CA2437876A1 - Method and system for fecal and ingesta detection during meat and poultry processing - Google Patents

Abstract

Imaging systems, containing at least one charge-coupled device detector, are used for determining contamination of foodstuffs, such as for example, animal carcasses. Image processing algorithms allow for the identification of contaminants.

Description

i METHOD AND SYSTEM FOR FECAL AND INGESTA DETECTION
DURING MEAT AND POUhTRY PROCESSING
Background of The Invention Field of the Invention The invention relates to imaging systems for detecting contamination on foods. The imaging systems can be used, for example, for real-time detection of fecal and ingesta on meat and poultry carcasses which may be present when carcasses are being processed. The systems include both hyperspectral and multispectral imaging systems including apparatus, methods, and computer readable mediums.
Description of the Related Art Microbial pathogens in food cause an estimated 76 million cases of human illnesses and up to 5,000 deaths annually, according to the Center for Disease Control and Prevention (Mead et al.
Emerging Infectious Diseases 5(5) 607-625, 1999). In 1996, the USDA Economic Research Service reported that the annual cost of the food-borne illnesses caused by six common bacterial pathogens: Campylobacter spp., Clostridium perfringens, Eseher.ichia coli 0157: H7, .Listeria monocytogenes, Salmonella spp., and Staphylococcus aureus; ranges from 2.9 billion to 6.7 billion dollars. The foods most likely to cause these illnesses are animal products such as red meat, poultry and eggs, seafood, and dairy products.
Contamination of meat and poultry in particular, with many bacterial food-borne pathogens, can occur as a result of exposure of the animal carcass to ingesta and/or fecal material during or after slaughter. Accordingly, in order to minimise the likelihood of such contamination, it has been necessary to examine each food item individually to detect the presence of contaminants. Historically,~such inspection has been performed visually by U.S.D.A. inspectors, who examine each individual food item as it passes through the processing system.
With poultry, for example, in a modern poultry processing plant, carcasses are placed on shackles of a processing line conveyor system for dressing and inspection. Typically, such conveyors operate at speeds of up to 140 carcasses per minute, with a six inch separation between shackles holding carcasses.
Even with multiple inspectors continuously performing such inspection, as little as two seconds are allotted for the inspection of each carcass.
During this inspection period, the inspector is required to check for evidence of eight different diseases as well as for certain quality characteristics, to verify that the chicken was alive when placed on the production line, and to check for evidence of ingesta or fecal contamination. Moreover, during a typical business day operating in two eight hour shifts, a productive poultry processing plant may produce as many as 250,000 processed chickens.
After slaughter, each carcass is examined for disease or evidence of contamination that would render all or part of the carcass unfit for human consumption. Currently the meat processing industry relies upon a variety of methods for the.
inspection of animal carcasses. These methods typically include human visual inspection, microbiological culture analysis, bioluminescent ATP-based assays, and antibody-based microbiological tests. Unfortunately, these procedures are labor intensive, time consuming, and do not meet the needs of the meat processing industry for an accurate high speed, non-invasive method that is amenable to inspection and real-time analysis.
It is apparent from this brief description that the historical inspection of meat carcases by human inspectors is problematic, and that it is poorly suited to the effective detection and elimination of contaminants in modern poultry processing plants. In particular, it requires the inspectors to make a subjective determination repeatedly. Such a system is prone to errors, which can lead to the entry of contaminated poultry products into the commercial distribution system.
In 1994, the Food Safety Inspection Service (FSIS) published a proposed rule, "Enhanced Poultry Inspection" (USDA, Proposed Rule, Fed. Reg. Volume 59, 35659, 1994) to clarify and strengthen the FSIS's zero-tolerance policy for visible fecal contamination on poultry carcasses. Prior to this rule, FSIS
ensured removal of all visible fecal contamination subsequent to postmortem inspection through off-line reinspection, direct on-line observations by an inspector, and application of finished product standards (FPS). Any bird found to be contaminated with feces was set aside for rework or condemnation. The proposed Enhanced Poultry Inspection rule removed "feces" from the list of defects in the FPS.
Since the proposed rule was published, FSIS has adopted the Pathogen Reduction; Hazard Analysis and Critical Control Points (HACCP) Systems (USDA, Final Rule, Fed. Reg., Volume 61, 28805-38855, 1996). The Pathogen Reduction/HACCP. system superceded the provisions of the Enhanced Poultry Inspection rule.
However, FSIS determined that the zero fecal tolerance provision would complement the Pathogen Reduction/HACCP
regulations. Therefore, FSIS finalized the zero fecal tolerance provision of the Enhanced Poultry Inspection proposal (USDA, Final Rule, Fed. Reg., Volume 62, 5139-5143, 1997).
The HACCP regulations require meat processing establishments to identify all food safety hazards likely to occur in a specific process, and to identify critical control points adequate to prevent them. Zero tolerance for visible fecal contamination is a standard that has been implemented by FSIS, forcing poultry processing plants to adopt some point in the evisceration process as a critical control point under HACCP
regulations which can be achieved by control, and therefore, is consistent with the HACCP framework. If evisceration machinery is not adjusted properly, the digestive tract of the bird may be torn during evisceration and its contents may leak onto the carcass. In meat processing establishments, fecal contamination of carcasses is a food safety hazard because of its link to microbiological contamination and food borne illness (USDA, 1997, supra). Pathogens may reside in fecal material and ingesta, both within the gastrointestinal tract and on the exterior surface of animals going to slaughter.
Therefore, without proper procedures during slaughter and processing, the edible portions of the carcass can become contaminated with bacteria capable of causing illness in humans. Preventing carcasses with visible fecal and ingesta contamination from entering the chlorinated ice water bath (chiller) is critical for preventing cross-contamination of other carcasses. Thus, the final carcass wash, before entering the chiller, has been adopted by many poultry processors as a HACCP system critical control point for preventing cross-contamination of other carcasses.
Compliance with zero tolerance in meat processing establishments is currently verified by visual observation.
Three criteria are used for identifying fecal contamination (USDA, 1997, supra). These are color, consistency, and composition. In general, fecal material color ranges from varying shades of yellow to green, brown and whites the consistency of feces is usually semi-solid to paste; and the composition of feces may include plant material. Inspectors use these guidelines to verify that establishments prevent carcasses with visible fecal contamination from entering the chillers. me Visual inspection is both labor intensive and prone to both human error and variability. In addition, there has been a dramatic increase in water usage in most plants as a result of the zero-tolerance fecal standard. Plants have nearly doubled their previous water usage and nationwide the usage has increased an estimated 2 billion gallons (Jones, Poultry, Volume 6, 38-41, 1999).
Efforts have been made to develop automated or semiautomated visual inspection systems for detecting the presence of contaminants on food products during processing. Most systems utilize a technique in which the food item is irradiated with light having a frequency, for example, in the UV range, such that it causes the emission of fluorescent radiation upon striking fecal matter or ingesta. Fluorescent light emanating from the target food item is then measured and compared with a threshold value. If the light gathered exceeds the threshold, a signal indicative of the presence of fecal contamination or ingesta is generated. Such a system is disclosed for example in U.S. Patent Nos. 5,621,215 and 5,895,921 to Waldroup et al., and U.S. Patent No. 5,821,546 to Xiao et al.
U.S. Patent No. 5,914,247 to Casey et al. discloses a fecal and ingesta contamination detection system which is based on the premise that the emission of fluorescent light having a i wavelength between about 660 and 680 nm is indicative of the presence of ingesta or fecal material. Thus, carcases being processed are illuminated with UV or visible light (suitable wavelengths being between 300 and 600 nm) and the illuminated surface is then examined for the emission of fluorescent light in the 660 and 680 range. In a preferred embodiment, the intensity of such fluorescence in the 660-680 nm range is compared with that in the 600-620 range as a baseline in order to distinguish fluorescent light emissions of the carcasses themselves.
Visible and near-infrared reflectance (Vis/NIR) spectroscopy is a technique that can be used to detect contamination on foodstuffs. It is a nonconsumptive, instrumental method for fast, accurate, and precise evaluation of the chemical composition of agricultural materials (Williams, Commercial near-infrared reflectance analyzers. In Williams and Norris, eds., Near Infrared Technology in the Agricultural and Food Industries, Am. Assoc. Cereal Chem., St. Paul, MN, 1987, pp.
107-142). The use of Vis/NIR spectroscopic techniques for classifying wholesome, septicemic, and cadaver carcasses have been reported by Chen and Massie (ASAE, Volume 36(3), 863-889,1993) and Chen et al. (Appl. Spectrosc., Volume 50, 920-916,1996b). These studies were conducted with a near-infrared reflectance (NIR) probe in contact with a stationary carcass.
More recently, Chen and Hruschka (ASAE Paper No. 983047, American Society of Agricultural Engineers, St. Joseph, MI, 1999) disclosed an on-line transportable Vis/NIR system (400 to 1700 nm) in which the probe was not in contact with the carcass and carcasses were moving at rates of either 60 or 90 birds per minute. Carcasses were classified as wholesome or unwholesome with an average accuracy of 94o and 97.50 when measured in room light and in the dark, respectively. On-line trials were conducted in a slaughter establishment where spectra of normal and abnormal carcasses were measured. The Vis/NIR system measured carcasses at a rate of 70 birds per minute and was able to classify the carcasses from the spectral data with a success rate of 950 (Chen and Hruschka, 1998, supra). The Vis/NIR method showed promise for separation of wholesome and unwholesome carcasses in a partially automated system. The use of the technique to detect fecal and ingesta surface contaminants on poultry carcasses has not been attempted in the processing plant.
Machine vision is a technology for automating production processes with vision capabilities. Even though machine vision has evolved into a promising technology for many agricultural product applications, such as grading or inspection, there are many factors to be considered in on-line applications:
processing speed, reliability, and applicability for industrial environments (Sakar and Wolfe, Trans. ASAE, Volume 28(3), 970-979, 1985; Miller and Delwiche, Trans. ASAE, Volume 32(4), 1484-1490,1989; Tao et al., Trans. ASAE Volume 38(5), 1555-1561, 1995; Steinmetz et al., Trans. ASAE, Volume 37(4), 1347-1353, 1994; Ni et al., ASAE Paper No. 933032, American Society of Agricultural Engineers, St. Joseph, MI, 1993; Daley et al., Proc. SPIE, Volume 2345, 403-411, 1994). Image processing techniques have made machine vision research possible to identify and classify agricultural commodities in the spatial domain (Guyer et al., Trans. ASAE, Volume 29(6), 863-869, 1986) as well as in the spectral domain (Meyer et al., Applied Engineering in Agriculture, Volume 8(5), 715-722, 1992).
Machine vision techniques are feasible for grading and parts identification in poultry production (Daley et al., Proceedings of Robotics and Vision '88, Society of Manufacturing Engineers, Dearborn, MI, 1988). Techniques for recognizing global or systemic defects on poultry carcasses with a color imaging system were reported by Daley et al. (1994, supra ) and Chin et al. (Experimental evaluation of neural networks for inspection of chickens. Research Report of Georgia Tech. Research Institute, 1993). However, this approach had a 90o accuracy for global defect classification and only a~60o accuracy for local defect classification (Daley and Carey, Color machine vision for defect detection: Algorithms and techniques, RIA
International Robots and Vision Conf., 1991). Even though a color imaging system has the ability to extract the salient image features, this system was not successful for totally automated inspection because of low accuracy (Daley, Color machine vision for industrial inspection advances and potential for the future, Research Report of Georgia Tech. Research Institute, 1992).
Multispectral imaging technology has potential for food inspection application. Since biological materials at different conditions have different spectral reflectance characteristics, the status of materials could be identified based on their spectral images by selecting optimum wavelengths. Several spectral image processing algorithms have been developed to differentiate wholesome carcasses from unwholesome carcasses (Park and Chen, ASAE Paper No. 946027, American Society of Agricultural Engineers, St. Joseph, MI, 1994a; Park et al., Trans. ASAE, Volume 39(5), 1933-1941, 1996a). Use of intensities, recorded in different spectral bands of a multispectral camera for segmentation, was effective for classification of poultry carcasses (Park and Chen, Trans.
ASAE, Volume 37(6), 1983-1988,.1994b: Park et al., 1996a, supra). Multispectral imaging was used for detecting unwholesome conditions, such as septicemia, cadaver, bruise, tumor, air-sacculitis, and ascites, in poultry carcasses (Park et al., 1996a, supra). Park and Chen (1994b, supra) developed a prototype multispectral imaging system for detecting abnormal poultry carcasses, specifically, to determine the optimal wavelengths of multispectral filters for discerning septicemic and cadaver carcasses from normal carcasses, and to develop a discriminate function for separation of the abnormal carcasses with an accuracy of 93o for normal, 83% for septicemic, and 970 for cadaver carcasses.
Textural feature analysis of multispectral images has potential to discriminate wholesome carcasses from septicemic and cadaver carcasses with high classification accuracy of about 940 (Park and Chen, Trans. ASAE, Volume 39(4), 1485-1491, 1996).
However, texture feature analysis would not be useful for an on-line system because of heavy computing time. To achieve real-time processing and analyzing of multispectral gray-scale images for on-line separation of septicemic, cadaver, tumorous, bruised, and other damaged carcasses from the wholesome carcasses, a neural network algorithm was found to be useful (Park et al., ASAE Paper No. 983070, American Society of Agricultural Engineers, St. Joseph, MI, 1998b). Thus, image texture analysis is an important process in scene analysis because it partitions an image into meaningful regions. Zumia et al., (Pattern Recognition, Volume 16(1), 39-46,1983) described a method for discriminating texture classes based on the measurements of small regions determined by an initial segmentation of the image for categorizing homogeneous regions.
Park and Chen (1996, supra) have reported that textural feature analysis of multispectral images containing Vis/NTR wavelengths based on co-occurrence matrices was feasible for discriminating abnormal from normal poultry carcasses at 542 nm.
Development of high speed and reliable inspection systems to ensure safe production of poultry processing has become an important issue. Two dual-wavelength vision systems were developed for on-line machine vision inspection of poultry carcasses (Chao et al., ASAE Paper No. 993118, American Society of Agricultural Engineers, St. Joseph, MI, 1999). A real-time multispectral image processing algorithm was developed from neural network models with different learning rules and transfer functions for on-line poultry carcass inspection (Park et al., Journal of Agricultural Engineering Research, Volume 69, 351-363, 1998c). The classification accuracy with dual-wavelength spectral images was much higher than single wavelength spectral images in identifying unwholesome poultry carcasses (Chao et al., 1999, supra). Object-oriented software was developed for on-line image capture, off-line development of classification models, and on-line prediction of wholesome and unwholesome carcasses.
An extension of multispectral imaging is known as hyperspectral imaging which is also referred to as imaging spectrometry.
Whereas multispectral imaging consists of measurements from two to about ten discrete wavelengths for a given image, hyperspectral imaging measures more than ten contiguous wavelengths, often many more. Like multispeetral imaging, hyperspectral imaging is an imaging technique that combines aspects of conventional imaging with spectrometry and radiometry. The result is a technique that is capable of providing an absolute radiometric measurement over a contiguous spectral range fox each. and every pixel of an image. Thus, data from a hyperspectral image contains two-dimensional spatial information plus spectral information over the spatial image. These data can be considered as a three-dimensional hypercube which can provide physical and geometric observations of size, dimension, orientation, shape, color, and texture, as well as chemical/molecular information such as water, fat, proteins, and other hydrogen-bonded constituent as described above in other Vis/NIR research. Hyperspectral imaging is often used in remote sensing applications (Schowengerdt, The nature of remote sensing, In Remote sensing: Models and methods for image processing, San Diego, Academic Press, 1997, pp. 1-33), but is also being utilized in medical, biological, agricultural, and industrial areas as well (Lu and Chen, SPIE, Volume 3544, 121-133, 1998; Heitschmidt et al., SPIE, Volume 3544, 134-137, 1998; Levenson et al., SPIE, Volume 3438, 300-312, 1998; Lu et al., ASAE Paper No.993120, American Society of Agricultural Engineers, St. Joseph, MI, 1999; Willoughby et al., SPIE, Volume 2599, 264-272, 1996).
Since the detectors used to measure hyperspectral data are two-dimensional focal plane arrays (FPA), while hyperspectral data are three-dimensional, there must be a technique to collect all the data. The two primary techniques for collecting hyperspectral images are collecting two-dimensional spatial images while sequentially varying a narrow bandwidth of incident energy, or collecting full spectral information of a line-scan image while sequentially varying the position of the line scan (Wolfe, Introduction to imaging spectrometers, SPIE
Optical Engineering Press, Bellingham, WA, 1997; Fisher et al., SPIE, Volume 3438, 23-30, 1998; Hart and Slough, SPIE, Volume 3389, 139-149, 1998). The first technique can typically be demonstrated with either an acousto-optic tunable filter (AOTF) or a liquid-crystal tunable filter (LCTF) in front of a FPA
where a two-dimensional spatial image is captured at successive wavelengths. The latter technique is usually implemented in remote sensing as either a push-broom or whisk-broom scanner where a line-scan spectrometer is positioned in front of the FPA so that the FPA successively captures one spatial dimension and one spectral dimension as the scanner or image travels normal to the line-scan direction (first spatial dimension).
With each technique, the successive images must be combined to build a hypercube of data for a given image. Each technique has advantages and disadvantages that dictate their use in varying applications. LCTF and AOTF systems can rapidly collect images at discrete wavelengths, which can be easily varied. However, they are better suited for stationary objects to avoid image shifting between discrete wavelength measurements. Push-broom and whisk-broom systems are better suited for moving objects but cannot measure at discrete wavelengths.
Hyperspectral imaging has recently been used to explore the feasibility of detecting defects and contaminates in poultry carcasses (Lu and Chen, 1998, supra; Heitschmidt et al., 1998, supra). Lu et al. (1999, supra) demonstrated that taking a second-derivative of the reflectance value could qualitatively distinguish between four normal carcasses and four cadaver, four septicemia, and three tumorous carcasses. Heitschmidt et al. (1998, supra) contaminated two carcasses with fecal material and were able to qualitatively identify the contaminants with principal component analysis (PCA). However, the time required to perform the PCA was over 40 minutes for a single carcass. Image ratios (wavelength ratios) were also examined. No specific wavelengths were identified as significant for detecting fecal contamination with the limited sample population.
Hyperspectral imaging is an extremely useful tool to throughly analyze the spectra of inhomogeneous materials that contain a wide range of spectral information. It can be an effective technique for identifying surface contaminant on poultry carcasses. At the current time though, it is not suitable for on-:line identification of fecal contamination because of lengthy image acquisition and processing times.
SUMMARY OF THE INVENTION
It is therefore, an object of the present invention to provide imaging systems and methods for detecting contamination on foods.

Another object of the present invention is to provide improved processes and apparatus for detection of c~ntamination on a food item, which achieves enhanced accuracy and dependability in positively identifying contaminants.
Another object of the present invention is to provide processes and apparatus which can reliably detect contaminants at a speed which is compatible with the rate at which a food is processed on a production line.
A still further object of the present invention is to provide real-time automated food inspection systems which can quickly and accurately identify contaminated food items in a food processing line.
Further objects and advantages of the invention will become apparent from the following description.
This and other objects and advantages are achieved by the imaging systems according to the invention, in which digital imaging sensors, such as multispectral or hyperspectral imaging camera units are used to collect reflectance data from a food source on which contamination is to be detected. Reflectance data gathered by the imaging system are then processed in a digital computer using specially derived algorithms for enhancing the detection of contamination.
The theoretical development of algorithms which are used for this purpose is based on the difference between spectral reflectance of contaminants versus that of uncontaminated food.
The assumption is made that a mathematical combination of remotely sensed spectral bands could be used to identify contaminants. The results generated by such a combination of spectral bands corresponds to the amount of contaminants in a given image pixel.
There are two categories of algorithms that have been developed for use in the detection of contaminants. The first is a ratio of two key wavelengths or bands that are determined. The purpose behind using a ratio is to alter the reflectance measurements of spectral bands using an illumination independent function, which will augment the spectral values for the contaminant while diminishing the values fox the food source or background.
Examples range from a simple ratio of two wavelength images, to a ratio of multiple wavelength image combinations, such as fix, ~.p~;., ~
(< ~x~1(';i where ~.1 , ~2 ~ ~3 ~ and~,4 are images at four key wavelengths, and x is a constant. Another example in the ratio category would be the well-known normalized difference vegetative index (NDVT) .
The second category of algorithm is defined as a linear combinations of wavelengths. The linear combinations category can range from a combination of two wavelengths ~~21 -!- ~z), to a linear combination of wavelength ratios, such as:
/~.1 + a,2 - W ~1 -I- /~,3 - W /~,1 -I- /~.4 ' ,y -I-~2 . x /~3 - x /~.4 -- Z

where ~1 , ~2 , ~3 ~ and~,4 are images at four key wavelengths, and W, x, y, and ~ are constants. This category also includes previously published remote sensing algorithms such as the Mahalonobis Distance and the rule file generation of the Spectral Angle Mapper. These formulas may need to be combined with a known filter for optimum results. Once an equation has been used, it may be necessary to apply any of a number of imaging filters to the resultant data set, either for clarity, to sharpen results, or even to limit the error. Some examples of these are low pass, high pass, median, gaussian, laplachian and texture filters.
BRIEF DESCRIPTION OF THE DRAV~1II~1GS
Figures la (Front view), 1b (Side View), and 1c (camera assembly) show a schematic of an imaging system 10 including a means for obtaining spectral images 12- SensiCam camera containing at least one charge-coupled device detector 1A with spectrograph 1C, and lens assembly 1B; two quartz-halogen line lights 2B; fiber-optic cables 2A, power supply 2C for lighting;
power supply 4 for detector 1A; battery backup 5; computer monitor 6, computer 7, and interface cable 7A. Figure 1d is a schematic diagram which shows the components of a multispectral contaminant detection system according to one embodiment of the present invention.
Figure 2 is a flowchart for the detection of contaminants on food such as feces and ingesta on the poultry carcasses with Vis/NIR monochromator (2.1-2.5) and hyperspectral imaging system 10 (2.6-2.17).
Figure 3 is a flowchart for the detection of contaminants on food such as feces and ingesta on poultry carcasses with a multispectral imaging system where key wavelengths have already been determined.
Figures 4a and 4b illustrate a digital filtering technique common to two embodiments of the present invention.
Figure 5 is a graph showing Vis/NIR reflectance average spectra from a scanning Vis/NIR monochromator of uncontaminated hard (#5.1) and soft (#5.2) scalded poultry carcass skin and pure feces from duodenum (#5.3), ceca (#5.4), and colon (#5.5) samples of poultry viscera.
Figure 6 is a graph showing discrimination of uncontaminated hard and soft scalded poultry carcass skin (#6.1) from duodenum (#6.2), ceca (#6.3 ), and colon (#6.4) feces by Principal Component Analysis of Vis/NIR reflectance spectra.
Figure 7 is a graph showing Principal Component Analysis loadings for Principal Components 1 (#7.1), 2 (#7.2), and 4 (#7.3) as a function of wavelength.
Figure 8 is a graph showing a non-linear cubic regression model for spectral calibration of the hyperspectral imaging system 10 with a binning of 4 by 2 .
Figure 9~shows a color composite and images of poultry carcasses contaminated with feces from duodenum, ceca, and colon locations of viscera at selected spectral wavelengths acquired by hyperspectral imaging to demonstrate image quality and spectral- image differences.
Figures 10a-a show a color-composite image (Figure 10a) and spectral images (Figures 10b-e) from image system 10 which correspond to key wavelengths capable of identifying fecal contamination as determined from a Vis/NIR monochromator.
Figures 11a-f show the ratio images at key wavelengths that identify feces (duodenum, ceca, and colon) and ingesta contaminants on a poultry oarcass. Figure 11a is a 517-nm image divided by 434-nm image. Figure 11b is a 565-nm image divided by 434-nm image. Figure 11c is a 628-nm image divided by 434-nm image; figure d is a 565-nm image divided by 517-nm image.
Figure 11e is a 628-nm image divided by 517-nm image. Figure 11f is a 628-nm image divided by 565-nm image.
Figure 12a shows a hyperspectral color-composite image for the identification of fecal and ingesta contaminants on a poultry carcass. The image shows blood hemorrhage (#12a.1) and contaminant in the wing shadow (#12a.2).
Figure 12b shows a ratio image (565-nm image divided by 517-nm image) for the identification of fecal and ingesta contaminants on a poultry carcass. The blood hemorrhage (#12b.1) is not identified while the fecal contaminant in the wing shadow (#12b.2) can be identified easily.
Figures 13a, b, and c are ratio images to show a masking procedure to eliminate background noise from algorithm-processed images. Figure 13a shows an unmasked image (565-nm image divided 517-nm image). Figure 13b shows a masking template from a image at 565 nm. Figure 13c shows the image of figure 13a after the masking template (Figure 13b) was applied.
Figures 14a-b show a ratio image (565-nm image divided by 517-nm image) before and after histogram stretching of the masked ratio image to qualitatively demonstrate the effectiveness of the histogram stretching routine. Figure 14a shows a ratio image, after the masking procedure, with contaminants somewhat visible in white. Figure 14b shows a ratio image, after the histogram stretching procedure, with contaminants clearly visible in white. #14b.1- contaminant below the tail, #14b.2-row of duodenum, #14b.3- row of ceca, #14b.4- .row of colon contaminant, and #14b.5- row of ingesta contaminants.
Figures 15a-f show graphs of contaminated and uncontaminated poultry carcasses for validation of the ratio-image algorithm before and after the masking and threshold procedures for identification of fecal and ingesta contamination. The figure shows a clean carcass (fig. 15a), clean carcass with masking procedure applied to eliminate background (fig. 15b), clean carcass after masking and threshold procedures (fig. 15c), carcass with contaminant (fig. 15d), contaminated carcass with masking procedure applied to eliminate background (fig. 15e), and contaminated carcass after masking and threshold procedures (fig. 15f) .
Detailed Description of the Invention Hyperspectral and multispectral imaging are imaging techniques that combine aspects of conventional imaging with spectrometry and radiometry. These techniques are capable of providing an absolute radiometric measurement over a contiguous spectral range for each and every pixel of an image. Data from an image contain two-dimensional spatial information as well as spectral information at each location in the spatial domain. These data can be considered as a three-dimensional hypercube (or data cube) which can provide physical and/or chemical information of a material under test. This information can include physical and geometric observations of size, orientation, shape, color, and texture, as well as chemical/molecular information such as water, fat, and protein.
Generally, for detecting contamination on food, such as for example animal carcasses, testing is conducted at one or more stations along the processing line, during transport along the line, or soon after completion of slaughter. For the purposes of this application, contamination of animal carcasses is to include but not be limited to, digestive tract material including fecal contamination, ingesta contamination, crop contents, bacterial. contamination, etc. At the testing stations, the carcasses may be imaged with,a hyperspectral or multispectral imaging system at any contiguous or discrete wavelengths of radiation from about 400 to about 2500 nm emitted therefrom and detected as described herein below.
Because processing facility practices vary with the particular meat producing animal, specific locations for testing along the processing line will vary. For instance, the typical processing line for poultry include the following steps in order: the bird is suspended b.y the legs in a shackle, electrically stunned, bled via a neck cut, hard or soft scalded, defeathered, decapitated, and eviscerated (usually by mechanical means), and chilled in chlorinated ice-water baths.
On the other hand, beef harvest procedures differ significantly, and include the following steps: the animal is inspected, rendered unconscious, shackled, hoisted, exsanguinated and placed onto a moving rail. The carcass is then skinned (primarily through the use of mechanical hide pulleys) and the head is removed for postmortem inspection of wholesomeness. Prior to evisceration, the brisket is split and the esophagus and anus are loosened (these may be tied to prevent fecal and ingesta contamination of the carcass). The abdominal cavity is then opened with a vertical incision through the abdominal muscles~and the internal organs (excluding the kidneys) and the entire gastrointestinal tract are removed onto a conveyor for postmortem inspection and further processing. The eviscerated carcass is then split into halves, cutting longitudinally through the spinal column, and inspected for wholesomeness. Onoe inspection is complete (passed), the carcass sides are mechanically washed (which may include a steam pasteurization step to minimize microbial contaminate~n), weighed, and chilled for 24 to 48 hours before fabrication into primal and subprimal cuts and subsequent shipment. Pork harvest procedures are similar to beef, with the exception where the skinning step in the beef process is replaced by a hair removal process in pork that leaves the skin on the carcass. Scalding the carcass in hot water to loosen the hair follicles, mechanically removing the hair, singeing to remove any residual hair, and subsequently washing and rehanging the carcass accomplish this. Evisceration is similar to beef and pork~,caroasses are split into sides through the spinal column; however, the skin and soft tissue are left intact at the anterior end of the carcass. Inspection, washing, and chilling procedures are also similar to beef. In some instances, pork carcass may be deboned while warm, ground with other ingredients such as spices, and rapidly~chilled to refrigeration temperatures. Testing may be conducted during or upon completion of any of the above-mentioned steps.
In one example, beef or pork carcasses can be inspected for contamination prior to chilling of the sides or carcass usually within approximately 2 - 3 minutes after splitting or in less than 10 minutes of initiation of harvest, depending on the species. Other sites for inspecting may include after skinning, after evisceration, and(or) after splitting. Poultry carcasses can be inspected for contamination after defeathering and/or evisceration. For quality control, poultry may also be inspected following removal from chilled chlorinated ice-water baths.
Imaging systems 20 (Figure 1d) include a means for obtaining spectral images 12, a lighting system 2, and data processing unit 9. One embodiment of the present invention includes a hyperspectral imaging system l0 (Figures 1a-1c). Hyperspectral imaging system 10 includes at least a means for obtaining spectral images 12, such as for example at least one charge-coupled device detector 1A; lighting system 2, and data processing unit 9. The means for collecting spectral images 12 for the purposes of this embodiment, includes at least one charge-coupled device.lA, a lens assembly 1B, and a line-scan spectrograph 1C. It further includes a power supply 4 and a battery back-up 5.
Device 1A can be a high resolution detector, such as for example, a Charge-Coupled Device detector (CCD). Examples of a charge-coupled device detector include, for example, a SensiCam 370 KL Camera (Cooke Cooperation, Auburn Hills, MI); an Orca 100 Digital CCD Camera system (Hamamatsu, Bridgewater, NJ); a SpectraVideo 16-bit Digital (PixelVision, Inc., Beaverton, OR);
etc.
Line-scan spectrograph 1C has a nominal spectral range of from about 400 nm to about 900 nm and attaches to the CCD detector 1A for generating line-scan images. Lens assembly 1B includes a 1.4/17-mm compact C-mount lens such as, for example, a Xenoplan (Schneider, Hauppauge, NY); Nikkor (Nikon Inc., Melville, NY); and attaches to spectrograph 1C.
Another embodiment of the present invention includes a multispectral imaging system 10. Multispectral imaging system includes a means for obtaining spectral images 12, a lighting system 2 and a data processing unit 9. In this embodiment, a means for obtaining spectral images 12 for a multispectral imaging system includes a common aperture camera having two or more detectors, such as two CCD detectors, for simultaneously acquiring multispectral images. The camera utilizes a wavelength-separating prism, a dichroic filter, to split broadband light, which enters the camera through the lens, into at least two independent optical channels. The degree of specific spectral separation between optical channels depends upon the dichroic filter and the subsequent trim filter properties. Specifically, the separation is a function of the desired key wavelengths as determined by the calibration model fox the specific food and its associated contaminants, their proximity to each other, and the bandwidth of the trim filters.
The wavelength separating prisms or filters contain different dichroic coatings on different faces of the prism which determines the performance of the camera. The optical trim filters, between the prism exit plane and the detectors, determine the spectral bandwidth reaching the detectors and are designed such that the central wavelength corresponds to one of the key wavelengths. As a result of this process, two or more spectral images are obtained simultaneously. By way of example, a common aperture camera system with two detectors is described. Common aperture cameras with three or more detectors are also feasible. These cameras would result in simultaneously acquiring three or more spectral images.
It is of course possible to achieve similar means for obtainng spectral images 12 by using multiple digital imaging devices, such as CCD devices, each having its own filter, for isolation of a preselected wavelength band. Another means for obtainng spectral images l~ is at least one charge-coupled device detector containing area scan filters, such as for example, a liquid crystal tunable filter, an acousto-optic tunable filter, etc. The bandwidths for these filters are specified according to the type of data to be collected. The determination of the bandwidths needed is well within the ordinary skill in the art in light of the detailed description of the present application. The at least one charge-coupled device detector with filters has to be capable of collecting at least two discrete spectral images.
The image signals provided by the means for obtaining spectral images 12 are input to a computer 7 via a known frame grabber l7, such as, for example, a 12-Bit PCI~interface board (Cooke Company, Auburn Hills, MI; National Instruments, Austin, TX).
The frame grabber 17 assembles the data into respective image frame files. These data are then processed by the computer 7 according to one of the different processes, including differing processing algorithms 20 and method steps, depending on the nature of the production line processing the food. The end result of such computer analysis is the generation of a qualitative analysis such as a "contaminated/uncontaminated"
determination for each unit of food that passes in front of the means for obtaining spectral images or a quantitative determination to determine, for example, types and/or si~e~of contamination.
The theoretical development of algorithms 20 which are used for this purpose is based on the difference between spectral reflectance of contaminants versus that of uncontaminated food.
The assumption is made that a mathematical combination of remotely sensed spectral bands could be used to identify contaminants. The results generated by such a combination of spectral bands corresponds to the amount of contaminants in a given image pixel.

There are two categories of algorithms that have been developed for use in the detection of contaminants. The first is a ratio of key wavelengths or bands that are determined. The purpose behind using a ratio is to alter the reflectance measurements of spectral bands using an illumination-independent function, which will augment the spectral values for the contaminant while diminishing the values for the food source or background.
Examples range from a simple ratio of two wavelength images,to a more complex ratio such as:
,x,.x~,r:-:.~'I
~ a .=oc~o , where ~,1 , ~,., , ~,3, and ~,4 are images at four key wavelengths, and x is a constant. Another example in the ratio category would be the well-known normalized difference vegetative index (NDVI) .
The second category of algorithm is defined as a linear combination of wavelengths. The linear combination category can range from a combination of two wavelengths, (~,1 + ~,2) , to a linear combination of wavelength ratios such as:
/~1 + ~2 - W /~, + /~,3 - W /~1 + /~4 - y ~2 -x + X23 -x where ~1 . ~,z . ~.3 , and ~,4 are images at four key wavelengths, and vv, x, y, and z are constants. This category also includes previously published remote sensing algorithms such as the Mahalonobis Distance and the rule file generation of the Spectral Angle Mapper. These formulas may need to be combined with a known filter for optimum results though. Once an equation has been used, it may be necessary to apply any of a number of imaging filters to the resultant data set, either for clarity, to sharpen results, or even to limit the error. Some examples of these are low pass, high pass, median, gaussian, laplachian and texture filters.
Both the hyperspectral and the multispectral systems require lighting system 2. Lighting system 2 includes an illuminator with at least 2400 lux (lumen/m2) intensity and excitation wavelengths between about 400 and about 2500 nm, such as for example, Fiber-bite A240 (Dolan-Jenner, Inc., Lawrence, MA);
lamp assembly 2B, for example quartz halogen line lights such as for example QF5048 (Dolan-Jenner, Inc.); lighting power supply 2C, and fiber-optic cables 2A.
Data analysis (Table A - Appendix) is needed in order to determine particular types of contamination on foodstuffs. A
calibration model correlates band numbers of the imaging system with actual wavelengths. Referring to Figure 2, the first step is to collect spectra of pure contaminants and uncontaminated food with a Vis/NIR monochromator, for example, a NIRSystems 6500 monochromator (NIRSystems, Silver Spring, MD) which has a spectral range from about 400 to about 2500 nm in about 2-nm intervals (Figure 2, Box 2.1). Before each sample is measured, a standard ceramic tile, having high reflectance, is measured to provide standard reflectance values. The reflectance values of the samples and the standard are converted to log (1/R) values where R is reflectance. It is known to convert reflectance data representing log (1/R) values, wherein R is reflectance, which values vary approximately linearly with the concentration of the absorber. Generally, any suitable monochromator may be utilized, provided that the resulting spectra covers both visible and NIR regions. This means that the wavelength of visible light will be in the range of about 400 to about 780 nm, and NIR light will be in the range of about 782 to about 2500 nm.
After the data are converted to log (l/R), the next step is to transform the converted data with standard normal variate and detrending or multiplicative scatter correction to remove interferences of scatter, particle size, variation in baseline shift, and curvilinearity (Figure 2, Box 2.2). Variation within individual Vis/NIR spectra is the result of three main sources: 1) nonspecific scatter of .radiation at the surface of the sample; 2) variable spectral pathlength through the sample;
and 3) chemical composition of the sample. Scatter is dependent on the physical nature of the sample particles and pathlength is largely dependent on sample particle size. There is a high degree of collinearity between data points in the log (1/R) spectra, which is a function of scatter and variable pathlength. The multiplicative combinations of these effects are unique to any one spectrum, and any corrections for these interference should be made on the same basis. These interferences can cause problems in quantitative and qualitative analysis and should be removed prior to calibration and analysis. Spectral measurements of pure contaminants and uncontaminated foodstuffs are transformed with standard normal variate and detrending procedures (Barnes et al., Appl.
Spectrosc., Volume 43 (5), 772-777, 1989 herein incorporated by reference) to remove interferences of scatter and particle size, and variations in baseline shift and curvilinearity. The invention is not limited to any one specific mathematical transformation to remove these interferences. It has been found that NIR ratio techniques (Norris, Karl, U.S. Patent NO.

5, 132,538; herein incorporated by reference), second derivative transformation, and multiplicative scatter correction (Geladi et al., Applied SpectrosC., Vol. 39(3), 491-500, 1985; herein incorporated by reference) show sufficiently similar corrections within the scope of the present invention.
After transforming the log (1/R) data, as described above, the transformed data is processed with Principal Component Analysis (PCA) for formation of scores and loadings (Figure 2, Box 2.3).
Examples of commercially available software for performing PCA
analysis include Winisi, Infrasoft, Port Matilde, PA;
Unscrambler, CAMO, Oslo, Norway; Grams/32, Galactic Industries Corp., Salem, NH; etc. Mathematical tools have been developed to help extract additional information from spectra.
Chemometrics have been described as the application of mathematical and statistical methods to extract more useful information from chemical and physical measurements. Recent advances have lead to new data analysis systems and commercially available Vis/NIR instruments that use one or more chemometric methods for qualitative and quantitative analysis.
Standard practices for chemometrics in infrared, multivariate, qualitative, and quantitative analysis are described elsewhere (American Society for Testing Materials (ASTM) Practice, E1655-94, 1995; ASTM Annual Book of Standards, West Conshohocken, PA., Volume 3.06, 1995; both herein incorporated by reference).
Principal Component Analysis (PCA) is one technique for identifying the underlying features of large data sets, and attempts to describe the variation in mufti-dimensional data by means of a small number of uncorrelated variables. The underlying concepts and properties of PCA are described in Barton et al., (U. S. Patent NO. 6,114,699; herein incorporated by reference). Briefly, PCA is a variable reduction procedure.
It is useful on a large data set with a large number of variables that are correlated to each other. Because of these intercorrelations, the observed variables can be reduced into a smaller number of artificial variables (principal components, eigenvectors, factors, or T-variables) that will account for most of the variance in the observed variables. Translated into principal components, the new coordinate system has fewer dimensions than the original set of variables, and the directions of the principal components describe the largest variations. The localization, or the coordinates of the samples related to the principal components, are called scores (Eigenvalues). The corresponding relationship between the original variables and the new principal components are called loadings (weights).
The next step is to compare scores with variations in principal components to select discrete principal components at which scores correlate with uncontaminated foods and contaminants (Figure 2, Box 2.4). The spectra of samples are analyzed by principal component analysis as described above, and scores and loadings are used to select key Vis/NTR wavelengths for discrimination between contaminated and uncontaminated foods.
It has been found that partial least squares regression (Workman et al., Applied Spectrosc. Reviews, Volume 31(1&2), 73-124, 1996; herein incorporated by reference) can also be used for modeling the variance within spectra of pure contaminants and uncontaminated foods to identify key wavelengths (Figure 2, Box 2.5). Once key wavelengths are identified for a particular food and its associated contaminants, these can be used in any embodiment of the present invention.
After key wavelengths are identified from the calibration model generated for a particular food and its associated contaminants, the systems of the present invention are ready to image foods for contamination. For hyperspectral imaging system 10, line scan images are collected (Figure 2, Box 2.6).
With hyperspectral imaging, all spectral wavelengths can be collected for every pixel of an object. Data are acquired with a high resolution CCD image detector, which is a two-dimensional focal-plane array sensor. Therefore, only two dimensions of an image can be collected at any given time. A
typical embodiment is~a single line-scan image which consists of spectral information for one spatial row. Line-scan images are taken while the food source is moving so that the successive line scans represent successive slices of the food source. Alternatively, the food source may remain stationary and the camera may move. Successive line-scan images are collected by computer 7 via a frame-grabber 17 that interfaces the CCD detector through interface cable 7a. Once the frame-grabber has stored line-scan images as data files in computer 7, they may be combined to create a full hyperspectral image that has two-dimensional spatial information as well as spectral information (Figure 2, Box 2.7). This full hyperspectral image is sometimes referred to as a three-dimensional hypercube. The hyperspectral image is created through software which combines the individual line-scan image files into a single hyperspectral image file such as for example Hypervisual (Provision Technologies, Stennis Space Center, MS; Interactive Data Language (IDL), Research Systems, Inc., Boulder, Colorado). However, any software capable of creating a hyperspectral image is useful in the present system and is well within the ordinary skill in the art given the detailed description of the present invention.
The next step in the process is to choose sensor binning (Figure 2, Box 2.8). The choice of hyperspectral imaging resolution is determined by the food source imaged, the minimum physical size of the contaminant, and the values of the key wavelengths determined from the Vis/NIR monochromator. The imaging resolution is a function of both the CCD detector dimensions and the binning selected during image capture.

Binning describes the process where photons collected in adjacent pixels are summed together. For example, a binning of 4 by 2 applied to a CCD with 1280 by 1024 pixels would result in the summing of photons collected over eight adjacent pixels (two rows of four columns). The result would be line-scan images with an image resolution of 320 pixels (1280 divided by 4) in the spatial dimension and 512 pixels (1024 divided by 2) in the spectral dimension. The spectral dimension is sometimes referred to as the bands.
The hyperspectral imaging system requires a wavelength calibration so that the intensities at various band numbers will correspond to an actual wavelength. Thus, after wavelength calibration, the same key wavelengths, identified with the Zlis/NIR monochromator (Figure 2, Box 2.5 and described above), can be evaluated with the hyperspectral imaging system.
The hyperspectral wavelength calibration equation was developed from separate hyperspectral image data (Figure 2, Box 2.9) of spectral calibration lamps (Oriel Instruments, Stratford, CT) and lasers (Edmund Scientific, Barrington, NJ) inserted into an integrating sphere (Optronic Laboratories, Inc., Orlando, Florida; Labsphere, North Sutton, NH). The integrating sphere disperses the energy from the calibration lamps so that any image system looking into the integrating sphere observes a spatially uniform image. The calibration lamps must have precise distinct wavelength peaks across the wavelength range of the hyperspectral imaging system needing calibration.
Determination of these is well within the ordinary skill in the art.
After imaging the spectral calibration sources, distinct wavelength peaks and their corresponding band numbers are identified (Figure 2, Box 2.10) and for a given binning, regressed (Figure 2, Box 2.11) against the corresponding image band numbers as follows:
wavelength (nm)= 380.277 + 0.905X + (4.369 X 10-4)X2 - (4.356 X 10-')X3 (r~=0.9999) where X is the band number ranging from about 0 to about 511 (Figure 2, Box 2.11). This wavelength calibration equation is independent of light intensity but is dependent on the individual CCD detector and will have different coefficients for different detectors and binning combinations. The wavelength calibration is then applied to all subsequent images of food (Figure 2, Box 2.12). Given this detailed description of the present invention, it is well within the ordinary skill in the art to develop a calibration equation for a given sensor.
The next step is to select hyperspectral images (Figure 2, Box 2.13) with the key wavelengths identified earlier (Figure 2, Box 2.5). Then a ratio image is calculated where, in the simple case, the intensity of one image is divided by the intensity of a corresponding image at a different key wavelength on a pixel-by-pixel level (Fi'gure 2, Box 2.14).
This allows for the generation of ratio images that are used to identify and locate contamination on the food souroe.
To further enhance an image, the image background is eliminated through a masking procedure (Figure 2, Box 2.15). Histogram stretching is used to visually enhance the contaminants (Figure 2, Box 2.16). Masking is a prooess where all pixel intensities below a minimum value are assigned to a fixed value (zero).
Since the calculated ratio-image values vary (Figure 2, Box 2.14) from about 0 to about 2, masking is performed on one of the original key wavelength images and then transferred to the ratio image by assigning all corresponding ratio image intensity values to zero. For example, the image at 565-nm has an intensity value of less than about 120 for the background.

Thus, any pixels below the minimum value of 120 in the 565-nm image are assigned a value of zero in the ratio image, which removes the background.
Histogram stretching is a method to enhance the contrast of a displayed image. Typically, it is a linear stretching of the image intensity values to the full-scale display range (0 to 255 gray-scale) where the minimum image intensity value is assigned to a full-scale display value (255 for 8-bit gray-scale). However, for the masked ratio images, a histogram stretch is applied such that all image intensity values below a low threshold value are assigned a zero display value, and all those above a high threshold value are assigned a full display-scale value, with intermediate values assigned linearly proportional display values between the zero and full display-scale values. The method to this point would be suitable as a decision tool for food inspectors in the identification of the presence of contaminants.
Another alternative to histogram stretching is to use a threshold routine to quantitatively identify contaminants (Figure 2, Box 2.17). The threshold routine changes all pixel intensity values below a given contaminant threshold value to zero. The contaminant threshold is chosen so that all pixels greater than the threshold value are identified as contaminants. This is applicable for real-time identification of food contamination in a processing line.
For the multispectral imaging system 10 embodiment, spectral images at least two wavelengths are collected (Figure 3, Box 3.1) with a means for collecting spectral images 12 as described above. The spectral data is then analyzed using a software program 19 containing the necessary algorithms 20 and process steps. A software program 19 is stored in the memory of a computer 7 (Figures 1a-1d).
One example of an algorithm 20 for a first step in analyzing the spectral data is to calculate a ratio image at two key wavelengths to detect contaminants (Figure 3, Box 3.2).
Multispectral wavelengths are first collected (Figure 3, Box 3.1) at the key wavelengths as discussed above for the hyperspectral imaging embodiment. Then the image at one key wavelength is divided by the image at another key wavelength.
When this type of calculation is used the next step, is to reduce the background noise by separating the contaminated foodstuff from its background with a masking procedure (Figure 3, Box 3.3) as discussed above for the hyperspectral imaging embodiment. The fourth step, for this embodiment, separates the contaminants from the foodstuff with histogram stretching (Figure 3, Box 3.6) for display or monitoring purposes (Figure 3, Box 3.7) for qualitative separation, while the fifth step, for this embodiment, is a threshold procedure which quantitatively separates the contaminants from the foodstuff (Figure 3, Box 3.8), as described for the hyperspectral imaging embodiment.
Another embodiment for analyzing spectral data is illustrated in Figure 3 with a system as depicted in Figure 1d which is used to detect contamination on poultry carcasses which. have been hard scalded prior to removal of feathers. The carcass is illuminated by a light source 2 which provides a predetermined spectral profile. In this case, it has been determined that a light source corrected to about 5600K is particularly advantageous.
Data representing a multispectral image of light reflected from the target carcass are generated by the spectral image detector 12 in four wavelengths (Figure 3, Box 3.1). In particular, it has been determined that the following four ranges of wavelengths are particularly advantageous for this purpose:
A1 = about 750-830 nm AZ = about 450-500 nm A3 = about 500-535 nm AQ = about 550-585 nm The image data generated (Figure 3, Box 3.1) thus provide four reflectance values, each represented by a digital number (DN), for each pixel included in the acquired image - one such digital number for each of the frequency bands A1-1~4. These values are combined to generate a value I for each pixel (Figure 3, Box 3.2), according to the following algorithm:
/~ 3~/~ 1 -~- /~. 2) wherein n is an integer constant. Subtraction of the constant n in the numerator as indicated in Equation 1, reduces the amount of background noise (Figure 3, Box 3.3) lay altering the DN values of the specified wavelength 1~1. The result of the above calculation, which is performed in computer 7 of Figure 1d is the creation of an image file having a single DN value for each pixel.
The DN values generated are then filtered by a process referred to as "texture analysis" (Figure 3, Box 3.4), which characterizes the image according to real variations in pixel brightness -- that is, DN values -- to generate two new output image files indicative of the mean and variance for pixels within a moving window of about a 3 by 3 pixel mask, as illustrated in Figures 4a and 4b.
Figure 4a shows the manner of calculation of mean values in the texture analysis. For each window of nine pixels (about a 3 by 3 pixel set) a mean value of the DN values is determined.
For example, in Figure 4a, three windows a,~b, and c are indicated by brackets, with window a being enclosed by a heavy line. The mean of DN values in window a is about 4.2.
Similarly, the mean value for window b, enclosed by a dashed line, is about 3.2, as also shown in the set of mean values.
As the window is moved over the whole of the image file, a complete new image file of mean values is created. It is of course apparent that different sizes and shapes of windows can be used for this purpose.
The technique of calculation of a new image data set of variance values, shown in Figure 4b, is similar to that used to calculate the mean values in Figure 4a.
As is apparent, the performance of the texture analysis (Figure 3, Box 3.4) yields two image data sets, representing the spatially distributed mean and variance values, respectively for the data values I (Figure 3, Box 3.2). The mean and variance values are then added together to form a final output image.
Finally the output image is analyzed by computer 7 to determine whether ingesta or fecal contamination is present on the imaged carcass. This can be done, for example, by establishing a threshold value, IF which is indicative of contamination. In this case, calculated values in the final output image data are compared with a threshold value and a decision is made based on such comparison. For example, a positive contamination judgement (Figure 3, Box 3.10) could be made if a single pixel value exceeds the threshold value, i.e., a judgement of contamination could be made if a specified minimum number of values exceed the threshold; or a minimum number within a defined proximity. In a preferred embodiment, such a determination is made if the carcass has at least one pixel that exceeds the threshold (Figure 3, Box 3.10); otherwise, the carcass is concluded to be contaminant free (Figure 3, Box 3.9) .
In another embodiment, i.e., for carcasses that are soft scalded, carcasses are illuminated by a 5600K color corrected light source 2 in the same manner as indicated in Figure 1d.
In this embodiment, however, three multispectral image-data sets are acquired by specral image detector 12 at the following key wavelengths, one image dataset per wavelength range:
A~ = about 497-537 nm 2~~ = about 525-565 nm A3 = about 608-648 nm That is, DN reflectance values are determined for every pixel at each of the three wavelength ranges A1 - A3. Thus, each pixel is characterized by a spectral pattern or "signature"
consisting of these three DN values - one for each wavelength range.
Next, an algorithm referred to as Spectral Angle Mapper is applied (Figure 3, Box 3.2), correlating each of the acquired image-data sets A with predetermined spectral signature values at the detected wavelength ranges A1, 1~2, and A3, for each of four types of ingesta/fecal contamination, which differ only in their source within the digestive tract of the carcass prior to dressing; that is in particular, the stomach, the duodenum, the colon, and the ceca. The reflectance values for each of these types of contaminant at the key wavelength ranges varies in a characteristic fashion, and accordingly this characteristic pattern can be used to detect its presence. It is of course apparent that the greater the number of wavelength bands which is used to characterize both the target carcass and the types of contamination, the greater the precision of the correlation.
However, it has been determined that the three wavelength range values indicated above are sufficient in practice.
In this embodiment, the Spectral Angle Mapper (SAM) algorithm is calculated (Figure 3, Box 3.2). The Spectral Angle Mapper is a mathematical function which can be used to determine the degree of spectral similarity between the spectral values for each pixel in the acquired image data set and the corresponding spectral signature values for the four contaminants noted alcove.
The formula for calculation of SAM is as follows:
nb ti1"i Q' = COS 1 n6 1112 nb 1/2 ( E q . 4 ) 2 ~~ z ti 1"i wherein ti = detected reflectance value of the target carcass in the it'' band, ri = reflectance value of the predetermined spectral signature of a subject contaminant in the it'' band;
and nb = number of bands i in the image.
In this case, since four different types of contaminants are to be detected, four values of a are calculated for each pixel- one for each of the respective reference spectra - resulting in four image data sets, referred to as Rule Files. By virtue of the SAM algorithm, the data in each of these Rule Files are indicative of the likelihood of contamination of the imaged carcass.
Next, the sum or product of all the Rule Files produces a new image file (Figure 3, Box 3.2). Thereafter, processing for soft scald carcasses in Figure 3 proceeds in the same manner as for hard scald carcasses as in Figure 3. That is, a texture analysis is performed (Figure 3, Box 3.4); the mean and variance values are combined: and contamination is determined based on the combined image data set (Figure 3, Box 3.8-3.10).
The following examples are intended only to further illustrate the invention and are not intended to limit the scope of the invention which is defined by the claims. Poultry carcasses are used as a model system for testing the system of the present invention.

Live birds were obtained from a local broiler house, transported to the grow-out facilities at USDA-Agricultural Research Service in Athens, Georgia and held for about 4 days. The feeding regime was scheduled for meal feeding to provide a consistent amount of fecal material in the digestive tract among birds.
Birds were stunned (12 VAC), bled for about 90 seconds, scalded at about 57.5 °C for approximately 2 minutes (e. g. hard scald), ~r about 53 °C for 50 seconds (e. g. soft scald), and picked.
Hard scalding removes the skin cuticle resulting in a white carcass, whereas soft scalding leaves the cuticle intact resulting in a yellow carcass. In order to collect feces and ingesta, four replicates of 20 birds were processed and eviscerated to obtain fecal material from the duodenum, ceca, and colon portions of the viscera, and ingesta from the proventriculus and gizzard. Samples of skin were also taken from the breast. See Table 1 below for control variables and fixed values for digestive tract contents.

Table 1. Control Variables and Corresponding Fixed Values CONTROL VARIABLES VALUES

Bird 6 week male Diet Corn/Soybean meal Feed Withdrawal 8 hours Water Withdrawal 4 hours In order to collect Vis/NIR spectra from carcasses, pure feces from duodenum, ceca, colon and both hard and soft scalded uncontaminated skin were scanned with an NIRSystems 6500 monochromator (McGee, U.S. Pat. No. 4,~6~,~/3~, herein incorporated by reference). Spectra were recorded from about 400 nm to about 2500 nm in about 2-nm intervals and analyzed from about 400 nm to about 900 nm. Samples of uncontaminated breast skin were presented in cylindrical sample cells (internal diameter-about 38 mm~ depth-about 9 mm) with an optical quartz surface and a cardboard backing. Samples of pure feces were presented in cylindrical sample cells (internal diameter-about 38 mm; depth-about 0.1, 0.2, or 0.3 mm) with an optical quartz surface and a locking back. Each sample was scanned about 32 times, averaged and transformed to log (1/R1, where R is reflectance). Figure 5 is the reflectance spectra of uncontaminated poultry carcass breast skin (hard and soft scalded), and pure feces from different sites in the digestive tract. Samples 5.1 and 5.2 are uncontaminated breast skin subjected to hard and soft scald treatments, respectively.
Samples 5.3-5.5 are pure feces from the duodenum (5.3), colon (5.4), and ceca (5.5).
A commercial spectral analysis program (NIRS3, Infrasoft International, Inc., Port Matilda, PA) was used to collect the spectra of pure contaminates and uncontaminated poultry carcass skin and for principal component analysis (PCA). The spectral data set (n=76 uncontaminated hard and soft scalded skin; N=42 duodenum; N=37 ceca; and N=25 colon) was transformed with standard normal variate and de-trending procedures to remove the interference of light scatter from the skin and differences in pathlength due to sample thickness. The spectra were mean centered and reduced by PCA. Translated into principal components, the new coordinate system has fewer dimensions than the original Vis/NIR data set, and the directions of the new coordinate axes (called principal components) were chosen to describe the largest variations.
The PCA algorithm creates scores, which represent the position of samples relative to the principal components. For each principal component, scores are derived by taking the sum across the spectrum of weights times the log (1/R) values. The corresponding relationship between the Vis/NIR spectra and the principal components are called loadings. Plots of loadings often resemble the spectra of samples and thus offer scope for interpretation. Four principal components were used in the PCA.
The components explained about 99.80 of the Vis/NIR spectral variation. Figure 6 shows a clear discrimination between uncontaminated skin and feces using principal components 1, 2, and 4. Principal component 1 (PC 1) was primarily responsible for the separation of uncontaminated skin (Figure 6, 6.1 hard and soft scald), from duodenum (Figure 6, 6.2), colon (Figure 6, 6.3), and ceca (Figure 6, 6.4). Uncontaminated skin had negative scores for PC 1, whereas pure feces had positive scores for PC 1. Zoadings are the regression coefficients for each Vis/NIR wavelength for each principal component and indicate which wavelengths are dominantly influencing the discrimination.
From PC analysis, four key wavelengths were identified for discrimination of uncontaminated skin from pure feces based on the loadings.
Figure 7 shows the loadings for PC 1, PC 2, and PC 4 as a function of wavelength. Key wavelengths are identified by maximum loadings at about 565 nm for PC 1 (Figure 7, 7.1), about 434 and 517 nm for PC 2 (Figure 7, 7.2), and about 628 nm for PC
4 (Figure 7, 7.3). These key wavelengths were selected and applied to hyperspectral images of uncontaminated and contaminated carcasses.

Spectral calibration was performed to correlate absolute wavelength data from known spectral light sources of Mercury Argon (HgAr) and Krypton (Kr) gas emission calibration lamps (Model 6035 and 6031, Oriel Instruments, Stratford, CT, respectively), and Helium-Neon lasers, to the 512-hyperspectral image bands (1024 pixels with a binning of 2) obtained from the CCD detector 1a. Figure 8 shows the data and the non-linear calibration for the hyperspectral imaging system 10, which was used to correlate the band numbers~of the hyperspectral images to the actual wavelengths. The wavelength calibration equation for hyperspectral imaging system 10 is as follows:
Wavelength (mn)=380.277 + 0.905X + (4.369 x 10-4) X2 - (4.356 x 10-' ) X3 (r' = 0.9999) where X is the band number ranging from about 0 to about 511.

Immediately after processing, as in Example 1, carcasses, fecal, and ingesta samples were used for image acquisition. Carcasses were contaminated with feces from the duodenum, ceca, and colon, and ingesta with varying contaminate size and location.
Carcasses were imaged in a shackle welded to a stainless steel rod, suspended across two stands. A laser beam was used to align fiber-optic line-light propagation to make light propagation on the carcasses as diffuse as possible. Light intensity and distribution on the carcasses were measured and optimized with a digital intensity meter (Mavolux 5032C, Gossen, Germany) before image data were collected. Carcasses were imaged with hyperspectral imaging system 10 with 4 by 2 binning and SensiCamTM software with the following control settings:
actual image size of about 320 (horizontal) by about 340 (vertical) pixels spatial resolution, and about 512 wavelengths spectral resolution. The spectral resolution of hyperspectral images was approximately 0.9 nm. The exposure time and delay time of camera control during image acquisition were about 50 msec. and zero, respectively. Even though scanning time depends on the size of a carcass and image resolution, the average time to scan a whole carcass was about 34 seconds. Hypercube image files were created from line scan image data with HyperVisual software (Provision Technologies, Stennis Space Center, MS), which converts 16-bit binary data into binary sequence mode data for hyperspectral image processing. First, hyperspectral images of uncontaminated carcasses were collected. Then, carcasses were contaminated with feces and ingesta varying in type of contaminant, contaminant spot size, and location on the carcass.
Hyperspectral images and spectral image files were further processed, analyzed, and displayed using Environment for Visualizing Images (ENVI) software (Research Systems, Inc., Boulder, CO).
Figure 9 shows spatial images at some selected spectral wavelengths acquired by the hyperspectral imaging system.l0 to demonstrate image quality and spectral differences. The spectral images less than 400-nm wavelengths contained noise compared with others, because the grating diffraction efficiency of the system below about 400 nm is less than about 30o and the nominal spectral range of spectrograph is between about 430 nm to about 900 nm. The fecal (top three rows on carcass) and ingesta spots (bottom row on carcass) on each carcass were displayed distinctively up to about 517 nm. However, the spots of duodenum and ingesta began to disappear as the wavelengths increased beyond 517 nm. Feces from the ceca were clearly found over all the wavelength spectral images. Thus, spectral images selected from hypercube image data were useful for the identification of feces and ingesta contamination on the poultry carcasses.

Using the wavelength calibration from example 3, the key wavelengths of about 434 nm, 517 nm, 565 nm, and 628 nm, identified with the Vis/NIR monochromator, corresponded to band numbers 58, 143, 190, and 251. Figures 10a-a show an approximate color composite (Red: 634 nm; Green: 520 nm; Blue:
446 nm) image and four spectral images at key wavelengths (434, 517, 565, and 628 nm) of a poultry carcass contaminated with.
feces (duodenum, ceca, colon) and ingesta. Cecal feces were detected from the raw spectral images from the four selected wavelengths including the false color image. However, it was difficult to detect feces from duodenum and ingesta samples with the about 5~5 nm and about 628 nm spectral images. With a single wavelength image, uncontaminated dark areas in the leg and wing folds were incorrectly identified as contaminates.
Two wavelength ratio images were determined from the four key wavelengths above. Six ratio images were obtained from the combination of different key wavelengths as shown in Figures 11a-f. Among ratio images, the image at 565 nm divided by the image at 517 nm could identify feces (duodenum, ceca, and colon) and ingesta contaminants including colon feces located below the tail as shown in figure 11d. The ratio images of the image at 517 nm divided by the image at 434 nm (Figure 11a), the image at 565 nm divided by the image at 434 nm (Figure 11b), and the image at 628 nm divided by the image at 434 nm (Figure 11c) show distinctive ceca (dark spots on the body) contamination.
However, other contaminated spots of duodenum, colon, and ingesta were not readily apparent. Even though the image at 628 nm divided by the image at 517 nm (Figure 11e) shows all the contaminated spots on the body, other white spots under the wings and the area between the legs caused false positive errors. Similarly, as seen on the image at 628 nm divided by the image at 565 nm (Figure 11f), false positive contamination between the carcass legs were actually caused by skin cuticle or blood hemorrhages on the skin of the carcass.
Other,contaminated carcasses showed this algorithm could detect contaminates of different sizes at different locations. As shown in Figure 12, blood clot (Figure 12, 12a.1) in the color composite disappeared (Figure 12, 12b.1) after the image ratio algorithm was applied. In addition, this algorithm could detect fecal contaminant in the shadow of the wing fold (Figure 12, 12a.2) in the composite image; (Figure 12, 12b.2 in the ratio image). Thus, image-ratio algorithm, particularly 565-nm and 517-nm wavelengths, identified fecal and ingesta contaminants on the surface of poultry carcasses extremely well, while minimizing false positive contaminates.

The background of the original two-wavelength ratio image is noisier than the chicken body and contains no useful information. To eliminate the background, a masking procedure was implemented for further processing to segregate the ratio image of a carcass from the background. To build a masking template for each carcass (Figure 13a), a single spectral image was selected from the 512- hvperspectral images. For example, a template (Figure 13b) was created by thresholding an image at 565 nm. Intensities below a minimum thresholding value of about 120, which corresponded to the background, were then assigned a value of zero. Figure 13c shows the ratio image after the masking procedure was applied. It was obvious that the masking procedure made the spots of contamination on the carcass more visually distinctive.

After eliminating the background noise with a masking procedure, a histogram stretching algorithm was used to separate feces and ingesta contaminates from a carcass as shown in Figure 14x.
Both linear and nonlinear histogram-stretching algorithms were tested. As shown in Figure 14b, top center white portion of vent area indicates natural contamination of colon feces (Figure 14, 141x.1); the second row represents duodenum (Figure 14, 14b.2); third row represents ceca (Figure 14, 14b.3); fourth row represents colon feces (Figure 14, 14b.4); and fifth row represents ingesta contaminates (Figure 14, 14b.5), , respectively. From numerous ratio images of both hard and soft scalded carcasses, parameter values of histogram-stretching algorithm for the sample in Figures 14a and 14b were determined as follows: minimum input = about 1.28; maximum input = about 1.60; minimum output = about 0.75; maximum output = about 2.38.
These parameter values force intensities below 1.28 to have a display value of zero, intensities between 1.28 and 1.60 to have display values that linearly vary from zero to full scale, and intensities above 1.60 to have display values at full scale.

The hyperspectral image processing algorithms (ratio of two-wavelength images) demonstrated in the previous examples were tested with sixteen poultry carcasses. As shown in Table 2 below, the histogram stretching algorithms were accurate for both the linear and nonlinear models. Even though threshold values of each sample varied slightly, the mean minimum and maximum input threshold values were about 0.76 (Standard Deviation (S.D.)= about -0.03) and about 2.29 (S. D.= about 0.25). For the linear model, the mean minimum and maximum values were about 1.25 (S. D. - about 0.05) and about 1.69 (S. D.=
about 0.08), respectively. For the square-root model, the mean minimum and maximum values were about 1.39 (S. D. - about 0.06) and about 1.55 (S. D.= about 0.13), respectively.
The square-root model of histogram stretching performed perfectly for identifying fecal and ingesta contaminants.
However, the linear model missed several contaminant spots (chicken sample #2, #3, and #7).
Table 2. Threshold values of histogram stretching for segregating feces and ingesta from chicken carcasses. ' Sample Inp ut Out put Number of ID Linear Square Contaminants root Predicted Actual M Lower Upper Lower Upper Linear /
Square Chicken#1 0.77 2.62 1.20 1.88 I.34 1.38 9 / 9 9 Chicken#2 0.77 2.03 1.28 1.8Q 1.38 1.46 8 / 9 9 Chicken#3 0.76 2.22 1.20 1.80 1.30 1.94 7 / 9 9 Chicken#4 0.72 2.28 I.26 1.60 1.36 1.56 10 / 10 10 Chicken#5 0.80 2.10 1.20 1.70 1.36 1.46 9 / 9 9 Chicken#6 0.75 2.33 1.28 1.60 1.36 1.46 11 / 11 11 Chicken#7 0:74 2.01 I.26 1.60 1.30 1.40 6 / 8 8 Chicken#8 0.80 2.30 1.20 1.70 .1.46 1.56 12 / 12 12 .

Chicken#9 0.74 2.46 1.30 1.70 1.50 1.60 14 / 14 14 Chicken#100.74 2.06 1.34 I.70 1.46 I.S6 16 / 16 16 Chicken# 0.76 2.14 1.20 1.64 1.41 1.5 16 / I 6 I 6 11 _ I

Chicken#120.74 2.51 1.30 1.64 1.44 1.57 Z4 / 14 14 Chicken#130.73 2.18 1.20 1.68 1.40 I.50 I3 / 13 13 Chicken#140.80 2.96 1.30 1.66 1.36 1.50 I2 / 12 12 Chicken#150.80 2.13 1.28 1.60 I.40 1.60 12 / 12 12 Chicken#160.73 2.34 1.20 1.74 1.44 1.66 16 / 16 16 Mean 0.76 2.29 1.25 1.69 1.39 1.55 Std. Dev. 0.03 0.25 0.05 0.08 0.06 0.13 Figure 15 shows the comparison of intensity distribution between a clean and contaminated carcass to demonstrate the performance of the masking procedure and the threshold algorithm. As shown in Figures 15a-e, there were no readily apparent differences of the intensity distribution of the original ratio images between the clean (Figure 15a) and fecal and ingesta contaminated (Figure 15d) carcass. Masking makes the carcass outline apparent (Figures 15b and 15e), but little distinctive between the contaminated and uncontaminated carcass can be seen. After the thresholding algorithm was applied, intensities above the threshold are shown in Figure 15c for the clean bird disappeared; whereas the peak intensities above the threshold in Figure 15f indicated the spots of feces and ingesta on the carcass. Therefore, the threshold algorithm can be further applied for the automatic detection of fecal and ingesta contaminants on poultry carcasses in conjunction with masking and the optimum threshold parameter values.
The foregoing detailed description is for the purpose of illustration. Such detail is solely for that purpose and those skilled in the art can make variations therein without departing from the spirit and scope of the invention.

Index of the Elements 1A. Charge-Coupled Device Detector 1B. Lens Assembly 1C. Spectrograph 2. Lighting System 2A. Fiber Optic Cable 2B. Lamp Assembly 2C. Power Supply for Lighting 4. Power Supply for at least one Charge-Coupled Device Detector 5. Battery Backup 6. Computer Monitor 7. Computer 7a. Interface Cable 9. Data Processing Unit 10. Imaging Systems 12. Means for Obtaining Spectral Images 17. Frame Grabber 19. Software 20. Algorithm f0 r O 07 h CO M ~ O (O CO M CO
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~z~~s File Name: e:~scaldltrimlabc.cal Spectra anc constituent Data File File Date: Tue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 1 2 3 4 5 6 7 8 9 Sample Nu 0102s "0103s" "0104s" "0105s" "0106s" "0107s" "0108s" "0109s"
"0110s"
400 0.463525 0.4788483 0.4838938 0.4695534 0.4793588 0.4137969 0.4523254 0.4422773 0.487329 402 0,485949 0.504639 0.5121277 0.4954112 0.5071897 0.4325764 0,4762123 0.4671833 0.5098276 404 0,509578 0.5322698 0.5380695 0.5253913 0.531085 0.4523951 0.502008 0.4860967 0.5328052 406 0,532265 0.557336 0.5598709 0.5429813 - 0.55439 0.4690523 0,5204953 0.5057206 0.5540736 408 0.550346 0.5759787 0.5819975 0.5596452 0.5731922 0.4845066 0,5395666 0.5242565 0.5748277 410 0.566941 0.5935451 0.602661 0.5755309 0.5892518 0.49801 0.5569966 0.5402868 0.5942198 412 0,582469 0.6111816 0.6206171 0.5898036 0.6049463 0.5117362 0.5732133 0.554607 0.6115218 414 0.595918 0.6249065 0.6343607 0.6021099 0.6183185 0.521819 0.5886634 0.567129 0.6275928 416 0.60708 0.637239 0.64663 0.6109515 0.629473 0.5314206 0.600623 0.5776856 0.6420978 418 0.616541 0.64675 0.6573169 0.6175108 0.6374405 0.5363703 0.6098161 0.5850421 0.6534052 420 0.622283 0.6540251 0.666238 0.6218126 x.5444607 0.5449553 0.6189444 0.5912401 0.6635942 422 0.625605 0.6578929 0.671261 0.6235379 0.6477855 0.5488715 0.623551 0.5937292 0.6708127 424 0.626405 0.6605738 0.6745231 0.623962 0.6502526' " 0.551026 0.626884 0,5944499 0.6756957 426 0.625136 0.6601317 0.6756846 0.6196387 0.6485714 0.5512289 0.6282412 0.592833 0.6785378 428 0.621402 0.6582128 0.674981 0.6143537 0.6458614 0.5498758 0.6270275 0,5893205 0.6783627 430 0.614919 0.6541668 0.6721152 0.6086149 0.6411394 0.5479244 0.6229709 0.5841771 0.6758286 432 0.606976 0.647888 0.6672614 0.60081 0.63547 0.5426617 0.6182697 0.5766237 0.6714652 434 0.596825 0.6403149 0.6604199 0.5916975 0.6273642 0.5362863 0.6107314 0.568948 0.6638387 436 0.586522 0.6331297 0.6524678 0.5828173 0.6187864 0.5294421 0.6020963 0.5589063 0.6549271 438 0.574681 0.6249124 0.6435721 0.5736563 0.6101949 0.5218401 0.5927975 0.5481378 0.6447909 440 0.562256 0.616564 0.6335912 0.5632824 0.5996118 0.5129365 0.5821788 0.5374465 0.6337761 442 0.549899 0.6079009 0.6230059 0.5538462 0.5893925 0.5047283 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0.2048067 0.2132351 0.1963614 0.2236237 942 0.216517 0.2083436 0.203404 0.2161877 0.2176169 0.2086374 0.216035 0.2000844 0.2257917 944 0.220606 0.212336 0.2055372 0.2186904 0.2201062 0.2129853 0.2193872 0.2042445 0.2285138 946 0.225534 0.2171637 0.2084179 0.2220391 0.2235123 0.218212 0.2236342 0.2091141 0.2321244 948 0.23099 0.2225062 0.2118083 0.2259733 0.227587 0.2239841 0.2284879 0.214387 0.2363649 950 0.236863 0.2282619 0.2156546 0.2304258 0.2322346 0.2301778 0.2338487 0.21997 0.241134 File Name: e:lscald\trimlabc.cal Spectra and Constituent Data File File Date: Flue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08;32 2000 Fite ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 10 11 12 13 14 15 16 17 18 Sample Numb "0201 s" "0202s" "0203s" "0204s" "0205s" "0206s" "0207s" "0208s"
"0209s"
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File Name: e:lscald\trim\abc.cal Spectra and Constituent Data File File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 19 20 21 22 23 24 25 26 27 Sample Numb "0210s" "0211s" "0212s" "0213s" "0214s" "0215s" "0216s" "0217s"
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0.2142523 936 0.2249781 0.2189677 0.206372 0.2127112 0.2137785 0.2194895 0.2163068 0,1938298 0.2154665 938 0.2256822 0.2218502 0.2082836 0.2148465 0.2169317 0.2224222 0.2161303 0.1960713 0.2167751 940 0.2266564 0.2251 1 59 0.2105042 0.2173394 0.220485 0.225773 0.2161383 0,1986426 . 0.2184117 942 0.2280967 0.2287099 0.2130883 0.2202222 0.2243402 0.2295065 0.2166091 0,2015176 0.22051 944 0.2301358 0.2328538 0.2161871 0.2236708 0.2287049 0.233839 0.2176408~~.
~0.2D48479 0.2232429 946 0.2331252 0.2378626 0.2201177 0.2280158 0.2338787 0.2391309 0.2195619 0.2088982 0.2269488 948 0.2368185 ~ 0.2434089 0.2246023 0.2329596 0.2395378 0.2450138 0.2221816 0.2134156 0.2313756 950 0.2411451 0.2493918 0.2295756 0.2384168 0.2455868 0.2514042 0.2254617 0.2183118 0.2364577 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 " Samples: 149 Deleted: 0 Constituents: 1 No. Data Points; 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 28 29 30 31 32 33 34 35 36 Sample Numb "0219s" "0220s" "0311S" "0312S" "0313S" "314S" "3155" "316S"
"318S"
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0.1824096 0.2343705 0.2434126 0.230997 0.2630665 0.2428006 0.2509701 0.2278456 0.2278782 940 0.1843384 0.2379803 0.2480415 0.2325255 0.2677678 0.245887 0.2554748 0.230869 0.2322005 942 0.1865782 0.2420154 0.2531175 0.2345541 0.2728732 0.2494192 0.2604302 0.2343082 0.2369459 944 0.1892557 0.2467044 0.258931 0.2372583 0.278679 0.2536096 0.2661076 0.238361 0.2423928 946 0.1926042 0.2524033 0.2658573 0.2409958 0.2855528 0.2588052 0.2728918 0.2433486 0.2488772 948 0.1964229 0.2587555 0.2735056 0.2455038 0.2930864 0.2646903 0.280367 0.2489665 0.2560433 950 0.2006547 0.2656851 0.2817457 0.2507215 0.3011398 0.2711724 0.288423 0.2551292 0.2637792 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data Fife File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal _ Masfer No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 37 38 39 40 41 42 43 44 45 Sample Numb "320S" "0513ss "0514ss" "0515ss" "0516ss" "0521ss" "0522ss"
"0523ss" "0524ss"
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0.2250048 0.1881052 0.214536 0.1977158 0.1713421 938 0.2455959 0.1948436 0.2030061 0.2006262 0.2276441 0.1896283 0.2158433 0.1981373 0.1717178 940 0.249667 0.1963908 0.2061109 0.2032282 0.2306904 0.1914054 0.2174509 0.1987531 0.1722413 942 0.2541123 0.1982309 0.2095072 0.2061636 0.234117 0.1934765 0.2194578 0.1997572 0.1730505 944 0.259152 0.2004744 0.2133698 0.2096056 0.23812 0.1959594 0.2219991 0.2012307 0.1742207 946 0.2651384 0.2033574 0.2179771 0.2138437 0.2430432 0.1990951 0.2253819 0.2034452 0.1759525 948 0.2716807 0,2066736 0.2230253 0.218591 0.2485323 0.2026666 0.2293322 0.2062185 0.1780996 950 0.2787375 0,2103622 0.2284542 0.223777 0.2545094 0.206621 0.2337918 0.2095101 0.1806421 File Name: e:\scald\trim\abc,cal Spectra and Constituent Data File File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 .
Segment 2 1100 - 2498, 0 Position 46 47 48 49 50 51 52 53 54 Sample Numb "0101 a" "0102a" "0103a" "0104a" "0105a" "0106a" "0107a" "0108a"
"0109a"
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0.3613961 0.3789951 0.4255117 0.3871592 0.3672308 0.4000627 0.409201 702 0.4194126 0.3575014 0.3610249 0.3788131 0.4250742 0.3870628 0.3667303 0.3996732 0.4088074 704 0.4186475 0.357174 0.3606578 0.3786556 0.4246812 0.3869697 0.3662194 0.3992771 0.4083872 706 0.4179046 0.3568903 0.3602863 0.3785004 0.4243562 0.3869476 0.3657371 0.3989183 0.4080233 708 0.4172852 0.3566876 0.3600554 0.3784355 0.424069 0.3869668 0.3653712 0.3986688 0.4077508 710 0.4167575 0.3564978 0.3598155 0.3784096 0.4238446 0.3870549 0.3650492 0.3984151 0.4075164 712 0.416284 0.3563907 0.3596331 0.3784339 0.4237056 0.3872186 0.3647736 0.3982231 0.4073521 714 0.4158753 0.356314 0.359511 B 0.3784819 0.4236037 0.3874266 0.3645721 0.3981017 0.4072319 716 0.4155684 0.3562888 0,3594258 0.3785936 0.423564 0.3876975 0.3644122 0.3980216 , 0.4071872 718 0.4153371 0.356338 0,3593937 0.3787436 0.4235595 0.3879987 0.3643036 0.3980061 0.407195 720 0.4151466 0.3565371 0,3595261 0.3789567 0.4236789 0.3883921 0.364257 0.3980084 0.4072932 722 0.4150342 0.3566742 0.359594 0.3791938 0.4237727 0.3887939 0.3643877 0.3980829 0.4073879 724 0.4149686 0.3568439 0,3597142 0.3795873 0.4239186 0.3892286 0.3644306 0.398187 0.40756 726 0.414995 0.3571284 0.3598881 0.3799411 0.4241699 0.3897822 0.3645617 0.3983531 0.407791 728 0.4150535 0.3574196 0.3600888 0.3803332 0.4244514 0.3904268 0.3647281 0.3986474 0.4080749 730 0.4151592 0.3577251 0.3603301 0.38073 0.4247372 0.3909796 0.3649106 0.398917 0.4083837 732 0.415347 0.358114 0.3606129 0.3811972 0.4250417 0.39158 0.3651588 0.399192 0.4088372 734 0.4155845 0.3585067 0.3609275 0.3816535 0.4254089 0.3922423 0.3654522 0.3994828 0.4092267 736 0.4157694 0.3588944 0.3612246 0.3821112 0.4258028 0.3928321 0.3657168 0.3998015 0.4095945 738 0.4159665 0.3593241 0.361549 0.3826253 0.4261315 0.3934748 0.366016 0.4001458 0.4100254 740 0.4162124 0.3597916 0.3619226 0.3831438 0.4264503 0.3941363 0.3663402 0.40048 0.41047 742 0.4164385 0.3602161 0.362242 0.3836547 0.4267298 0.3948012 0.3666683 0.4008354 0.4109129 744 0.4166786 0.3606743 0.3625972 0.3841592 0.4270332 0.3954521 0.36701 0,401221 0.4113717 746 0.4169279 0.361138 0.3629472 0.3846779 0.427291 0.3961083 0.3673649 0.4015834 0.4118482 748 0.4171755 0.3616666 0.3633026 0.3852445 0.4275706 0.3967775 0.3677315 0.4019861 0.4123771 750 0.4174551 0.3621908 0.363717 0.3858126 0.427833 0.3974355 0.3681179 0.402387 0.4128507 752 0.4177344 0.3627132 0.3641005 0.3863955 0.4281212 0.3980905 0.3685869 0.4027975 0.4133655 754 0.4180884 0.3633062 0.364561 0.3870488 0.428424 0.3988028 0.3690353 0.4032546 0.4139429 756 0.4184087 0.3639078 0.3650013 0.3877002 0.4287333 0.399504 0.3695317 0.4037235 0.4145056 758 0.4187507 0.3645211 0.3654718 0.3883601 0.4290567 0.4001973 0.3700421 0.4041989 0.4150611 760 0.4191331 0.3651546 0.3659837 0.3890747 0.4293989 0.4009074 0.370588 0.4046968 0.41564 762 0.4195161 0.3657874 0.3664665 0.3898082 0.4297639 0.4016252 0.3711504 0.4051901 0.4161905 764 0.41987 0.3663049 0.366881 0.3904774 0.4301298 0.4022532 0.3716466 0.4056388 0.4166829 766 0.4202587 '0.3668026 0.3672498 0.3911688 0.4305208 0.4028775 0.372124 0.4061034 0.4171669 768 0.4206733 0.3672104 0.3675422 0.3918465 0.4309739 0.4034717 0.3725874 0.4065662 0.4176591 770 0.4210329 0.3676042 0.3677933 0.3924651 0.4314331 0.4040624 0.3729849 0.4070475 0.4180908 772 0.4213438 0.3680565 0.3680909 0.3930205 0.4318348 0.4046353 0.3733736 0.4075085 0.4185699 774 0.4216868 0.3686257 0.3685249 0.3936443 0.4322392 0.405283 0.373832 0.4080392 0.4191148 776 0.4220451 0.3693008 0.369045 0.3942727 0.4326369 0.4060047 0.3743625 0.4085838 0.4197688 778 0.4224846 0.3700173 0.3696292 0.3949823 0.4330459 0.40679 0.3749303 0.4091755 0.420513 780 0.4229472 0.3707435 0.3702455 0.3957054 0.4334487 0.407596 0.3755235 0.4098101 0.4213042 782 0.4234517 0.3714523 0.3708466 0.3964794 0.4338585 0.4083721 0.3761289 0.4104034 0.4220667 784 0.423956 0.3721638 0.371431 0.3972542 0.4342915 0.4091288 0.3767461 0.4110081 0.4228189 786 0.42439 0.3727898 0.37194 0.3979676 0.4346951 0.4097804 0.3772832 0.4115407 0.4234642 788 0.4248152 0.3733927 0.3724348 ' 0.3986663 0.435099 0.4104364 0.3778242 0.412075 ' 0.4240993 790 0.4252135 0.3739655 0.3729049 0.3993192 0.4354967 0.4110675 0.3783303 0.4125725 0.4246922 792 0.4255692 0.3744972 0.3733406 0.3999504 0.4358599 0.4116518 0.3787881 0.4130185 0.4252414 794 0.425947 0.3750358 0.3737881 0.4005779 0.4362326 0.4122492 0.3792581 0.4134768 0.4258124 796 0.4262979 0.3755724 0.3742292 0.4011947 0.4365919 0.412838 0.3797222 0.4139332 0.4263706 798 0.4266448 0.3760802 0.3746373 0.4017817 0.4369425 0.4133916 0.3801519.
0:4143644 0.4268985 80D 0.426968 0.3765489 0.3750259 0.402333 0.4372606 0.413923 0.3805592 0.4147692 0.4273928 802 0.4272747 0.3770018 0.3754078 0.4028702 0.4375799 0.4144383 0.380951 0.4151434 0.4278741 804 0.4275649 0.3774389 0.3757728 0.4033996 0.437897 0.4149364 0.381324 0.4155207 0.428346 806 0.4278234 0.3778265 0.3760939 0.4038662 0.4381877 0.4154033 0.3816659 0.4158413 0.4287827 SDB 0.4280674 0.3781703 0.3763705 0.4042851 0.4384608 0.4158217 0.3819667 0.4161366 0.429177 810 0.4282863 0.3784928 0.3766559 0.4046955 0.4387323 0.4162332 0.3822341 0.4164081 0.4295433 812 0.4284449 0.3787771 D.3Y68711 0.4050454 0.4389679 0.4165931 0.3824568 0.4166267 0.4298324 814 0.4285601 0.3789856 0.3770314 0.405324 0.4391546 0.416876 0.3826281 0.4167832 0.4300808 816 0.4286343 0.3791704 0.3771759 0,4055594 0.4393493 0.4171519 0.3827783 0.4169178 0.4302852 818 0.428673 0.3793002 0.3772748 0:405754 0.4395295 0.4173677 0.3828687 0.4170023 0.4304361 820 0.4286688 0.3793819 0.3773167 0.4058869 0.4396963 0.4175264 0.3829159 0.4170268 0.430551 822 0.4286157 0.3794123 0.377334 0.4059891 0.4398309 0.4176379 0.3829117 0.4170068 0.4306077 824 0.4285116 0.3794003 0.3772932 0.4060053 0.4399449 0.4176974 0.3828691 0.4169307 0.4305993 826 0.4283561 0,3793247 0.3772021 0.4059691 0.4400342 0.4176971 0.3827752 0.4167951 0.4305419 828 0.4281574 0.3792049 0.3770792 0.4058664 0.4400824 0.4176323 0.3826212 0.4166076 0.4304377 830 0.4278708 0.3790198 0.3768608 0.4056922 0.4400801 0.4174945 0.3823968 0.4163486 0.4302314 832 0.4275157 0.378757 0.3765807 0.4054403 0.4400069 0.4173003 0.3821337 0.4160093 0.4299589 834 0.42709 0.3784271 0.376242 0.4051068 0.4398658 0.4170065 0.3817766 0.4155989 0.4296098 836 0.4265983 0.3780243 0.3758287 0.4047265 0.4396459 0.4166377 0.3813495 0.4151115 0.4291912 838 0.4259852 0.3775289 0.375316 0.4041985 0.4392931 0.4161586 0.3808246 0.414515 0.4286436 840 0.425291 0.3769488 0.374738 0.403588 0.4388272 0.4155783 0.3802088 0.4138226 0.4280234 842 0.4245247 0.3763015 0.3740633 0.4029099 0.4382678 0.4149199 0.3795432 0.4130661 0.4273149 844 0.4236584 0.3755715 0.3733274 0.4021265 0.4376244 0.4141879 0,3787682 0.4122085 0.4265075 846 0.4226919 0.374742 0.3724943 0.4012766 0.4368787 0.4133313 0.3779178 0.411247 0.4255779 848 0.4216746 0.3738696 0.3716144 0.4003386 0.4360681 0.4124197 0,3770025 0.4102299 0.4246187 850 0.4206019 0.3729373 0.3706766 0.3993349 0.4351816 0.4114378 0.3760458 0.4091752 0.4235728 852 0.4194056 0.3718997 0.369619 0.3982291 0.434198 0.4103693 0,3749661 0.4079776 0.4224103 854 0.4181615 0.3708001 0.3685322 0.3970613 0.433157 0.4092231 0.3738405 0.4067273 0.4211821 856 0.4168938 0.3696727 0,3673955 0.3958486 0.4320716 0.4080308 0.3726892 0.4054401 0.4199397 858 0.4155754 0.3685074 0,3662159 0.3945954 0.4309499 0.4068143 0.3714933 0.4041224 0.4186295 860 0.4141444 0.3672489 0.36496 0.3932438 0.4297315 0.405474 0,3701946 0.4026777 0.4172115 862 0.4126962 0.3659552 0.3636642 0.3918581 0.4284892 0.4041107 0.3688829 0.4012299 0.4157556 864 0.4112589 0.3646654 0,3623588 0.3904749 0.4272396 0.4027709 0.3675703 0.3997804 0.4143188 866 0.4097243 0.3632841 0.3609853 0.3890045 0.4259075 0.4013177 0.3661654 0.3982247 0.4127668 868 0.4081236 0.361847 0.3595494 0.3874661 0.4245244 0.3998069 0.3647045 0.3966179 0.4111604 870 0.4065745 0.3604566 0.358151 0.3859885 0.423186 0.3983555 0.3632834 0.3950633 0.4095946 872 0.4049802 0.3590243 0.356718 0.384464 0.4218065 0.3968423 0.3618272 0.3934461 0.4079659 874 0.4033192 0.3575253 0.3551976 0.382862 0.4203805 0.395281 B 0.3603002 0.3916014 0.406288 876 0.4016594 0.3560312 0:3536951 0.3812676 0.4189583 0.3937309 0.3587864 0.3901341 0.4046069 878 0.4000391 0.3545586 0.3522263 0.3796949 0.4175494 0.3921921 0.3572748 0.388502 0.4029488 880 0.3983669 0.353067 0.3507148 0.3781019 0.4161189 0.3906225 0.3557447 0.3868273 0.401252 882 0.3965697 0.3514447 0.3490846 0.3763685 0.4145692 0.3889279 0.3540989 0.3850252 0.3994102 864 0.394884 0.3499342 0.3475462 0.3747415 0.4131356 0.3873419 0.3525372 0.3833392 0.397687 886 0.3932441 0.3484864 0.3460543 0.3731565 0.4117223 0.3857884 0.3510365 0.3816924 0.396003 888 0.3915084 0.3469063 0.3444719 0.3714911 0.4102407 0.3841499 0.3494329 0.3799505 0.3942236 890 0.3897659 0.3453555 0.3428881 0.3698307 0.4087494 0,3825115 0.3478404 0.3782092 0.3924277 892 0.3881069 0.3438711 0.3413747 0.3682255 0.4073197 0.380948 0.3463046 0.376562 0.3907244 894 0.3863853 0.3423365 0.3397995 0.3665701 0.4058542 0.3793396 0.3447234 0.3748415 0.3889472 896 0.3846301 0.3407538 0.3382007 0.364861 0.4043486 0.3776815 0.3430862 0.3730723 0,387128 898 0.3829026 0.3392026 ' 0.3366143 0.3631926 0.4026744 0.3760629 0.3415002 0.3713463 0.3853431 900 0.3811699 0.3376428 0.3350179 0.3615019 0.401414 0.3744465 0.3398867 0.369619 0.383549 902 0.379399 0.3360252 0.3333715 0.3597742 0.3999452 0.3727962 0.3382372 0.3678392 0.3817061 904 0.3775901 0.3343808 0.3316866 0.357998 0.3984639 0.3711149 0.3365328 0.3660397 0.3798307 906 0.3758495 0.3327572 0.3300461 0.3562726 0.3970456 0.3695062 0.3348887 0.3642966 0.3780174 908 0.3741361 0,3311706 0.3284288 0.3545658 0.3956816 0.3679174 0.3332713 0.3625894 0.3762304 910 0.3723726 0,3295153 0.3267618 0.3528129 0.3943337 0.3662949 0.3315821 0.3608076 0.37439 912 0.3706511 0,3278922 0.3251398 0.3510842 0.3930699 0.3647201 0.3299439 0.3591033 0.3725984 914 0.3690682 0.326404 0.3236137 0.3494749 0.3919605 0.3632651 0.3284478 0.3575076 0.3709279 916 0.3674915 0.3248986 0.3221017 0.3479052 0.3909418 0.361815 0.326933 0.3559284 0.3692745 918 0.3659731 0.3234444 0.3206343 0.3463842 0.3900687 0.3604563 0.3254869 '0.3544221 0.3676738 920 0.3646063 0.3221195 0.3193147 0.3449956 0.389381 0.3592468 0.3241676 0.3530657 0.3662603 922 0.3633357 0.3208696 0.3180831 0.3436843 0.388872 0.3580928 0.3229315 0.3517816 0.3649071 924 0.3620984 0.3196986 0.3169004 0.3424638 0.3885573 0.3570377 0.3217774 0.3505743 0,3636374 926 0.360967 0.318632 0.3158231 0.3413082 0,3884649 0.3560667 0.3207202 0.349465 0.3624645 928 0.3599924 0.3176963 0.3149103 0.3403282 0.3885792 0.355254 0.319803 0.3485027 0.3614416 930 0.3590991 0.3168448 0.3140706 0.339423 0.3888754 0.3545208 0.3189772 0.3476312 0.3605332 932 0.3582681 0.3160588 0.3133045 0.3386022 0,3894023 0.3538728 0.3182211 0.3468345 0.3596775 934 0.3575536 0.3153915 0.312655 0.3378806 0.3901492 0.3533434 0.3175865 0.3461614 0,3589587 936 0.3569815 0.3148347 0.3121329 0.3372883 0.3910667 0.3529418 0.3170764 0.3456154 0.3583711 938 0.356497 0.3143649 0.3116876 0.3367796 0,3922794 0.3526521 0.3166521 0.34516 0.3578906 940 0.3561454 0.3140063 0.3113701 0.336386 0.3938118 0.3524892 0.3163401 0.3448351 0.3575341 942 0.3559591 0.3137922 0.3112038 0.33615 0.3956065 0.3524923 0.316197 0.3446731 0.3573533 944 0.3559222 0.3137005 0.3111676 0.3360471 0.3977312 0.3526423 0.3161888 0.3446612 0.3573131 946 0.3560968 0.3138055 0.3113814 0.3361121 0.4003858 0.3530101 0.3163558 0.34484 0.3574786 948 0.3564299 0.3140748 0.311664 0.3363151 0.4033691 0.3535331 0.3166708 0.3451695 0.3578008 950 0.3569177 0.3144257 0.31208 0.3366557 0.4066259 0.3542121 0.3172067 0.3456637 0.3582763 File Name: e:lscald\trim\abc.cal Spectra and Constituent Data File File Date; Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 55 56 57 58 59 60 61 62 63 Sample Numb "0110a" "0111a" "0112a" "0113a" "0114a" "0115a" "0116a" "0117a"
"0118a"
400 0.7781149 0.711133 0.7276059 0.7414168 0.7096283 0.726806 0.6520679 0.7062058 0.7235578 402 0.8152934 0.7441609 0.7592626 0.7768544 0.7424356 0.7664696 0.6781524 0.7410896 0.7575393 404 0.8532405 0.7764477 0.7907139 0.8119965 0.7741118 0.804824 0.7036831 0.7745296 0.7918769 406 0.8865668 0.8062578 0.820738 0.8451538 0.8057745 0.8400198 0.7267594 0.8070427 0.8225921 408 0.9173819 0.8333197 0.8467461 0.8747025 0.8326628 0.8735433 0.7462119 0.8358712 0.8488684 410 0.9465238 0.8576241 0.8700937 0.9030079 0.8574487 0.9047379 0.763615 0.8639351 0.8740572 412 0.9723306 0.8791239 0.8903223 0.9276273 0.880748 0.932003 0.7782986 0.8877831 0.8956627 414 0,9966792 0.89831 0.9070696 0.9505416 0.9006869 0.9571711 0.7904943 0.9086488 0.9154182 416 1.0179734 0.9161957 0.921847 0.9694513 0.9177868 0.9786899 0.7999783 0.9276601 0.9327745 418 1.0359228 0.9301274 0.9328002 0.9858555 0.9314166 0.996402 0.8061763 0.9425284 0.9473872 420 1.0530052 0.9432647 0.9438729 0.9996681 0.9439312 1.0138838 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0.9141741 0.8982551 1.0166111 0.8999671 1.0583847 0.7103643 0.9407302 0.9517043 466 1.1059344 0.9038124 0.8907772 1.0097246 0,891206 1.0507371 0.7044207 0.9330907 0.9462121 468 1.099825 0.8934965 0.8829331 1.0015619 0.8809881 1.0416863 0.6986516 0.9253832 0.9395604 470 1.0912046 0.8837292 0.8754516 0.9926005 0.8697956 1.0316148 0.6924608 0.9173459 0.9322527 472 1.0828639 0.8739421 0.868138 0.9832443 0.8594591 1.0208073 0.6864046 0.9094525 0.9235209 474 1.0724667 0.8638009 0.8596976 0.9714003 0.8487721 1.009231 0.6797347 0.9004516 0.911886 476 1.0605316 0.8540106 0.8513375 0.9593387 0.8379521 0.9957235 0.6729682 0.8895953 0.8990023 478 1.0484513 0.8439404 0.842543 0.9461163 0.8266311 0.9817205 0.6662664 0.8781092 0.8858204 480 1.0345089 0.8326203 0.832204 0.9316933 0.8143405 0.9659842 0.6593721 0.8654027 0.8727197 482 1.0203742 0.8220173 0.8220726 0.9173145 0.8025752 0.9491874 0.6524624 0.852569 0.8591528 484 1.0058839 0.8114411 0.8113631 0.9014647 0.7898272 0.931765 0.6456535 0.8392113 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0.3143658 0.3463127 0.3185058 0.3504838 936 0.3651743 0.3520813 0.3592212 0.3556544 0.3394766 0.3138564 0.3456 0.3180247 0.3499814 938 0.3648248 0.3515769 0.358851 0.3553005 0.3387613 0.313421 0,3449471 0.3176216 0.3495638 940 0.3646116 0.3512003 0.3586191 0.3550802 0.3381486 0.3131138 0,3443937 0.3173375 0.3492736 942 0.3645926 0.3509835 0.3585534 0.355042 0.3376778 0.3129496 0,3439835 0.3172253 0.3491496 944 0.3647284 0.3509076 0.3586367 0.3551514 0.3373213 0.3129112 0,3436813 0.3172388 0.349168 946 0.3650796 0.3510184 0.3589238 0.3554849 0.3370885 0.313071 0,3435189 0.3174459 0.3493872 948 0.3656061 0.3512774 0.3593771 0.355988 0.3369819 0.3133883 0.3434916 0.317793 0.3497621 950 0.3662972 0.3516921 0.3599938 0.3566513 0.3369939 0.3137855 0.3435686 0.3183032 0.3502853 7g File Name: e:lscald\trim\abc.cal Spectra and Constituent Data File File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 64 65 66 67 68 69 70 71 72 Sample Numb "0119a" "0120a" "0201 a" "0202a" "0203a" "0204a" "0205a" "0206a"
"0207a"
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0.8669986 486 0.8184046 0.9399856 0.755189 0.7556266 0.8759153 0.8417417 0.7909923 0.7978548 0.8529091 488 0.802389 0.9241765 0.7451321 0,7443326 0.8636908 0.8246559 0.7770393 0.7829896 0.8399464 490 0.7868157 0.9084101 0.7366189 0,7339182 0.8509438 0.8070785 0.7642572 0.7688 0.825462 492 0.7713383 0.890781 0.7268169 0,7225326 0.8382567 0.7889768 0.7505925 0.7523332 0.8049486 494 0.7539124 0.871398 0.7163423 0,7110875 0.8250429 0.7718275 0.7369483 0.7373837 0.7821285 496 0.7374328 0.8537958 0.7055618 0,6999447 0.8122501 0.7542114 0.7230074 0.7209849 0.7621243 498 0.7221508 0.8356091 0.6960651 0,6900995 0.8009276 0.7387639 0.709788 0.7066782 0.7454754 500 0.7061467 0.8164935 0.6844386 0,6787956 0.7901468 0.7217524 0.6963052 0.6920355 0.7270095 502 0.6881058 0.7985888 0.6738975 0,6684611 0.7814251 0.7041543 0.6834839 0.6766347 0.7091714 504 0.671547 0.7845511 0.6629688 0,6584474 0.7716876 0.6877218 0.6704593 0.6618831 0.6931255 506 0.6556081 0.7674752 0.6526864 0,6486439 0.7575434 0.6724019 0.6568518 0.6477187 0.6769873 508 0.63931 0.7475438 0.6419469 0.6385173 0.7396822 0.6552614 0.6444548 0.6340905 0.661166 510 0.6247146 0.7275177 0.6329312 0.6300263 0.7259545 0.6395571 0.6332269 0.6218799 0.6453269 512 0.6114838 0.7097652 0.6235548 0.622227 0.7157763 0.6261745 0.6228794 0.6093594 0.632134 514 0.5995892 0.6937302 0.6144519 0.6146804 0.7055309 0.6119312 0.6122148 0.5983999 0.6187017 516 0.5863938 0.6802044 0.6068681 0.6080846 0.6963952 0.6000074 0.6028446 0.5895905 0.6069573 518 0.5753158 0.6670907 0.6000651 0.6002431 0.6890445 0.5912879 0.5958053 0.5797077 0.5966974 520 0.5655767 0.6546608 0.5933662 0.5940045 0.6817546 0.5792836 0.5880261 0.5711949 0.5876033 522 -0.55655 0.6440278 0.5870909 0.5874453 0.6745826 0.5706475 0.5804079 0.5631212 0.5780027 524 0.5489284 0.6348592 0.5811674 0.5828757 0.6691421 0.5627599 0.573783 0.5567659 0.5703343 526 0.5411783 0.6264863 0.5760974 0.5782084 0.6630983 0.5558417 0.5678874 0.5506632 0.5637948 528 0.5357453 0.619719 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0.4343992 0.4204787 0.3995846 0.4276984 0.4323707 0.3992368 0.3833999 0.3922375 830 0.3801858 0.4341447 0.4203547 0.3993641 0.4274408 0.4321828 0.3990867 0.3832408 0.3919845 832 0.379878 0.4338257 0.4201748 0.3990926 0.4271072 0.4319064 0.3988887 0.3830471 0.3916667 834 0.379499 0.4334246 0.4199281 0.3987251 0.4267096 0.4315539 0.3985884 0.3827516 0.3912669 836 0.3790643 0.4329714 0.4196335 0.3983054 0.4262425 0.4311356 0.3982406 0.3824015 0.390819 838 0.3785162 0.4323875 0.4192187 0.3977734 0.4256797 0.4305859 0.3977672 0,3819367 0.3902806 840 0.3779127 0.4317192 0.4187212 0.3971463 0.4250295 0.4299537 0.3972121 0.3813778 0.3896407 842 0.3772268 0.4309896 0.4181776 0.3964781 0.4242949 0.4292379 0.3966154 0.3807692 0.3889339 844 0.3764595 0.4301588 0.4175318 0.3956891 0.4234787 0.4284136 0.3958906 0.380062 0.3881482 846 0.3755971 0.4292297 0.4167983 0.394805 0.4225801 0.4275087 0.3950748 0.3792613 0.38725 848 0.3746879 0.4282437 0.4160142 0.3938722 0.4216321 0.4265178 0.3942113 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0.4073154 0.4115354 0.3808894 0.3652855 0.3719712 872 0.359495 0.4117143 0.4025891 0,378293 0.405804 0.4099343 0.3794665 0.3638812 0.3704494 874 0.3579673 0.4100589 0.4012294 0.3767351 0.4042407 0.4082685 0.3779727 0.3624023 0.3686435 876 0.3564525 0.4083998 0.3998862 0.3751847 0.4026645 0.4065922 0.3764804 0.3609179 0.3672614 878 0.3549434 0.4067548 0.3985452 0.3736525 0.4011323 0.4049463 0.3750118 0.3594571 0.3656889 880 0.3534259 0.4051089 0.3971888 0.3720989 0.3995577 0.4032861 0.3735057 0.3579719 0.3640961 882 0.3517644 0.4033087 0.3957247 0.3704151 0.3978542 0.4014849 0.371867 0.3563824 0.3623609 884 0.3502199 0.4016247 0.394331 0.3688259 0.3962684 0.3997839 0.3703451 0.3548642 0.3607331 886 0.3487168 0.3999811 0.3929939 0.3672919 0.3947229 0.3981326 0.3688637 0.3534046 0.3591597 888 0.3471168 0.3982321 0.3915654 0.3656748 0.3930908 0.3963789 0.3672962 0.351839 0.3574801 890 0.3455239 0.3964917 0.3901333 0.3640507 0.3914453 0.3946279 0.3657193 0.3502811 0.3558008 892 0.3439973 0.3948258 0.3887815 0.3625031 0.3898908 0.3929587 0.3642091.
0.3487957 0.3541928 894 0.3424156 0.3931063 0.387367 0.3608857 0.3882613 0.3912296 0.3626282 0.3472523 0.3525362 896 0.3407965 0.3913361 0.3859386 0.3592617 0.386615 0.3894509 0.361034 0.3456645 0.350822 898 0.3392092 0.3896137 0.3845333 0.3576618 0.384997 0.387734 0.3594685 0.3441335 0.3491372 900 0.337604 0.3878762 0.3831373 0.3560594 0.3833745 0.3859791 0.3578893 0.3425904 0.347461 902 0,3359476 0.3860985 0.3817138 0.3544367 0.3817243 0.384192 0.3562991 0.3410161 0.3457245 904 0.3342479 0.3842867 0.3802741 0.3527868 0.3800663 0.3823698 0.3546945 0.3394136 0.3439612 906 0.3326064 0.3825296 0.3789078 0.3511883 0.3784596 0.3806263 0.3531163 0.3378696 0.342233 908 0.3309844 0.3808244 0.3775883 0.3496457 0.3769074 0.3789109 0.3516115 0.3363675 0.3405667 910 0.3293014 0.3790401 0.3762269 0.3480586 0.3753105 0.377129 0.3500549 0.334813 0.338814 912 0.3276658 0.3773245 0.374911 0.3465179 0.3737512 0.3753992 0.348552 0.3333244 0.3371041 914 0.3261594 0.3757049 0.3737204 0.3450903 0.3723294 0.3737994 0.3471912 0.331954 0.3355516 916 0.3246424 0.3741247 0.3725575 0.3437301' 0.3709279 0.3722475 0.3458712 0.3306084 0.3340104 918 0.3231778 0.3725695 0.3714663 0.3424166 0.3695667 0.3707232 0.3446037 0.3293213 0.3325256 920 0.3218664 0.3711808 0.3705135 0.3412499 0.3683639 0.369393 0.3434835 0.3281697 0.3311434 922 0.3206334 0.3698985 0.3696572 0.3401622 0.3672417 0.3681397 0.342466 0.3270969 0.3298596 924 0.3194585 0.3686586 0.368867 0.3391772 0.3661784 0.3669675 0.3415362 0.3261217 0.3286377 926 0.3184001 0.3675123 0.3681756 0.3382938 0.3652113 0.365892 0.3407143 0.3252561 0.3275027 928 0.3174745 0.3665082 0.3676371 0.3375706 0.3643841 0.3649681 0.3400538 0.3245407 0.3265102 930 0.3166386 0.3656084 0.3671899 0.3369568 0.3636377 0.3641567 0.3394857 0.3239161 0.3255897 932 0.3158696 0.3647526 0.3668419 0.3364401 0.3629793 ' 0.3634218 0.339039 0.3233922 0.3247252 934 0.3152116 0.3640338 0.3666143 0.3360464 0.3624319 0.3628174 0.3387026 0.322987 0.323975 936 0.3146726 0.3634308 0.3665164 0.3358007 0.3620145 0.3623473 0.3385108 0.3227129 0.3233274 938 0.3142195 0.3629254 0.3665431 0.3356649 0.3616915 0.3619742 0.3384463 0.3225662 0.3227788 940 0.3138869 0.3625464 0.3667293 0.3356919 0.3615176 0.3617596 0.3385422 0.3225691 0.3223222 942 0.3136992 0.3623425 0.3670964 0.3359041 0.3615071 0.3617163 0.3388283 0.3227347 0.3220149 944 0.3136341 0.3622646 0.3676408 0.3362848 0.3616541 0.3618275 0.3392754 0.3230874 0.32184 946 0.3137721 0.3623835 0.368446 0.3369221 0.362014 0.3621532 0.3400251 0.3236599 0.3218128 948 0.3140922 0.3626599 0.3694312 0.3377335 0.3625391 0.3626482 0.3409472 0.3244175 0.3219338 950 0.3144848 0.3630871 0.3705812 0,3387186 0.3632277 0.3632995 0.3420404 0.3253341 0.3221777 ~2 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Flue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File-ID: . pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 73 74 75 76 77 78 79 80 81 Sample Numb "0209x" "0208x" "0212x" "0210x" "0213x" "0211x" "0214x" "0215x"
"0216x"
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0.3738795 0.3362387 942 0.3289621 0.3492977 0.3185455 0.3218573 0.3685149 0.334818 0.3638836 0.3737421 0.3362159 944 0.3293398 0.349438 0.3186255 0.321672 0.369012 0.3350686 0.3642217 0.3737682 0.3363374 946 0.3299633 0.3497945 0.3189051 0.3216221 0.3697522 0.3355524 0.3647925 0.3740128 0.3366756 948 0.3307557 0.3503163 0.319337 0.3217171 0.3706781 0.3362037 0.3655452 0.3744444 0.3371819 950 0.3317223 0.3509842 0.3199081 0.3219429 0.3717666 0.3369997 0.3664486 0.3750274 0.3378492 File Name: e:Gscaldltrim~abc.cal Spectra and Constituent Data File File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 82 83 84 85 86 87 88 89 90 Sample Numb "0217a" "0218a" "0219a" "0220a" "0221 a" "0223a" "0101 b" "0102b"
"0103b"
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0.9090649 1.0325527 0.9384839 1.2235065 1.4057252 1.5571012 444 1.0951041 1.1335357 1.1474177 0.9040605 1.033076 0.9331411 1.2259789 1.4062884 1.5614731 446 1.0949993 1.1345503 1.1481022 0.8998799 1.0321751 0.9298828 1.2246784 1.4034185 1.5616608 448 1.0944295 1.1341773 1.1485574 0.8949602 1.0317308 0.9281855 1.2251356 1.4025161 1.5594975 450 1.0924929 1.135795 1.1499048 0.8908779 1.03113 0.9268618 1.2240415 1.4006176 1.5628227 452 1.0897217 1.1343383 1.148973 0.8869584 1.0294045 0.9197826 1.2235395 1.3979011 1.5618312 454 1.0877637 1.1339585 1.1476581 0.8820932 1.0269593 0.9094177 1.2224531 1.394981 1.5600672 456 1.0864832 1.1324054 1.1456635 0.8773789 1.0256147 0.9014629 1.2202456 1.3913498 1.5604932 458 1.0829953 1.1300099 1.1430848 0.8717079 1.0216049 0.8948041 1.2174585 1.3864317 1.5569876 460 1.0802618 1.1272742 1.1396612 0.8655372 1.0186746 0.8881471 1.2146616 1.3810952 1.5542946 462 1.0767924 1.1251851 1.1370414 0.8593416 1.0149789 0.8818558 1.2122767 1.3768898 1.5505424 464 1.0724417 1.1201974 1.1315556 0.8525786 1.0103872 0.8737603 1,2060418 1.3708978 1.5470035 466 1.0668958 ~ 1.1141422 1.1261855 0.8450422 1.0051291 0.8653742 1.2047465 1.3654904 1.5440534 468 1.0612849 1.1082218 1.1185752 0.8369424 0.9999484 0.8567702 1.2005599 1.361375 1.5386028 470 1.0555055 1.1025207 1.1124107 0'.8286447 0.9938587 0.8483008 1.195019 1.3514377 1.5322104 472 1.0474197 1.0940664 1.1046107 0.8204008 0.9683428 0.8391868 1.1900573 1.3467231 1.5279782 474 1.0393236 1.0834452 1.095415 0.8108706 0.981212 0.8304378 1.1842778 1.3382806 1'.5204906 476 1.0295403 1.073536 1.0863626 0.8014127 0.974355 0.8202724 1.1780524 1.331136 1.5153453 478 1.0193856 1.0643179 1.0759995 0.7905688 0.9670821 0.8111047 1.1711454 1.3243291 1.5073969 480 1.0082984 1.0515407 1.0655185 0.7795646 0.9573056 0.8000124 1.1637254 1.3157514 1.498971 482 0.9966021 1.0386169 1.052955 0.7679353 0.9477741 0.7891526 1.1559839 1.3070779 1.4896424 464 0.9849771 1.0260748 1.0405531 0.7562934 0.9372067 0.779087 1.1477957 1.2982457 1.4819627 486 0.9732614 1.010305 1.026788 0.744257 0.9270549 0.767179 1.1403322 1.2887263 1.4722002 488 0.960057 0.9949149 1.012665 0.7313917 0.9160465 0.7551548 1.1317219 1.2796757 1.461832 490 0.9477094 0.9784557 0.9987 0.7191616 0.9053904 0.7435713 1.1235163 1.2713904 1.4559317 492 0.9340348 0.9618473 0.9829113 0.7056342 0.8936495 0.7313023 1.1135905 1.2607241 1.4444852 494 0.9204561 0.9436691 0.9678801 0.6922151 0.883045 0.7186837 1.1053159 1.2509396 1.4335157 496 0.9069919 0.924639 0.9519529 0.6787115 0.869239 0.7039936 1.0971981 1.24298 1.4252899 498 0.8934405 0.9065438 0.9389001 0.6667786 0.8557186 0.6899396 1.0884714 1.2330837 1.4156344 500 0.8806871 0.8882593 0.9228069 0.6538217 0.8386142 0.676974 1.080935 1.225477 1.4082565 502 0.8675665 0.8689805 0.9063527 0.6391616 0.820491 0.662245 1.0741496 1.217283 1.400239 504 0.8540555 0.8507096 0.8934319 0.6262069 0.8048784 0.6495571 1.0671453 1.2109733 1.3913277 506 0.8414811 0.8329493 0.8788125 0.6127409 0.7919865 0.6367747 1.0608979 1.2042627 1.3843307 508 0.8287389 0.8141609 0.8647388 0.600672 0.7800313 0.622278 1.0535038 1.197705 1.3796451 510 0.8173102 0.7962537 0.851702 0.5888858 0.7667624 0.6104112 1.0484968 1.1896558. 1.3711197 512 0.8069345 0.7800184 0.8416476 0.5778809 0.7542161 0.5993536 1.0425274 1.1846224 1.3660352 514 0.7964238 0.7689557 0.8355366 0.5674009 0.743013 0.5894846 1.0383017 1.179422 1.3615482 516 0.788307 0.7568222 0.8256293 0.5587133 0.7325082 0.5787871 1.034214 1.1751139 1.3555398 518 0.7848924 0.7383006 0.8106193 0.5510016 0.7243434 0.5700092 1.0323732 1.1714671 1.3531637 g7 520 0.7829098 0.7253157 0.7983251 0.5428872 0.7130837 0.56191 1.0301847 1.1686858 1.3526192 522 0.7708292 0.7130408 0.7908146 0.5365101 0.7049567 0.554764 1.0290465 1.165072 1.3516567 524 0.7622131 0.7043641 0.7820198 0.5311598 0.6981791 0.5490896 1.0282333 1.163239 1.3525555 526 0.7563452 0.6957031 0.7741317 0.5259109 0.6918174 0.5431534 1.0264566 1.1617272 1.3530321 5'28 0.7518083 0.6865774 0.7698119 0.5212191 0.6881145 0.5389624 1.0269327 1.1613832 1.3558328 530. 0.7490704 0.6807417 0.7634228 0.5170813 0.6820232 0.5357717 1.0294702 1.1618643 1.3589191 532 0.7454782 0.6754727 0.7575499 0.5137995 0.677317 0.5314236 1.0299935 1.1614937 1.3627287 534 0.7412235 0.6689775 0.7532491 0.5101891 0.6729716 0.5285786 1.0319527 1.1606081 1.3644309 536 0.7383006 0.663807 0.7497336 0.5077313 0.6703006 0.5260876 ' 1.033319 1.1614184 1.3658392 538 0.735242 0.6594172 0.7437053 0.5048297 0.6660475 0.5242192 1.0335622 1.1615465 1.3659052 540 0.7327432 0.655158 0.738968 0,5023 0.6628777 0.5225492 1.0339237 1.1613226 1.3685918 542 0.7289094 0.650534 0.734028 0.4994526 0.6615193 0.5201269 1.034405 1.1599743 1.3691399 544 0.7243575 0.6466983 0.7281928 0.4974107 0.6557476 0.5176084 1.0335279 1.1584394 1.3709424 546 0.7210217 0.6422781 0.7218824 0.4946229 0.6518602 0.5153228 1.0310179 1.1577196 1.3722675 548 0.7162002 0.6369606 0.7164981 0.4911145 0.6471378 0.5129632 1.0296731 1.1558983 1.3701972 550 0.7120224 0.6322168 0.7101858 0.4879396 0.6424568 0.5104398 1.0273705 1.1530976 1.3706694 552 0.7090891 0.6260949 0.7036427 0.4850542 0.6386624 0.506663 1.0289456 1.1516941 1.36971 554 0.7023218 0.6203408 0.6964988 0.4811352 0.6328917 0.503123 1.0227927 1.1482391 1.3681288 556 0.6974118 0.6132724 0.6887915 0.4775454 0.6278908 0.4991791 1.0183461 1.1455429 1.3643305 558 0.6917498 0.6063862 0.6815413 0.4736434 0.622209 0.4949207 1.0156832 1.1425922 1.3631523 560 0.686304 0.5995207 0.6741269 0.4702057 0.6175466 0.4904336 1.006911 1.1388783 1.3612201 562 0.6807526 0.5924554 0.6666256 0.4663479 0.6119694 0.4860818 1.0013118 1.1358891 1.3577914 564 0.6750043 0,58529 0.6592308 0.4624881 0.6064246 0.4821079 0.9949135 1.1329496 1.3580902 566 0.6691065 0.5782111 0.6527452 0.4589051 0.6012113 0.4779475 0.9896959 1.1298972 1.3555547 568 0.6633973 0.5713537 0.6448032 0.4552401 0.5959187 0.4739801 0.9820971 1.1282227 1.3520172 570 0.6578125 0.5644183 0.6373566 0.4522095 0.5903099 0.4700988 0.975933 1.1259754 1.3479891 572 0.6523026 0.5584196 0.6304666 0.4488134 0.5849744 0.466902 0.9684719 1.1236286 1.3444834 574 0.646225 0.5523908 0.623496 0.4454615 0.5800548 0.463833 0.9620727 1.1164422 1.3408984 576 0.6402727 0.5463935 0.6161563 0.4424415 0.5744802 0.4607311 0.954017 1.1114742 1.3378578 578 ~ 0.63411 0.5408142 0.6098561 0.4394776 0.5695744 0.4579075 0.9460249 1.1072881 1.3324761 580 0.6279362 0.5357514 0.6034621 0.4363846 0.5642745 0.4549705 0,9383469 1.103487 1.3280122 582 0.6217489 0.5306717 0.5965703 0.4338126 0.5589947 0.452147 0,9308336 1.0987959 1.325119 584 0.6152533 0.5262727 0.5905665 0.4311174 0.5543321 0.44993 0.9228153 1.0948085 1.3206577 586 0.6088248 0.5214642 0.5843503 0.4287069 0.549232 0.4476955 0,9148987 1.0912893 1.3167123 588 0.6022706 0.5172259 0.5785605 0.4263068 0.5440913 0.4453918 0,9064091 1.0873412 1.3118494 590 0.5958167 0.5130029 0.572907 0.4237978 0.5393257 0.4433932 0,8988041 1.0832293 1.3080742 592 0.5901284 0.5090849 0.5674573 0.4216204 0.5345187 0.4415403 0.8910747 1.0793791 1.3045859 594 0.5839326 0.5052158 0.5617945 0.4193893 0.5300362 0.4397057 0.8833501 1.0754589 1.3017199 596 0.5776271 0.5013016 0.5566709 0.4171861 0.525346 0.4375372 0.8757336 1.0717406 1.3046265 598 0.571814 0.4976661 0.5515345 0.4149013 0.52106 0.4357805 0.869445 1.0685816 1.2959485 600 0.5663009 0.4939731 0.546648 0.4131914 0.5170964 0.433785 0.8622085 1.0656569 1.2902709 602 0.5608878 0.4900295 0.541 846 0.4110976 0.5127629 0.4318727 0.8553745 1.062519 1.2858391 604 0.5557315 0.4862046 0.5371408 0.4091525 0.508894 0.4300607 0.84875 1.05898 1.2830927 606 0.5509517 0.4825645 0.5326861 0.407199 0.5053302 0.4281279 0.8425187 1.0557436 1.2788507 608 0.5461627 0.4787425 0.5282317 0.405204 0.501542 0.4260031 0.8365824 1.0525768 1.2755592 610 0.5417675 0.4749202 0.523909 0.403243 0.4983144 0.4238694 0.830695 1.0494037 1.2719564 612 0.5377001 0.4714825 0.5200318 0.4014817 0.4950816 0.4222095 0.8252443 1.0463665 1.2694962 614 0.5341311 0.4678943 0.5163932 0.3997957 0.492081 0.4203447 0.8199133 1.0436966 1.2658887 616 0.5301931 0.4643388 0.5122978 0.3980655 0.4888855 0.418471 0.8145252 1.0404464 1.2627227 618 0.5267499 0.4612663 0.5089015 0.396726 0.4861718 0.4168592 0,8095118 1.037957 1.2593814 620 0.5234166 0.4581798 0.5055243 0.3951796 0.4834443 0.4155153 0.8044742 1.0350286 1.2558768 622 0.5201685 0.4552896 0.5024432 0.3939338 0.4809156 0.4141718 0.7991906 1.0320113 1.2525263 624 0.5170689 0.4527601 ' 0.4992628 0.392719 0.4783523 0.4128999 0.7937593 1.0289738 1.2503612 626 0.5140624 0.4501734 0.4964172 0.3916019 0.475899 0.411817 0.7887802 1.0262953 1.2460281 628 0.5110461 0.4477436 0.493615 0.3905212 0.4736778 0.4108146 0.7833931 1.0233369 1.2423202 630 0.5079669 0.4453055 0.4907745 0.389481 0.4712426 0.4098409 0.7778897 1.020505 1.2387044 632 0.5051199 0.4430765 0.487964 0.3885531 0.4688346 0.408981 0.7727234 1.0176862 1.2353991 634 0.5022353 0.4409609 0.4852764 0.3875959 0.4665862 0.4082196 0.7674575 1.0149961 1.2318623 636 0.499194 0.4387479 0.4825579 0.386674 0.4641761 0.4073494 0.7620881 1.0121406 1.2283826 638 0.4961793 0.4366249 0.4798633 0.3857189 0.4618463 0.4065596 0.7568306 1.0093607 1.2248023 640 0.4933201 0.4346123 0.4773884 0.3848416 0.4597435 0.405774 0.7520667 1.0067749 1.2217991 642 0.4902805 0.4325909 0.4748749 0.3840067 0.4573981 0.4050208 0.7474591 1.0044606 1.2186272 644 0.4873576 0.4306185 0.4723006 0.383117 0.455044 0.4042763 0.7425024 1.0017226 1.2149971 646 0.4845113 0.428713 0.4700111 0.3823122 0.4528258 0.4035881 0.7381269 0.9992442 1.2117021 648 0.4816273 0.4269347 0.4676815 0.3815443 0.4505287 0.402872 0.7337685 0.9966645 1.2081797 650 0.4787297 0.4251438 0.465359 0.3807447 0.4482476 0.4021918 0.7294711 0.9940071 1.2049471 652 0.4759095 0.4234417 0.4632095 0.3800465 0.4460779 0.4015629 0.7255368 0.9915063 1.2013766 654 0.4731362 0.4218203 0.4611329 0.3793833 0.4438506 0.4009488 0.7216038 0.9889179 1.1980054 656 0.4703576 0.4201515 0.4590216 0.3786082 0.4415679 0.4003597 0.7176353 0.9861997 1.1944306 658 0.4675534 0.4186299 0.4569378 0.3778535 0.4393194 0.3998224 0.7137721 0.9834995 1.1907952 660 0.4647558 0.4171584 0.4548849 0.3771195 0.4371103 0.3992586 0.7100853 0.9808701 1.1871036 662 0.4620564 0.4157072 0.4528847 0.3763691 0.434877 0.3986772 0.7065091 0.9782919 1.1830633 664 0.45926 0.41419 0.4508278 0.3756451 0.4327661 0.3981195 0.7027417 0.9755076 1.1790187 666 0.4565117 0.4127082 0.4488486 0.3748753 0.4305323 0.3976004 0.6990574 0.9726738 1.1748805 $g 668 0.4539376 0.4112153 0.4467989 0.3740606 0.4283141 0.3970288 0.6954624 0.9698781 1.1706576 670 0.4511126 0.4097525 0.4447005 0.3732844 0.4261017 0.3965461 0.6916735 0.9669051 1.1660185 672 0.4483537 0.4083802 0.4426376 0.3725255 0.4239175 0.3960203 0.6879966 0.9640207 1.1617163 674 0.4457999 0.4070792 0.440694 0.3718138 0.4219009 0.3955364 0.6845006 0.9612191 1.1570885 676 0.4431607 0.40571 0.438706 0,3711048 0.4198794 0.3950742 0.6809818 0.9583395 1.1524476 678 0.4406002 0.4043893 0.4367283 0.3704115 0.417861 0.3946433 0.6774004 0.9555357 1.1475806 680 0.4382234 0.4031406 0.4348507 0.3697556 0.4159675 0.3942264 0.6741337 0.9530205 1.14293 682 0.4358439 0.4019604 0.4330229 0.3691243 0.4141877 0.3938496 0.6707641 0.950222 1.1387033 684 0.4335687 0.400758 0.4312321 0.3685197 0.4124644 0.3935166 0.6673891 0.947468 1.1338036 686 0.4314174 0.3996333 0.4295333 0.3680055 0.4108514 0.3932005 0.6642206 0.9447651 1.1292946 688 0.4294463 0.3985813 0.4279831 0.3675275 0.4093228 0.3928803 0.661124 0.94216 1.1248641 690 0.4275293. 0.3975439 0.4264539 0.3670887 0.4078989 0.3926693 0.6580771 0.9395906 1.1204473 692 0.4257122 0.3965904 0.4250267 0.3667216 0.4065555 0.3924781 0.6550218 0.9370475 1.1161261 694 0.4240213 0.3956662 0.4237053 0.3664894 0.4052924 0.3923101 0.6521934 0.9346544 1.1118768 696 0.4225295 0.3948195 0.4224981 0.3661967 ~ 0.4041703 0.3921912 0.6494478 0.9322895 1.1077645 698 0.4210551 0.3940176 0.421299 0.3660164 0.4030685 0.3921423 0.6465994 0.929896 1.1036303 700 0.4196696 0.3933041 0.4201667 0.3658637 0.402077 0.3921485 0.6438582 0.9275928 1.0995919 702 0.4184693 0.3926443 0.4192144 0.3657347 0.4012125 0.3921146 0.6413488 0.9253906 1.0957417 704 0.4173172 0.3919851 0.4183124 0.3656551 0.4002959 0.3921228 0.6388035 0.9232405 1.0918977 706 0.4161464 ' 0.3913678 0.4174215 0.365541 B 0.3994479 0.3922041 0.6362218 0.9210023 1.0886445 708 0.4151861 0.39085 0.4166279 0.36562 0.3987434 0.392287 0.6338036 0.9189562 1.0646239 71 D 0.4142857 0.3903466 0.4159006 0.3656292 0.3980562 0.3924073 0.6313971 0.9168954 1.0808766 712 0.4133814 0.389927 0.4152319 0.3657539 0.3974073 0.3925724 0.6290003 0.9147623 1.0771587 714 0.412586 0.3895756 0.4146098 0.3658788 0.3968454 0.3927843 0.6267253 0.9127516 1.073513 716 0.4118308 0.3892559 0.4140354 0.3660372 0.396346 0.3930167 0.6244692 0.9108238 1.0700049 718 0.4111959 0.3889812 0.4135461 0.366248 0.3958903 0.3932956 0.6222137 0.9088022 1.0664723 720 0.4105489 0.3887785 0.4130697 0.366485 0.395462 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0.4110315 0.3711476 0.3939045 0.3990029 0.5981312 0.8859795 1.0245135 744 0.4065814 0.3892422 0.4109719 0.3716423 0.3938843 0.3995674 0.5963128 0.8840461 1.0210351 746 0.4064111 0.389387 0.4109351 0.3721523 0.3938907 0.4000986 0.5944096 0.8820601 1.0174997 748 0.4062577 0.3895307 0.4109402 0.3726642 0.3939061 0.4006503 0.5925198 0.8800297 1.0139239 750 0.4060884 0.3897107 0.4109216 0.3731933 0.3938952 0.4012086 0.5907011 0.8780031 1.0105374 752 0.405977 0.3898859 0.4109574 0.3737039 0.3939254 0.4017675 0.5889424 0.8760625 1.0073071 .
754 0.4058475 0.3901016 0.4110028 0.37427 0.3939216 0.4023573 0.5871973 0.8740699 1.0040894 756 0.4057716 0.3903868 0.4111297 0.3748398 0.3939354 0.403 0.5855317 0.872162 1.0011301 758 0.4057044 0.3906545 0.411218 0.375389 0.3939582 0.4035858 0.5840253 0.870347 0.998327 760 0.4056439 0.390977 0.4113419 0.376024 0.393972 0.4042335 0.5825565 0.8684725 0.9954889 762 0.4055484 0.3912484 0.4114541 0.3766173 0.3939432 0.4048645 0.5811119 0.8665403 0.9925804 764 0.4054554 0.3915074 0.4115506 0.377195 0.3938649 0.4054762 0.579859 0.8646843 0.9899717 766 0.4053994 0.3917699 0.411646 0.3778643 0.3937992 0.4060729 0.5786579 0.8627838 0.9871989 768 0.4054281 0.3920842 0.4117t21 0.3785643 0.3938279 0.4067294 0.5774771 0.8608575 0.9842545 770 0.4054945 0.3923886 0.4118263 0.3793105 0.3938793 0.40737 0.5763075 0.8588375 0.9812142 772 0.4055797 0.3926587 0.411886 0.3799553 0.3940485 0.4079928 0.574909 0.8566179 0.9779142 774 0.4056065 0.3929359 0.4120141 0.3805054 0.3942593 0.4086388 0.57339 0.8542756 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946 0.3461557 0,33262 0.3382122 0.3251558 0.3452341 0.350124 0.4378681 0.7070193 0.7536087 948 0.3466275 0.3332751 0.3383549 0.325429 0.3462134 0.3505654 0.4373981 0.7066917 0.7519478 950 0.347236 0.3340882 0.3386376 0.3258418 0.3473681 0.3511558 0.437061 0.7064996 0.7504103 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Flue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, D
Position 91 92 93 94 95 96 97 98 99 Sample Numb "0104b" "0106b" "0107b" "0108b" "0108b" "0109b" "0110b" "0111b"
"0112b"
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0.8900093 0.625144 0.4274743 936 0.5375952 0.4657153 0.5290006 0.5572603 0.5623381 0.6741113 0.8887459 0.6237183 0.4262343 938 0.5366632 0.4646996 0.5284293 0.555972 0.5610012 0.6718253 0.8875052 0.6223235 0.4250476 940 0.5358325 0.4637788 0.527999 0.55475 0.5597477 0.6695712 0.8863152 0.6210144 0.4239146 942 0.5351523 0.4630198 0.527773 0.553712 0.5586723 0.6674908 0.8852651 0.6198995 0.4229359 944 0.5345773 0.4623729 0.5277272 0.5527822 0.5577069 0.6654763 0.8843046 0.6188962 0.4220464 946 0.5341496 0.4618595 0.5279465 0.551976 0.5568843 0.663491 0.8834033 0.6180108 0.4212605 948 0.5338378 0.4614906 0.5283613 0.5513123 0.5561836 0.6616066 0.8826269 0.6172729 0.4205876 950 0.5336826 0.4612474 0.5289872 0.5508193 0.5556307 0.6598487 0.8819568 0.6166958 0.4200421 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Flue Nov 23 13:16;04 1999 Last Update: Fri Nov 03 14:08:32 2000 File !D: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 100 101 102 103 104 105 108 1 D7 108 Sample Numb "01136" "01146" "01166" "'01186" "01196" "01206" "02026" "02036"
"02046"
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0.6824399 0.4315349 934 0.7757302 0.4624916 0.7591336 0.701915 0.6349388 0.5388603 0.5614702 0.6817406 0.430089 936 0.7745473 0.4611799 0.7572659 0.7005757 0.6334614 0.5375395 0.560238 0.6811567 0.4287722 938 0.7733775 0.4599158 0.755373 0.699244 0.6320111 0.5362487 0.5590302 0.6806418 0.4275055 940 0.7722569 0.4587242 0.7535355 0.6979612 0.6306533 0.535037 0.5579137 0.6802227 0,4262989 942 0.7712662 0.457711 0.7516795 0.6968403 0.6294763 0.5340042 0.5569616 0.679972 0.4252598 944 0.7703354 0.4568063 0.7503002 0.6957808 0.6284089 0.5330642 0.5561262 0.6798368 0.42431 946 0.7694952. 0.4560367 0.7487772 0.6948118 0.6274669 0.5322495 0.5553955 0.6798681 0.4234593 948 0.7687511 0.4553992 0.7473587 0.6939523 0.6266829 0.5315666 0.5548016 0.6800597 0.4227315 950 0.7681139 0.4549053 0.7460846 0.6932141 0.6260359 0.5310195 0.5543545 0.680398 04221123 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Flue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab. Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 109 110 111 112 113 114 115 116 117 Sample Numb "0205b" "0206b" "0208b" "0209b" "0212b" "0211b" "0213b" "0214b"
"0215b"
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0.8604355 1.2049916 0.8413554 1.1089762 572 1.1339997 0.9858653 1.0162193 1.0126106 0.9834502 0.8554672 1.2076054 0.8361626 1.1034259 574 1.1246033 0.9798868 1.0104768 1.0075725 0.9767778 0.8495843 1.1953398 0.8316531 1.0995774 576 1.1189407 0.9739632 1.0046347 1.0029373. 0.9701349 0.8435121 1.1885555 0.8266334 1.0955307 578 1.1145058 0.9676726 0.9983683 0.9973374 0.9640607 0.8377665 1.1822263 0.821951 1.0899934 580 1.1102449 0.9613371 0.9915789 0.9922176 0.9575202 0.8319591 1.1776677 0.8174128 1.0851675 582 1.1042643 0.9548958 0.9847782 0.985608 0.9509946 0.8262931 1.1743815 0.8122207 1.0804411 564 1.0990826 0.9488458 0.9779356 0.980131 0.9453142 0.8198965 1.1685094 0.8078967 1.0763875 586 1.0942487 0.942426 0.9722424 0.9742202 0.9379414 0.8144242 1.163653 0.8035101 1.0714216 588 1.0903831 0.9353173 0.9645382 0.9680288 0.9311526 0.8080168 1.1589723 .
0.800057 1.0666296 590 1.0843236 0.9285112 0.9574649 0.9621947 0.9242493 0.8027293 1.1552647 0.7950061 1.0620239 592 1.080078 0.921718 0.9500761 0.9560927 0.9175919 0.796659 1.1513152 0.7904949 1.0586811 594 1.0755671 0.91498 0.9434129 0.9503068 0.9110015 0.7911576 1.1466088 0.7866023 1.0537318 596 1.0708966 0.9078853 0.9362552 0.9438925 0.9038966 0.7857616 1.1419721 0.7822046 1.0493982 598 1.066691 0.9012836 0.9292632 0.9390118 0.8972371 0.7800053 1.138182 0.7782274 1.0464323 600 1.064021 0.8956825 0.9226407 0.9332018 0.8917307 ~ 0.7746132 1.1344124 0.7747343 1.0415633 602 1.0588002 0.8888402 0.9168262 0.9278594 0.8843555 0.7693882 1.1304156 0.7704637 1.0373034 604 1.0551143 0.8825092 0.9100236 0.9235409 0.8777685 0.7643834 1.1281798 0.7665646 1.0334576 606 1.0511011 0.8768153 0.9039245 0.9184589 0.871938 0.7596149 1.1240335 0.7629123 1.0292544 608 1.0472386 0.8709481 0.8980076 0.9135731 0.8652112 0.7545028 1.1203806 0.7590417 1.0254558 610 1.043715 0.8650599 0.8921711 0.9089978 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0.5073127 0.5157467 0.580693 0.4630605 0.4664042 0.8726808 0.4994063 0.7273669 870 0.7827064 0.5058492 0.5141706 0.5785329 0.4615276 0.4648679 0.8710986 0.4972361 0.7255593 872 0.7809627 0.5043566 0.5125812 0.5762841 0.4599725 0.4632894 0.8694848 0.495009 0.7236976 874 0.7791523 0.5028389 0.5109506 0.5739929 0.4583609 0.4616544 0.8678625 0.4927089 0.7217972 876 0.7773652 0.5013469 0.5093533 0.5717375 0.4568057 0.4600553 0.8662861 0.4904477 0.7199404 878 0.7756431 0.4998722 0.5077662 0.5695056 0.455268 0.4584693 0.8646725 0.4881944 0.7180867 880 0.7738695 0.4983987 0.5061969 0.5672547 0.4537356 0.4568658 0.863058 0.4859269 0.7161911 882 0.7719641 0.496799& 0.5044942 0.5648739 0.452076 0.4551498 0.8613343 0.4835246 0.7141567 884 0.7702085 0.4953334 0.5029122 0.5626274 0.4505432 0.4535237 0.8597018 0.4812346 0.7122496 886 0.7664872 0.4939256 0.5013899 0.5604579 0.4490775' 0.4519411 0.8561521 0.47904 0.7104104 888 0.7666467 0.4924466 0.4998072 0.5582057 0.44754 0.4503122 0.8565016 0.4766987 0.7084504 890 0.7648228 0.4909889 0.4982372 0.5559646 0.4459939 0.4486568 0.8548513 0.4743591 0.7065107 892 0.7631027 0.4896126 0.4967267 0.5538307 0.4445701 0.4470902 0,8532453 0.4721466 0.7046711 894 0.7613917 0.4882182 0.4952089 0.5516452 0.4430873 0.4454746 0,8515786 0.4698483 0.7027784 896 0.7595938 0.4867917 0.4936561 0.5494297 0.4415919 0.4438495 0.8498953 0.4675116 0.7008473 898 0.7578214 0.4854257 0.4921606 0.5472782 0.4401484 0.4422674 0,8482128 0.4652341 0.6989777 900 0.7560602 0.4840697 0.4906655 0.5451635 0.4387282 0.4406808 0,8465864 0.4629515 0.6971127, 902 0.7543001 0.4827294 0.4891734 0.5430195 0.4372937 0.4390835 0.8448606 0.4606546 0.6952426 904 0.7524924 0.4813667 0.487667 0.5408648 0.4358683 0.4374827 0.8431386 0.4583153 0.6933401 906 0.7507814 0.4800783 0.4861975 0.5388145 0.4344907 0.4359298 0.8415141 0.456078 0.6915475 908 0.7491006 0.4788288 0.4848031 0.5368273 0.4331615 0.4344229 0.8399184 0.4538713 0.6897753 910 0.7473723 0.4775464 0.4833722 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0.467501 0.4721527 0.5178743 0.4223531 0.4202493 0.8239825 0.4313681 0.6707914 934 0.7299301 0.4670447 0.4716569 0.5168561 0.42209 0.419588 0.8228767 0.4299688 0,6695456 936 0.7288041 0.4666918 0.4712847 0.5159467 0,4219743 0.4190786 0.8218101 0.4286962 0.6684141 938 0.7276813 0.4664273 0.4710033 0.5151331 0.4219616 0.418605 0.8207498 0.4274893 0.6673176 940 0.7266487 0.4662755 0.4708776 0.5144273 0.4221078 0.4182687 0.819709 0.4263522 0.6662936 942 0.7257403 0.466272 0.470909 0.5139136 0.4224209 0.4180913 0.8188081 0.4253668 0.66541 944 0.7249073 0.4664052 0.4710958 0.5135281 0.4229232 0.4180517 0.817959 0.4244744 0.6646274 946 0.7241722 0.4667336 0.471512 0.513329 0.4236854 0.4182036 0.8171744 0.4237094 0.6639622 948 0.7235584 0.4672073 0.4721001 0.5132955 0.4246428 0.4185114 0.8164775 0.4230678 0.6634177 950 0.7230472 0.4678329 0.4728448 0.5134066 0,4257846 0,4189768 0.8158767 0.4225348 0.663002 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 118 119 120 121 122 123 124 125 126 SampIeNumb "02166" "02176" "02196" "02206" "02216" "02236" "02076" "0101 c"
"0102c"
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0.344813 0.3864117 934 0,4773011 0.5608654 0.5191131 0.6105149 0.7773495 0.9197753 0.6926152 0.3438058 0.3855757 936 0,4762527 0.559399 0.5182213 0.6091244 0.7760985 0.9182481 0,6911562 0.3429103 0.3848667 938 0,4752666 0.5579462 0.5173917 0.6077735 0.7748353 0.9166807 0.6897258 0.3420547 0.3842316 940 0,4743939 0.5565468 0.5166896 0.6064948 0,7736334 0.9151207 0.6883742 0.3412835 0.383708 942 0.4736973 0.5552911 0.5161586 0.6054077 0.7725978 0.9137545 0.6871845 0.3406469 0.3833456 944 0.4731369 0.5541152 0.5157539 0.6044244 0.7716272 0.9123992 0.6860791 0.3400868 0.3831187 946 0.4727297 0.5530114 , 0.5155347 0.603586 0.770741 0.9110714 0.6851138 0.3396524 0.3830596 948 0.4724929 0.5520268 0.5154728 0.6028777 0.7699631 0.9098408 0.6842603 0.3393104 0.383148 950 0.4723983 0.5511472 0.5155752 0.6023258 0.7693163 0.90867 0.6835186 0.3390722 0.3833656 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Flue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 127 128 129 130 131 132 133 134 135 Sample Numb "0106c" "0107c" "0110c" "0112c" "0114c" "0116c" "0117c" "0118c"
"0120c"
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0.3437996 0.3642936 0.4747716 0.3430384 0.334896 936 0.4780407 0.3240206 0.3733552 0.3829921 0.3429443 0.3634838 0.4741834 0.3423328 0.3338498 938 0.4776745 0.3233736 0.3729381 0.3822937 0.3421369 0.362716 0.4736973 0.3416854 0.3328168 940 0.4774584 0.3228101 0.372623 0.3817165 0.341422 0.3620424 0.4733573 0.3411342 0.3318433 942 0.4774248 0.3223867 0.3724571 0.3813182 0.340855 0.3615145 0.4732146 0.3407267 0.3310074 944 0.4775698 0.3220677 0.3724191 0.3810533 0.3403895 0.3610829 0.4732367 0.3404309 0.3302245 946 0.477954 0.3218931 0.3725613 0.3809709 0.340051 0.3607725 0.4734818 0.3402824 0.3295044 948 0.4785219 0.3218311 0.3728422 0.381045 0.3398303 0.3605729 0.4739159 0.3402592 0.3288795 950 0.4792535 0.321888 0.3732554 0.3812611 0.3397272 0.3605012 0.4745115 0.3403447 0.328338 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from skinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 136 137 138 139 140 141 142 143 144 Sample Numb "0201 c" "0202c" "0204c" "0205c" "0208c" "0209c" "0210c" ' -"0211c" "0213c"
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0.8547881 1.1422503 1.0820068 1.3262172 1.1475967 1.1612496 444 0.8966652 0.7910594 1.1347125 0.847322 1.1437113 1.0794406 1.325178 1.1485896 1.1624237 446 0.8928857 0.7869183 1.1330929 0.8374792 1.1438293 1.0768777 1.3279809 1.1477971 1.1632841 448 0.8903057 0.7839059 1.130416 0.8292565 1.1422445 1.072031 1.3304942 1.1477847 1.1645582 450 0.8880193 0.7806001 1.1270245 0.8191263 1.1398997 1.0673658 1.3292459 1.1460378 1.1625316 452 0.8848013 0.7758723 1.1222059 0.810484 1.1384495 1.0616385 1.3290452 1.1429397 1.1619252 454 0.8817778 0.7700683 1.1185566 0.8014591 1.1372939 1.054253 1.3278102 1.1415716 1.1618446 456 0.8793352 0.7654425 1.1114267 0.7936963 1.1342014 1.0500809 1.3280952 1.1385537 1.1602894 458 0.876571 0.7598909 1.1057742 0.7861148 1.1314827 1.0428098 1.3236278 1.1333176 1.1576126 460 0.8727791 0.7550438 1.0998077 0.7775266 1.1266587 1.0359092 1.322064 1.1305985 1.156404 462 0.8703191 0.7498809 1.0940777 0.7693245 1.1232812 1.02877 1.3186029 1.1261448 1.1538708 464 0.8662836 0.7445416 1.0869586 0.7616783 1.1189803 1.0207454 1.3154787 1.1218259 1.1498343 466 0.8630772 0.7392951 1.0800378 0.7544584 1.1151924 1.0138487 1.310608 1.116273 1.1461139 468 0.8573686 0.7336626 1.0711997 0.7481766 1.1094084 1.0058632 1.3066375 1.1109413 1.1412238 470 0.8531492 0.7275254 1,0633175 0.7412183 1.1026111 0.996719 1.2995913 1.1053021 1.1357982 472 0.8474907 0.7216573 1.053817 0.7341438 1.0974263 0.988342 1.2958275 1.09861 1.1306813 474 0.841794 0.7155551 1.044449 0.7273812 1.0904086 0.9789166 1.2891879 1.0916326 1.1245769 476 0.8356993 0.7092999 1,0354778 0.7208703 1.0818347 0.9694948 1.2794486 1.0835764 1.116829 478 0.8294923 0.7025868 1,0258303 0.7147943 1.0748987 0.9596596 1.2736325 1.0753263 1.1087549 480 0.8228333 0.6959527 1,0148134 0.7082984 1.0667197 0.9491446 1.2630582 1.0677423 1.0998445 482 0.8156949 0.6896291 1.0048053 0.7015674 1.059652 0.9394442 1.2533059 1.0588521 1.0913551 484 0.8087353 0.6825333 0.9926985 0.6954755 1.0503688 0.9289156 1.2423924 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0.3629111 0.5209714 0.4291281 0.496446 0.4803697 0.6682126 0.5352238 0.5685862 744 0.3903056 0.3633193 0.5199155 0.4291788 0.4953656 0.4798012 0.6658144 0.5339339 0.5658656 746 0.3902925 0.3637194 0.5188267 0.4292402 0.4942663 0.4792419 0.6633601 0.5326324 0.5631053 748 0.3902812 0.3641506 0.5177558 0.4292997 0.4931506 0.4786901 0.6609153 0.5313342 0.560277 750 0.3902662 0.3646577 0.5166703 0.4294335 0.4920639 0.4781955 0.6585249 0.5300533 0.5574504 752 0.3902566 0.365122 0.5156766 0.4295934 0.4910324 0.4777721 0.656229 0.5288113 0.5547003 754 0.3902471 0.3656488 0.5146473 0.4298284 0.4899724 0.4773998 0.6539026 0.5275294 0.5518983 756 0.3903119 0.3662127 0.5137201 0.4301009 0.4889674 0.4770288 0.6517127 0.5263478 0.5491964 758 0.3903614 0.366757 0.5129067 0.4303541 0.4880222 0.4766372 0.6497732 0.5252375 0.5467354 760 0.3904587 0.3673345 0.5121221 0.4306048 0.4870155 0.4762963 0.6479931 0.5241411 0.5441794 762 0.3905671 0.3678897 0.511343 0.4307913 0.4860157 0.4759078 0.6465808 0.5230482 0.5416534 764 0.390611 0.3683911 0.5106962 0.4308946 0.485043 0.4755467 0.6457286 0.5220618 0.5392421 766 0.3906572 0.3688586 0.5100957 0.430873 0.4839896 0.4751882 0.6452985 0.5211073 0.5366998 768 0.390686 0.3692934 0.5095622 0.4307045 0.4829428 0.4748363 0.6451463 0.5200952 0.5340108 770 0.3906885 0.3697428 0.5090705 0.4305246 0.4819723 0.4745262 0.6446057 0.5192039 0.5312688 772 0.3906321 0.3701805 0.508399 0.4304785 0.4810802 0.4742021 0.6432888 0.5180933 0.5286173 774 0.390639 0.3706943 Ø5075689 0.4306391 0.4802047 0.4738891 0.6412771 0.5169514 0.5259502 776 0.3906778 0.3712309 0.5066449 0.4310196 0.4793386 0.4735409 0.6388581 0.5157292 0.5235212 778 0.3908562 0.3718163 0.5056627 0.4314826 0.4786725 0.4732184 0.6364356 0.5146154 0.5213614 780 0.3910421 0.3724059 0.5047194 0.4318902 0.4779742 0.4728671 0.6342275 0.513556 0.519355 782 0.3912044 0.3730646 0.5038092 0.4321941 0.4772245 0.4724796 0.6322702 0.5125001 0.5172694 784 0.3914174 0.3737506 0.5030235 0.4324779 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0.3636483 0.3921305 0.3758622 0.5212833 0.433846 0.4241091 936 0.3289319 0.3134184 0.3881328 0.3628816 0.3916267 0.3749412 0.5205485 0.4339399 0.4237621 938 0.3283835 0.3128329 0.3870715 0.3621723 0.391229 0.3740895 0.5198827 0.43421 0.4235292 940 0.3279412 0.3123612 0.3861026 0.3615652 0.3909446 0.3733351 0.5193344 0.4346648 0.4233945 942 0.3276224 0.3120404 0.3852885 0.3611022 0.3908151 0.3727523 0.5189489 0.4353454 0.4234065 944 0.3274196 0.3118044 0.3845898 0.360728 0.3908265 0.3722872 0.5186859 0.4362249 0.42356 946 0.3273337 0.3118078 0.3839959 0.3605141 0.3910311 0.3719617 0.5185989 0.4373959 0.4239229 948 0.3273766 0.3118615 0.3835361 0.3604132 0.3913997 0.3717778 0.5186472 0.4387821 0.4244433 950 0.3275197 0.3120226 0.3831945 0.3604282 0.3919011 0.3717216 0.5188276 0.4403504 0.4251137 File Name: e:\scald\trim\abc.cal Spectra and Constituent Data File File Date: Ffue Nov 23 13:16:04 1999 Last Update: Fri Nov 03 14:08:32 2000 File ID: pure a,b,c from sleinabc.cal Master No: 13609408 Instrument Model: NIRSystem 6500 Serial No: 13609408 Samples: 149 Deleted: 0 Constituents: 1 No. Data Points: 276 Lab Basis Dry Matter Segment 1 400 - 950 , 2 Segment 2 1100 - 2498, 0 Position 145 146 147 148 149 Sample Numb "0216c" "0217c" "0220c" "0221 c" "0223c"
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0.9160168 0.4763954 0.8321976 0.7882897 550 0.6822558 0.9126806 0.4739144 0.8278783 0.785938 552 0.6777733 0.9109063 0.4707433 0.8246504 0.7832481 554 0.6719667 0.9062871 0.4679972 0.8198802 0.7803773 556 0.6664567 0.9026319 0.464891 0.8151644 0.776645 558 0.6600328 0.8990676 0.4615611 0.8104362 0.7733378 560 0.6541973 0.8959302 0.458452 0.805649 0.7713196 562 0.6474939 0.8905312 0.4550882 0.8006413 0.7664394 564 0.6415437 0.8863392 0.4517503 0.7954692 0.7626661 566 0.6352116 0.8811715 0.4481649 0.7898079 0.7589164 568 0.6283728 0.8772309 0.4447554 0.7839684 0.7544836 570 0.6212784 0.8716289 0.441633 0.7778363 0.7502472 572 0.6151943 0.8666 0.4382357 0.7719705 0.7459499 574 0.608323 0.8623247 0.4349775 0.7659849 0.7416216 576 0.601738 0.8567368 0.4318145 0.7593095 0.7370524 578 0.5958281 0.8513469 0.4288577 0.7531761 0.7325222 580 0.5893502 0.8457721 0.4258719 0.7470694 0.7284421 582 0.5832701 0.8402579 0.4230041 0.740328 0.7235251 584 0.5773574 0.8348585 0.4204176 0.733713 0.7190369 586 0.5716538 0.8294111 0.4178556 0.7276382 0.7147701 588 0.5663262 0.8243843 0.4157245 0.7211146 0.7101786 590 0.5610324 0.8188618 0.4134547 0.714493 0.7056344 592 0.5558935 0.8144917 0.4113725 0.7087787 0.701555 594 0.5509317 0.8090065 0.4093577 0.7029411 0.6977847 596 0.5457693 0.8034124 0.4072993 0.6968589 0.6934866 598 0.5410087 0.7988179 0.4054735 0.6913882 0.6898495 600 0.5364707 0.7937625 0.4039409 0.6862743 0.6862393 602 0.5320387 0.7889121 0.4020889 0.6810421 0.6829572 604 0.5276141 0.7843795 0.4003755 0.6758568 0.6793834 606 0,5234666 0.7796771 0.3986292 0.6708949 0.6763373 608 0.5192826 0.7752385 0.3969036 0.6662018 0.6731479 610 0.5152016 0.7706174 0.3952245 0.6615066 0.6702081 612 0.5118811 0.7667782 0.3936937 0.6572298 0.667127 614 0.5080733 0.7626748 0.3921828 0.6530495 0.6642991 616 0.5043293 0.7583733 0.3904408 Ø6488333 0.6613481 618 0.5008725 0.7542329 0.38923 0.6448737 0.6588336 620 0.4976848 0.7505496 0.3879334 0.6411068 0.6564013 622 0.4944031 0.7463494 0.3866608 0.6372734 0.6537165 624 0.4914021 0.7424091 0.3654316 0.6336866 0.6511056 626 0.4884474 0.7384449 0.3843547 0.6304725 0.64868 628 0.4856891 0.7346725 0.3833165 0.6271098 0.6465855 630 0.4829712 0.7305942 0.3824099 0.6236736 0.6439435 632 0.4803702 0.7267716 0.3614592 0.620472 0.6416508 634 0.4779651 0.72298 0.3805366 0.617219 0.639391 636 0.4755307 0.719323 0.3797464 0.6140699 0.6370375 638 0.4731892 0.71549 0.3789697 0.6108863 0.6347502 640 0.47107 0.7118132 0.3782979 0.6079268 0.6326051 642 0.4690747 0.7081909 0.3776196 0.6049305 0.6303716 644 0.4670406 0.704462 0.3769898 0.6020047 0.6282816 646 0.4652589 0.7011185 0.3764446 0.5992439 0.6262698 648 0.4635021 0.6976833 0.3759073 0.5964494 0.6242697 650 0.4618028 0.6943123 0.37539 0.5936112 0.6221767 652 0.4602391 0.6910081 0.3750345 0.5909382 0.6201448 654 0.4587461 0.6878561 0.374653 0.5882421 0.6181457 656 0.4572886 0.6846632 0.3742403 0.5855205 0.6161678 658 0.455802 0.6814682 0.3739032 0.5828117 0.6139118 660 0.4544875 0.678335 0.3735509 0.5801326 0.6117613 662 0.4530839 0.6752459 0.3731858 0.5773622 0.609524 664 0.4516875 0.6721172 0.3728399 0.5745329 0.6071107 666 0.4504874 0.6689762 0.3725357 0.5716497 0.6047605 668 0.449075 ~ 0.6658621 0.3721251 0.5687441 0.6021076 670 0.447698 0.6627073 0.3717152 0.5657305 0.599345 672 0.4462948 0.6594967 0.3713656 0.5626506 0.5964601 674 0.4450348 0.6565981 0.371033 0.5598657 0.593656 676 0.4437208 0.6536015 0.3707294 0.5569586 0.5906803 678 0.4423619 0.6505089 0.3703701 0.5539742 0.5876203 680 0.4411023 0.6476468 0.3699976 0.5512204 0.5647077 682 0.4397833 0.6447608 0.3696302 0.5484023 0.5817852 684 0.4384694 0.6418949 0.369293 0.5456962 0.5788236 686 0.437201 0.6391175 0.3689532 0:5431612 0.5760524 688 0.4360415 0.6364371 0.3686466 0.5406755 0.5733167 690 0.434828 0.6338736 0.3683308 0.5382873 0.5707556 692 0.433598 0.6313348 0.3679847 0.5359557 0.5683393 694 0.432438 0.6288546 0.3677697 0.5337651 0.5659674 696 0.4313443 0.6265444 0.3674645 0.5317338 0.5637238 698 0.430269 0.6241702 0.3672623 0.5296415 0.5614578 700 0.429222 0.6219327 0.3670507 0.527625 0.5592688 702 0.4282376 0.6199366 0.3668792 0.5257937 0.5573265 704 0.4272884 0.6178581 0.3667528 0.5238946 0.5552896 706- 0.4263202 0.6157758 0.366585 0.5220147 0.5532934 708 0.4254263 0.6138982 0.3665225 0.5203063 0.5513921 710 0.4245445 0.6120133 0.3665003 0.518585 0.5495577 712 0.423717 0.6101812 0.366495 0.5168272 0.5476987 714 0.4228984 0.6084642 0.3665139 0.5152021 0.5459087 716 0.4221288 0.6067633 0.3665843 0.5135683 0.5441366 718 0.4213865 0.6051067 0.36669 0.5119182 0.5424077 720 0.4206809 0.6034808 0.3668439 0.5102964 0.540646 722 0.4199805 0.6020007 0.3671574 0.5087695 0.538952 724 0.4193247 0.6005519 0.367354 0.5072364 0.5372984 726 0.4186894 0.5990527 0.3676091 0.5056483 0.5356103 728 0.4180409 0.5976452 0.367908 0.504086 0.5339668 730 0.4174413 0.5963188 0.3682029 0.5025924 0.5323635 732 0.4168926 0.5950033 0.3685312 0.5010108 0.5306409 734 0.4162905 0.593658 0.3688456 0.4993929 0.5289022 736 0.4156974 0.592436 0.3691763 0.4978754 0.5272537 738 0.4150482 0.5911701 0.3695115 0.4963101 0.5254892 740 0.4144311 0.5899066 0.3698317 0.4946778 0.523691 B
742 0.4137459 0.588713 0.3701558 0.4930744 0.5219388 744 0.4130562 0.5874714 0.3704596 0.4914391 0.5201281 746 0.4123675 0.5862358 0.3707291 0.4898238 0.5182775 748 0.4116682 0.5850304 0.3710107 0.4881621 0.5163677 750 0.4109533 0.583854 0.3712754 0.4865595 0.5144659 752 0.4102879 0.5827314 0.3715389 0.4850141 0.5126262 754 0.4095695 0.5816349 0.3718148 0.4833987 0.510747 756 0.408939 0.5806736 0.3721565 0.4819154 0.5089796 758 0.4083829 0.5797487 0.3724427 0.4805421 0.5073225 760 0.4078175 0.5788504 0.372604 0.4791285 0.5056287 762 0.4072594 0.5779803 0.3731306 0.4776468 0.5039551 764 0.4067922 0.5771835 0.3734053 0.476285 0.5024278 766 0.4063288 0.5764383 0.3736656 0.4748707 0.5008581 768 0.4058586 0.5757139 0.3738768 0.4735254 . 0.4994111 770 0.4053912 0.5750104 0.374074 0.4722324 0.4978536 772 0.4048326 0.5741311 0.374294 0.4708784 0.4962657 774 0.4042228 0.5731176 0.3746513 0.469585 0.4945859 776 0.4036951 0.5720717 0.3750713 0.4682748 0.4928837 778 0.4032448 0.5709022 0.3754998 0.4671013 0.4912306 780 0.402882 0.5697891 0.3758769 0.4659669 0.489584 782 0.4024901 0.5686967 0.3762332 0.464825 0.4878682 784 0.4022149 0.5677291 0,3766144 0.4637833 0.4863315 786 0.4019426 0.5668648 0.3769798 0.4628401 0.4849764 788 0.4016855 0.5660013 0.377322 0.4618651 0.4836252 790 0.4014175 0.5651134 0.3776888 0.4609129 0.4823117 792 0.4011752 0.5642881 0.3780297 0.4600466 0.4811392 794 0.4009371 0.5634428 0.3783895 0.4591822 0.4799582 796 0.4007224 0.5625987 0.3787521 0.4583113 0.4787562 798 0.4004928 0.5617309 0.3790959 0.4574716 0.4775994 800 0.4002854 0.56094 0.3794298 0.4566448 0.4764692 802 0.4000646 0.5600674 0.3797551 0.4558335 0.4753563 804 0.3998532 0.5591919 0.3800806 0.4550352 0.4742598 806 0.3996308 0.5583628 0.3803591 0.4542603 0.4732098 808 0.3994158 0.5575404 0.380612 0.4535207 0.4722047 810 0.3991748 0.5566575 0.3808561 0.452751 0.4711727 812 0.3989146 0.5557905 0.3810574 0.4519961 0.4701659 814 0.3986726 0.5549267 0.3812049 0.4512785 0.4691884 816 0.3983554 0.5540195 0.3813455 0.450536 0.4682251 818 0.3980253 0.553104 0.3814288 0.449789 0.4672353 820 0.3977004 0.552218 0.3814706 0.4490877 0.4663098 822 0.3973359 0.5512895 0.3814629 0.4483692 0.4653624 824 0.3969297 0.5503154 0.3814203 0.4476447 0.4644306 826 0.3964996 0.5493389 0.3813162 0.4469391 0.4635034 828 0.3960499 0.5483463 0.3811698 0.4462245 0.4625664 830 0.3955382 0.5473062 0.3809687 0.4454735 0.4616343 832 0.3949534 0.5461777 0.3806919 0.4446973 0.4606537 834 0.3943123 0.5450403 0.3803424 0.4438971 0.4596877 836 0.3936341 0.5439028 0.3799503 0.4431024 0.4587382 ..
838 0.3928526 0.5425927 0.3794507 0.4422292 0.4576657 840 0.3919998 0.5412174 0.3788751 0.4413073 0.4565751 842 0.3910874 0.5398296 0.3782394 0.4403886 0.4554887 844 0.3900843 0.5383474 0.3775198 0.4394038 0.4543514 846 0.3889984 0.5367988 0.3767019 0.4383714 0.4531308 848 0.3878845 0.5352155 0.375856 0.4373424 0.4519257 850 0.3867134 0.5335917 0.3749506 0.4362776 0.4506954 852 0.3854426 0.5318701 0.3739341 0.4351326 0.4494099 854 0.3841207 0.5301296 0.3728621 0.4339913 0.4480924 856 0.3827892 0.5283544 0.3717712 0.4328214 0.4467807 858 0.3814222 0.5265827 0.3706383 0.4316501 0.4454617 860 0.3799529 0.5246776 0.3693976 0.4304092 0.4440842 -.
862 0.3784687 0.5227813 0.3681477 0.4291894 0.4426863 864 0.3769956 0.5209458 0.366899 0.4279663 0.441329 866 0.3754381 0.5189784 0.3655623 0.4266776 0.4399109 868 0.3738363 0.5169743 0.3641693 0.4253715 0.4384555 870 0.3722741 0.5150577 0.3628068 0.4241036 0.4370489 872 0.3706713 0.5130914 0.3614101 0.4228036 0.4356279 874 0.3690167 0.5110446 0.3599441 0.4214822 0.4341548 876 0.367362 0.5090429 0.3584901 0.4201479 0.4326881 878 0.365713 0.507047 0.357026 0.4188398 0.4312625 880 0.3640649 0.5050442 0.3555586 0.4175229 0.4298118 882 0.3622749 0.5028917 0.3539555 0.4160869 0.4282487 884 0.3605945 0.5008547 0.352443 0.4147703 0.4267936 886 0.358967 0.4988869 0.3509622 0.4134811 0.4253861 888 0.35724 0.4968111 0.3493918 0.412104 0.4238823 890 0.3555117 0.4947244 0.3478312 0.410737 0.4223897 892 0.3538722 0.4927521 0.3463244 0.4094243 0.4209844 894 0.3521684 0.4907194 0.344774 0.4080813 0.4195099 896 0.3504354 0.488636 0.3431666 0.406701 0.4180086 898 0.3487425 0.4866243 0.3416176 0.4053638 0.4165595 900 0.3470547 0.4846062 0.3400413 0.4040297 0.4150939 902 0.3453133 0.4825581 0.3384388 0.4026847 0.4136262 904 0.3435809 0.4805031 0.3367971 0.4013291 0.4121575 906 0.3418768 0.4785347 0.3352155 0.4000212 0.4107319 908 0.3402362 0.4766145 0.3336778 0.3987628 0.4093462 910 0.3385493 0.4746552 0.3320546 0.3974733 0.4079503 912 0.3368975 0.4727381 0.3304605 0.3962334 0.4065816 914 0.3353996 0.4709925 0.3290515 0.395071 B 0.4053452 916 0.3339308 0.46925 0.3275746 0.3939349 0.4041027 ' 918 0.3325078 0.4675742 0.3261693 0.3928247 0.4029104 920 0.3312344 0.4660449 0.324863 0.3918134 0.4018206 922 0.3300393 0.4645999 0.3236278 0.3908681 0.4008114 924 0.3289293 0.4632356 0.3224495 0.389935 0.3998442 926 0.3279205 0.4619686 0.321337 0.3891255 0.3989625 928 0.3270661 0.4608487 0.320363 0.3883709 0.3982035 930 0.3263066 0.4598163 0.3194476 0.3876821 0.3975108 932 0.3256305 0.4588652 0.3185813 0.3870362 0.3968761 934 0.3250817 0.4580333 0:3178117 0.3864627 0.3963263 936 0.3246517 0.4573446 0.3171474 0.3659949 0.3958866 938 0.3243285 0.4567467 0.3165408 0.3855928 0.3955297 940 0.3241421 0.4562886 0.316028 0.3852975 0.3953042 942 0.3241409 0.4560253 0.3156572 0.385145 0.3952252 944 0.3242962 0.455921 0.3153989 0.3851249 0.39527 946 0.3246594 0.4560199 0.3152594 0.3852732 0.3955232 948 0.3251947 0.456308 0.3153235 0.3855746 0.3959109 950 0.3258833 0.4567421 0.3154229 0.3860068 0.3964458

Claims (21)

We claim:

1. An imaging system for determination of contamination on food comprising:
at least one charge-coupled device detector with an optical filter capable of collecting at least two discrete narrow-band images, a lighting system, a data processing unit operatively connected to said detectors for receiving images for analysis of the spectral properties of an image created by said detector, and a computer readable memory encoded with a computer program containing a detection algorithm based on mathematical analysis of selected key wavelengths of radiation detected by said detector wherein said selected key wavelengths are derived by using a calibration process including:
(a) collecting spectra with a visible/near infrared monochromator by irradiating samples of uncontaminated food and pure contaminants representative of the types of contamination to be determined with visible/near infrared radiation and digitally recording reflectance intensity from about 400 nm to about 2500 nm in about 2-nm intervals, (b) transforming said spectra recorded in step (a) for each sample to log10 spectra in absorbence units, (c) transforming said log10 spectra with standard normal variate and detrending procedures to remove interferences of scatter, particle size, and variations in baseline shift and curvilinearity, (d) processing said transformed spectra in step (c) with at least one of Principal Component Analysis and Partial Least Squares regression for formation of scores and loadings, (e) comparing said scores with variations in Principal Components for selecting discrete Principal Components at which scores correlate with uncontaminated foods and contaminants, (f) evaluating loadings of said discrete Principal Components for extreme variations in absolute value to identify key wavelengths, (g) selecting images at key wavelengths identified in step f, and (h) calculating algorthim to detect contaminants.

2. The imaging system of claim 1 wherein said optical filter is selected from the group consisting of a line-scan spectrograph, a liquid crystal tunable filter, an acousto-optic tunable filter, and a narrow band-pass filter; wherein said filters are capable of collecting at least two discrete spectral images each taken at a different wavelength.

3. A method for identifying contamination on food comprising:
(a) identifying key wavelengths by performing the following steps:
(i) preparing samples of uncontaminated and pure contaminants representative of the types of contamination to be determined, (ii) collecting spectra of said samples, (iii) transforming spectra of said samples to log10 in absorbence units, (iv) transforming said log10 spectra with standard normal variate and detrending procedures to remove interferences of scatter, particle size, and variations in baseline shift and curvilinearity, (v) processing said transformed spectra in step (iv) with at least one of Principle Component Analysis and Partial Least Squares regression for formation of scores and loadings, (vi) comparing said scores with variations in Principal Components for selecting discrete Principal Components at which scores correlate with uncontaminated foods and contaminants, (vii) evaluating loadings of said discrete principal components for extreme variations in absolute value for identifying key wavelengths, (viii) identifying said key wavelengths based on the results of step (vii), (b) calibrating image wavelengths wherein said calibration includes selecting sensor binning to determine band numbers, imaging known wavelength standards to identify wavelength peaks and band numbers, performing a non-linear regression on said wavelengths against said band numbers, and applying said regression to subsequent images, (c) creating hyperspectral or multispectral images of said samples, (d) selecting said images at said key wavelengths based on the results of step (viii), (e) applying algorithms using key wavelengths identified in step (viii) to form an image dataset for the identification of contamination.

4. A method for identifying contamination on food comprising:
(a) identifying key wavelengths by performing the following steps:
(i) preparing samples of uncontaminated and pure contaminants representative of the types of contamination to be determined, (ii) collecting spectra of said samples, (iii) transforming spectra of said samples to log10 in absorbence units, (iv) transforming said log10 spectra in absorbence units, (v) processing said transformed spectra in step (iv) with at least one of Principle Component Analysis and Partial Least Squares regression for formation of scores and loadings, (vi) comparing said scores with variations in Principal Components for selecting discrete Principal Components at which scores correlate with uncontaminated foods and contaminants, (vii) evaluating loadings of said discrete principal components for extreme variations in absolute value for selecting key wavelengths, (viii) identifying key wavelengths based on the results of step (vii), (b) calibrating image wavelengths wherein said calibration includes selecting sensor binning to determine band numbers, imaging known wavelength standards to identify wavelength peaks and band numbers, performing a non-linear regression on said wavelengths against said band numbers, and applying said regression to subsequent images, (c) creating hyperspectral or multispectral images of said samples, (D) selecting said images at said key wavelengths based on the results of step (viii), (e) calculating a ratio of two images at said two wavelengths to form a ratio image, (f) performing a masking procedure on said ratio image to remove background from said image, (g) applying histogram stretching to said ratio image to qualitatively identify contaminants in real-time, and/or (h) applying thresholding to said ratio image from step m to quantitatively identify contaminants in real-time.

5. The method of claim 4 further including transforming said log10 spectra with standard normal variate and detrending procedures to remove interference of scatter, particle size, and variations in baseline shift and curvilinearity.

6. The method of claim 4 wherein hyperspectral images are collected by a line-scan spectrograph with a charge-coupled detector.

7. The method of claim 4 wherein multispectral images are collected using an imaging device selected from the group consisting of a common aperture camera with at least two charge-coupled device detectors, at least one charge-coupled device detector with a liquid crystal tunable filter, at least one charge-coupled device with an acousto-optic tunable filter, at least one charge-coupled device with a line-scan spectrograph, and multiple charge-coupled device detectors with narrow band-pass filters.

8. The method of claim 4 wherein said ratio image is determined by dividing an image at a first said key wavelength by an image at a second key wavelength on a pixel by pixel basis of said collected images from step (4d).

9. A method comprising:
(a) preparing samples of uncontaminated food and pure contaminants representative of the types of contamination to be determined, (b) collecting spectra of said samples, (c) transforming spectra of said samples to log10 spectra in absorbence units, (d) processing said transformed spectra in step (c) with at least one of Principal Component Analysis and Partial Least Squares regression for formation of scores and loadings, (e) comparing said scores with variations in Principal Components for selecting discrete Principal Components at which scores correlate with uncontaminated foods and contaminants,, (f) evaluating loadings of said discrete principal components for extreme variations in absolute value for selecting key wavelengths, and (g) identifying key wavelengths for identification of contaminants based on the results of step (f).

10. A process for detecting contamination on food comprising:
(a) illuminating said food with a source of electromagnetic radiation having a predetermined spectral content, (b) detecting radiation from said source reflected by said food item in each of four predetermined wavelengths .lambda.1 , .lambda.2, .lambda.3, .lambda.4, and generating a first data set comprising digital values indicative of reflected radiation intensity in each of said wavelengths;
(c) processing said digital values according to an algorithm as follows wherein nis a constant integer; and I is an indication of fecal contamination.

11. The process of claim 10 wherein .lambda.1 is from about 750 to about 830 nm .lambda.2 is from about 450 to about 500 nm .lambda.3 is from about 500 to about 535 nm .lambda.4 is from about 550 to about 585 nm.

12. The process of claim 10 further comprising calculating, for each digital value in said first data set, mean and variance values, based on a set of proximate digital values, thereby creating a mean value data set and a variance value data set;
adding said mean value and variance value data sets to create a final data set;

determining presence or absence of contamination based on values in said final data set.

13. ~The process of claim 12 wherein said determining step comprises:
comparing data values in said final data set to a predetermined threshold value; and determining presence of contamination based on results of said contamination.

14. ~A process for detecting contamination on food comprising:
(a) illuminating said food with a source of electromagnetic radiation having a predetermined spectral content;
(b) detecting radiation from said source reflected by said food in a plurality of predetermined wavelengths and generating a data set comprising signals indicative of reflected radiation intensity in each of said wavelengths;
(c) processing said data set according to a predetermined mathmatic function to generate a plurality of rule files comprising respective image files;
(d) combining said rule files to generate a combined data set;
(e) performing a texture analysis on said combined data set to generate spatially distributed mean and variance data;
(f) summing said mean and variance data to yield output data; and (g) detecting contamination based on said output data.

15. The process of claim 14 wherein said predetermined mathematic function is defined by:
wherein nb - number of said predetermined wavelengths;
t = detected reflected radiation value from said food for a defined wavelength; and r = reflectance value of a contaminant at said defined wavelength.

16. A method for determining contamination on poultry or livestock carcasses comprising:
(a) obtaining poultry or livestock carcasses for which contamination is to be determined, (b) creating hyperspectral or multispectral images of said carcasses, (c) selecting images at key wavelength, (d) applying an algorithm to detect contaminants to images selected in step (c) to obtain contaminant containing images, (e) applying masking to eliminate background in images of step (d), (f) applying histogram stretching to images of step (e) qualitatively identify contaminants, and (g) applying thresholding to images of step (e) or (f) to quantitatively identify contaminants in real-time,

17. The method of claim 16 wherein said algorithms are selected from the group consisting of a ratio of key wavelengths and a linear combination of key wavelengths.

18. A computer readable medium encoded with a computer program for detecting contamination on food by causing a computer to process image signals indicative fo intensity of radiation reflected from said food in four wavelengths .lambda.1-.lambda.4, using an algorithm:
wherein I is an indication of contamination.

19. An apparatus for detecting contamination on food comprising:
(a) a plurality of sensors for detecting spatially distributed radiation reflected from a food at four wavelengths .lambda.1, .lambda.2, .lambda.3, and .lambda.4;
a computer; and a computer readable medium coupled to said computer for causing said computer to process said image signals using an algorithm:
wherein I is an indication of contamination.

20. A computer readable medium encoded with a computer program for detecting contamination on a food by causing a computer to process spectrally resolved image information indicative of intensity of radiation reflected from said food in a plurality of wavelength by performing the following steps:

(a) calculating data sets in the form of rule files, using said spectrally resolved image information, comprising values of a wherein wherein nb - number of bands of spectrally resolved image information;

t - detected spectrally resolved image information values for an i th band; and r = spectrally resolved image information value for an i th band for a contaminant whose presence is to be detected.

(b) combining said rule files to generate a combined data set;

(c) performing a texture analysis on said combined data set to generate spatially distributed mean and variance data; and (d) summing said mean and variance data to yield output data indicative of contamination.

21. An apparatus for detecting contamination on food comprising:

(a) a plurality of sensors for detecting spatially distributed spectrally resolved image information indicative of intensity radiation reflected from a food in a plurality of wavelength bands;
(b) a computer readable medium coupled to said computer for causing said computer to process said image signals by calculating data sets in the form of Rule Files, using said spectrally resolved image information, using a formula~
wherein nb = number of bands of spectrally resolved image information;
t = detected spectrally-resolved image information values for an i th band, and r = spectrally resolved image information value for an i th band for a contaminant whose presence is to be detected;
(c) combining said Rule Files to generate a combined data set;
(d) performing a texture analysis on said combined data set to generate spatially distributed mean and variance data; and (e) summing said mean and variance data to yield output indicative of contamination.