J. Anim Sci. 2008. 86:211-219. doi:10.2527/jas.2007-0084
© 2008 American Society of Animal Science
Predicting beef tenderness using near-infrared spectroscopy1
S. R. Rust2,
D. M. Price,
J. Subbiah3,
G. Kranzler,
G. G. Hilton,
D. L. Vanoverbeke and
J. B. Morgan4
Oklahoma State University, Stillwater 74075
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Abstract
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The objective of this multiple-phase study was to determine the accuracy of an on-line near-infrared (NIR) spectral reflectance system to predict 14-d-aged cooked beef tenderness. In phase I, 292 carcasses (140 US Select, 152 US Choice) were selected (d 2) from 2 commercial beef processing facilities. After carcass selection, longissimus lumborum (LL) muscle sections (ribs 9 to 12) were individually identified, vacuum-packaged, and transported to the Oklahoma State University Meats Laboratory, where a 2.54-cm-thick steak (n = 1) was fabricated and stored in refrigerated conditions (1°C ± 1). Following a 30-min oxygenation period, a NIR spectral scan was obtained on the 12th-rib LL steak. Steaks (d 3) were individually vacuum-packaged and aged at 4°C for a total of 14 d before cooking slice shear force (SSF) analysis. In phases II and III, 476 carcasses (258 US Select, 218 US Choice) were immediately NIR scanned after carcass presentation to in-plant USDA grading personnel. In a similar fashion, all LL steaks were aged (1°C ± 1) for 14 d before cooking (70°C) and conducting SSF. Of the phase I and II samples, 39 (6.77%) were categorized as being tough (i.e., 
25 kg of SSF after the 14-d postmortem aging period). Of these 39 tough samples, 20 (3.7% error rate) were correctly placed in the 90% certification level. Another 10 tough samples were placed in the 80% certification level (2.0% error rate). The overall NIR certified tender group was 1.67 kg more tender (P < 0.05) than LL samples from the noncertified samples. When the NIR predicted samples to be tough, 10% of the samples were eliminated from the phase I and II LL populations at 90% certification. The population SSF mean improved in excess of 6.5 kg. For phase III, SSF evaluation by an independent third party indicated the NIR system was able to successfully sort tough from tender LL samples to 70% certification levels. It was concluded that NIR scanning offers an in-plant opportunity to sort carcasses into tenderness outcome groups for guaranteed-tender branded beef programs.
Key Words: beef • near-infrared • quality grade • tenderness
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INTRODUCTION
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Beef tenderness is a primary consideration in consumer satisfaction. The beef industry continues to struggle with the challenge of producing lean beef products that are consistent in their palatability characteristics. Working with products that originate from multiple breeds and hundreds of thousands of producers who use different production systems, the variability within the carcass population is immense. Despite its significance to eating satisfaction, tenderness is not a factor directly incorporated into the quality grading process. In order to improve beef tenderness and overall quality, identification of tough carcasses must be a top priority for the beef industry. The primary method used today as a marketing tool to differentiate between tenderness levels is the indirect, marbling-based method of USDA quality grading.
Several technologies have been developed to evaluate beef tenderness. Near-infrared reflectance (NIR) spectroscopy is a rapid, nondestructive system that gathers information about samples through measurements of reflected light. The light reflected back through the NIR system contains information about properties associated with meat tenderness (Byrne et al., 1998
; Park et al., 2001). Shackelford et al. (2004, 2005) have developed a commercially available tenderness prediction system based on visible and near-infrared reflectance spectroscopy that could be used on-line to identify carcasses that excel in longissimus tenderness.
The objective of this experiment was to investigate the feasibility of using NIR spectrometer to predict tenderness of aged beef in a real-world processing plant environment.
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MATERIALS AND METHODS
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Animal Care and Use Committee approval was not obtained for this study because the samples were obtained from federally inspected slaughter facilities.
Description of the Instrument
Near-infrared (NIR) analyses were performed in reflectance mode with a VIS/NIR spectrophotometer (Field Spec Pro Jr., Analytical Spectral Devices Inc., Boulder, CO). The spectrometer used in this study was capable of collecting light in the visible and NIR regions (400 to 2,500 nm). A fiber-optic contact probe was used to transmit light reflected from the beef surface to 3 internal detectors. Light was supplied by a 20-W halogen light source and a diffuse reflection probe with 35° geometry with an effective measuring area of 1 mm2. The halogen lamp was powered by a feedback controller to stabilize illumination level. The detectors consisted of a silicon photodiode array, a thermoelectrically (TE) cooled Indium Gallium Arsenide (InGaAs) detector, and a TE-cooled extended InGaAs detector to measure the 350 to 1,000 nm, 1,001 to 1,670 nm, and 1,671 to 2,500 nm wavelength domains, respectively.
Inside the instrument, a diffraction grating split the reflected light into narrow wavelength bands. A 512-channel silicon photodiode array was geometrically positioned to receive light within a narrow bandwidth (1.4 nm) in the region of 350 to 1,000 nm. The photodiodes converted the accumulated light to an electronic signal. The signal was digitized by and transferred to the computer. Spectral resolution in this region was 3 nm. The total volume of sample analyzed varied with the spectral range (Hoving-Bolink et al., 2005). In the visible region of light (400 to 750 nm), meat was relatively opaque, and information could be collected up to approximately 1 cm. In the NIR region, the penetration depth was at most 1 mm, due to the high water content.
The 2 InGaAs detectors were the scanning type. They differed from the first sensor in that they measured wavelengths sequentially rather than simultaneously. Each sensor consisted of a concave holographic grating and a single TE-cooled InGaAs detector. The gratings were mounted on a shaft that oscillates with a period of 200 ms (100 ms/scan). As the grating oscillated, the detector measured different wavelength bands. The resolution in these spectral regions was 30 nm. For in-plant operation, the spectrometer was secured in a backpack, with a notebook computer supported in view of the operator (Figure 1). The fiberoptic cable from the spectrometer terminated in a contact probe that projected broadband lighting and positioned the cable to collect the light reflected from the beef surface (Figure 2). A total of 32 scans were averaged for every spectrum, and each spectrum took 10 s to be recorded.
Meat Samples
Beef longissimus lumborum (LL) from the ribeye roll samples (n = 768) were collected from 2 regional packing plants (n = 304, Sam Kane’s, Corpus Christi, TX; and n = 464, Excel Packing, Plainview, TX). It should be noted that the US Quality Grades of the tested carcasses (approximately 50% US Select and 50% US Choice) were selected primarily to reflect typical quality grade distributions of US beef processing plants. This project was conducted in 3 phases. In phase I (n = 292), after a 48-h chill (1°C), carcasses passed the quality grading stand where they were selected for inclusion in the investigation. Selected carcasses were individually identified and moved off onto separate rails segmented by USDA Quality Grade. Carcass grade data factors were collected for preliminary yield grade, adjusted fat thickness, ribeye area, KPH percentage, lean maturity, skeletal maturity, marbling score, and quality grade, as evaluated and stamped by USDA graders (USDA, 1997). Hot carcass weight and carcass identification numbers were recorded directly from the plant identification tags.
Following grade data collection, phase I carcasses were fabricated, and individually identified ribeye rolls (IMPS# 112) were collected, vacuum-packaged, packed into refrigerated chests with ice packs, and transported to the Oklahoma State University (OSU) Food and Agricultural Products Research Center. At approximately 72-h postmortem, a 2.54-cm LL steak was cut from the anterior end of each subprimal using a band saw, individually identified, placed on a plastic tray, and allowed to bloom for 30 min. After pH and Hunter L*, a*, and b* (Miniscan XE Plus, Reston, VA) values were collected, trays were transported to the Oklahoma State University Biosystems & Agricultural Engineering Machine Vision Laboratory for spectral scanning under controlled conditions. Following scanning, transported steaks were vacuum-packaged, aged for 14 d, and frozen ( –2.0°C) until further analysis.
In phase II, carcasses (n = 276, 50% US Low Choice and 50% US Select) were selected at the USDA grading stand and transferred onto separate rails segmented by USDA Quality Grade. Carcass grade data factors were collected in a manner similar to procedures as outlined in phase I. After NIR scanning each identified carcass, ribeye rolls were collected during fabrication, vacuum-packaged, and transferred to OSU. At 72-h postmortem, a LL steak (2.54 cm thick) was uniformly removed from each subprimal. Following a 30-min bloom period, pH and Hunter L*, a*, and b* values were obtained from the steak surfaces. Following baseline pH and objective lean color information collection, LL steaks were individually vacuum-packaged and allowed to age for an additional 11 d (1.0°C).
The concluding portion of the project (phase III) consisted of a third-party verification stage that included overnight shipping (FedEx Inc., Stillwater, OK) LL steaks (n = 200, 50% US Low Choice and 50% US Select) to the US Meat Animal Research Center (USMARC) in Clay Center, Nebraska, for slice shear force (SSF) measurements. Accompanying the 14-d aged and frozen steaks, a NIR-predicted tenderness classification rating was provided to the USMARC personnel. These tenderness ratings were established from the in-plant spectral scans obtained from the 2 cooperating beef processing facilities. The OSU spectrometer was based on phase I and II and was used to predict SSF values. The predicted values were then compared with the SSF values determined at USMARC.
Slice Shear Measurement
Slice shear force steaks at USMARC were thawed for 24 h at 1 to 2°C and cooked on a belt-fed impingement oven (TBG060 Magigrill, MagiKitch’n Inc., Quakertown, PA), as described by Wheeler et al. (1998). Preliminary test cooking was conducted to determine the appropriate cooking times to reach 70°C internal temperature. After the steaks exited the belt oven, they were held at room temperature for 2 min for postcooking temperature rise to complete the cooking process. Slice shear force was measured after the cooked steaks were allowed to chill for 24 h at 4°C. Using the procedures outlined by Shackelford et al. (1999a, b), a first cut was made approximately 1 cm from the lateral end of the cooked steak. The SSF sample was removed at an angle of 45° using a knife with 2 parallel blades separated by a 1-cm space. This procedure generated a cooked meat sample measuring 5 cm in length by 1 cm in thickness and 2.5 cm in width. This sample location was selected so that limited connective tissue would be located within the slice shear sample. Slice shear force was measured using a flat, blunt-end blade (SSF blade) mounted on an Instron Universal Testing Machine (Instron Corp., Canton, MA). The force required to shear the muscle fibers of the slice was recorded as a SSF. Increased SSF values indicated tougher beef.
Instrument Operation-Optimization of Parameters
Integration time (IT) is an important parameter for the photodiode array detector. Integration time, also known as exposure time, is defined as the time during which the photodiode array accumulates the signal. The greater the IT, the greater the signal. However, the photodiode array begins to saturate if the signal exceeds the dynamic range of the detector. Therefore, optimization of the IT is required. In this experiment, the contact probe was placed on a white reference plate (Spectralon Diffuse Reflectance Targets, LabSphere Inc., North Sutton, NH) that reflected light across the spectrum of interest. The spectrometer automatically adjusted the IT to allow maximum signal without saturation.
A certain amount of electric current, called dark current, is generated by thermal electrons and is added to the signal generated by reflected light. Dark current is a property of the detector and associated electronics (not the light source) and varies with temperature. It also varies linearly with the integration time for the photodiode array. Dark current was produced by the detector when the mechanical shutter blocked the entrance slit of the spectrometer. This signal was detected from all readings to eliminate the effect of temperature variation. Dark current was read every 5 min during spectral collections.
Another factor that was used to optimize instrument spectral capabilities was a white reference plate. Because a white surface reflects nearly 100% of incident light, the resulting measure is an estimate of incident light intensity (Io). White reference spectra were collected every 5 min during carcass spectral readings. To avoid soiling the white plate, it was protected with a 1.59-mm cover glass made of fused quartz borosilicate. This glass has more than 90% transmission over the spectral range of interest.
Reflectance
The spectrum reflected from the sample (I) must be collected under conditions similar to those used for the white reference. Because a cover glass was placed over the white reference plate, a glass plate with identical specifications was placed over the sample. By dividing the reflected spectrum by incident light (white reference spectrum), reflectance (I/Io) was obtained. The acquired reflectance, which is the fraction of incident light that is reflected from beef surface, depends on the property of the material as well as the design of the probe and spectrophotometer. In addition, for a given scan, 10 spectra were collected consecutively and averaged to minimize the effect of electronic noise. Three spectra were collected at 3 locations near the lateral end of the LL, in an effort to avoid connective tissue.
The median of 3 spectra was calculated and saved as a reflectance spectrum for that sample. Median calculations aid in the prevention of outlier data points, such as spectra over a thick marbling spot or connective tissue, or low signal at water absorption bands. Reflectance (R) was converted to absorbance (1/R) by log transformation. This transformation is commonly employed to linearize the relationship between the concentration of an absorbing compound and the absorption spectrum.
Model Development
Model development was generated from data collected in phases I and II. Each spectrum generated 2,150 data points, or independent variables, and the SSF as the dependent variable. To accommodate this scale of variables, a multivariate, dimensionality reduction technique was employed to avoid overfitting. Partial least squares regression was used to produce new features. These features are linear combinations of original spectral data points yielding new factors that are not correlated and that explain most of the variation in both the dependent and independent variables (CAMO, 1998). Absorbance spectra in the region of 400 to 1,500 nm were used to predict SSF. Spectra beyond 1,500 nm were not found to be useful. The model was developed with Unscrambler software (Camo Inc., Corvallis, OR). Cross-validation (Esbensen, 2001) was employed to select the number of partial least squares factors included in the models.
Evaluation of the Statistical Model
Our evaluation of system performance followed procedures described by Wheeler et al. (2002). They assessed performance of 3 instrumented tenderness prediction systems on the basis of progressive certification of steak sample tenderness in 10% certification increments. We classified any steaks with 14-d SSF greater than 25 kg as tough and the rest as tender. In the description that follows, observed values refers to the reference SSF values. Predicted values refers to the 14-d SSF predicted by the spectral reflectance system.
Samples were first sorted and ordered on the basis of predicted values. For 10% certification levels, 10% of the steaks having the lowest predicted values were classified into a certified tender group and the remaining into a not-certified-tender group. The mean observed SSF values were compared for the certified tender and not certified tender groups using a t-test for independent samples (
= 0.05). Equality of variance for the 2 groups was tested. If the variances were equal, a pooled variance estimate was used in the t-test. If the variances were not equal, Satterthwaite approximation was used to estimate the variance. When there was a significant difference in mean observed shear force values between the 2 groups, we concluded that the spectral reflectance system had successfully sorted the tender from the tough samples at that certification level. Any tough sample (observed 14-d SSF value 25 kg) in the certified-tender group was an error. This procedure was repeated for certification levels up to 90%, in 10% increments. A 100% certification level classified all samples as tender (no sorting). The proportion of tough samples with no sorting was the error rate for 100% certification for comparison to other certification levels.
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RESULTS AND DISCUSSION
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As anticipated, simple statistics for various carcass and muscle traits to characterize the samples were highly variable for all measured traits (Table 1). It should be mentioned that objective lean color traits were similar to those reported by Wulf and Page (2000) for a 1,000 beef carcass population selected to represent the US-fed beef population. A great deal of effort was taken to assure that potential tough LL steak samples were included in the current investigation. In order to best test and challenge the NIR spectral instrument, tough samples were needed in the population. According to the most recent National Beef Tenderness Survey (Brooks et al., 2000), only 1.5% of the LL steak samples had Warner-Bratzler shear force values that exceeded 4.6 kg. Certainly, many factors could have contributed to this low percentage of tough samples. Variables such as LL steaks from food service and retail sectors were sampled, the reported postmortem aging time average exceeded 21 d, and an attempt was made to include representatives from the entire national beef processing industry in the survey. In our tested ribeye steak population (Figure 3), even following a 14-d postmortem aging period, 6.8% (39 of 568 steaks) displayed SSF values that were classified as being tough (i.e., 25 kg). It should be mentioned that in the instrument-testing project summarized by Wheeler et al. (2002), their initial population included in excess of 14% samples that were classified as tough. It should be noted that several distinct differences were observed between that particular investigation (Wheeler et al., 2002) and the current study: 1) those authors utilized strip loin samples, whereas we tested ribeye steaks; 2) a belt grill broiler was utilized by Wheeler and others, whereas an convection impingement oven was utilized in the current study (the validation part of our study involved a belt grill broiler); and 3) the SSF procedure was used to determine actual tenderness rating, whereas Wheeler used the more traditional Warner-Bratzler shear force procedure. However, it appears that both studies were fortunate in that several samples classified as being tough following an extended postmortem aging were included in both test populations.
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Figure 3. Distribution of slice-shear force values for longissimus steaks included in phases I and II (n = 568 samples).
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If one further investigates the distribution of tenderness ratings as determined by SSF values, it becomes apparent that ribeye samples originating from select quality graded carcasses were tougher and more variable in their tenderness ranges than samples from steaks from choice graded carcasses (Table 2). In both phases of the investigation, SSF values exceeding 25 kg were greater for the US Select samples (i.e., 9.0%) when compared with only 3.4 and 5.9% tough samples from US Choice carcasses in phases I and II, respectively (Table 2). Substantial variation in tenderness was noted for the entire population with a range in SSF from 9.87 to 39.87 kg. In fact, 12 longissimus samples had SSF values that exceeded 28 kg.
In order to prevent biased comparisons between the 3 tested tenderness prediction procedures, Wheeler and coworkers (2002) utilized the use of progressive certification as tender in 10% increments (10 to 90% certified as tender). In our study, 6.8% of the carcasses were tough. This result stems from the lower number of tough carcasses in testing. Note that in this certification method, we grouped the samples into 10 categories. At each certification level, there are 2 groups: certified tender and not certified tender. Significant differences in mean observed SSF indicate successful sorting at that certification level. The best-case scenario would be to have all tough carcasses in the toughest category and zero error rate at all certification levels. These results were not obtainable using the Oklahoma State University spectral instrument, but compared with the previous tested instruments, progress was made. In the initial phases of the project, 39 of the 568 carcass samples were categorized as tough (i.e., 25 kg of SSF at 14 d of postmortem aging). This performance reflects into 6.8% error in certification at the 100% level (Figure 4). A very high percentage of the LL samples were correctly classified as being tender when the population was categorized in expected certification levels. Of the 39 tough samples, 20 tough carcasses were correctly placed in the not-certified-tender category. Another 19 tough samples were incorrectly placed in the not-certified-tender category (3.7% error rate). At the 80% certification level, 30 tough carcasses (10 more tough samples were correctly identified in addition to the 20 tough carcasses identified at the 90% certification level) were correctly placed in the not-certified-tender category. Nine tough carcasses were incorrectly placed in the certified-tender category (1.98% error rate). In the Wheeler et al. (2002) study, the error rate for 100% certification using all carcasses in phase II was 9.3%. Slice shear force certification levels up to 80% had lower (P < 0.05) error rates than 100% certification.
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Figure 4. Error rates for certifying choice and select quality carcasses as tender in increments of 10% of the sample population in phases I and II (n = 568).
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Of the 400 tested samples, 37 samples (9.3%) exhibited WBSF values > 5 kg at 14 d of postmortem aging (i.e., 100% certification). Using SSF at 3 d postmortem to segment carcasses into a 90% (i.e., predict toughest 10% of population) and 80% certification group (i.e., predict toughest 20% of population) resulted in error rates (percentage of carcasses certified as tender that had WBSF of >5 kg at 14 d postmortem) of 6.4 and 4.1%, respectively. This means that following 90 and 80% certification, 23 of 37 and 13 of 37 carcasses were certified as being tender, but they were actually tough. The errors of SSF at 3 d postmortem as a predictor of aged beef tenderness are due to the carcass-to-carcass variation in tenderization during aging.
In another attempt to better understand the data, we separated the predicted SSF estimates from the 3-d spectral scans and segmented them into projected palatability groups (Figure 5). The mean SSF value for this group (10% certification) was 14.90 kg. In addition, no tough samples were included in this subset. Mean SSF values increased as predicted. The overall mean for the samples predicted as toughest (100% certification) was 25 kg, with 20 of the 39 tough samples being correctly placed in this category. One concern is one of the tough samples was predicted to be one of the most tender samples (20% certification). Although much progress has been made, more work is needed.
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Figure 5. Mean slice shear force in each predicted tenderness category (n = 56 or 57) at 14 d of age. Number of tough (actual SSF > 25 kg) steaks in each predicted tenderness category.
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Regardless of percentage certified, the difference in mean longissimus SSF value between certified tender and not certified tender was significant (P < 0.05) for spectral analysis in both USDA Choice and Select carcasses (Table 3). Removing the toughest 10% improved the mean SSF in excess of 6.5 kg. A similar trend was observed, in that when predicted tough samples were removed from the population, improvements resulted in the certified tender population. The magnitude of difference was not greatly improved after the toughest 40% (60% certified as tender) was segmented as not certified. The SSF values for certified tender and not certified tender among the USDA Select samples are also significant (Table 4). In the Wulf et al. (1997) study the colorimeter appeared to be useful at identifying guaranteed tender beef using an independent sample, but not within the narrow range of marbling in USDA Select carcasses.
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Table 3. Effect of percentage certified tender and not certified tender on 14-d slice shear force of US Choice and Select longissimus steaks (phase I and II, n = 554)
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Table 4. Effect of percentage certified tender and not certified tender on 14-d slice shear force of USDA Select longissimus steaks (phase I and II, n = 291)
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The certification for phase III (validation) samples is represented in Table 5. Up to 70% certification levels, significant differences were observed, meaning that the system sorted the tough from tender carcasses successfully. The implication is that if we remove the top 30% of carcasses sorted as tough by this system, the remaining 70% carcasses can be sold as guaranteed tender for premium markets such as restaurants. At 20% certification level, the means of 2 categories were different (P = 0.06). The number of samples in the certified-tender group was 40, and the number in the not-certified-tender group was 160. The difference in sample size for each group may be an indicator of misrepresented significance when the analysis of the data was performed. However, the P-value for the means of the categories (P = 0.06) is close to the significance level (P = 0.05) chosen and may be considered significant for practical purposes.
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Table 5. Mean shear force values of certified tender and not certified tender groups for phase III (validation) samples
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As previously observed in all samples in our study (Choice and Select), segmenting carcasses based on their predicted SSF value is a very effective tool. Utilizing the 70% certified tender as a sorting tool for eliminating tough carcasses improved SSF values in excess of 4.0 kg. In fact, in the Select samples in phase I and II, the mean SSF values between the extreme 10% certified-tender (14.80 kg) and not-certified-tender (21.56 kg) categories is 6.76 kg (Table 4
). It appears that spectral reflectance has promise segmenting less consistent, lower Quality grading carcasses into palatability groups. In this study, the Oklahoma State University Spectrometer appears to perform with a similar level of accuracy as the system described by Shackelford et al. (2005), but it is unclear if it would perform as well with a higher percentage of tough carcasses, such as the 17.8% tough in the sample tested by Shackelford et al. (2005).
The TenderTec (George et al., 1997; Belk et al., 2001), connective tissue probe (Swatland, 1995), elastography (Berg et al., 1999), ultrasound (Park and Whittaker, 1991), image analysis (Li et al., 1999, 2001), colorimeter (Wulf and Page, 2000), and SSF (Shackelford et al., 1999a,b, 2001) are methods that have been developed at attempting beef tenderness prediction. More recently, a noninvasive system based on visible and near-infrared reflectance spectroscopy has been successfully developed (Shackelford et al., 2004, 2005). Today, the beef industry needs an on-line system, not to eliminate USDA Quality grading, but to serve as a complementary tool to assist in quality grading. This is especially true for the lower quality carcasses in the US Standard and Select grades. To date, the Oklahoma State University Spectral Reflectance systems appears to meet most industry criteria, in that it is an objective, noninvasive, tamper-proof, and accurate system that appears to be applicable across various carcass quality levels in a harsh, packing-plant environment. An NIR system with contact probe was developed and evaluated on-line. The contact probe provided stable broadband light and fixed the geometry of light and fiber optic probe in relation to the meat surface. Spectral reflectance values were collected at 3 d postmortem and were used to predict 14-d slice shear-force tenderness values. A low correlation coefficient between the observed and predicted slice-shear force values indicated that the system did not predict exact tenderness categories with high accuracy. Up to 70% certification levels, the system sorted the carcasses into tender and tough categories successfully. The practical implication to the beef industry is that at or below 70% certified tender carcasses could be sold as guaranteed tender to premium markets like restaurants.
Tenderness is a critical factor in consumer perception of beef palatability. Direct evaluation is absent because there is currently no accepted method available for predicting tenderness on-line. Carcasses are not priced on the basis of tenderness; therefore, producers lack incentive to supply a tender product. As a result, consumer preference is not routed back to the producers. The Oklahoma State University near-infrared reflectance spectrometer responds to the need for objective measurement. It is a rapid, nondestructive method for online evaluation that is an accurate predictor of tenderness at the consumer level.
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Footnotes
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1 Appreciation is expressed to Tommy Wheeler (US Meat Animal Research Center, Clay Center, NE) for his contributions to the study. 
2 Current address: Smithfield Beef Group, Green Bay, WI 54311.
3 Current address: University of Nebraska, Lincoln 68583.
4 Corresponding author: brad.morgan{at}okstate.edu
Received for publication February 7, 2007.
Accepted for publication September 6, 2007.
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Abstract
INTRODUCTION
