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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Meat Safety & Quality Research » Research » Publications at this Location » Publication #340541

Research Project: Mitigation Approaches for Foodborne Pathogens in Cattle and Swine for Use During Production and Processing

Location: Meat Safety & Quality Research

Title: Predicting aged pork quality using a portable raman device

item Santos, Carolina - Universidade Federal De Santa Catarina (UFSC)
item Longergan, Steven - Iowa State University
item Zhao, Jinhui - Jiangxi Agricultural University
item Yu, Chenxu - Iowa State University
item Shackelford, Steven
item Wheeler, Tommy
item King, David - Andy
item Prusa, Kenneth - Iowa State University
item Dong, Xiuping - Dalian University Of Technology
item Newman, David - Arkansas State University

Submitted to: American Meat Science Association Conference Reciprocal Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2017
Publication Date: 6/18/2017
Citation: Santos, C., Longergan, S.M., Zhao, J., Yu, C., Shackelford, S.D., Wheeler, T.L., King, D.A., Prusa, K.J., Dong, X., Newman, D. 2017. Predicting aged pork quality using a portable raman device. [Abstract]. American Meat Science Association Conference Reciprocal Proceedings. June 18-21, 2017, College Station, Texas. No. 50.

Interpretive Summary:

Technical Abstract: Objectives: A need exists for a better on-line evaluation method for pork quality. Raman spectroscopy evaluates structure and composition of food samples, with advantage of being portable, non-invasive and insensitive to water. The objectives of this study were to evaluate the correlation between Raman spectral (RS) data measured from fresh and aged pork with sensory characteristics and slice shear force (SSF), to develop classification models for prediction of fresh pork sensory. Materials and Methods: Eight hundred-- pork loins, from 4 plants, were removed from the carcass at 24 h postmortem and selected based on color and marbling. Six hundred loins from 3 plants were subjected to onsite RS measurements in which the ventral side of each loin was scanned with RS for 6 seconds. All loins were then transported to USMARC and held for 14 days at 0°C. The aged loins were cut into 2.54 cm chops for RS, SSF and sensory analysis. For the sensory analysis only 75 loins from each plant were chosen. One chop for RS measurements and two for sensory were vacuum packed and transported to ISU Labs. At 14 d, the chops (cross section) were scanned under same conditions. SSF on 800 samples was determined following Wheeler et al., 2005. Sensory tenderness was evaluated by a trained sensory panel (n=10). All spectral data were analyzed using R and Matlab. Support Vector Machine was used to develop the classification model, where 300 pork loin samples were divided into groups according to the percentile (25%) of values of sensory tenderness or SSF. Results: A weak correlation (R2=0.20) between SSF and sensory tenderness was obtained using a least square regression model. The prediction accuracies for d15 postmortem samples are significantly higher than that for d1 postmortem samples, both for tenderness scores and SSF values (Table 1). These observations strongly suggest that aging of the meat samples from day 1 to 15 has significantly affected their chemical properties that are directly correlated to their tenderness. For d15 postmortem samples however, a substantial improvement in classification accuracies for the four quality grade groups was observed. In general, pork samples that belong to the medium quality category are more difficult to predict based on their Raman spectroscopic characteristics. Table 1. The average accuracies for classifying pork Raman spectra into 4 groups based on percentiles. Conclusion: It was demonstrated that sensory attributes of pork loins are moderately correlated to their Raman spectroscopic characteristics. The classification model developed yielded moderate performance in identifying pork loins that belong to extreme categories of sensory quality (i.e., superior and inferior) in freshly cut loins. The spectra obtained from aged samples showed a more accurate classification. Raman spectroscopy, in combination with performance-enhancing data processing and multivariate statistical discriminant modeling, has the potential to become a rapid on-line screening tool for the pork producers to quickly select meats with superior quality and/or poor quality to better serve customers. This project was funded in part by a grant from the National Pork Board. The scholarship for the first author was granted by CNPq-Brazil.