Location: Meat Safety and QualityTitle: Predicting aged pork quality using a portable Raman device
|SANTOS, C.C. - Iowa State University|
|ZHAO, J - Iowa State University|
|DONG, X - Iowa State University|
|LONERGAN, S.M. - Iowa State University|
|HUFF-LONERGAN, E - Iowa State University|
|OUTHOUSE, A - Iowa State University|
|CARLSON, K.B. - Iowa State University|
|PRUSA, K.J. - Iowa State University|
|FEDLER, C.A. - Iowa State University|
|YU, C - Iowa State University|
|King, David - Andy|
Submitted to: Meat Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/28/2018
Publication Date: 7/2/2018
Publication URL: https://handle.nal.usda.gov/10113/5984801
Citation: Santos, C., Zhao, J., Dong, X., Lonergan, S., Huff-Lonergan, E., Outhouse, A., Carlson, K., Prusa, K., Fedler, C., Yu, C., Shackelford, S.D., King, D.A., Wheeler, T.L. 2018. Predicting aged pork quality using a portable Raman device. Meat Science. 145:79-85. https://doi.org/10.1016/j.meatsci.2018.05.021.
Interpretive Summary: A rapid and accurate method to sort pork meat into quality groups based on measurements taken early postmortem would enhance the industry’s ability to market consistent products to meet consumer demands. Many methods studied for this purpose are not amenable to commercial line-speed measurement. Raman spectroscopy can evaluate structure and composition of food samples and might meet industry needs for on-line classification of pork quality. The objective of this study was to evaluate the ability of Raman data from fresh and aged pork to classify pork loins into tenderness categories. Results show that tenderness attributes of pork loins were lowly correlated to their Raman spectroscopic characteristics. However, a model to classify pork loins into tender and tough categories was moderately accurate and may have potential to be applied on-line commercially to sort for pork quality.
Technical Abstract: The utility of Raman spectroscopic signatures of fresh pork loin (1 d & 15 d postmortem) in predicting fresh pork tenderness and slice shear force (SSF) was determined. Partial least square models showed that sensory tenderness and SSF are weakly correlated (R2=0.2). Raman spectral data were collected in 6 s using a portable Raman spectrometer (RS). A PLS regression model was developed to predict quantitatively the tenderness scores and SSF values from Raman spectral data, with very limited success. It was discovered that the prediction accuracies for day 15 post mortem samples are significantly greater than that for day 1 postmortem samples. Classification models were developed to predict tenderness at two ends of sensory quality as “poor” vs. “good”. The accuracies of classification into different quality categories (1st to 4th percentile) are also greater for the day 15 postmortem samples for sensory tenderness (93.5% vs 76.3%) and SSF (92.8% vs 76.1%). RS has the potential to become a rapid on-line screening tool for the pork producers to quickly select meats with superior quality and/or cull poor quality to meet market demand/expectations.