期刊
MEAT SCIENCE
卷 145, 期 -, 页码 79-85出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.meatsci.2018.05.021
关键词
On-line data collection; Pork quality; Raman spectral; Support vector machine; Tenderness prediction
资金
- National Pork Board [15-078]
- Iowa Agricultural and Home Economics Experiment Station [3721]
- CNPq-Brazil
- CSC-China
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 (R-2 = 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.
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