Abstract

The objective of this research was to develop a rapid noninvasive method for quantitative and qualitative determination of chilled pork spoilage. Microbiological, physicochemical, and organoleptic characteristics such as the total viable count (TVC), Pseudomonas spp., total volatile basic-nitrogen (TVB-N), pH value, and color parameter L* were determined to appraise pork quality. The hyperspectral scattering characteristics from 54 meat samples were fitted by four-parameter modified Gompertz function accurately. Support vector machines (SVM) was applied to establish quantitative prediction model between scattering fitting parameters and reference values. In addition, partial least squares discriminant analysis (PLS-DA) and Bayesian analysis were utilized as supervised and unsupervised techniques for the qualitative identification of meat spoilage. All stored chilled meat samples were classified into three grades: “fresh,” “semi-fresh,” and “spoiled.” Bayesian classification model was superior to PLS-DA with overall classification accuracy of 92.86%. The results demonstrated that hyperspectral scattering technique combined with SVM and Bayesian possessed a powerful capability for meat spoilage assessment rapidly and noninvasively.

© 2016 The Author(s)

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