Characterization of the Freshness of Pork by Near-Infrared Spectroscopy (NIRS) and Ensemble Learning

Abstract

Pork is a perishable food and often needs to be stored in the refrigerator to maintain its quality as much as possible. Traditional methods for discriminating fresh and refrigerated pork are subjective, time-consuming, or destructive. The feasibility of using near-infrared (NIR) spectroscopy combined with chemometrics was explored to discriminate fresh and refrigerated pork. A total of 104 samples including 40 fresh and 64 refrigerated samples were first prepared and split into the training and test sets. Both partial least squares (PLS) and a subspace-based ensemble algorithm were used to establish classifiers. Also, both the number of learners and the size of subspace were optimized for ensemble modeling. On the independent test set, three measures, that is, the sensitivity, specificity, and total accuracy of the ensemble classifier were 95%, 93.8%, and 94.2%, respectively, each of which is superior to that of the PLS classifier. In addition, the influence of training set composition on classifier performance was also studied, indicating that ensemble modeling is robust. The results show that the NIR spectroscopy coupled with such an ensemble model can serve as a potential tool of discriminating fresh and refrigerated pork.