Rapid identification of lamb freshness grades using visible and near-infrared spectroscopy (Vis-NIR)

Abstract

In the meat industry, it is essential to monitor and identify meat freshness grades due to its impact on the safety of human diets. This study aimed to identify premium, sub-fresh, and spoiled lamb samples using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) in the range of 400–1000 nm coupled with chemometrics methods. The two-dimensional correlation spectroscopy (2D-CS) was utilized to select effective wavelengths for simplifying the model and increasing the calculation speed. The capabilities of the four models including partial least squares discriminant analysis (PLS-DA), soft independent modelling of class analogy (SIMCA), back propagation neuron network (BP), decision tree (DT) and random forest (RF) were compared to select the best identification model. The results showed that the RF model generated excellent performance, the accuracies of the training and test sets were 93% and 91%, respectively. In summary, this study showed that it was feasible to rapidly and non-destructively identify and evaluate the freshness grades of lamb using Vis-NIR.