Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion

Total volatile basic nitrogen (TVB-N) content is an important indicator in evaluating pork meat's freshness. This paper attempted a new strategy for measurement of TVB-N content in the pork meat by integrating two nondestructive sensing tools of hyperspectral imaging (HSI) and colorimetric sensors. First, HSI system and colorimetric sensors array system were used for data acquisition and further data processing, respectively. Herein, we proposed a novel efficient back propagation adaptive boosting (BP-AdaBoost) algorithm for data fusion and modeling, and we compared it with the classic modeling algorithm based on principal component analysis and back propagation artificial neural network (PCA-BPANN). Experiments results showed that the model based on data fusion was superior to the model based on the single sensing tool, and BP-AdaBoost has stronger capacity in solution to the complicated data fusion in contrast the PCA-BPANN. And the optimum results were achieved with the ratio of prediction to deviation (RPD) = 2.885, and the correction coefficient (R) = 0.932 in the prediction set. This work demonstrates that it has the potential to nondestructive detection of TVB-N content in pork meat by integrating HSI technique and colorimetric sensors technique combined with BP-AdaBoost nonlinear data fusion algorithm.