Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques

Abstract

Total volatile basic nitrogen (TVB-N) content is an important reference index for evaluating pork freshness. This paper attempted to measure TVB-N content in pork meat using integrating near infrared spectroscopy (NIRS), computer vision (CV), and electronic nose (E-nose) techniques. In the experiment, 90 pork samples with different freshness were collected for data acquisition by three different techniques, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from 3 different sensors data. Back-propagation artificial neural network (BP-ANN) was used to construct the model for TVB-N content prediction, and the top principal components (PCs) were extracted as the input of model. The result of the model was achieved as follows: the root mean square error of prediction (RMSEP)=2.73mg/100g and the determination coefficient (Rp2)=0.9527 in the prediction set. Compared with single technique, integrating three techniques, in this paper, has its own superiority. This work demonstrates that it has the potential in nondestructive detection of TVB-N content in pork meat using integrating NIRS, CV and E-nose, and data fusion from multi-technique could significantly improve TVB-N prediction performance.