Quantifying of total volatile basic nitrogen (TVB-N) content in chicken using a colorimetric sensor array and nonlinear regression tool

Abstract

Total volatile basic nitrogen (TVB-N) content is an important indicator for evaluating meat's freshness. This study attempts to quantify TVB-N content non-destructively in chicken using a colorimetric sensors array with the help of multivariate calibration. First, we fabricated a colorimetric sensor array by printing 12 chemically responsive dyes on a C2 reverse silica-gel flat plate. A color change profile was obtained by differentiating the images of the sensor array before and after exposure to volatile organic compounds (VOCs) released from a chicken sample. In addition, we proposed a novel algorithm for modeling, which is a back propagation artificial neural network (BPANN), and an adaptive boosting (AdaBoost) algorithm, namely, AdaBoost–BPANN, and we compared it with the commonly used algorithms. Experimental results showed that the optimum model was achieved by the AdaBoost–BPANN algorithm with RMSEP = 7.7124 mg/100 g and R = 0.8915 in the prediction set. This study demonstrated that the colorimetric sensors array has a high potential in the non-destructive sensing of chicken's freshness and that the AdaBoost–BPANN algorithm performs well as a solution to a complex data calibration.