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

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.