Rapid measurement of total polyphenols content in cocoa beans by data fusion of NIR spectroscopy and electronic tongue

Abstract

Total polyphenols content (TPC) is an important measure of phytochemicals in cocoa beans due to its numerous health benefits. This work attempts to measure the total polyphenols content in cocoa beans by using a novel approach of integrating near infrared spectroscopy (NIRS) and electronic tongue (ET). 110 samples of cocoa beans with different polyphenol content were used for data acquisition by NIRS and ET. The optimum individual characteristic variables were extracted and scaled by normalization in principal component analysis (PCA). Support vector machine regression (SVMR) was used to construct the model. The performance of the final model was evaluated according to: correlation coefficient (R_pre), root mean square error of prediction (RMSEP) and bias in the prediction set. Compared with a single technique (NIRS or ET), the data fusion was superior for the determination of TPC in cocoa beans. The optimal data fusion model was achieved with: R_pre = 0.982, RMSEP = 0.900 g g⁻¹ and bias = 0.013 in the prediction set. The overall results demonstrate that integrating NIRS and ET is possible and could improve the prediction of TPC in cocoa beans.