Potential of multispectral imaging combined with chemometric methods for rapid detection of sucrose adulteration in tomato paste

Confirmation of the authenticity of tomato paste is an increasing challenge for food processors and regulatory authorities. This study focuses on the rapid qualitative and quantitative detection of sucrose adulteration in tomato paste using multispectral imaging (405-970 nm) combined with chemometric methods. Partial least squares (PLS), least squares-support vector machines (LS-SVM), and back propagation neural network (BPNN) were used to develop quantitative models. Compared with PLS and BPNN, LS-SVM considerably improved the prediction performance with coefficient of determination in prediction (RP2) of 0.936 and 0.966, the root-mean-square error of prediction (RMSEP) of 0.521% and 0.445%, and residual predictive deviation (RPD) of 5.014 and 5.865 for batch 1 and batch 2 of tomato paste, respectively. Besides, multispectral imaging was feasible to detect sucrose adulteration in tomato paste at very low proportions (1%) with no misclassification using chemometric methods. It was concluded that multispectral imaging has an excellent potential for rapid determination of sucrose adulteration in tomato paste.