Feasibility study on the use of Fourier transform near-infrared spectroscopy together with chemometrics to discriminate and quantify adulteration in cocoa beans

Fourier transform near-infrared (FT-NIR) spectroscopy combined with Support Vector Machine (SVM) and synergy interval partial least square (Si-PLS) was attempted in this study for cocoa bean authentication. SVM was used to develop an identification model to discriminate between fermented cocoa beans (FC), unfermented cocoa beans (UFC) and adulterated cocoa bean (5–40 wt/wt.% content of UFC). Si-PLS model was used to quantify the addition of UFC in FC. SVM model accurately discriminated the cocoa bean samples used. After cross-validation, the optimal identification rate was 100% in both the training set and prediction set at three principal components. For quantitative analysis, Si-PLS model was evaluated according to root mean square error of prediction (RMSEP) and coefficient of correlation in prediction (R_pred). The results revealed that Si-PLS model in this work was promising. The optimal performance of Si-PLS model showed an excellent predictive potential, RMSEP = 1.68 and R_pred = 0.98 in the prediction set. The overall results indicated that FT-NIR spectroscopy together with an appropriate multivariate algorithm could be employed for rapid identification of fermented and unfermented cocoa beans as well as the quantification of UFC down to 5% in FC for quality control management.