Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis

•Smooth-1der PCA gave a satisfactory cluster score plot.•The best classification model was derived by Smooth-1der BPANN.•Efficient variable selection by synergy interval PLS (SiPLS).•Optimal regression model was achieved by Si-BPANNR for pH and fermentation index.•The relationship between NIRS and cocoa bean category, pH and FI has been developed. Rapid analysis of cocoa beans is an important activity for quality assurance and control investigations. In this study, Fourier transform near infrared spectroscopy (FT-NIRS) and chemometric techniques were attempted to estimate cocoa bean quality categories, pH and fermentation index (FI). The performances of the models were optimised by cross-validation and examined by identification rate (%), correlation coefficient (Rpre) and root mean square error of prediction (RMSEP) in the prediction set. The optimal identification model by back propagation artificial neural network (BPANN) was 99.73% at 5 principal components. The efficient variable selection model derived by synergy interval back propagation artificial neural network regression (Si-BPANNR) was superior for pH and FI estimation. Si-BPANNR model for pH was Rpre=0.98 and RMSEP=0.06, while for FI was Rpre=0.98 and RMSEP=0.05. The results demonstrated that FT-NIRS together with BPANN and Si-BPANNR model could successfully be used for cocoa beans examination.