Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr.)

Abstract

Fourier-transform near infrared spectroscopy coupled with chemometric algorithms was applied comparatively for the quantification of chemical compositions in black wolfberry. The compositional parameters, i.e. total flavonoid content, total anthocyanin content, total carotenoid content, total sugar, and total acid were performed for quantification. Model results were evaluated using the correlation coefficients of determination for calibration (R2) and prediction (r2), root-mean-square error of prediction and residual predictive deviation. The findings revealed that the performances of models based on variable selection such as synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS were better than the classical PLS. The performance of the developed models yielded 0.88 ≤ R2 ≤ 0.97, 0.87 ≤ r2 ≤ 0.94 and 1.75 ≤ RPD ≤ 4.00. The overall results showed that the FT-NIR spectroscopy in conjunction with chemometric algorithms could be used for the quantification of the chemical composition of black wolfberry samples.