Food adulteration presents a major challenge in the food industry, impacting product quality and consumer trust. This study investigates the use of Fourier Transform Infrared (FTIR) spectroscopy combined with chemometric methods, Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA), for detecting cornstarch and powdered sugar adulterants in Ziziphus jujuba Mill. fruit powder. The PCA model effectively separated pure and adulterated jujube powders, clustering samples by adulterant type and concentration. Brands A, B, and E of the retail jujube powder samples were identified as likely adulterated with corn starch, while Brands C and D showed minimal adulteration. PLS-DA further differentiated adulteration levels, suggesting high corn starch adulteration levels in Brand A, B, and E samples. A Partial Least Squares Regression (PLSR) model yielded high accuracy (R² = 0.9859) and precision, with low error rates (RMSEE = 5.50% and RMSECV = 9.16%), supporting the model’s robustness in adulteration prediction. Our findings highlight FTIR spectroscopy coupled with chemometric modelling as an efficient, reliable tool for rapid adulteration detection in jujube powder. This approach offers significant potential for ensuring product authenticity and quality control in functional food products.
Crystal et al. (Fri,) studied this question.