Detection of tea seed oil adulteration based on near-infrared and Raman spectra information fusion

Abstract

The adulteration of tea seed oil severely harms consumer interests, such as affecting the taste experience and economic losses, creating an urgent need for a rapid, efficient, and non-destructive detection method. In this study, near-infrared and Raman spectroscopy were used to detect the adulteration of tea seed oil with four common edible vegetable oils. After preprocessing the raw spectral data, three variable selection methods namely uninformative variable elimination, competitive adaptive reweighted sampling, and successive projections algorithm were used individually and consecutively to select key spectral variables. And the NIR and Raman characteristic variables were then fused to develop a discrimination model. The results indicate that normalization is the superior preprocessing method and consecutive variable selection methods are generally superior to single ones. The best NIR and Raman models, based on consecutive variable selection methods, achieve accuracy and F1 score of the prediction set of 95.652%, 97.479% and 92.754%, 95.868%, respectively. Spectral information fusion can fully utilize the spectral information, the accuracy and F1 score of the optimal binary adulteration discrimination model based on information fusion for calibration and prediction sets are all 100%. Meanwhile, the accuracy and F1 score of the multiple adulteration discrimination model are 98.225% and 98.932%, respectively. Therefore, the fusion of NIR and Raman spectral information can enable highly accurate discrimination of adulterated oil. The proposed method in this study will aid in developing portable detection devices for tea seed oil adulteration.