Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging

Abstract

Stable isotopes, multi‐elements, metabolic profiles, and integrated spectroscopic fingerprints are priority options for food geographical origin traceability. However, til now, it is still hard to detect adteration with the same one from other geographic origins, which is harder than geographical origin traceability. In this study, partial least square discriminant analysis was employed to build a classification model to discriminate the domestic and imported soybeans after variable selection by uninformative variable elimination using near infrared hyperspectral imaging. As a result, this model could completely discriminate domestic and imported soybeans. Moreover, the developed model was used to detect the adulterated domestic soybean was adulterated with 13.3%, 20.0%, 26.7%, and 33.3% of imported soybean. When the skewness value was less than 0.76 and kurtosis value was less than 1.57 of a sample, the sample was considered as the adulterated. The results indicated that the domestic soybeans adulterated with 20.0%, 26.7%, and 33.3% of imported soybeans were successfully identified. This method could not only identify origin traceability but also detect adulteration of soybeans, which will be beneficial to guarantee the quality and safety of soybean.