基于LIF与PLS-DA的高品质食用油掺伪鉴别OA

This study proposed a method for identifying adulteration in high-quality edible oils by combining laser-induced fluorescence(LIF)technology with partial least squares-discriminant analysis(PLS-DA),aiming to quickly detect low-quality edible oils in the market.Firstly,a laboratory-built LIF system was used to collect fluorescence spectral data of olive oil,sesame oil,peanut oil,and their adulterated samples.Subsequently,PLS-DA was employed to construct adulteration identification models for olive oil,sesame oil,and peanut oil respectively.Finally,the performance of these models was evaluated using a prediction set.The results indicate that the PLS-DA model can accurately capture the differential characteristics in fluorescence spectra between adulterated samples and authentic samples.Under the verification of the experimentally obtained data,a 100%correct classification rate is achieved.This method enables high-precision identification of adulterated edible oil,providing a scientific identification tool for food safety supervision and offers support for technical research.