A rapid and low-cost method for detection of nine kinds of vegetable oil adulteration based on 3-D fluorescence spectroscopy

Abstract

The integrity of vegetable oil has become a focal concern within the global food industry, particularly in specific regions where economic interests drive authenticity issues. In this study, we used three-dimensional (3-D) fluorescence spectroscopy for the qualitative and quantitative analysis of adulteration in different vegetable oils, offering the advantages of rapid and low-cost detection. Nine types of adulterated vegetable oils were modeled using five machine learning methods: K-nearest neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), and Convolutional Neural Network (CNN). In the qualitative analysis, the Rp2 and RMSEP values ranged from 0.89 to 0.99 and 0.01 to 0.05, respectively. For qualitatively analysis, a threshold of 5 % was established for detecting adulteration in soybean oil with palm oil and sesame oil with palm oil, achieving an accuracy (ACC) greater than 90 %. Meanwhile, a threshold of 10 % was effective for remaining seven adulterated oils, with an ACC exceeding 90 %. Moreover, an ACC rate of over 95 % was attained when the threshold was set at 15 %. The findings demonstrate that 3-D fluorescence spectroscopy presents a robust tool for detecting various adulterated oils, heralding a valuable contribution to food safety and quality control.