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

文献类型： 外文期刊

第一作者： Wu, Meifeng

作者： Wu, Meifeng;Li, Minmin;Fan, Bei;Sun, Yufeng;Tong, Litao;Wang, Fengzhong;Li, Long;Wu, Meifeng;Wang, Fengzhong

作者机构：

关键词： Vegetable oil; Oil adulteration; 3-D fluorescence spectroscopy; Chemometrics; Machine learning

期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：6.0； 五年影响因子：6.0 ）

ISSN： 0023-6438

年卷期： 2023 年 188 卷

页码：

收录情况： SCI

摘要： 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.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序