Simultaneous determination of vegetable oil frying frequency and peroxide value based on the three-dimensional fluorescence spectroscopy and machine learning

文献类型： 外文期刊

第一作者： Yang, Huihui

作者： Yang, Huihui;Wang, Yutang;Fan, Bei;Chen, Qing;Wu, Meifeng;Wang, Fengzhong;Li, Long;Yang, Huihui;Wang, Fengzhong;Li, Long

作者机构：

关键词： Frying oil; Peroxide value; Fluorescence spectroscopy; Machine learning

期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）

ISSN： 0308-8146

年卷期： 2025 年 471 卷

页码：

收录情况： SCI

摘要： The practice of deep-frying introduces various health concerns. Assessing the quality of frying oil is paramount. This study employs three-dimensional fluorescence spectroscopy to evaluate the peroxide value of vegetable oils after varying frying times. Three feature preprocessing techniques combined with three machine learning methods to predict the frying frequency and the peroxide value. For the prediction of frying frequency, SG-RF model performed the best for soybean oil, while for peanut and corn oils, SG-PLS excelled, with the Rp2 of 0.98, 0.98, and 0.97, respectively. Regarding the quantification of peroxide value, normalize-kNN performs the best for soybean frying oil, for peanut frying oil SG-RF are optimal, and the optimal combination is normalization-RF for corn frying oil, with the Rp2 of 0.87, 0.89, and 0.93, respectively. This rapid assessment method enables early detection of oil safety issues, protecting consumer health by preventing the use of degraded oils.

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