{"title":"基于电化学和机器学习的四种食用油不同储藏期酸败综合研究","authors":"Song Wan , Lin Tang","doi":"10.1016/j.ijoes.2024.100799","DOIUrl":null,"url":null,"abstract":"<div><p>This study investigates the rancidity development in four edible oils (corn, mustard, soybean, and sunflower) over a 12-month storage period using a novel approach combining electrochemical techniques and machine learning. Cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry were employed to characterize oil oxidation. Electrochemical parameters showed strong correlations with traditional chemical indicators, such as the DPV peak current at +0.2 V with p-anisidine value (r = 0.94, p < 0.001). A Random Forest model, trained on electrochemical data, accurately predicted Total Oxidation (TOTOX) values, achieving an R² of 0.96 and RMSE of 2.18 for the test set. The model effectively captured oxidation trends across oil types, with the highest accuracy for mustard oil (MAE: 1.21) and lower performance for sunflower oil (MAE: 2.15). Feature importance analysis revealed charge transfer resistance and DPV peak currents as the most influential predictors. This approach offers rapid, non-destructive assessment of oil quality, potentially improving quality control in the food industry. However, challenges such as electrode fouling and complex sample preparation need to be addressed for practical implementation.</p></div>","PeriodicalId":13872,"journal":{"name":"International Journal of Electrochemical Science","volume":"19 11","pages":"Article 100799"},"PeriodicalIF":1.3000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1452398124003419/pdfft?md5=0f08bb25c65699c6f2ee0d6a7c61e071&pid=1-s2.0-S1452398124003419-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Comprehensive electrochemical and machine learning-based study of rancidity in four edible oils over various storage periods\",\"authors\":\"Song Wan , Lin Tang\",\"doi\":\"10.1016/j.ijoes.2024.100799\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study investigates the rancidity development in four edible oils (corn, mustard, soybean, and sunflower) over a 12-month storage period using a novel approach combining electrochemical techniques and machine learning. Cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry were employed to characterize oil oxidation. Electrochemical parameters showed strong correlations with traditional chemical indicators, such as the DPV peak current at +0.2 V with p-anisidine value (r = 0.94, p < 0.001). A Random Forest model, trained on electrochemical data, accurately predicted Total Oxidation (TOTOX) values, achieving an R² of 0.96 and RMSE of 2.18 for the test set. The model effectively captured oxidation trends across oil types, with the highest accuracy for mustard oil (MAE: 1.21) and lower performance for sunflower oil (MAE: 2.15). Feature importance analysis revealed charge transfer resistance and DPV peak currents as the most influential predictors. This approach offers rapid, non-destructive assessment of oil quality, potentially improving quality control in the food industry. However, challenges such as electrode fouling and complex sample preparation need to be addressed for practical implementation.</p></div>\",\"PeriodicalId\":13872,\"journal\":{\"name\":\"International Journal of Electrochemical Science\",\"volume\":\"19 11\",\"pages\":\"Article 100799\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1452398124003419/pdfft?md5=0f08bb25c65699c6f2ee0d6a7c61e071&pid=1-s2.0-S1452398124003419-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Electrochemical Science\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1452398124003419\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ELECTROCHEMISTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Electrochemical Science","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1452398124003419","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ELECTROCHEMISTRY","Score":null,"Total":0}
Comprehensive electrochemical and machine learning-based study of rancidity in four edible oils over various storage periods
This study investigates the rancidity development in four edible oils (corn, mustard, soybean, and sunflower) over a 12-month storage period using a novel approach combining electrochemical techniques and machine learning. Cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry were employed to characterize oil oxidation. Electrochemical parameters showed strong correlations with traditional chemical indicators, such as the DPV peak current at +0.2 V with p-anisidine value (r = 0.94, p < 0.001). A Random Forest model, trained on electrochemical data, accurately predicted Total Oxidation (TOTOX) values, achieving an R² of 0.96 and RMSE of 2.18 for the test set. The model effectively captured oxidation trends across oil types, with the highest accuracy for mustard oil (MAE: 1.21) and lower performance for sunflower oil (MAE: 2.15). Feature importance analysis revealed charge transfer resistance and DPV peak currents as the most influential predictors. This approach offers rapid, non-destructive assessment of oil quality, potentially improving quality control in the food industry. However, challenges such as electrode fouling and complex sample preparation need to be addressed for practical implementation.
期刊介绍:
International Journal of Electrochemical Science is a peer-reviewed, open access journal that publishes original research articles, short communications as well as review articles in all areas of electrochemistry: Scope - Theoretical and Computational Electrochemistry - Processes on Electrodes - Electroanalytical Chemistry and Sensor Science - Corrosion - Electrochemical Energy Conversion and Storage - Electrochemical Engineering - Coatings - Electrochemical Synthesis - Bioelectrochemistry - Molecular Electrochemistry