Yuan Ting CHEN, Maurice HT LING*, Hong YANG, Martin Hui CAI, Rui Wen KOH, Rick YH TAN and Xuejia Xue

School of Applied Science, Temasek Polytechnic, Singapore

*Corresponding Author: Maurice HT LING and Xuejia Xue, School of Applied Science, Temasek Polytechnic, Singapore.

Received: June 04, 2024; Published: June 30, 2024

Abstract

Natural antioxidants (NATOs) derived from sources like rosemary, green tea, and oregano have acquired extensive attention for their efficacy in preserving edible oils, presenting a promising alternative to synthetic ATOs due to their superior safety profile. However, integrating NATOs into the food industry faces challenges stemming from the variability in their chemical composition, necessitating prolonged stability tests based on peroxide values (PV). This study explores the predictability of PV in peanut oil using three chemical parameters (total phenolic content, total antioxidant content, and total carotenoid content), one physical parameter (partition coefficient), and storage duration. Six machine learning classifiers (logistic regression, multilayer perceptron, radial basis function, Gaussian Naïve Bayes classifier, support vector machine, and decision tree) were employed. The results have shown significant correlations between the chemical parameters and antioxidant activity. Our findings indicate that PV in peanut oil can be accurately predicted using these parameters and storage duration, with the multilayer perceptron demonstrating the highest predictive performance, achieving an accuracy of at least 89.8% in determining whether PV remains within acceptable limits post-storage.

Keywords: Peanut Oil; Shelf-Life; Peroxide Value; Antioxidants; Natural Antioxidants; Machine Learning Classifiers; Artificial Neural Networks; Multilayer Perceptron

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Citation

Citation: Maurice HT LING., et al. “Predicting Peroxide Value of Peanut Oil using Machine Learning Models". Acta Scientific Nutritional Health 8.7 (2024): 116-122.

Copyright

Copyright: © 2024 Maurice HT LING., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.