Sun, X.; Hu, Y.; Liu, C.; Zhang, S.; Yan, S.; Liu, X.; Zhao, K. Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms. Preprints2024, 2024040285. https://doi.org/10.20944/preprints202404.0285.v1
APA Style
Sun, X., Hu, Y., Liu, C., Zhang, S., Yan, S., Liu, X., & Zhao, K. (2024). Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202404.0285.v1
Chicago/Turabian Style
Sun, X., Xuecong Liu and Kun Zhao. 2024 "Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202404.0285.v1
Abstract
Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.
Engineering, Safety, Risk, Reliability and Quality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
