Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms

• Published in: Foods Vol. 13; no. 9; p. 1420
• Main Authors: Sun, Xiaorong, Hu, Yiran, Liu, Cuiling, Zhang, Shanzhe, Yan, Sining, Liu, Xuecong, Zhao, Kun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2024
• Subjects: Algorithms; Chromatography; Data mining; Dielectric properties; Edible oils; Fatty acids; feature importance scores; Food safety; Fourier transforms; Health aspects; Lasers; Learning algorithms; Light; Machine learning; oblique-incidence reflectivity difference; Oils & fats; Olive oil; Physical properties; Quality assessment; Raw materials; Reflectance; Resonant frequencies; Scientific imaging; Signal classification; Soybeans; Vegetable oils; China; edible oils; machine learning; feature importance scores; oblique-incidence reflectivity difference
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods13091420

Due to the significant price differences among different types of edible oils, 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 all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.