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Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms.
Sun, Xiaorong; Hu, Yiran; Liu, Cuiling; Zhang, Shanzhe; Yan, Sining; Liu, Xuecong; Zhao, Kun.
Afiliación
  • Sun X; College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
  • Hu Y; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
  • Liu C; College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
  • Zhang S; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
  • Yan S; College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
  • Liu X; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
  • Zhao K; College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
Foods ; 13(9)2024 May 06.
Article en En | MEDLINE | ID: mdl-38731791
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Foods Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Foods Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza