Quantitatively Detecting Camellia Oil Products Adulterated by Rice Bran Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Algorithms and Chemometric Algorithms

Quantitatively Detecting Camellia Oil Products Adulterated by Rice Bran Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Algorithms and Chemometric Algorithms

The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predic...

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Bibliographic Details
Main Authors: Henan Liu, Sijia Ma, Ni Liang, Xin Wang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Foods
Subjects:
adulteration quantification
camellia oil products
Raman spectroscopy
back propagation neural network
rice bran oil products
corn oil products
Online Access:https://www.mdpi.com/2304-8158/13/24/4182
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