Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods.

Journal: Food chemistry
PMID: 39303476

Abstract

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with R, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.

Authors

  • Jiahua Wang
    Key Laboratory for Deep Processing of Major Grain and Oil (Wuhan Polytechnic University), Ministry of Education, Hubei Key Laboratory for Processing and Transformation of Agricultural Products, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
  • Jiangjin Qian
    College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, Hubei, China.
  • Mengting Xu
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. Electronic address: xumengting@nuaa.edu.cn.
  • Jianyu Ding
    College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, Hubei, China.
  • Zhiheng Yue
    College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, Hubei, China.
  • Yanpeng Zhang
    Department of Microbiology, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
  • Huang Dai
    Key Laboratory for Deep Processing of Major Grain and Oil (Wuhan Polytechnic University), Ministry of Education, Hubei Key Laboratory for Processing and Transformation of Agricultural Products, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China. Electronic address: huangdai9@126.com.
  • Xiaodan Liu
    Key Laboratory for Deep Processing of Major Grain and Oil (Wuhan Polytechnic University), Ministry of Education, Hubei Key Laboratory for Processing and Transformation of Agricultural Products, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
  • Fuwei Pi
    Key Laboratory for Deep Processing of Major Grain and Oil (Wuhan Polytechnic University), Ministry of Education, Hubei Key Laboratory for Processing and Transformation of Agricultural Products, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China; State Key Laboratory of Food Science and Resources, School of Food Science, Jiangnan University, Wuxi, 214122, China.

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