Enhanced food authenticity control using machine learning-assisted elemental analysis.

Autor: Yang Y; School of Quality and Technical Supervision, Hebei University, Baoding 071002, China; National&Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China; Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University, Baoding 071002, China., Zhang L; School of Quality and Technical Supervision, Hebei University, Baoding 071002, China; National&Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China; Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University, Baoding 071002, China., Qu X; College of Traditional Chinese Medicine, Hebei University, Baoding 071002, China., Zhang W; School of Quality and Technical Supervision, Hebei University, Baoding 071002, China; National&Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China; Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University, Baoding 071002, China., Shi J; Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China., Xu X; College of Traditional Chinese Medicine, Hebei University, Baoding 071002, China. Electronic address: xgxu@hbu.edu.cn.
Jazyk: angličtina
Zdroj: Food research international (Ottawa, Ont.) [Food Res Int] 2024 Dec; Vol. 198, pp. 115330. Date of Electronic Publication: 2024 Nov 13.
DOI: 10.1016/j.foodres.2024.115330
Abstrakt: With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namely food traceability and food quality control. More explicitly, they are the traceability of food origin and organic food, detection of food adulteration and heavy metals. It also points out the limitations of the commonly used morphology and organic compound detection methods, and highlights the advantages of combining the elements in food as detection indicators using machine learning technology to solve the problem of food authenticity. Taking elements as detection objects has the significant advantages of stability, machine learning technology can combine large data samples, ensuring both the accuracy and efficiency. In addition, the most suitable algorithm can be found by comparing their accuracy.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: