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The integration of machine learning into proteomics advances food authentication and adulteration control.

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Title: The integration of machine learning into proteomics advances food authentication and adulteration control.
Authors: Li, Hongfei1,2 (AUTHOR), Mo, Hanlin3,4 (AUTHOR), Song, Yu-Chen1,2 (AUTHOR), Chen, Guanying5 (AUTHOR), Wu, Cai-E6 (AUTHOR), Zhu, Fu-Yuan1,2 (AUTHOR) fyzhu@njfu.edu.cn
Source: Trends in Food Science & Technology. Jul2025, Vol. 161, pN.PAG-N.PAG. 1p.
Subjects: Food adulteration, Food chemistry, Food science, Food safety, Machine learning, Adulterations
Abstract: The escalating frequency of global food fraud poses a serious threat to food safety, necessitating the development of effective countermeasures. Machine learning (ML), as a cutting-edge technology, has greatly bolstered conventional methods in advancing food safety. Proteomics, a high-throughput tool, is extensively utilized for comprehensive understandings of food authentication and adulteration detection. However, integrating ML into proteomics is both indispensable and fraught with challenges. The primary objective of this review is to delve into the optimization of food proteomics required for seamless ML integration, drawing insights from its applications in food authentication and adulteration control. Subsequently, we aim to underscore recent studies that have harnessed ML-assisted proteomics for enhancing food safety. Lastly, we evaluate the potential applications of advanced ML integration within proteomics methodologies. While ML-assisted proteomics is a rapidly evolving field that has yet to become standard practice in food science or industry, recent applications have demonstrated its capacity to enhance the efficiency and accuracy of food proteomics analysis when combined with ML. Moreover, the integration of ML into proteomics methodologies promises further advancements in model depth and robustness, underscoring the immense potential of ML-assisted proteomics in safeguarding food safety in the future. • Proteomics suffer from food component complexity and lack of comprehensive databases. • Machine learning enhances efficiency and accuracy of food proteomic analysis. • Machine learning-assisted proteomic methodology offers promising food safety applications. • Machine learning efficiently, reliably links protein features to food safety tasks. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The escalating frequency of global food fraud poses a serious threat to food safety, necessitating the development of effective countermeasures. Machine learning (ML), as a cutting-edge technology, has greatly bolstered conventional methods in advancing food safety. Proteomics, a high-throughput tool, is extensively utilized for comprehensive understandings of food authentication and adulteration detection. However, integrating ML into proteomics is both indispensable and fraught with challenges. The primary objective of this review is to delve into the optimization of food proteomics required for seamless ML integration, drawing insights from its applications in food authentication and adulteration control. Subsequently, we aim to underscore recent studies that have harnessed ML-assisted proteomics for enhancing food safety. Lastly, we evaluate the potential applications of advanced ML integration within proteomics methodologies. While ML-assisted proteomics is a rapidly evolving field that has yet to become standard practice in food science or industry, recent applications have demonstrated its capacity to enhance the efficiency and accuracy of food proteomics analysis when combined with ML. Moreover, the integration of ML into proteomics methodologies promises further advancements in model depth and robustness, underscoring the immense potential of ML-assisted proteomics in safeguarding food safety in the future. • Proteomics suffer from food component complexity and lack of comprehensive databases. • Machine learning enhances efficiency and accuracy of food proteomic analysis. • Machine learning-assisted proteomic methodology offers promising food safety applications. • Machine learning efficiently, reliably links protein features to food safety tasks. [ABSTRACT FROM AUTHOR]
ISSN:09242244
DOI:10.1016/j.tifs.2025.105029