Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods DOI Creative Commons
Panagiota‐Kyriaki Revelou, Efstathia Tsakali, Anthimia Batrinou

и другие.

Foods, Год журнала: 2025, Номер 14(6), С. 922 - 922

Опубликована: Март 8, 2025

Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing data analysis capabilities and have proven to be powerful tools assessing the of Animal-Source Foods (ASFs). Studies that link ML with HACCP in ASFs are limited. The present review provides an overview ML, feature extraction, selection employed safety. Several non-destructive presented, including spectroscopic methods, smartphone-based sensors, paper chromogenic arrays, machine vision, hyperspectral imaging combined algorithms. Prospects include enhancing predictive models development hybrid Artificial Intelligence (AI) automation quality control processes using AI-driven computer which could revolutionize inspections. However, handling conceivable inclinations AI is vital guaranteeing reasonable exact hazard assessments assortment nourishment generation settings. Moreover, moving forward, interpretability will make them more straightforward dependable. Conclusively, applying allows real-time analytics can significantly reduce risks associated ASF consumption.

Язык: Английский

Advances in Starch-Based Nanocomposites for Functional Food Systems: Harnessing AI and Nuclear Magnetic Resonance Technologies for Tailored Stability and Bioactivity DOI Creative Commons
Yue Sun, Ziyu Wang,

Jian Ye

и другие.

Foods, Год журнала: 2025, Номер 14(5), С. 773 - 773

Опубликована: Фев. 24, 2025

Starch-based nanocomposites (SNCs) are at the forefront of innovations in food science, offering unparalleled opportunities for enhancing stability, bioactivity, and overall functionality systems. This review delves into potential SNCs to address contemporary challenges formulation, focusing on synergistic effects their components. By integrating cutting-edge technologies, such as artificial intelligence (AI) nuclear magnetic resonance (NMR), we explore new avenues precision, predictability, SNCs. AI is applied optimize design SNCs, leveraging predictive modeling fine-tune material properties streamline production processes. The role NMR also critically examined, with particular emphasis its capacity provide high-resolution structural insights, monitor stability over time, elucidate molecular interactions within matrices. Through detailed examples, highlights impact unraveling complex behaviors bioactive compounds encapsulated Additionally, discuss integration functional assays AI-driven analytics assessing bioactivity sensory these systems, providing a robust framework rational advanced products. synergy between AI, NMR, opens pathways developing tailored, high-performance formulations that both health consumer preferences.

Язык: Английский

Процитировано

0
Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods DOI Creative Commons
Panagiota‐Kyriaki Revelou, Efstathia Tsakali, Anthimia Batrinou

и другие.

Foods, Год журнала: 2025, Номер 14(6), С. 922 - 922

Опубликована: Март 8, 2025

Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing data analysis capabilities and have proven to be powerful tools assessing the of Animal-Source Foods (ASFs). Studies that link ML with HACCP in ASFs are limited. The present review provides an overview ML, feature extraction, selection employed safety. Several non-destructive presented, including spectroscopic methods, smartphone-based sensors, paper chromogenic arrays, machine vision, hyperspectral imaging combined algorithms. Prospects include enhancing predictive models development hybrid Artificial Intelligence (AI) automation quality control processes using AI-driven computer which could revolutionize inspections. However, handling conceivable inclinations AI is vital guaranteeing reasonable exact hazard assessments assortment nourishment generation settings. Moreover, moving forward, interpretability will make them more straightforward dependable. Conclusively, applying allows real-time analytics can significantly reduce risks associated ASF consumption.

Язык: Английский

Процитировано

0