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

Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk DOI
Yi Wang,

Yihang Feng,

Zhenlei Xiao

и другие.

Food Chemistry, Год журнала: 2024, Номер 463, С. 141115 - 141115

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

6
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