Integrating Risk-based Quality Assurance and Predictive Process Control in Food Manufacturing: A Critical Review of Contamination Prevention, Statistical Monitoring, and Regulatory Compliance
Abstract
Food manufacturing is increasingly able to measure process conditions continuously, yet measurements do not automatically establish that microbiological, chemical, allergenic or physical hazards are controlled. This critical narrative review examines how risk-based quality assurance, preventive food safety management, statistical process monitoring and predictive analytics can be integrated without confusing operational prediction with demonstrated food safety. The review draws on peer-reviewed evidence from food microbiology, process engineering and organisational research, alongside recognised international and jurisdiction-specific food safety requirements. It differentiates hazard analysis, control-measure validation, real-time monitoring and independent verification as related but non-substitutable functions. Evidence is strongest for product- and process-specific preventive controls, scientifically justified critical parameters and targeted environmental verification. Multivariate monitoring and predictive microbiology provide plausible means of recognising process deviation earlier, but their outputs depend on representative training conditions, valid sensors, sampling design, and explicit links between measured signals and hazards. Published machine-learning studies demonstrate a broad range of detection and classification applications, whereas prospective, multi-site evidence that predictive alerts reduce contamination, consumer exposure or regulatory non-compliance remains relatively sparse. Studies of food safety management systems further indicate that organisational context and the quality of hazard analysis can limit the effectiveness of technically sophisticated monitoring. An integrated architecture is therefore proposed in which hazard severity and exposure inform monitoring priorities, validated preventive measures retain primacy, statistical warnings prompt proportionate investigation, and microbiological or chemical verification tests the performance of the whole system. Particular attention is given to rare-event detection, model drift, preventive-control exemptions and differences between international standards and binding regional regulations. The most defensible route towards predictive assurance is controlled, documented decision support with prospective outcome evaluation, rather than unqualified automation of food safety decisions.
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BibTeX
@article{asefon2026integrating,
title = {{Integrating Risk-based Quality Assurance and Predictive Process Control in Food Manufacturing: A Critical Review of Contamination Prevention, Statistical Monitoring, and Regulatory Compliance}},
author = {Temitope Isaiah Asefon},
journal = {Journal of Scientific Research and Reports},
year = {2026},
month = nov,
doi = {10.9734/jsrr/2026/v32i114701},
url = {https://hal.science/hal-05856815v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating Risk-based Quality Assurance and Predictive Process Control in Food Manufacturing: A Critical Review of Contamination Prevention, Statistical Monitoring, and Regulatory Compliance
AU - Temitope Isaiah Asefon
PY - 2026
DA - 2026/11/05
JO - Journal of Scientific Research and Reports
DO - 10.9734/jsrr/2026/v32i114701
UR - https://hal.science/hal-05856815v1
AB - Food manufacturing is increasingly able to measure process conditions continuously, yet measurements do not automatically establish that microbiological, chemical, allergenic or physical hazards are controlled. This critical narrative review examines how risk-based quality assurance, preventive food safety management, statistical process monitoring and predictive analytics can be integrated without confusing operational prediction with demonstrated food safety. The review draws on peer-reviewed evidence from food microbiology, process engineering and organisational research, alongside recognised international and jurisdiction-specific food safety requirements. It differentiates hazard analysis, control-measure validation, real-time monitoring and independent verification as related but non-substitutable functions. Evidence is strongest for product- and process-specific preventive controls, scientifically justified critical parameters and targeted environmental verification. Multivariate monitoring and predictive microbiology provide plausible means of recognising process deviation earlier, but their outputs depend on representative training conditions, valid sensors, sampling design, and explicit links between measured signals and hazards. Published machine-learning studies demonstrate a broad range of detection and classification applications, whereas prospective, multi-site evidence that predictive alerts reduce contamination, consumer exposure or regulatory non-compliance remains relatively sparse. Studies of food safety management systems further indicate that organisational context and the quality of hazard analysis can limit the effectiveness of technically sophisticated monitoring. An integrated architecture is therefore proposed in which hazard severity and exposure inform monitoring priorities, validated preventive measures retain primacy, statistical warnings prompt proportionate investigation, and microbiological or chemical verification tests the performance of the whole system. Particular attention is given to rare-event detection, model drift, preventive-control exemptions and differences between international standards and binding regional regulations. The most defensible route towards predictive assurance is controlled, documented decision support with prospective outcome evaluation, rather than unqualified automation of food safety decisions.
SN - 2320-0227
SN - 2320-0227
ER -