Authors : Gloria Otwiwaa Larbi; Moses Mayonu; Joy Onma Enyejo

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/5t5yhr2w

Scribd : https://tinyurl.com/4epvssmt

DOI : https://doi.org/10.38124/ijisrt/26May649

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Foodborne contamination within industrial food processing environments continues to present significant public health, regulatory, and economic challenges due to inconsistent hygiene protocol implementation, ineffective sanitation monitoring, and delayed contamination response mechanisms. This study presents a novel intelligent quality assurance framework termed the Adaptive Hygiene Compliance and Microbial Risk Optimization Algorithm (AHCMROA) for evaluating hygiene protocol compliance and predicting microbial load reduction in food processing facilities using datadriven quality assurance metrics. The proposed framework integrates Internet of Things (IoT)-enabled environmental sensing, ATP bioluminescence monitoring, surface swab microbial analysis, employee sanitation tracking logs, and machine learning-driven risk analytics to establish a real-time hygiene performance evaluation system. The AHCMROA framework combines Extreme Gradient Boosting (XGBoost), Temporal Convolutional Networks (TCN), Fuzzy Rule-Based Risk Classification, and an Attention-Guided Long Short-Term Memory (AG-LSTM) architecture to dynamically model contamination propagation patterns and sanitation effectiveness across multiple processing zones. A novel Hygiene Compliance Index (HCI) and Microbial Reduction Efficiency Score (MRES) are introduced to quantify protocol adherence and microbial load minimization efficiency under varying operational conditions.

Keywords : Food Processing, Hygiene Protocol Compliance, Microbial Load Reduction, Data-Driven Quality Assurance Metrics, Machine Learning.

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