ReviewFoods (Basel, Switzerland)2026
A Review of Machine Learning and AI Applications in Enhancing HACCP Systems for Ice Cream Manufacturing.
Review in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Hazard analysis and critical control point (HACCP) systems provide a preventive framework for food safety by implementing quality assurance plans, continuous monitoring, corrective actions, and risk mitigation strategies at critical control points throughout food processing, including dairy products such as ice cream. Artificial intelligence (AI) is increasingly transforming food safety management by enabling real-time monitoring, predictive analytics, and automated decision-making within food processing systems. This review critically examines the integration of AI technologies into HACCP systems for ice cream manufacturing, with an emphasis on improving hazard detection, process control, traceability, and the efficiency of corrective actions. The review evaluates the application of Internet of Things sensors, computer vision, and machine learning-based predictive monitoring systems across critical processing stages, including raw material reception, pasteurization, continuous freezing, and hardening/storage. Compared to conventional HACCP systems, AI-assisted technologies offer greater capabilities for anomaly detection, predictive maintenance, automated verification, and data-driven risk management. Nevertheless, their industrial implementation remains constrained by data quality limitations, infrastructure cost, cybersecurity risks, regulatory uncertainty, and limited model explainability. Accordingly, this review highlights key research gaps related to industrial scalability, validation under dynamic processing conditions, and the scarcity of ice cream-specific AI datasets. Finally, the review identifies future research directions and emerging opportunities for applying AI technologies in food processing and quality control systems, providing a framework for the evolution of intelligent HACCP systems in frozen dairy manufacturing.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.