ReviewComprehensive reviews in food science and food safety2025
Approaches for Measuring and Predicting Fouling During Thermal Processing of Dairy Solutions.
Review in Comprehensive reviews in food science and food safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hybrid Intelligence for Fouling Prediction and Adaptive Clean-in-Place Optimization in Food Processing Heat Exchangers: A Systematic Review.Journal of food science · 2026Pooled it
- Effectiveness of an artificial intelligence-assisted training program on cleaning competency among hospital environmental service staff.BMC health services research · 2026Trial
- Influence of Protein Concentration on Heat-Induced Fouling of Oat Drink.Foods (Basel, Switzerland) · 2026Article
- A generalized fractional model for one-step identification of membrane filtration clogging mechanisms.Scientific reports · 2026Article
- The case for computational and artificial intelligence-based approaches for sustainable functional food systems for sub-Saharan Africa.Frontiers in nutrition · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Fouling during the thermal processing of dairy products remains a significant challenge, reducing operational efficiency, increasing energy consumption, and complicating cleaning cycles. This review critically assesses current methods for measuring and predicting fouling during thermal processing in the dairy industry, emphasizing scientific principles, technical maturity, and industrial applicability. Unlike existing reviews, which are mostly focused on fouling quantification, this work highlights the shift toward prediction-driven approaches for fouling control and minimization. Traditional measurement techniques, such as monitoring thermal resistance and pressure drop, are evaluated alongside emerging methods, including acoustic, spectroscopic, and electrochemical sensors. Their respective limitations and strengths are discussed in terms of sensitivity, scalability, and industrial robustness. Advanced predictive tools, including deep learning, computational fluid dynamics, and dimensional analysis techniques, are explored for their ability to model the dynamic nature of fouling and support real-time decision-making. The integration of artificial intelligence with real-time process data acquisition is identified as a key innovation for improving fouling management and optimizing cleaning schedules. The review also considers the importance of small-scale experimental systems in linking laboratory-scale research with industrial applications. Development and utilization of tools for enhanced process efficiency through prediction, prevention, and control of in-process fouling are growing. Greater control in this regard offers substantial opportunity to meet future challenges in process optimization, shorten cleaning-in-place times, and advance sustainable dairy manufacturing through real-time monitoring, predictive analytics, and industrial-scale implementation. Addressing these challenges will require a multidisciplinary approach between researchers, engineers, and industry stakeholders to translate emerging technologies into practical, scalable solutions.
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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.