ReviewInternational journal of microbiology2024
Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models.
Review in International journal of microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed.
- Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology.iScience · 2026Review
- Microbial Survival, Sublethal Injury, and Recovery in Frozen Food Systems: Implications for Safety, Quality Deterioration, and Cold-Chain Control.Comprehensive reviews in food science and food safety · 2026Review
- Fungal Growth Risk Prediction and Optimal Regulation Method for Food Storage Based on the Forward Reachable Set.Foods (Basel, Switzerland) · 2026Article
- Biofilm Formation and Spore-Mediated Persistence of Clostridium perfringens in Meat and Poultry Processing Environments and Their Implications for Control Strategies.Journal of food science · 2026Review
- Review
- Vendor Hygiene Practices, Temporal Variation, and Microbial Quality of Soya Kebabs Sold in Public and Private Basic Schools in Sunyani, Ghana.Food science & nutrition · 2026Article
- Review
- Next-Generation Strategies for Controlling Foodborne Pathogens: Precision Antimicrobials, Biofilm Disruption, and Emerging Molecular Interventions.Foods (Basel, Switzerland) · 2026Review
- Mixed-species biofilms in food systems: from community assembly to ecology-informed management.Frontiers in microbiology · 2026Review
- A Comprehensive Review of Technological Advances in Meat Safety, Quality, and Sustainability for Public Health.Foods (Basel, Switzerland) · 2025Review
- Exploring Intervention Strategies for Microbial Biofilms in the Food Industry Based on a Biomolecular Mechanism Perspective: Recent Advances and Emerging Trends.Foods (Basel, Switzerland) · 2025Review
- Modelling the Combined Effects of Oxalic Acid, Water Activity, and pH on the Growth and Mycotoxin Production ofFoods (Basel, Switzerland) · 2025Article
- Modeling the Growth ofFood science & nutrition · 2025Article
- Application of Bioinformatics and Machine Learning Tools in Food Safety.Current nutrition reports · 2025Review
- Exploring the Complexities of Seafood: From Benefits to Contaminants.Foods (Basel, Switzerland) · 2025Review
- Optimization of Haskap Extract and Tannic Acid Combined with Mild Heat Treatment: A Predictive Study on the Inhibition ofFoods (Basel, Switzerland) · 2025Article
- Applying advanced predictive microbiology techniques to static and dynamic growth studies ofEFSA journal. European Food Safety Authority · 2024Article
- Impact of Metabolites from Foodborne Pathogens on Cancer.Foods (Basel, Switzerland) · 2024Review
- Mathematical Modeling ofFoods (Basel, Switzerland) · 2024Article
- Plant Microbe Interaction-Predicting the Pathogen Internalization Through Stomata Using Computational Neural Network Modeling.Foods (Basel, Switzerland) · 2024Article
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
No grant is acknowledged in the PubMed record.
Abstract
Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.
Identifiers
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.