Arificial Intelligence (AI) has entered the research and development (R&D) sector as a “predictive engine”: machine learning models use environmental data (temperature/humidity), sensor signals (volatile gases, NIR/IR spectroscopy), and images (vision, hyperspectral) to estimate shelf life and anticipate spoilage. Recent reviews show that neural networks and hybrid models, trained on real-world process data, can improve shelf life estimation compared to static approaches by connecting microbiological dynamics, sensory quality, and the cold chain. So-called “predictive microbiology” enhanced by ML also helps choose optimal packaging and conditions to extend product shelf life without compromising safety.
On the allergen front, AI is accelerating non-destructive methods: combining imaging/spectroscopy (e.g., NIR) with classification algorithms allows for the recognition of traces or cross-contact in complex matrices without laborious extractions. Recent studies report promising performance (high accuracy on specific target proteins), and technical reviews confirm that the models are able to “distill” useful signals from noisy data, paving the way for faster in-line controls. This is also an important change for labeling: reducing false negatives improves the protection of allergy sufferers, while reducing false positives avoids excessive “may contain” statements that unnecessarily penalize product categories.
However, representative data sets, robust validation protocols, and integration with official methods (e.g., ELISA/LC-MS) are needed to ensure regulatory reliability. Finally, the supply chain: the integration of IoT, blockchain, and AI enables end-to-end traceability and early warning. Sensors record real-world conditions during transportation and storage, blockchain creates an immutable log of batches and events, and predictive models identify anomalies (cold chain breaks, recall patterns, fraud) with faster and more targeted responses. Europe and technical agencies are working on frameworks and tools (EFSA has launched a process on innovative methods, data, and the responsible use of AI in risk assessment), strengthening industrial adoption.
For companies, the “musts” are data quality, model transparency, continuous validation, cybersecurity and integration with HACCP/ISO and market requirements: only in this way can AI move from “proof of concept” to a structural lever for safety, sustainability and product development more focused on real shelf life and nutritional needs. So, don’t worry: Artificial Intelligence won’t replace workers or other human resources in the agri-food industry; instead, it will be a valuable addition to optimize processes, ensure safety, and predict errors that are part of human nature. For entrepreneurs, it will instead be useful for predicting competitively advantageous models.
- Edited by Dr. Gianfrancesco Cormaci, PhD, specialist in Clinical Biochemistry.
Scientific references
Balta I et al. Trends Food Sci Technol. 2025 Nov; 165:105278
Rodriguez-Alonso A et al. Food Analysis Method. 2025; 18:2331.
Yu Q et al. Comput Electronics Agricult. 2024 Sep; 224:109191.
