Food industry turns to machine learning to boost efficiency, cut waste and improve quality

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture evolution, and energy efficiency under varying conditions. Drying remains one of the most energy-intensive and technically complex food preservation methods, and the review highlights how neural networks outperform traditional regression models in capturing nonlinear drying behavior across diverse food materials. The adoption of machine learning in drying systems has accelerated sharply in recent years, reflecting industry demand for faster design cycles and improved energy control.

Machine learning is becoming a key tool in food production, reshaping how food is processed, preserved, monitored, and delivered across global supply chains. Climate volatility, rising energy costs, labor shortages, and sustainability targets are prompting the food industry to turn to data-driven intelligence to stabilize operations and improve efficiency at scale.

A comprehensive review titled “Machine Learning in Transforming the Food Industry,” published in Foods, documents how machine learning models are now embedded across food processing operations, from drying and frying to baking, extrusion, canning, and supply chain management. 

Machine learning replaces rigid models in core food processing operations

Food processing operations are defined by simultaneous heat, mass, and momentum transfer, making them difficult to model accurately using conventional theoretical approaches. Processes such as drying, frying, baking, and extrusion involve nonlinear interactions between material properties, environmental conditions, and equipment parameters. Traditional physics-based models often require simplifying assumptions, heavy computational resources, and specialized expertise, limiting their use in real-world production environments.

The study documents how machine learning models, particularly artificial neural networks, have emerged as a dominant solution to these constraints. Neural networks are shown to predict processing kinetics, temperature profiles, moisture content, oil absorption, density changes, and product quality indicators with high accuracy and lower computational cost. By training on experimental and operational datasets, these models bypass the need for fully specified physical equations while still delivering reliable performance.

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture evolution, and energy efficiency under varying conditions. Drying remains one of the most energy-intensive and technically complex food preservation methods, and the review highlights how neural networks outperform traditional regression models in capturing nonlinear drying behavior across diverse food materials. The adoption of machine learning in drying systems has accelerated sharply in recent years, reflecting industry demand for faster design cycles and improved energy control.

Frying operations, particularly in snack food production, present similar challenges. Frying kinetics are influenced by product geometry, oil temperature, moisture diffusion, fat absorption, and heat transfer coefficients. The review shows that machine learning models can predict frying outcomes more efficiently than mechanistic models, enabling better control over texture, oil uptake, and product consistency. Advanced neural network configurations are now capable of handling large datasets and multi-output predictions, supporting real-time quality monitoring during industrial frying.

Extrusion, widely used in cereal-based foods and snacks, benefits from machine learning’s ability to manage complex parameter interactions. Product quality in extrusion depends on raw material composition, moisture content, screw speed, barrel temperature, and mechanical energy input. The study details how machine learning models outperform response surface methodologies in predicting expansion ratio, texture, solubility, and sensory attributes. These models allow manufacturers to fine-tune extrusion parameters without extensive trial-and-error experimentation.

Baking processes, which involve fermentation, moisture evaporation, protein denaturation, and crust formation, have also seen growing adoption of machine learning. Neural network models are used to predict baking stages, thermal properties, dough behavior, and final product quality. The review notes that adaptive machine learning systems integrated with sensors and vision technologies can outperform fixed-time baking approaches, delivering more consistent outcomes while reducing waste and energy use.

Canning, a preservation method with long shelf-life expectations, presents another domain where machine learning is gaining ground. Predicting thermal processing conditions that balance microbial safety with quality retention remains a major challenge. The study shows that neural networks can accurately predict optimal temperature and time parameters, minimizing nutrient degradation while maintaining safety standards. Machine learning frameworks are also being applied to predict aroma profiles and fermentation outcomes linked to canned food characteristics.

Food supply chains adopt machine learning to cut waste and improve traceability

The study further highlights the expanding role of machine learning in food supply chain management. As global food systems grow more complex and interconnected, machine learning is increasingly used to address inefficiencies, food loss, and safety risks from farm to consumer.

Machine learning algorithms are now applied to forecast demand, optimize inventory levels, manage staffing, detect spoilage, and assess consumer purchasing behavior. The review outlines how data-driven systems analyze historical sales, weather patterns, local events, and logistics data to improve supply chain responsiveness. These tools allow retailers and distributors to reduce overstocking, minimize waste, and respond more quickly to shifts in consumer demand.

Food safety monitoring represents another critical application. Machine learning-powered sensors and inspection systems are used to detect contamination risks, monitor storage conditions, and identify defects in food products through image recognition and machine vision. These technologies reduce reliance on manual inspection and enable continuous quality assurance across high-volume operations.

Traceability and transparency are also strengthened through machine learning integration. By combining sensor data, blockchain systems, and predictive analytics, machine learning supports more reliable tracking of food provenance. This capability is increasingly important as consumers demand clearer information about origin, quality, and sustainability credentials.

Supply chain applications deliver both economic and environmental benefits. Reduced waste translates directly into cost savings, lower emissions, and improved resource efficiency. Machine learning-driven optimization is therefore positioned as a strategic tool not only for profitability but also for meeting sustainability targets across the agri-food sector.

Limits of data-driven AI and the rise of physics-informed machine learning

The authors identify structural limitations that currently constrain its effectiveness in food processing and supply chain applications.

A central challenge is data quality. Food materials are inherently heterogeneous, with properties that change across time and space. Experimental data can be noisy, incomplete, or difficult to reproduce, limiting the reliability of purely data-driven models. In many cases, key process variables at the microscale remain unobservable, creating gaps in training datasets.

Another major concern is interpretability. Many machine learning models function as black boxes, providing accurate predictions without clear physical explanations. In regulated industries such as food processing, lack of transparency can hinder trust, validation, and adoption, particularly when safety and compliance are involved.

To address these challenges, the study highlights physics-informed machine learning as a critical emerging direction. This approach integrates physical laws, governing equations, and boundary conditions directly into the learning process. By combining data-driven flexibility with physics-based constraints, physics-informed models improve accuracy, generalizability, and interpretability.

Physics-informed machine learning allows models to operate effectively even with limited data, a common scenario in food processing research. By embedding known physical relationships into the training process, these models maintain consistency with established scientific principles while still benefiting from machine learning’s predictive power.