Food industry embraces AI to automate quality testing and optimize processing

The study discusses how AI-driven traceability systems can monitor food through complex supply chains, enabling the fast identification and recall of contaminated products. Deep learning algorithms, informed by historical data, help detect risks before they escalate, improving risk management protocols and consumer protection. AI systems can predict the likelihood of recalls based on textual data analysis and time-series forecasting, strengthening early warning systems for food safety threats.

Artificial intelligence is driving breakthroughs in the food industry, promising smarter production, safer products, and tailored consumer experiences. A new editorial study titled "Applications of Artificial Intelligence in Food Industry," published in Foods, provides an extensive overview of how AI-powered technologies are rapidly transforming the industry, addressing key questions: How is AI enhancing food quality and safety? What role does it play in classification and process optimization? And how might it shape the industry’s future?

One of the primary questions investigated is how AI improves the assessment of food quality and nutritional content. The research highlights advancements using deep learning combined with imaging and spectroscopy techniques. For instance, multispectral imaging and image texture parameters have demonstrated strong correlations with chemical properties in peppers and apples. Deep learning models integrated with near-infrared spectroscopy have been used to determine the soluble solid content of fruits like apples, and to classify maturity levels based on color and moisture indicators. These technologies, when coupled with optimization algorithms, have proven effective not only in assessing quality but also in predicting and controlling drying processes, as in the case of kiwifruit dehydration.

The study also asks how AI can automate classification processes for fruits and vegetables in real time. The authors cite research where artificial neural networks classified Persian lemons with 96.6% accuracy based on image analysis. Similarly, a near-infrared-based online system successfully distinguished maize seed vitality with over 91% accuracy. These models mark a shift toward industrial-scale, real-time classification that minimizes human error and enhances processing efficiency.

Beyond quality and classification, the research examines AI’s ability to optimize processes and maximize output. For instance, the use of an Adaptive Neuro-Fuzzy Inference System in supercritical CO2 extraction from pomegranate seeds identified ideal operating conditions, while deep learning improved the drying process of fingerroot extract to preserve bioactive compounds. These implementations not only enhanced the retention of nutritional value but also improved energy efficiency, demonstrating how AI reduces waste and cost across food processing systems.

Another central focus is AI’s role in ensuring food safety. The study discusses how AI-driven traceability systems can monitor food through complex supply chains, enabling the fast identification and recall of contaminated products. Deep learning algorithms, informed by historical data, help detect risks before they escalate, improving risk management protocols and consumer protection. AI systems can predict the likelihood of recalls based on textual data analysis and time-series forecasting, strengthening early warning systems for food safety threats.

The study further investigates how AI can extract insights from unstructured data to understand evolving consumer preferences. AI tools that mine social media, online reviews, and surveys help detect emerging dietary trends and foodborne illness concerns. One cited system uses social media mining to predict illness outbreaks, while another employs cross-modal alignment techniques to retrieve recipes from food images. Additionally, multi-task learning models now generate personalized food recommendations that align taste preferences with health needs, representing a major advance in dietary personalization.

The research also tackles the limitations in the real-world implementation of these tools. While AI applications are increasingly being deployed in product analysis and logistics, the study notes a persistent gap between technological development and practical usage, especially in agriculture and crop science. Barriers include data standardization, infrastructure readiness, and integration with existing industrial systems. Nonetheless, hybrid approaches combining traditional sensor technologies with AI, such as intelligent SERS sensors for pesticide detection on apples, have shown promise in addressing these challenges.

The study delves into AI’s potential to further reshape the food sector by integrating with complementary technologies like the Internet of Things, blockchain, and robotics. In future applications, these integrations are expected to enhance transparency, traceability, and predictive accuracy across supply chains. Blockchain, for example, can authenticate the origins of fresh produce, while AI can evaluate distribution patterns to reduce losses. The convergence of these systems will not only support real-time decisions but also advance sustainability goals.

Sustainability is a major theme in the paper’s projections. The authors argue that AI will be central to optimizing resource usage and minimizing waste. From intelligent crop planning models that account for economic and environmental constraints to fermentation processes that valorize food waste, AI’s data-driven insights are redefining sustainability in food manufacturing and agriculture. Technologies like hyperspectral imaging and clustering algorithms are being used to predict dissolved oxygen levels in lakes for aquaculture, while AI-generated recommendations can guide dietary choices aligned with environmental and health metrics.

In the future, as the paper suggests, AI will continue to play a critical role in personalized nutrition. Deep generative models and AI chatbots are already being used to develop individualized dietary recommendations. These tools analyze user profiles and feedback to provide real-time meal suggestions, monitor eating disorders, and support healthy consumption habits. As these systems mature, they may become essential in integrating health, technology, and food systems.

Notably, the authors caution that ethical considerations must guide implementation. AI systems must be transparent, interpretable, and fair, particularly when making health-related decisions or engaging in predictive modeling. Data privacy and user consent are also flagged as critical issues, especially in applications that involve social media mining or biometric data.