AI in Food Safety: A Game-Changer for Quality and Accountability

India has a robust regulatory framework for the food industry, yet violations of food safety guidelines persist, posing significant risks to consumer well-being. Food safety and policy experts believe that artificial intelligence (AI) can address these challenges effectively. Let’s delve into how AI is transforming the food industry.

India’s AI market is projected to grow from $8 billion in 2025 at a 40% compound annual growth rate (CAGR) to $17 billion by 2027, according to a BCG report. In laboratories, next-generation AI tools are driving greater efficiency, improving data accuracy, and automating complex analytical tasks. This reduces rework and retesting, directly lowering operational costs while enabling faster turnaround times and streamlining workflows.

A recent Deloitte report highlights that AI-driven automation can deliver 10-12% revenue gains in life sciences and 20-30% reductions in operational labor costs for routine lab tasks. Key AI tools in food safety management include blockchain, machine learning (ML), predictive analytics, computer vision, the Internet of Things (IoT), natural language processing, smart monitoring, and automated quality control.

Agilent is at the forefront of this transformation, incorporating AI into many of its analytical solutions. Agilent’s AI-integrated tools focus on value creation beyond cost reduction. Platforms such as SLIMS (Sample Lifecycle Management System) and CrossLab Connect support incremental AI adoption, enabling labs to start with workflow or software upgrades and scale AI capabilities over time. OpenLab CDS, for example, seamlessly integrates with a wide range of instruments and data sources, making it easier for labs to transition from older systems. SLIMS and CrossLab Connect offer digital integration and centralized data management, streamlining operations and improving data accessibility.

The food industry is undergoing a significant transformation driven by new-age technology, particularly AI. Machine learning and next-generation tools are redefining how laboratory testing is conducted to ensure that food products remain safe. This shift is reshaping the development of food products, keeping all safety standards in mind.

Beyond detection, AI supports real-time decision-making. Predictive analytics models, trained on environmental data, storage conditions, and microbial behavior, forecast spoilage risks and optimize shelf-life management. AI-driven compliance monitoring tools proactively flag gaps in documentation and regulatory adherence, minimizing the risk of non-compliance before it becomes an issue. Future-ready laboratories are adopting digital twins, smart workflows, and AI-powered dashboards aligned with global lab management standards like ISO 17025:2017. These advancements ensure more accurate testing, process standardization, faster turnaround times, and continuous calibration of instruments and analyst performance.

AI is impacting food products from raw materials to predictive modeling that can help identify shelf life in a short amount of time. Earlier, this was done through accelerated testing, but predictive models using algorithms are now the game-changers. Smart sensors help monitor temperature and humidity, which in turn gauge how the product behaves under specific conditions. These AI-based testing applications process real-time batch analysis, helping food business operators and manufacturers save time and improve operational efficiency.

Dr. H G Koshia, Commissioner of the Food & Drugs Control Administration in Gujarat, emphasizes the role of advanced machine learning algorithms. These algorithms can analyze vast datasets in real time, flagging anomalies in ingredients or detecting contaminants that would otherwise go unnoticed by conventional methods. AI enhances traceability throughout the supply chain, ensuring transparency and accountability from farm to table. This heightened scrutiny not only fills existing loopholes but also fosters trust among consumers, who are increasingly demanding rigorous standards in food safety and quality.

While the food processing industry is rapidly adopting advanced technologies, such as rapid testing kits and digital traceability, these innovations must work in tandem with ground-level reforms to create a robust food safety ecosystem. Dr. Harinder Singh Oberoi, Director of the National Institute of Food Technology Entrepreneurship and Management in Sonepat, Haryana, underscores the importance of a transparent, technology-driven food system. He states, “The future of food safety lies in harnessing emerging technologies like blockchain, IoT, and AI to revolutionize compliance monitoring, traceability, and quality assurance.”

Chinmayee Deulgaonkar, Managing Director of FoodChain ID India, highlights the importance of intelligent systems built on real-time data, predictive modeling, and end-to-end traceability. As the global food economy scales and diversifies, the imperative is to build ecosystems that guarantee safety, quality, and authenticity at every step. In this new era, food safety is no longer reactive; it is designed.

AI offers capabilities far beyond conventional detection and analysis. In food adulteration, AI-enabled analytical tools can scan massive chemical, molecular, and physical datasets to detect deviations and patterns that often elude manual testing. Techniques such as AI-enhanced spectroscopy, NMR (nuclear magnetic resonance), chromatography with machine learning integration, and hyperspectral imaging allow for rapid, non-destructive, and highly sensitive adulteration detection.

Dr. Nilesh Amritkar, Managing Director of Envirocare Labs, a Mumbai-based NABL-accredited food testing laboratory, notes the unprecedented complexity in the global food industry. Rising demand, longer supply chains, evolving regulatory standards, and growing consumer awareness have raised the bar for quality, safety, and transparency. Despite these challenges, AI-powered next-generation analytical testing can act as a true game-changer.

Accelerating product development cycles can bring a product to market quickly. With consumer trends and business uncertainties, there is a high demand to accelerate R&D processes where AI using advanced technologies is an integrated multi-disciplinary approach. Fatima Jabalpurwala, Section Manager of Global Analytical and Digital Lead at Mondelez International, explains how AI translates consumer needs into innovative products. AI enables ease of conducting physical trials where digital models and predictive models link up recipe formulation changes using the analytical characterization of food matrices.

Vikram Kelkar, MD of Hexagon Nutrition, emphasizes the proactive approach AI can take in identifying risks, automating inspections, ensuring compliance, and improving traceability. AI helps close safety loopholes and maintain the highest food safety standards. While AI tools require an upfront investment, their ability to prevent losses, optimize labor, enhance compliance, and improve efficiency typically makes them highly cost-effective over time. The return on investment grows as these systems scale and mature in operation.

Despite its immense potential, AI faces several challenges. AI models require large, diverse, and high-quality datasets for training, but data in the food industry is often fragmented, in heterogeneous formats, and can be incomplete or inaccurate. The initial investment in AI automation equipment, software systems, and infrastructure can be substantial, particularly for small and medium-sized enterprises (SMEs). Additionally, getting the right workforce capable of developing, implementing, and managing AI systems is a major challenge.

Nandakumar Kalathil, Country General Manager of Agilent Technologies in India, highlights the primary obstacles to large-scale AI implementation in food safety testing. Data standardization is crucial for AI models to function effectively, but achieving this across diverse food categories and testing protocols can be difficult. Another significant challenge is integrating AI with legacy systems, which can complicate the adoption of advanced AI technologies. There is also a general lack of awareness and readiness among lab personnel and management regarding the capabilities and requirements of AI, which can hinder its adoption.

Many employees lack the technical proficiency required to operate AI tools effectively, necessitating comprehensive training programs and fostering a culture of technological acceptance. Institutions can play a crucial role in bridging this gap by offering specialized training and education in AI and related technologies.

In summary, AI is a game-changer in food safety, quality, and regulatory compliance. While challenges exist, the benefits of AI in the food industry are undeniable. By leveraging AI's capabilities, the food sector can move from reactive to proactive risk management, ensuring the highest standards of safety and quality for consumers.