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India does not have a paucity of Regulations, Acts and Statutes, even from the food industry perspective. Yet, violations of food safety guidelines have remained a long-term thorn in the side of consumer well-being and rights. Ignoring food regulations has also reportedly led to incidents of malnutrition and even deaths. Food safety and policy experts believe artificial intelligence (AI) could be effectively leveraged to address these key concerns. Let’s explore further.
Significance of AI
India’s AI market is projected to grow from $8 billion in 2025 at 40 per cent CAGR to $17 billion by 2027, highlights the BCG report. Across laboratories, Next-Gen AI tools are driving greater efficiency, improving data accuracy and automating complex analytical tasks to reduce rework and retesting, directly lowering operational costs. They also enable faster turnaround times, boost lab throughput, and simplify workflows, easing staff training and helping maintain consistent performance.
A recent Deloitte report highlights that AI-driven automation can deliver 10–12 per cent revenue gains in life sciences and 20–30 per cent reductions in operational labour costs for routine lab tasks.
A mention can be made of some key AI tools in food safety management, and these are blockchain, Machine Learning (ML), predictive analytics, computer vision, The Internet of Things (IoT), Natural Language Processing, Smart Monitoring, and Automated Quality Control.
Agilent is playing a pivotal role in this transformation by incorporating AI into many of its analytical solutions. Agilent’s AI-integrated tools focus on value creation beyond simple 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, is designed to integrate seamlessly 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 centralised data management, streamlining operations and improving data accessibility.
The food industry is currently going through a transformation. This transformation shift is basically because of the new-age technology known as AI. Besides, ML with a lot of next-generation tools and automation are redefining how laboratory testing is carried out to ensure that the food to be sold in the market remains safe. This, in turn, is reshaping how food products are being developed, keeping all the safety standards in mind.
Beyond detection, AI supports real-time decision-making. Predictive analytics models, trained on environmental data, storage conditions, and microbial behaviour, forecast spoilage risks and optimise shelf-life management. AI-driven compliance monitoring tools proactively flag gaps in documentation and regulatory adherence, minimising the risk of non-compliance before it manifests. Future-ready laboratories are adopting digital twins, smart workflows, and AI-powered dashboards aligned with global lab management standards like ISO 17025:2017. These enable not only more accurate testing but also process standardisation, faster turnaround times, and continual calibration of instruments and analyst performance.
AI is impacting food products right from raw materials, like inventory management, to predictive modelling that can help to identify the shelf life in a short amount of time. Earlier, it used to be through accelerated testing, but predictive models are the game-changer that uses algorithms.
With the help of amazing AI tools, one can identify and set the shelf life of products. Smart sensors help to monitor temperature and humidity, which in turn help to gauge how the product is behaving under a particular temperature, with a certain humidity. These AI-based testing applications help to process real-time batch analysis. Food business operators and manufacturers can save a lot of time, and real-time data is helping aid in the operational efficiency of the entire food product.
Dr H G Koshia, Commissioner, Food & Drugs Control Administration, Government of Gujarat, who has played a vital role in the implementation of e-governance in the Food & Drugs Control Administration, Gujarat, mentions, “Advanced machine learning algorithms can analyse vast datasets in real time, flagging anomalies in ingredients or detecting contaminants that would otherwise go unnoticed by conventional methods. Additionally, 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.
Talking about the advantages of AI in the food industry, Dr Harinder Singh Oberoi, Director, National Institute of Food Technology Entrepreneurship and Management, Kundli in Sonepat, Haryana, reinforces the importance of building a transparent, technology-driven food system. Dr Oberoi mentions, “The future of food safety lies in harnessing emerging technologies like blockchain, IoT, and AI to revolutionise compliance monitoring, traceability, and quality assurance.”
By leveraging AI’s ability to process vast amounts of data, identify patterns, and make predictions, the food sector can move from reactive to proactive risk management. Says Chinmayee Deulgaonkar, Managing Director, FoodChain ID India, “As the global food economy continues to scale and diversify, the future of safety and transparency lies in intelligent systems built on real-time data, predictive modelling, and end-to-end traceability. Whether you are a spice processor in Kerala, a soya protein supplier in Missouri, or a seafood aggregator in Da Nang, the imperative is the same: build ecosystems that can guarantee safety, quality, and authenticity at every step. In this new era, food safety is no longer reactive — it’s designed.”
Alan Huang, Chief Engineer, Anko Food Machine Company, a Taiwan-based food industry player, mentions how AI can track and analyse supply chain data to ensure traceability, flag anomalies, and identify sources of contamination faster, thereby strengthening food safety and regulatory compliance. Huang adds, “Computer vision can detect defects, contamination, or foreign objects on production lines more accurately than manual inspection. These systems continue to learn and adapt over time, leading to continuous improvement.”
A true game changer
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, Envirocare Labs, a Mumbai-based NABL-accredited food testing laboratory since the last 43 years that has set its benchmark for quality testing in India says, “The global food industry is navigating unprecedented complexity: rising demand, longer supply chains, evolving regulatory standards, and growing consumer awareness have raised the bar for quality, safety, and transparency. Yet, persistent loopholes: adulteration, storage failures, and supply chain opacity continue to challenge the sector despite best efforts. This is where AI-powered next-generation analytical testing can act as a true game-changer.”
Accelerating product development cycles can bring a product to the market in a really short time to the market. With the consumer trends and a lot of business uncertainties and geopolitical issues in place, there is always a very high demand to accelerate R&D processes where AI using advanced technologies is an integrated multi-disciplinary approach.
Fatima Jabalpurwala, Section Manager, Global Analytical, AMEA & Digital Lead, Mondelez International, says, “Keeping consumer sentiments in mind, AI tends to translate the consumers’ needs into what innovations are going to appeal to our consumers. AI enables ease of conducting physical trials where digital models and predictive models link up recipe formulation changes using the analytical characterisation of food matrices.”
Says Vikram Kelkar, MD, Hexagon Nutrition, “AI can be used to implement a proactive approach by identifying risks, automating inspections, ensuring compliance, and improving traceability, thereby closing safety loopholes and helping the food industry maintain the highest food safety standards. It also helps in informing about new changes, updates and aids human efforts towards quality assurance and food safety. While AI tools require an upfront investment, their ability to prevent losses, optimise labour, enhance compliance, and improve efficiency typically makes them highly cost-effective over time. The RoI grows as these systems scale and mature in operation.”
Cost-effectiveness
Despite immense potential, there are several challenges that need to be looked into. AI models require large, diverse, and high-quality datasets for training. Data in the food industry is often fragmented, in heterogeneous formats, and can be incomplete or inaccurate. Also, the initial investment in AI automation equipment, software systems, and infrastructure can be substantial, particularly for small and medium-sized enterprises (SMEs).
Apart from this, getting the right workforce capable of developing, implementing and managing AI systems is a major challenge. Existing regulations seem not to fully address the unique aspects of AI in food safety, requiring new guidelines and standards. Ensuring fairness, transparency, and accountability in AI algorithms, particularly in decision-making processes, is important.
Says Nandakumar Kalathil, Country General Manager (CGM) – India, Agilent Technologies, “Despite its transformative potential, the large-scale implementation of AI in food safety testing is not without challenges. One of the primary obstacles is data standardisation. AI models require clean, structured, and consistent data to function effectively, but achieving this across diverse food categories and testing protocols can be difficult. Another significant challenge is the integration of AI with legacy systems. Many laboratories still rely on manual or semi-automated processes, which can complicate the adoption of advanced AI technologies. Additionally, there may be a general lack of awareness and readiness among lab personnel and management regarding the capabilities and requirements of AI, which can hinder its adoption.”
Manpower constraints
Many employees lack the technical proficiency required to operate AI tools effectively, necessitating comprehensive training programmes and fostering a culture of technological acceptance. Institutions can play a larger role in nurturing students to study more about AI in food safety.
This is where many institutes in India have come forward to offer courses on AI in food safety. Apart from NIFTEM, Lovely Professional University, Shooini University, several IITs across India, Chandigarh University, SRM Institute of Science and Technology, MIT School of Food Technology, etc, offer courses on AI in food safety.
Dr Sawinder Kaur, Professor and Associate Dean, Department of Food Technology and Nutrition, School of Agriculture, Lovely Professional University, mentions, “AI plays a crucial role in helping the food industry maintain top-quality standards by minimising human error, improving efficiency, and ensuring compliance with safety regulations. Lovely Professional University (LPU) integrates Artificial Intelligence (AI), Machine Learning (ML), and emerging technologies into its food technology curriculum by offering a course on ‘Introduction to AI, ML and Emerging technologies’ to prepare students for modern challenges in food safety and innovation.”
Sanjiv Das