How AI Uncovers Hidden Food Frauds with Up to 98% Accuracy?
Artificial Intelligence (AI) transforms food authentication, enabling fast, accurate, and cost-effective fraud detection in global food supply chains. A study titled “The Journey of Artificial Intelligence in Food Authentication”, published in Foods, highlights how AI tools such as convolutional neural networks (CNNs), support vector machines (SVMs), and random forests (RFs) are increasingly used with image, spectral, and chemical data to detect adulteration and mislabelling.
CNN-based models, for example, have achieved over 97% accuracy in spotting adulteration in red meat via hyperspectral imaging (HSI). In honey, thermographic imaging combined with CNNs can detect 1% rice syrup adulteration with over 92% accuracy, while Raman spectroscopy models achieve nearly 98% success in identifying complex syrup mixtures.
AI vs Traditional Methods: A Smarter, Faster Approach
AI is superior to traditional lab-based methods, replacing human subjectivity and manual data interpretation with non-invasive, automated analysis. These systems can process complex datasets without handcrafted inputs and are gradually replacing chemometric techniques in many quality control settings.
In dairy, portable AI sensors analyzing RGB and UV-Vis data have achieved over 98% accuracy in detecting adulterants. CNN and SVM models in edible oil analysis have reached 97–100% accuracy in identifying adulterants in olive oil.
Also Read: Microsoft and National FFA Organization Expand FarmBeats to Students Across the Country
Challenges and Future Outlook
While AI shows enormous promise, challenges include a lack of standardized data protocols and limited availability of large, labeled datasets. AI tools currently serve best as screening solutions, with chemical validation still required for regulatory purposes.
Future developments may include mobile and edge AI systems for real-time fraud detection in remote settings, as well as hybrid models combining deep learning with traditional analytical techniques to enhance food safety and quality control.
