Advancements in Saffron Authentication: A Review of ML and DL Techniques for Adulteration Prediction

Saffron, regarded as one of the most expensive spices in the world, is often adulterated to increase profitability at the expense of its authenticity and quality. The paper describes a comparison between artificial intelligence (AI) techniques and traditional laboratory methods for detecting fishery mislabeling. Various AI driven models such as machine learning algorithms and computer vision have been compared for accuracy, proficiency, and feasibility against the classical laboratory chromatographic and spectroscopic methods which are mostly based on easy detection. The present paper attempts to represent the comparative merits and demerits of both approaches concerning their detection precision, costeffectiveness, and easy implementation. AI approaches have an advantage for rapid analysis and scalability, whilst the lab methods give accurate results but are much slower and expensive. The present paper represents the integration of AI tools for solving quality control problems, thereby enhancing the food safety and safeguard the saffron market.