Artificial Intelligence and Internet of Things-Based Food Authenticity and Adulteration Detection in Spices: A Review Paper

Culinary spices represent one of the most commonly adulterated agricultural commodities worldwide due to the complexity of their supply chain and the high economic value of the product. Traditional laboratory analyses (e.g., High-performance liquid chromatography, Mass spectrometry, and Wet chemical tests) are destructive, time-consuming, and insufficient for the high-throughput screening of spice powders or botanicals. This paper conducts a narrative literature review of 22 selected sources published between 2016 and 2026 that fall under seven themes: spice supply-chain risks and adulteration profiles, computer vision and deep learning approaches, portable Vis–NIR spectroscopy, electronic nose and gas sensing, multi-sensor fusion and chemometric analysis, machine learning methods for quantifying adulteration concentrations, and Edge-IoT hardware solutions. The reviewed articles demonstrate that Convolutional Neural Networks and transfer learning can reach an excellent level of precision in distinguishing between whole spices and botanical intruders, while compact Vis-NIR spectral sensors can detect food dyes and starch fillers in ground powders. SVM/Random Forest classifier-based MOS gas-sensor arrays can detect spoiled spices and vision-spectral fusion can have more accurate detection than a single modality. Nevertheless, most of the data presented in the literature is based on experiments performed on an oversimplified scenario of adulteration, and there are not enough publicly available multi-modal sensor datasets. This research presents a thematic literature review, a comparison of features in fourteen selected papers, a proposed conceptual framework, and recommendations.