Abstract
Honey has long been an essential component of human nutrition, valued for its health benefits and economic significance. However, honey adulteration poses a significant challenge, whether by adding sweeteners or mixing high-value single-flower honey with lower-quality multi-flower varieties. Traditional detection methods, such as melissopalynological analysis and chromatography, are often time-consuming and costly. This study proposes an artificial intelligence-based approach using the BME688 gas sensor to detect honey adulteration rapidly and accurately. The sensor captures the gas composition of honey mixtures, creating a unique digital fingerprint that can be analysed using machine learning techniques. Experimental results demonstrate that the proposed method can detect adulteration with high precision, distinguishing honey mixtures with up to 5% resolution. The findings suggest that this approach can provide a reliable, efficient, and scalable solution for honey quality control, reducing dependence on expert analysis and expensive laboratory procedures.