Abstract
Honey, a valued natural sweetener with nutritional and therapeutic benefits, is often subject to adulteration, compromising its authenticity. This study introduces a novel method combining headspace solid-phase microextraction (HS-SPME), ion mobility spectrometry (IMS), and chemometric analysis to detect honey adulteration. Authentic coriander honey was mixed with invert sugar and high fructose corn syrup (HFCS) at 5-50% concentrations, alongside sugar-fed honey, simulating direct and indirect adulteration. Volatile compounds were extracted via HS-SPME and analyzed using IMS, with data processed through Principal Component Aanalysis (PCA), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Decision Tree (DT) models. PCA revealed clear clustering while SVM achieved the highest accuracy (99 %), followed by DT (98 %) and LDA (97 %). This rapid, sensitive, and cost-effective method effectively addresses the drawbacks of traditional approaches, facilitating precise discrimination between authentic and adulterated honey, and thus significantly advancing honey authentication and quality control in the food industry.