Multivariate and machine learning approach to honey adulteration: Integrating Principal Component Analysis and artificial neural network models

Abstract Honey adulteration is a major issue in India. It affects product quality and can pose health risks to consumers. This study examined the concentration of adulterants' impacts on honey quality using four types from different floral sources. The samples were mixed with brown rice syrup, corn syrup, and malt syrup at levels of 10%, 20%, and 30%. We analyzed nine physicochemical properties: moisture content, color, pH, electrical conductivity, density, viscosity, total soluble solids (TSS), hydroxymethylfurfural (HMF) content, and diastase activity. The results showed moisture content ranging from 15.23% to 18.00%, pH between 3.77 and 4.20, color ΔE from 20.71 to 29.71, TSS between 75.07°Bx and 80.83°Bx, electrical conductivity from 0.20 to 0.32 mS/cm, viscosity ranging from 5.51 to 16.80 Pa·s, density from 1.35 to 1.44 kg/m³, HMF content between 17.24 and 21.57 mg/kg, and diastase activity from 10.74 to 13.56. The analysis of variance (ANOVA) showed significant increases in HMF content and pH with the addition of adulterants. At the same time, viscosity, diastase activity, and electrical conductivity decreased significantly (P