Assessment of Physicochemical and Microbiological Characteristics of Honey in Southwest Ethiopia: Detection of Adulteration through Analytical Simulation

This study aimed to evaluate the quality of honey in the supply chain from the Gera district to Jimma town in southwest Ethiopia and develop a predictive model to detect adulteration. A preliminary survey revealed that poor handling practices and adulteration negatively impacted honey's physicochemical and microbial quality. For laboratory analysis, 268 honey samples were collected from households, cooperatives, chira markets, Agaro markets, and Jimma markets. They were mixed separately to create composite samples representing different value chain actors. Laboratory results indicated that honey samples from supply chain actors confirmed significant differences (p < 0.05) in physicochemical and microbial quality. The study found that the extent of adulteration and physicochemical quality loss increased from producers to Jimma retailers, indicating multiple-stage adulteration along the supply chain that could pose a risk to the safety and quality of the product. The physicochemical quality parameters of the honey samples in the study varied within the following ranges: moisture (18.35-19.42%), water activity (0.48-0.61), viscosity (7.45-10.28 Pas), pH (3.41-4.0), titratable acidity (34.01-36.03 meq/kg), ash (0.1-0.23%), electrical conductivity (0.25-0.39 mS/cm), Total Soluble Solid (75.9-77.5 degrees Brix), Water insoluble Solid (0.16-2.48 g/100 g), Diastase Activity (6-14 DN), and Hydroxymethylfurfural (0.2-27.7 mg/kg). Microbiological analyses showed that total aerobic bacterial and fungal load ranged from 2.7 x 10(1)-2.29 x 10(2) and 3.2 x 10(1)-4.57 x 10(2), respectively. A predictive model was developed using adulteration indicator parameters, showing good linearity (R-2>90%) and predictive capacity for detecting adulteration with sugar syrup.

Automated summary

This study evaluated the quality of honey in the supply chain in southwest Ethiopia and found significant differences in physicochemical and microbial quality among different actors. The study also developed a predictive model to detect adulteration, which showed good linearity and predictive capacity.