Leveraging advanced transformer neural networks for accurate detection of honey adulteration through thermal image analysis

Abstract

The research suggests a novel approach for the determination of honey adulteration through the Vision Transformer (ViT) model and thermography techniques in honey sample analysis and grading. Honey adulteration, being a natural and highly prized dietetic food item, is a major economic and health hazard. Conventional procedures for the determination of honey adulteration require long processing time and are not as sensitive. Thermal imaging constitutes a distinctive benefit in that it permits temperature difference determination among honey samples and hence facilitates the determination of differences in sugar, moisture, and adulterants. Thermal imaging technique offers a great advantage in adulterant detection since it has the potential to detect temperature variations in honey samples as a result of differences in sugar content, moisture levels, and other adulterants. To find a trustworthy method for honey classification, we gathered a large dataset of thermal images of 9 pure honey samples, and 45 honey samples adulterated at various levels ranging from 1% to 20% during their cooling processes. The dataset was employed to train and fine-tune the model in this work. The findings indicated that the model achieved a level of accuracy at 99.9% with sensitivity of 99.5% and specificity of 100%. The finding of the current study provides the proof to establish the effectiveness of thermal image analysis using Transformers as a capable instrument for the prompt and accurate detection of instances of honey adulteration. The above-mentioned approach presents a likely useful method of implementing quality control policies in the honey industry such that authenticity and safety of this valuable organic resource is ensured.