Webcam based visible light spectrometer for accurate honey adulteration detection using variational autoencoder and Bayesian neural network
Year: 2025
Type: article
Abstract: The authenticity of honey is often compromised due to adulteration, posing health risks and disadvantaging genuine producers. This study presents a compact, cost-effective visible light spectrophotometer, employing a webcam sensor and a DVD disc as the dispersive element, for honey purity classification. The device, measuring 17.8 × 8 × 7 cm, captured spectral absorbance data from four types of honey samples: pure and those adulterated with orange syrup, cane syrup, and palm syrup at varying concentrations. Extracted features included absorbance and first-order differential absorbance, which were analyzed using Principal Component Analysis (PCA) and Variational Autoencoder (VAE). The optimal VAE latent space features were used for classification via Support Vector Machine (SVM), achieving 100% accuracy with 5-fold cross-validation for differential absorbance. Additionally, a Bayesian Monte Carlo Dropout Neural Network (BNN) was trained on the VAE latent space to predict sweetener concentration, achieving high performance on testing data (MAE: 0.012, MSE: 0.007, R²: 0.9978). These results demonstrate the potential of the proposed webcam-based spectrophotometer as a reliable and affordable tool for honey adulteration detection.
Source: Applied Food Research
Institutions: State University of Jakarta, Universitas Gadjah Mada
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,050
OpenAlex ID: W4415371395