Honey Adulteration Detection using Hyperspectral Imaging and Machine Learning

TL;DR

This study presents a machine learning system using hyperspectral imaging to accurately detect honey adulteration and identify botanical origin, offering a non-destructive alternative to chemical testing with high accuracy.

Contribution

The paper introduces a novel hyperspectral imaging and machine learning approach for honey adulteration detection and botanical classification, achieving over 96% accuracy.

Findings

96.39% cross-validation accuracy in adulteration detection

Effective identification of honey botanical origin

Potential to replace chemical-based testing methods

Abstract

This paper aims to develop a machine learning-based system for automatically detecting honey adulteration with sugar syrup, based on honey hyperspectral imaging data. First, the floral source of a honey sample is classified by a botanical origin identification subsystem. Then, the sugar syrup adulteration is identified, and its concentration is quantified by an adulteration detection subsystem. Both subsystems consist of two steps. The first step involves extracting relevant features from the honey sample using Linear Discriminant Analysis (LDA). In the second step, we utilize the K-Nearest Neighbors (KNN) model to classify the honey botanical origin in the first subsystem and identify the adulteration level in the second subsystem. We assess the proposed system performance on a public honey hyperspectral image dataset. The result indicates that the proposed system can detect adulteration in honey with an overall cross-validation accuracy of 96.39%, making it an appropriate alternative to the current chemical-based detection methods.