Classification of Honey Botanical and Geographical Sources using Mineral Profiles and Machine Learning

TL;DR

This study develops a machine learning approach using mineral profiles to accurately classify honey's botanical and geographical origins, achieving over 98% accuracy with Random Forest classifiers.

Contribution

It introduces a two-step method combining preprocessing and supervised classification to identify honey sources based on mineral content, demonstrating high accuracy.

Findings

Random Forest achieved 99.30% accuracy for botanical classification.

Random Forest achieved 98.01% accuracy for geographical classification.

Mineral profiles are effective discriminators for honey source identification.

Abstract

This paper proposes a machine learning-based approach for identifying honey floral and geographical sources using mineral element profiles. The proposed method comprises two steps: preprocessing and classification. The preprocessing phase involves missing-value treatment and data normalization. In the classification phase, we employ various supervised classification models for discriminating between six botanical sources and 13 geographical origins of honey. We test the classifiers' performance on a publicly available honey mineral element dataset. The dataset contains mineral element profiles of honeys from various floral and geographical origins. Results show that mineral element content in honey provides discriminative information useful for classifying honey botanical and geographical sources. Results also show that the Random Forests (RF) classifier obtains the best performance on this dataset, achieving a cross-validation accuracy of 99.30% for classifying honey botanical origins and 98.01% for classifying honey geographical origins.