Classification of Honey Botanical and Geographical Sources using Mineral Profiles and Machine Learning
This work addresses honey authentication for quality control and traceability, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of identifying honey floral and geographical sources by using mineral profiles and machine learning, achieving cross-validation accuracies of 99.30% for botanical origins and 98.01% for geographical origins with a Random Forests classifier.
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.