Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment

Abstract

The application of artificial intelligence for the development of recognition models for food and beverages differentiation has benefited from increasing attention in recent years. For this scope, different machine learning (ML) algorithms were used in order to find the most suitable model for a certain purpose. In the present work, three ML algorithms, namely artificial neural networks (ANN), support vector machines (SVM) and k-nearest neighbors (KNN), were applied for constructing honey geographical classification models, and their performance was assessed and compared. A preprocessing step consisting of either a component reduction method or a supervised feature selection technique was applied prior to model development. The most efficient geographical differentiation models were obtained based on ANN, when a subset of features corresponding to the markers having the highest discrimination potential was used as input data. Therefore, when the samples aimed to be classified at an intercountry level, an accuracy of 95% was achieved; namely, 99% of the Romanian samples and 73% of the ones originating from other countries were correctly predicted. Promising results were also obtained for the intracountry honey discrimination; namely, the model built for classifying the Transylvanian samples from the ones produced in other Romanian regions had an 85% accuracy.

Keywords

• honey geographical assessment
• isotope fingerprint
• elemental profile
• artificial intelligence