Application of multivariate analysis and Kohonen Neural Network to discriminate bioactive components and chemical composition of kosovan honey

Abstract

The diversity of botanical origins may influence the composition of honey and thus its recognition as a functional and healthy food. This study examined the standard physicochemical properties, bioactive components and antioxidant activity of Kosovan honeys according to their floral source (monofloral, blossom, acacia, and mountain blossom honey). Then the Kohonen Neural Network (KNN), which transforms complex multivariate data into two-dimensional space, and Principal Component Analysis (PCA) were used to identify and group botanical origin of honey samples based on their component features. Physicochemical characteristics, total phenolic content, and antioxidant activity varied significantly between the individual distinct varieties of honeys. Statistical analysis showed the usefulness of KNN and PCA for dimensionality reduction and detecting the structure and general regularities in the values of variables describing the tested honeys of the same botanical origin. KNNs have proven to be a particularly effective data mining tool, enabling the detection of subtle differences and clearer separation of clusters occurring in honey samples. The developed KNN model revealed proximity between the AC and MBL clusters, as well as between the MF and BL clusters, indicating similarity of their features. The arrangement of honey groups on the matrix map also suggested that the properties of AC and MBL honeys were significantly different from those of MF and BL honeys. The research showed that both methods used could be used as additional statistical tools supporting the recognition of the type of honey according to its chemical composition, mineral content, bioactive components and the antioxidant activity of honey as a functional food.