期刊名称：Polish Journal of Food and Nutrition Sciences
印刷版ISSN：1230-0322
电子版ISSN：2083-6007
出版年度：2019
卷号：69
期号：2
页码：191-201
DOI：10.31883/pjfns/108526
出版社：Walter de Gruyter GmbH
摘要：The aim of this study was to develop models based on Linear Discriminant Analysis (LDA), Classi cation and Regression Trees (C&RT), and Arti cial Neural Network (ANN) for the prediction of the botanical origin of honeys using their physicochemical parameters as well as their antioxidative and thermal properties. Also Principal Component Analysis (PCA) and Cluster Analysis (CA) were performed as initial steps of data mining. The datasets consisted of 72 honey samples (false acacia, rape, buckwheat, honeydew, linden, nectar-honeydew and multi oral) obtained from different regions of Poland and collected between April 2014 and November 2016. Ash content, pH, free acidity, colorimetric coordinates in the CIELAB space (L*, a*, b*, h*, C*), total phenolics content, antioxidant activity, and glass transition temperatures (Tg ) of the honey samples were determined. The rst four principal components accounted for about 85% of the total variance. PC1 was highly correlated with colour intensity, the hue angle (h*), and total phenolics content, whereas PC2 was dominated by chroma (C*) value and glass transition temperatures (Tg ). The CA dendrogram displays two clusters: one with light coloured honey samples and second with dark coloured honey samples. On the basis of the LDA analysis, the colour parameters possessed the highest discrimination power according to the botanical origin of honey samples. The models based on ANN and C&RT algorithms were characterized by 100% accuracy. Study results demonstrate that the chemometric approach enables high-accuracy classi cation of honeys according to their botanical origin.
