Tsankova D.Lekova S.2024-07-162024-07-162024-07-162024-07-162015-01-011314-79781314-7471SCOPUS_ID:84944325902https://rlib.uctm.edu/handle/123456789/1119The aim of the article is to present results on the potential of honey discrimination (on the base of its botanical origins) by Vis-NIR spectroscopy and subsequent statistical cluster analysis. Thirty-five samples from three types of honey (acacia, linden, and honeydew) are measured by a spectrophotometer ``Cary100`` with recorded wavelength range of 350-900 nm for calibration of honey classifier. Firstly, principal components analysis (PCA) is used for reducing the number of inputs (wavelengths) and proper visualization of the experimental results. Next, the first two principal components (PCs) are combined separately with Naïve Bayes classification (NBC) and k-means clustering (KMC) to develop PC-NBC and PCKMC models. An additional reduction of the number of used wavelengths by selecting the values at equal, predetermined intervals by a power of 2 is proposed. The PC-NBC and PC-KMC models developed for these reduced wavelengths produce almost the same performance as compared with those developed for the original number of wavelengths and thus facilitate developing a simpler and cheaper sensor for honey discrimination in practice. The high accuracy of the proposed honey classifiers is confirmed by leave-one-out cross-validation test conducted in MATLAB environment.enArticle