Clustering Honey Samples with Unsupervised Machine Learning Methods using FTIR Data

Abstract

Abstract Honey is a food item that people consume because of its taste and positive effects on health. The importance of honey is increasing day by day because of the difficulties in production, the threat of the bee population due to environmental conditions and climate changes, and the increasing population. In this work, data obtained from Fourier transform infrared (FTIR) spectra of honey samples were used for clustering of honey data. First of all, the number of clusters was determined by applying elbow method to the spectrum data obtained from the samples. After this process, the data was divided into 5 clusters. The data were reduced to 2 dimensions with principal components analysis (PCA), clusters of samples were determined by applying Hierarchical clustering (HCA). 20% of the data whose clusters were determined were randomly selected to be used as test data. The rest of the data was used as training data in Deep Learning. After the training, the test data was checked and the accuracy was found to be 96.15%. The proposed method gives reliable results in clustering of honey samples with the advantages of being fast, cheap and not requiring preprocess procedure.