University of Tehran Journal of Sciences, Islamic Republic of Iran 1016-1104 24 1 2013 03 01 Linear and Nonlinear Multivariate Classification of Iranian Bottled Mineral Waters According to Their Elemental Content Determined by ICP-OES 15 22 31920 EN J.B. Ghasemi Department of Chemistry, Faculty of Sciences, K.N. Toosi University of Technology, Tehran, Islamic Republic of Iran E. Zolfonoun Department of Chemistry, Faculty of Sciences, K.N. Toosi University of Technology, Tehran, Islamic Republic of Iran R. Khosrokhavar Food and Drug Laboratory Research Center, MOH & ME, Tehran, Islamic Republic of Iran Journal Article 2012 02 01 The combinations of inductively coupled plasma-optical emission spectrometry (ICP-OES) and three classification algorithms, i.e., partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM) and soft independent modeling of class analogies (SIMCA), for discriminating different brands of Iranian bottled mineral waters, were explored. ICP-OES was used for the determination of Li, Na, K, Ca, Mg, Sr, Ba, B, Si and Zn in bottled mineral waters (150 samples) from 30 brands. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed differences in water samples according to the mineral composition. 120 samples (4 for each brand) were selected randomly for the calibration set, and 30 samples (1 for each brand) for the prediction set. PLS-DA, LS-SVM and SIMCA were implemented for calibration models. The results suggest that ICP-OES combined with PLS-DA, LS-SVM and SIMCA models had the capability to discriminate the different brands of mineral waters with high accuracy. The model can resolve the tap water samples from classified mineral waters accordingly. https://jsciences.ut.ac.ir/article_31920_02a7e3b93f2f0a6e3c5cab457e06c859.pdf