Volume 3, Issue 2, June 2013, Pages 493–503
Wafa Terouzi1, Stefan Platikanov2, Anna de Juan Capdevila3, and Abdelkhalek Oussama4
1 Laboratory of applied and environmental Spectrochemistry, Faculty of Science and Technology of Beni Mellal, University of Sultan Moulay slimane, 21000-Beni Mellal, Morocco
2 Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034 Barcelona, Catalonia, Spain
3 Department of Analytical Chemistry, Faculty of Chemistry, University of Barcelona, Mart? i Franquès 1-11, 08028 Barcelona, Catalonia, Spain
4 Laboratory of applied and environmental Spectrochemistry, Faculty of Science and Technology of Beni Mellal, University of Sultan Moulay slimane, 21000-Beni Mellal, Morocco
Original language: English
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The aim of this work was to characterize and classify three close regions of olives by direct analysis on the olive without any preliminary treatment. This study was focused on the olive samples picked in the three zones: named Bazaza, oled ayad and oled hamdan, in the Moroccan region of Beni Mellal. All samples were also analysed by FT-IR spectroscopy, the spectral data were subjected to a preliminary derivative transform based on the gap segment algorithm to reduce the noise and extract a largest number of analytical information from spectra. A multivariate statistical procedure based on cluster analysis (CA) coupled to support vector machines (SVM), was elaborated, providing an effective classification method. On the basis of a hierarchical agglomerative CA and principal component analysis (PCA), three distinctive clusters were recognized. The SVM procedure was then applied to classify samples from the same regions. The model resulted able to separate the three classes and classify new objects into the appropriate defined classes with a percentage prediction of 93%. The results showed that FTIR spectroscopy coupled with chemiometric methods are an interesting technique for classifying olive samples according to their geographical origins.
Author Keywords: Olives, FT-IR spectroscopy, Chemometrics, Geographical origin, Support Vector Machines.
Wafa Terouzi1, Stefan Platikanov2, Anna de Juan Capdevila3, and Abdelkhalek Oussama4
1 Laboratory of applied and environmental Spectrochemistry, Faculty of Science and Technology of Beni Mellal, University of Sultan Moulay slimane, 21000-Beni Mellal, Morocco
2 Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034 Barcelona, Catalonia, Spain
3 Department of Analytical Chemistry, Faculty of Chemistry, University of Barcelona, Mart? i Franquès 1-11, 08028 Barcelona, Catalonia, Spain
4 Laboratory of applied and environmental Spectrochemistry, Faculty of Science and Technology of Beni Mellal, University of Sultan Moulay slimane, 21000-Beni Mellal, Morocco
Original language: English
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The aim of this work was to characterize and classify three close regions of olives by direct analysis on the olive without any preliminary treatment. This study was focused on the olive samples picked in the three zones: named Bazaza, oled ayad and oled hamdan, in the Moroccan region of Beni Mellal. All samples were also analysed by FT-IR spectroscopy, the spectral data were subjected to a preliminary derivative transform based on the gap segment algorithm to reduce the noise and extract a largest number of analytical information from spectra. A multivariate statistical procedure based on cluster analysis (CA) coupled to support vector machines (SVM), was elaborated, providing an effective classification method. On the basis of a hierarchical agglomerative CA and principal component analysis (PCA), three distinctive clusters were recognized. The SVM procedure was then applied to classify samples from the same regions. The model resulted able to separate the three classes and classify new objects into the appropriate defined classes with a percentage prediction of 93%. The results showed that FTIR spectroscopy coupled with chemiometric methods are an interesting technique for classifying olive samples according to their geographical origins.
Author Keywords: Olives, FT-IR spectroscopy, Chemometrics, Geographical origin, Support Vector Machines.
How to Cite this Article
Wafa Terouzi, Stefan Platikanov, Anna de Juan Capdevila, and Abdelkhalek Oussama, “Classification of olives from Moroccan regions by using direct FT-IR analysis: Application of support vector machines (SVM),” International Journal of Innovation and Applied Studies, vol. 3, no. 2, pp. 493–503, June 2013.
