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Classification of olives from Moroccan regions by using direct FT-IR analysis: Application of support vector machines (SVM)

Journal Name:

  • International Journal of Innovation and Applied Studies

Publication Year:

  • 2013

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
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.
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  • English

REFERENCES

References: 

[1] Baeten, V., Fernandez Pierna, J.A., Dardenne, P., Meurens, M., García González,D.L. and Aparicio Ruiz, R. (2005).
Detection of the presence of hazelnut oil in olive oil by FT-raman and FT-MIR spectroscopy. J. Agric. Food Chem. 53 (16),
6201-6206.
[2] Uceda, M., Hermoso, M., Garcia-Ortiz, A., Jimenez, A., et al. Intraspecific variation of oil content and the characteristics
of oils in olive cultivars. Acta Hortic. ISHS, 1999, 474.
[3] Bertran, E., Blanco, M., Iturriaga, H., Maspoch, S., et al. Near infrared spectometry and pattern recognition as screening
methods for the authentification of virgin olive oils of very close geographical origins. J. Near Infrared Spec., 2000, 8,
45–52.
[4] Galtier, O., Dupuy, N., Le Dre´au, Y., Ollivier, D., et al. Geographic origins and compositions of virgin olive oils
determinated by chemometric analysis of NIR spectra. Anal. Chim. Acta, 2007, 595, 136–144.
[5] Yang, H., Irudayaraj, J., Paradkar, M.M. Food Chem. 2005, 93, 25–32.
[6] Tapp, H. S., Defernez, M., Kemsley, E. K. FTIR Spectroscopy and multivariate analysis can distinguish the geographical
origin of extra virgin olive oils. J. Agric. Food Chem., 2003, 51, 6110–6115.
[7] Liu, H. X. and Sun, S.Q. Modern Instrum., 2005, 11, 6.
[8] Maquelin, K., Kirschner, C., Choo-Smith, L.P. and Microbiol, J. Methods, 2002, 51, 255.
[9] Reid, L.M., O’Donnell, C.P. and Downey, G. Trends Food Sci. Technol., 2006, 17, 344.
[10] Lerma-García, M.J., Ramis-Ramos, G., Herrero-Martínez, J.M. and Simó-Alfonso, E.F. Food Chem. 2010, 118, 78-83.
[11] Ozen, B.F. and Mauer, L.J. J. Agric. Food Chem., 2002, 50, 3898-3901.
[12] Sinelli, N., Cerretani, L., Di Egidio, V., Bendini, A. and Casiraghi, E. Food Res. Int. , 2010, 43, 369-375.
[13] De Luca, M., Terouzi, W., Ioele, G., Kzaiber, F., Oussama, A., Oliverio, F., Tauler, R., Ragno, G. Food Chem., 2011.
doi:10.1016/j.foodchem.2010.07.010.
[14] Gurdeniz, G., Tokatli, F. and Ozen, B. Eur. J. Lipid Sci. Technol., 2007, 109, 1194-1202.
[15] Zagonel, G.F., Peralta-Zamora, P. and Ramos, L.P. Talanta, 2004, 63, 1021-1025.
[16] Pinheiro, P. B. M. and da Silva, J. Chemometric classification of olives from three Portuguese cultivars of Olea europaea
L. Anal. Chim. Acta, , 2005, 544, 229–235.
[17] Terouzi, W., DeLuca, M., Bolli, A., Oussama, A., Patumi, M., Ioele , G., Ragno, G., (2011). A discriminant method for
classification of Moroccan olive varieties by using direct FT-IR analysis of the mesocarp section, Vib. Spectrosc.,
doi:10.1016/j.vibspec.2011.01.004
[18] DeLuca, M., Terouzi, W., Kzaiber, F., Ioele , G., Oussama, A. and Ragno, G. Classification of moroccan olive cultivars by
linear discriminant analysis applied to ATR–FTIR spectra of endocarps, International Journal of Food Science and
Technology, 2012.
doi:10.1111/j.1365-2621.2012.02972.x
[19] Dupuy, N., Galtier, O., Le Dréau, Y., Pinatel, C., Kister, J. and Artaud, J. Chemometric analysis of combined NIR and MIR
spectra to characterize French olives. Eur. J. Lipid Sci. Technol. 2010, 112, 463–475.
[20] Reynolds, A. P., Richards, G., de la Iglesia, B., and Rayward-Smith, V. J. (2006). Clustering rules: A comparison of
partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms, 5, 475–504.
[21] Patrik D'haeseleer. How does gene expression clustering work?. Journal of Nature biotechnology, volume 23, number
12, December 2005, pp 1499-1501.
[22] Esbensen, K. H. Multivariate data analysis – In practice. An introduction to multivariate data analysis and experimental
design. Oslo: CAMO Process, 2002.
[23] Michiel de Hoon, Seiya Imoto, Satoru Miyano. The C Clustering Library for cDNA microarray data. The University of
Tokyo, institute of midecal science, human genome center, 5 July 2008, p 3.
[24] Wold, S., Esbensen, K. and Geladi, P. Chem. Intell. Lab. Syst., 1987, 2, 37.
Wafa Terouzi, Stefan Platikanov, Anna de Juan Capdevila, and Abdelkhalek Oussama
ISSN : 2028-9324 Vol. 3 No. 2, June 2013 503
[25] Brereton, R.G. Multivariate Pattern Recognition in Chemometrics, Illustrated by Case Studies, Elsevier Science,
Netherlands, 1992.
[26] Vapnik, V.The Nature of Statistical Learning Theory. Second edition. New York: Springer, 1999.
[27] Drucker, H., Burges, CJ., Kaufman, L., Smola, A. and Vapnik, V. Support Vector Regression Machines. Adv Neural Inf
Process Syst, 1997, 9:155-161.
[28] Belousov AI, Verzakov SA, von Frese J. Support Vector Machines: A Versatile and Powerful Approach to Data Analysis,
Poster at the Gordon Conf. On Statistics and Chemical Engineering, Williamstown, MA, 2001.
[Online] Available: http://www. Amstat.org./sections/spes/GRC2001.htm (30 July 2004)
[29] Belousov AI, Verzakov SA, von Frese J. Applicational aspects of support vector machines. J. Chemometrics 2002; 16:
482–489.
[30] Ding, C.Q. and Dubchak, I. Multi-class protein fold recognition using support vector machines and neural network.
Bioinformatics, vol 17, n° 4 (2001), 349-358.
[31] Broun, M.P.S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T.S., Manuel Ares, Jr. and Haussler, D.
Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS, vol. 97, n° 1,
262-267, Jannuary 4, 2000.
[32] Iñón, F. A., Garrigues, J. M., Garrigues, S., Molina, A., and de la Guardia, M. Selection of calibration set samples in
determination of olive oil acidity by partial least squares-attenuated total reflectance-Fourier transform infrared
spectroscopy. Analytica Chimica Acta, 2003, 489, 59–75.
[33] Maggio, R. M., Kaufman, T. S., Del Carlo, M., Cerretanil, L., Bendini, A., Cichelli, A., et al. Monitoring of fatty acid
composition in virgin olive oil by Fourier transformed infrared spectroscopy coupled with partial least squares. Food
Chemistry, 2009, 114, 1549–1554.
[34] Pandey, K. K.A. Study of Chemical Structure of Soft and Hardwood and Wood Polymers by FTIR Spectroscopy. J. Appl.
Polym. Sci., 1999, 71, 1969–1975.
[35] Sathle, L. and Wold, S. Partial least squares analysis with crossvalidation for the two-class problem: a Monte Carlo study.
Journal of Chemometrics, 1987, 1, 185–196.

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