Four cultivars of olives picked up in the Moroccan region of Beni Mellal were subjected to a characterizationand classification study. Analytical data were collected by Fourier transform infrared spectroscopy(FTIR), applied on the mesocarp of the fresh olives without any preliminary treatment. The spectral datawere pre-treated by derivative elaboration based on the Savitzky–Golay algorithm to reduce noise andincrease analytical information. Partial least squares discriminant analysis (PLS-DA) was performed toelaborate the measurement data and assess the discriminant features of the four cultivars. The PLS modelwas optimized by applying the Martens’ uncertainty test which provided to select the vibrational frequenciesgiving the most useful information. The optimized model resulted able to separate the fourclasses and classify new objects into the appropriate defined classes with a percentage prediction of 97%.The proposed method represents a real novelty to classify olives of different varieties by means of a rapid,inexpensive and reliable procedure.

A discriminant method for classification of Moroccan olive varieties by using direct FT-IR analysis of the mesocarp section

De Luca M;IOELE, Giuseppina;RAGNO, Gaetano
2011

Abstract

Four cultivars of olives picked up in the Moroccan region of Beni Mellal were subjected to a characterizationand classification study. Analytical data were collected by Fourier transform infrared spectroscopy(FTIR), applied on the mesocarp of the fresh olives without any preliminary treatment. The spectral datawere pre-treated by derivative elaboration based on the Savitzky–Golay algorithm to reduce noise andincrease analytical information. Partial least squares discriminant analysis (PLS-DA) was performed toelaborate the measurement data and assess the discriminant features of the four cultivars. The PLS modelwas optimized by applying the Martens’ uncertainty test which provided to select the vibrational frequenciesgiving the most useful information. The optimized model resulted able to separate the fourclasses and classify new objects into the appropriate defined classes with a percentage prediction of 97%.The proposed method represents a real novelty to classify olives of different varieties by means of a rapid,inexpensive and reliable procedure.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/140246
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