Beni Mellal, Morocco
Beni Mellal, Morocco

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Hirri A.,University Moulay Soulymane | Bassbasi M.,University Moulay Soulymane | Platikanov S.,Jordi Girona | Tauler R.,Jordi Girona | Oussama A.,University Moulay Soulymane
Food Analytical Methods | Year: 2015

Fourier transform infrared (FTIR) coupled to chemometrics was shown to be a useful method to classify and predict the quality of four commercial grade virgin olive oils (VOO). FTIR and physicochemical data were collected using a set of 70 samples representing extra virgin (EV), virgin (V), ordinary virgin (OV), and lampante (L) commercial grade olive oils collected in Beni Mellal region (central Morocco). Two partial least squares discriminant analysis (PLS-DA) models using physicochemical data and FTIR data were established and compared. The PLS-DA model using only physicochemical data was not accurate enough to distinguish satisfactorily among OV, V, and EV olive oil grades. On the contrary, the PLS-DA model on FTIR data was better in the calibration, able to describe 98 % of the spectral information and predicting 93 % of the VOO grades. In the external validation, this PLS-DA model accurately classified VOO commercial grades with prediction accuracy of 100 %. The proposed procedure is fast, nondestructive, simple, and easy to operate, and it is recommended for the quick monitoring of olive oil’s quality. © 2015 Springer Science+Business Media New York


Terouzi W.,University Moulay Soulymane | De Luca M.,University of Calabria | Bolli A.,University Moulay Soulymane | Oussama A.,University Moulay Soulymane | And 3 more authors.
Vibrational Spectroscopy | Year: 2011

Four cultivars of olives picked up in the Moroccan region of Beni Mellal were subjected to a characterization and 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 data were pre-treated by derivative elaboration based on the Savitzky-Golay algorithm to reduce noise and increase analytical information. Partial least squares discriminant analysis (PLS-DA) was performed to elaborate the measurement data and assess the discriminant features of the four cultivars. The PLS model was optimized by applying the Martens' uncertainty test which provided to select the vibrational frequencies giving the most useful information. The optimized model resulted able to separate the four classes 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. © 2011 Elsevier B.V. All Rights Reserved.


De Luca M.,University of Calabria | Terouzi W.,University Moulay Soulymane | Kzaiber F.,University Moulay Soulymane | Ioele G.,University of Calabria | And 2 more authors.
International Journal of Food Science and Technology | Year: 2012

The potential of FTIR combined with chemometrics was studied to classify five Moroccan varieties of olives by analysis on the endocarps. Attenuated total reflectance (ATR) enabled the samples to be examined directly in the solid state. The spectral data were subjected to a preliminary derivative elaboration based on the Norris gap algorithm to reduce the noise and extract larger analytical information. Linear discriminant analysis (LDA) was adopted as classification method, and Principle component analysis (PCA) was employed to compress the original data set into a reduced new set of variables before LDA. The calibration set was built by using the IR data from seventy-five samples scanned in reflectance mode, and the ranges 3000-2400 and 2300-600cm -1 were selected because furnishing the most useful analytical information. PCA allowed clustering the samples in five classes by using the first two principal components with an explained variance of 98.16%. Application of LDA on an external test set of twenty-five samples enabled to classify them into five variety groups with a correct classification of 92.0%. © 2012 The Authors. International Journal of Food Science and Technology © 2012 Institute of Food Science and Technology.


Hirri A.,University Moulay Soulymane | Bassbasi M.,University Moulay Soulymane | Platikanov S.,CSIC - Institute of Environmental Assessment And Water Research | Tauler R.,CSIC - Institute of Environmental Assessment And Water Research | Oussama A.,University Moulay Soulymane
Food Analytical Methods | Year: 2016

Fourier transform infrared (FTIR) coupled to chemometrics was shown to be a useful method to classify and predict the quality of four commercial grade virgin olive oils (VOO). FTIR and physicochemical data were collected using a set of 70 samples representing extra virgin (EV), virgin (V), ordinary virgin (OV), and lampante (L) commercial grade olive oils collected in Beni Mellal region (central Morocco). Two partial least squares discriminant analysis (PLS-DA) models using physicochemical data and FTIR data were established and compared. The PLS-DA model using only physicochemical data was not accurate enough to distinguish satisfactorily among OV, V, and EV olive oil grades. On the contrary, the PLS-DA model on FTIR data was better in the calibration, able to describe 98 % of the spectral information and predicting 93 % of the VOO grades. In the external validation, this PLS-DA model accurately classified VOO commercial grades with prediction accuracy of 100 %. The proposed procedure is fast, nondestructive, simple, and easy to operate, and it is recommended for the quick monitoring of olive oil’s quality. © 2015, Springer Science+Business Media New York.

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