Four varieties of red garlic (Allium sativum L.) cultivated in different Italian territories, Sulmona (Abruzzo), Proceno and Castelliri (Lazio), and Nubia (Sicily), were analysed by Attenuated Total Reflectance-Fourier Transformed Infrared (ATR-FTIR) spectroscopy. ATR-FTIR spectra of bulbils and bulbil tunics were separately acquired and processed by Partial Least Squares Discriminant Analysis (PLS-DA) with the aim of classifying the garlic samples on the basis of their geographical origin. Finally, two multi-block strategies (based on Sequential and Orthogonalized Partial Least Squares and Sequential and Orthogonalized Covariance Selection, coupled with Fisher’s Linear Discriminant Analysis) have been applied in order to test whether a joint analysis of data could lead to higher prediction rates. Eventually, the best results were achieved by the multi-block approach based on SO-PLS, which allows obtaining a total classification rate of 95 % (corresponding to one misclassified sample over 20) in external validation.

Geographical discrimination of red garlic (Allium sativum L.) using fast and non-invasive Attenuated Total Reflectance-Fourier Transformed Infrared (ATR-FTIR) spectroscopy combined with chemometrics

Biancolillo A.
;
D'Archivio A. A.
2020-01-01

Abstract

Four varieties of red garlic (Allium sativum L.) cultivated in different Italian territories, Sulmona (Abruzzo), Proceno and Castelliri (Lazio), and Nubia (Sicily), were analysed by Attenuated Total Reflectance-Fourier Transformed Infrared (ATR-FTIR) spectroscopy. ATR-FTIR spectra of bulbils and bulbil tunics were separately acquired and processed by Partial Least Squares Discriminant Analysis (PLS-DA) with the aim of classifying the garlic samples on the basis of their geographical origin. Finally, two multi-block strategies (based on Sequential and Orthogonalized Partial Least Squares and Sequential and Orthogonalized Covariance Selection, coupled with Fisher’s Linear Discriminant Analysis) have been applied in order to test whether a joint analysis of data could lead to higher prediction rates. Eventually, the best results were achieved by the multi-block approach based on SO-PLS, which allows obtaining a total classification rate of 95 % (corresponding to one misclassified sample over 20) in external validation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/139011
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