New phenolic compounds from Olea europaea, identified by high performance liquid chromatography/electrospray ionization tandem mass spectrometry, are suitable markers for differentiation between different varieties of olive trees cultivated in the same geographical area (Rende, Italy). Five cultivars (Carolea, Cassanese, Coratina, Nocellara and Leccino) were considered for the discrimination. Samples of Carolea, cultivated in three different geographical zones (Rende, Mirto and Spoleto. Italy), were as well checked to evaluate possible differences. Three supervised pattern recognition procedures, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA) and K-nearest neighbours (KNN) were used to classify samples in five groups corresponding to the five cultivars and in three groups corresponding to the three areas of production. The results show that KNN provides a model unable to predict a proper assignment of the cultivar, at least for those olive trees considered in this work, whereas LDA and SIMCA allow the achievement of good percentage of prediction for the cultivars as well as cultivation zones.

New phenolic compounds from Olea europaea, identified by high performance liquid chromatography/electrospray ionization tandem mass spectrometry, are suitable markers for differentiation between different varieties of olive trees cultivated in the same geographical area (Rende, Italy). Five cultivars (Carolea, Cassanese, Coratina, Nocellara and Leccino) were considered for the discrimination. Samples of Carolea, cultivated in three different geographical zones (Rende, Mirto and Spoleto. Italy), were as well checked to evaluate possible differences. Three supervised pattern recognition procedures, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA) and K-nearest neighbours (KNN) were used to classify samples in five groups corresponding to the five cultivars and in three groups corresponding to the three areas of production. The results show that KNN provides a model unable to predict a proper assignment of the cultivar, at least for those olive trees considered in this work, whereas LDA and SIMCA allow the achievement of good percentage of prediction for the cultivars as well as cultivation zones. (C) 2009 Elsevier Ltd. All rights reserved.

Secondary metabolites of Olea europaea leaves as markers for the discrimination of cultivars and cultivation zones by multivariate analysis

DI DONNA, Leonardo;Mazzotti F;Naccarato A;TAGARELLI, Antonio;
2010

Abstract

New phenolic compounds from Olea europaea, identified by high performance liquid chromatography/electrospray ionization tandem mass spectrometry, are suitable markers for differentiation between different varieties of olive trees cultivated in the same geographical area (Rende, Italy). Five cultivars (Carolea, Cassanese, Coratina, Nocellara and Leccino) were considered for the discrimination. Samples of Carolea, cultivated in three different geographical zones (Rende, Mirto and Spoleto. Italy), were as well checked to evaluate possible differences. Three supervised pattern recognition procedures, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA) and K-nearest neighbours (KNN) were used to classify samples in five groups corresponding to the five cultivars and in three groups corresponding to the three areas of production. The results show that KNN provides a model unable to predict a proper assignment of the cultivar, at least for those olive trees considered in this work, whereas LDA and SIMCA allow the achievement of good percentage of prediction for the cultivars as well as cultivation zones. (C) 2009 Elsevier Ltd. All rights reserved.
New phenolic compounds from Olea europaea, identified by high performance liquid chromatography/electrospray ionization tandem mass spectrometry, are suitable markers for differentiation between different varieties of olive trees cultivated in the same geographical area (Rende, Italy). Five cultivars (Carolea, Cassanese, Coratina, Nocellara and Leccino) were considered for the discrimination. Samples of Carolea, cultivated in three different geographical zones (Rende, Mirto and Spoleto. Italy), were as well checked to evaluate possible differences. Three supervised pattern recognition procedures, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA) and K-nearest neighbours (KNN) were used to classify samples in five groups corresponding to the five cultivars and in three groups corresponding to the three areas of production. The results show that KNN provides a model unable to predict a proper assignment of the cultivar, at least for those olive trees considered in this work, whereas LDA and SIMCA allow the achievement of good percentage of prediction for the cultivars as well as cultivation zones.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/132274
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