Title of article :
Chemometric classification of several olive cultivars from Trلs-os-Montes region (northeast of Portugal) using artificial neural networks
Author/Authors :
Peres، نويسنده , , Antَnio M. and Baptista، نويسنده , , Paula and Malheiro، نويسنده , , Ricardo and Dias، نويسنده , , Luيs G. and Bento، نويسنده , , Albino and Pereira، نويسنده , , José Alberto، نويسنده ,
Issue Information :
دوفصلنامه با شماره پیاپی سال 2011
Pages :
9
From page :
65
To page :
73
Abstract :
This work aimed to use artificial neural networks for fruit classification according to olive cultivar, as a tool to guarantee varietal authenticity. So, 70 samples, each one containing, in general, 40 olives, belonging to the six most representative olive cultivars of Trلs-os-Montes region (Cobrançosa, Cordovil, Madural, Negrinha de Freixo, Santulhana and Verdeal Transmontana) were collected in different groves and during four crop years. Five quantitative morphological parameters were evaluated for each fruit and endocarp, respectively. In total, ten biometrical parameters were used together with a multilayer perceptron artificial neural network allowing the implementation of a classification model. Its performance was compared with that obtained using linear discriminant analysis. The best results were obtained using artificial neural networks. In fact, the external validation procedure for linear discriminant analysis, using olive data from olive trees not included in the model development, showed an overall sensibility and specificity in the order of 70% and varying between 45 and 97% for the individual cultivars. On the other hand, the artificial neural network model was able to correctly classify the same unknown olives with a global sensibility and specificity around 75%, varying from 58 and 95% for each cultivar. The predictive results of the artificial neural network model selected was further confirmed since, in general, it correctly or incorrectly classified the unknown olive fruits in each one of the six cultivars studied with, respectively, higher and lower probabilities than those that could be expected by chance. The satisfactory results achieved, even when compared with previous published works, regarding olive cultivarʹs classification, show that the neural networks could be used by olive oil producers as a preventive and effective tool for avoiding adulterations of Protected Designation of Origin or monovarietal olive oils with olives of non-allowed cultivars.
Keywords :
linear discriminant analysis , OLEA EUROPAEA L. , authenticity , Artificial neural networks , Protected designation of origin , cultivars
Journal title :
Chemometrics and Intelligent Laboratory Systems
Serial Year :
2011
Journal title :
Chemometrics and Intelligent Laboratory Systems
Record number :
1489935
Link To Document :
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