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Neural network models to classify olive oils within the protected denomination of origin framework
Author(s) -
Torrecilla José S.,
Cancilla John C.,
Matute Gemma,
DíazRodríguez Pablo
Publication year - 2013
Publication title -
international journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.831
H-Index - 96
eISSN - 1365-2621
pISSN - 0950-5423
DOI - 10.1111/ijfs.12245
Subject(s) - olive oil , artificial neural network , multilayer perceptron , pattern recognition (psychology) , perceptron , artificial intelligence , mathematics , computer science , data mining , chemistry , food science
Summary A computerised approach to vastly reduce the experimental information required (number of independent variables) to classify similar extra virgin olive oils ( EVOO s) is presented. It is based on the application of a multilayer perceptron ( MLP ) and further analysis of the obtained results using differential calculations. To validate this new model, it has been applied for the classification of 147 EVOO samples into four similar families. The oil samples employed came from two types of protected denomination of origin ( PDO ) oils and two non‐ PDO from the same S panish province ( G ranada). This approach results in a new method that reduces the necessary size of the databases used, without an appreciable loss of information, by over 82%. The percentage of misclassifications using less data points is similar to the results achieved using the whole database (less than 0.90%).