Using 1H and 13C NMR techniques and artificial neural networks to detect the adulteration of olive oil with hazelnut oil

Autor: Marco D’Imperio, Anna Laura Segre, Ramón Aparicio, Luisa Mannina, Diego L. García-González
Rok vydání: 2004
Předmět:
Zdroj: 219 (2004): 545–548.
info:cnr-pdr/source/autori:D. L. Garcia-González, L. Mannina, M. D’Imperio, A. L. Segre, R. Aparicio/titolo:Using 1H and 13C NMR and Artificial Neural Networks to Detect the Adulteration of Olive Oil with Hazelnut Oil/doi:/rivista:/anno:2004/pagina_da:545/pagina_a:548/intervallo_pagine:545–548/volume:219
ISSN: 1438-2385
1438-2377
Popis: The lack of any official analytical method to detect the adulteration of olive oil with a low percentage of hazelnut oil is explained by the similarities in the chemical compositions of both kinds of oils. To counter this problem, an artificial neural network based on 1H-NMR and 13C-NMR data has been developed to detect olive oil adulteration, and the results from this ANN are presented here. A training set consisting of hazelnut oils, pure olive oils, and olive oils blended with 2–20% hazelnut oils was used to design and train a multilayer perceptron with 100% correct classifications. This mathematical model was also validated using an external validation set of blend samples (3–15%) and genuine samples. The detection limit of the model was around 8%.
Databáze: OpenAIRE
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