Electronic nose based discrimination of a perfumery compound in a fragrance

An electronic nose (e-nose) device developed at the University of Buenos Aires was applied to detect the presence of a given perfumery compound (also so-called the perfumery note, refereed as mangone) in a fragrance, at very low weight percentages. The results were compared with sensorial analysis p...

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Autores principales: Branca, A., Simonian, P., Ferrante, M., Novas, E., Negri, R.M.
Formato: JOUR
Materias:
Electronic noses
Neural networks
Perfumery notes
Principal component analysis
Sensorial panels
Data reduction
Database systems
Fragrances
Gas chromatography
Radial basis function networks
Sensory perception
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_09254005_v92_n1-2_p222_Branca
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_09254005_v92_n1-2_p222_Branca
record_format dspace
spelling todo:paper_09254005_v92_n1-2_p222_Branca2023-10-03T15:46:17Z Electronic nose based discrimination of a perfumery compound in a fragrance Branca, A. Simonian, P. Ferrante, M. Novas, E. Negri, R.M. Electronic noses Neural networks Perfumery notes Principal component analysis Sensorial panels Data reduction Database systems Fragrances Gas chromatography Neural networks Principal component analysis Radial basis function networks Electronic noses Sensory perception An electronic nose (e-nose) device developed at the University of Buenos Aires was applied to detect the presence of a given perfumery compound (also so-called the perfumery note, refereed as mangone) in a fragrance, at very low weight percentages. The results were compared with sensorial analysis performed by trained panelists and gas chromatography mass spectroscopy (GCMS) measurements. The triangle test for detection of the perfumery note in the fragrance was performed by a set of 20 trained panelists. Less than 40% of the panelist could identify the presence of the strange note for concentration 10-2% (w/w), and similar percentages were obtained for lower concentrations. Detection by CGMS was difficult at those concentrations, because of the low percentages of the perfumery note and the similar retention times obtained for the note and other compounds included in the fragrance. The developed electronic nose provided fingerprints for different odors, associated to different samples that were used to build up an odor database. Then, two different multivariate data analysis were performed, the non-supervised principal component analysis (PCA) and an artificial neural network (ANN), in order to discriminate the samples with or without mangone. Measurements of several dilutions of mangone up to 10-4% (w/w) were performed to obtain the database. Both methods, PCA and ANN, were successful in the discrimination process of samples with from those without mangone. In particular a 100% success was obtained by using a radial basis function (RBF) artificial neural network, even when considering the more diluted samples. © 2003 Elsevier Science B.V. All rights reserved. Fil:Negri, R.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_09254005_v92_n1-2_p222_Branca
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Electronic noses
Neural networks
Perfumery notes
Principal component analysis
Sensorial panels
Data reduction
Database systems
Fragrances
Gas chromatography
Neural networks
Principal component analysis
Radial basis function networks
Electronic noses
Sensory perception
spellingShingle Electronic noses
Neural networks
Perfumery notes
Principal component analysis
Sensorial panels
Data reduction
Database systems
Fragrances
Gas chromatography
Neural networks
Principal component analysis
Radial basis function networks
Electronic noses
Sensory perception
Branca, A.
Simonian, P.
Ferrante, M.
Novas, E.
Negri, R.M.
Electronic nose based discrimination of a perfumery compound in a fragrance
topic_facet Electronic noses
Neural networks
Perfumery notes
Principal component analysis
Sensorial panels
Data reduction
Database systems
Fragrances
Gas chromatography
Neural networks
Principal component analysis
Radial basis function networks
Electronic noses
Sensory perception
description An electronic nose (e-nose) device developed at the University of Buenos Aires was applied to detect the presence of a given perfumery compound (also so-called the perfumery note, refereed as mangone) in a fragrance, at very low weight percentages. The results were compared with sensorial analysis performed by trained panelists and gas chromatography mass spectroscopy (GCMS) measurements. The triangle test for detection of the perfumery note in the fragrance was performed by a set of 20 trained panelists. Less than 40% of the panelist could identify the presence of the strange note for concentration 10-2% (w/w), and similar percentages were obtained for lower concentrations. Detection by CGMS was difficult at those concentrations, because of the low percentages of the perfumery note and the similar retention times obtained for the note and other compounds included in the fragrance. The developed electronic nose provided fingerprints for different odors, associated to different samples that were used to build up an odor database. Then, two different multivariate data analysis were performed, the non-supervised principal component analysis (PCA) and an artificial neural network (ANN), in order to discriminate the samples with or without mangone. Measurements of several dilutions of mangone up to 10-4% (w/w) were performed to obtain the database. Both methods, PCA and ANN, were successful in the discrimination process of samples with from those without mangone. In particular a 100% success was obtained by using a radial basis function (RBF) artificial neural network, even when considering the more diluted samples. © 2003 Elsevier Science B.V. All rights reserved.
format JOUR
author Branca, A.
Simonian, P.
Ferrante, M.
Novas, E.
Negri, R.M.
author_facet Branca, A.
Simonian, P.
Ferrante, M.
Novas, E.
Negri, R.M.
author_sort Branca, A.
title Electronic nose based discrimination of a perfumery compound in a fragrance
title_short Electronic nose based discrimination of a perfumery compound in a fragrance
title_full Electronic nose based discrimination of a perfumery compound in a fragrance
title_fullStr Electronic nose based discrimination of a perfumery compound in a fragrance
title_full_unstemmed Electronic nose based discrimination of a perfumery compound in a fragrance
title_sort electronic nose based discrimination of a perfumery compound in a fragrance
url http://hdl.handle.net/20.500.12110/paper_09254005_v92_n1-2_p222_Branca
work_keys_str_mv AT brancaa electronicnosebaseddiscriminationofaperfumerycompoundinafragrance
AT simonianp electronicnosebaseddiscriminationofaperfumerycompoundinafragrance
AT ferrantem electronicnosebaseddiscriminationofaperfumerycompoundinafragrance
AT novase electronicnosebaseddiscriminationofaperfumerycompoundinafragrance
AT negrirm electronicnosebaseddiscriminationofaperfumerycompoundinafragrance
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