An electronic nose based on an array of 6 metal oxide semiconductor sensors was used, jointly with linear discriminant analysis (LDA) and artificial neural network (ANN) method, to classify oils containing the five typical virgin olive oil (VOO) sensory defects (fusty, mouldy, muddy, rancid and winey). For this purpose, these defects, available as single standards of the International Olive Council, were added to refined sunflower oil. According to the LDA models and the ANN method, the defected samples were correctly classified. On the other hand, the electronic nose data was used to predict the defect percentage added to sunflower oil using multiple linear regression models. All the models were able to predict the defect percentage with average prediction errors below 0.90%. Then, the develop is a useful tool to work in parallel to panellists, for realizing a rapid screening of large set of samples with the aim of discriminating defective oils.

Use of electronic nose to determine defect percentage in oils. Comparison with sensory panel results / M.J. Lerma-García; L. Cerretani; C. Cevoli; E.F. Simó-Alfonso; A. Bendini; T. Gallina Toschi. - In: SENSORS AND ACTUATORS. B, CHEMICAL. - ISSN 0925-4005. - STAMPA. - 147:(2010), pp. 283-289. [10.1016/j.snb.2010.03.058]

Use of electronic nose to determine defect percentage in oils. Comparison with sensory panel results

CERRETANI, LORENZO;CEVOLI, CHIARA;BENDINI, ALESSANDRA;GALLINA TOSCHI, TULLIA
2010

Abstract

An electronic nose based on an array of 6 metal oxide semiconductor sensors was used, jointly with linear discriminant analysis (LDA) and artificial neural network (ANN) method, to classify oils containing the five typical virgin olive oil (VOO) sensory defects (fusty, mouldy, muddy, rancid and winey). For this purpose, these defects, available as single standards of the International Olive Council, were added to refined sunflower oil. According to the LDA models and the ANN method, the defected samples were correctly classified. On the other hand, the electronic nose data was used to predict the defect percentage added to sunflower oil using multiple linear regression models. All the models were able to predict the defect percentage with average prediction errors below 0.90%. Then, the develop is a useful tool to work in parallel to panellists, for realizing a rapid screening of large set of samples with the aim of discriminating defective oils.
2010
Use of electronic nose to determine defect percentage in oils. Comparison with sensory panel results / M.J. Lerma-García; L. Cerretani; C. Cevoli; E.F. Simó-Alfonso; A. Bendini; T. Gallina Toschi. - In: SENSORS AND ACTUATORS. B, CHEMICAL. - ISSN 0925-4005. - STAMPA. - 147:(2010), pp. 283-289. [10.1016/j.snb.2010.03.058]
M.J. Lerma-García; L. Cerretani; C. Cevoli; E.F. Simó-Alfonso; A. Bendini; T. Gallina Toschi
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/89143
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 44
  • ???jsp.display-item.citation.isi??? 43
social impact