Title of article :
A chemometric approach based on a novel similarity/diversity measure for the characterisation and selection of electronic nose sensors Original Research Article
Author/Authors :
Davide Ballabio، نويسنده , , Maria Stella Cosio، نويسنده , , Saverio Mannino، نويسنده , , Roberto Todeschini، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2006
Abstract :
Electronic nose sensor signals provide a digital fingerprint of the product in analysis, which can be subsequently investigated by means of chemometrics. In this paper, the fingerprint characterisation of electronic nose data has been studied by means of a novel chemometric approach based on the partial ordering technique and the Hasse matrix. This matrix can be associated to each data sequence and the similarity between two sequences can be evaluated with the definition of a distance between the corresponding Hasse matrices. Since all the signals achieved along time are intrinsically ordered, the data provided by electronic nose can be also considered as sequential data and consequently characterized by means of the proposed approach. The similarity/diversity measure has been here applied in order to characterize the class discrimination capability of each electronic nose sensor: extra virgin olive oil samples of different geographical origin have been considered and Hasse distances have been used to select the sensors which appear more able to discriminate the olive oil origins. The distance based on the Hasse matrix has showed some useful properties and proved to be able to link each electronic nose time profile to a meaningful mathematical term (the Hasse matrix), which can be consequently studied by multivariate analysis.
Keywords :
Partial ordering , Class discrimination , Electronic nose , Time profile , Similarity/diversity , Hasse
Journal title :
Analytica Chimica Acta
Journal title :
Analytica Chimica Acta