This paper presents an experiment making use of the near-infrared spectrum for distinguishing the wines produced in two close provinces of Abruzzo region of Italy. A collection of 32 wines was considered, 18 of which were produced in the province of Chieti, while the other 14 were from the province of Teramo. A conventional dual-beam spectrophotometer was used for absorption measurements in the 1300-1900 nm spectroscopic range. Principal Component Analysis was used for explorative analysis. Score maps in the PC1-PC2 or PC2-PC3 spaces were obtained, which successfully grouped the wine samples in two distinct clusters, corresponding to Chieti and Teramo provinces, respectively. A modelling of dual-band spectroscopy was also proposed, making use of two LEDs for illumination and a PIN detector instead of the spectrometer. These data were processed using Linear Discriminant Analysis which demonstrated satisfactory classification results. © 2014 Copyright SPIE.

Near-infrared spectroscopy and pattern-recognition processing for classifying wines of two Italian provinces

Mignani Anna Grazia;Ciaccheri Leonardo;Mencaglia Andrea Azelio;
2014

Abstract

This paper presents an experiment making use of the near-infrared spectrum for distinguishing the wines produced in two close provinces of Abruzzo region of Italy. A collection of 32 wines was considered, 18 of which were produced in the province of Chieti, while the other 14 were from the province of Teramo. A conventional dual-beam spectrophotometer was used for absorption measurements in the 1300-1900 nm spectroscopic range. Principal Component Analysis was used for explorative analysis. Score maps in the PC1-PC2 or PC2-PC3 spaces were obtained, which successfully grouped the wine samples in two distinct clusters, corresponding to Chieti and Teramo provinces, respectively. A modelling of dual-band spectroscopy was also proposed, making use of two LEDs for illumination and a PIN detector instead of the spectrometer. These data were processed using Linear Discriminant Analysis which demonstrated satisfactory classification results. © 2014 Copyright SPIE.
2014
Istituto di Fisica Applicata - IFAC
Inglese
Advanced Environmental, Chemical, and Biological Sensing Technologies XI
9106
9781628410433
http://www.scopus.com/record/display.url?eid=2-s2.0-84907372487&origin=inward
classification
geographic origin
multivariate data analysis
NIR
spectroscopy
wine
7
none
Mignani, ANNA GRAZIA; Ciaccheri, Leonardo; Gordillo, Belén; Mencaglia, ANDREA AZELIO; GonzálezMiret María, Lourdes; Heredia Francisco, José; Cichelli,...espandi
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/263998
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