title: Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin.
abstract: A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.
authors.affiliation: The Australian Wine Research Institute, Chemistry Department, Waite Road, Urrbrae, P.O. Box 197, Glen Osmond - Urrbrae Adelaide, Adelaide, SA 5064, Australia; The Cooperative Research Centre for Viticulture, P.O. Box 154, Glen Osmond, SA 5064, Australia.
authors.forename: Daniel
authors.initials: D
authors.lastname: Cozzolino
authors.forename: Heather E
authors.initials: HE
authors.lastname: Smyth
authors.forename: Wies
authors.initials: W
authors.lastname: Cynkar
authors.forename: Robert G
authors.initials: RG
authors.lastname: Dambergs
authors.forename: Mark
authors.initials: M
authors.lastname: Gishen