Classification of Chardonnay Grapes According to Geographical Indication and Quality Grade Using Attenuated Total Reflectance Mid-infrared Spectroscopy
- 99 Downloads
Rapid analytical methods based on infrared spectroscopy in combination with chemometrics have found wide application in the food and beverage industry. These methods have the potential to qualitatively analyse and classify or authenticate samples including grapes and wines, or be used as a tool for objective decision-making while grapes are still ripening, ultimately offering better control over the winemaking process. Thus, an initial investigation examined the use of attenuated total reflectance (ATR) mid-infrared (MIR) spectroscopy to discriminate Chardonnay grape samples from different geographical origins and industry-allocated quality grades with minimal sample preparation. Classification of samples according to region of origin using partial least squares discriminant analysis (PLS-DA) of the fingerprint region of the MIR spectra (1500–800 cm−1) had an overall success rate of 83 and 81% for the 2014 and 2016 vintages, respectively. It was also possible to classify sample quality successfully using this same approach. Correct classification of Chardonnay grapes according to quality grade was of the order of 83% in 2014 and 79% in 2016. The ability to predict juice titratable acidity and total soluble solids was also shown. We have demonstrated the potential use of ATR-MIR as a rapid tool to classify samples according to geographical origins and quality grades, which has implications for authenticity determination and for optimising the streaming of fruit to the most appropriate winemaking processes.
KeywordsVitis vinifera Infrared spectroscopy Geographical origin Quality Chemometrics
We are grateful to collaborating members of the Australian wine industry for allowing access to their vineyards, provision of information and ongoing support. We thank Emily Nicholson, Paul Boss, Sue Maffei and Claudia Schueuermann of CSIRO, and Jiaming Wang and Merve Darici of The University of Adelaide, for their help during vintage periods.
J.M.G. was financially supported by the Turner Family Scholarship from The University of Adelaide and supplementary scholarship from Wine Australia (GWR Ph1210).
Compliance with Ethical Standards
Conflict of Interest
Joanna Gambetta received a supplementary scholarship from funding agency Wine Australia. Daniel Cozzolino, Susan Bastian and David Jeffery declare that they have no conflict of interest.
This article does not contain any studies with human or animal subjects.
Informed consent is not applicable in this study.
- Fernandes A, Gomes V, Melo-Pinto P (2018) A review of the application to emergent subfields in viticulture of local reflectance and interactance spectroscopy cobined with soft computing and multivariate analysis. In: Cruz Corona C (ed) Soft computing for sustainability science, vol 358. Studies in fuzziness and soft computing. Springer, Cham, pp 87–115Google Scholar
- Gambetta J, Bastian S, Jeffery D (2016) Snapshot of Australian production practices for chardonnay wines. Wine Vitic J 31:27–32Google Scholar
- Iland PG, Bruer N, Edwards G, Weeks S, Wilkes E (2004) Chemical analysis of grapes and wine: techniques and concepts. Patrick Iland Wine Promotions, AdelaideGoogle Scholar
- Jolliffe IT (2002) Principal component analysis. 2nd edn. Springer, New YorkGoogle Scholar
- Longbottom M, Simos C, Krstic M, Johnson D (2013) Grape quality assessments: a survey of current practice. Wine Vitic J 28:33–37Google Scholar
- Naes T, Isaksson T, Fearn T, Davies T (2002) A user friendly guide to multivariate calibration and classification. NIR Publications, ChichesterGoogle Scholar
- Smith P (2015) Assessment of relationships between grape chemical composition and grape allocation grade for cabernet sauvignon, shiraz and chardonnay. Aust NZ Grapegrower Winemaker 620:30–32Google Scholar
- Xanthopoulos P, Pardalos PM, Trafalis TB (2013) Linear discriminant analysis. In: Robust data mining. Springer, New York, p 27–33Google Scholar