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Classification of Chardonnay Grapes According to Geographical Indication and Quality Grade Using Attenuated Total Reflectance Mid-infrared Spectroscopy

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Abstract

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.

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References

  • Alessandrini M, Gaiotti F, Belfiore N, Matarese F, D'Onofrio C, Tomasi D (2017) Influence of vineyard altitude on glera grape ripening (Vitis vinifera L.): effects on aroma evolution and wine sensory profile. J Sci Food Agric 97:2695–2705

    Article  CAS  PubMed  Google Scholar 

  • Anderson K (2016) Evolving varietal and quality distinctiveness of Australia’s wine regions. J Wine Res 27:173–192

    Article  Google Scholar 

  • Arana I, Jarén C, Arazuri S (2005) Maturity, variety and origin determination in white grapes (Vitis vinifera L.) using near infrared reflectance technology. J Near Infrared Spectrosc 13:349–357

    Article  CAS  Google Scholar 

  • Barker M, Rayens W (2003) Partial least squares for discrimination. J Chemom 17:166–173

    Article  CAS  Google Scholar 

  • Bevilacqua M, Bucci R, Magrì AD, Magrì AL, Nescatelli R, Marini F (2013) Classification and class-modelling. In: Marini F (ed) Chemometrics in food chemistry. Elsevier, Oxford, pp 171–187

    Chapter  Google Scholar 

  • Bevin CJ, Dambergs RG, Fergusson AJ, Cozzolino D (2008) Varietal discrimination of Australian wines by means of mid-infrared spectroscopy and multivariate analysis. Anal Chim Acta 621:19–23

    Article  CAS  PubMed  Google Scholar 

  • Cadot Y, Caillé S, Samson A, Barbeau G, Cheynier V (2012) Sensory representation of typicality of cabernet franc wines related to phenolic composition: impact of ripening stage and maceration time. Anal Chim Acta 732:91–99

    Article  CAS  PubMed  Google Scholar 

  • Cozzolino D, Cynkar W, Shah N, Smith P (2011) Technical solutions for analysis of grape juice, must and wine: the role of infrared spectroscopy and chemometrics. Anal Bioanal Chem 401:1475–1484

    Article  CAS  PubMed  Google Scholar 

  • Cozzolino D, Cynkar W, Shah N, Smith P (2012) Varietal differentiation of grape juice based on the analysis of near- and mid-infrared spectral data. Food Anal Methods 5:381–387

    Article  Google Scholar 

  • Cozzolino D, Holdstock M, Dambergs RG, Cynkar WU, Smith PA (2009) Mid infrared spectroscopy and multivariate analysis: a tool to discriminate between organic and non-organic wines grown in Australia. Food Chem 116:761–765

    Article  CAS  Google Scholar 

  • Cozzolino D, Smyth HE, Cynkar W, Dambergs RG, Gishen M (2005) Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin. Talanta 68:382–387

    Article  CAS  PubMed  Google Scholar 

  • Dal Santo S, Tornielli GB, Zenoni S, Fasoli M, Farina L, Anesi A, Guzzo F, Delledone M, Pezzotti M (2013) The plasticity of the grapevine berry transcriptome. Genome Biol 14:R54

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dennis EG, Keyzers RA, Kalua CM, Maffei SM, Nicholson EL, Boss PK (2012) Grape contribution to wine aroma: production of hexyl acetate, octyl acetate, and benzyl acetate during yeast fermentation is dependent upon precursors in the must. J Agric Food Chem 60:2638–2646

    Article  CAS  PubMed  Google Scholar 

  • Edelmann A, Diewok J, Schuster KC, Lendl B (2001) Rapid method for the discrimination of red wine cultivars based on mid-infrared spectroscopy of phenolic wine extracts. J Agric Food Chem 49:1139–1145

    Article  CAS  PubMed  Google Scholar 

  • 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 87–115

    Google Scholar 

  • Gambetta JM, Bastian SEP, Cozzolino D, Jeffery DW (2014) Factors influencing the aroma composition of chardonnay wines. J Agric Food Chem 62:6512–6534

    Article  CAS  PubMed  Google Scholar 

  • Gambetta J, Bastian S, Jeffery D (2016) Snapshot of Australian production practices for chardonnay wines. Wine Vitic J 31:27–32

    Google Scholar 

  • Gambetta JM, Cozzolino D, Bastian SEP, Jeffery DW (2016) Towards the creation of a wine quality prediction index: correlation of chardonnay juice and wine compositions from different regions and quality levels. Food Anal Methods 9:2842–2855

    Article  Google 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, Adelaide

    Google Scholar 

  • Jolliffe IT (2002) Principal component analysis. 2nd edn. Springer, New York

    Google Scholar 

  • Liu L, Cozzolino D, Cynkar WU, Gishen M, Colby CB (2006) Geographic classification of Spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. J Agric Food Chem 54:6754–6759

    Article  CAS  PubMed  Google Scholar 

  • Lockshin L, Jarvis W, d’Hauteville F, Perrouty J-P (2006) Using simulations from discrete choice experiments to measure consumer sensitivity to brand, region, price, and awards in wine choice. Food Qual Prefer 17:166–178

    Article  Google Scholar 

  • Longbottom M, Simos C, Krstic M, Johnson D (2013) Grape quality assessments: a survey of current practice. Wine Vitic J 28:33–37

    Google Scholar 

  • Milosevic M (2004) Internal reflection and ATR spectroscopy. Appl Spectrosc Rev 39:365–384

    Article  CAS  Google Scholar 

  • Naes T, Isaksson T, Fearn T, Davies T (2002) A user friendly guide to multivariate calibration and classification. NIR Publications, Chichester

    Google Scholar 

  • Niimi J, Boss PK, Bastian SEP (2018) Sensory profiling and quality assessment of research cabernet sauvignon and chardonnay wines; quality discrimination depends on greater differences in multiple modalities. Food Res Int 106:304–316

    Article  CAS  PubMed  Google Scholar 

  • Pereira GE, Gaudillere J-P, van Leeuwen C, Hilbert G, Maucourt M, Deborde C, Moing A, Rolin D (2006) 1H NMR metabolite fingerprints of grape berry: comparison of vintage and soil effects in Bordeaux grapevine growing areas. Anal Chim Acta 563:346–352

    Article  CAS  Google Scholar 

  • Riovanto R, Cynkar WU, Berzaghi P, Cozzolino D (2011) Discrimination between shiraz wines from different Australian regions: the role of spectroscopy and chemometrics. J Agric Food Chem 59:10356–10360

    Article  CAS  PubMed  Google Scholar 

  • Ristic R, Cozzolino D, Jeffery DW, Gambetta JM, Bastian SEP (2016) Prediction of phenolic composition of shiraz wines using attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy. Am J Enol Vitic 67:460–465

    Article  CAS  Google Scholar 

  • Robinson AL, Boss PK, Solomon PS, Trengove RD, Heymann H, Ebeler SE (2014) Origins of grape and wine aroma. Part 1. Chemical components and viticultural impacts. Am J Enol Vitic 65:1–24

    Article  Google Scholar 

  • Savitsky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639

    Article  Google Scholar 

  • Schindler R, Vonach R, Lendl B, Kellner R (1998) A rapid automated method for wine analysis based upon sequential injection (SI)-FTIR spectrometry. Fresenius J Anal Chem 362:130–136

    Article  CAS  Google Scholar 

  • Shah N, Cynkar W, Smith P, Cozzolino D (2010) Use of attenuated total reflectance midinfrared for rapid and real-time analysis of compositional parameters in commercial white grape juice. J Agric Food Chem 58:3279–3283

    Article  CAS  PubMed  Google 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 620:30–32

  • Xanthopoulos P, Pardalos PM, Trafalis TB (2013) Linear discriminant analysis. In: Robust data mining. Springer, New York, p 27–33

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Acknowledgments

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.

Funding

J.M.G. was financially supported by the Turner Family Scholarship from The University of Adelaide and supplementary scholarship from Wine Australia (GWR Ph1210).

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Author notes

  1. Joanna M. Gambetta

    Present address: National Wine and Grape Industry Centre, School of Agricultural and Wine Science, Charles Sturt University, Wagga Wagga, NSW, Australia

Authors and Affiliations

  1. School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1, Glen Osmond, SA, 5064, Australia

    Joanna M. Gambetta, Susan E. P. Bastian & David W. Jeffery

  2. School of Medical and Applied Sciences, Central Queensland Innovation and Research Precinct, Central Queensland University, Bruce Highway, North Rockhampton, QLD, 4701, Australia

    Daniel Cozzolino

Authors
  1. Joanna M. Gambetta
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  4. David W. Jeffery
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Corresponding author

Correspondence to David W. Jeffery.

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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.

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This article does not contain any studies with human or animal subjects.

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Gambetta, J.M., Cozzolino, D., Bastian, S.E.P. et al. Classification of Chardonnay Grapes According to Geographical Indication and Quality Grade Using Attenuated Total Reflectance Mid-infrared Spectroscopy. Food Anal. Methods 12, 239–245 (2019). https://doi.org/10.1007/s12161-018-1355-2

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  • DOI: https://doi.org/10.1007/s12161-018-1355-2

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