Skip to main content
Log in

Classification of Polish wines by application of ultra-fast gas chromatography

  • Original Paper
  • Published:
European Food Research and Technology Aims and scope Submit manuscript

Abstract

The potential of ultra-fast gas chromatography (GC) combined with chemometric analysis for classification of wine originating from Poland according to the variety of grape used for production was investigated. A total of 44 Polish wine samples differing in the type of grape (and grape growth region) used for the production as well as parameters of the fermentation process, alcohol content, sweetness, and others which characterize wine samples were analysed. The selected features coming from ultra-fast GC analysis were subsequently used as inputs for both principal component analysis (PCA) and supervised machine learning. Using the proposed classification algorithm, it was possible to classify white and red wines according to the variety of grape used for production with a 98.7 and 98.2% accuracy, respectively. The model was characterized by good recall and area under receiver operating characteristic which was 1.000 for white wines and 0.992 for red wines. Cuveé wines (made from various types of grapes) were also successfully classified which leads to the conclusion that the proposed classification method can be used not only to differentiate between wines made from different grapes but also to detect possible adulterations, provided known; non-adulterated samples are available as a reference. The model was also used to classify wine samples based on other features, such as the geographic region in which the vineyard is situated, type of yeast used, the temperature of fermentation, sweetness, etc. In all cases, a high classification accuracy (in most cases > 90%) was achieved. The obtained results could be applied in the wine industry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Alcalde-Eon C, Escribano-Bailon MT, Santos-Buelga C, Rivas-Gonzalo JC (2006) Changes in the detailed pigment composition of red wine during maturity and ageing. A comprehensive study. Anal Chim Acta 563(1–2):238–254

    Article  CAS  Google Scholar 

  2. Amann A, Costello BDL, Miekisch W, Schubert J, Buszewski B, Pleil J, Ratcliffe N, Risby T (2014) The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva. J Breath Res 8:34001

    Article  CAS  Google Scholar 

  3. Boser BE,. Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory - COLT’92, pp. 144–152

  4. De la Calle García D, Reichenbächer M, Danzer K, Hurlbeck C, Bartzsch C, Feller KH (1998) Analysis of wine bouquet components using headspace solid-phase microextraction-capillary gas chromatography. J High Resolut Chromatogr 21:373–377

    Article  Google Scholar 

  5. Demšar J, Curk T, Erjavec A, Hočevar T, Milutinovič M, Možina M, Polajnar M, Toplak M, Starič A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013) Orange: data mining toolbox in Python. J Mach Learn Res 14:2349–2353

    Google Scholar 

  6. Everstine K, Spink J, Kennedy S (2013) Economically motivated adulteration (EMA) of food: common characteristics of EMA incidents. J Food Prot 76:723–735

    Article  PubMed  Google Scholar 

  7. Fang F, Li JM, Pan QH, Huang WD (2007) Determination of red wine flavonoids by HPLC and effect of aging. Food Chem 101(1):428–433

    Article  CAS  Google Scholar 

  8. Gliszczyńska-Świgło A, Chmielewski J (2017) Electronic nose as a tool for monitoring the authenticity of food. A review. Food Anal Methods 10:1800–1816

    Article  Google Scholar 

  9. Hernandez T, Estrella I, Carlavilla D, Martin-Alvarez PJ, Moreno-Arribas MV (2006) Phenolic compounds in red wine subjected to industrial malolactic fermentation and ageing on lees. Anal Chim Acta 563(1–2):116–125

    Article  CAS  Google Scholar 

  10. Hernanz D, Gallo V, Recamales AF, Melendez-Martínez AJ, GonzalezMiret ML, Heredia FJ (2009) Effect of storage on the phenolic content, volatile composition and colour of white wines from the varieties Zalema and Colombard. Food Chem 113(2):530–537

    Article  CAS  Google Scholar 

  11. Hrbek V, Vaclavik L, Elich O, Hajslova J (2014) Authentication of milk and milk-based foods by direct analysis in real time ionization-high resolution mass spectrometry (DART-HRMS) technique: a critical assessment. Food Control 36:138–145

    Article  CAS  Google Scholar 

  12. Hristov H, Nedyalkova M, Madurga S, Simeonov V (2017) Boron oxide glasses and nanocomposites: synthetic, structural and statistical approach. J Mater Sci Technol 33:535–540

    Article  Google Scholar 

  13. Majchrzak T, Wojnowski W, Dymerski T, Gębicki J, Namieśnik J (2017) Electronic noses in classification and quality control of edible oils: a review. Food Chem 246:192–201

    Article  CAS  PubMed  Google Scholar 

  14. Nedyalkova M, Donkova B, Simeonov V (2017) Chemometrics expertise in the links between ecotoxicity and physicochemical features of silver nanoparticles: environmental aspects. J AOAC Int 100(2):359–364

    Article  CAS  PubMed  Google Scholar 

  15. Pereira AC, Reis MS, Saraiva PM, Marques JC (2011) Madeira wine ageing prediction based on different analytical techniques: UV–vis, GC-MS, HPLC-DAD. Chemometr Intell Lab 105(1):43–55

    Article  CAS  Google Scholar 

  16. Pillonel L, Ampuero S, Tabacchi R, Bosset JO (2003) Analytical methods for the determination of the geographic origin of Emmental cheese: volatile compounds by GC/MS-FID and electronic nose. Eur Food Res Technol 216:179–183

    Article  CAS  Google Scholar 

  17. Shen F, Ying Y, Li B, Zheng Y, Zhuge Q (2011) Multivariate classification of rice wines according to ageing time and brand based on amino acid profiles. Food Chem 129(2):565–569

    Article  CAS  Google Scholar 

  18. Sotirchos DG, Danezis GP, Georgiou CA (2017) Introduction, definitions and legislation. In: Georgiou CA, Danezis GP (eds) Food authentication: management, analysis and regulation. Wiley-Blackwell, Greece, pp 3–18

    Google Scholar 

  19. Szczepanska N, Kudlak B, Nedyalkova M, Simeonov V, Namiesnik J (2017) Application of chemometric techniques in studying of toxicity of selected commercially available products for infants and children. Environ Monit Assess 189:309

    Article  PubMed  Google Scholar 

  20. Wieczerzak M, Kudlak B, Yotova G, Nedyalkova M, Tsakovski S, Simeonov V, Namiesnik J (2016) Modeling of pharmaceuticals mixtures toxicity with deviation ratio and best-fit functions models. Sci Total Environ 571:259–268

    Article  CAS  PubMed  Google Scholar 

  21. Wiśniewska P, Śliwińska M, Namieśnik J, Wardencki W, Dymerski T (2016) The verification of the usefulness of electronic nose based on ultra-fast gas chromatography and four different chemometric methods for rapid analysis of spirit beverages. J Anal Methods Chem 2016:1–12

    Article  CAS  Google Scholar 

  22. Wiśniewska P, Śliwińska M, Dymerski T, Wardencki W, Namieśnik J (2016) Differentiation between spirits according to their botanical origin. Food Anal Methods 9:1029–1035

    Article  Google Scholar 

  23. Yu H, Dai X, Yao G, g Xiao Z (2014) Application of gas chromatography-based electronic nose for classification of Chinese rice wine by wine age. Food Anal Methods 7:1489–1497

    Article  Google Scholar 

Download references

Funding

This study was funded by the Faculty of Chemistry, Gdańsk University of Technology for financial support within the minigrant program (Decision No. 4914/E-359/M/2017).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Justyna Płotka-Wasylka.

Ethics declarations

Conflict of interest

Justyna Płotka-Wasylka has received research mini-grant from by the Faculty of Chemistry, Gdańsk University of Technology. All authors declare that they have no conflict of interest.

Compliance with ethics requirements

This article does not contain any studies with human participants or animals performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Majchrzak, T., Wojnowski, W. & Płotka-Wasylka, J. Classification of Polish wines by application of ultra-fast gas chromatography. Eur Food Res Technol 244, 1463–1471 (2018). https://doi.org/10.1007/s00217-018-3060-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00217-018-3060-1

Keywords

Navigation