European Food Research and Technology

, Volume 245, Issue 12, pp 2697–2703 | Cite as

Cross-flow filtration of lees grape juice for non-aromatic white wine production: a case study on an Italian PDO

  • Lorenzo GuerriniEmail author
  • Luca Calamai
  • Alessio Cappelli
  • Giulia Angeloni
  • Piernicola Masella
  • Alessandro Parenti
Original Paper


During white winemaking, after clarification, a considerable amount of juice remains trapped in the lees. Recently, cross-flow filtration has become popular to recover a large amount of juice from lees. However, high juice recovery is observed to be consistent with a decrease in wine quality, while low recovery reduces the amount of wine that can be produced. Therefore, this study aims to find the optimal compromise between quantity and quality. We tested at industrial scale and in three replicates, qualitative changes in an elite Italian white wine as a function of different amounts of juice recovered. The length of the filtration cycle affected the juice’s chemical parameters. In some cases, uncontrolled alcoholic fermentation started before the yeast inoculum, leading to a small increase in ethanol, and higher acetic acid and ethyl acetate concentrations that can be find in the resulting wines. The phenomenon was particularly apparent in longer fermentation cycles. Furthermore, longer filtration cycles increased juice acidity and potassium, while the resulting wines changed in terms of pH, glycerol, and volatile profile. Several grape-related compounds were found in wines at different concentrations as a function of the amount of juice recovered. In particular, Z3-hexen-1-ol, p-cymene, β-ionone, and benzyl alcohol were found to increase, reaching a maximum at 65% of recovered juice before decreasing. Furthermore, several fermentation-related compounds were found to change according to the length of the filtration cycle. Our work identifies an optimal recovery point of 65%. This percentage appears the best trade-off between the flavor of wines, fermentation risk management, and the amount of juice recovered.


Grape compounds Juice recovery Process optimization Tangential filtration Wine quality 



The authors would like to thank the Teruzzi and Puthod company for hosting the trials and Dr. Tiberio Profita which carried out part of the work during his thesis. Furthermore, the authors would like to thank the TMCI Padovan company for their technical support during the trials.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest to disclose.

Compliance with ethics requirements

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


  1. 1.
    Ribéreau-Gayon P, Denis D, Bernard D, Aline L (2006) Handbook of enology. In: The microbiology of wine and vinifications, vol 1, 2nd ed. Wiler, England, p 497Google Scholar
  2. 2.
    Guerrini L, Masella P, Angeloni G et al (2018) Harvest of Sangiovese grapes: the influence of material other than grape and unripe berries on wine quality. Eur Food Res Technol. CrossRefGoogle Scholar
  3. 3.
    Parenti A, Spugnoli P, Masella P et al (2015) Comparison of grape harvesting and sorting methods on factors affecting the must quality. J Agric Eng 46:19–22. CrossRefGoogle Scholar
  4. 4.
    El Rayess Y, Albasi C, Bacchin P et al (2011) Cross-flow microfiltration applied to oenology: a review. J Memb Sci 382:1–19. CrossRefGoogle Scholar
  5. 5.
    Cassano A, Mecchia A, Drioli E (2008) Analyses of hydrodynamic resistances and operating parameters in the ultrafiltration of grape must. J Food Eng 89:171–177. CrossRefGoogle Scholar
  6. 6.
    Mauro C, Vignani R, Jacopo B et al (2014) Vineyards genetic monitoring and Vernaccia di San Gimignano wine molecular fingerprinting. Adv Biosci Biotechnol 05:142–154. CrossRefGoogle Scholar
  7. 7.
    Caramês ETS, Alamar PD, Poppi RJ, Pallone JAL (2017) Rapid assessment of total phenolic and anthocyanin contents in grape juice using infrared spectroscopy and multivariate calibration. Food Anal Methods 10:1609–1615. CrossRefGoogle Scholar
  8. 8.
    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. CrossRefGoogle Scholar
  9. 9.
    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 401:1479–1488. CrossRefGoogle Scholar
  10. 10.
    Wu D, He Y, Nie P et al (2010) Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Anal Chim Acta 659:229–237. CrossRefPubMedGoogle Scholar
  11. 11.
    Garde-Cerdán T, Diago MP, Fernández-Novales J et al (2019) Assessment of amino acids and total soluble solids in intact grape berries using contactless Vis and NIR spectroscopy during ripening. Talanta 199:244–253. CrossRefPubMedGoogle Scholar
  12. 12.
    Pereira Ramos R, dos Santos Costa D, Teruel Mederos BJ et al (2019) Development of predictive models for quality and maturation stage attributes of wine grapes using vis-nir reflectance spectroscopy. Postharvest Biol Technol 150:166–178. CrossRefGoogle Scholar
  13. 13.
    Hu L, Yin C, Ma S, Liu Z (2018) Rapid detection of three quality parameters and classification of wine based on Vis-NIR spectroscopy with wavelength selection by ACO and CARS algorithms. Spectrochim Acta Part A Mol Biomol Spectrosc 205:574–581. CrossRefGoogle Scholar
  14. 14.
    Ríos-Reina R, García-González DL, Callejón RM, Amigo JM (2018) NIR spectroscopy and chemometrics for the typification of Spanish wine vinegars with a protected designation of origin. Food Control 89:108–116. CrossRefGoogle Scholar
  15. 15.
    Shen F, Liu X, Tang P et al (2016) Evaluation of near-infrared and mid-infrared spectroscopy for the determination of routine parameters in Chinese rice wine. J Food Process Preserv 41:e12952. CrossRefGoogle Scholar
  16. 16.
    Wang L, Sun DW, Pu H, Cheng JH (2017) Quality analysis, classification, and authentication of liquid foods by near-infrared spectroscopy: a review of recent research developments. Crit Rev Food Sci Nutr 57:1524–1538. CrossRefPubMedGoogle Scholar
  17. 17.
    Guerrini L, Pantani O, Politi S et al (2019) Does bottle color protect red wine from photo-oxidation? Packag Technol Sci. CrossRefGoogle Scholar
  18. 18.
    Varela F, Calderón F, González MC et al (1999) Effect of clarification on the fatty acid composition of grape must and the fermentation kinetics of white wines. Eur Food Res Technol 209:439–444. CrossRefGoogle Scholar
  19. 19.
    Jaeger SR, McRae JF, Salzman Y et al (2010) A preliminary investigation into a genetic basis for cis-3-hexen-1-ol odour perception: a genome-wide association approach. Food Qual Prefer 21:121–131. CrossRefGoogle Scholar
  20. 20.
    Bertrand A, Skouroumounis GK, Baumes RL, Kotseridis Y (1999) Quantitative determination of [beta]-ionone in red wines and grapes of Bordeaux using a stable isotope dilution assay. J Chromatogr A 848:317–325. CrossRefPubMedGoogle Scholar
  21. 21.
    Cabrita MJ, Freitas Costa AM, Laureano O, Di Stefano R (2006) Glycosidic aroma compounds of some Portuguese grape cultivars. J Sci Food Agric 86:922–931. CrossRefGoogle Scholar
  22. 22.
    Konig H, Unden G, Frolich J (2009) Biology of microorganisms on grapes, in must and in wineGoogle Scholar
  23. 23.
    Giles CH, MacEwan TH, Nakhwa SN, Smith D (1960) Studies in adsorption. Part XI. A system of classification of solution adsorption isotherms, and its use in diagnosis of adsorption mechanisms and in measurement of specific surface areas of solids. J Chem Soc. CrossRefGoogle Scholar
  24. 24.
    Guerrini L, Angeloni G, Masella P et al (2018) A technological solution to modulate the aroma profile during beer fermentation. Food Bioprocess Technol. CrossRefGoogle Scholar
  25. 25.
    Guerrini L, Masella P, Spugnoli P et al (2016) A condensor to recover organic volatile compounds during vinification. Am J Enol Vitic 2:163–168. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lorenzo Guerrini
    • 1
    Email author
  • Luca Calamai
    • 1
  • Alessio Cappelli
    • 1
  • Giulia Angeloni
    • 1
  • Piernicola Masella
    • 1
  • Alessandro Parenti
    • 1
  1. 1.Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali (DAGRI)Università degli Studi di FirenzeFlorenceItaly

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