Literature DB >> 25523419

Rapid monitoring of grape withering using visible near-infrared spectroscopy.

Roberto Beghi1, Valentina Giovenzana1, Simone Marai1, Riccardo Guidetti1.   

Abstract

BACKGROUND: Wineries need new practical and quick instruments, non-destructive and able to quantitatively evaluate during withering the parameters that impact product quality. The aim of the work was to test an optical portable system (visible near-infrared (NIR) spectrophotometer) in a wavelength range of 400-1000 nm for the prediction of quality parameters of grape berries during withering.
RESULTS: A total of 300 red grape samples (Vitis vinifera L., Corvina cultivar) harvested in vintage year 2012 from the Valpolicella area (Verona, Italy) were analyzed. Qualitative (principal component analysis, PCA) and quantitative (partial least squares regression algorithm, PLS) evaluations were performed on grape spectra. PCA showed a clear sample grouping for the different withering stages. PLS models gave encouraging predictive capabilities for soluble solids content (R(2) val  = 0.62 and ratio performance deviation, RPD = 1.87) and firmness (R(2) val  = 0.56 and RPD = 1.79).
CONCLUSION: The work demonstrated the applicability of visible NIR spectroscopy as a rapid technique for the analysis of grape quality directly in barns, during withering. The sector could be provided with simple and inexpensive optical systems that could be used to monitor the withering degree of grape for better management of the wine production process.
© 2014 Society of Chemical Industry.

Entities:  

Keywords:  chemometrics; firmness; grape withering; postharvest; soluble solids content; visible NIR spectroscopy

Mesh:

Substances:

Year:  2015        PMID: 25523419     DOI: 10.1002/jsfa.7053

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  4 in total

1.  Destructive and optical non-destructive grape ripening assessment: Agronomic comparison and cost-benefit analysis.

Authors:  Sara Savi; Stefano Poni; Alessandro Moncalvo; Tommaso Frioni; Irene Rodschinka; Linda Arata; Matteo Gatti
Journal:  PLoS One       Date:  2019-05-29       Impact factor: 3.240

2.  Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction.

Authors:  Teodora Basile; Antonio Domenico Marsico; Rocco Perniola
Journal:  Foods       Date:  2022-01-20

Review 3.  Postharvest Water Loss of Wine Grape: When, What and Why.

Authors:  Chiara Sanmartin; Margherita Modesti; Francesca Venturi; Stefano Brizzolara; Fabio Mencarelli; Andrea Bellincontro
Journal:  Metabolites       Date:  2021-05-14

4.  Near Infrared Spectroscopy as a Green Technology for the Quality Prediction of Intact Olives.

Authors:  Silvia Grassi; Olusola Samuel Jolayemi; Valentina Giovenzana; Alessio Tugnolo; Giacomo Squeo; Paola Conte; Alessandra De Bruno; Federica Flamminii; Ernestina Casiraghi; Cristina Alamprese
Journal:  Foods       Date:  2021-05-11
  4 in total

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