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Spectra evolution over on-vine holding of Italia table grapes: prediction of maturity and discrimination for harvest times using a Vis-NIR hyperspectral device

Francesca Piazzolla, Maria Luisa Amodio, Giancarlo Colelli
  • Francesca Piazzolla
    Department of Science of Food, Agriculture and Environment, University of Foggia, Italy
  • Maria Luisa Amodio
    Department of Science of Food, Agriculture and Environment, University of Foggia, Italy | marialuisa.amodio@unifg.it
  • Giancarlo Colelli
    Department of Science of Food, Agriculture and Environment, University of Foggia, Italy

Abstract

Measurement of certain grape quality parameters (sugars, acidity, and pH-value) is essential for the determination of the optimum harvest time. Non-destructive analytical techniques, including near infrared (NIR) spectroscopy, can be valid alternatives to traditional analytical methods for the determination of maturity indexes, enabling the possibility of on-field applications. This work aims to study the reliability to monitor spectra changes related with ripening of table grapes and to select optimal wavelengths for the discrimination of bunches from different harvests, in addition to the prediction of total soluble solids, pH, titratable acidity, phenols and antioxidant activity of table grapes. Grapes were harvested four times from the same plants at day 0 (I HT), and after 11 (II HT), 27 (III HT) and 48 (IV HT) days. Spectra were acquired from the images obtained using a spectral scanner Vis-NIR (ver 1.4.; DV Srl, Padova, Italy), with a detector in the region between 400-1000 nm principal component analysis was used to remove outliers followed by spectra pre-treatment. The best prediction model was achieved for soluble solids with the regression coefficient values of 0.91 for calibration and 0.88 for validation followed by titratable acidity (0.71 and 0.78) and antioxidant activity (0.68 and 0.62). In addition an excellent correlation was observed between spectra and days before harvest (R2 of 0.98 for calibration and prediction models) indicating that is possible to relate spectra changes with ripening, leading also to the effective discrimination of the fruits from the different harvest times. The results showed that this technique may be a valid support to select the optimal harvest time also based on the prediction of the maturity related constituents.

Keywords

Hyperspectral; partial least squares regression; linear discriminant analysis; classification.

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Submitted: 2016-11-25 17:39:15
Published: 2017-06-01 13:33:13
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Copyright (c) 2017 Francesca Piazzolla, Maria Luisa Amodio, Giancarlo Colelli

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