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

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 (R 2 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.

Hyperspectral Imaging to Characterize Table Grapes

“…The obtained values of R 2 of prediction were 0.92 for both PLS regression and Neural Networks with RMSEP of 0.94 • Brix and 0.96 • Brix, respectively. Hence, a good capacity of correlation was achieved in numerous other works on prediction of TSS for table and wine grapes [24,38,57,58].…”

Non-Invasive Monitoring of Berry Ripening Using On-the-Go Hyperspectral Imaging in the Vineyard

“…In these works, two different spectral ranges (400-1000 nm and 900-1700 nm) were used to determine the grape quality parameters such as Phenolic compounds, TSS, pH, Titratable Acidity and Antioxidant activity. The best prediction results gathered with the PLS regression achieved R 2 of 0.86 and standard error of prediction (SEP) values of 2.62 and 3.05 mg/g for Nonacylated and Total Anthocyanins [2,39]; R 2 of 0.88, 0.89 and 0.84 and RMSEP values of 0.95 • Brix, 35.6 mg/L and 0.13 g/L CE for TSS, Anthocyanins and Tannins, respectively [3,40,42]. Additionally, in these works, values of R 2 of 0.81, 0.78 and 0.62 and RMSEP of 0.25, 0.04 g/100g tartaric acid and 48.98 mg/100g Trolox for pH, Titratable Acidity and Antioxidant Activity, respectively were reported [40,42].…”

“…The best prediction results gathered with the PLS regression achieved R 2 of 0.86 and standard error of prediction (SEP) values of 2.62 and 3.05 mg/g for Nonacylated and Total Anthocyanins [2,39]; R 2 of 0.88, 0.89 and 0.84 and RMSEP values of 0.95 • Brix, 35.6 mg/L and 0.13 g/L CE for TSS, Anthocyanins and Tannins, respectively [3,40,42]. Additionally, in these works, values of R 2 of 0.81, 0.78 and 0.62 and RMSEP of 0.25, 0.04 g/100g tartaric acid and 48.98 mg/100g Trolox for pH, Titratable Acidity and Antioxidant Activity, respectively were reported [40,42]. These outcomes are very close to the results presented in this study.…”

“…Nowadays, most of the studies developed with hyperspectral images to quantify grape composition parameters are performed under controlled conditions (indoor environments) using individual grape berries samples or samples composed of reduced number of grape berries randomly collected from many grape clusters [2,3,[39][40][41][42]. In these works, two different spectral ranges (400-1000 nm and 900-1700 nm) were used to determine the grape quality parameters such as Phenolic compounds, TSS, pH, Titratable Acidity and Antioxidant activity.…”

Spectral and Hyperspectral Technologies as an Additional Tool to Increase Information on Quality and Origin of Horticultural Crops

“…Authors obtained 100% correct classification of samples belonging from five harvest times [8]. Moreover, in a recent study, [42] showed that analyzing spectra changes over time during on-vine holding of table grapes was possible to monitor ripening and to correctly classify grapes by harvest time, using only 14 wavelengths. These findings encourage further implementation of this method to monitor ripening of table grapes in the vineyard and better define the most suitable harvest time.…”