Optimization of NIR Spectral Data Management for Quality Control of Grape Bunches during On-Vine Ripening

Abstract

NIR spectroscopy was used as a non-destructive technique for the assessment of chemical changes in the main internal quality properties of wine grapes (Vitis vinifera L.) during on-vine ripening and at harvest. A total of 363 samples from 25 white and red grape varieties were used to construct quality-prediction models based on reference data and on NIR spectral data obtained using a commercially-available diode-array spectrophotometer (380–1,700 nm). The feasibility of testing bunches of intact grapes was investigated and compared with the more traditional must-based method. Two regression approaches (MPLS and LOCAL algorithms) were tested for the quantification of changes in soluble solid content (SSC), reducing sugar content, pH-value, titratable acidity, tartaric acid, malic acid and potassium content. Cross-validation results indicated that NIRS technology provided excellent precision for sugar-related parameters (r2 = 0.94 for SSC and reducing sugar content) and good precision for acidity-related parameters (r2 ranging between 0.73 and 0.87) for the bunch-analysis mode assayed using MPLS regression. At validation level, comparison of LOCAL and MPLS algorithms showed that the non-linear strategy improved the predictive capacity of the models for all study parameters, with particularly good results for acidity-related parameters and potassium content.

Implementation of an on-line near infrared/visible (NIR/VIS) spectrometer for rapid quality assessment of grapes upon receival at wineries

“…The RMSEP of tartaric acid prediction amounted to a value between 0.4 and 0.5 g/kg with a relatively low R 2 val, ranging from 0.179 to 0.540, thus showing a limited predictability of this parameter, which is in accordance with the data of González-Caballero et al (2011) reporting an SEP of 1.60 g/L and an R 2 val of 0.46. Malic acid prediction performed relatively well with an RMSEP of 0.4-1.4 g/kg and an R 2 val of 0.48-0.88 compared with the prediction ability in grape bunches with an SEP = 1.39 g/L and R 2 val = 0.30 (González-Caballero et al 2011). Significant effects of minor constituents on the NIR spectra of fruits and vegetables are doubtful, particularly due to the predominance of water absorption bands.…”

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

“…The error rates for this parameter are lower than those reported by other authors using a different instrument in the NIR wavelength range from 900-1700 nm (Nogales-Bueno et al, 2014). Hence, a good capacity of correlation was achieved in numerous other works on prediction of TSS for wine and table grapes (González-Caballero et al, 2011;Parpinello et al, 2013).…”

“…Reducing sugar content study during the stages of grape ripening, wine making and ageing was conducted by Fernández-Novales et al (2009). González-Caballero et al (2011) also studied the changes in the internal quality attributes of wine grapes and excellent precision was obtained for TSS and reducing sugars with an R 2 as high as 0.94 and good precision was obtained for pH, TA, malic acid and tartaric acid with R 2 values ranging from 0.73 to 0.87. et al (2006) found that the predictions of the pH and anthocyanins were based on the visible range and those for the TSS were based on the near infrared (NIR) range.…”

Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks

“…reported R 2 values as large as 0.91 with RMSE of 0.63°Brix for a 250 ml recipient full of berries González-Caballero et al (2011). showed R 2 values of 0.94 and RMSE of 1°Brix for whole bunches.…”