Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: A preliminary approach

Nogales-Bueno, Julio; Hernández-Hierro, José Miguel; Rodríguez-Pulido, Francisco J.

doi:10.1016/j.foodchem.2013.12.030

Cited by 123 publications

(98 citation statements)

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“…As result of this external validation the standard errors of prediction (SEP) were obtained for each reference variable, these values were also included in Table 2. These values are comparatively similar to the errors previously reported for total or extractable content of these compounds using near infrared spectroscopy taking into the account the applicability range (Cozzolino, 2009;Ferrer-Gallego, Hernández-Hierro, Rivas-Gonzalo, & EscribanoBailón, 2011;Hernández-Hierro et al, 2013;Nogales-Bueno et al, 2014;Rodríguez-Pulido et al, 2014).…”

Section: Calibration Processsupporting

confidence: 90%

Use of near infrared hyperspectral tools for the screening of extractable polyphenols in red grape skins

et al. 2015

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a b s t r a c tHyperspectral images of intact grapes were recorded at harvest time using a near infrared hyperspectral imaging system (900-1700 nm). Spectral data have been correlated with red grape skin extractable polyphenols (total phenolic, anthocyanins and flavanols) by modified partial least squares regression (MPLS) using a number of spectral pretreatments. The obtained results (coefficient of determination (RSQ) and standard error of prediction (SEP), respectively) for the developed models were: 0.82 and 0.92 mg g À1 of grape skin for extractable total phenolic content, 0.79 and 0.63 mg g À1 of grape skin for extractable anthocyanin content, 0.82 and 0.45 mg g À1 of grape skin for extractable flavanol content. The obtained results present a good potential for a fast and reasonably inexpensive screening of the extractable polyphenolic compounds in intact grapes. Moreover, the heterogeneity of extractable polyphenols within the ripeness stage has been also evaluated using the proposed method.

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Section: Calibration Processsupporting

confidence: 90%

Use of near infrared hyperspectral tools for the screening of extractable polyphenols in red grape skins

et al. 2015

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“…González-Neves et al (2010) reported a value of (R 2 ) of 0.98 for total phenols in validation model using red grapes Graciano. Similar results were generated in a study conducted by Nogales-Bueno et al (2014) in which the results of R 2 values of 0.89 for red grapes, 0.80 for white grapes, and 0.77 for global models, were reported. Results demonstrated that, while good performances were observed for TSS, followed by TA, pH and antioxidant activity, in agreement with the results of the other authors with research on grapes (Cao et al, 2010;González-Caballero et al, 2010Baiano et al, 2012;Nogales-Bueno et al, 2014), a limited potential was seen in the performance of the PLS model in case of phenol content.…”

Section: Articlesupporting

confidence: 88%

“…These results presented a better performance as compared to González- (2010) work in which TA for the berries were measured over a wavelength range of 380-1650 nm giving an R 2 of 0.33 in cross validation. The R values for TA in the present study are lower as compared to the study conducted by Baiano et al (2012) in which the prediction values for TA were as high as 0.95 and 0.82 for white and red grapes, respectively, but similar to those obtained by Nogales-Bueno et al (2014). The better results obtained in prediction for TA and pH compared to calibration, may be explained by the fact that models were not enough robust and may be by having selected by chance external data set which better described the correlation between spectra and analytic measure, compared to sample used for the calibration model.…”

Section: Articlesupporting

confidence: 53%

“…These authors predicted the TSS of red and white grape berries with a value of R 2 for prediction to be 0.93 and 0.94, respectively. 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).…”

Section: Articlesupporting

confidence: 52%

“…Nogales- Bueno et al (2014) used the NIR in the range of 900-1700 nm for non-destructive prediction of phenolic content of grape skin, TSS, TA and pH developing both individual models and global models. The values of R 2 for for the individual models of these parameters were very high (more than 0.90) for red grapes.…”

Section: Introductionmentioning

confidence: 99%

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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

Piazzolla

Amodio

Colelli

2017

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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.

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Rapid Estimation of Bioactive Phytochemicals in Vegetables and Fruits Using Near Infrared Reflectance Spectroscopy

Kumar

Singh

Dhanani

2017

Fruit and Vegetable Phytochemicals

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Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: A preliminary approach

Cited by 123 publications

References 20 publications

Use of near infrared hyperspectral tools for the screening of extractable polyphenols in red grape skins

Use of near infrared hyperspectral tools for the screening of extractable polyphenols in red grape skins

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

Rapid Estimation of Bioactive Phytochemicals in Vegetables and Fruits Using Near Infrared Reflectance Spectroscopy

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