First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery

González-Caballero, Virginia; Sánchez, María‐Teresa; López, María-Isabel; Pérez‐Marín, Dolores

doi:10.1016/j.jfoodeng.2010.06.016

Cited by 67 publications

(74 citation statements)

References 23 publications

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“…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. In case of antioxidant activity, no comparison was possible since it has not been previously reported in any paper.…”

Section: Articlesupporting

confidence: 90%

“…Similarly, in a study conducted by Cao et al (2010) the models resulted in an R value of 0.97 for wine grapes. González-Caballero et al (2010) on single berries of wine-grapes with coefficient of determination (R 2 ) of 0.64 in prediction using Vis-NIR range. The main reason for this difference on the results may be the method for setting the Y vector during the reference development.…”

Section: Articlementioning

confidence: 99%

“…González-Caballero et al (2010), achieved high R 2 values of 0.89 and 0.87 for the TSS and reducing sugar content with the standard error of cross validation (SECV) of 1.41°Brix and 17.13 g/L, respectively. Reducing sugar content study during the stages of grape ripening, wine making and ageing was conducted by Fernández-Novales et al (2009).…”

Section: Introductionmentioning

confidence: 98%

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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

J Agricult Engineer

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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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Section: Articlesupporting

confidence: 90%

Section: Articlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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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

J Agricult Engineer

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“…Figure 2 shows the UV/VIS/NIR raw spectra of mixtures with all the main wine compounds. In the NIR region, the spectrum was dominated by an absorption band at around 1200 nm region related with sugars (González-Caballero et al 2010). Water-related absorption bands were also found at around 950 nm and 1460 nm, which are related to the third overtone of O-H, as it is usually the case for fruits and vegetables and their juices, with 70-80% of water (Murra 1987;McGlone & Kawano 1998;Williams 2001).…”

Section: Resultsmentioning

confidence: 96%

“…Quantitative calibrations were developed to predict the studied compounds. For comparative purposes, two model techniques were applied: partial leastsquare (PLS) and principal component regression (PCR) (Urbano-Cuadrado et al 2004;González-Caballero et al 2010).…”

Section: Samplesmentioning

confidence: 99%

Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

Martelo-Vidal¹,

Vázquez²

2014

Czech J. Food Sci.

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Martelo-Vidal M.J., Vázquez M. (2014): Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds. Czech J. Food Sci., 32: 37-47.Spectroscopy of UV-VIS-NIR combined with chemometric analyses was used as a non-destructive technique to build models for the quantitative characterisation of the main compounds of wine. The work in mixtures can give insight into how interferences affect the performance of calibrations in wines. Ethanol, glycerol, glucose, tartaric acid, malic acid, lactic acid, and acetic acid were evaluated as pure compounds and in mixtures. Different pre-treatments for the spectra and modelling strategies such as partial least squares (PLS) regression or Principal Component Regression (PCR) were evaluated. All pure compounds studied showed a good relationship between spectra and concentrations. However, interferences were observed in the mixtures and only good models for ethanol, tartaric acid, and malic acid were obtained. The best model was obtained in the NIR region for ethanol and in the UV region for tartaric acid and malic acid. The results indicate that NIR spectroscopy could be used as an alternative to conventional chemical methods for ethanol determination and UV spectroscopy for the determination of tartaric acid and malic acid.

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Nutritive quality prediction of peaches during storage

Zhong

Bao

Chen

et al. 2021

Food Science & Nutrition

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Peaches (Prunus persica L. Batsch) are commonly consumed fruits with high nutritional value. We evaluated the nutritive qualities of peach fruit during storage. Heatmap analysis showed that protein, ash, and crude fiber contents clustered together, whereas fat and reducing sugars clustered separately. We then classified the nutrients into two clusters; cluster 1 showed low fat and reducing sugar levels and high protein, crude fiber, and ash levels, whereas cluster 2 showed high fat and reducing sugar levels and low protein, cruder fiber, and ash levels. Partial least squares regression and random forest analyses showed accuracies of 67% and 61%, respectively. Spectra at 1,439 and 1,440 nm indicated reducing sugars, and the spectrum at 2,172 nm indicated protein. Thus, Fourier transform‐near infrared spectroscopy could predict the two clusters based on five nutritive qualities. Our findings may help to establish guidelines for promoting the acceptability of peach fruits among consumers.

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First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery

Cited by 67 publications

References 23 publications

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

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

Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

Nutritive quality prediction of peaches during storage

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