Soluble solids content and pH prediction and varieties discrimination of grapes based on visible–near infrared spectroscopy

Cao, Feilong; Wu, Di; He, Yong

doi:10.1016/j.compag.2009.05.011

Cited by 91 publications

(73 citation statements)

References 12 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%

“…As for residual prediction deviation, the obtained value close to 2 (1.92) indicates that that coarse quantitative predictions are possible by using the model (Nicolaï et al, 2007). These results were similar to those reported in the studies of Cao et al (2010) in which the TSS of grape berries belonging to three different varieties were measured by using 2 different model development techniques i.e., PLS and genetic algorithm coupled with least square support vector machine (GA-LS-SVM). The obtained values of R of prediction were approximately equal to the values obtained in the present study being 0.91 for both PLS and GA-LS-SVM with RMSEP of 0.93 and 0.96°Brix.…”

Section: Articlesupporting

confidence: 85%

“…In the present study the highest value of Rcal obtained was 0.58 with a RMSEC of 0.15, which is much lower as compared to the results of Baiano et al (2012) in which R value for white grapes was 0.80. 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.…”

Section: Articlementioning

confidence: 87%

See 2 more Smart Citations

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: Articlesupporting

confidence: 85%

Section: Articlementioning

confidence: 87%

See 1 more Smart Citation

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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show abstract

“…These chemical analyses are not only time-consuming and expensive but also destructive, i.e. part of the grapes is lost (Cao et al 2010). Only in case of sugar content an easy relatively fast, traditional physical method is applied (refractometer reading of °Brix), but this method is also destructive.…”

Section: ---mentioning

confidence: 99%

Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries

Navrátil¹,

Buschmann²

2016

Photosynt.

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In vivo reflectance and fluorescence spectra from berry skins of a white (Riesling) and red (Cabernet Sauvignon) grapevine variety were measured during a ripening season with a new CMOS radiometer instrument. Classical reference measurements were also carried out: sugar content of the berry juice [°Brix], pigment content (chlorophyll a and b, carotenoids, anthocyanins) from methanol extracts of the berry skin. We showed that the spectra and the colors analyzed from them could be taken as an unambiguous indicator of grapevine ripening. Reflectance spectra, which were affected by the content of pigments (chlorophylls and anthocyanins), effects of surface (wax layers), and tissue structure (cell size) of the berries well correlated (R 2 = 0.89) with the °Brix measurements of the berries. The fast data acquisition of both reflectance and fluorescence spectra in one sample with our radiometer instrument made it superior over the timeconsuming, traditional, and mostly destructive chemical analysis used in vine growing management.

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“…Infrared spectroscopy has been widely used to evaluate the internal quality (SSC, pH, acidity and firmness et al) of fruits such as apple (Bobelyn et al 2010;Fan et al 2009), pear (Paz et al 2009;Sun et al 2009), orange (Liu et al 2010a;Liu et al 2010b), grape (Cao et al 2010), peach (Kurz et al 2010;Ziosi et al 2008), and kiwifruit (Clark et al 2004;Moghimi et al 2010) et al. The first attempts to establish the relationship between sensory texture and NIRS measurements were made by Mehinagic et al (2003) who reported good correlations between penetrometry and sensory texture but poor correlations between NIR and sensory data were found.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive measurement of fracturability and chewiness of apple by FT-NIRS

Ren

et al. 2013

J Food Sci Technol

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In order to assess quickly and non-destructively the fracturability and chewiness of apple fruit by FT-NIR spectra in the wavelength range of 4000 cm −1 -12000 cm −1 , multivariate models were built using multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR). Fracturability and chewiness reference data were instrumentally measured using a Texture Profile Analysis (TPA) test. The effects of various pre-processing methods of the spectroscopic data on the performance of the multivariate models were analyzed. Standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Min-Max normalization(MMN) and first derivative (FD) were tested. The performance of the fracturability prediction models was better for the PLSR model (R 2 =0.91, RMSEP=101.90) than for the PCR and MLR models. With regard to chewiness, the performance of the PCR model (R 2 =0.88, RMSEP=13.46) was similar to the one of the PLSR model but better than the one of the MLR model. The results demonstrated that NIR spectra together with stoichiometry could determine precisely fracturability and chewiness of apple, and the predictive ability of the models developed by other methods may be improved in the future.

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Soluble solids content and pH prediction and varieties discrimination of grapes based on visible–near infrared spectroscopy

Cited by 91 publications

References 12 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

Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries

Non-destructive measurement of fracturability and chewiness of apple by FT-NIRS

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