Journal article published in Scientia Horticulturae, volume 197, pp. 272–279.

Authors: , , , , , , , and .

Abstract

Flavonoids are a class of bioactive compounds extremely important in food and wine industry. The development of rapid methods for their quantification in grape berries is one of the modern challenges inviticulture and enology research. Total flavonoid (TF) amount changes during grape ripening and also berry physicalmechanical properties, as evaluated by instrumental texture analysis, change in the same period. In this work, TF and berry physical-mechanical parameters were linked together through predictive models. Models were developed for each of four red wine grape cultivars: Brancellao, Cabernet Franc, Mencía and Merenzao, and another one considered all cultivars together. These models reached high accuracy and allowed to predict TF in grape berries with a low error (RMSE from 0.15 ± 0.07 mg g−1 to 0.35 ± 0.10 mg g−1in prediction, as evaluated by cross-validation). Berry weight (BW) was the parameter having the largest influence on TF predictions, and also was the only variable having part in all models. BW and chewiness had a similar behavior and when berry weight was excluded, chewiness was able to substitute its role in all models. The other physical-mechanical characteristics displayed a different behavior across cultivars. In conclusion, this work shows that it is possible to predict TF from physical-mechanical predictors in grape berries and that cultivar specific models reach higher accuracy for this purpose than the multi-cultivar model.

Key words: Multivariate adaptive regression splines (MARS), Total flavonoids, Berry weight, Mechanical properties, Red wine grape cultivars

BibTeX entry: click to show

@article{
	2318_1567475,
	url = {https://hdl.handle.net/2318/1567475},
	author = {Brillante, Luca and Tomasi, Diego and Gaiotti, Federica and Giacosa, Simone and Torchio, Fabrizio and Río Segade, Susana and Siret, René and Zouid, Imen and Rolle, Luca},
	title = {Relationships between skin flavonoid content and berry physical-mechanical properties in four red wine grape cultivars (Vitis vinifera L.)},
	year = {2015},
	journal = {Scientia Horticulturae},
	volume = {197},
	abstract = {Flavonoids are a class of bioactive compounds extremely important in food and wine industry. The development of rapid methods for their quantification in grape berries is one of the modern challenges inviticulture and enology research. Total flavonoid (TF) amount changes during grape ripening and also berry physicalmechanical properties, as evaluated by instrumental texture analysis, change in the same period. In this work, TF and berry physical-mechanical parameters were linked together through predictive models. Models were developed for each of four red wine grape cultivars: Brancellao, Cabernet Franc, Mencía and Merenzao, and another one considered all cultivars together. These models reached high accuracy and allowed to predict TF in grape berries with a low error (RMSE from 0.15 ± 0.07 mg g−1 to 0.35 ± 0.10 mg g−1in prediction, as evaluated by cross-validation). Berry weight (BW) was the parameter having the largest influence on TF predictions, and also was the only variable having part in all models. BW and chewiness had a similar behavior and when berry weight was excluded, chewiness was able to substitute its role in all models. The other physical-mechanical characteristics displayed a different behavior across cultivars. In conclusion, this work shows that it is possible to predict TF from physical-mechanical predictors in grape berries and that cultivar specific models reach higher accuracy for this purpose than the multi-cultivar model.},
	keywords = {Multivariate adaptive regression splines (MARS), Total flavonoids, Berry weight, Mechanical properties, Red wine grape cultivars},
	doi = {10.1016/j.scienta.2015.09.053},	
	pages = {272--279}
}

View or request article

You can find the postprint version of this article in the Open Access Repository of the University of Torino:
IRIS-AperTO record 2318/1567475

View the final version at publisher:
doi:10.1016/j.scienta.2015.09.053

File not available?

You can contact me and request this article through the following form:





2019 Simone Giacosa

Tema di Anders Norén

Cookie and privacy policy. This website uses only technical cookies which are essential for the proper operation of the website. User data is kept indefinetely and used exclusively for the website operation. Users may contact the site owner for further information regarding this policy, for data or removal requests.