Assessment of grape cluster yield components based on 3D descriptors using stereo vision

NOTICE: this is the author’s version of a work that was accepted for publication in Food Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Food Control, [Volume 50, April 2015, Pages 273–282] DOI 10.1016/j.foodcont.2014.09.004Wine quality depends mostly on the features of the grapes it is made from. Cluster and berry morphology are key factors in determining grape and wine quality. However, current practices for grapevine quality estimation require time-consuming destructive analysis or largely subjective judgment by experts. The purpose of this paper is to propose a three-dimensional computer vision approach to assessing grape yield components based on new 3D descriptors. To achieve this, firstly a partial three-dimensional model of the grapevine cluster is extracted using stereo vision. After that a number of grapevine quality components are predicted using SVM models based on new 3D descriptors. Experiments confirm that this approach is capable of predicting the main cluster yield components, which are related to quality, such as cluster compactness and berry size (R2 > 0.80, p < 0.05). In addition, other yield components: cluster volume, total berry weight and number of berries, were also estimated using SVM models, obtaining prediction R2 of 0.82, 0.83 and 0.71, respectively.This work has been partially funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA - Spanish National Institute for Agriculture and Food Research and Technology) through research project RTA2012-00062-C04-02, support of European FEDER funds, UPV-SP20120276 and AGL2011-23673 project.Ivorra Martínez, E.; Sánchez Salmerón, AJ.; Camarasa Baixauli, JG.; Diago, M.; Tardaguila, J. (2015). Assessment of grape cluster yield components based on 3D descriptors using stereo vision. Food Control. 50:273-282. https://doi.org/10.1016/j.foodcont.2014.09.004S2732825

Genetic bases for the metabolism of the DMS precursor S-methylmethionine by Saccharomyces cerevisiae

Dimethyl sulfide (DMS) is a sulfur containing volatile that enhances general fruity aroma and imparts aromatic notes in wine. The most important precursor of DMS is S-methylmethionine (SMM), which is synthesized by grapes and can be metabolized by the yeast S. cerevisiae during wine fermentation. Precursor molecules left after fermentation are chemically converted to DMS during wine maturation, meaning that wine DMS levels are determined by the amount of remaining precursors at bottling. To elucidate SMM metabolism in yeast we performed quantitative trait locus (QTL) mapping using a population of 130 F2-segregants obtained from a cross between two wine yeast strains, and we detected one major QTL explaining almost 30% of trait variation. Within the QTL, gene YLL058W and SMM transporter gene MMP1 were found to influence SMM metabolism, from which MMP1 has the bigger impact. We identified and characterized a variant coding for a truncated transporter with superior SMM preserving attributes. A population analysis with 85 yeast strains from different origins revealed a significant association of the variant to flor strains and minor occurrence in cheese and wine strains. These results will help selecting and improving S. cerevisiae strains for the production of wine and other fermented foods containing DMS such as cheese or beer.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.BT/Industriele Microbiologi