Viticultural and chemical characteristics of Muscat Hamburg preselected clones grown for table grapes.

BACKGROUND: Clonal selection is one of the tools used for grapevine improvement and therefore is very important for obtaining clones with better characteristics than the variety population. The aim of this study was to select superior grapevines of Vitis vinifera L. cv. Muscat Hamburg grown for fresh consumption. RESULTS: The viticultural parameters and fruit composition of 35 selected vines were determined during a 5-year period. The evaluated parameters showed high variability among selected vines. The significant effect of vintage was observed for all descriptors with the exception of the number of seeds per berry and sugar concentration. Additionally, all vines were examined for their tolerance to low temperatures and the results showed 73% and 90% of primary bud injury at -20 and -25 °C, respectively. In relation to berry classification, the percentage of first-class grapes ranged from 60% to 69% for all selected grapevines. Multivariate statistical analysis was performed to classify grapevines based on their performance. CONCLUSION: Fourteen grapevines were identified as the most promising among the 35 vines initially planted, based on high yield, bunch and berry weight, sugar content and percentage of first-grade grapes. Those grapevines were selected for the next phase of the clonal selection. This study highlighted the importance of clonal selection for improvement of the variety population. © 2016 Society of Chemical Industry.

Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters.

BACKGROUND: Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two-dimensional (2D) and three-dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation. RESULTS: The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R(2) = 84.5 and 71.1%, respectively). CONCLUSION: The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry.