Screening table grape cultivars for berry firmness and quality traits using cell wall ELISA and glycan microarrays
John Moore (South Africa)1 2; Terri-Lynne Naidoo (South Africa)1; Eugene Badenhorst (South Africa)1; Miguel Hendriques (South Africa)1; Bodil Jorgensen (Denmark)3; Talitha Venter (South Africa)1;
1 - South African Grape and Wine Research Institute, Stellenbosch University, Stellenbosch, South Africa; 2 - Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa; 3 - Department of Plant and Environmental Science, University of Copenhagen, Copenhagen, Denmark;
Keywords: Grapes; ELISA; CoMPP;
Abstract Topics: Theme 12: Cell Walls in Crop Quality, Biomass Utilisation and Sustainability
Type of Presentation: Poster

Abstract text: The crisp texture of table grapes is a critical quality attribute influencing consumer acceptance and market value. Berry firmness varies among cultivars and is affected by harvest timing and vineyard performance. Cell wall composition and architecture play a central role in determining berry quality, particularly pericarp firmness and structural integrity at harvest and during postharvest storage. Currently, producers commonly assess firmness through visual inspection of cross-sectioned berries prior to harvest. This study explores an integrated approach combining carbohydrate microarrays with 25 to 30 probes targeting cell wall glycans via immunochemical analyses and rapid infrared spectroscopy to chemotype grape berry cell walls at defined stages of ripeness. Immunochemical datasets were generated from more than 25 table grape cultivars, including well-characterised varieties such as ‘Crimson Seedless’ and ‘Prime’, alongside less studied cultivars such as ‘Autumn Crisp’ and ‘Sugar Crisp’. These datasets enable the generation of spectroscopic fingerprints that capture cultivar-specific cell wall characteristics. The ultimate objective is to advance understanding of berry cell wall composition across key cultivars and to support the development of infrared sensing technologies capable of predicting textural outcomes. Resulting datasets and predictive models are correlated with instrumental firmness and textural measurements across cultivars and ripening stages.