@article{oh_stapleton_giongo_johanningsmeier_mollinari_mainland_perkins-veazie_iorizzo_2024, title={Prediction of blueberry sensory texture attributes by integrating multiple instrumental measurements}, volume={218}, ISSN={["1873-2356"]}, DOI={10.1016/j.postharvbio.2024.113160}, journal={POSTHARVEST BIOLOGY AND TECHNOLOGY}, author={Oh, Heeduk and Stapleton, Lee and Giongo, Lara and Johanningsmeier, Suzanne and Mollinari, Marcelo and Mainland, Charles M. and Perkins-Veazie, Penelope and Iorizzo, Massimo}, year={2024}, month={Dec} }
 @article{trandel-hayse_johanningsmeier_oh_iorizzo_perkins-veazie_2023, title={Blueberry Cell Wall Polysaccharide Composition of Three Distinct Fruit Firmness Phenotypes}, volume={3}, ISSN={["2692-1944"]}, DOI={10.1021/acsfoodscitech.3c00284}, abstractNote={Blueberry (Vaccinium corymbosum) cultivars vary in firmness, and these phenotypic differences may be associated with peel and pulp cell wall polysaccharides. Three blueberry cultivars of distinctive texture phenotypes, Indigocrisp (crisp), Emerald (firm, industry standard), and Jewel (soft), were evaluated for cell wall polysaccharide composition. Alcohol-insoluble residues (AIRs) from both peel and pulp were reduced, methylated, hydrolyzed, acetylated, and quantified using gas chromatography–mass spectrometry (GC-MS). Monosaccharide composition (μg·mg–1 AIR) differed among cultivars, with "Indigocrisp" pulp highest in glucuronic acid (22.23), "Emerald" pulp highest in glucose (106.31), and "Jewel" peel highest in arabinose (38.73) and mannose (11.88). Forty-five cell wall polysaccharide linkages were identified, and specific linkages were associated with blueberry peel and pulp among the texture phenotypes. Polysaccharide classifications were then estimated from the 45 cell wall polysaccharide linkages. "Indigocrisp" and "Emerald" pulp were highest in arabinan and type II arabinogalactan, which are less susceptible to depolymerization. "Indigocrisp" pulp had a greater abundance of heteromannan, xyloglucan, and cellulose, while "Jewel" was highest in rhamnogalacturonan I, which typically depolymerizes first. The greater abundances of arabinan and type II arabinogalactan in the pulp of the firm and crisp cultivars likely contribute to the texture characteristics of these phenotypes.}, number={11}, journal={ACS FOOD SCIENCE & TECHNOLOGY}, author={Trandel-Hayse, Marlee and Johanningsmeier, Suzanne and Oh, Heeduk and Iorizzo, Massimo and Perkins-Veazie, Penelope}, year={2023}, month={Nov}, pages={1920–1930} }
 @article{oh_pottorff_giongo_mainland_iorizzo_perkins-veazie_2024, title={Exploring shelf-life predictability of appearance traits and fruit texture in blueberry}, volume={208}, ISSN={["1873-2356"]}, DOI={10.1016/j.postharvbio.2023.112643}, abstractNote={Improving the shelf-life of blueberries (Vaccinium spp.) has become a crucial breeding priority for the industry. However, the breeders have sparse empirical data to select genotypes with extended shelf-life. In this study, a large set of cultivars was evaluated for mechanical texture and appearance characteristics at harvest and after storage to understand their relationship and test multiple statistical models to assess the predictability of shelf-life. Blueberries harvested from 61 cultivars with extensive phenotypic variation were stored at 2 oC and 80% relative humidity (RH) for six weeks. The results indicated that weight loss, texture change, and fruit wrinkling could be predicted using fruit characteristics measured at harvest (T0) or two weeks post-harvest (T2). The berry size at T0 was able to predict postharvest weight loss with high accuracy; the larger the initial berry size, the less weight loss. This trend plateaued with berries larger than 18 mm in diameter. For texture, the measurements at T0 and six weeks after storage (T6) were positively correlated in all mechanical texture parameters, indicating that the initial texture is highly related to the final texture after storage. The overall change of texture could be best predicted using the texture parameter 'distance to maximum force' (DFM) measured at T0. Although the prediction accuracy was relatively low (R2 = 0.34), the model still effectively predicted the cultivars with the most texture change and those with the least. Interestingly, the prediction power improved to a moderate level (R2 = 0.45–0.66) when using all the texture and appearance parameters measured at T0 and T2. Wrinkling was best predicted by either the initial fruit size or the texture parameter 'force linear distance' (FLD) with low accuracy (R2 = 0.35–0.37); the larger the berry or FLD at T0, the less wrinkle after storage. These findings provide empirical data that blueberry breeders could use to select for shelf-life in blueberry. Predicting the variation of shelf-life indicators in a germplasm can substantially reduce the cost and time required to phenotype shelf-life performance.}, journal={POSTHARVEST BIOLOGY AND TECHNOLOGY}, author={Oh, Heeduk and Pottorff, Marti and Giongo, Lara and Mainland, Charles M. and Iorizzo, Massimo and Perkins-Veazie, Penelope}, year={2024}, month={Feb} }