Abstract
Background and goals Grape berry texture (gumminess, springiness, chewiness) and juiciness are important traits when breeding new table grape cultivars. Measuring these traits has previously relied on evaluating single berries at multiple stages of ripening.
Methods and key findings Bulk grape berry samples of the table grape cultivar Scarlet Royal were evaluated weekly for texture, weight, and juiciness postveraison to determine changes in these characteristics during ripening. Most traits evaluated did not show consistent significant differences after 1 wk postveraison, with the exception of total soluble solids, which consistently increased. Based on these data, variability in texture across wine, table, and breeding lines was compared at a single time point postveraison. Significant differences were detected in berry box weight, chewiness, juiciness, and berry box number associated with use-type (wine and table). Berry box weight, berry box number, and juiciness were highly correlated (r ≥ 0.60), but had low-to-moderate (r ≤ 0.41) significant correlation with gumminess, chewiness, and springiness.
Conclusions and significance Quantitative trait locus (QTL) mapping in a bi-parental population identified regions on chromosomes 13, 18, and 19 associated with gumminess, chewiness, springiness, and juiciness. These are the first QTL associated with “juiciness” in grape.
Introduction
Grape, Vitis vinifera, is an important fruit crop grown for fresh (table grapes) and processed (wine and raisin grapes) markets worldwide, with an estimated market value of $5.93 billion in the United States, as found on the Statista website (https://www.statista.com/statistics/193910/total-us-grape-production-value-from-2000). Fresh and processed grape market classes have different berry trait requirements for texture and juiciness, which are directly related to their use and regional consumer preferences. Table grapes for fresh eating in the U.S. have a moderately juicy, firm, or crisp flesh, while winegrapes for processing are juicy with gelatinous flesh. Within and among grape use-types and varieties, differences are partially based on berry texture traits. Winemaker preferences are based on complex relationships between berry size, skin thickness, and juice extractability, depending on the type of wine (Rolle et al. 2012a, Shanshiashvili et al. 2025). Consumer demand for fresh market table grapes is based on perceived quality related to berry size, color, juiciness, and fruit firmness, which can vary widely across varieties and storage length. In the U.S., table grape storage length is dependent on variety, harvest time, and market saturation; grapes can be stored for a few days, up to 15 wk (Crisosto et al. 1994).
The texture of many table grape varieties can be characterized using parameters such as gumminess and chewiness or firmness (Rolle et al. 2011, 2012b, 2013). Gumminess (hardness × cohesiveness) is often used for semisolid foods (e.g., scrambled eggs, cottage cheese), while chewiness (hardness × cohesiveness × springiness) is usually used for solid foods (e.g., bananas, apples). Springiness is the ratio or percentage of the height of the food product between the first and second compression (i.e., first bite and second bite). Firmness (maximum force required to compress a food) can be applied to a food product but is not an adequate descriptor for the many components that comprise “grape berry texture”. Metrics such as gumminess and chewiness, which include but are not exclusively based on force (in Newtons [N]), may be better descriptors for consumer preference. Juiciness, another critical sensory attribute in consumer-preferred table grapes, is often evaluated simultaneously, although its exact relationship with texture is unclear (Giacosa et al. 2015, Ma et al. 2016). While some berry quality traits such as naturally enhanced berry size and improved color are easily selected when breeding, others such as fruit texture or berry juiciness are harder to measure (Rolle et al. 2012b). Breeding for texture has been heavily dependent on phenotypic selection by breeder and consumer taste panels, although more recently, molecular markers have been identified, allowing for marker-assisted selection in both public and private breeding programs.
Molecular markers have been developed for several berry-related traits in wine and table grapes. For example, markers for naturally larger grape berry size have been identified in multiple inter- and intraspecific crosses, as well as markers for berry weight and shape, color, seedlessness, and cuticle thickness (Cabezas et al. 2006, Doligez et al. 2013, Herzog et al. 2015, Muñoz-Espinoza et al. 2020, Sun et al. 2020, Underhill et al. 2020, Wang et al. 2020, 2022, Crespan et al. 2021, Wu et al. 2022, de Sousa-Moreira et al. 2024). However, few markers are available for berry texture and ripening-related traits.
There are many challenges to evaluating berry quality traits that are related to ripening in mapping populations. Berry ripeness is highly variable among genotypes, processing large-numbers of individuals is time-intensive, and most analyses use individual berries which can vary in ripeness within a cluster (Coombe and McCarthy 2000, Gouthu et al. 2014). Berry development is a well-established process, but how berry texture changes postveraison is not well understood nor is it often addressed (Dokoozlian and Kliewer 1996, Iland 2011, Fasoli et al. 2018, Choi et al. 2022), partly because berry ripeness is a dynamic process and not a single event. Prior studies have shown that skin break force and energy, as well as berry cohesiveness, affected berry texture and varied among winegrape varieties across ripening stages, and changes in berry chewiness, gumminess, and springiness were cultivar dependent (Río Segade et al. 2011, 2013). These results were similar to another study that found that berry texture characteristics changed among early and late ripening individual berries for 21 winegrape cultivars, but those changes were cultivar dependent (Brillante et al. 2017). Each of these studies used total soluble solids (TSS) across multiple time points to divide individual berries into “early” (low TSS) and “late” (high TSS) ripening categories. TSS, while important, do not always indicate “ripeness”. Some cultivars, such as the table grape Valley Pearl, are commercially harvested at lower TSS (17 Brix) than other table grape cultivars because the berries have lower acid-to-soluble solid ratios (Ledbetter 2016). Another study using genome-wide associations explored categorically-defined (e.g., firm, soft) berry flesh texture differences in a large germplasm collection, instead of sorting berries into ripe or unripe categories by measuring TSS, and found a significant quantitative trait locus (QTL) located on chromosome (chr) 16 (Guo et al. 2019). In that study, clusters were defined as “ripe”, with no information on what metrics were used to determine ripeness or how berries were evaluated for fruit texture. This suggests that using a single “ripe” time point may be sufficient to detect meaningful differences among lines.
Previous studies have identified potential QTL associated with berry quality traits and texture on chr 1, 4, 5, 8, 9, 10, 13, and 18, depending on the year and the cross (Carreño et al. 2015, Correa et al. 2016). Later studies confirmed that chr 18 has a major QTL associated with berry texture, and an additional QTL on chr 16 was found by using an association mapping approach (Guo et al. 2019, Crespan et al. 2021). The QTL on chr 18 was linked to the gene for seedlessness (VviAGL11). While the authors postulated that the VviAGL11 gene may play a role in texture, it may also be an artifact of the cross (i.e., a seeded winegrape with poor texture crossed with a seedless table grape with good texture) used for analyses (Crespan et al. 2021). The QTL on chr 16 was co-located with genes associated with calcium, which is believed to play a role in berry firmness both pre- and postharvest (Ejsmentewicz et al. 2015, Balic et al. 2022). In a separate study, a single gene (VvPeI, pectate lyase) on a different chromosome (14) was associated with texture variation using an association mapping approach with a panel of table grapes (Vargas et al. 2013). More than 30 single nucleotide polymorphisms (SNPs) were identified in this gene (located on chr 14), explaining 7 to 42% of the variability observed, depending on the year and the SNP (Vargas et al. 2013).
Each of these studies relied on evaluation of individual berries, in place of evaluating a bulk set of berries or a full cluster. In their review, Rolle et al. (2012b) suggested that it was “essential” to use mean values for compression measurements in place of individual berry evaluations. One such tool for measuring a single value to represent texture across a large number of individual berries is the Kramer Shear cell, which was designed to test a non-uniform sample, particularly in fresh fruits and vegetables (Kramer et al. 1951, Álvarez et al. 2020). While not routinely used in grapes, the Kramer Shear cell has been used with other fresh fruit and vegetables, including apples, beans, beets, peaches, raspberries, raisins, and cucumbers (Kramer et al. 1951, Szczesniak et al. 1970, Christofi et al. 2021).
The objectives of this study were to evaluate if textural changes occur in ripening grapes postveraison, to compare textural differences between table and wine grapes, and to identify molecular markers associated with fruit quality traits in a biparental mapping population, using a bulk sample approach with the Kramer Shear cell.
Materials and Methods
Site and plant material
The table grape cultivar Scarlet Royal, described as “firm” and “meaty”, was grown at the San Joaquin Valley Agricultural Sciences Center (SJVASC) in Parlier, CA. The site also included 44 grape lines (wine and table grape cultivars, advanced breeding lines [included with the “table” category for analyses], and breeding lines), as well as ~215 mature vines of a previously-described mapping population with variability in fruit traits (Cadle-Davidson et al. 2016, de Sousa-Moreira et al. 2024). All vines were planted ~1 m apart with 2-m row spacing. Vines were fertilized, watered, and treated for powdery mildew as needed. No cultural treatments to specifically affect berry texture were used (e.g., girdling or gibberellin). Vines received water through daily drip irrigation during the fruit development period to replace what was lost to evapotranspiration. Calcium ammonium nitrate (CAN-17) was applied annually in the drip line during rapid shoot growth to provide ≈25 kg nitrogen (N)/ha to replace what was lost to prior crop removal (Peacock et al. 1998). All vines were grown at the SJVASC unless otherwise noted. For the mapping population, each of the 215 vines represented a genetically unique F1 individual from a Vitis interspecific hybrid cross (11-3527) (C81-227 [Vitis cinerea] × Y315-43-04 [V. vinifera]). These vines were thinned to 10 clusters or fewer per plant to minimize variability due to crop load. Not all vines had sufficient clusters for analysis each year, and when fewer than three clusters were available for a vine, that vine was removed from analysis. Each vine was represented by 2 yr of data.
Developmental time course
At veraison, 10 Scarlet Royal clusters were randomly selected for evaluation from the vineyard. An additional 10 clusters were randomly selected weekly for evaluation for five additional weeks. Clusters were returned to the lab for evaluation of berry number, berry weight, berry texture, berry juiciness, and berry TSS for each of the six time points (Table 1).
Grape berry texture and sensory attributes measured across three experiments in table grapes, winegrapes, breeding lines, and a biparental mapping population. TSS, total soluble solids.
Cultivar variability
Samples included five clusters each from the 44 grape lines and the ~215 vines of the mapping population. The clusters were collected at full (>80% of the berries exhibiting) color (red and black lines only) or at 2 to 5 wk postveraison (green lines only) (Supplemental Table 1). All clusters for a vine were harvested at a single time.
Phenotyping
All samples were stored in paper bags in a cold room at 4°C and evaluated once berries had returned to room temperature (~23°C) within 2 wk of harvest to minimize postharvest storage effects. For the cultivar variability study (four table grape varieties), an additional set of clusters was stored in a cold room at 4°C for 6 to 8 wk to determine fruit quality and texture changes from short (<2 wk) to long (≥6 wk) postharvest storage. For each experiment, berries were removed from the rachis and a representative subset based on berry size and color from each individual cluster was used for texture analysis. The representative subset was based on the number of berries needed to fill a Kramer Shear cell attachment ~64 × 82 × 99 mm3 (internal dimensions of the Zwick Roell Kramer Shear cell attachment 014778). For each genotype, the number of berries and weight (g) of the subset was recorded. Berry texture was determined for each cluster using the Kramer Shear cell on the TAII Texture Analyzer (Zwick USA) and analyzed using the included TestXpert II software. Force (N) was applied to the berries at a rate of 50 mm/min, with an initial force requirement of 3 N to begin data collection. Texture metrics measured were gumminess (measured in N), springiness, and chewiness (measured in N) (Chiabrando et al. 2009). For chewiness, gumminess, and springiness trait evaluations, genotypes were only included if they were represented by at least three individual clusters, and if the individual clusters were large enough to fill (minimum of three-quarters full) the Kramer Shear cell. Juice from the berries was collected after maceration by the Kramer Shear cell blades and total volume (mL) was recorded for each sample. TSS were calculated using a Refracto 30GS portable refractometer (Mettler Toledo).
Statistical analyses
Data were analyzed for normality using the Shapiro-Wilks test and the distribution of residuals in R (ver. 3.6.1) (R Development Core Team 2012). For cultivar comparisons, springiness, chewiness, and berry number were log-transformed. Berry box weight was square root-transformed. For developmental time course evaluations with table grape cultivar Scarlet Royal comparisons, TSS, berry weight by volume, and berry springiness were log-transformed. Analysis of variance was performed using R. Significance of means for each line was separated using Tukey’s honestly significant difference test, with the package ‘agricolae’ (de Mendiburu and Yaseen 2020). Correlations among variables were calculated using Pearson’s correlation coefficients (r) on normalized values implemented in R, using the cor function with values for each individual cluster. Best linear unbiased predictors (BLUPs) were calculated in R.
QTL identification
A previously generated genetic map for the 11-3527 population was used for all potential QTL evaluations (Cadle-Davidson et al. 2016, de Sousa-Moreira et al. 2024) (Supplemental Table 2). A mean value for each trait was calculated for 2019 and 2020, averaged across all clusters collected for each genotype. QTL analyses were performed using the ‘R/QTL’ package (ver. 3.6.1) under the interval mapping standards with the scanone function, Kosambi map function, “em” algorithm method, and 4way cross-type (Broman and Sen 2009). The minimum logarithm of odds (LOD) score required for QTL detection was determined by the genome-wide LOD significance threshold (α = 0.10), based on 1000 permutations. The contribution of a QTL or set of QTL on a phenotypic variance of a trait was estimated using the fitQTL function. For traits with a significant correlation, additive and interactive covariates were tested using the addcovar and intcovar functions.
Results
Scarlet Royal time course
Across timepoints, Scarlet Royal clusters showed variability within and among clusters for size and color development (Figure 1). During berry ripening, from veraison to 5 wk postveraison, significant differences that were consistent across sequential time points were detected in berry chewiness, juiciness, box weight, and TSS. Chewiness, berry box weight, and juiciness did not significantly change between 2 and 5 wk postveraison (Table 2). TSS showed a consistent increase across timepoints from veraison to 5 wk postveraison. Berry gumminess, springiness, and box weight significantly varied among samples and time points, but not across sequential time (Table 2). Springiness had the most variation across weeks but had a downward trend as berries ripened.
Six clusters of table grape cultivar Scarlet Royal showing cluster variability at A) 1 and B) 3 wk postveraison.
Scarlet Royal grape berry quality and texture traits during ripening. TSS, total soluble solids; StDev, standard deviation.
Cultivars
For the fruit quality and texture metrics that were evaluated, significant differences were evident among the wine, table (cultivars and advanced breeding lines), and breeding lines examined in this study (p < 0.0001) (Supplemental Table 3). Because of insufficient numbers of juice and multiuse grapes, these use-types were excluded from significance comparisons. On average, the evaluated winegrape cultivars (Cabernet Sauvignon, French Colombard, and Touriga Nacional) had a higher berry box number and box weight per sample, and a corresponding higher volume of juice on average, compared to the table grape cultivars and breeding lines that were evaluated (Supplemental Table 3). The single juice grape cultivar (Niagara) had a high box weight similar to the winegrapes, but a low berry box number. Table grape cultivars and breeding lines had a higher mean chewiness compared to winegrape cultivars, although within table grape cultivars and breeding lines, there were no significant differences (Figure 2 and Supplemental Table 4). Springiness was significantly higher in breeding lines compared to wine and table grape cultivars. Correlations between berry-related traits (berry box number, berry box weight, and juiciness) were positive and high (r ≥ 0.50) (Table 3). Correlations among texture-related traits (gumminess, chewiness, and springiness) were positive and low (r = 0.24) to high (r = 0.89). Low (r = −0.10 to −0.29) negative correlations were detected among berry-related traits (berry box weight and berry box number) and berry texture traits. Berry juiciness had moderate (r = −0.36 to −0.41) negative correlations with berry texture (springiness, gumminess, and chewiness). TSS were positively correlated with most traits, except for springiness (which had a negative relationship) and chewiness, wherein no correlation was observed.
Comparison of mean springiness, chewiness, gumminess, berry box weight, berry box number, and juiciness for select grape use-types: juice (n = 1), table (n = 19), and wine (n = 4), and table grape/raisin breeding lines (n = 18). Mean gumminess and chewiness were measured as force in Newtons (N) across replicates.
Pearson’s correlation coefficient among grape berry quality traits, using a panel of combined wine and table grape cultivars and advanced breeding lines. TSS, total soluble solids.
For the four lines that experienced additional cold storage, cultivar had a significant effect on berry quality traits, but harvest storage length did not (p ≥ 0.05) (Table 4). Within a cultivar, gumminess, chewiness, springiness, and berry box weight all showed a decrease compared to the short cold storage treatment (~2 wk), but this was not significant.
Table grape fruit quality and texture metrics for berries evaluated at 2 and 6 wk postharvest under cold storage (4°C).
11-3527 Mapping population
Significant variability in berry weight by volume, number, juiciness, springiness, gumminess, and chewiness was observed among genotypes in the 11-3527 mapping population (Table 5 and Supplemental Table 5). For gumminess, a single QTL was found on chr 13 (2019) and 18 (2020) in each year, explaining 16% and 14% of the variability observed, respectively (Table 6). Similarly-located individual QTL were also associated with chewiness (2019 and 2020) and springiness (2020 only) in the same respective years. Berry box number had three associated QTL identified in 2019, located on chr 14, 18, and 19. These same QTL on 18 and 19 were also confirmed to be associated with berry box number in 2020. Using an additive model, these QTL explained 40 to 48% of the variability observed in each year, respectively. A single QTL associated with berry box weight was observed on chr 14. A single QTL was also identified for juiciness on chr 19 in 2019 (pos 53.4 Mb) and 2020 (pos 61.7 Mb), explaining 15 to 17% of the variability that was observed.
Variability in fruit quality values for 11-3527 mapping population, evaluated in 2019 (19) and 2020 (20). TSS, total soluble solids.
Fruit quality-associated quantitative trait locus (QTL) identified in the 11-3527 grape population. Chr, chromosome; pos, position of the marker in megabases (Mb) on each respective chromosome; LOD, logarithm of odds; % var, percentage of the observed variation explained by the marker; BLUPs, best linear unbiased predictors.
Using BLUPs, QTL associated with gumminess and chewiness were identified on chr 15 (pos 59.9 Mb), 17 (pos 25.2 Mb), and 18 (pos 14 Mb). Individual QTL explained 13 to 16% of the variability that was observed. Using an additive model, the QTL explained 34.4% and 27.3% of the variability for gumminess and chewiness, respectively, that was observed across years. An additional QTL for berry box number was found on chr 18 (pos 74.1 Mb), and a QTL for juiciness was found on chr 19 (pos 53.4 Mb). These QTL explained 35% and 13% of the variability that was observed across years for berry box number and juiciness, respectively. No significant QTL were found for berry box weight or TSS.
Discussion
Berry quality traits such as texture, juiciness, and size are important characteristics for consumer acceptance and breeding of table grape cultivars. However, breeding for fruit quality can be complicated because non-table grape types (including species or individuals with desirable characteristics for abiotic and biotic stress resistance) often have “poor” texture with chewy, gelatinous flesh and thick skins. In addition, it has been shown that traits like berry weight can strongly correlate with berry texture, and that texture can vary within a cluster (Coombe and McCarthy 2000, Brillante et al. 2017, Cheng et al. 2023). While most grape berry texture studies have evaluated hundreds of individual berries within a use-type using probe-based techniques, few have looked at bulk samples within or among use-types.
Using a bulk sample approach, berry texture traits did not vary consistently after 2 wk postveraison across ripening in the table grape Scarlet Royal, with the exception of TSS. This contrasted with previous work that evaluated individual berries sorted by TSS, where significant differences in texture were observed between “early” and “late” ripening berries (Río Segade et al. 2013). Small changes were observed from week to week, highlighting the variability in cluster ripening across the vineyard, but these changes were not typically significant when compared across weeks for Scarlet Royal. Different cultivars and even grape types (wine versus table versus raisin) may have more ripening variability postveraison. While this work will need to be repeated with more cultivars, this study suggests that bulk sampling and testing could eliminate the time-consuming tasks of sorting and selecting individual berries for texture analyses, and for at least some cultivars, could allow for a single “ripeness” timepoint evaluation for texture.
Previously, berry weight has been negatively associated with texture traits (gumminess, springiness, and chewiness), with lighter berries having lower springiness and chewiness values (Brillante et al. 2017, Cheng et al. 2023). These prior studies have mainly been performed with winegrapes, which have a smallish berry with thick skin and gelatinous flesh, or with a small number of table grapes. However, our data, which combined a diverse set of table, wine, and breeding line grapes, showed a weak correlation among berry box weight or berry box number and texture traits. Only juiciness, which had a high correlation with berry box weight and number, had a moderate (r ≥ 0.3) strongly significant association with berry texture. However, these data are based on bulk sampling of a cluster, and not on TSS-sorted berries. The observed differences relative to previous studies could be based on the diversity of the evaluated cultivars or on the methodology used (single berries versus bulk, Kramer Shear cell versus penetration test). Texture differences among the cultivars and use-types showed wide variability within a use-type, but also significant differences among types.
On average, winegrapes had more than five times as many berries per box compared to table grapes, but only 15% more box weight and almost twice (47%) as much juice. These data would suggest that while weight does play a large role in juice availability, texture traits related to use-type also play a role, consistent with previous results where texture traits grouped strongly by use-type (Sato and Yamada 2003). However, no single texture trait that was measured in this study was sufficient to differentiate cultivars from among or within use-types. Winegrapes with their gelatinous flesh and thick skin had lower mean springiness, chewiness, and juiciness compared to table grape cultivars. Yet individual cultivars such as Cabernet Sauvignon from California State University, Fresno, had similar values to table grape cultivars such as Solbrio, which has thin skin and crisp flesh (Ledbetter 2019). Among table grape cultivars, Valley Pearl and Scarlet Royal are known to have a firm and meaty, but not crisp, texture, while Ivory Seedless and Solbrio are known for having crisp texture (Ledbetter 2016, 2019). Nevertheless, texture values (springiness, gumminess, and chewiness) had no similarities for the firm and meaty cultivars or for the crisp cultivars. This suggests that a combination of metrics, rather than just a single metric, may be needed to more accurately select grapes that match certain qualitative descriptors. Subsequently, both food scientists and sensory panels should be included in future work on texture.
Using a biparental mapping population, QTL were identified that were associated with berry chewiness, springiness, gumminess, berry box weight, berry box number, and juiciness. Only two QTL that were associated with berry texture (gumminess, springiness, and chewiness) were identified on the beginning of chr 13 and 18, explaining 14 to 17% of the observed variability. These QTL were only identified in either 2019 or 2020 (i.e., not in both years) and were associated with all three texture metrics. These QTL regions were consistent with a previous study that identified QTL on chr 13 and 18 that were associated with berry firmness (Carreño et al. 2015). These individual QTL explained 12 to 19% of the observed variability, and for any given cross and year, a QTL was identified on chr 13 or 18, but not on both. However, based on BLUPs, which use data across years, a similarly located QTL identified on chr 18 was found, and additional QTLs on chr 15 and 17 were identified. While chr 13, 17, and 18 have been associated with berry texture in previous studies, to our knowledge, this is the first report of QTL on chr 15 that is associated with texture (Carreño et al. 2015, Guo et al. 2019, Lin et al. 2023).
In this study, QTL for berry box weight and box number were identified on chr 14, rather than on chr 7, 9, or 18, where QTL for berry weight have previously been identified (Doligez et al. 2013, de Sousa-Moreira et al. 2024). However, this QTL was co-located near previously identified QTL for berry number using the same population, highlighting that berry box weight may be a function of berry number and not true berry weight (de Sousa-Moreira et al. 2024). Interestingly, a novel QTL for juiciness was found on chr 19, and was not associated with berry weight and number or berry box weight or box number. This juice-related QTL region was present in both 2019 and 2020, suggesting that it is stable across years. It was also confirmed using BLUPs. To our knowledge, this is the first QTL associated with grape berry juiciness, and future studies on texture may want to include juice content. Future work should examine genes in this region to determine if any candidate genes may be present.
Conclusion
Grape berry texture is a critical component of a successful table grape cultivar. Breeding for berry texture is complex but should improve as molecular markers found in our work and in others’ are implemented into breeding programs. Future work should look further into the traits, or combination of traits, that result in the regional-specific textures that consumers seek.
Supplemental Data
The following supplemental materials are available for this article in the Supplemental tab above:
Supplemental Table 1 Grape lines (cultivars and breeding lines) evaluated for berry texture traits in this study.
Supplmental Table 2 Genotype file used for input into R/QTL, indicating each specific vine (ID) and the haplotype (1 to 4) at each locus.
Supplemental Table 3 Mean values for a panel of table and wine grape cultivars and breeding lines evaluated in this study. TSS, total soluble solids.
Supplemental Table 4 Ad hoc separation of mean values for a panel of table and wine grape cultivars and breeding lines evaluated in this study using Tukey’s honestly significant difference test (p = 0.05).
Supplemental Table 5 Mean values for berry fruit quality traits measured in 2019 and 2020 in an F1 biparental mapping population (Vitis cinerea × Vitis vinifera). TSS, total soluble solids.
Data Availability
All data underlying this study are included in the manuscript and its supplemental information.
Footnotes
We acknowledge the technical assistance of Marcos Alvarez, Jenny Vasquez, Cameron Saunders, Molly Irwin, Kern Vasquez, and Jeff DeLong. We thank Dr. Sonet van Zyl of California State University at Fresno for providing grape samples.
Burhans A and Naegele RP. 2025. Bulk sample evaluation of grape berry texture identifies differences among breeding lines and cultivars and identifies novel QTL associated with berry texture and juiciness. Am J Enol Vitic 76:0760018. DOI: 10.5344/ajev.2025.24060
By downloading and/or receiving this article, you agree to the Disclaimer of Warranties and Liability. If you do not agree to the Disclaimers, do not download and/or accept this article.
- Received November 2024.
- Accepted May 2025.
- Published online July 2025
This is an open access article distributed under the CC BY 4.0 license.
References
Vol 76 Issue 2
Jump to section
Related Articles
Cited By...
More from this TOC section
Similar Articles
