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35 results for “fruit quality”
Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach
Background <p>Fruit quality traits have a significant effect on consumer acceptance and subsequently on peach (<i>Prunus persica</i> (L.) Batsch) consumption. Determining the genetic bases of key fruit quality traits is essential for the industry to improve fruit quality and increase consumption. Pedigree-based analysis across multiple peach pedigrees can identify the genomic basis of complex traits for direct implementation in marker-assisted selection. This strategy provides breeders with better-informed decisions and improves selection efficiency and, subsequently, saves resources and time.</p> Results <p>Phenotypic data of seven F<sub>1</sub> low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. One major QTL for fruit blush was found on linkage group 4 (LG4) at 40–46 cM that explained from 20 to 32% of the total phenotypic variance and showed three QTL alleles of different effects. For soluble solids concentration (SSC), one QTL was mapped on LG5 at 60-72 cM and explained from 17 to 39% of the phenotypic variance. A major QTL for titratable acidity (TA) co-localized with the major locus for low-acid fruit (<i>D</i>-locus). It was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles.</p> Conclusions <p>A multi-family-based QTL discovery approach enhanced the ability to discover a new TA QTL at the distal end of LG5 and validated other QTLs which were reported in previous studies. Haplotype characterization of the mapped QTLs distinguishes this work from the previous QTL studies. Identified predictive SNPs and their original sources will facilitate the selection of parents and/or seedlings that have desired QTL alleles. Our findings will help peach breeders develop new predictive, DNA-based molecular marker tests for routine use in marker-assisted breeding.</p>
Data from: Shelf life and quality of tomato (Lycopersicon esculentum Mill.) fruits as affected by neem leaf extract dipping and beeswax coating
<p>The data was generated to investigate the effect of Beeswax (BW) coating and Neem leaf extract (NLE) dipping on the shelf life and quality of tomatoes (<em>Lycopersicon esculentum</em> Mill.) over a storage period of 36 days. A factorial combination of four levels of Neem plant extract (control, 15%, 20% and 25%) and four levels of beeswax coating (control, 3%, 6% and 9%) storage treatments with three replications were applied on fully matured green tomatoes in the study. The treatments were arranged in a randomized complete block design. The average storage room air temperature and relative humidity varied from 15.2 ºC to 20.4 ºC and 55.53% to 69.46% RH during 36 days of storage period at Haramaya University from February to April 2020. Data were recorded on 4, 8, 12, 16, 20, 24, 28, 32, and 36 days after storage. Data on physiological loss in weight, chemical compositions (total soluble solids, pH, titratable acidity, and ascorbic acid), decay (%), percentage marketability, and shelf life were assessed at an interval of four days during 36 days of storage period under ambient conditions.</p>
Data from: Shelf life and quality of tomato (Lycopersicon esculentum Mill.) fruits as affected by neem leaf extract dipping and beeswax coating
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Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach
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Data from: No evidence of positive assortative mating for genetic quality in fruit flies
In sexual populations the effectiveness of selection will depend on how gametes combine with respect to genetic quality. If gametes with deleterious alleles are likely to combine with one another, deleterious genetic variation can be more easily purged by selection. Assortative mating, where there is a positive correlation between parents in a phenotype of interest such as body size, is often observed in nature, but does not necessarily reveal how gametes ultimately combine with respect to genetic quality itself. We manipulated genetic quality in fruit fly populations using an inbreeding scheme designed to provide an unbiased measure of mating patterns. While inbred flies had substantially reduced reproductive success, their gametes did not combine with those of other inbred flies more often than expected by chance, indicating a lack of positive assortative mating. Instead, we detected a negative correlation in genetic quality between parents, i.e., disassortative mating, which diminished with age. This pattern is expected to reduce the genetic variance for fitness, diminishing the effectiveness of selection. We discuss how mechanisms of sexual selection could produce a pattern of disassortative mating. Our study highlights that sexual selection has the potential to either increase or decrease genetic load.
Figure 2 in Influence of Trichoderma harzianum and Bacillus thuringiensis with reducing rates of NPK on growth, physiology, and fruit quality of Citrus aurantifolia
Figure 2. Effect of two biofertilizers mixing with different level of NPK on specific leaf weight of limau nipis. Error bars indicate ± S. E. Different small case letters in mean value bars represent statistical difference at 5% level. T0, control; T1, NPK 100% (100 g); T2, T.harzianum 50% (5g) + NPK 50%; T3, B. thuringiensis 50% (5g) + NPK 50%; T4, T.harzianum 75% (7.5g) + NPK 25%; T5,B. thuringiensis 75% (7.5 g) + NPK 25%, T6, 100% T. harzianum (10 g); T7, 100% B. thuringiensis (10 g).
Supplementary material 1 from: Ruland KT, Montague T, Helwi P (2023) Impact of hail-netting on Vitis vinifera L. canopy microclimate, leaf gas exchange, fruit quality, and yield in a semi-arid environment. Viticulture Data Journal 555: e108805. https://doi.org/10.3897/vdj.555.e108805
Hail-netting on vines
Supplementary material 3 from: Ruland KT, Montague T, Helwi P (2023) Impact of hail-netting on Vitis vinifera L. canopy microclimate, leaf gas exchange, fruit quality, and yield in a semi-arid environment. Viticulture Data Journal 555: e108805. https://doi.org/10.3897/vdj.555.e108805
Compact canopy under hail-netting
Supplementary material 2 from: Ruland KT, Montague T, Helwi P (2023) Impact of hail-netting on Vitis vinifera L. canopy microclimate, leaf gas exchange, fruit quality, and yield in a semi-arid environment. Viticulture Data Journal 555: e108805. https://doi.org/10.3897/vdj.555.e108805
Li-Cor-6400 machines below hail-netting
Data from: No evidence of positive assortative mating for genetic quality in fruit flies
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Single and double mutations in tomato ripening transcription factors have distinct effects on fruit development and quality traits
GEO Series GSE163745. Solanum lycopersicum. 8 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic and Epigenetic Signatures of Tomato Fruit After Postharvest UV-C Irradiation Are Associated with the Maintenance of Fruit Quality
GEO Series GSE290063. Solanum lycopersicum. 18 samples. Type: Methylation profiling by high throughput sequencing; Other.
Comparative gene expression profiles on contrasting mealy phenotypes in peach fruits reveal candidate genes associated with fruit quality
GEO Series GSE33027. Prunus persica. 8 samples. Type: Expression profiling by array.
The effect of Epigallocatechin-3-Gallate (EGCG), a main active ingredient in tea residues, on improving fruit quality and prolonging postharvest storage in apple
GEO Series GSE248662. Malus domestica. 6 samples. Type: Expression profiling by high throughput sequencing.
microRNA396 is a coordinative regulator that can be modulated to reconcile the reverse relationship between fruit size and quality in tomato
GEO Series GSE265866. Solanum lycopersicum. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
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