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Effect of environment and genotypes on the physicochemical quality of the grains of newly developed wheat inbred lines.

Mutwali NI, Mustafa AI, Gorafi YS, Mohamed Ahmed IA - Food Sci Nutr (2015)

Bottom Line: To meet the increased demand for wheat consumption, wheat cultivation in Sudan expanded southward to latitudes lower than 15°N, entering a new and warmer environment.In this study, we assessed the end-use quality attributes of 20 wheat genotypes grown in three different environments in the Sudan (Wad Medani, Hudeiba, and Dongola).The findings obtained, covered wide ranges of test weight (TW, 76.6-85.25 kg/hL), thousand kernel weight (TKW, 28.70-48.48 g), protein (PC, 9.96-14.06%), wet gluten (WG, 28.63-46.53%), gluten index (GI, 36.36-92.77%), water holding capacity (WHC, 168.42-219.32%), falling number (FN, 508.00-974.67 sec), and sedimentation value (SV, 19.00-40.00 mL).

View Article: PubMed Central - PubMed

Affiliation: Department of Food Science and Technology Faculty of Agriculture University of Khartoum Shambat 13314 Khartoum Sudan.

ABSTRACT
To meet the increased demand for wheat consumption, wheat cultivation in Sudan expanded southward to latitudes lower than 15°N, entering a new and warmer environment. Consequently, wheat breeders developed several wheat genotypes with high yields under these environmental conditions; however, the evaluation of the end-use quality of these genotypes is scarce. In this study, we assessed the end-use quality attributes of 20 wheat genotypes grown in three different environments in the Sudan (Wad Medani, Hudeiba, and Dongola). The results showed significant differences (P ≤ 0.01) in all quality tests among environments, genotypes and genotypes Versus environments. The findings obtained, covered wide ranges of test weight (TW, 76.6-85.25 kg/hL), thousand kernel weight (TKW, 28.70-48.48 g), protein (PC, 9.96-14.06%), wet gluten (WG, 28.63-46.53%), gluten index (GI, 36.36-92.77%), water holding capacity (WHC, 168.42-219.32%), falling number (FN, 508.00-974.67 sec), and sedimentation value (SV, 19.00-40.00 mL). Analysis of the traits, genotypes, and traits versus genotypes showed varied correlations in the three growing environments. The genotype G3 grown in either one or all of the three environments exhibits worthy performance and stability for most of the tested quality traits. The crossing of this genotype with high yield genotypes could produce cultivars with sufficient quality and marketability.

No MeSH data available.


Related in: MedlinePlus

Biplot based on principal component analysis for grain quality traits in 20 wheat genotypes (G1–G20) grown in three different environments (Wad Medani, Hudeiba, and Dongola). The biplots showed the interrelations between the quality traits (A) and the environments (B). Bidimensional clustering analysis is presenting the relationships between the genotypes (C). TKW, Thousand kernel weight (g); TW, Test weight (kg/hL); AC, Ash content; FC, Fat content; PC, Protein content; FN, Falling number; WHC, Water holding capacity; GI, Gluten index; SV, Sedimentation value; WG, Wet gluten; DG, Dry gluten.
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fsn3313-fig-0001: Biplot based on principal component analysis for grain quality traits in 20 wheat genotypes (G1–G20) grown in three different environments (Wad Medani, Hudeiba, and Dongola). The biplots showed the interrelations between the quality traits (A) and the environments (B). Bidimensional clustering analysis is presenting the relationships between the genotypes (C). TKW, Thousand kernel weight (g); TW, Test weight (kg/hL); AC, Ash content; FC, Fat content; PC, Protein content; FN, Falling number; WHC, Water holding capacity; GI, Gluten index; SV, Sedimentation value; WG, Wet gluten; DG, Dry gluten.

Mentions: To profoundly determine the multivariate relationships between the grain end‐use quality traits and the growing environments of 20 wheat genotypes, biplot analysis was carried out by comparing the eigenvalues of PC1 and PC2 of principal component analysis (PCA) for both the genotypes and the quality traits (Fig. 1A–C). Regarding the interrelation between the traits and genotypes, the results of the first two PC axes (PC1, 39.89% and PC2, 23.37%) accounted for about 63.26% of the total variability reflecting the complexity of the variation between the plotted components (Fig. 1A). In the biplot, vectors of traits (variables) showing acute angle are positively correlated, whereas those formed obtuse or straight angles are negatively correlated, and those with right angle have no correlation. The distance between the raw (genotypes) is interpreted in terms of similarity. Regarding the traits, PC1 had the breadmaking quality parameters (DG, WG, PC, GI, and WHC) as the principal components, and FN and MC to a lesser extent while, PC2 had the SV, FC, and TW as the primary elements. The cosine of the angles between vectors indicated a high positive correlation between WHC, FN, and GI in the positive direction. These three traits were also positively correlated with FC in the positive direction and AC in the negative direction. High positive correlation was also observed between PC, WG, and DG and between SV and FC, and similarly between TW, TKW, and MC. In contrast, WHC, FN, and GI were negatively correlated with other breadmaking quality parameters mainly PC, WG, and DG and with grain physical characteristics such as TW, TKW, and MC. The SV was also negatively correlated with AC, TW, and TKW. Overall, the biplot analysis exhibits three groups of the traits based on their phenotypic associations, those include; gluten, starch, and milling quality characteristics (GI, WHC, FN, and AC) group, breadmaking quality attributes (SV, PC, WG, and DG) group, and grain physical and marketing characteristics (MC, TKW, and TW). These results shows some differences from that of the correlation analysis among pairs of characters as the biplot describes the interrelationships among all characters concurrently based on the overall contribution of the data (Yan and Fregeau‐Reid 2008).


Effect of environment and genotypes on the physicochemical quality of the grains of newly developed wheat inbred lines.

Mutwali NI, Mustafa AI, Gorafi YS, Mohamed Ahmed IA - Food Sci Nutr (2015)

Biplot based on principal component analysis for grain quality traits in 20 wheat genotypes (G1–G20) grown in three different environments (Wad Medani, Hudeiba, and Dongola). The biplots showed the interrelations between the quality traits (A) and the environments (B). Bidimensional clustering analysis is presenting the relationships between the genotypes (C). TKW, Thousand kernel weight (g); TW, Test weight (kg/hL); AC, Ash content; FC, Fat content; PC, Protein content; FN, Falling number; WHC, Water holding capacity; GI, Gluten index; SV, Sedimentation value; WG, Wet gluten; DG, Dry gluten.
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Related In: Results  -  Collection

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fsn3313-fig-0001: Biplot based on principal component analysis for grain quality traits in 20 wheat genotypes (G1–G20) grown in three different environments (Wad Medani, Hudeiba, and Dongola). The biplots showed the interrelations between the quality traits (A) and the environments (B). Bidimensional clustering analysis is presenting the relationships between the genotypes (C). TKW, Thousand kernel weight (g); TW, Test weight (kg/hL); AC, Ash content; FC, Fat content; PC, Protein content; FN, Falling number; WHC, Water holding capacity; GI, Gluten index; SV, Sedimentation value; WG, Wet gluten; DG, Dry gluten.
Mentions: To profoundly determine the multivariate relationships between the grain end‐use quality traits and the growing environments of 20 wheat genotypes, biplot analysis was carried out by comparing the eigenvalues of PC1 and PC2 of principal component analysis (PCA) for both the genotypes and the quality traits (Fig. 1A–C). Regarding the interrelation between the traits and genotypes, the results of the first two PC axes (PC1, 39.89% and PC2, 23.37%) accounted for about 63.26% of the total variability reflecting the complexity of the variation between the plotted components (Fig. 1A). In the biplot, vectors of traits (variables) showing acute angle are positively correlated, whereas those formed obtuse or straight angles are negatively correlated, and those with right angle have no correlation. The distance between the raw (genotypes) is interpreted in terms of similarity. Regarding the traits, PC1 had the breadmaking quality parameters (DG, WG, PC, GI, and WHC) as the principal components, and FN and MC to a lesser extent while, PC2 had the SV, FC, and TW as the primary elements. The cosine of the angles between vectors indicated a high positive correlation between WHC, FN, and GI in the positive direction. These three traits were also positively correlated with FC in the positive direction and AC in the negative direction. High positive correlation was also observed between PC, WG, and DG and between SV and FC, and similarly between TW, TKW, and MC. In contrast, WHC, FN, and GI were negatively correlated with other breadmaking quality parameters mainly PC, WG, and DG and with grain physical characteristics such as TW, TKW, and MC. The SV was also negatively correlated with AC, TW, and TKW. Overall, the biplot analysis exhibits three groups of the traits based on their phenotypic associations, those include; gluten, starch, and milling quality characteristics (GI, WHC, FN, and AC) group, breadmaking quality attributes (SV, PC, WG, and DG) group, and grain physical and marketing characteristics (MC, TKW, and TW). These results shows some differences from that of the correlation analysis among pairs of characters as the biplot describes the interrelationships among all characters concurrently based on the overall contribution of the data (Yan and Fregeau‐Reid 2008).

Bottom Line: To meet the increased demand for wheat consumption, wheat cultivation in Sudan expanded southward to latitudes lower than 15°N, entering a new and warmer environment.In this study, we assessed the end-use quality attributes of 20 wheat genotypes grown in three different environments in the Sudan (Wad Medani, Hudeiba, and Dongola).The findings obtained, covered wide ranges of test weight (TW, 76.6-85.25 kg/hL), thousand kernel weight (TKW, 28.70-48.48 g), protein (PC, 9.96-14.06%), wet gluten (WG, 28.63-46.53%), gluten index (GI, 36.36-92.77%), water holding capacity (WHC, 168.42-219.32%), falling number (FN, 508.00-974.67 sec), and sedimentation value (SV, 19.00-40.00 mL).

View Article: PubMed Central - PubMed

Affiliation: Department of Food Science and Technology Faculty of Agriculture University of Khartoum Shambat 13314 Khartoum Sudan.

ABSTRACT
To meet the increased demand for wheat consumption, wheat cultivation in Sudan expanded southward to latitudes lower than 15°N, entering a new and warmer environment. Consequently, wheat breeders developed several wheat genotypes with high yields under these environmental conditions; however, the evaluation of the end-use quality of these genotypes is scarce. In this study, we assessed the end-use quality attributes of 20 wheat genotypes grown in three different environments in the Sudan (Wad Medani, Hudeiba, and Dongola). The results showed significant differences (P ≤ 0.01) in all quality tests among environments, genotypes and genotypes Versus environments. The findings obtained, covered wide ranges of test weight (TW, 76.6-85.25 kg/hL), thousand kernel weight (TKW, 28.70-48.48 g), protein (PC, 9.96-14.06%), wet gluten (WG, 28.63-46.53%), gluten index (GI, 36.36-92.77%), water holding capacity (WHC, 168.42-219.32%), falling number (FN, 508.00-974.67 sec), and sedimentation value (SV, 19.00-40.00 mL). Analysis of the traits, genotypes, and traits versus genotypes showed varied correlations in the three growing environments. The genotype G3 grown in either one or all of the three environments exhibits worthy performance and stability for most of the tested quality traits. The crossing of this genotype with high yield genotypes could produce cultivars with sufficient quality and marketability.

No MeSH data available.


Related in: MedlinePlus