Starch Granule Size Distribution and Pasting Properties from 14 Soft Wheat Varieties in Huaihe River Basin

Abstract

Starch granule size distribution plays a vital role in determining the physicochemical properties and processing quality of soft wheat. This study analyzed fourteen soft wheat varieties cultivated in the Huaihe River Basin, an agriculturally important but underrepresented region, to evaluate starch granule size distribution, pasting properties, and their interrelationship. The starch granules were categorized into four size classes, with the volume dominated by A-type granules (>10 μm), while numerically, the majority were <2.8 μm. Pasting characteristics measured by the Rapid Visco Analyzer revealed substantial variation among genotypes. Varieties with a higher proportion of A-type granules exhibited stronger pasting profiles, including higher peak and final viscosities, whereas those with more B-type granules showed lower values. These observations indicate a clear relationship between granule morphology and starch functionality. In the present study, there was a significant positive correlation between peak viscosity, final viscosity, and set-back viscosity. The volume % of granules > 10 μm showed a positive correlation with peak viscosity (r = 0.53 *), final viscosity (r = 0.57 *), and set-back (r = 0.53 *), while the volume percentage of granules < 10 μm was significantly negatively correlated with peak viscosity (r = −0.53 *), final viscosity (r = −0.57 *), and set-back (r = −0.53 *) value. It indicated that the higher the percentage of granules > 10 μm, the higher the peak viscosity, final viscosity, and set-back value in soft wheat grain.

1. Introduction

Wheat (Triticum aestivum L.) is a key cereal crop that contributes around 35% of global grain production after rice and maize. Its broad cultivation and consumption can be attributed to its nutritional value, versatility, and affordability. Due to its ability to grow in a wide range of temperatures, including latitudes between 47° S and 57° N, wheat can be produced in a variety of agroecological zones [1]. Starch, which makes up around 70% of the dry weight of wheat kernels and is one of their most significant biochemical constituents, is crucial for establishing the quality of food products as well as their end-use process [2]. The way starch gelatinizes and retrogrades affects the texture, shelf life, and structural integrity of processed wheat-based foods such as bread, noodles, pastries, and biscuits. Amylose and amylopectin are the two primary polymers that make up wheat starch. The physicochemical and pasting characteristics of wheat flour are greatly influenced by the ratio and molecular structure of these polysaccharides. Although amylose content can affect gelatinization [3], some studies report that higher amylose may decrease peak viscosity, while others report an increase, depending on conditions.

Granules of starch that differ in size, shape, and content are found in wheat. Generally, these granules fall into one of three categories: A-type (>10 μm), B-type (5–10 μm), or C-type (<5 μm) [4]. One important factor influencing the pasting characteristics of wheat starch is the size distribution of the granules, specifically the proportions of A- and B-type granules. Prior studies have mostly focused on the morphological characterization of individual granules or the starch composition (such as the amylose/amylopectin ratio), despite the growing emphasis on improving wheat quality. The wider impact of granule size distribution on pasting performance, particularly in soft wheat cultivars, has received less attention [3,5], especially in regions like China’s Huaihe River Basin, where temperature, humidity, and irrigation during grain filling can affect starch deposition and, ultimately, pasting properties [6]. Soft wheat, which is frequently used to make cakes and biscuits, needs particular starch properties to guarantee a delicate crumb structure and reduced viscosity. Differences in granule size distribution contribute to these characteristics, but these mechanisms are still not fully understood

With its distinct meteorological, soil, and agronomic circumstances, the Huaihe River Basin is one of China’s most significant wheat-producing regions. Although it is strategically important for soft wheat production, little is known about the starch structure and functionality of wheat genotypes from this area. Temperature, humidity, and irrigation levels throughout the grain-filling stage can affect the production and deposition of starch granules, resulting in variations in the distribution of granule sizes and, ultimately, pasting properties. Rarely, though, have these regional dynamics been included in international debates about the quality of wheat starch. The current study is novel since it specifically examines fourteen genotypes of soft wheat grown in the Huaihe River Basin. These genotypes were chosen because of their diversity in grain and starch properties as well as their agronomic performance. The work sheds light on how genotype interactions and regional environmental factors impact starch performance, an issue that has not received enough attention in the literature, by examining the link between pasting qualities and starch granule size distribution across this sample.

Additionally, a number of recent studies have highlighted the fact that the properties of starch granules are influenced by processing and environmental factors in addition to genotype. For instance, starch size affects gluten rearrangement during dough mixing, which in turn affects dough strength and the polymerization of glutenin subunits [7]. The development of a fine, continuous network structure in noodle dough has been demonstrated to be significantly influenced by the B-type granules [8]. In soft wheat flour post-milling purification, the presence of small-diameter granules, namely C-type, has been linked to improved flour yield and clarity [9,10]. Furthermore, both cultivar and agronomic practices influence the formation of A-type starch granules, characterized by diameters greater than 10 μm [11].

Starch pasting and retrogradation behavior are also affected by its interaction with other food components and additives. For instance, soluble fibers such as Tara gum and κ-carrageenan have been reported to increase peak viscosity while reducing setback viscosity and pasting temperature [12]. In contrast, insoluble fibers show minimal effect. Additionally, wheat oligopeptides (WOP) help reduce retrogradation during storage by improving water-holding capacity and decreasing gel hardness [13]. Fructose and sucrose affect the beginning of gelatinization and the viscosity of starch paste [14]. Pasting and retrogradation behavior are also influenced by how starch interacts with other components and food additives. For example, it has been shown that soluble fibers, such as Tara gum and k-carrageenan, increase peak viscosity and decrease setback viscosity and pasting temperature [1], whereas insoluble fibers have minimal impact. Similarly, wheat oligopeptides (WOP) aid in minimizing retrogradation during storage by enhancing water-holding capacity and decreasing gel hardness [15,16].

To guarantee lower gluten content and ideal starch qualities for baking applications, soft wheat refinement techniques and, in particular, post-milling purification flour are essential. In order to achieve the fine, consistent texture needed for cakes and biscuits, methods that improve flour softness, such as sieving and re-grinding, have become crucial [17]. Concerns with protein content, moisture content, gluten strength, and farinograph stability have been brought to light by studies on Chinese wheat cultivars, which frequently fall short of national quality standards [18]. Bimodal granule distribution patterns and Solvent Retention Capacity (SRC) profiles are two analytical techniques that have become effective markers for evaluating the functional quality of Chinese soft wheat cultivars [19,20].

Given this background, the main hypothesis of the current study is that key pasting parameters like peak viscosity, setback viscosity, final viscosity, and retrogradation behavior are significantly influenced by variations in the starch granule size distribution, particularly the proportions of A- and B-type granules. Improving soft wheat’s processing efficiency and end-use quality requires an understanding of these effects, particularly for product categories that require specific starch textures and stability profiles. The Huaihe River Basin experiences unique climate patterns during grain filling, which may influence starch deposition differently than in other regions.

This study evaluates starch granule size and pasting properties in 14 soft wheat genotypes grown in the Huaihe River Basin to improve understanding of their functional quality. It also provides useful information for breeders and processors looking to maximize the functionality of soft wheat.

2. Results

2.1. Yield

Grain yield showed considerable variation among the 14 soft wheat cultivars during the 2022–2023 and 2023–2024 growing seasons. As illustrated in Figure 1, grain yield varied significantly among cultivars. Huacheng 2019 produced the highest yield (11.572 t/ha) during the 2022–2023 season, followed by Xunong 029 and Lomai 28, indicating strong yield potential. In contrast, Zhoumai 30 recorded the lowest yield (7.667 t/ha), reflecting its relatively weaker performance. Similar trends were observed in the 2023–2024 season, with Huacheng 2019, Lomai 28, and Xunong 029 maintaining consistently high yields across the years. These results emphasize the importance of stable high-yielding genotypes and their potential role in improving wheat productivity under similar agro-climatic conditions.

2.2. Volume Distibution

In terms of volume percentage, the laser diffraction analysis showed that the starch granule size distribution differed significantly between genotypes, especially between A-type (>10 μm) and B-type (≤10 μm) starch granules (

Table 1. Comparison of wheat starch volume of 14 soft wheat varieties during 2022–2024.

Year	Variety	Particle Diameter of Starch Granule/μm
		<2.8 μm	2.8–10 μm	10–22 μm	>22 μm	≤10 μm	>10 μm
2022–2023	Annon 0711	9.33 ± 0.06 h	27.20 ± 0.08 c	30.61 ± 0.12 e	32.86 ± 0.18 d	36.53 ± 0.06 gh	63.47 ± 0.06 de
	Quanmai 31	9.20 ± 0.02 hi	24.93 ± 0.13 f	31.79 ± 0.11 d	34.08 ± 0.08 c	34.13 ± 0.14 j	65.87 ± 0.14 b
	Mengmai 0818	9.81 ± 0.19 g	30.41 ± 0.1 b	27.88 ± 0.31 h	31.9 ± 0.39 e	40.22 ± 0.09 e	59.78 ± 0.09 g
	Lomai 28	8.38 ± 0.29 j	22.50 ± 0.34 g	32.64 ± 1.18 c	36.48 ± 1.09 a	30.88 ± 0.23 k	69.12 ± 0.23 a
	Zheng Mai 132	10.64 ± 0.26 e	30.31 ± 0.21 b	28.94 ± 0.21 g	30.1 ± 0.45 fg	40.95 ± 0.29 d	59.05 ± 0.29 h
	Bainong 307	10.07 ± 0.19 fg	30.56 ± 0.4 b	29.84 ± 0.1 f	29.54 ± 0.32 g	40.63 ± 0.38 de	59.37 ± 0.38 gh
	Zhou Mai 30	11.86 ± 0.06 b	31.37 ± 0.03 a	29.77 ± 0.03 f	27.00 ± 0.07 i	43.23 ± 0.07 b	56.77 ± 0.07 j
	Huacheng 865	10.62 ± 0.01 e	30.29 ± 0.02 b	29.16 ± 0.07 fg	29.92 ± 0.1 g	40.92 ± 0.03 d	59.08 ± 0.03 h
	Longke 1109	11.04 ± 0.01 d	26.79 ± 0.06 d	33.86 ± 0.02 b	28.31 ± 0.1 h	37.83 ± 0.08 f	62.17 ± 0.08 f
	XuNong 029	11.39 ± 0.02 c	25.48 ± 0.13 e	35.04 ± 0.14 a	28.10 ± 0.04 h	36.86 ± 0.15 g	63.14 ± 0.15 e
	Huacheng 2019	10.36 ± 0.05 ef	25.82 ± 0.15 e	33.10 ± 0.14 bc	30.72 ± 0.17 f	36.18 ± 0.16 h	63.82 ± 0.16 d
	Huaimai 44	12.65 ± 0.25 a	31.18 ± 0.14 a	30.80 ± 0.11 e	25.36 ± 0.10 j	43.83 ± 0.12 a	56.17 ± 0.12 k
	Wanken 22	8.99 ± 0.09 i	26.72 ± 0.11 d	28.70 ± 0.05 g	35.59 ± 0.23 b	35.70 ± 0.20 i	64.30 ± 0.20 c
	Wanken 9	11.83 ± 0.25 b	30.26 ± 0.23 b	30.67 ± 0.47 e	27.24 ± 0.09 i	42.09 ± 0.39 c	57.91 ± 0.39 i
	F-Value	92.89 **	187.73 **	66.434 **	384.371 **	144.52 **	144.52 **
2023–2024	Annon 0711	8.56 ± 0.41 i	28 ± 0.63 c	30.11 ± 0.68 e	33.33 ± 1.36 d	36.56 ± 0.83 gh	63.44 ± 0.83 cd
	Quanmai 31	11.04 ± 0.65 bc	25.73 ± 0.55 f	32.6 ± 0.69 bc	30.62 ± 1.12 f	36.77 ± 0.43 g	63.23 ± 0.43 d
	Mengmai 0818	9.04 ± 0.39 h	31.21 ± 0.54 b	27.38 ± 0.59 h	32.36 ± 1.33 e	40.25 ± 0.8 e	59.75 ± 0.8 f
	Lomai 28	7.61 ± 0.73 j	23.3 ± 0.84 g	32.14 ± 0.85 c	36.94 ± 0.1 a	30.92 ± 0.95 j	69.08 ± 0.95 a
	Zheng Mai 132	9.87 ± 0.67 ef	31.11 ± 0.84 b	28.44 ± 0.69 g	30.57 ± 1.63 f	40.99 ± 0.99 d	59.01 ± 0.99 g
	Bainong 307	9.3 ± 0.4 gh	31.36 ± 0.45 b	29.34 ± 0.61 f	30 ± 0.9 f	40.66 ± 0.53 de	59.34 ± 0.53 fg
	Zhou Mai 30	9.6 ± 0.41 fg	26.62 ± 0.77 e	29.27 ± 0.6 f	34.51 ± 1.32 c	36.21 ± 0.92 hi	63.79 ± 0.92 bc
	Huacheng 865	9.86 ± 0.45 ef	31.09 ± 0.62 b	28.66 ± 0.51 fg	30.39 ± 1.09 f	40.95 ± 0.75 d	59.05 ± 0.75 g
	Longke 1109	10.27 ± 0.44 de	27.59 ± 0.63 d	33.36 ± 0.55 b	28.78 ± 1.09 g	37.86 ± 0.72 f	62.14 ± 0.72 e
	XuNong 029	10.62 ± 0.46 cd	26.28 ± 0.75 e	34.54 ± 0.48 a	28.56 ± 1.18 gh	36.9 ± 0.93 g	63.1 ± 0.93 d
	Huacheng 2019	11.88 ± 0.45 a	31.98 ± 0.52 a	31.29 ± 0.57 d	24.84 ± 1.31 j	43.87 ± 0.88 a	56.13 ± 0.88 j
	Huaimai 44	11.1 ± 0.43 b	32.17 ± 0.62 a	30.3 ± 0.49 e	26.43 ± 1.04 i	43.26 ± 0.72 b	56.74 ± 0.72 i
	Wanken 22	8.22 ± 0.54 i	27.52 ± 0.69 d	28.2 ± 0.61 g	36.06 ± 1.4 b	35.74 ± 0.96 i	64.26 ± 0.96 b
	Wanken 9	11.06 ± 0.32 bc	31.06 ± 0.56 b	30.17 ± 1.04 e	27.7 ± 1.27 h	42.13 ± 0.7 c	57.87 ± 0.7 h
	F-Value	58.973 **	432.32 **	63.236 **	138.485 **	342.416 **	342.415 **

Letters (a, b, c, …): Values within the same column followed by different letters indicate significant differences among varieties at p < 0.05 (Duncan’s multiple range test). Asterisks (**): F-Value with ** indicates significance at p < 0.01.

). Between 56.13% and 69.12% of the total starch volume was made up of A-type granules, with Lomai 28 having the largest percentage of big granules. Huacheng 2019, on the other hand, had the lowest A-type volume (56.13%), indicating a more evenly distributed granule composition. By volume, B-type granules ranged from 30.88% to 43.87%, with larger concentrations observed in genotypes such as Zhoumai 30 and Bainong 307.

Subsequent subclassification showed that the 2.8–10 μm class accounted for 22.50–32.17% of the overall volume, while the <2.8 μm starch granules provided 7.61–12.65%. Variations in amyloplast development and filling were reflected in the 24.84–36.94% of the volume that consisted of granules larger than 22 μm. The increased A-type volume in genotypes such as Lomai 28 and Xunong 029 points to a structural starch advantage that could improve pasting properties and favorably affect the texture and stability of food products.

2.3. Grain Quality Characteristics

Significant differences were also observed among cultivars in protein and wet gluten levels, key indicators of wheat processing quality. Protein content ranged from 9.87% in Zhoumai 30 to 12.68% in Huacheng 2019, reflecting substantial genetic variability in crude protein accumulation. Wanken 9 and Quanmai 31 exhibited relatively higher protein levels (>12%), suggesting suitability for products requiring moderately strong dough. Wet gluten content followed a similar trend, ranging from 20.15% to 28.49%, with Huacheng 2019 again showing the highest value. Cultivars such as Zhoumai 30 and Huaimai 44, with lower protein and gluten content, may be more appropriate for soft end-products like cakes and biscuits. Genotypes with higher B-type granule surface area may also influence pasting properties, indicating potential implications for processing quality. These findings underscore the importance of selecting cultivars based on grain composition for specific end-use applications, alongside yield considerations. Although protein and wet gluten were measured.

Table 2. Comparison of starch granule volume distribution in 14 soft wheat varieties during 2022–2024.

Year	Variety	Diameter of Starch Granule (%)
		<2.8 μm	2.8–10 μm	10–22 μm	>22 μm	≤10 μm	>10 μm
2022–2023	Annon 0711	47.9 ± 0.37 e	32.19 ± 0.24 f	12.79 ± 0.09 c	7.12 ± 0.06 c	80.09 ± 0.13 gh	19.91 ± 0.13 bc
	Quanmai 31	46.92 ± 0.03 f	31.65 ± 0.08 g	13.74 ± 0.07 a	7.68 ± 0.03 b	78.57 ± 0.09 i	21.43 ± 0.09 a
	Mengmai 0818	44.70 ± 0.45 h	37.32 ± 0.38 a	11.37 ± 0.09 e	6.61 ± 0.15 ef	82.02 ± 0.08 e	17.98 ± 0.08 e
	Lomai 28	49.34 ± 0.17 d	33.3 ± 0.11 e	11.40 ± 0.06 e	5.96 ± 0.01 g	82.64 ± 0.06 d	17.36 ± 0.06 f
	Zheng Mai 132	45.37 ± 0.88 gh	35.7 ± 0.48 c	12.19 ± 0.32 d	6.74 ± 0.09 de	81.07 ± 0.41 f	18.93 ± 0.41 d
	Bainong 307	47.81 ± 0.7 e	32.44 ± 0.31 f	12.88 ± 0.29 bc	6.87 ± 0.10 d	80.25 ± 0.39 g	19.75 ± 0.39 c
	Zhou Mai 30	49.79 ± 0.2 cd	30.41 ± 0.04 h	13.24 ± 0.1 b	6.56 ± 0.07 f	80.2 ± 0.16 gh	19.8 ± 0.16 bc
	Huacheng 865	47.81 ± 0.47 e	36.44 ± 0.16 b	10.44 ± 0.37 g	5.31 ± 0.09 i	84.25 ± 0.36 ab	15.75 ± 0.36 hi
	Longke 1109	45.63 ± 0.23 g	34.14 ± 0.07 d	12.35 ± 0.08 d	7.89 ± 0.10 a	79.76 ± 0.17 h	20.24 ± 0.17 b
	XuNong 029	51.03 ± 0.58 b	32.65 ± 0.44 f	11.04 ± 0.23 ef	5.28 ± 0.04 i	83.69 ± 0.26 c	16.31 ± 0.26 g
	Huacheng 2019	52.19 ± 0.63 a	32.26 ± 0.43 f	10.87 ± 0.16 f	4.68 ± 0.06 j	84.45 ± 0.21 a	15.55 ± 0.21 i
	Huaimai 44	49.43 ± 0.16 d	34.55 ± 0.10 d	10.82 ± 0.03 fg	5.21 ± 0.03 i	83.97 ± 0.06 bc	16.03 ± 0.06 gh
	Wanken 22	50.51 ± 0.02 bc	30.62 ± 0.07 h	13.00 ± 0.02 bc	5.87 ± 0.02 gh	81.13 ± 0.05 f	18.87 ± 0.05 d
	Wanken 9	52.41 ± 0.06 a	28.75 ± 0.11 i	13.05 ± 0.09 bc	5.79 ± 0.00 h	81.16 ± 0.09 f	18.84 ± 0.09 d
	F-Value	92.89 **	187.73 **	66.434 **	384.371 **	144.52 **	144.52 **
2023–2024	Annon 0711	43.36 ± 0.76 bc	28.07 ± 1.1 ef	12.46 ± 0.26 cd	16.11 ± 0.8 c	71.43 ± 0.84 de	28.57 ± 0.84 cd
	Quanmai 31	36.76 ± 0.31 ef	31.48 ± 0.38 cd	11.66 ± 0.23 de	20.1 ± 0.61 a	68.24 ± 0.38 efg	31.76 ± 0.38 abc
	Mengmai 0818	47.18 ± 0.98 a	30.59 ± 0.55 cdc	10.52 ± 0.58 e	11.71 ± 0.48 fgh	77.77 ± 1.06 ab	22.23 ± 1.06 fg
	Lomai 28	46.95 ± 1.23 a	26.27 ± 2.11 fg	12.92 ± 1.96 bcd	13.86 ± 0.87 de	73.22 ± 2.83 cd	26.78 ± 2.83 de
	Zheng Mai 132	35.74 ± 0.36 f	34.27 ± 0.72 ab	11.49 ± 0.22 de	18.5 ± 0.32 ab	70.01 ± 0.52 def	29.99 ± 0.52 bcd
	Bainong 307	41.99 ± 1.66 cd	28.33 ± 0.87 ef	14 ± 1.09 abc	15.69 ± 0.87 cd	70.31 ± 1.94 def	29.69 ± 1.94 bcd
	Zhou Mai 30	42.18 ± 0.7 cd	23.69 ± 2.38 g	14.35 ± 0.25 ab	18.08 ± 0.57 b	65.87 ± 2.45 g	32.43 ± 0.62 ab
	Huacheng 865	44.62 ± 0.1 b	32.51 ± 0.85 bc	11.38 ± 0.63 de	11.49 ± 0.17 fgh	77.13 ± 0.8 ab	22.87 ± 0.8 fg
	Longke 1109	37.86 ± 0.55 e	29.71 ± 0.51 de	14.86 ± 1.18 a	19.27 ± 1.29 ab	67.57 ± 0.62 fg	34.13 ± 2.45 a
	XuNong 029	40.91 ± 0.97 d	36.27 ± 0.5 a	10.75 ± 0.72 e	12.06 ± 0.49 efg	77.18 ± 1.19 ab	22.82 ± 1.19 fg
	Huacheng 2019	47.4 ± 1.15 a	31.18 ± 0.21 cd	12.79 ± 0.69 bcd	10.28 ± 0.58 gh	78.59 ± 1.03 a	23.07 ± 1.26 fg
	Huaimai 44	42.87 ± 0.09 c	32.72 ± 0.74 bc	12.15 ± 0.38 de	12.26 ± 0.32 ef	75.59 ± 0.69 abc	24.41 ± 0.69 efg
	Wanken 22	47 ± 0.3 a	28.12 ± 3.28 ef	12.84 ± 1.04 bcd	12.03 ± 2.54 efg	75.13 ± 3.53 bc	24.87 ± 3.53 ef
	Wanken 9	46.72 ± 0.13 a	30.21 ± 1.22 cde	11.55 ± 0.54 de	9.87 ± 0.49 h	76.93 ± 1.26 ab	21.41 ± 1.03 g
	F-Value	45.78 **	12.629 **	5.462 **	28.666 **	14.411 **	14.411 **

Letters (a, b, c, …): Values within the same column followed by different letters indicate significant differences among varieties at p < 0.05 (Duncan’s multiple range test). Asterisks (**): F-Value with ** indicates significance at p < 0.01.

presents only starch granule volume distribution.

2.4. Starch Granule Number Distribution

In all genotypes, the distribution of starch granule numbers showed a clear bimodal pattern, with B-type granules predominating (

Table 3. Proportion of starch granule number distribution of 14 soft wheat varieties during 2022–2024.

Year	Variety	Diameter of Starch Granule (%)
		<2.8 μm	2.8–10 μm	10–22 μm	>22 μm	≤10 μm	>10 μm
2022–2023	Annon 0711	97.34 ± 0.08 ab	2.54 ± 0.07 f	0.10 ± 0.00 a	0.02 ± 0.00 a	99.88 ± 0.00 b	0.12 ± 0.00 de
	Quanmai 31	96.91 ± 0 cd	2.94 ± 0.00 d	0.13 ± 0.00 a	0.02 ± 0.00 a	99.85 ± 0.00 b	0.15 ± 0.00 a
	Mengmai 0818	95.89 ± 0.07 f	3.97 ± 0.07 a	0.12 ± 0.00 b	0.02 ± 0.00 a	99.86 ± 0.00 a	0.14 ± 0.00 bc
	Lomai 28	96.41 ± 0.21 f	3.45 ± 0.21 b	0.13 ± 0.01 b	0.01 ± 0.00 b	99.86 ± 0.00 b	0.14 ± 0.00 ab
	Zheng Mai 132	96.64 ± 0.12 def	3.23 ± 0.11 c	0.11 ± 0.01 b	0.02 ± 0.00 a	99.87 ± 0.01 b	0.13 ± 0.01 c
	Bainong 307	96.86 ± 0.07 cde	3.01 ± 0.07 d	0.11 ± 0.00 b	0.02 ± 0.00 a	99.87 ± 0.00 b	0.13 ± 0.00 c
	Zhou Mai 30	97.25 ± 0.04 b	2.62 ± 0.04 ef	0.11 ± 0.00 bc	0.02 ± 0.00 a	99.87 ± 0.00 b	0.13 ± 0.00 cd
	Huacheng 865	97.21 ± 0.07 b	2.68 ± 0.06 ef	0.09 ± 0.00 bc	0.01 ± 0.00 c	99.9 ± 0.00 b	0.10 ± 0.00 fg
	Longke 1109	96.59 ± 0.03 ef	3.27 ± 0.02 c	0.12 ± 0.00 cd	0.02 ± 0.00 a	99.86 ± 0.00 b	0.14 ± 0.00 bc
	XuNong 029	97.13 ± 0.05 bc	2.77 ± 0.05 e	0.09 ± 0.00 de	0.01 ± 0.00 c	99.90 ± 0.00 b	0.10 ± 0.00 g
	Huacheng 2019	97.25 ± 0.09 b	2.64 ± 0.09 ef	0.10 ± 0.00 de	0.01 ± 0.00 c	99.89 ± 0.00 b	0.11 ± 0.00 f
	Huaimai 44	96.63 ± 0.03 def	3.26 ± 0.03 c	0.10 ± 0.00 de	0.01 ± 0.00 c	99.89 ± 0.00 b	0.11 ± 0.00 f
	Wanken 22	97.28 ± 0.01 b	2.60 ± 0.01 f	0.11 ± 0.00 ef	0.01 ± 0.00 c	99.88 ± 0.00 b	0.12 ± 0.00 e
	Wanken 9	97.6 ± 0.01 a	2.29 ± 0.01 g	0.10 ± 0.00 f	0.01 ± 0.00 c	99.89 ± 0.00 b	0.11 ± 0.00 f
	F-Value	13.42 **	69.44 **	14.376 **	31.692 **	3.842 **	31.00 **
2023–2024	Annon 0711	96.69 ± 0.77 abc	3.08 ± 0.68 fgh	0.21 ± 0.09 de	0.01 ± 0 b	99.78 ± 0.09 ab	0.22 ± 0.09 de
	Quanmai 31	93.82 ± 0.06 g	5.83 ± 0.05 b	0.32 ± 0 bc	0.02 ± 0 a	99.66 ± 0 de	0.34 ± 0 ab
	Mengmai 0818	96.42 ± 0.26 bcde	3.39 ± 0.24 defg	0.18 ± 0.02 e	0.01 ± 0 b	99.81 ± 0.02 a	0.19 ± 0.02 e
	Lomai 28	96.78 ± 0.35 ab	3.01 ± 0.33 fgh	0.19 ± 0.04 e	0.02 ± 0 a	99.79 ± 0.04 ab	0.21 ± 0.04 de
	Zheng Mai 132	92.96 ± 0.31 h	6.67 ± 0.3 a	0.35 ± 0.01 ab	0.02 ± 0 a	99.63 ± 0.01 de	0.37 ± 0.01 ab
	Bainong 307	95.91 ± 0.08 def	3.82 ± 0.09 cde	0.26 ± 0.02 cd	0.01 ± 0 b	99.73 ± 0.02 bc	0.27 ± 0.02 cd
	Zhou Mai 30	94.2 ± 0.56 g	5.4 ± 0.55 b	0.39 ± 0.01 a	0.01 ± 0 b	99.6 ± 0.01 e	0.4 ± 0.01 a
	Huacheng 865	95.8 ± 0.06 ef	3.98 ± 0.07 cd	0.21 ± 0.01 de	0.01 ± 0 b	99.78 ± 0.01 ab	0.22 ± 0.01 de
	Longke 1109	96.05 ± 0.31 cdef	3.63 ± 0.28 def	0.31 ± 0.04 bc	0.01 ± 0 b	99.68 ± 0.04 cd	0.32 ± 0.04 bc
	XuNong 029	94.44 ± 0.24 g	5.32 ± 0.21 b	0.22 ± 0.02 de	0.02 ± 0 a	99.76 ± 0.02 ab	0.24 ± 0.02 de
	Huacheng 2019	97.24 ± 0.13 a	2.58 ± 0.13 h	0.17 ± 0.01 e	0.01 ± 0 b	99.82 ± 0.01 a	0.18 ± 0.01 e
	Huaimai 44	95.36 ± 0.14 f	4.4 ± 0.14 c	0.23 ± 0 de	0.01 ± 0 b	99.76 ± 0 ab	0.24 ± 0 de
	Wanken 22	96.85 ± 0.2 ab	2.96 ± 0.21 gh	0.18 ± 0.03 e	0.01 ± 0 b	99.81 ± 0.03 a	0.19 ± 0.03 e
	Wanken 9	96.56 ± 0.4 abcd	3.25 ± 0.38 efg	0.18 ± 0.02 e	0.01 ± 0 b	99.81 ± 0.02 a	0.19 ± 0.02 e
	F-Value	28.789 **	30.707 **	9.864 **	12.444 **	10.24 **	10.24 **

Letters (a, b, c, …): Values within the same column followed by different letters indicate significant differences among varieties at p < 0.05 (Duncan’s multiple range test). Asterisks (**): F-Value with ** indicates significance at p < 0.01.

). In the 2022–2023 season, B-type granules (≤10 μm) accounted for 99.85% to 99.90% of all granules, while A-type granules (>10 μm) represented only 0.10% to 0.15%. In the 2023–2024 season, a slight increase in A-type granules was observed in several genotypes, ranging from 0.18% in Huacheng 2019 to 0.40% in Zhou Mai 30, with Lomai 28 and Mengmai 0818 showing intermediate values (0.21% and 0.19%, respectively). Despite this minor variation, B-type granules remained overwhelmingly predominant in number. This predominance of B-type granules in quantity, contrasted with the larger volume contribution of A-type granules, highlights the complex granule architecture that should be considered in wheat breeding for functionality.

2.5. Pasting Properties of Wheat Starch

Significant differences in viscosity behavior among the genotypes were evident in the pasting profiles produced by the Rapid Visco Analyzer (RVA) (

Table 4. Comparison of viscosity parameters (cP) of 14 soft wheat varieties during 2022–2024.

Year	Variety	Peak Viscosity	Trough Viscosity	Final Viscosity	Breakdown Viscosity	Setback
2022–2023	Annon 0711	988 ± 52 de	445 ± 11 e	1388 ± 52 de	543 ± 63 de	943 ± 63 de
	Quanmai 31	930 ± 2 ef	484 ± 13 d	1330 ± 2 ef	446 ± 12 ef	846 ± 12 ef
	Mengmai 0818	1113 ± 56 bc	396 ± 111 f	1513 ± 56 bc	717 ± 45 bc	1117 ± 45 bc
	Lomai 28	1180 ± 111 b	358 ± 13 g	1580 ± 111 b	822 ± 124 ab	1222 ± 124 ab
	Zheng Mai 132	856 ± 6 f	575 ± 16 b	1256 ± 6 f	282 ± 16 h	682 ± 16 h
	Bainong 307	909 ± 22 ef	505 ± 3 cd	1309 ± 22 ef	404 ± 20 fg	804 ± 20 fg
	Zhou Mai 30	745 ± 8 g	614 ± 9 a	1145 ± 8 g	130 ± 7 i	530 ± 7 i
	Huacheng 865	1073 ± 33 cd	433 ± 5 e	1473 ± 33 cd	640 ± 31 cd	1040 ± 31 cd
	Longke 1109	1135 ± 36 bc	387 ± 19 f	1535 ± 36 bc	749 ± 55 b	1149 ± 55 b
	XuNong 029	1001 ± 28 de	440 ± 15 e	1401 ± 28 de	561 ± 43 d	961 ± 43 d
	Huacheng 2019	1282 ± 67 a	373 ± 4 fg	1682 ± 67 a	908 ± 68 a	1308 ± 68 a
	Huaimai 44	891 ± 21 f	523 ± 20 c	1291 ± 21 f	368 ± 41 fgh	768 ± 41 fgh
	Wanken 22	866 ± 6 f	570 ± 17 b	1266 ± 6 f	296 ± 20 h	696 ± 201 h
	Wanken 9	874 ± 8 f	563 ± 15 b	1274 ± 8 f	311 ± 14 gh	711 ± 14 gh
	F-Value	22.04 **	75.04 **	22.04 **	40.11 **	40.11 **
2023–2024	Annon 0711	974 ± 54 bc	440 ± 17 e	1151 ± 90 cd	534 ± 68 cd	711 ± 93 cde
	Quanmai 31	916 ± 4 cdef	479 ± 20 d	1258 ± 39 bcd	437 ± 23 ef	779 ± 33 bcd
	Mengmai 0818	1050 ± 92 ab	392 ± 14 f	1316 ± 92 bc	658 ± 96 ab	924 ± 88 bc
	Lomai 28	970 ± 47 bcd	369 ± 9 fg	1369 ± 142 abc	601 ± 49 bc	1000 ± 134 b
	Zheng Mai 132	853 ± 10 efg	570 ± 21 ab	1211 ± 357 bcd	283 ± 25 gh	641 ± 357 de
	Bainong 307	835 ± 30 fg	500 ± 10 cd	1366 ± 30 abc	335 ± 22 gh	865 ± 20 bcd
	Zhou Mai 30	829 ± 29 g	591 ± 17 a	1052 ± 102 d	239 ± 34 h	461 ± 106 e
	Huacheng 865	927 ± 26 cde	429 ± 4 e	1438 ± 38 ab	498 ± 25 de	1009 ± 41 b
	Longke 1109	817 ± 10 g	382 ± 22 fg	1177 ± 98 cd	435 ± 12 ef	795 ± 95 bcd
	XuNong 029	999 ± 66 abc	435 ± 14 e	1409 ± 87 abc	564 ± 72 bcd	974 ± 84 b
	Huacheng 2019	1072 ± 45 a	353 ± 9 g	1625 ± 110 a	718.33 ± 54 a	1272 ± 111 a
	Huaimai 44	884 ± 19 defg	518 ± 27 c	1192 ± 88 bcd	366 ± 45 fg	674 ± 104 cde
	Wanken 22	859 ± 9 efg	565 ± 16 ab	1581 ± 38 a	294 ± 18 gh	1016 ± 22 b
	Wanken 9	867 ± 10 efg	558 ± 11 b	1204 ± 65 bcd	309 ± 9 gh	645 ± 75 de
	F-Value	7.83 **	61.43 **	3.41 **	20.74 **	5.67 **

Letters (a, b, c, …): Values within the same column followed by different letters indicate significant differences among varieties at p < 0.05 (Duncan’s multiple range test). Asterisks (**): F-Value with ** indicates significance at p < 0.01.

). Peak viscosity, which reflects water absorption and starch swelling, ranged from 745 cp (Zhou Mai 30) to 1282 cp (Huacheng 2019), indicating significant variations in gelatinization behavior. Final viscosity, a measure of retrogradation tendency, varied from 1145 cp to 1682 cp, with Huacheng 2019 having the highest value. Breakdown viscosity, which indicates starch granule stability under heat and shear, ranged from 130 cp (Zhou Mai 30) to 908 cp (Huacheng 2019), demonstrating -genomic resistance to breakdown. The highest setback viscosities, reflecting gel-forming and retrogradation potential, were observed in Huacheng 2019 (1308 cp) and Lomai 28 (1222 cp). Genotypes with relatively low pasting temperatures, such as Wanken 22 and Huaimai 44, displayed narrower gelatinization thresholds. Overall, genotypes with higher A-type starch content, particularly Huacheng 2019 and Lomai 28, exhibited more advantageous viscosity profiles suitable for noodles and baked products.

2.6. Correlation Between Granule Size and Pasting Properties

Pasting parameters and starch granule size fractions were shown statistically to be significantly correlated. Peak viscosity (r = 0.53 *), final viscosity (r = 0.57 *), and setback viscosity (r = 0.53 *) all exhibited positive correlations with A-type granule volume, suggesting that a greater number of bigger granules results in stronger thickening and gelling tendencies. B-type granules, on the other hand, showed a negative correlation with every viscosity parameter, indicating that a greater percentage of smaller granules weakens the paste’s stability and dilutes its consistency. In 2022–2023, significant positive correlation was found between peak viscosity (r = 0.53 *), final viscosity (r = 0.57 *), and setback viscosity (r = 0.53 *) However, no significant correlation was found in 2023–2024.

Interestingly, there was a high negative correlation (r = −0.48) between the <2.8 μm fraction and breakdown viscosity (Figure 2), indicating that microgranules are more likely to disintegrate when exposed to heat stress. These correlations support the study’s main hypothesis and highlight how crucial granule size distribution selection is to enhancing soft wheat’s functional qualities.

3. Discussion

This study thoroughly assessed how the distribution of starch granule sizes affected the pasting characteristics and yield components of fourteen soft wheat cultivars cultivated in the Huaihe River Basin. According to our research, granule size—particularly the percentage of large A-type granules (>10 μm)—is a significant factor influencing starch functionality and is associated with yield metrics. While our results showed yield variation among genotypes, a statistical correlation with yield metrics was not conducted in this study [21].

In line with other regional studies of soft wheat [22,23], the volume percentage of A-type granules in the cultivars under study ranged from 56.13% to 69.12%. Improved pasting properties, such as higher peak and breakdown viscosities, were linked to a larger percentage of A-type granules. This confirms previous findings that bigger starch granules have better pasting and gelatinization properties due to their increased swelling power and water absorption capabilities [24]. The unique climatic and edaphic characteristics of the Huaihe River Basin, which include fertile soil profiles and moderate temperature ranges during grain filling, may be the reason for the narrower granule size distribution seen in our samples as compared to some earlier research. Granule form and starch production are probably influenced by these environmental conditions [25].

Huacheng 2019 was the cultivar with the highest grain yield, as well as the best balance of A- and B-type granules. These results support the idea that the distribution of starch granule size is linked to both yield and quality characteristics [26]. A disproportionate ratio favoring smaller granules can negatively impact starch functional quality and productivity, as demonstrated by Bainong 307, which had a preponderance of smaller B-type granules and accordingly worse pasting viscosity and yield performance [27]. The necessity of including starch physicochemical characteristics into breeding selection criteria for wheat improvement initiatives is highlighted by the correlation shown between granule size distribution and yield.

With clear size ranges demonstrating genetic variability, the notable variations in starch granule diameter among cultivars (Table 4) are consistent with previous genetic and phenotypic investigations [28]. The idea that cultivar-specific genetic variables control the development of starch granules is supported by the statistical significance of the variation in granule size (F-value 69.44 **). The end-use quality of wheat is significantly impacted by these granule size variations because granule size affects processing properties including dough rheology and milling performance in addition to pasting behavior [29].

These findings suggest that starch functioning may be enhanced by a granule size distribution favoring large A-type granules. The dynamics of starch granule formation are probably mediated by phenological diversity among cultivars, specifically in grain filling time and developmental timing, as well as environmental influences, most notably temperature. It has been demonstrated that high temperatures during the grain filling phase lower the A/B granule ratio by inhibiting the production or swelling of big A-type granules, changing the pasting profiles and starch content [30]. This temperature effect emphasizes the interplay between genotype and environment (GxE) in determining wheat quality attributes and may help to explain some cultivar differences found in our study. The Huaihe River Basin’s local microclimate, which is marked by moderate but erratic temperatures during grain loading, has a significant impact on the shape of starch granules and, consequently, the quality of wheat processing [31].

As lipids like fats and oils have been shown to interact with starch molecules and alter viscosity parameters like peak viscosity, setback viscosity, and gelatinization temperatures, our findings further highlight the complexity of starch pasting properties in the presence of these substances [32]. Because lipid concentration and composition vary and impact functional qualities, this interaction is especially pertinent to soft wheat flour uses in baking and confectionery. Therefore, in order to completely comprehend and maximize the quality of wheat flour, future research should take compositional effects into account in addition to granule size distribution.

Large starch granules have a crucial role in providing advantageous pasting properties, as evidenced by the strong positive correlations that have been found between the volume of A-type granules and important viscosity metrics like peak, final viscosity, and setback value. On the other hand, these parameters were inversely correlated with the quantity of smaller B-type granules, which is in line with research showing that smaller granules have a lower role in the development of swelling and viscosity [33]. These findings provide credence to the idea that starch functioning is improved by a larger size distribution with a greater percentage of large granules, which may result in better processing quality and possibly better texture in the finished product [34].

Our results imply that choosing wheat cultivars with an ideal ratio of starch granule sizes especially large A-type granules—could enhance starch functionality from an agronomic and practical breeding standpoint. Higher percentages of ultra-small granules (<2.8 μm) were found in genotypes like Wanken 9 and Quanmai 31, which suggests more amyloplast fragmentation or incomplete formation. In contrast, Mengmai 0818 and Zhengmai 132 showed wider granule size ranges, which may have been caused by interactions between environment and genotype.

These results imply that although while B-type granules predominate numerically, even slight variations in A-type granule counts can have a big effect on the flour’s pasting and functional properties. This might guide customized breeding techniques aiming for cultivars adaptable to environmental stressors like heat during grain filling, and it would help wheat production in areas with climates similar to the Huaihe River Basin. Our study also shows how starch granule size manipulation can be used to create new wheat-based food products with the appropriate pasting and textural properties [35].

Finally, this study lays the foundation for further research into the physiological and molecular processes controlling the distribution of starch granule sizes, namely the genetic control of enzymes involved in starch production in different environmental settings. By comprehending these processes, more accurate breeding strategies to maximize wheat yield and quality will be possible, addressing the issues of food security in the face of climate change [36]. Although this study provides valuable insights into the relationship between starch granule size distribution and pasting properties in soft wheat cultivars, it is important to note certain limitations. The set of fourteen genotypes used in this study were regionally adapted cultivars, and detailed pedigree information was not available for all of them. While these varieties represent the practical germplasm used in the Huaihe River Basin, the absence of pedigree records limits the ability to link results directly to specific genetic backgrounds. In addition, detailed yield component data (grains per spike and thousand-kernel weight) were collected only in the 2023 season due to logistical constraints. However, grain yield was measured in both years, and the primary objective of this study focused on starch physicochemical characteristics rather than yield performance. Future research with larger genotype panels, known pedigrees, and more comprehensive multi-year agronomic data would strengthen the generalization of these findings.

4. Materials and Methods

4.1. Experimental Site and Plant Material

The field experiment was conducted at the Fengyang Experimental Station of Anhui Science and Technology University, located in Fengyang County, Anhui Province, China (latitude: 32.88° N, longitude: 117.56° E), during the winter wheat growing seasons of 2022–2023 and 2023–2024. This site lies within the Huaihe River Basin, an important region for soft wheat cultivation, characterized by a temperate monsoon climate with an average annual temperature of 15 °C and approximately 900 mm of rainfall. The pre-sowing physicochemical analysis of the clay loam soil at the station indicated an organic matter content of 16.65 mg kg⁻¹, alkali-hydrolyzable nitrogen of 72.75 mg kg⁻¹, available phosphorus of 18.30 mg kg⁻¹, and available potassium of 96.05 mg kg⁻¹.

For this study, fourteen soft wheat (Triticum aestivum L.) genotypes were selected based on variations in agronomic performance, grain quality traits, and regional adaptability (Table 1). These cultivars were obtained from regional breeding programs within the Huaihe River Basin and represent widely cultivated varieties in the area. Although detailed pedigree information was not available for all genotypes, the selected set reflects the practical germplasm resources currently employed by farmers and breeders. The primary objective of the study was to evaluate starch granule size distribution and its association with pasting properties; therefore, pedigree details were not considered within the scope of this work.

4.2. Experimental Design and Field Management

The field experiments employed a randomized complete block design (RCBD) with three replications of each genotype. Five rows, spaced 25 cm apart, made up each five-meter-square plot. The planting density of 270 seeds per square meter was maintained in every plot. Fertilizers were sprayed at 120 kg/ha for nitrogen, 90 kg/ha for phosphorus (P₂O₅), and 90 kg/ha for potassium (K₂O) in compliance with regional high-yield norms. Phosphorus and potassium were applied fully as basal fertilizers, whereas nitrogen was split into two applications: 70% prior to sowing and the remaining 30% as a topdressing during the green-up stage. Techniques for field management, such as irrigation, weed control, and insect control, were regularly used. Sowing took place on October 31 in both years. All plots were harvested by hand, and yield data were based on a 1 m² sub-sample. on May 29. Following harvest, grain samples were sun-dried and stored at 4 °C for further analysis.

4.3. Yield and Grain Quality Evaluation

Grain yield was determined at physiological maturity in both experimental years (2022–2023 and 2023–2024) using hand-harvested 1 m² subplots. Due to resource constraints, detailed yield component traits, including the number of grains per spike and thousand-kernel weight, were recorded only during the 2023 season. Effective spikes per square meter, grains per spike, and thousand-kernel weight were measured, with 15 randomly selected spikes manually threshed for grain counting. The harvested grain from each subplot was weighed to calculate grain yield, which was then expressed in tonnes per hectare. These yield and component data serve as supporting agronomic information, while the primary focus of the study remains on starch granule size distribution and pasting properties.

Grain quality traits, including protein and wet gluten contents, were analyzed using a DA7200 Near-Infrared Reflectance (NIR) Analyzer (Perten Instruments, Sweden). Prior to analysis, grain samples were ground into fine flour using a Foss CyclotecTM laboratory mill. To ensure accuracy and repeatability, all measurements were performed in triplicate.

4.4. Starch Extraction and Granule Size Analysis

With a few minor adjustments, starch extraction was carried out using the [21] method. To soften the endosperm, two grams of mature grain samples from each genotype were immersed in distilled water for a whole day. A 200-mesh sieve was used to filter the resultant slurry after the dehulled grains were manually ground in a porcelain mortar. After collecting the filtrate, it was centrifuged for ten minutes at 3000 rpm. In order to exclude non-starch components including proteins and lipids, the pellet was thereafter successively cleaned with 2 μmol·L⁻¹ NaCl, 0.2% NaOH, 2% SDS, and distilled water. After being cleaned, the starch was held at −15 °C until analysis, air-dried at ambient temperature, and then further cleaned with acetone. A BT-9300 SE laser diffraction particle size analyzer was used to examine the size distribution of starch granules. Four sizes of the granules were identified: <2.8 μm, 2.8–10 μm, 10–22 μm, and >22 μm. Granules smaller than 10 μm were categorized as B-type for this study, whereas those larger than 10 μm were categorized as A-type. Granule number distribution, surface area, and volume percentage were used to document the distribution of starch granules.

4.5. Pasting Properties Analysis

The pasting behavior of wheat starch was evaluated using a Super 3 Rapid Visco Analyzer (RVA, Newport Scientific, Australia). For each genotype, 3 g of starch (adjusted to 14% moisture basis) was dispersed in 25 mL of distilled water, and the RVA was programmed with the following temperature-time profile: heating from 50 °C to 95 °C at 12 °C/min, holding at 95 °C for 2.5 min, cooling back to 50 °C at 12 °C/min, followed by a final holding stage at 50 °C for 2 min. Standard pasting parameters, including peak viscosity, trough viscosity, breakdown viscosity, final viscosity, setback viscosity, and pasting temperature, were recorded. All measurements were performed in triplicate, and mean values were used for statistical analysis.

4.6. Statistical Analysis

Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA) was used to compile data collected from agronomic attributes, starch granule size distribution, and RVA parameters. Version 7.05 of the DPS software was used. All replicates were included in statistical analysis to account for experimental variation. For all assessed variables, significant differences between genotypes were identified using analysis of variance (ANOVA) in a randomized full block design. A two-way ANOVA was performed using year and genotype as fixed factors to test for interaction effects. Significant interactions were explored by presenting year-wise results. The Least Significant Difference (LSD) test was used to separate means at a significance threshold of p < 0.05 when significant differences were discovered. The associations between starch granule size fractions and pasting characteristics such peak viscosity, setback viscosity, and final viscosity were examined using Pearson correlation analysis. Principal component analysis (PCA) was also used to display genotype grouping based on multivariate attributes and to determine the main factors influencing variance in starch properties.

5. Conclusions

The findings of this study show the considerable differences in yield and yield components. Starch granule distribution across the 14 soft wheat types were also investigated from 2022 to 2024. Notably, yield performance differed greatly, with Huacheng 2019 having the highest yield and Zhoumai 30 having the lowest. These changes were related to variations in the number of spikes, grains per spike, 1000-kernel weight and other yield components, all of which varied significantly between types over both years of the trial. The grain analysis of the particles carried out with starch showed that big-sized granules (particle size > 10 μm) usually represent the major starch volume than the small-sized granules (particle size ≤ 10 μm). However, the surface area distribution showed that B-type starch granules contributed more to the surface area than A-type granules. These findings underline the complex quantitative relationship between starch granule size distributions and yield constituents, with implications for the physiological properties of wheat varieties in the Huaihe River Basin. This study confirms that granule size distribution significantly influences starch pasting behavior in soft wheat. Genotypes rich in A-type granules showed favorable viscosity profiles for baking applications. Nevertheless, the study was based on fourteen regionally adapted varieties with incomplete pedigree records, and detailed yield component traits were measured only in one season. These factors should be considered when interpreting the results, and future work should expand the germplasm base and include more extensive agronomic evaluation.

This study yields important findings that give future studies more critical data about the paper and it also raises hope that plant breeders could develop new sets of wheat through the optimal rearrangement of starch granule distributions, which will in turn lead to better protection and productivity of wheat. Subsequent research could further unravel the molecular basis of the differences and thus will be able to develop the breeding programs for wheat to enhance its performance. Further future research is require in the research field.