Interrelationships between appearance quality and starch and protein structures in superior and inferior grains of soft and non-soft rice

Interrelationships between appearance quality and starch and protein structures in superior and inferior grains of soft and non-soft rice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Interrelationships between appearance quality and starch and protein structures in superior and inferior grains of soft and non-soft rice Xi Chen, Jianghui Yu, Ying Zhu, Guodong Liu, Guangyan Li, Fangfu Xu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7486609/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Rice → Version 1 posted 10 You are reading this latest preprint version Abstract This study investigates the material foundations underpinning the variations in appearance quality among different types of japonica rice and within different grain positions, thereby providing a theoretical basis for enhancing the quality of soft rice varieties. The experimental results indicate that relative to non-soft rice, soft varieties demonstrate markedly higher levels of chalkiness and reduced transparency in both grain classifications. Structural analyses indicate that soft rice grains, particularly the inferior ones, exhibit lower amylose content along with higher proportions of small starch granules and Fa chains, alongside enhanced crystallinity and short-range order. These characteristics compromise the crystalline integrity and amplify light scattering. Furthermore, the protein network in soft rice is characterized by increased levels of albumin and glutelin, a reduction in prolamin content, and a transition towards α-helix and random coil structures. These changes suggest a diminished integration of starch and protein and introduce spatial limitations. The compounded defects across multiple scales in starch and protein structures are more accentuated in inferior grains, leading to enhanced porosity and optical heterogeneity within the endosperm. This synergistic degradation of starch and protein architectures emerges as the primary mechanism responsible for the relatively poor appearance quality of soft rice. Rice Grain position Appearance quality Starch Protein Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Rice (Oryza sativa L.) serves as a fundamental food crop globally, and its qualitative enhancement is pivotal for ensuring food security and nutritional health (Ma et al., 2024 ). Although the proliferation of high-grade rice varieties in China is notable, the appearance and eating qualities still pose significant challenges to industrial progress and market competitiveness (Zheng, 2021 ). In recent years, China has developed numerous soft rice japonica varieties that are prized for their tender texture and excellent flavor (Zhu et al., 2022 ). However, the endosperm of these varieties tends to be opaque and lacks translucency, reducing their appeal to consumers and their competitiveness in the market (Zhu, 2022 ). Therefore, advancements in the appearance quality of soft rice are crucial for boosting its market competitiveness. Chalkiness, characterized by the opacity of the endosperm, emerges as a critical quality determinant. Its development is closely linked to the distinct microstructure of the chalky regions within the endosperm (Wang et al., 2022 ; Lin et al., 2016 ). These regions typically feature starch granules with spherical shapes, loosely arranged with considerable gaps between them (Zheng et al., 2022). As the main components of the endosperm, the composition (amylose and amylopectin) and fine structure of starch significantly influence the transparency of the endosperm (Zhong et al., 2020 ). A lower amylose content leads to a translucent or opaque “cloudy appearance,” adversely affecting the visual appeal (Zhu, 2022 ; Liu et al., 2022 ). Moreover, a higher proportion of short amylopectin chains (DP 6–12) disrupts the cohesion between crystalline and amorphous areas, resulting in underdeveloped starch granules, smaller granule size, and increased intergranular voids (Zhou, 2017 ). These microstructural changes significantly extend the paths of light scattering within the endosperm, reduce light transmittance, and thereby decrease transparency while increasing the degree of chalkiness (Yong et al., 2024 ; Fan et al., 2024 ). The protein architecture also critically influences endosperm transparency. Disruptions in protein synthesis or anomalies in protein body distribution impair starch-protein interactions, creating intercellular spaces that enhance chalkiness (Wang, 2016 ). Research indicates that chalky regions tend to have higher concentrations of albumin and lower levels of globulin, a disparity that underscores a fundamental molecular mechanism underlying the degradation of appearance quality (Tang et al., 2018 ). Additionally, the spatial distribution and three-dimensional conformational changes in proteins affect the optical properties of the endosperm. Disrupted or fragmented protein networks lead to structural laxity, which exacerbates light scattering and further diminishes transparency (Jia et al., 2021 ). Hence, the interplay and structural integrity of starch and proteins are integral to the microstructural regulation that underpins the appearance quality of rice endosperm. In addition to the inherent factors related to grain properties, the variation in grain position within the panicle significantly affects the appearance quality of rice (Wei et al., 2015 ). Notable disparities in nutrient supply and the progression of grain filling across different grain positions result in spatial heterogeneity in the development and quality characteristics of the grains (Zhu, 2018 ; Wang et al., 2019 ; Jiang et al., 2022 ). Typically, grains located at the upper part of the panicle, classified as superior grains, are characterized by fully developed structures with densely packed starch granules and enhanced endosperm transparency (Ge et al., 2023 ). Conversely, grains from the lower part of the panicle, referred to as inferior grains, often display a disorganized arrangement of starch granules and increased light scattering due to incomplete grain filling and a relative enrichment in protein content (Zhang, 2012 ). This results in increased chalkiness and a noticeable reduction in appearance quality. Although existing research has explored the impact of grain position on rice quality, there remains a lack of systematic and thorough investigations into the fine structural characteristics of starch and proteins within different grain positions of soft rice japonica, their synergistic interactions, and the specific mechanistic connections to variations in appearance quality among these positions. Most current studies focus primarily on individual grain positions or single components (Li et al., 2020 ), often neglecting comparative analyses of the structural differences in starch and protein between dominant and subordinate grains in both soft and non-soft japonica rice varieties, or how these components collectively regulate appearance quality. To bridge these research gaps, we have chosen prominent southern cultivars including soft rice varieties Nanjing 5718 and Nanjing 9108, alongside non-soft rice varieties Huaidao 5 and Huajing 5. By differentiating between dominant and subordinate grains within the panicle, this study systematically analyzes the interrelationships between starch and protein content, composition, structural features, and appearance quality. Our aim is to elucidate the structural foundations and synergistic mechanisms of starch and proteins that account for variations in appearance quality between different grain positions in soft rice, making direct comparisons to non-soft rice. This research is intended to provide a theoretical basis for enhancing the market competitiveness of these varieties. 2. Materials and methods 2.1 Experimental site, rice materials, and field management The study was conducted at the experimental farm of Yangzhou University, located in Yangzhou, Jiangsu, China (119°42′E, 32°39′N), in the year 2022. The field soil was characterized as sandy loam with 0.13% total N, 165.9 mg kg -1 alkali hydrolyzable N, 33.7 mg kg -1 Olsen-P, 77.6 mg kg -1 exchangeable K, and 30.9 g kg -1 organic matter. The rice varieties selected for this study included two high-quality soft rice cultivars, NJ5718 (Nanjing 5718) and NJ9108 (Nanjing 9108), and two non-soft rice cultivars, HD5 (Huaidao 5 hao) and HJ5 (Huajing 5 hao). The field experiment was conducted using a randomized block design, with each plot encompassing an area of 36 m 2 (6 m × 6 m) across three replicates. Rice seeds were initially sown in a seedling nursery on May 19 and subsequently transplanted on June 11, planting four seedlings per hill with a row spacing of 30 cm and a plant spacing of 12 cm. The total nitrogen application was set at 270 kg ha -1 , divided among base, tillering, and panicle fertilizers at a distribution ratio of 35:35:30, respectively. The tillering fertilizer was administered seven days post-transplantation, and the panicle fertilizer was applied at the stage of the fourth leaf. The nutrient composition maintained a ratio of nitrogen, phosphorus, and potassium at 2:1:2. Phosphorus was applied solely as a base fertilizer, while potassium was evenly distributed prior to tillage and the jointing stage. The irrigation regimes and the management of weeds and pests during the rice growth periods adhered to local agricultural guidelines. 2.2 Sampling and measurement At the heading stage, five hundred rice panicles that flowered synchronously were selected and labeled for each cultivar. Upon reaching maturity, grains from these designated panicles were categorized into superior grains (SG) and inferior grains (IG). SGs were collected from the upper third of the primary branches, whereas IGs were sourced from the lower third of the secondary branches. Both superior and inferior grains for each cultivar were harvested at maturity to facilitate subsequent assessments. 2.2.1 Appearance quality The assessment of the chalkiness characteristics of mature seeds adhered to the standards specified in GB/T 17891-2017. The rate of chalky grains and the degree of chalkiness were determined using the WS-SC-E system, developed in China. Additionally, the transparency of the rice samples was quantified by measuring the transmission rate through a cuvette 1 cm in thickness. This analysis was performed using a colorimeter equipped with a D65 light source (CM-5, Konica Minolta, Tokyo, Japan). 2.2.2 Proportion of grain type Number proportion: Superior grains are defined as the percentage of grains located on the upper third of the primary branches of the panicle relative to the total grain count of the entire panicle. Conversely, inferior grains represent the percentage of grains situated on the upper third of the secondary branches of the panicle compared to the overall grain count of the panicle. Weight proportion: The proportion of high grain weight is described as the percentage of grains whose weight exceeds the average weight of the strongest grains at maturity, relative to the total number of grains per panicle. Similarly, the proportion of low grain weight denotes the percentage of grains whose weight falls below the average weight of the weakest grains at maturity, in relation to the total number of grains per panicle. 2.2.3 Contribution analysis of grain positions to appearance quality The contributions of high-quality granules (SG), medium-quality granules (MG, excluding superior and inferior granules), and inferior granules (IG) to the overall appearance quality were quantified using a multiple linear regression weighting method (Bocianowski, 2012). Contribution = trait value × number proportion Predicted value = SG contribution + MG contribution +IG contribution Contribution ratio (%) = (contribution / Predicted value) × 100 Model validation: Relative error (%) = |Measured value - Predicted value| / Measured value × 100 2.2.4 Starch extraction Measure and weigh 10 g of rice flour into a container, add 35 mL of NaOH solution (pH 8–9), and introduce 50 mg of alkaline protease along with two glass beads. Following this, add 25 μL of sodium azide (0.04 g/mL). Seal the container with parafilm and place it in a shaker set to 42℃ and 180 rpm for 24 hours. Filter the homogenized suspension through a 300-mesh sieve into a 250 mL beaker and transfer the filtrate to a 50 mL centrifuge tube. Centrifuge at 3000 rpm for 10 minutes, discard the supernatant, and remove the yellow layer from the starch surface. Resuspend the precipitate in deionized water and repeat centrifugation under the same conditions. Wash the precipitate with deionized water 3–5 times to eliminate ions and impurities. Further clean the precipitate with 95% ethanol and a chloroform-methanol mixture (V/V = 1:1) to remove lipids. Finally, dry the sample at 40℃ for two days and sieve through a 200-mesh screen to obtain the starch. 2.2.5 Starch and its components Rice flour samples were utilized for the analysis of starch and its components. The total starch content (TSC) was quantified using a Total Starch Kit (Megazyme, Bray, Ireland). Initially, a 10 mg sample was placed into a 10 mL centrifuge tube, to which 5 mL of 80% ethanol was added. The mixture was then incubated in a water bath at 80°C for 30 minutes. After centrifugation at 5000 rpm for 10 minutes, the supernatant was discarded. This procedure was repeated twice. Subsequently, 400 μL of 2 mol/L KOH was added, and the mixture was placed in an ice bath for 30 minutes. Upon completion of the ice bath incubation, 1.6 mL of 1.2 mol/L sodium acetate buffer (pH 3.8), 10 μL of thermostable α-amylase, and 10 μL of amyloglucosidase were added. The tube was thoroughly mixed and returned to a water bath at 50°C for 30 minutes. Afterwards, the sample was transferred to a 10 mL volumetric flask to adjust the volume to the mark. Then, 2 mL of the supernatant was taken and centrifuged at 8000 rpm for 10 minutes. 1 mL of the resulting supernatant was used for analysis, and the total starch content was determined using the GOPOD kit. The amylose content (AC) was measured using the iodine-binding method as described by Tan et al. (2000). Amylopectin content (AP) was calculated by subtracting AC from TSC. 2.2.6 Analysis of SEM and starch granule size Starch granules were affixed to circular aluminum stubs using double-sided adhesive tape, coated with gold, and examined under a scanning electron microscope (Gemini SEM 300, Carl Zeiss, Oberkochen, Germany) at a magnification of 5000 ×. Images were captured at an accelerating potential of 5 kV. The distribution of starch granule sizes was assessed following the method previously described by Shi et al. (2018) using a laser diffraction particle size analyzer (Mastersizer 2000, Malvern, Worcestershire, England). The size distribution was expressed in terms of the volume of equivalent spheres, and the average granule size was calculated as the volume-weighted mean. The experiments in this study were replicated three times. 2.2.7 Analysis of starch molecular size distribution The pure starch was debranched and freeze-dried in strict accordance with the methodology established by Wu et al. (2014). Approximately 4 mg of isolated starch was combined with 0.7 mL of LiBr/DMSO (0.5% w/v) and agitated continuously at 80°C overnight. Subsequently, the mixture was centrifuged at 4000 g for 10 minutes, and the supernatant was precipitated using four volumes of anhydrous ethanol. The precipitate was then dispersed in 0.9 mL of hot water and mixed with 0.1 mL of acetate buffer (0.1 M, pH 3.5), 5 μL of 4% sodium azide solution (w/v), and 2.5 μL of isoamylase (Megazyme E-ISAMY), followed by incubation at 37°C with continuous agitation for 3 hours. The sample was subsequently freeze-dried prior to analysis. The molecular weight distribution of the debranched starch was determined using an LC-20 CE Shimadzu system equipped with an RID-10A refractive index detector (Shimadzu Corporation, Kyoto, Japan), as described by Gu et al. (2019). Calibration of the column was performed using standard dextrans with known molecular weights (2800, 18,500, 111,900, 410,000, 1,050,000, 2,900,000, and 6,300,000). 2.2.8 Crystalline structure of starch Starch samples were subjected to analysis to determine their crystalline structure. Utilizing the method described by Zhu et al., the X-ray diffraction patterns of starch were acquired using a powder X-ray diffractometer. The measurements were performed at 40 kV and 200 mA, with the diffractometer set to scan from a 2θ angle of 3° to 40° at a sampling interval of 0.6 seconds. The relative crystallinity of the starch was quantified using XPERT HighScore Plus software. Following the procedure outlined by Man et al. (2012), the external regions of starch granules were examined using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) employing a Fourier Transform Infrared Spectrometer (Varian 7000 system, Palo Alto, CA, USA). The system used a DTGS detector and a germanium crystal ATR reflector unit. For sample preparation, 100 mg of starch was suspended in 100 μL of ultrapure water, vigorously shaken, and then stored at 4°C for future analysis. Water was used as the blank control. The spectral range was set from 400 to 4000 cm -1 with a resolution of 2 cm -1 , and each sample was subjected to 64 scans. For spectral deconvolution, the range of 800–1200 cm -1 was selected with a half-bandwidth of 19 cm -1 and an enhancement factor of 1.9. The lamellar structure of starch was investigated using a small-angle X-ray scattering instrument (Bruker NanoStar, Vantec 2000, Bruker, Germany) following the methodology of Yuryev et al. (2004). The SAXS data were analyzed using DIFFRAC Plus NanoFit software, and SAXS spectrum parameters were determined using a straightforward graphical method (Cai et al., 2014). 2.2.9 Protein and its components Analyses of protein and its components were conducted using rice flour samples. The nitrogen content in rice was quantified with an automated Kjeldahl analyzer (Kjeltec 8200, Foss, Hillerød, Denmark), and the protein content was subsequently calculated by applying a conversion factor of 5.95. The identification of specific protein components followed the method outlined by Sapan et al. (1999). Among these components, glutelin levels were assessed via the biuret colorimetric method, whereas albumin, globulin, and prolamin were quantified using the Coomassie blue colorimetric method. Measurements were replicated thrice for each sample, and the results were expressed as mean values. 2.2.10 Protein secondary structure The secondary structure of proteins in rice flour samples was investigated using a Fourier Transform Infrared (FTIR) spectrometer (Varian 7000 system, Palo Alto, CA, USA), adhering to the protocol described by Shi et al. (2023). Samples were prepared by combining rice flour with potassium bromide (KBr) in a 1:100 ratio; the mixture was then compressed into thin pellets for FTIR analysis. The spectral range was set from 400 to 4000 cm -1 , with each sample undergoing 32 scans and the air background receiving 64 scans. Post-deconvolution, the secondary protein structures were identified from the spectral region spanning 1600 cm -1 to 1700 cm -1 , encompassing random coil (1637–1645 cm -1 ), β-turn (1664–1681 cm -1 ), β-sheet (1615–1637 cm -1 and 1682–1700 cm -1 ), and α-helix (1646–1664 cm -1 ). 2.3 Data processing and analysis Data processing was performed using Excel 2019, while statistical analyses were conducted via SPSS Version 20.0. Graphical representations were created with Origin 2021. Both principal components analysis (PCA) and correlation analysis were employed to evaluate the appearance qualities and the starch and protein characteristics of various rice varieties using Origin 2021. 3. Results and analysis 3.1 Appearance quality 3.1.1 Overall appearance quality Significant disparities in appearance quality were noted between soft and non-soft rice varieties (Table 1). Compared with non-soft rise, soft rice exhibited a 40.81% to 48.36% higher chalky grain rate and a 66.52% to 73.66% increase in chalkiness degree. Conversely, the transparency of soft rice was reduced by 3.23% to 26.71% relative to non-soft varieties. Table 1 Difference in appearance quality between soft and non-soft rice (%) Cultivar Chalky Grain Rate Chalkiness Degree Transparency NJ5718 25.50±0.11b 6.88±0.33b 9.57±0.18c NJ9108 29.13±0.17a 7.95±0.29a 8.86±0.21d HD5 15.04±0.19c 2.30±0.11c 11.85±0.25b HJ5 15.10±0.12c 2.09±0.24c 12.08±0.24a Data are presented as mean ± standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p < 0.05). 3.1.2 Appearance quality of superior and inferior grains Significant disparities were observed in the appearance quality of superior and inferior grains between soft and non-soft rice (Fig. 1). Compared with non-soft rice, the superior grains of soft rice demonstrated increases of 79.00%–115.22% in chalky grain rate and 410.94%–481.03% in chalkiness degree, while their transparency decreased by 21.67%–27.27%. In inferior grains, the chalky grain rate and chalkiness degree of soft rice showed increases of 70.72%–89.17% and 121.03%–136.31%, respectively, with a decrease in transparency of 34.19%–36.70%. In soft rice, compared with superior grains, the inferior grains exhibited higher rates of 67.51%–71.43% in chalky grain rate and 88.47%–89.26% in chalkiness degree, along with a decrease in transparency of 40.55%–43.34%. Similarly, in non-soft rice, the inferior grains displayed increases of 69.80%–74.23% in chalky grain rate and 95.03%–95.61% in chalkiness degree, and a decrease in transparency of 29.88%–34.30% relative to superior grains. 3.1.3 Proportion of different types of grains Notable differences were found in the proportions of superior and inferior grains between soft and non-soft rice (Table 2). In terms of numerical proportion, compared with non-soft rice, the percentage of superior grains in soft rice was reduced by 8.60%–17.17%, whereas the proportion of inferior grains increased by 7.33%–13.03%. Regarding weight proportion, the percentage of high grain weight was 13.12%–26.18% lower, and that of low grain weight was 7.09%–10.37% higher in soft rice. Table 2 Difference in proportion between superior and inferior grains of soft and non-soft rice Cultivar Number Proportion（%） Weight Proportion（%） Superior Grains Inferior Grains High-weight grains Low-weight grains NJ5718 13.92±2.94a 15.27±0.68a 22.08±0.12c 18.89±0.17d NJ9108 13.46±1.25a 15.01±0.73ab 22.97±0.09c 19.29±0.21d HD5 15.23±1.11a 13.91±0.87bc 29.91±0.15a 17.55±0.87e HJ5 16.25±1.67a 13.28±0.50c 26.44±0.45b 17.29±0.18e Data are presented as mean ± standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p < 0.05). 3.1.4 Analysis of the contribution of superior and inferior grains to appearance quality In this study, we developed an approximate weighted contribution model to quantify the effects of superior and inferior grains on the overall appearance quality of rice panicles, considering the appearance quality traits and grain proportions (Table 3). Despite some deviations between predicted and actual values, the model consistently reflected the general trends, underscoring its utility in elucidating the relative contributions of different grain positions to the overall appearance quality of the panicle. Specifically, in comparison to non-soft rice, soft rice exhibited a 58.62%–77.29% greater contribution from the degree of chalkiness and a 10.17%–22.49% reduced contribution from transparency in superior grains. For inferior grains, the rate of chalky grains was 7.16%–27.75% higher, while the degree of chalkiness was 6.17%–12.95% lower. Table 3 Analysis of the contribution of superior and inferior grains to appearance quality Cultivar Trait Predicted Relative Error (%) SG Contribution IG Contribution SG Contribution ratio (%) IG Contribution ratio (%) NJ5718 CR 29.61 16.13 2.55 8.79 8.68 29.69 CD 8.20 19.13 0.45 4.68 5.52 57.16 Tra 8.36 12.66 1.46 0.90 17.45 10.80 NJ9108 CR 29.47 1.16 2.21 8.43 7.50 28.59 CD 8.10 1.89 0.45 4.42 5.62 54.56 Tra 8.15 7.98 1.32 0.83 16.23 10.16 HD5 CR 17.17 14.17 1.39 4.58 8.08 26.68 CD 3.08 33.82 0.10 1.88 3.17 60.92 Tra 9.68 18.33 1.88 1.21 19.47 12.48 HJ5 CR 17.32 14.72 1.38 4.03 7.96 23.24 CD 2.71 29.55 0.09 1.70 3.48 62.68 Tra 10.43 13.67 2.18 1.13 20.94 10.81 CR: Chalk grain rate, CD: Chalkiness degree, Tra: Transparency. Data are presented as mean ± standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p < 0.05). 3.2 Starch component, morphology and structure 3.2.1 Starch content and its components Distinct variations were observed in the starch content and its components between superior and inferior grains of both soft and non-soft rice types (Fig. 2). In soft rice, compared with non-soft rice, superior grains exhibited decreases in the TSC, apparent AC, and the amylose-to-total starch ratio (AC/TSC) by 1.83%–2.47%, 40.95%–44.80%, and 39.46%–43.79%, respectively. Conversely, AP and the amylopectin-to-total starch ratio (AP/TSC) increased by 8.66%–10.80% and 11.41%–12.86%, respectively. In inferior grains of soft rice, decreases in TSC, AC, and AC/TSC were noted at 2.63%–3.07%, 36.57%–39.36%, and 34.88%–37.43%, respectively, while increases in AP and AP/TSC were 5.11%–5.32% and 7.96%–8.66%, respectively. In soft rice, compared with superior grains, inferior grains demonstrated reductions in TSC, AC, and AC/TSC by 0.25%–0.61%, 5.45%–13.98%, and 5.17%–15.57%, respectively. Increases in AP and AP/TSC were 0.46%–1.54% and 0.76%–2.07%, respectively. In non-soft rice, compared with superior grains, inferior grains showed reductions of 0.09%–0.67%, 16.24%–17.72%, and 16.27%–22.17% in TSC, AC, and AC/TSC, respectively, while AP and AP/TSC increased by 4.76%–5.90% and 4.71%–5.33%, respectively. 3.2.2 Starch granule size A marked variance was noted in the distribution of starch granule size between superior and inferior grains as well as between soft and non-soft rice varieties, as indicated in Table 4. Specifically, compared with non-soft rice, in the superior grains of soft rice, the percentage of small starch granules was elevated by 8.94% to 16.06%, whereas the mean granule size and the proportion of larger granules decreased by 4.41% to 8.11% and 16.93% to 23.16%, respectively. Conversely, in the inferior grains of soft rice, the occurrence of small granules was 6.28% to 10.53% higher, and there was a reduction in the average granule size and the proportion of large granules by 7.45% to 8.65% and 25.42% to 31.72%, respectively. In soft rice, compared with superior grains, inferior grains exhibited an increase in the proportion of small granules by 4.72% to 8.41%, accompanied by decreases in the average granule size and the proportion of large granules by 1.84% to 6.38% and 20.39% to 23.20%, respectively. In non-soft rice, compared with superior grains, inferior grains exhibited a 6.86% to 14.36% higher proportion of small granules, and decreases in average granule size and large granule proportion by 2.03% to 2.53% and 6.57% to 17.98%, respectively. Table 4 Difference in starch granule size between superior and inferior grains of soft and non-soft rice Cultivar Grain position Average size (μm) Small particle content (＜2μm) % Medium particle content (2-10μm) % Large particle content (＞10μm) % NJ5718 SG 5.44±0.02d 15.03±0.16b 79.33±0.37a 5.64±0.21c NJ9108 5.64±0.04c 14.62±0.40b 79.69±0.22a 5.69±0.18c HD5 5.90±0.01a 13.42±0.04c 79.74±0.20a 6.85±0.16ab HJ5 5.92±0.03a 12.95±0.34c 79.71±0.21a 7.34±0.55a NJ5718 IG 5.28±0.03e 15.85±0.31a 79.78±0.07a 4.37±0.24d NJ9108 5.34±0.06e 15.74±0.05a 79.77±0.14a 4.49±0.18d HD5 5.77±0.05b 14.81±0.34b 79.17±0.25a 6.02±0.09b HJ5 5.78±0.08b 14.34±0.16b 79.27±0.09a 6.40±0.07b Data are presented as mean ± standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p < 0.05). 3.2.3 Starch microscopic morphology The microstructural analysis of starch, conducted using SEM, is presented in Fig. 3. Although the starch granules in both superior and inferior grains of soft and non-soft rice are predominantly irregular polygons, a differential pattern of surface integrity was observed. Compared to non-soft rice, the starch granules in both superior and inferior grains of soft rice exhibited varying degrees of fragmentation. Notably, this fragmentation was more pronounced in inferior grains, with some granules appearing severely collapsed (Fig. 3-A, B, a, b). 3.2.4 Starch molecular structure Marked disparities in the molecular structure of starch were noted between superior and inferior grains of both soft and non-soft rice varieties (Fig. 4, Table 5). Compared with non-soft rice, in superior grains of soft rice, the proportions of Fa and Fb 1 in amylopectin chains (DP < 100) were found to be significantly higher, ranging from 6.18% to 7.32% and from 2.59% to 2.98%, respectively, compared to non-soft rice. Conversely, the proportions of Fb2, Fb3, and the average chain length were notably lower, ranging from 0.92% to 11.62%, from 3.06% to 13.64%, and from 1.86% to 5.24%, respectively. In the inferior grains of soft rice, the increases in the proportions of Fa and Fb1 were from 6.35% to 7.05% and from 2.07% to 2.36%, respectively, while decreases in Fb 2 , Fb 3 , and average chain length ranged from 2.23% to 15.77%, from 1.36% to 9.84%, and from 0.67% to 8.73%, respectively. In soft rice, compared with superior grains, inferior grains demonstrated higher proportions of Fa and Fb 1 , ranging from 3.88% to 4.57% and from 0.38% to 1.30%, respectively. Additionally, the proportions of Fb 2 , Fb 3 , and average chain length were lower, ranging from 8.20% to 9.05%, from 4.53% to 7.46%, and from 11.50% to 13.14%, respectively. In non-soft rice, compared with superior grains, inferior grains exhibited increases in Fa and Fb1 proportions, ranging from 1.21% to 3.71% and from 0.35% to 2.17%, respectively, and decreases in Fb 2 , Fb 3 , and average chain length, ranging from 3.68% to 4.31%, from 8.19% to 9.79%, and from 8.11% to 8.33%, respectively. Furthermore, significant variations were also observed in the distribution of amylose chains (DP > 100) among superior and inferior grains, across both soft and non-soft rice varieties (Table 2, Fig. 4-B). Compared with non-soft rice, in superior grains of soft rice, the proportions of A chains, B chains, and C chains were significantly lower, ranging from 50.93% to 53.13%, from 37.53% to 42.89%, and from 26.14% to 31.72%, respectively. In inferior grains of soft rice, the reductions in A chain, B chains, and C chains were from 55.00% to 57.46%, from 4.10% to 10.38%, and from 21.47% to 26.89%, respectively. Within the soft rice variety, inferior grains, compared to superior ones, showed lower proportions of A chains, B chains, and C chains, ranging from 18.69% to 22.85%, from 3.56% to 14.29%, and from 6.61% to 17.58%, respectively. Similarly, within non-soft rice, compared with superior grains, inferior grains exhibited reductions in A chain, B chains, and C chains, ranging from 11.01% to 15.30%, from 40.50% to 47.40%, and from 5.21% to 7.55%, respective. Table 5 Difference in amylopectin and amylose chain length distribution between superior and inferior grains of soft and non-soft rice Cultivar Grain position Amylopectin chain length distribution Amylose chain length distribution (%) Average chain length (DP) Fa (DP 6-12) % Fb 1 (DP 13-24) % Fb 2 (DP 25-36) % Fb 3 (DP 37-100) % A-chain (DP 100-1000) B-chain (DP 1001-2000) C-chain (DP 2001-10000) NJ5718 SG 20.09±0.04b 29.19±0.42b 47.66±0.30b 10.80±0.10c 12.35±0.08e 4.99±0.05c 2.53±0.21b 2.11±0.09f NJ9108 20.47±0.04b 28.88±0.3bc 47.48±0.21b 10.90±0.42c 12.74±0.04d 4.87±0.01c 2.73±0.05b 2.26±0.02e HD5 21.20±0.14a 27.53±0.28e 46.28±0.17c 12.22±0.05a 14.30±0.17a 10.39±0.47a 4.37±0.28a 3.06±0.03b HJ5 21.25±0.05a 27.20±0.13e 46.12±0.45c 12.30±0.07a 14.05±0.07b 10.17±0.21a 4.43±0.37a 3.09±0.06b NJ5718 IG 17.78±0.08d 30.20±0.14a 48.10±0.06a 9.91±0.14d 11.79±0.27f 3.96±0.05d 2.34±0.23b 2.42±0.03d NJ9108 17.90±0.10d 30.00±0.10a 48.23±0.08a 10.14±0.10d 11.63±0.05f 3.85±0.03d 2.44±0.02b 2.56±0.06c HD5 19.48±0.11c 28.21±0.06d 47.12±0.28b 11.77±0.07b 12.90±0.11c 9.05±0.11b 2.33±0.3b 3.26±0.02a HJ5 19.53±0.10c 28.04±0.12d 47.43±0.20b 11.93±0.05b 12.60±0.10d 8.80±0.07b 2.60±0.24b 3.31±0.01a Data are expressed as the mean ± standard deviation, n = 3. Different superscript letters in the same column indicate significant differences in the LSD test (p < 0.05). The area ratio of AM/AM+AP represented the amylose content. 3.2.5 Starch crystal structure In this investigation, both superior and inferior grains of soft and non-soft rice varieties demonstrated A-type crystalline structures. The diffraction analysis revealed two prominent peaks at 15° and 23° (2θ), accompanied by a bimodal pattern at 17° and 18° (2θ) (Fig. 5-A). These findings suggest that the crystalline type of japonica rice starch remains consistent across different rice varieties. Nonetheless, compared with non-soft rice, soft rice exhibited a notably higher relative crystallinity, ranging from 7.28% to 8.25% in superior grains, and from 7.37% to 7.99% in inferior grains (Table 3). Within the varieties of soft rice, the crystallinity of inferior grains was marginally higher by 0.49% to 1.64% compared to that of superior grains; similarly, in non-soft rice, inferior grains showed a crystallinity increase of 0.96% to 1.32% over superior grains. Further analysis using ATR-FTIR (Fig. 5B, Table 6) revealed significant differences in the short-range ordered structure of starch granules. Compared with non-soft rice, in soft rice, superior grains demonstrated significantly lower 1022/995 cm⁻¹ ratios, with a decrease ranging from 6.86% to 10.13%, and higher 1045/1022 cm⁻¹ ratios, with an increase ranging from 15.93% to 21.37%. Inferior grains of soft rice also showed a 7.32% to 8.86% decrease in the 1022/995 cm⁻¹ ratio and a 14.47% to 20.22% increase in the 1045/1022 cm⁻¹ ratio compared to non-soft rice. Within the soft rice category, the inferior grains exhibited significantly lower ratios of 1045/1022 cm⁻¹ and significantly higher ratios of 1022/995 cm⁻¹ compared to superior grains, decreasing by 2.00% to 6.87% and increasing by 0.23% to 3.45%, respectively. In non-soft rice, the corresponding ratios in inferior grains were reduced by 0.79% to 5.78% for 1045/1022 cm⁻¹ and increased by 0.31% to 2.43% for 1022/995 cm⁻¹, respectively, compared to superior grains. Additionally, compared with non-soft rice, soft rice displayed a significantly higher peak intensity, increasing by 19.10% to 20.91%, and a reduced lamellar thickness, decreasing by 0.97% to 1.51%, in superior grains. A similar trend was observed in inferior grains, with an increase in peak intensity of 18.82% to 20.71% and a decrease in lamellar thickness of 0.98% to 1.41%. Within the soft rice category, inferior grains exhibited a slightly higher peak intensity ranging from 0.15% to 1.20% and a reduced lamellar thickness ranging from 0.22% to 0.66% compared to superior grains. Conversely, in non-soft rice, inferior grains showed a 0.14% to 1.92% increase in peak intensity and a 0.22% to 0.75% decrease in lamellar thickness compared to superior grains (Table 6). Table 6 Difference in crystal structure indexes between superior and inferior grains of soft and non-soft rice Cultivar Grain position Relative crystallinity (%) 1045/1022 cm -1 1022/995 cm -1 Peak intensity (counts) Lamellar thickness (nm) NJ5718 SG 12.33±0.07ab 0.778±0.05a 0.883±0.05b 84.30±2.68a 9.15±0.07c NJ9108 12.23±0.08b 0.764±0.05a 0.869±0.03b 83.87±1.87a 9.16±0.03c HD5 11.40±0.06d 0.659±0.04c 0.948±0.03a 70.42±1.46b 9.25±0.07a HJ5 11.39±0.06d 0.641±0.03c 0.967±0.03a 69.72±0.65b 9.29±0.07a NJ5718 IG 12.43±0.02a 0.749±0.03b 0.899±0.01b 84.88±1.26a 9.10±0.16d NJ9108 12.39±0.09a 0.728±0.05b 0.885±0.03b 84.43±0.42a 9.13±0.15d HD5 11.51±0.03c 0.636±0.02d 0.970±0.02a 71.06±1.10b 9.22±0.04b HJ5 11.54±0.06c 0.623±0.03d 0.971±0.02a 70.32±1.25b 9.23±0.06b Data are presented as mean ± standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p < 0.05). 3.3 Protein component and structure 3.3.1 Protein content and its components Marked disparities were noted in the protein content and its various fractions between the superior and inferior grains of both soft and non-soft rice varieties, as detailed in Table 7. Compared with non-soft rice, the superior grains of soft rice exhibited increased levels of total protein, ranging from 7.33% to 8.57%, and enhanced percentages of albumin, globulin, and glutelin, measuring 21.23% to 26.75%, 6.53% to 46.85%, and 13.21% to 17.52%, respectively. Conversely, the prolamin content was reduced by 2.58% to 4.04%. In the inferior grains of soft rice, there was also a notable increase in total protein content (7.10% to 8.52%), albumin (8.10% to 29.10%), globulin (12.35% to 33.59%), and glutelin (13.47% to 16.50%), accompanied by a decrease in prolamin content ranging from 6.97% to 13.16%. Within the soft rice category, the inferior grains demonstrated higher levels of total protein (4.00% to 5.60%), albumin (7.29% to 23.86%), prolamin (1.95% to 3.55%), and glutelin (1.82% to 5.51%) compared to the superior grains. Similarly, in non-soft rice, the inferior grains contained greater amounts of total protein (4.45% to 5.43%), albumin (16.31% to 25.80%), globulin (2.61% to 21.62%), prolamin (6.37% to 14.41%), and glutelin (2.53% to 5.46%) than the superior grains. Furthermore, the proportions of each protein fraction relative to the total protein content displayed significant variations between soft and non-soft rice, as shown in Table 7. Compared with non-soft rice, in the superior grains of soft rice, there was a higher proportion of albumin (an increase of 6.90% to 10.00%) and glutelin (an increase of 0.61% to 1.13%), while the proportion of prolamin was considerably reduced, ranging from 12.38% to 34.18%. The inferior grains of soft rice similarly exhibited an increased proportion of glutelin (0.84% to 1.76%) and a diminished prolamin proportion (16.22% to 34.76%). Within the soft rice variants, the inferior grains maintained a higher proportion of albumin (4.13% to 17.29%) but a lower proportion of glutelin (0.39% to 1.25%) when compared to the superior grains. In contrast, the proportion of albumin in the inferior grains of non-soft rice was significantly higher (10.69% to 19.55%), while the proportion of glutelin was reduced (0.96% to 1.54%) relative to the superior grains. Table 7 Difference in protein content and its components between superior and inferior grains of soft and non-soft rice Cultivar Grain position Protein content (%) Content (mg/g) Proportion (%) Albumin Globulin Prolamin Glutelin Albumin Globulin Prolamin Glutelin NJ5718 SG 8.93±0.05bc 3.94±0.10cd 4.08±0.04d 4.51±0.10e 68.91±0.73c 4.84±0.08c 5.02±0.07d 5.31±0.12d 84.82±0.2a NJ9108 8.99±0.03b 3.98±0.07c 4.89±0.02b 4.53±0.03f 70.23±0.92b 4.8±0.12c 5.89±0.07a 4.64±0.02e 84.66±0.21ab HD5 8.32±0.03d 3.25±0.03e 3.83±0.08e 4.65±0.08de 60.87±1.00e 4.49±0.03d 5.3±0.16bc 6.06±0.06c 84.15±0.15cd HJ5 8.28±0.10d 3.14±0.24e 3.33±0.04f 4.7±0.07b 59.76±0.83f 4.4±0.35d 4.67±0.04e 7.05±0.02a 83.87±0.39de NJ5718 IG 9.35±0.06a 4.27±0.08b 4.55±0.04c 4.62±0.08d 71.51±0.14ab 5.04±0.09bc 5.36±0.04b 5.27±0.1d 84.33±0.17bc NJ9108 9.43±0.03a 4.88±0.08a 5.25±0.08a 4.67±0.05f 72.71±0.28a 5.63±0.1a 6.04±0.1a 4.56±0.05e 83.77±0.16e HD5 8.73±0.08c 3.95±0.02cd 4.05±0.09d 5.02±0.11c 62.41±0.46de 5.26±0.06b 5.38±0.12b 6.29±0.11b 83.07±0.03f HJ5 8.69±0.06c 3.78±0.20d 3.93±0.01e 5.32±0.02a 63.02±0.18d 4.97±0.24c 5.16±0.03cd 6.99±0.05a 82.87±0.17f Data are presented as mean ± standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p < 0.05). 3.3.2 Protein secondary structure Significant differences were observed in the secondary structure of proteins between the superior and inferior grains of both soft and non-soft rice varieties, as depicted in Fig. 6. Compared with non-soft rice, the superior grains of soft rice demonstrated higher proportions of α-helix, increasing by 1.35% to 2.54%, and random coil, which increased by 4.41% to 6.90%. Conversely, these grains exhibited reductions in the proportions of β-sheet and β-turn by 2.12% to 2.80% and 1.44% to 3.87%, respectively. In the case of inferior grains, soft rice similarly displayed an increase in α-helix content (ranging from 1.27% to 3.44%) and random coil content (ranging from 2.44% to 5.56%), alongside reductions in β-sheet (ranging from 2.28% to 3.24%) and β-turn proportions (ranging from 0.21% to 1.71%) relative to non-soft rice. Within the soft rice category, inferior grains exhibited higher levels of α-helix, β-sheet, β-turn, and random coil compared to superior grains, with increases observed between 1.52% to 2.78%, 0.60% to 1.09%, 0.43% to 2.89%, and 4.70% to 7.89%, respectively. A similar trend was noted in non-soft rice, where inferior grains displayed elevated levels of α-helix (0.90% to 2.78%), β-sheet (0.56% to 1.77%), β-turn (0.16% to 1.24%), and random coil (3.39% to 5.86%) compared with their superior counterparts. 3.4 PCA analysis and correlations analysis 3.4.1 PCA analysis The PCA loading plot indicated that the first principal component (PC1) accounted for 63.9% of the total variance, while the second principal component (PC2) explained an additional 17.9% (Fig. 7A). The analysis delineated four distinct clusters. The upper left cluster predominantly correlated with traits such as the chalky grain rate, chalkiness degree, presence of small starch granules, albumin, and Fb 1 (DP 6–12). The upper right cluster was associated with total starch, C-chain DP (2000–10000), β-sheet, and β-turn. Meanwhile, the lower left cluster showed correlations with medium starch granules, the 1045/1022 cm⁻¹ ratio, random coil, amylopectin, and crystallinity. The lower right cluster was linked with transparency, average chain length of amylopectin, α-helix, B-chain DP (1001–2000), and Fb 2 (DP 25–36) along with Fb 3 (DP 37–100). These findings suggest that the starch and protein structural characteristics investigated in this study are both directly and indirectly associated with the appearance quality traits of rice. 3.4.2 Correlation analysis 3.4.2.1 Correlation between appearance quality and starch structure Correlation analyses conducted on chalkiness traits, transparency, and starch characteristics of superior and inferior grains in both soft and non-soft rice varieties (Fig. 7-B) demonstrated that chalkiness traits—which include the rate of chalky grains and the degree of chalkiness—exhibited a negative correlation with several parameters: total starch content, amylose content, average size of starch granules, proportion of large starch granules, long-chain amylopectin structures (Fb 2 , Fb 3 ), average chain length of amylopectin, A-chains, B-chains, the 1022/995 cm⁻¹ ratio, and lamellar thickness. Conversely, these traits were positively correlated with amylopectin content, the proportion of small starch granules, short-chain amylopectin (Fa, Fb 1 ), relative crystallinity, and peak intensity. Transparency showed a negative correlation with amylopectin content, the proportion of small starch granules, short-chain amylopectin (Fa, Fb 1 ), relative crystallinity, the 1045/1022 cm⁻¹ ratio, and peak intensity. Conversely, it was positively correlated with total starch content, amylose content, average size of starch granules, proportion of large starch granules, long-chain amylopectin (Fb 2 , Fb 3 ), average chain length of amylopectin, A-chains, B-chains, C-chains, the 1022/995 cm⁻¹ ratio, and lamellar thickness. 3.4.2.2 Correlation between appearance quality and protein structure The correlation analysis concerning chalkiness traits, transparency, and protein structure in both superior and inferior grains of soft and non-soft rice (Fig. 7-C) indicated that chalkiness traits were significantly inversely correlated with α-helix content, while exhibiting strong positive correlations with total protein content, albumin, globulin, glutelin, β-sheet, and β-turn structures. Transparency displayed negative correlations with total protein, albumin, globulin, glutelin, and β-sheet content. Conversely, it showed positive correlations with prolamin and α-helix content. 4. Discussion This study has elucidated that the overall appearance quality of soft rice is significantly inferior compared to non-soft rice (Table 1 ), with both dominant and inferior grains displaying relatively high levels of chalkiness and low transparency (Fig. 1). Further investigations revealed that even though dominant grains occupy favorable positions within soft rice, their significant contributions to high chalkiness and low transparency (Table 3 ) limit their ability to positively enhance the overall quality of the panicle. Consequently, they fail to offset the negative effects posed by inferior grains. Moreover, soft rice is characterized by a higher proportion of low-weight grain kernels (Table 2 ), and the rate of chalky grains among inferior samples markedly influences the appearance quality of the entire panicle (Table 3 ). As a result, the detrimental attributes of inferior grains—predominantly high chalkiness and low transparency—are significantly magnified at the panicle level, thus being the primary contributors to the relatively poor appearance quality observed in soft rice. 4.1 Interrelationships between appearance quality variations and starch-protein structural characteristics The appearance quality of rice is fundamentally a reflection of the homogeneity of light transmission and the structural compactness within the endosperm tissue (Cao et al., 2024 ). These characteristics are predominantly governed by the synergistic regulation of starch and protein structures (He et al., 2024 ). This study demonstrates that the differences in appearance quality observed across grain positions in both soft and non-soft rice types stem from multiscale structural deterioration, which manifests in the morphological architecture, molecular composition, and spatial conformations of both starch granules and protein networks (Fig. 7). With respect to starch structure, compared with non-soft rice, both superior and inferior grains of soft rice display finer fragmentation, increased breakage, and looser granule packing—characteristics that are particularly pronounced in inferior grains (Fig. 3 , Table 4 ). These morphological anomalies directly compromise the structural continuity of the endosperm tissue, augmenting light-scattering interfaces and pathways while diminishing optical homogeneity. Consequently, this leads to reduced transparency and enhanced chalkiness (Lin, 2015 ). At the molecular level, the elevated ratio of Fa-chains, reduced proportion of Fb3-chains, and lower amylose content in soft rice grains (Fig. 2 , Table 5 ) collectively result in a shift towards shorter amylopectin chain lengths. This dominance of short chains weakens intermolecular entanglement strength and reduces hydrogen-bonding density, leading to loosening within the lamellar structures of crystalline regions (Fig. 3 ) and a significant reduction in mechanical stability (Wu et al., 2021 ). The compromised integrity of these crystalline structures diminishes granule robustness, making the inferior grain endosperm more susceptible to macro-defects such as pores and fissures during mechanical stress or desiccation. Moreover, soft rice grains exhibit higher crystallinity and elevated short-range molecular order (Table 6 ), creating a “high-crystallinity–high-order” architecture. This structural formation is likely due to the increased presence of short amylopectin chains (Fa) (Table 5 ), which promote localized double-helix formation and ordered stacking (Ma et al., 2025 ). Paradoxically, such short-chain-dominated configurations may concurrently reduce crystalline lamellar thickness and increase intra-granular porosity (Fig. 3 , Table 6 ), blurring the interfaces between crystal and amorphous regions (Fan et al., 2024 ). This porous, thin-layered microstructure substantially increases the frequency of light scattering and the complexity of refraction, visually amplifying the appearance of chalkiness (Fan et al., 2024 ) and ultimately degrading the appearance quality of the rice. Beyond starch, the composition of proteins and their spatial conformation are also critical in regulating the structural compactness and optical homogeneity of the endosperm (Shi, 2017 ). Both superior and inferior grains of soft rice are characterized by a compositional imbalance, evidenced by elevated levels of albumin and glutelin with a concurrent reduction in prolamin content (Table 7 ). The decreased hydrophobic capacity of prolamin diminishes its ability to embed compactly on the surfaces of starch granules (Wang et al., 2024 ), while the increased hydrophilic properties of albumin and glutelin fractions contribute to the enlargement of interfacial voids (Lu et al., 2023 ). This aberrant composition leads to a reduction in starch-protein interfacial density, which in turn triggers heterogeneous light scattering, significantly compromising the optical uniformity of the endosperm (Lu et al., 2008 ; Liu et al., 2023 ). Furthermore, the secondary structures of proteins in soft rice grains exhibit increased proportions of α-helix and random coil structures, along with decreased fractions of β-sheet and β-turn structures (Fig. 6 ). This indicates a progressive disordering within the protein network. Such conformational instability undermines the structural integrity of the protein matrix, reducing its capacity to spatially confine starch granules and exacerbating their disordered arrangement (Fig. 3 ). This collectively degrades the optical homogeneity of the endosperm (Zhu, 2020 ; Li et al., 2023 ). Notably, in inferior grains, synergistic interactions between destabilized protein networks and inherent starch structural defects amplify the porosity and interfacial discontinuity of the endosperm, ultimately leading to a degradation of optical performance. 4.2 Synergistic interaction mechanisms between starch and protein structures The significant deterioration in the appearance quality of soft-textured japonica rice—particularly in inferior grains—is fundamentally attributed to the mutually inducing and exacerbating interactions between starch structural defects and aberrant protein networks under unfavorable developmental conditions. This synergy establishes a vicious cycle of progressive structural degradation. On one hand, defects in starch structure initiate abnormalities in the protein network: severe fragmentation, micronization, and disordered arrangement of starch granules in inferior grains (Table 4 , Fig. 3 ) physically disrupt starch-protein interfaces and interfere with normal protein assembly, leading to disordered transitions in secondary structure (Fig. 6 ) that result in loose, disorganized protein matrices. On the other hand, these aberrant protein networks amplify starch defects: the disordered protein matrix, potentially compromised by impaired grain filling, diminishes spatial confinement and physical support for starch granules. This not only fails to prevent further granule fragmentation and misalignment but also exacerbates granule displacement and defect formation (e.g., pores) during desiccation or processing, intensifying structural disorder. The mechanisms described above demonstrate significant superposition and cumulative effects in grains of lower quality. These grains exhibit severe structural defects in starch and protein, which markedly enhance their negative interactions. Such defects destabilize the starch-protein matrix within the endosperm, compromising its structural integrity and significantly increasing the number of light-scattering interfaces and the complexity of light refraction. These changes decrease transparency, accentuate visual chalkiness, and ultimately impair the aesthetic quality of soft rice. 5. Conclusion This study, through a comparative analysis of the starch and protein characteristics of superior and inferior grains among varieties of soft and non-soft rice, elucidates the structural underpinnings of their differing appearance qualities. Soft rice, in comparison to non-soft rice, typically displays heightened chalkiness and diminished transparency across both grain types. This deterioration in quality is attributed to the dual destabilization of the starch and protein structures within the endosperm. Specifically, the starch granules in soft rice are characterized by increased fragmentation, an elevated proportion of smaller granules, and disorganized branching structures, which together reduce granule density and optical homogeneity. Concurrently, an imbalance in protein components and looser conformational arrangements further undermine the compactness of the endosperm structure, leading to a marked increase in chalkiness. These structural defects are especially prominent in inferior grains and, due to their greater prevalence within the panicle, have a predominant adverse effect on the overall visual quality of the rice panicle. Declarations Acknowledgements Not applicable. Author Contributions HW and HZ conceived and designed the experiment and provided financial assistance; XC, JY, and JC performed the experiments and analyzed data; YZ, GL, GL, FX, and QH contributed reagents/materials/analysis tools; XC wrote the paper. All authors have read and agreed to the published version of the manuscript. Funding This work was funded by the National Natural Science Foundation of China (32372215, 32372212, 32201891), the Earmarked Fund for CARS (Rice, CARS-01), the National Key Research Program of China (2022YFD2301401), the Collaborative Promotion Project for Major Agricultural Technologies (2024-ZYXT-03-1), the Changzhou Modern Agricultural Science and Technology Innovation Center Project (CAIC(2023)005), the Priority Subject Program Development of Jiangsu Higher Education Institutions (PAPD) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX24_3790). Data availability Data will be made available on request. Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Competing Interests The authors declare no competing interests. References Bocianowski, J. (2012). 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Study on influence of rice protein and its composition on rice cooking and eating quality. Zhejiang Gongshang University. Wang, G. Q., Li, H. X., Feng, L., et al. (2019). Transcriptomic analysis of grain filling in rice inferior grains under moderate soil drying. Journal of Experimental Botany, 70(5), 1597–1611. Wu, Y. F., Zhang, Y., Wang, L. L., et al. (2021). Starch quality of rice grain: Research progress on influencing factors and mechanism. Chinese Agricultural Science Bulletin, 37(6), 1–8. Wang, C. L., Guo, W., Hu, P. S., et al. (2022). Differences of physicochemical properties between chalky and translucent parts of rice grains. Rice Science, 29(6), 577–588. Wang, S.S., Luo, L.Y., Tang, Q.H., et al. (2024). Preparation of zein nanoparticles-citric acid cross-linked starch composite edible film for preventing rapid fusion of cream and tea soup. Food and Fermentation Industries, 50(22), 9–18. Yuryev, V. P., Krivandin, A. V., Kiseleva, V. I., et al. (2004). Structural parameters of amylopectin clusters and semi-crystalline growth rings in wheat starches with different amylose content. Carbohydrate Research, 339(16), 2683–2691. Yong, M. L., Ye, M., Zhang, Y., et al. (2024). Differences in rice starch structure, physicochemical properties, and their responses to nitrogen among different eating-quality rice varieties. Chinese Journal of Rice Science, 38(1), 57–71. Zhang, Q. L. (2012). Rice proteins affect cooking and eating quality of cooked indica rice. Sichuan Agricultural University. Zhou, H. Y. (2017). Association analysis of amylopectin structure in rice endosperm and its relationship with quality and starch synthesis-related genes. Jiangxi Agricultural University. Zhu, H. L. (2018). Analysis of the cause of the difference between superior and inferior spikelets in different genotype rice. Nanjing Agricultural University. Zhong, Y., Liu, L., Qu, J., et al. (2020). Amylose content and specific fine structures affect lamellar structure and digestibility of maize starches. Food Hydrocolloids, 108, 105994. Zhu, L. (2020). Mechanism of rice taste quality based on structural analysis and real-time monitoring. Jiangnan University. Zheng, X. L. (2021). Difference of grain characters and physiological and biochemical mechanism in different panicle of japonica rice. Journal of Nuclear Agricultural Sciences, 35(6), 1234–1242. Zhu, Y., Xu, D., Chen, X., et al. (2022). Quality characteristics of semi-glutinous japonica rice cultivated in the middle and lower reaches of the Yangtze River in China. Journal of the Science of Food and Agriculture, 102(9), 3712–3723. Zhu, Y. (2022). Quality characteristics and carbon and nitrogen metabolism mechanism of good quality, high yield and high nitrogen efficiency of soft japonica rice in Yangtze River Delta. Yangzhou University. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Rice → Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 31 Aug, 2025 Submission checks completed at journal 31 Aug, 2025 First submitted to journal 29 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7486609","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509979830,"identity":"46dace4d-1345-4560-9b10-b40224200c7a","order_by":0,"name":"Xi Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACNvmDDQYfKv7J8TMzH3yQUFFDWAufBHND4YwzB4wl29uSDR6cOUZYi5wEe8Nn3rYDiQZnzphJPmxhJsJh0o2NG2ecuZNgcCPBrCKxgY2Bv707Ab8WmYPNQL88y5O8kZB2I3GHDIPEmbMb8GthSGwznHGGuZjvRsKxG4ln2BgMJHIJamn/zdvGnNhwI7GtILGNmQgtEokNxrxthxMnnDkMspEYLTwHG4AOSwMFMrNEwpljPAT9It/e/gDofRtgVPJ//PijokaOv70XvxYMwEOa8lEwCkbBKBgFWAEARlFTyG51i64AAAAASUVORK5CYII=","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Xi","middleName":"","lastName":"Chen","suffix":""},{"id":509979831,"identity":"bb798c6a-67fe-4f7f-a33c-9716f77d813a","order_by":1,"name":"Jianghui Yu","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jianghui","middleName":"","lastName":"Yu","suffix":""},{"id":509979832,"identity":"d85d74cb-0546-41c6-a2b3-c542d96a280d","order_by":2,"name":"Ying Zhu","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhu","suffix":""},{"id":509979833,"identity":"9e4558eb-7124-414c-991a-3432362be1ea","order_by":3,"name":"Guodong Liu","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Guodong","middleName":"","lastName":"Liu","suffix":""},{"id":509979834,"identity":"95f4fb48-a04d-4cf8-8467-8eec483f25ad","order_by":4,"name":"Guangyan Li","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Guangyan","middleName":"","lastName":"Li","suffix":""},{"id":509979835,"identity":"430033f0-5456-42fc-ae1e-cdde376b0f84","order_by":5,"name":"Fangfu Xu","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Fangfu","middleName":"","lastName":"Xu","suffix":""},{"id":509979836,"identity":"b8a57f6c-6954-4aed-b6db-482ae74a6636","order_by":6,"name":"Qun Hu","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Qun","middleName":"","lastName":"Hu","suffix":""},{"id":509979837,"identity":"35f12940-33aa-4240-abd6-8b65fe27367b","order_by":7,"name":"Jiale Cao","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiale","middleName":"","lastName":"Cao","suffix":""},{"id":509979838,"identity":"8b465e9a-eff6-474b-8e94-7b21ee322392","order_by":8,"name":"Haiyan Y. Wei","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"Y.","lastName":"Wei","suffix":""},{"id":509979839,"identity":"77b9b70f-3f59-4f40-abd4-3c9530c964cd","order_by":9,"name":"Hongcheng Zhang","email":"","orcid":"","institution":"Agricultural College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hongcheng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-08-29 08:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7486609/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7486609/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12284-026-00886-9","type":"published","date":"2026-03-03T15:59:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90837907,"identity":"ee62ce82-4da8-4b9a-a751-accc20927056","added_by":"auto","created_at":"2025-09-08 18:17:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifference of appearance quality between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA represents the difference in chalky grain rate between superior and inferior grains of both soft and non-soft rice. B represents the difference in chalkiness degree between superior and inferior grains of both soft and non-soft rice. C represents the difference in transparency between superior and inferior grains of both soft and non-soft rice. D illustrates the percentage difference between superior and inferior grains of both soft and non-soft rice. Vertical bars denote means ± standard error. Distinct letters signify significant differences as determined by the LSD test (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7486609/v1/ef40155e11e5bc50bfa272a2.png"},{"id":90837910,"identity":"cb0bdefb-1f96-484c-ba8d-f8c87347f0b8","added_by":"auto","created_at":"2025-09-08 18:17:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22445,"visible":true,"origin":"","legend":"\u003cp\u003eDifference in starch content and its components between superior and inferior grains of soft and non-soft rice\u003c/p\u003e\n\u003cp\u003eVertical bars represent means ± standard error. Different letters indicate significant differences in the LSD test (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7486609/v1/d473cae066748653eba2d07f.png"},{"id":90838755,"identity":"67f1582e-312c-48fd-bb3d-41800186aa8f","added_by":"auto","created_at":"2025-09-08 18:33:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":751180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in scanning electron microscope observations of starch granules between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA and a were NJ5718, B and b were NJ9108, C and c were HD5, D and d were HJ5; Capital letters were superior grains, small letters were inferior grains.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7486609/v1/a2f47f1bee1cff2add1d874e.png"},{"id":90838454,"identity":"af9d22ee-5a46-4fac-930d-a9a640a57ae3","added_by":"auto","created_at":"2025-09-08 18:25:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural differences debranched starch between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFine structure of debranched starch as determined by SEC (A), and B showed the CLD above DP 100. S: superior grain, I: inferior grain.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7486609/v1/60bf815037c6ea582134bab8.png"},{"id":90839100,"identity":"4eb014f3-1b5a-4252-8d22-13ed96c78786","added_by":"auto","created_at":"2025-09-08 18:41:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifference in X-ray diffractogram and infrared spectrum between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS: superior grain, I: inferior grain.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7486609/v1/c0795f662b9c18c3809a04e8.png"},{"id":90838455,"identity":"592cdb2b-4624-42d6-92b4-c34b1d839d73","added_by":"auto","created_at":"2025-09-08 18:25:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":15378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in protein secondary structure between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as mean ± standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7486609/v1/1863f4e064ed6929276b869b.png"},{"id":90838458,"identity":"d110254a-ecfa-4e29-ac52-821681b37d86","added_by":"auto","created_at":"2025-09-08 18:25:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":36265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA (principal component analysis) of appearance quality and other properties (A), correlation analysis of rice appearance quality and other properties (B、C)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB is the correlation analysis of appearance quality and starch structure of soft and non-soft rice superior and inferior grains. C is the correlation analysis of appearance quality and protein structure of soft and non-soft rice superior and inferior grains. Abbreviations corresponding to letters: Cul: Cultivar, GP: Grain position, CR: Chalk grain rate, CD: Chalkiness degree, Tra: Transparency, TSC: Total starch content, AC: Amylose content, AP: Amylopectin content, AS: Average size, SC: Small particle content, MC Medium particle content, LC: Large particle content, FbF: Fb1, FbS: Fb2, FbT: Fb3, AL: Average chain length, DPF: DP (100-1000), DPS: DP (1001-2000), DPT: DP (2000-10000), RC: Relative crystallinity, A: 1045/1022 cm\u003csup\u003e-1\u003c/sup\u003e, B: 1022/995 cm\u003csup\u003e-1\u003c/sup\u003e, PI: Peak intensity, LT: Lamellar thickness, Pc: Protein content, Alb: Albumin, Glo: Globulin, Pro: Prolamin , Glu: Glutelin, C: α-helix, D: β- sheet, E: β-turn, F: Random coil. * p\u0026lt;=0.05, ** p\u0026lt;=0.01, *** p\u0026lt;=0.001.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7486609/v1/c4150f6a8d815119074e0cea.png"},{"id":104252202,"identity":"0dc89ff6-980d-42ac-9d60-8d398bd7a720","added_by":"auto","created_at":"2026-03-09 16:17:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2821974,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7486609/v1/22a931be-b53b-42e7-80b0-869b4d25e65d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interrelationships between appearance quality and starch and protein structures in superior and inferior grains of soft and non-soft rice","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRice (Oryza sativa L.) serves as a fundamental food crop globally, and its qualitative enhancement is pivotal for ensuring food security and nutritional health (Ma et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although the proliferation of high-grade rice varieties in China is notable, the appearance and eating qualities still pose significant challenges to industrial progress and market competitiveness (Zheng, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In recent years, China has developed numerous soft rice japonica varieties that are prized for their tender texture and excellent flavor (Zhu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the endosperm of these varieties tends to be opaque and lacks translucency, reducing their appeal to consumers and their competitiveness in the market (Zhu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, advancements in the appearance quality of soft rice are crucial for boosting its market competitiveness.\u003c/p\u003e\u003cp\u003eChalkiness, characterized by the opacity of the endosperm, emerges as a critical quality determinant. Its development is closely linked to the distinct microstructure of the chalky regions within the endosperm (Wang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These regions typically feature starch granules with spherical shapes, loosely arranged with considerable gaps between them (Zheng et al., 2022). As the main components of the endosperm, the composition (amylose and amylopectin) and fine structure of starch significantly influence the transparency of the endosperm (Zhong et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A lower amylose content leads to a translucent or opaque \u0026ldquo;cloudy appearance,\u0026rdquo; adversely affecting the visual appeal (Zhu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, a higher proportion of short amylopectin chains (DP 6\u0026ndash;12) disrupts the cohesion between crystalline and amorphous areas, resulting in underdeveloped starch granules, smaller granule size, and increased intergranular voids (Zhou, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These microstructural changes significantly extend the paths of light scattering within the endosperm, reduce light transmittance, and thereby decrease transparency while increasing the degree of chalkiness (Yong et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The protein architecture also critically influences endosperm transparency. Disruptions in protein synthesis or anomalies in protein body distribution impair starch-protein interactions, creating intercellular spaces that enhance chalkiness (Wang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Research indicates that chalky regions tend to have higher concentrations of albumin and lower levels of globulin, a disparity that underscores a fundamental molecular mechanism underlying the degradation of appearance quality (Tang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, the spatial distribution and three-dimensional conformational changes in proteins affect the optical properties of the endosperm. Disrupted or fragmented protein networks lead to structural laxity, which exacerbates light scattering and further diminishes transparency (Jia et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, the interplay and structural integrity of starch and proteins are integral to the microstructural regulation that underpins the appearance quality of rice endosperm.\u003c/p\u003e\u003cp\u003eIn addition to the inherent factors related to grain properties, the variation in grain position within the panicle significantly affects the appearance quality of rice (Wei et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notable disparities in nutrient supply and the progression of grain filling across different grain positions result in spatial heterogeneity in the development and quality characteristics of the grains (Zhu, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Typically, grains located at the upper part of the panicle, classified as superior grains, are characterized by fully developed structures with densely packed starch granules and enhanced endosperm transparency (Ge et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, grains from the lower part of the panicle, referred to as inferior grains, often display a disorganized arrangement of starch granules and increased light scattering due to incomplete grain filling and a relative enrichment in protein content (Zhang, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This results in increased chalkiness and a noticeable reduction in appearance quality. Although existing research has explored the impact of grain position on rice quality, there remains a lack of systematic and thorough investigations into the fine structural characteristics of starch and proteins within different grain positions of soft rice japonica, their synergistic interactions, and the specific mechanistic connections to variations in appearance quality among these positions. Most current studies focus primarily on individual grain positions or single components (Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), often neglecting comparative analyses of the structural differences in starch and protein between dominant and subordinate grains in both soft and non-soft japonica rice varieties, or how these components collectively regulate appearance quality. To bridge these research gaps, we have chosen prominent southern cultivars including soft rice varieties Nanjing 5718 and Nanjing 9108, alongside non-soft rice varieties Huaidao 5 and Huajing 5. By differentiating between dominant and subordinate grains within the panicle, this study systematically analyzes the interrelationships between starch and protein content, composition, structural features, and appearance quality. Our aim is to elucidate the structural foundations and synergistic mechanisms of starch and proteins that account for variations in appearance quality between different grain positions in soft rice, making direct comparisons to non-soft rice. This research is intended to provide a theoretical basis for enhancing the market competitiveness of these varieties.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003ch2\u003e2.1 Experimental site, rice materials, and field management\u003c/h2\u003e\n\u003cp\u003eThe study was conducted at the experimental farm of Yangzhou University, located in Yangzhou, Jiangsu, China (119°42′E, 32°39′N), in the year 2022. The field soil was characterized as sandy loam with 0.13% total N, 165.9 mg kg\u003csup\u003e-1\u003c/sup\u003e alkali hydrolyzable N, 33.7 mg kg\u003csup\u003e-1\u003c/sup\u003e Olsen-P, 77.6 mg kg\u003csup\u003e-1\u003c/sup\u003e exchangeable K, and 30.9 g kg\u003csup\u003e-1\u003c/sup\u003e organic matter.\u003c/p\u003e\n\u003cp\u003eThe rice varieties selected for this study included two high-quality soft rice cultivars, NJ5718 (Nanjing 5718) and NJ9108 (Nanjing 9108), and two non-soft rice cultivars, HD5 (Huaidao 5 hao) and HJ5 (Huajing 5 hao).\u003c/p\u003e\n\u003cp\u003eThe field experiment was conducted using a randomized block design, with each plot encompassing an area of 36 m\u003csup\u003e2\u003c/sup\u003e (6 m × 6 m) across three replicates. Rice seeds were initially sown in a seedling nursery on May 19 and subsequently transplanted on June 11, planting four seedlings per hill with a row spacing of 30 cm and a plant spacing of 12 cm. The total nitrogen application was set at 270 kg ha\u003csup\u003e-1\u003c/sup\u003e, divided among base, tillering, and panicle fertilizers at a distribution ratio of 35:35:30, respectively. The tillering fertilizer was administered seven days post-transplantation, and the panicle fertilizer was applied at the stage of the fourth leaf. The nutrient composition maintained a ratio of nitrogen, phosphorus, and potassium at 2:1:2. Phosphorus was applied solely as a base fertilizer, while potassium was evenly distributed prior to tillage and the jointing stage. The irrigation regimes and the management of weeds and pests during the rice growth periods adhered to local agricultural guidelines.\u003c/p\u003e\n\u003ch2\u003e2.2 Sampling and measurement\u003c/h2\u003e\n\u003cp\u003eAt the heading stage, five hundred rice panicles that flowered synchronously were selected and labeled for each cultivar. Upon reaching maturity, grains from these designated panicles were categorized into superior grains (SG) and inferior grains (IG). SGs were collected from the upper third of the primary branches, whereas IGs were sourced from the lower third of the secondary branches. Both superior and inferior grains for each cultivar were harvested at maturity to facilitate subsequent assessments.\u003c/p\u003e\n\u003ch3\u003e2.2.1 Appearance quality\u003c/h3\u003e\n\u003cp\u003eThe assessment of the chalkiness characteristics of mature seeds adhered to the standards specified in GB/T 17891-2017. The rate of chalky grains and the degree of chalkiness were determined using the WS-SC-E system, developed in China.\u003c/p\u003e\n\u003cp\u003eAdditionally, the transparency of the rice samples was quantified by measuring the transmission rate through a cuvette 1 cm in thickness. This analysis was performed using a colorimeter equipped with a D65 light source (CM-5, Konica Minolta, Tokyo, Japan).\u003c/p\u003e\n\u003ch3\u003e2.2.2 Proportion of grain type\u003c/h3\u003e\n\u003cp\u003eNumber proportion: Superior grains are defined as the percentage of grains located on the upper third of the primary branches of the panicle relative to the total grain count of the entire panicle. Conversely, inferior grains represent the percentage of grains situated on the upper third of the secondary branches of the panicle compared to the overall grain count of the panicle.\u003c/p\u003e\n\u003cp\u003eWeight proportion: The proportion of high grain weight is described as the percentage of grains whose weight exceeds the average weight of the strongest grains at maturity, relative to the total number of grains per panicle. Similarly, the proportion of low grain weight denotes the percentage of grains whose weight falls below the average weight of the weakest grains at maturity, in relation to the total number of grains per panicle.\u003c/p\u003e\n\u003ch3\u003e2.2.3\u0026nbsp;Contribution analysis of grain positions to appearance quality\u003c/h3\u003e\n\u003cp\u003eThe contributions of high-quality granules (SG), medium-quality granules (MG, excluding superior and inferior granules), and inferior granules (IG) to the overall appearance quality were quantified using a multiple linear regression weighting method (Bocianowski, 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContribution = trait value × number proportion\u003c/p\u003e\n\u003cp\u003ePredicted value = SG contribution + MG contribution +IG contribution\u003c/p\u003e\n\u003cp\u003eContribution ratio (%) = (contribution / Predicted value) × 100\u003c/p\u003e\n\u003cp\u003eModel validation: Relative error (%) = |Measured value - Predicted value| / Measured value × 100\u003c/p\u003e\n\u003ch3\u003e2.2.4 Starch extraction\u003c/h3\u003e\n\u003cp\u003eMeasure and weigh 10 g of rice flour into a container, add 35 mL of NaOH solution (pH 8–9), and introduce 50 mg of alkaline protease along with two glass beads. Following this, add 25 μL of sodium azide (0.04 g/mL). Seal the container with parafilm and place it in a shaker set to 42℃ and 180 rpm for 24 hours. Filter the homogenized suspension through a 300-mesh sieve into a 250 mL beaker and transfer the filtrate to a 50 mL centrifuge tube. Centrifuge at 3000 rpm for 10 minutes, discard the supernatant, and remove the yellow layer from the starch surface. Resuspend the precipitate in deionized water and repeat centrifugation under the same conditions. Wash the precipitate with deionized water 3–5 times to eliminate ions and impurities. Further clean the precipitate with 95% ethanol and a chloroform-methanol mixture (V/V = 1:1) to remove lipids. Finally, dry the sample at 40℃ for two days and sieve through a 200-mesh screen to obtain the starch.\u003c/p\u003e\n\u003ch3\u003e2.2.5 Starch and its components\u003c/h3\u003e\n\u003cp\u003eRice flour samples were utilized for the analysis of starch and its components. The total starch content (TSC) was quantified using a Total Starch Kit (Megazyme, Bray, Ireland). Initially, a 10 mg sample was placed into a 10 mL centrifuge tube, to which 5 mL of 80% ethanol was added. The mixture was then incubated in a water bath at 80°C for 30 minutes. After centrifugation at 5000 rpm for 10 minutes, the supernatant was discarded. This procedure was repeated twice. Subsequently, 400 μL of 2 mol/L KOH was added, and the mixture was placed in an ice bath for 30 minutes. Upon completion of the ice bath incubation, 1.6 mL of 1.2 mol/L sodium acetate buffer (pH 3.8), 10 μL of thermostable α-amylase, and 10 μL of amyloglucosidase were added. The tube was thoroughly mixed and returned to a water bath at 50°C for 30 minutes. Afterwards, the sample was transferred to a 10 mL volumetric flask to adjust the volume to the mark. Then, 2 mL of the supernatant was taken and centrifuged at 8000 rpm for 10 minutes. 1 mL of the resulting supernatant was used for analysis, and the total starch content was determined using the GOPOD kit. The amylose content (AC) was measured using the iodine-binding method as described by Tan et al. (2000). Amylopectin content (AP) was calculated by subtracting AC from TSC.\u003c/p\u003e\n\u003ch3\u003e2.2.6 Analysis of SEM and starch granule size\u003c/h3\u003e\n\u003cp\u003eStarch granules were affixed to circular aluminum stubs using double-sided adhesive tape, coated with gold, and examined under a scanning electron microscope (Gemini SEM 300, Carl Zeiss, Oberkochen, Germany) at a magnification of 5000 ×. Images were captured at an accelerating potential of 5 kV. The distribution of starch granule sizes was assessed following the method previously described by Shi et al. (2018) using a laser diffraction particle size analyzer (Mastersizer 2000, Malvern, Worcestershire, England). The size distribution was expressed in terms of the volume of equivalent spheres, and the average granule size was calculated as the volume-weighted mean. The experiments in this study were replicated three times.\u003c/p\u003e\n\u003ch3\u003e2.2.7 Analysis of starch molecular size distribution\u003c/h3\u003e\n\u003cp\u003eThe pure starch was debranched and freeze-dried in strict accordance with the methodology established by Wu et al. (2014). Approximately 4 mg of isolated starch was combined with 0.7 mL of LiBr/DMSO (0.5% w/v) and agitated continuously at 80°C overnight. Subsequently, the mixture was centrifuged at 4000 g for 10 minutes, and the supernatant was precipitated using four volumes of anhydrous ethanol. The precipitate was then dispersed in 0.9 mL of hot water and mixed with 0.1 mL of acetate buffer (0.1 M, pH 3.5), 5 μL of 4% sodium azide solution (w/v), and 2.5 μL of isoamylase (Megazyme E-ISAMY), followed by incubation at 37°C with continuous agitation for 3 hours. The sample was subsequently freeze-dried prior to analysis. The molecular weight distribution of the debranched starch was determined using an LC-20 CE Shimadzu system equipped with an RID-10A refractive index detector (Shimadzu Corporation, Kyoto, Japan), as described by Gu et al. (2019). Calibration of the column was performed using standard dextrans with known molecular weights (2800, 18,500, 111,900, 410,000, 1,050,000, 2,900,000, and 6,300,000).\u003c/p\u003e\n\u003ch3\u003e2.2.8 Crystalline structure of starch\u003c/h3\u003e\n\u003cp\u003eStarch samples were subjected to analysis to determine their crystalline structure. Utilizing the method described by Zhu et al., the X-ray diffraction patterns of starch were acquired using a powder X-ray diffractometer. The measurements were performed at 40 kV and 200 mA, with the diffractometer set to scan from a 2θ angle of 3° to 40° at a sampling interval of 0.6 seconds. The relative crystallinity of the starch was quantified using XPERT HighScore Plus software.\u003c/p\u003e\n\u003cp\u003eFollowing the procedure outlined by Man et al. (2012), the external regions of starch granules were examined using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) employing a Fourier Transform Infrared Spectrometer (Varian 7000 system, Palo Alto, CA, USA). The system used a DTGS detector and a germanium crystal ATR reflector unit. For sample preparation, 100 mg of starch was suspended in 100 μL of ultrapure water, vigorously shaken, and then stored at 4°C for future analysis. Water was used as the blank control. The spectral range was set from 400 to 4000 cm\u003csup\u003e-1\u003c/sup\u003e with a resolution of 2 cm\u003csup\u003e-1\u003c/sup\u003e, and each sample was subjected to 64 scans. For spectral deconvolution, the range of 800–1200 cm\u003csup\u003e-1\u003c/sup\u003e was selected with a half-bandwidth of 19 cm\u003csup\u003e-1\u003c/sup\u003e and an enhancement factor of 1.9.\u003c/p\u003e\n\u003cp\u003eThe lamellar structure of starch was investigated using a small-angle X-ray scattering instrument (Bruker NanoStar, Vantec 2000, Bruker, Germany) following the methodology of Yuryev et al. (2004). The SAXS data were analyzed using DIFFRAC Plus NanoFit software, and SAXS spectrum parameters were determined using a straightforward graphical method (Cai et al., 2014).\u003c/p\u003e\n\u003ch3\u003e2.2.9 Protein and its components\u003c/h3\u003e\n\u003cp\u003eAnalyses of protein and its components were conducted using rice flour samples. The nitrogen content in rice was quantified with an automated Kjeldahl analyzer (Kjeltec 8200, Foss, Hillerød, Denmark), and the protein content was subsequently calculated by applying a conversion factor of 5.95. The identification of specific protein components followed the method outlined by Sapan et al. (1999). Among these components, glutelin levels were assessed via the biuret colorimetric method, whereas albumin, globulin, and prolamin were quantified using the Coomassie blue colorimetric method. Measurements were replicated thrice for each sample, and the results were expressed as mean values.\u003c/p\u003e\n\u003ch3\u003e2.2.10 Protein secondary structure\u003c/h3\u003e\n\u003cp\u003eThe secondary structure of proteins in rice flour samples was investigated using a Fourier Transform Infrared (FTIR) spectrometer (Varian 7000 system, Palo Alto, CA, USA), adhering to the protocol described by Shi et al. (2023). Samples were prepared by combining rice flour with potassium bromide (KBr) in a 1:100 ratio; the mixture was then compressed into thin pellets for FTIR analysis. The spectral range was set from 400 to 4000 cm\u003csup\u003e-1\u003c/sup\u003e, with each sample undergoing 32 scans and the air background receiving 64 scans. Post-deconvolution, the secondary protein structures were identified from the spectral region spanning 1600 cm\u003csup\u003e-1\u003c/sup\u003e to 1700 cm\u003csup\u003e-1\u003c/sup\u003e, encompassing random coil (1637–1645 cm\u003csup\u003e-1\u003c/sup\u003e), β-turn (1664–1681 cm\u003csup\u003e-1\u003c/sup\u003e), β-sheet (1615–1637 cm\u003csup\u003e-1\u003c/sup\u003e and 1682–1700 cm\u003csup\u003e-1\u003c/sup\u003e), and α-helix (1646–1664 cm\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e\n\u003ch2\u003e2.3 Data processing and analysis\u003c/h2\u003e\n\u003cp\u003eData processing was performed using Excel 2019, while statistical analyses were conducted via SPSS Version 20.0. Graphical representations were created with Origin 2021. Both principal components analysis (PCA) and correlation analysis were employed to evaluate the appearance qualities and the starch and protein characteristics of various rice varieties using Origin 2021.\u003c/p\u003e"},{"header":"3. Results and analysis","content":"\u003ch2\u003e3.1 Appearance quality\u003c/h2\u003e\n\u003ch3\u003e3.1.1 Overall appearance quality\u003c/h3\u003e\n\u003cp\u003eSignificant disparities in appearance quality were noted between soft and non-soft rice varieties (Table 1). Compared with non-soft rise, soft rice exhibited a 40.81% to 48.36% higher chalky grain rate and a 66.52% to 73.66% increase in chalkiness degree. Conversely, the transparency of soft rice was reduced by 3.23% to 26.71% relative to non-soft varieties.\u003c/p\u003e\n\u003cp\u003eTable 1 Difference in appearance quality between soft and non-soft rice (%)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eChalky Grain Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eChalkiness Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eTransparency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e25.50\u0026plusmn;0.11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e6.88\u0026plusmn;0.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e9.57\u0026plusmn;0.18c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e29.13\u0026plusmn;0.17a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e7.95\u0026plusmn;0.29a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e8.86\u0026plusmn;0.21d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e15.04\u0026plusmn;0.19c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.30\u0026plusmn;0.11c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e11.85\u0026plusmn;0.25b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e15.10\u0026plusmn;0.12c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.09\u0026plusmn;0.24c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e12.08\u0026plusmn;0.24a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003ch3\u003e3.1.2 Appearance quality of superior and inferior grains\u003c/h3\u003e\n\u003cp\u003eSignificant disparities were observed in the appearance quality of superior and inferior grains between soft and non-soft rice (Fig. 1). Compared with non-soft rice, the superior grains of soft rice demonstrated increases of 79.00%\u0026ndash;115.22% in chalky grain rate and 410.94%\u0026ndash;481.03% in chalkiness degree, while their transparency decreased by 21.67%\u0026ndash;27.27%. In inferior grains, the chalky grain rate and chalkiness degree of soft rice showed increases of 70.72%\u0026ndash;89.17% and 121.03%\u0026ndash;136.31%, respectively, with a decrease in transparency of 34.19%\u0026ndash;36.70%.\u003c/p\u003e\n\u003cp\u003eIn soft rice, compared with superior grains, the inferior grains exhibited higher rates of 67.51%\u0026ndash;71.43% in chalky grain rate and 88.47%\u0026ndash;89.26% in chalkiness degree, along with a decrease in transparency of 40.55%\u0026ndash;43.34%. Similarly, in non-soft rice, the inferior grains displayed increases of 69.80%\u0026ndash;74.23% in chalky grain rate and 95.03%\u0026ndash;95.61% in chalkiness degree, and a decrease in transparency of 29.88%\u0026ndash;34.30% relative to superior grains.\u003c/p\u003e\n\u003ch3\u003e3.1.3 Proportion of different types of grains\u003c/h3\u003e\n\u003cp\u003eNotable differences were found in the proportions of superior and inferior grains between soft and non-soft rice (Table 2). In terms of numerical proportion, compared with non-soft rice, the percentage of superior grains in soft rice was reduced by 8.60%\u0026ndash;17.17%, whereas the proportion of inferior grains increased by 7.33%\u0026ndash;13.03%. Regarding weight proportion, the percentage of high grain weight was 13.12%\u0026ndash;26.18% lower, and that of low grain weight was 7.09%\u0026ndash;10.37% higher in soft rice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Difference in proportion between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNumber Proportion（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 265px;\"\u003e\n \u003cp\u003eWeight Proportion（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSuperior Grains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eInferior Grains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHigh-weight grains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLow-weight grains\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.92\u0026plusmn;2.94a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e15.27\u0026plusmn;0.68a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e22.08\u0026plusmn;0.12c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e18.89\u0026plusmn;0.17d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.46\u0026plusmn;1.25a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e15.01\u0026plusmn;0.73ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e22.97\u0026plusmn;0.09c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e19.29\u0026plusmn;0.21d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e15.23\u0026plusmn;1.11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.91\u0026plusmn;0.87bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e29.91\u0026plusmn;0.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e17.55\u0026plusmn;0.87e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e16.25\u0026plusmn;1.67a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.28\u0026plusmn;0.50c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e26.44\u0026plusmn;0.45b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e17.29\u0026plusmn;0.18e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003ch3\u003e3.1.4 Analysis of the contribution of superior and inferior grains to appearance quality\u003c/h3\u003e\n\u003cp\u003eIn this study, we developed an approximate weighted contribution model to quantify the effects of superior and inferior grains on the overall appearance quality of rice panicles, considering the appearance quality traits and grain proportions (Table 3). Despite some deviations between predicted and actual values, the model consistently reflected the general trends, underscoring its utility in elucidating the relative contributions of different grain positions to the overall appearance quality of the panicle. Specifically, in comparison to non-soft rice, soft rice exhibited a 58.62%\u0026ndash;77.29% greater contribution from the degree of chalkiness and a 10.17%\u0026ndash;22.49% reduced contribution from transparency in superior grains. For inferior grains, the rate of chalky grains was 7.16%\u0026ndash;27.75% higher, while the degree of chalkiness was 6.17%\u0026ndash;12.95% lower.\u003c/p\u003e\n\u003cp\u003eTable 3 Analysis of the contribution of superior and inferior grains to appearance quality\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003ePredicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eRelative Error (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSG Contribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eIG Contribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSG Contribution ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eIG Contribution ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e29.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e29.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e19.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e57.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eTra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e29.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e28.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e54.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eTra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e7.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e16.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e17.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e26.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e33.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e60.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eTra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e19.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e12.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e17.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e23.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e29.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e62.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eTra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e20.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e10.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCR: Chalk grain rate, CD: Chalkiness degree, Tra: Transparency.\u003c/p\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003ch2\u003e3.2 Starch component, morphology and structure\u003c/h2\u003e\n\u003ch3\u003e3.2.1 Starch content and its components\u003c/h3\u003e\n\u003cp\u003eDistinct variations were observed in the starch content and its components between superior and inferior grains of both soft and non-soft rice types (Fig. 2). In soft rice, compared with non-soft rice, superior grains exhibited decreases in the TSC, apparent AC, and the amylose-to-total starch ratio (AC/TSC) by 1.83%\u0026ndash;2.47%, 40.95%\u0026ndash;44.80%, and 39.46%\u0026ndash;43.79%, respectively. Conversely, AP and the amylopectin-to-total starch ratio (AP/TSC) increased by 8.66%\u0026ndash;10.80% and 11.41%\u0026ndash;12.86%, respectively. In inferior grains of soft rice, decreases in TSC, AC, and AC/TSC were noted at 2.63%\u0026ndash;3.07%, 36.57%\u0026ndash;39.36%, and 34.88%\u0026ndash;37.43%, respectively, while increases in AP and AP/TSC were 5.11%\u0026ndash;5.32% and 7.96%\u0026ndash;8.66%, respectively.\u003c/p\u003e\n\u003cp\u003eIn soft rice, compared with superior grains, inferior grains demonstrated reductions in TSC, AC, and AC/TSC by 0.25%\u0026ndash;0.61%, 5.45%\u0026ndash;13.98%, and 5.17%\u0026ndash;15.57%, respectively. Increases in AP and AP/TSC were 0.46%\u0026ndash;1.54% and 0.76%\u0026ndash;2.07%, respectively. In non-soft rice, compared with superior grains, inferior grains showed reductions of 0.09%\u0026ndash;0.67%, 16.24%\u0026ndash;17.72%, and 16.27%\u0026ndash;22.17% in TSC, AC, and AC/TSC, respectively, while AP and AP/TSC increased by 4.76%\u0026ndash;5.90% and 4.71%\u0026ndash;5.33%, respectively.\u003c/p\u003e\n\u003ch3\u003e3.2.2 Starch granule size\u003c/h3\u003e\n\u003cp\u003eA marked variance was noted in the distribution of starch granule size between superior and inferior grains as well as between soft and non-soft rice varieties, as indicated in Table 4. Specifically, compared with non-soft rice, in the superior grains of soft rice, the percentage of small starch granules was elevated by 8.94% to 16.06%, whereas the mean granule size and the proportion of larger granules decreased by 4.41% to 8.11% and 16.93% to 23.16%, respectively. Conversely, in the inferior grains of soft rice, the occurrence of small granules was 6.28% to 10.53% higher, and there was a reduction in the average granule size and the proportion of large granules by 7.45% to 8.65% and 25.42% to 31.72%, respectively.\u003c/p\u003e\n\u003cp\u003eIn soft rice, compared with superior grains, inferior grains exhibited an increase in the proportion of small granules by 4.72% to 8.41%, accompanied by decreases in the average granule size and the proportion of large granules by 1.84% to 6.38% and 20.39% to 23.20%, respectively. In non-soft rice, compared with superior grains, inferior grains exhibited a 6.86% to 14.36% higher proportion of small granules, and decreases in average granule size and large granule proportion by 2.03% to 2.53% and 6.57% to 17.98%, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Difference in starch granule size between superior and inferior grains of soft and non-soft rice\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eGrain position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eAverage size\u003c/p\u003e\n \u003cp\u003e(\u0026mu;m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSmall particle content\u003c/p\u003e\n \u003cp\u003e(＜2\u0026mu;m) %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMedium particle content\u003c/p\u003e\n \u003cp\u003e(2-10\u0026mu;m) %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eLarge particle content\u003c/p\u003e\n \u003cp\u003e(＞10\u0026mu;m) %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.44\u0026plusmn;0.02d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.03\u0026plusmn;0.16b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.33\u0026plusmn;0.37a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.64\u0026plusmn;0.21c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.64\u0026plusmn;0.04c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e14.62\u0026plusmn;0.40b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.69\u0026plusmn;0.22a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.69\u0026plusmn;0.18c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.90\u0026plusmn;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.42\u0026plusmn;0.04c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.74\u0026plusmn;0.20a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.85\u0026plusmn;0.16ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.92\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.95\u0026plusmn;0.34c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.71\u0026plusmn;0.21a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.34\u0026plusmn;0.55a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003eIG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.28\u0026plusmn;0.03e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.85\u0026plusmn;0.31a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.78\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.37\u0026plusmn;0.24d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.34\u0026plusmn;0.06e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.74\u0026plusmn;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.77\u0026plusmn;0.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.49\u0026plusmn;0.18d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.77\u0026plusmn;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e14.81\u0026plusmn;0.34b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.17\u0026plusmn;0.25a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.02\u0026plusmn;0.09b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.78\u0026plusmn;0.08b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e14.34\u0026plusmn;0.16b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e79.27\u0026plusmn;0.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.40\u0026plusmn;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003ch3\u003e3.2.3 Starch microscopic morphology\u003c/h3\u003e\n\u003cp\u003eThe microstructural analysis of starch, conducted using SEM, is presented in Fig. 3. Although the starch granules in both superior and inferior grains of soft and non-soft rice are predominantly irregular polygons, a differential pattern of surface integrity was observed. Compared to non-soft rice, the starch granules in both superior and inferior grains of soft rice exhibited varying degrees of fragmentation. Notably, this fragmentation was more pronounced in inferior grains, with some granules appearing severely collapsed (Fig. 3-A, B, a, b).\u003c/p\u003e\n\u003ch3\u003e3.2.4 Starch molecular structure\u003c/h3\u003e\n\u003cp\u003eMarked disparities in the molecular structure of starch were noted between superior and inferior grains of both soft and non-soft rice varieties (Fig. 4, Table 5). Compared with non-soft rice, in superior grains of soft rice, the proportions of Fa and Fb\u003csub\u003e1\u003c/sub\u003e in amylopectin chains (DP \u0026lt; 100) were found to be significantly higher, ranging from 6.18% to 7.32% and from 2.59% to 2.98%, respectively, compared to non-soft rice. Conversely, the proportions of Fb2, Fb3, and the average chain length were notably lower, ranging from 0.92% to 11.62%, from 3.06% to 13.64%, and from 1.86% to 5.24%, respectively. In the inferior grains of soft rice, the increases in the proportions of Fa and Fb1 were from 6.35% to 7.05% and from 2.07% to 2.36%, respectively, while decreases in Fb\u003csub\u003e2\u003c/sub\u003e, Fb\u003csub\u003e3\u003c/sub\u003e, and average chain length ranged from 2.23% to 15.77%, from 1.36% to 9.84%, and from 0.67% to 8.73%, respectively.\u003c/p\u003e\n\u003cp\u003eIn soft rice, compared with superior grains, inferior grains demonstrated higher proportions of Fa and Fb\u003csub\u003e1\u003c/sub\u003e, ranging from 3.88% to 4.57% and from 0.38% to 1.30%, respectively. Additionally, the proportions of Fb\u003csub\u003e2\u003c/sub\u003e, Fb\u003csub\u003e3\u003c/sub\u003e, and average chain length were lower, ranging from 8.20% to 9.05%, from 4.53% to 7.46%, and from 11.50% to 13.14%, respectively. In non-soft rice, compared with superior grains, inferior grains exhibited increases in Fa and Fb1 proportions, ranging from 1.21% to 3.71% and from 0.35% to 2.17%, respectively, and decreases in Fb\u003csub\u003e2\u003c/sub\u003e, Fb\u003csub\u003e3\u003c/sub\u003e, and average chain length, ranging from 3.68% to 4.31%, from 8.19% to 9.79%, and from 8.11% to 8.33%, respectively.\u003c/p\u003e\n\u003cp\u003eFurthermore, significant variations were also observed in the distribution of amylose chains (DP \u0026gt; 100) among superior and inferior grains, across both soft and non-soft rice varieties (Table 2, Fig. 4-B). Compared with non-soft rice, in superior grains of soft rice, the proportions of A chains, B chains, and C chains were significantly lower, ranging from 50.93% to 53.13%, from 37.53% to 42.89%, and from 26.14% to 31.72%, respectively. In inferior grains of soft rice, the reductions in A chain, B chains, and C chains were from 55.00% to 57.46%, from 4.10% to 10.38%, and from 21.47% to 26.89%, respectively. Within the soft rice variety, inferior grains, compared to superior ones, showed lower proportions of A chains, B chains, and C chains, ranging from 18.69% to 22.85%, from 3.56% to 14.29%, and from 6.61% to 17.58%, respectively. Similarly, within non-soft rice, compared with superior grains, inferior grains exhibited reductions in A chain, B chains, and C chains, ranging from 11.01% to 15.30%, from 40.50% to 47.40%, and from 5.21% to 7.55%, respective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Difference in amylopectin and amylose chain length distribution between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"96%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003eGrain position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 47px;\"\u003e\n \u003cp\u003eAmylopectin chain length distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 35px;\"\u003e\n \u003cp\u003eAmylose chain length distribution (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eAverage chain length (DP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eFa\u003c/p\u003e\n \u003cp\u003e(DP 6-12) %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eFb\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(DP 13-24) %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eFb\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(DP 25-36) %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eFb\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(DP 37-100) %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eA-chain\u003c/p\u003e\n \u003cp\u003e(DP 100-1000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eB-chain\u003c/p\u003e\n \u003cp\u003e(DP 1001-2000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eC-chain\u003c/p\u003e\n \u003cp\u003e(DP 2001-10000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 6px;\"\u003e\n \u003cp\u003eSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.09\u0026plusmn;0.04b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e29.19\u0026plusmn;0.42b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e47.66\u0026plusmn;0.30b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e10.80\u0026plusmn;0.10c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.35\u0026plusmn;0.08e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4.99\u0026plusmn;0.05c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.53\u0026plusmn;0.21b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.11\u0026plusmn;0.09f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.47\u0026plusmn;0.04b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e28.88\u0026plusmn;0.3bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e47.48\u0026plusmn;0.21b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e10.90\u0026plusmn;0.42c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.74\u0026plusmn;0.04d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4.87\u0026plusmn;0.01c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.73\u0026plusmn;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.26\u0026plusmn;0.02e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e21.20\u0026plusmn;0.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e27.53\u0026plusmn;0.28e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e46.28\u0026plusmn;0.17c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.22\u0026plusmn;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e14.30\u0026plusmn;0.17a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e10.39\u0026plusmn;0.47a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4.37\u0026plusmn;0.28a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.06\u0026plusmn;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e21.25\u0026plusmn;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e27.20\u0026plusmn;0.13e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e46.12\u0026plusmn;0.45c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.30\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e14.05\u0026plusmn;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e10.17\u0026plusmn;0.21a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4.43\u0026plusmn;0.37a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.09\u0026plusmn;0.06b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 6px;\"\u003e\n \u003cp\u003eIG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e17.78\u0026plusmn;0.08d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e30.20\u0026plusmn;0.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e48.10\u0026plusmn;0.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e9.91\u0026plusmn;0.14d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.79\u0026plusmn;0.27f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.96\u0026plusmn;0.05d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.34\u0026plusmn;0.23b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.42\u0026plusmn;0.03d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e17.90\u0026plusmn;0.10d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e30.00\u0026plusmn;0.10a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e48.23\u0026plusmn;0.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e10.14\u0026plusmn;0.10d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.63\u0026plusmn;0.05f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.85\u0026plusmn;0.03d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.44\u0026plusmn;0.02b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.56\u0026plusmn;0.06c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.48\u0026plusmn;0.11c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e28.21\u0026plusmn;0.06d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e47.12\u0026plusmn;0.28b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.77\u0026plusmn;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.90\u0026plusmn;0.11c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9.05\u0026plusmn;0.11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.33\u0026plusmn;0.3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.26\u0026plusmn;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.53\u0026plusmn;0.10c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e28.04\u0026plusmn;0.12d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e47.43\u0026plusmn;0.20b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.93\u0026plusmn;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.60\u0026plusmn;0.10d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e8.80\u0026plusmn;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.60\u0026plusmn;0.24b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.31\u0026plusmn;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are expressed as the mean \u0026plusmn; standard deviation, n = 3. Different superscript letters in the same column indicate significant differences in the LSD test (p \u0026lt; 0.05). The area ratio of AM/AM+AP represented the amylose content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.5 Starch crystal structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this investigation, both superior and inferior grains of soft and non-soft rice varieties demonstrated A-type crystalline structures. The diffraction analysis revealed two prominent peaks at 15\u0026deg; and 23\u0026deg; (2\u0026theta;), accompanied by a bimodal pattern at 17\u0026deg; and 18\u0026deg; (2\u0026theta;) (Fig. 5-A). These findings suggest that the crystalline type of japonica rice starch remains consistent across different rice varieties. Nonetheless, compared with non-soft rice, soft rice exhibited a notably higher relative crystallinity, ranging from 7.28% to 8.25% in superior grains, and from 7.37% to 7.99% in inferior grains (Table 3). Within the varieties of soft rice, the crystallinity of inferior grains was marginally higher by 0.49% to 1.64% compared to that of superior grains; similarly, in non-soft rice, inferior grains showed a crystallinity increase of 0.96% to 1.32% over superior grains.\u003c/p\u003e\n\u003cp\u003eFurther analysis using ATR-FTIR (Fig. 5B, Table 6) revealed significant differences in the short-range ordered structure of starch granules. Compared with non-soft rice, in soft rice, superior grains demonstrated significantly lower 1022/995 cm⁻\u0026sup1; ratios, with a decrease ranging from 6.86% to 10.13%, and higher 1045/1022 cm⁻\u0026sup1; ratios, with an increase ranging from 15.93% to 21.37%. Inferior grains of soft rice also showed a 7.32% to 8.86% decrease in the 1022/995 cm⁻\u0026sup1; ratio and a 14.47% to 20.22% increase in the 1045/1022 cm⁻\u0026sup1; ratio compared to non-soft rice. Within the soft rice category, the inferior grains exhibited significantly lower ratios of 1045/1022 cm⁻\u0026sup1; and significantly higher ratios of 1022/995 cm⁻\u0026sup1; compared to superior grains, decreasing by 2.00% to 6.87% and increasing by 0.23% to 3.45%, respectively. In non-soft rice, the corresponding ratios in inferior grains were reduced by 0.79% to 5.78% for 1045/1022 cm⁻\u0026sup1; and increased by 0.31% to 2.43% for 1022/995 cm⁻\u0026sup1;, respectively, compared to superior grains.\u003c/p\u003e\n\u003cp\u003eAdditionally, compared with non-soft rice, soft rice displayed a significantly higher peak intensity, increasing by 19.10% to 20.91%, and a reduced lamellar thickness, decreasing by 0.97% to 1.51%, in superior grains. A similar trend was observed in inferior grains, with an increase in peak intensity of 18.82% to 20.71% and a decrease in lamellar thickness of 0.98% to 1.41%. Within the soft rice category, inferior grains exhibited a slightly higher peak intensity ranging from 0.15% to 1.20% and a reduced lamellar thickness ranging from 0.22% to 0.66% compared to superior grains. Conversely, in non-soft rice, inferior grains showed a 0.14% to 1.92% increase in peak intensity and a 0.22% to 0.75% decrease in lamellar thickness compared to superior grains (Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 Difference in crystal structure indexes between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eGrain position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eRelative crystallinity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1045/1022\u003c/p\u003e\n \u003cp\u003ecm\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1022/995\u003c/p\u003e\n \u003cp\u003ecm\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003ePeak intensity (counts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eLamellar thickness\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 53px;\"\u003e\n \u003cp\u003eSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e12.33\u0026plusmn;0.07ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.778\u0026plusmn;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.883\u0026plusmn;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e84.30\u0026plusmn;2.68a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e9.15\u0026plusmn;0.07c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e12.23\u0026plusmn;0.08b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.764\u0026plusmn;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.869\u0026plusmn;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e83.87\u0026plusmn;1.87a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e9.16\u0026plusmn;0.03c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e11.40\u0026plusmn;0.06d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.659\u0026plusmn;0.04c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.948\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e70.42\u0026plusmn;1.46b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e9.25\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e11.39\u0026plusmn;0.06d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.641\u0026plusmn;0.03c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.967\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e69.72\u0026plusmn;0.65b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e9.29\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 53px;\"\u003e\n \u003cp\u003eIG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e12.43\u0026plusmn;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.749\u0026plusmn;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.899\u0026plusmn;0.01b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e84.88\u0026plusmn;1.26a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e9.10\u0026plusmn;0.16d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e12.39\u0026plusmn;0.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.728\u0026plusmn;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.885\u0026plusmn;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e84.43\u0026plusmn;0.42a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e9.13\u0026plusmn;0.15d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e11.51\u0026plusmn;0.03c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.636\u0026plusmn;0.02d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.970\u0026plusmn;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e71.06\u0026plusmn;1.10b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e9.22\u0026plusmn;0.04b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e11.54\u0026plusmn;0.06c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.623\u0026plusmn;0.03d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.971\u0026plusmn;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e70.32\u0026plusmn;1.25b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e9.23\u0026plusmn;0.06b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Protein component and structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1 Protein content and its components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarked disparities were noted in the protein content and its various fractions between the superior and inferior grains of both soft and non-soft rice varieties, as detailed in Table 7. Compared with non-soft rice, the superior grains of soft rice exhibited increased levels of total protein, ranging from 7.33% to 8.57%, and enhanced percentages of albumin, globulin, and glutelin, measuring 21.23% to 26.75%, 6.53% to 46.85%, and 13.21% to 17.52%, respectively. Conversely, the prolamin content was reduced by 2.58% to 4.04%. In the inferior grains of soft rice, there was also a notable increase in total protein content (7.10% to 8.52%), albumin (8.10% to 29.10%), globulin (12.35% to 33.59%), and glutelin (13.47% to 16.50%), accompanied by a decrease in prolamin content ranging from 6.97% to 13.16%. Within the soft rice category, the inferior grains demonstrated higher levels of total protein (4.00% to 5.60%), albumin (7.29% to 23.86%), prolamin (1.95% to 3.55%), and glutelin (1.82% to 5.51%) compared to the superior grains. Similarly, in non-soft rice, the inferior grains contained greater amounts of total protein (4.45% to 5.43%), albumin (16.31% to 25.80%), globulin (2.61% to 21.62%), prolamin (6.37% to 14.41%), and glutelin (2.53% to 5.46%) than the superior grains.\u003c/p\u003e\n\u003cp\u003eFurthermore, the proportions of each protein fraction relative to the total protein content displayed significant variations between soft and non-soft rice, as shown in Table 7. Compared with non-soft rice, in the superior grains of soft rice, there was a higher proportion of albumin (an increase of 6.90% to 10.00%) and glutelin (an increase of 0.61% to 1.13%), while the proportion of prolamin was considerably reduced, ranging from 12.38% to 34.18%. The inferior grains of soft rice similarly exhibited an increased proportion of glutelin (0.84% to 1.76%) and a diminished prolamin proportion (16.22% to 34.76%). Within the soft rice variants, the inferior grains maintained a higher proportion of albumin (4.13% to 17.29%) but a lower proportion of glutelin (0.39% to 1.25%) when compared to the superior grains. In contrast, the proportion of albumin in the inferior grains of non-soft rice was significantly higher (10.69% to 19.55%), while the proportion of glutelin was reduced (0.96% to 1.54%) relative to the superior grains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7 Difference in protein content and its components between superior and inferior grains of soft and non-soft rice\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003eGrain position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 92px;\"\u003e\n \u003cp\u003eProtein content (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 331px;\"\u003e\n \u003cp\u003eContent (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 338px;\"\u003e\n \u003cp\u003eProportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eGlobulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eProlamin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eGlutelin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eGlobulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eProlamin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eGlutelin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 53px;\"\u003e\n \u003cp\u003eSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e8.93\u0026plusmn;0.05bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.94\u0026plusmn;0.10cd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.08\u0026plusmn;0.04d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.51\u0026plusmn;0.10e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e68.91\u0026plusmn;0.73c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.84\u0026plusmn;0.08c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.02\u0026plusmn;0.07d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5.31\u0026plusmn;0.12d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e84.82\u0026plusmn;0.2a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e8.99\u0026plusmn;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.98\u0026plusmn;0.07c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.89\u0026plusmn;0.02b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.53\u0026plusmn;0.03f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e70.23\u0026plusmn;0.92b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.8\u0026plusmn;0.12c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.89\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.64\u0026plusmn;0.02e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e84.66\u0026plusmn;0.21ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e8.32\u0026plusmn;0.03d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.25\u0026plusmn;0.03e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3.83\u0026plusmn;0.08e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.65\u0026plusmn;0.08de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e60.87\u0026plusmn;1.00e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.49\u0026plusmn;0.03d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.3\u0026plusmn;0.16bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6.06\u0026plusmn;0.06c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e84.15\u0026plusmn;0.15cd\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e8.28\u0026plusmn;0.10d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.14\u0026plusmn;0.24e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3.33\u0026plusmn;0.04f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.7\u0026plusmn;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e59.76\u0026plusmn;0.83f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.4\u0026plusmn;0.35d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.67\u0026plusmn;0.04e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7.05\u0026plusmn;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e83.87\u0026plusmn;0.39de\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNJ5718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 53px;\"\u003e\n \u003cp\u003eIG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9.35\u0026plusmn;0.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4.27\u0026plusmn;0.08b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.55\u0026plusmn;0.04c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.62\u0026plusmn;0.08d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e71.51\u0026plusmn;0.14ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.04\u0026plusmn;0.09bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.36\u0026plusmn;0.04b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5.27\u0026plusmn;0.1d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e84.33\u0026plusmn;0.17bc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eNJ9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9.43\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4.88\u0026plusmn;0.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5.25\u0026plusmn;0.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.67\u0026plusmn;0.05f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e72.71\u0026plusmn;0.28a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.63\u0026plusmn;0.1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e6.04\u0026plusmn;0.1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.56\u0026plusmn;0.05e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e83.77\u0026plusmn;0.16e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e8.73\u0026plusmn;0.08c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.95\u0026plusmn;0.02cd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.05\u0026plusmn;0.09d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.02\u0026plusmn;0.11c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e62.41\u0026plusmn;0.46de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.26\u0026plusmn;0.06b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.38\u0026plusmn;0.12b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6.29\u0026plusmn;0.11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e83.07\u0026plusmn;0.03f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHJ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e8.69\u0026plusmn;0.06c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.78\u0026plusmn;0.20d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3.93\u0026plusmn;0.01e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.32\u0026plusmn;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e63.02\u0026plusmn;0.18d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.97\u0026plusmn;0.24c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.16\u0026plusmn;0.03cd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6.99\u0026plusmn;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e82.87\u0026plusmn;0.17f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation, with n = 3. Different superscript letters within the same column denote statistically significant differences according to the LSD test (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2 Protein secondary structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences were observed in the secondary structure of proteins between the superior and inferior grains of both soft and non-soft rice varieties, as depicted in Fig. 6. Compared with non-soft rice, the superior grains of soft rice demonstrated higher proportions of \u0026alpha;-helix, increasing by 1.35% to 2.54%, and random coil, which increased by 4.41% to 6.90%. Conversely, these grains exhibited reductions in the proportions of \u0026beta;-sheet and \u0026beta;-turn by 2.12% to 2.80% and 1.44% to 3.87%, respectively. In the case of inferior grains, soft rice similarly displayed an increase in \u0026alpha;-helix content (ranging from 1.27% to 3.44%) and random coil content (ranging from 2.44% to 5.56%), alongside reductions in \u0026beta;-sheet (ranging from 2.28% to 3.24%) and \u0026beta;-turn proportions (ranging from 0.21% to 1.71%) relative to non-soft rice.\u003c/p\u003e\n\u003cp\u003eWithin the soft rice category, inferior grains exhibited higher levels of \u0026alpha;-helix, \u0026beta;-sheet, \u0026beta;-turn, and random coil compared to superior grains, with increases observed between 1.52% to 2.78%, 0.60% to 1.09%, 0.43% to 2.89%, and 4.70% to 7.89%, respectively. A similar trend was noted in non-soft rice, where inferior grains displayed elevated levels of \u0026alpha;-helix (0.90% to 2.78%), \u0026beta;-sheet (0.56% to 1.77%), \u0026beta;-turn (0.16% to 1.24%), and random coil (3.39% to 5.86%) compared with their superior counterparts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 PCA analysis and correlations analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1 PCA analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCA loading plot indicated that the first principal component (PC1) accounted for 63.9% of the total variance, while the second principal component (PC2) explained an additional 17.9% (Fig. 7A). The analysis delineated four distinct clusters. The upper left cluster predominantly correlated with traits such as the chalky grain rate, chalkiness degree, presence of small starch granules, albumin, and Fb\u003csub\u003e1\u003c/sub\u003e (DP 6\u0026ndash;12). The upper right cluster was associated with total starch, C-chain DP (2000\u0026ndash;10000), \u0026beta;-sheet, and \u0026beta;-turn. Meanwhile, the lower left cluster showed correlations with medium starch granules, the 1045/1022 cm⁻\u0026sup1; ratio, random coil, amylopectin, and crystallinity. The lower right cluster was linked with transparency, average chain length of amylopectin, \u0026alpha;-helix, B-chain DP (1001\u0026ndash;2000), and Fb\u003csub\u003e2\u003c/sub\u003e (DP 25\u0026ndash;36) along with Fb\u003csub\u003e3\u003c/sub\u003e (DP 37\u0026ndash;100). These findings suggest that the starch and protein structural characteristics investigated in this study are both directly and indirectly associated with the appearance quality traits of rice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2 Correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.4.2.1 Correlation between appearance quality and starch structure\u003c/p\u003e\n\u003cp\u003eCorrelation analyses conducted on chalkiness traits, transparency, and starch characteristics of superior and inferior grains in both soft and non-soft rice varieties (Fig. 7-B) demonstrated that chalkiness traits\u0026mdash;which include the rate of chalky grains and the degree of chalkiness\u0026mdash;exhibited a negative correlation with several parameters: total starch content, amylose content, average size of starch granules, proportion of large starch granules, long-chain amylopectin structures (Fb\u003csub\u003e2\u003c/sub\u003e, Fb\u003csub\u003e3\u003c/sub\u003e), average chain length of amylopectin, A-chains, B-chains, the 1022/995 cm⁻\u0026sup1; ratio, and lamellar thickness. Conversely, these traits were positively correlated with amylopectin content, the proportion of small starch granules, short-chain amylopectin (Fa, Fb\u003csub\u003e1\u003c/sub\u003e), relative crystallinity, and peak intensity.\u003c/p\u003e\n\u003cp\u003eTransparency showed a negative correlation with amylopectin content, the proportion of small starch granules, short-chain amylopectin (Fa, Fb\u003csub\u003e1\u003c/sub\u003e), relative crystallinity, the 1045/1022 cm⁻\u0026sup1; ratio, and peak intensity. Conversely, it was positively correlated with total starch content, amylose content, average size of starch granules, proportion of large starch granules, long-chain amylopectin (Fb\u003csub\u003e2\u003c/sub\u003e, Fb\u003csub\u003e3\u003c/sub\u003e), average chain length of amylopectin, A-chains, B-chains, C-chains, the 1022/995 cm⁻\u0026sup1; ratio, and lamellar thickness.\u003c/p\u003e\n\u003cp\u003e3.4.2.2 Correlation between appearance quality and protein structure\u003c/p\u003e\n\u003cp\u003eThe correlation analysis concerning chalkiness traits, transparency, and protein structure in both superior and inferior grains of soft and non-soft rice (Fig. 7-C) indicated that chalkiness traits were significantly inversely correlated with \u0026alpha;-helix content, while exhibiting strong positive correlations with total protein content, albumin, globulin, glutelin, \u0026beta;-sheet, and \u0026beta;-turn structures.\u003c/p\u003e\n\u003cp\u003eTransparency displayed negative correlations with total protein, albumin, globulin, glutelin, and \u0026beta;-sheet content. Conversely, it showed positive correlations with prolamin and \u0026alpha;-helix content.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study has elucidated that the overall appearance quality of soft rice is significantly inferior compared to non-soft rice (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with both dominant and inferior grains displaying relatively high levels of chalkiness and low transparency (Fig.\u0026nbsp;1). Further investigations revealed that even though dominant grains occupy favorable positions within soft rice, their significant contributions to high chalkiness and low transparency (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) limit their ability to positively enhance the overall quality of the panicle. Consequently, they fail to offset the negative effects posed by inferior grains. Moreover, soft rice is characterized by a higher proportion of low-weight grain kernels (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the rate of chalky grains among inferior samples markedly influences the appearance quality of the entire panicle (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As a result, the detrimental attributes of inferior grains\u0026mdash;predominantly high chalkiness and low transparency\u0026mdash;are significantly magnified at the panicle level, thus being the primary contributors to the relatively poor appearance quality observed in soft rice.\u003c/p\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Interrelationships between appearance quality variations and starch-protein structural characteristics\u003c/h2\u003e\u003cp\u003eThe appearance quality of rice is fundamentally a reflection of the homogeneity of light transmission and the structural compactness within the endosperm tissue (Cao et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These characteristics are predominantly governed by the synergistic regulation of starch and protein structures (He et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study demonstrates that the differences in appearance quality observed across grain positions in both soft and non-soft rice types stem from multiscale structural deterioration, which manifests in the morphological architecture, molecular composition, and spatial conformations of both starch granules and protein networks (Fig.\u0026nbsp;7).\u003c/p\u003e\u003cp\u003eWith respect to starch structure, compared with non-soft rice, both superior and inferior grains of soft rice display finer fragmentation, increased breakage, and looser granule packing\u0026mdash;characteristics that are particularly pronounced in inferior grains (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These morphological anomalies directly compromise the structural continuity of the endosperm tissue, augmenting light-scattering interfaces and pathways while diminishing optical homogeneity. Consequently, this leads to reduced transparency and enhanced chalkiness (Lin, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). At the molecular level, the elevated ratio of Fa-chains, reduced proportion of Fb3-chains, and lower amylose content in soft rice grains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) collectively result in a shift towards shorter amylopectin chain lengths. This dominance of short chains weakens intermolecular entanglement strength and reduces hydrogen-bonding density, leading to loosening within the lamellar structures of crystalline regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and a significant reduction in mechanical stability (Wu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The compromised integrity of these crystalline structures diminishes granule robustness, making the inferior grain endosperm more susceptible to macro-defects such as pores and fissures during mechanical stress or desiccation. Moreover, soft rice grains exhibit higher crystallinity and elevated short-range molecular order (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), creating a \u0026ldquo;high-crystallinity\u0026ndash;high-order\u0026rdquo; architecture. This structural formation is likely due to the increased presence of short amylopectin chains (Fa) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which promote localized double-helix formation and ordered stacking (Ma et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Paradoxically, such short-chain-dominated configurations may concurrently reduce crystalline lamellar thickness and increase intra-granular porosity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), blurring the interfaces between crystal and amorphous regions (Fan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This porous, thin-layered microstructure substantially increases the frequency of light scattering and the complexity of refraction, visually amplifying the appearance of chalkiness (Fan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and ultimately degrading the appearance quality of the rice.\u003c/p\u003e\u003cp\u003eBeyond starch, the composition of proteins and their spatial conformation are also critical in regulating the structural compactness and optical homogeneity of the endosperm (Shi, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Both superior and inferior grains of soft rice are characterized by a compositional imbalance, evidenced by elevated levels of albumin and glutelin with a concurrent reduction in prolamin content (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The decreased hydrophobic capacity of prolamin diminishes its ability to embed compactly on the surfaces of starch granules (Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while the increased hydrophilic properties of albumin and glutelin fractions contribute to the enlargement of interfacial voids (Lu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This aberrant composition leads to a reduction in starch-protein interfacial density, which in turn triggers heterogeneous light scattering, significantly compromising the optical uniformity of the endosperm (Lu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, the secondary structures of proteins in soft rice grains exhibit increased proportions of α-helix and random coil structures, along with decreased fractions of β-sheet and β-turn structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This indicates a progressive disordering within the protein network. Such conformational instability undermines the structural integrity of the protein matrix, reducing its capacity to spatially confine starch granules and exacerbating their disordered arrangement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This collectively degrades the optical homogeneity of the endosperm (Zhu, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, in inferior grains, synergistic interactions between destabilized protein networks and inherent starch structural defects amplify the porosity and interfacial discontinuity of the endosperm, ultimately leading to a degradation of optical performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Synergistic interaction mechanisms between starch and protein structures\u003c/h2\u003e\u003cp\u003eThe significant deterioration in the appearance quality of soft-textured japonica rice\u0026mdash;particularly in inferior grains\u0026mdash;is fundamentally attributed to the mutually inducing and exacerbating interactions between starch structural defects and aberrant protein networks under unfavorable developmental conditions. This synergy establishes a vicious cycle of progressive structural degradation. On one hand, defects in starch structure initiate abnormalities in the protein network: severe fragmentation, micronization, and disordered arrangement of starch granules in inferior grains (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) physically disrupt starch-protein interfaces and interfere with normal protein assembly, leading to disordered transitions in secondary structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e) that result in loose, disorganized protein matrices. On the other hand, these aberrant protein networks amplify starch defects: the disordered protein matrix, potentially compromised by impaired grain filling, diminishes spatial confinement and physical support for starch granules. This not only fails to prevent further granule fragmentation and misalignment but also exacerbates granule displacement and defect formation (e.g., pores) during desiccation or processing, intensifying structural disorder.\u003c/p\u003e\u003cp\u003eThe mechanisms described above demonstrate significant superposition and cumulative effects in grains of lower quality. These grains exhibit severe structural defects in starch and protein, which markedly enhance their negative interactions. Such defects destabilize the starch-protein matrix within the endosperm, compromising its structural integrity and significantly increasing the number of light-scattering interfaces and the complexity of light refraction. These changes decrease transparency, accentuate visual chalkiness, and ultimately impair the aesthetic quality of soft rice.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study, through a comparative analysis of the starch and protein characteristics of superior and inferior grains among varieties of soft and non-soft rice, elucidates the structural underpinnings of their differing appearance qualities. Soft rice, in comparison to non-soft rice, typically displays heightened chalkiness and diminished transparency across both grain types. This deterioration in quality is attributed to the dual destabilization of the starch and protein structures within the endosperm. Specifically, the starch granules in soft rice are characterized by increased fragmentation, an elevated proportion of smaller granules, and disorganized branching structures, which together reduce granule density and optical homogeneity. Concurrently, an imbalance in protein components and looser conformational arrangements further undermine the compactness of the endosperm structure, leading to a marked increase in chalkiness. These structural defects are especially prominent in inferior grains and, due to their greater prevalence within the panicle, have a predominant adverse effect on the overall visual quality of the rice panicle.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHW\u0026nbsp;and\u0026nbsp;HZ\u0026nbsp;conceived and designed the experiment and provided financial assistance;\u0026nbsp;XC,\u0026nbsp;JY, and\u0026nbsp;JC\u0026nbsp;performed the experiments and analyzed data;\u0026nbsp;YZ, GL, GL, FX, and QH\u0026nbsp;contributed reagents/materials/analysis tools;\u0026nbsp;XC\u0026nbsp;wrote the paper. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (32372215, 32372212, 32201891), the Earmarked Fund for CARS (Rice, CARS-01), the National Key Research Program of China (2022YFD2301401), the Collaborative Promotion Project for Major Agricultural Technologies (2024-ZYXT-03-1), the Changzhou Modern Agricultural Science and Technology Innovation Center Project (CAIC(2023)005), the Priority Subject Program Development of Jiangsu Higher Education Institutions (PAPD) and the Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province (KYCX24_3790).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBocianowski, J. 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Jiangnan University.\u003c/li\u003e\n\u003cli\u003eZheng, X. L. (2021). Difference of grain characters and physiological and biochemical mechanism in different panicle of japonica rice. Journal of Nuclear Agricultural Sciences, 35(6), 1234\u0026ndash;1242.\u003c/li\u003e\n\u003cli\u003eZhu, Y., Xu, D., Chen, X., et al. (2022). Quality characteristics of semi-glutinous japonica rice cultivated in the middle and lower reaches of the Yangtze River in China. Journal of the Science of Food and Agriculture, 102(9), 3712\u0026ndash;3723. \u003c/li\u003e\n\u003cli\u003eZhu, Y. (2022). Quality characteristics and carbon and nitrogen metabolism mechanism of good quality, high yield and high nitrogen efficiency of soft japonica rice in Yangtze River Delta. Yangzhou University.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"rice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rice","sideBox":"Learn more about [Rice](http://thericejournal.springeropen.com)","snPcode":"12284","submissionUrl":"https://submission.nature.com/new-submission/12284/3","title":"Rice","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rice, Grain position, Appearance quality, Starch, Protein","lastPublishedDoi":"10.21203/rs.3.rs-7486609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7486609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the material foundations underpinning the variations in appearance quality among different types of japonica rice and within different grain positions, thereby providing a theoretical basis for enhancing the quality of soft rice varieties. The experimental results indicate that relative to non-soft rice, soft varieties demonstrate markedly higher levels of chalkiness and reduced transparency in both grain classifications. Structural analyses indicate that soft rice grains, particularly the inferior ones, exhibit lower amylose content along with higher proportions of small starch granules and Fa chains, alongside enhanced crystallinity and short-range order. These characteristics compromise the crystalline integrity and amplify light scattering. Furthermore, the protein network in soft rice is characterized by increased levels of albumin and glutelin, a reduction in prolamin content, and a transition towards α-helix and random coil structures. These changes suggest a diminished integration of starch and protein and introduce spatial limitations. The compounded defects across multiple scales in starch and protein structures are more accentuated in inferior grains, leading to enhanced porosity and optical heterogeneity within the endosperm. This synergistic degradation of starch and protein architectures emerges as the primary mechanism responsible for the relatively poor appearance quality of soft rice.\u003c/p\u003e","manuscriptTitle":"Interrelationships between appearance quality and starch and protein structures in superior and inferior grains of soft and non-soft rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 18:17:12","doi":"10.21203/rs.3.rs-7486609/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T04:03:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T08:43:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156520466112601316759427927458401233162","date":"2025-11-03T00:13:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T17:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338746262445709342363194120586813665696","date":"2025-10-21T00:13:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236226536536177117874295849693558001026","date":"2025-09-04T03:22:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-03T05:25:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T02:33:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-01T02:33:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Rice","date":"2025-08-29T08:30:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"rice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rice","sideBox":"Learn more about [Rice](http://thericejournal.springeropen.com)","snPcode":"12284","submissionUrl":"https://submission.nature.com/new-submission/12284/3","title":"Rice","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a542b73-d263-4870-b036-e9cff8a2da41","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:15:51+00:00","versionOfRecord":{"articleIdentity":"rs-7486609","link":"https://doi.org/10.1186/s12284-026-00886-9","journal":{"identity":"rice","isVorOnly":false,"title":"Rice"},"publishedOn":"2026-03-03 15:59:16","publishedOnDateReadable":"March 3rd, 2026"},"versionCreatedAt":"2025-09-08 18:17:12","video":"","vorDoi":"10.1186/s12284-026-00886-9","vorDoiUrl":"https://doi.org/10.1186/s12284-026-00886-9","workflowStages":[]},"version":"v1","identity":"rs-7486609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7486609","identity":"rs-7486609","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}