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Article

Evaluation of the Kernel Test Weight and Selection of Identification Indexes of Maize Inbred Lines

by
Tao Shen
1,†,
Jianping Li
1,†,
Chao Wang
2,
Haihong Fan
1,
Yunxiao Zheng
2,
Yifan Liu
1,3,
Shuzhen Zhang
4,5,
Liying Zhu
1,
Xiaoyan Jia
1,
Yongfeng Zhao
1,2,
Wei Song
6,* and
Jinjie Guo
1,*
1
State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Sub-Center of National Maize Improvement Center of China, College of Agronomy, Hebei Agricultural University, Baoding 071051, China
2
State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing 100107, China
3
Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture and Rural Affairs, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
4
Hengshui Taocheng District Agricultural Technology Extension Centre, Hengshui 053000, China
5
Hengshui Taocheng District Agricultural and Rural Bureau, Hengshui 053000, China
6
Key Laboratory of Crop Genetics and Breeding of Hebei Province, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(8), 1807; https://doi.org/10.3390/agronomy15081807
Submission received: 26 June 2025 / Revised: 19 July 2025 / Accepted: 22 July 2025 / Published: 26 July 2025
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

Kernel test weight (KTW) is one of the important assessment indexes of maize quality grade and one of the important influencing factors of yield. This study analyzed 12 traits related to KTW in 321 maize inbred lines using multivariate methods. The principal component analysis (PCA) indicated that the four PCs covered 78.176% of the information of the 12 traits in 321 maize inbred lines. Cluster analysis categorized the maize lines into six groups, identifying 16 elite inbred lines with the highest KTW. A stepwise regression model for KWT evaluation was developed using four PCA traits: starch content, amylopectin content, 100-kernel weight, and kernel circumference. The findings of this study serve as a valuable reference point for the genetic improvement of maize germplasm re-sources in kernel test weight and the creation of high kernel test weight maize resources.

1. Introduction

Maize (Zea mays L.) is a widely cultivated crop worldwide, utilized for human food, animal feed, industrial raw materials, and biofuels. The primary objective of breeding is to achieve high yielding, stable, and high-quality kernels. The kernel test weight is not only an indicator for evaluating the quality grade of maize [1] but also one of the key determinants of yield. There is a close correlation between the kernel quality and yield [2]. The genetic mechanism of maize kernel test weight is more complex than that of other agronomic traits [3]. The kernel test weight is a quantitative trait, the expression of which is influenced by multiple genes, which covers a wide range of characteristics, including kernel shape, weight, quality, maturity, uniformity, integrity, and potential nutritive value [4]. It is susceptible to environmental influences and environmental interactions [5]. The improvement in the maize kernel test weight is a crucial objective in the context of agricultural production. Consequently, the evaluation of maize inbred lines for kernel test weight and the screening of related identification indexes represent central foci of current breeding work.
In previous studies, a close correlation was found between the kernel test weight and yield and quality. For example, Li et al. found that the level of the thousand-kernel weight, production capacity, protein content, and starch content all affected the level of the kernel test weight, and they all showed extremely significantly correlated or significantly positive correlation with the kernel test weight [6]. In contrast, a negative correlation was identified between the kernel test weight and the fat content of the kernels. Shea et al. reported a negative connection between the kernel test weight and fat content in their research on genes associated with soybean weight capacity [7]. Conversely, the study on seed quality conducted by Holland et al. revealed a significant positive correlation between the kernel test weight and fat content [8]. Blandino et al. conducted an analysis of the quality traits of 33 maize hybrids, and the results demonstrated that the starch content, protein content, and amylopectin and amylose contents were significantly correlated with the kernel test weight [9]. In their study, Zhang et al. examined the correlation between the kernel test weight and several other traits, including the fat content, protein content, and lysine content [10]. Their findings revealed a significant positive correlation between the starch content, fat content, and kernel test weight, while the moisture and lysine content, protein content, and kernel test weight exhibited a significant negative correlation. Additionally, the starch content showed a significant negative correlation with the protein content and fat content. Kernel hardness is also an important indicator of corn quality. The kernel test weight can be used as an evaluation index of the kernel hardness, which was found to be significantly correlated with the kernel test weight and hardness in both maize and soybean [11]. Furthermore, the starch content, amylopectin/amylose, and protein content were all correlated with kernel hardness. Consequently, it is of considerable practical importance for the agricultural production sector to investigate and assess features associated with the kernel test weight in order to cultivate maize varieties with elevated kernel weight, thereby enhancing maize kernel yield and quality. This study investigated 12 traits associated with the kernel test weight in an experimental investigation of 321 maize inbred lines. These features comprised many aspects of maize kernel composition and morphology. Multivariate statistical analysis ultimately constructed a series of mathematical evaluation models for maize germplasm resources concerning the kernel test weight. Maize planting resources with a high kernel test weight were then screened, with the objective of providing a reference for the breeding of high kernel test weight varieties.

2. Materials and Methods

2.1. Plants Materials and Field Design

The experimental materials were provided by the Hebei Sub-center of the National Maize Improvement Center of Hebei Agricultural University. The center’s collection contains 321 genetically rich and widely sourced maize inbred lines from breeding practices in China and the United States [12]. The 321 inbred lines are primarily Chinese maize inbred lines belonging to hybrid dominant groups. They represent the germplasm resource base of the main maize-producing areas in China.
The materials were planted in 2021 at the Hainan Experimental Station of Hebei Agricultural University (Sanya) (19°22′ N, 113°66′ E, altitude 896 m) (Figure 1a); planting occurred in November 2022, followed by harvesting in April 2023. The soil is primarily sandy loam, situated in a tropical monsoon environment characterized by an average temperature of approximately 27 degrees Celsius from November to March and an average precipitation of around 20 mm. In May 2022, the experimental materials were planted at the Hebei Branch Station of the National Maize Improvement Centre, Hebei Agricultural University (Baoding) (39°38′ N, 115°96′ E, elevation 1066 m) (Figure 1b). In June 2023, the experimental materials were planted at the Hebei Academy of Agricultural and Forestry Sciences (Shijiazhuang) (37°88′ N, 114°55′ E, elevation 78 m) (Figure 1b). Harvesting occurred in October 2023 and October 2024, respectively. The dominant soil type in both Baoding and Shijiazhuang is cinnamon soil, characterized by a temperate monsoon climate. During the growing season, the mean monthly temperature approximates 32 °C, with average precipitation around 80 mm. The planting method was implemented using a completely randomized block design, comprising three sets of replications. The experimental row length was 4 m, the row spacing was 60 cm, and the plant spacing was 25 cm. Field management procedures were the same across all three locations. Fertilizations involved the application of organic fertilizers at a rate of 1.5 kg/m2 and compound fertilizer at 0.06 kg/m2. Chemical protection entailed the application of pesticides and herbicides throughout the agricultural area, while watering was conducted via drip systems.

2.2. Phenotypic Data Collection

2.2.1. Measurement of Quality Traits

(1) Near-infrared spectrometer (Perten Instruments, Stockholm, Sweden) scanning: The harvested maize kernels were sun-dried to achieve equilibrium moisture content, and impurities were removed. Two portions of kernels were removed from each maize inbred line, with the number of kernels in each portion ranging from 100 to 200. Prior to the commencement of spectral collection, the instrument was subjected to a 30 min warm-up period, with the objective of stabilizing the collected spectral data [13]. The maize kernel model was employed for the determination of protein content, fat content, and starch content [14], with the data from the three scans being averaged. Each inbred variety was subjected to analysis using two biological replicates and three technical replicates, with the resulting data averaged.
(2) Chemical determination of amylose: Current methods for the determination of amylose and amylopectin content include differential scanning calorimetry (DSC) [15], near-infrared spectroscopy (NIR) [16], thermogravimetry (TG) [17], high-performance spatial exclusion chromatography (HPSEC) [18], enzyme assays [19], and spectrophotometric methods [20]. The majority of the 321 test materials were common maize inbred lines. Previous research has indicated that the single wavelength spectrophotometer method set out in GB/T15683-2008 is a more accurate means of determining amylose [21]. Therefore, the present study was conducted to determine the starch content of autogenous maize kernels according to the single wavelength colorimetric method of the national standard GB/T15683-2008 [22]. The experiment was conducted with three technical replicates, and the resulting data were averaged. Eleven mixed solutions were prepared with amylose (Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China) –amylopectin (Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China) ratios of 0:100, 5:95, 10:90, 15:85, 20:80, 25:75, 30:70, 35:65, and 40:60. We placed 5 mL of each gradient solution into a 100 mL volumetric flask with 50 mL of ultrapure water (Aquapro, Chongping, China), added 1 mL of 1 mol/mL acetic acid solution (Sinopharm, Shanghai, China), shook well, and then added 2 mL of iodine reagent (Sinopharm, Shanghai, China). The solution was then made up to 100 mL with ultrapure water, the stopper was covered, and the solution was shaken well. It was left to stand for 10 min. The same 0.09% NaOH (Aladdin, Shanghai, China) solution from the national standard method described above was used as a control.

2.2.2. Measurement of Grain Shape Traits

The kernel length, width, area, circumference, and 100-kernel weight of maize inbred lines were measured using a TPKZ-3 Intelligent Seed Analyzer (Zhejiang Top Cloud-Agri Technology Co., Ltd., Hangzhou, China) [23,24]. The test kernels were randomly divided into three portions of 100 to 200 kernels each, and each sample was measured once. The resulting values were averaged across the three measurements. The measurement of each trait was conducted using two biological replicates and three technical replicates, with the resulting data averaged.

2.3. Data and Statistical Analysis

2.3.1. Data Collation and Statistical Analysis

The data for each trait trial were collected and processed using Microsoft Excel (version 2308; Microsoft Corporation, Redmond, WA, USA) [25]. The 12 traits were protein content (PC), fat content (FC), starch content (SC), amylopectin content (AP), amylose content (AS), amylopectin/amylose (AP/AS), 100-kernel weight (HKW), kernel length (KL), kernel width (KW), kernel width/kernel length (KW/KL), kernel circumference (KC), and kernel area (KS), with subsequent analysis in IBM SPSS Statistics software 26 [26] and R-4.3.3 [12].

2.3.2. Membership Function Values of Maize Inbred Lines for Each Composite Trait

U ( X j ) = ( X j − X m i n ) / ( X m a x − X m i n ) × 100 ,   j = 1 , 2 … n
In the above formula, the value of the affiliation function for each composite trait is represented by U, while Xmax is the maximum value for each composite trait, and Xmin is the minimum value for each composite trait [27].

2.3.3. Weights for Each Composite Trait in Maize Inbred Lines

W j = P j ∑ n = 1 n P j ,   j = 1,2 … n
In the above formula, P is the contribution of each composite trait, and Wj represents the weight of each composite trait among all the composite traits [12], respectively.
D = ∑ n = 1 n U X j × W j ,   j = 1,2 … n
where D is the combined evaluation value of kernel test weight traits for each maize inbred line [12].
In this experiment, to reduce the influence of other factors on the experimental results, the data means were standardized using Z-scores [28]. Statistical analyses were conducted utilizing R software (version 4.3.3; R Core Team, Vienna, Austria, 2024) and IBM SPSS Statistics (version 26; IBM Corp., Armonk, NY, USA). Cluster analysis was executed in R, whereas principal component analysis (PCA) and stepwise regression analysis were carried out using SPSS.

3. Results

3.1. Single Wavelength Spectrophotometric Standard Curve for Amylose

The absorbance measurements were conducted on 11 mixed solutions at 720 nm using a spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The standard curve was plotted using amylose content and absorbance as the horizontal and vertical coordinates, respectively (Figure 2). As illustrated in Figure 2, the standard curve y = 0.0066x + 0.045, with a correlation coefficient R2 = 0.9921, was obtained for single-wavelength spectrophotometry. This indicates that amylose exhibits a linear relationship with absorbance within the range of 0% to 60% in this test.

3.2. Descriptive Statistical Analysis of Each Trait

A descriptive statistical study of 321 maize inbred lines for kernel test weight-related features was performed utilizing SPSS 26 software, with the results displayed in Table 1. The traits exhibited disparate variability across the maize inbred lines, with a high variability observed in the starch content (59.18–74.29), amylose content (10.01–32.09), amylopectin content (33.49–57.23), 100-kernel weight (27.87–90.83), and kernel area (27.44–183.79). The analysis revealed a considerable range of coefficients of variation (3.37% to 38.21%) for the 12 traits, indicating a high level of phenotypic diversity. Among the quality traits, starch (3.37%) exhibited the lowest coefficient of variation, whereas among the grain shape traits, the coefficient of variation was high in all traits, with the highest being grain area (38.21%). The data analysis demonstrated that all traits significantly influenced the evaluation of the kernel test weight (Table 1).

3.3. Principal Component Analysis Was Conducted for Each Trait

The 12 kernel test weight-related traits were subjected to PCA using SPSS 26 software to determine the number of PCs to the eigenvalues greater than 1 [29]. The eigenvalues and contribution rate of each PC were derived from the analysis (Table 2). PCA was employed to transform the 12 original traits into four composite traits. The sum of the PCA contributions of the four composite traits was found to be 78.176%, which essentially encapsulates the information conveyed by the 12 traits. It is generally considered that a cumulative contribution rate of 70% and above is more desirable [30]. Accordingly, these four traits may be employed in a comprehensive evaluation of kernel test weight in maize for analysis.
The first PCA primarily constituted five traits, namely, the kernel length, kernel width, kernel length/kernel width, kernel circumference, and kernel area. This analysis indicated that 4.39 primitive traits collectively reflected 36.581% of the original amount of data. The second PCA primarily comprised two traits: amylopectin content and amylopectin/amylose composition. It represents 2.221 original traits, reflecting 18.506 original data volume. The third PCA primarily constituted two traits, namely starch content and amylose content, which collectively indicated 1.718 primitive traits, representing 14.314% of the original data quantity. The fourth PCA primarily constituted three traits, namely fat content, 100-kernel weight, and kernel width, which collectively indicated 1.053 primitive traits, representing 8.775% of the original data quantity.

3.4. Cluster Analysis

As demonstrated by Equations (1)–(3), the combined evaluation value of the kernel test weight for each maize inbred line can be calculated. The Euclidean squared distance was employed as the genetic distance, and the deviation square sum method was utilized to perform a cluster analysis of the D value (Figure 3) [31]. As illustrated in Figure 3, the 321 inbred lines were classified into six major classes. Category I comprised 25 inbred lines, including KN3, B100, 78551S, 1538, and so forth, which were assigned to the class with the lowest kernel test weight. Category II contained 72 inbred lines, including E588, 1028, PHN11, MBST, and so forth, which were characterized by low kernel test weight. Category III comprised 77 inbred lines, including PHNV9, SC14, PHN66, and S8324, which were classified as belonging to the lower category of kernel test weight. Category IV comprised 80 inbred lines, including Yan38, HHe01, Va26, and MM402A, which were classified as belonging to the higher kernel test weight category. Category V comprised 51 inbred lines, including XF77, 3489a, A679, ning45, etc., which were categorized as belonging to the category of high kernel test weight. Category VI comprised 16 inbred lines, including Lu65, DF24, 441950, and PN2, which were representative of the highest category of kernel test weight (Table S1).

3.5. Stepwise Regression Analysis

In the present study, among the 12 kernel traits, four composite traits related to kernel test weight were screened out. A set of accurate mathematical models for evaluating maize germplasm resources for kernel test weight was established by using stepwise regression analysis, with the value of D as the dependent variable, the four traits determining the PCA as the independent variables, and unstandardized coefficients being chosen to finally establish the regression equation. The equation is as follows: D = 0.037 + 0.079 × KC + 0.02 × SC + 0.017 × HKW + 0.01 × AP. The coefficient of determination R2 = 0.957, the adjusted R2 = 0.956, F = 1751.691, and P = 0.000 < 000.1. The equation reaches a highly significant level. The data demonstrate a statistically significant linear relationship between the kernel circumference, starch content, 100-kernel weight, amylopectin content, and D value. The standardized data for each of the four traits were brought into the regression line equation to obtain D values, and the root mean square error was calculated using the original D values and the D values obtained from the regression line equation. The resulting RMSE was 0.00031. According to the results, the relatively low root mean square error value suggests a high degree of correlation between the original two D values. In other studies, the regression line equation derived exhibits a low degree of error and a high degree of accuracy. Consequently, it can be employed to predict the size of kernel test weight in maize autogenous lines in practical applications.

3.6. Comparative Characterization of Maize Inbred Lines’ Germplasm Resources

The kernel test weights of the six clusters were subjected to a further comparison based on the results of stepwise regression and cluster analysis (Table 3).
As illustrated in the accompanying Table 3, category I was classified as the lowest kernel test weight class, representing 7.78% of the germplasm resources. Among the aforementioned characteristics, those pertaining to starch content, amylopectin content, 100-kernel weight, and kernel circumference exhibited the lowest values when compared to those of other classes.
Category II was classified as belonging to the low kernel test weight class and accounted for 22.43% of the germplasm resources. The starch content, amylopectin content, 100-kernel weight, and kernel circumference were found to be lower than in other groups.
Category III was classified as a lower category of kernel test weight and accounted for 23.99% of the germplasm resources. Among the aforementioned characteristics, those with high starch content, high amylopectin content, 100-kernel weight, and kernel circumference were found to be of a lower class, in addition to others.
Category IV was classified as belonging to the high kernel test weight class, which accounted for 24.92% of the germplasm resources. Among the aforementioned characteristics, the highest starch content, the highest amylopectin content, a high 100-kernel weight, and a high kernel circumference were observed.
Category V was classified as a high kernel test weight, representing 15.89% of the germplasm resources. The starch content was found to be lower, while the 100-kernel weight, amylopectin content, and kernel circumference were higher than in the other groups.
Category VI was the highest kernel test weight class and represented 4.98% of the germplasm resources. Among the aforementioned categories, the starch content was notably high, while the amylopectin content was relatively low. Additionally, the 100-kernel weight and grain circumference were among the highest.

4. Discussion

4.1. Influence of Grain Quality Traits and Grain Shape Traits on Kernel Test Weight

Previous studies have demonstrated that grain quality traits significantly influence kernel test weight formation. Blandino et al. identified synergistic effects among grain hardness (H/S ratio), test weight, protein content, and amylopectin/amylose ratio, collectively enhancing the kernel test weight [9]. Zhangyu et al. further indicated that starch content positively contributes to the kernel test weight, whereas protein and fat contents exhibit inhibit inhibitory effects [32]. Notably, the amylopectin/amylose ratio showed no significant impact on the kernel test weight. Moreover, protein and fat contents indirectly reduce the kernel test weight by negatively regulating starch accumulation, with amylose and amylopectin demonstrating antagonistic effects against protein and total starch content, respectively [32]. Milašinović et al. revealed that protein content modulates the kernel test weight through dual pathways: promoting amylose synthesis to increase grain compactness (enhancing kernel test weight) while suppressing amylopectin accumulation (restricting hundred-kernel weight development). Hard endosperm significantly improves grain compressive strength, thereby increasing the kernel test weight [33]. Bonfil et al. observed in wheat that excessively high protein content leads to endosperm structural porosity, exerting significant negative effects on the kernel test weight [34]. Conversely, Gürsoy et al. reported contradictory findings, suggesting that under specific genetic backgrounds, protein may enhance grain structural compactness to elevate the kernel test weight [35]. These discrepancies may arise from genotype × environment interactions but collectively confirm quality traits as key determinants of kernel test weight.
Grain shape traits similarly exert significant regulatory effects on the kernel test weight. Among maize genotypes, the kernel test weight follows the order: popcorn > flint > dent [36], attributable to endosperm development-mediated kernel shaping—where the endosperm filling degree influences the kernel width and kernel test weight by altering seed dimensions and morphology [37]. Wang et al. confirmed strong synergistic enhancement between thousand-kernel weight and the kernel test weight [38], while Ramya et al. further established that kernel length and width jointly regulate thousand-kernel weight [39]. Xin et al., however, emphasized the complexity of morphological regulation: in wheat, grain area, perimeter, width, and length all significantly affect the thousand-kernel weight and kernel test weight [40]. Genetic studies in maize and rice consistently support the positive contributions of kernel length and width to both hundred-kernel weight and kernel test weight [36,41]. In summary, grain morphological characteristics serve as key physical determinants of kernel test weight formation by modulating spatial packing efficiency and endosperm distribution patterns.
In this study, 12 grain quality traits and grain shape traits were analyzed in 321 maize inbred lines, which allowed for a more comprehensive evaluation of the kernel test weight. This approach proves beneficial in accurately assessing the kernel test weight in maize.

4.2. Multivariate Statistical Analysis

In this study, multivariate statistical analysis was employed to examine the traits related to the kernel test weight of 321 maize inbred lines. PCA is the process of converting several highly correlated and non-significantly different metrics into one or more independent composite metrics with less loss of information [42]. Cluster analysis is a technique employed in the classification of germplasm resources and the study of kinship relationships. Stepwise regression analysis is a frequently employed regression method in the presence of covariance issues [12].
Previous studies on maize [12,43,44,45,46], wheat [47,48], soybeans [49,50,51], and cotton [52,53,54] have also verified the feasibility of this comprehensive evaluation method. For example, Zarei et al. employed a series of analytical techniques, including correlation analysis, factor analysis, cluster analysis, and stepwise regression analysis, to investigate the relationship between hard grain yield and associated traits in the context of drought conditions [55]. Gao et al. employed correlation analysis, PCA, component analysis, cluster analysis, discriminant analysis, and stepwise regression to examine the kernel dehydration rates of maize inbred lines, formulate regression equations, and identify superior inbred lines characterized by rapid kernel dehydration, thereby offering guidance for future breeding efforts [56]. Chen et al. employed PCA in conjunction with the affiliation function method to thoroughly assess the low phosphorus tolerance of wheat varieties. They classified phosphorus-tolerant types via cluster analysis to identify representative evaluation indices for phosphorus tolerance, thereby establishing a foundation for the selection and breeding of low phosphorus-tolerant and high phosphorus-efficient wheat varieties [57]. Zhang et al. employed PCA and affiliation function analysis to determine the comprehensive assessment D value of soybean sprout quality and subsequently ranked the quality of soybean sprouts [58]. They then conducted stepwise regression analysis to formulate a regression equation for predicting soybean sprout quality. The study serves as a significant reference for soybean sprout production, the screening of superior varieties, and the prediction of soybean sprout quality [58]. Xiuxiu et al. examined 630 land cotton germplasm utilizing PCA, component analysis, cluster analysis, discriminant analysis, and stepwise regression, revealing a substantial association among the features through correlation analysis. PCA reduced 17 attributes to six independent composite indices. The affiliation function approach was employed to achieve a thorough assessment of F values, with which all attributes exhibited strong correlation. The yield, quality, and agronomic characteristics of high F-value germplasm were markedly superior to those of low F-value. Stepwise regression identified eight essential properties for formulating prediction equations [59]. Saeed et al. used multivariate analysis to study and analyze traits related to disease resistance, fiber quality, and yield in cotton [60].
The kernel test weight of maize is closely related to the yield and quality of the grain produced. Consequently, improving the kernel test weight is one of the key objectives of breeding. This study focuses on the evaluation indexes for high kernel test weight in maize germplasm resources. Following comprehensive evaluation and analysis, the twelve related traits were ultimately reduced to four closely associated with the kernel test weight. This approach facilitates precise and objective assessment of maize inbred lines with varying kernel test weights. Moreover, the outcomes of the screening process provide helpful information regarding future breeding improvements.

4.3. Genetic Improvement and Variety Selection in Maize

Maize is an important grain, feed, and economic crop. It plays a pivotal role in maintaining global economic stability and ensuring global food security [61]; the demand for it is increasing, and there is a growing focus on yield and quality. The kernel test weight is an important factor influencing kernel yield and quality. Consequently, improving kernel test weight-related traits is a key objective of current breeding programs. The current study scrutinized 12 traits closely linked to kernel test weight, encompassing the chemical composition and morphological structure of maize kernels. This approach facilitates a more scientific and accurate analysis of the traits related to kernel test weight. The twelve traits were converted into four comprehensive traits, namely, the kernel circumference, starch content, 100-kernel weight, and amylopectin content. A modeling system for the assessment of kernel test weight in maize inbred lines was constructed using multivariate statistical analysis, and the model can be used for kernel test weight testing in real production. A total of 16 maize inbred lines with high kernel test weight were selected from the 321 inbred lines evaluated, which included 441950, Dan 9064, P007, Deer 65, and others. The varieties may be employed as a point of reference for subsequent breeding and screening of high-kernel test-weight maize inbred lines. According to the results of a population structure analysis by Zhizhai et al. and Hou et al., 6.2% of the inbred lines belong to the Luda Red Cob, 12.5% belong to the Lancaster subpopulation, 37.5% belong to the Reid subpopulation, and 43.7% belong to the P subpopulation [62,63]. Therefore, in the selection of high-performing inbred lines, it is advisable to prioritize the Reid and P subgroups.

5. Conclusions

This study examined 321 maize inbred germplasm lines for kernel test weight attributes using multivariate statistical analysis. After PCA, the twelve linked features were transformed into four composite traits: amylopectin, starch content, 100-kernel weight, and kernel perimeter. Multivariate statistical analysis established mathematical evaluation models for maize germplasm resources for the kernel test weight. Cluster analysis divided the 321 maize inbred lines into six groups, with category VI having the greatest kernel test weight. The Reid and P subgroups made up 81.2% of the total; therefore, they should be prioritized when breeding high kernel test weight maize lines. This study provides scientific evidence for screening maize inbred materials with high kernel test weight, determining kernel test weight, and breeding and improving high kernel test weight maize inbred breeding.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15081807/s1, Table S1. Classification of 321 maize inbred lines.

Author Contributions

J.G. and W.S. designed the study; J.G., W.S., Y.Z. (Yunxiao Zheng), Y.Z. (Yongfeng Zhao), and X.J. developed the populations; T.S., C.W., and J.L. generated the data; T.S., H.F., Y.Z. (Yunxiao Zheng), and L.Z. analyzed the data. T.S. and J.L. drafted the manuscript; T.S., J.L., C.W., Y.L., S.Z., Y.Z. (Yunxiao Zheng), X.J., W.S., and J.G. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Innovation Team of Maize Modern Seed Industry in Hebei, Grant/Award Number: 21326319D; S&T Program of Hebei, Grant/Award Number: 24466301D; HAAFS International Science and Technology Cooperation Project (2023KJCXZX-LYS-GH01); and HAAFS Agriculture Science and Technology Innovation Project (2022KJCXZX-LYS-4).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We thank professors Jinsheng Lai and Weibin Song of the National Maize Improvement Center, College of Agronomy, China Agricultural University for providing the maize population.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of planting locations of 321 maize inbred lines. (a) shows China’s Hebei Province, and (b) shows China’s Hainan Province, with the triangular symbols representing the planting locations.
Figure 1. Map of planting locations of 321 maize inbred lines. (a) shows China’s Hebei Province, and (b) shows China’s Hainan Province, with the triangular symbols representing the planting locations.
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Figure 2. Single wavelength spectrophotometric standard curve. The horizontal coordinate is the percentage of amylose, and the vertical coordinate is the light absorption value; each blue dot represents a sample, and R2 is the correlation coefficient of the standard curve.
Figure 2. Single wavelength spectrophotometric standard curve. The horizontal coordinate is the percentage of amylose, and the vertical coordinate is the light absorption value; each blue dot represents a sample, and R2 is the correlation coefficient of the standard curve.
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Figure 3. Clustering analysis of 321 maize inbred lines. Red is group II, yellow is group I, green is group V, light blue is group III, dark blue is group IV, and pink is group VI.
Figure 3. Clustering analysis of 321 maize inbred lines. Red is group II, yellow is group I, green is group V, light blue is group III, dark blue is group IV, and pink is group VI.
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Table 1. Descriptive statistical analysis of 12 traits in 321 maize inbred lines.
Table 1. Descriptive statistical analysis of 12 traits in 321 maize inbred lines.
TraitRangeMeanSDSkewnessKurtosisCV%
PC (%)10.12–18.4613.681.590.41−0.0211.64
FC (%)3.12–10.195.180.721.147.0313.82
SC (%)59.18–74.2968.222.30−0.360.363.37
AS (%)10.01–32.0918.872.590.381.9013.75
AP (%)33.49–57.2349.363.04−0.462.126.15
AP/AS1.04–5.712.680.511.164.6419.18
HKW (g)27.87–90.8362.5211.720.09−0.2918.74
KL (mm)6.45–17.449.531.951.471.9720.42
KW (mm)5.50–11.227.080.851.442.9812.05
KW/KL1.02–1.841.340.140.980.9110.45
KS (mm2)27.44–183.7952.9220.222.257.0538.21
KC (mm)20.58–55.5929.665.941.522.3120.04
PC, FC, SC, AS, AP, AP/AS, HKW, KL, KW, KW/KL, KS, and KC represent protein content, fat content, starch content, amylose content, amylopectin content, amylopectin/amylose, 100-kernel weight, kernel length, kernel width, kernel width/kernel length, kernel area, and kernel circumference, respectively. SD and CV% denote standard deviation and coefficient of variation, respectively.
Table 2. Principal component analysis (PCA) eigenvalues and contribution of 12 traits in 321 maize inbred lines.
Table 2. Principal component analysis (PCA) eigenvalues and contribution of 12 traits in 321 maize inbred lines.
TraitPrincipal Component
1234
PC (%)−0.131−0.1−0.369−0.58
FC (%)0.129−0.353−0.6560.18
SC (%)−0.0450.3440.86−0.049
AS (%)−0.026−0.7680.546−0.034
AP (%)−0.0090.9470.071−0.004
AP/AS−0.0520.67−0.3230.028
HKW (g)0.212−0.061−0.0360.764
KL (mm)0.992−0.0120.023−0.061
KW (mm)0.9010.0740.0350.123
KW/KL0.813−0.0980.004−0.268
KS (mm2)0.9280.108−0.009−0.071
KC (mm)0.9940.0080.031−0.029
Eigenvalue4.392.2211.7181.053
Contribution rate (%)36.58118.50614.3148.775
Cumulative contribution rate (%)36.58155.08769.40178.176
PC, FC, SC, AS, AP, AP/AS, HKW, KL, KW, KW/KL, KS, and KC represent protein content, fat content, starch content, amylose content, amylopectin content, amylopectin/amylose, 100-kernel weight, kernel length, kernel width, kernel width/kernel length, kernel area, and kernel circumference, respectively.
Table 3. Description of the six groups in the hierarchical clustering results.
Table 3. Description of the six groups in the hierarchical clustering results.
GroupOriginal Mean
SC (%)AP (%)HKW (g)KC (mm)Number
I65.8146.2752.3523.9425
II67.5348.6457.9625.6272
III68.6249.9664.3526.8977
IV68.8250.364.7930.0680
V68.5250.1164.3836.6451
VI68.5549.4472.7945.9116
SC, AP, HKW, and KC represent starch content, amylopectin content, 100-kernel weight, and kernel circumference, respectively.
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Shen, T.; Li, J.; Wang, C.; Fan, H.; Zheng, Y.; Liu, Y.; Zhang, S.; Zhu, L.; Jia, X.; Zhao, Y.; et al. Evaluation of the Kernel Test Weight and Selection of Identification Indexes of Maize Inbred Lines. Agronomy 2025, 15, 1807. https://doi.org/10.3390/agronomy15081807

AMA Style

Shen T, Li J, Wang C, Fan H, Zheng Y, Liu Y, Zhang S, Zhu L, Jia X, Zhao Y, et al. Evaluation of the Kernel Test Weight and Selection of Identification Indexes of Maize Inbred Lines. Agronomy. 2025; 15(8):1807. https://doi.org/10.3390/agronomy15081807

Chicago/Turabian Style

Shen, Tao, Jianping Li, Chao Wang, Haihong Fan, Yunxiao Zheng, Yifan Liu, Shuzhen Zhang, Liying Zhu, Xiaoyan Jia, Yongfeng Zhao, and et al. 2025. "Evaluation of the Kernel Test Weight and Selection of Identification Indexes of Maize Inbred Lines" Agronomy 15, no. 8: 1807. https://doi.org/10.3390/agronomy15081807

APA Style

Shen, T., Li, J., Wang, C., Fan, H., Zheng, Y., Liu, Y., Zhang, S., Zhu, L., Jia, X., Zhao, Y., Song, W., & Guo, J. (2025). Evaluation of the Kernel Test Weight and Selection of Identification Indexes of Maize Inbred Lines. Agronomy, 15(8), 1807. https://doi.org/10.3390/agronomy15081807

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