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Background: The rapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from the use of costly equipment, stringent environmental requirements, or limited accuracy. This study proposes a simple and effective method for detecting the moisture content of single maize kernels based on viscoelastic properties., Results: Two types of viscoelastic experiments were conducted involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency-sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force-time graphs and viscoelastic parameters were extracted based on the four-element Maxwell model. Then, viscoelastic parameters and data of force-time graphs were employed as input variables to explore the relationships with moisture content separately. The impact of different preprocessing methods and feature time variables on model accuracy was explored based on force-time graphs. The results indicate that models utilizing the force-time data were more accurate than those utilizing viscoelastic parameters. The best model was established by partial least squares regression based on S-G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set were 0.954 and 0.021, respectively. The corresponding values of the prediction set were 0.905 and 0.029, respectively., Conclusions: This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Maize (Zea mays L.), a staple food and significant economic crop, is enriched with riboflavin, micronutrients and other compounds that are beneficial for human health. As emphasis on the nutritional quality of crops increases maize research has expanded to focus on both yield and quality. This study exploreed the genetic factors influencing micronutrient levels in maize kernels through a comprehensive genome-wide association study (GWAS). We utilized a diverse panel of 244 inbred maize lines and approximately 3 million single nucleotide polymorphisms (SNPs) to investigate the accumulation of essential and trace elements including cadmium (Cd), cobalt (Co), copper (Cu), nickel (Ni), selenium (Se) and zinc (Zn). Our analysis identified 842 quantitative trait loci (QTLs), with 12 QTLs shared across multiple elements and pinpointed 524 potential genes within a 100 kb radius of these QTLs. Notably ZmHMA3 has emerged as a key candidate gene previously reported to influence the Cd accumulation. We highlighted ten pivotal genes associated with trace element transport including those encoding heavy metal ATPases, MYB transcription factors, ABC transporters and other crucial proteins involved in metal handling. Additionally, haplotype analysis revealed that eight inbred linesaccumulated relatively high levels of beneficial elements while harmful elements were minimized. These findings elucidate the genetic mechanisms underlying trace element accumulation in maize kernels and provide a foundation for the breeding of nutritionally enhanced maize varieties., (© 2024. The Author(s).)

The objective of study was to investigate the relationships between maize kernel properties and carotenoid release during simulated gastrointestinal digestion of 103 hybrids of dent type. Commercial maize hybrids significantly differed in kernel hardness, chemical composition and carotenoid profile. Across all hybrids, the amount of digestible individual carotenoids increased as follows: β-carotene < α-cryptoxanthin < β-cryptoxanthin < lutein < zeaxanthin. The amount of digested carotenoids correlated negatively with amylose content and amylose-to-amylopectin ratio, while it correlated positively with the content of neutral detergent fibres and amylopectin as well as the Stenvert index. However, the content of endosperm lipids could not be related to carotenoid digestibility. Findings clearly indicate that the carotenoid release from the kernel during digestion is related to specific physical and chemical properties, leading to a better understanding of the effects of kernel matrix structure on carotenoid digestibility in dent-type maize hybrids., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: The functional roles of the Wall Associated Kinase (WAK) and Wall Associated Kinase Like (WAKL) families in cellular expansion and developmental processes have been well-established. However, the molecular regulation of these kinases in maize development is limited due to the absence of comprehensive genome-wide studies., Results: Through an in-depth analysis, we identified 58 maize WAKL genes, and classified them into three distinct phylogenetic clusters. Moreover, structural prediction analysis showed functional conservation among WAKLs across maize. Promoter analysis uncovered the existence of cis-acting elements associated with the transcriptional regulation of ZmWAKL genes by Gibberellic acid (GA). To further elucidate the role of WAKL genes in maize kernels, we focused on three highly expressed genes, viz ZmWAKL38, ZmWAKL42 and ZmWAKL52. Co-expression analyses revealed that their expression patterns exhibited a remarkable correlation with GA-responsive transcription factors (TF) TF5, TF6, and TF8, which displayed preferential expression in kernels. RT-qPCR analysis validated the upregulation of ZmWAKL38, ZmWAKL42, ZmWAKL52, TF5, TF6, and TF8 following GA treatment. Additionally, ZmWAKL52 showed significant increase of transcription in the present of TF8, with ZmWAKL52 localizing in both the plasma membrane and cell wall. TF5 positively regulated ZmWAKL38, while TF6 positively regulated ZmWAKL42., Conclusions: Collectively, these findings provide novel insights into the characterization and regulatory mechanisms of specific ZmWAKL genes involved in maize kernel development, offering prospects for their utilization in maize breeding programs., (© 2023. The Author(s).)

Members of the ADP-ribosylation factor family, which are GTP-binding proteins, are involved in metabolite transport, cell division, and expansion. Although there has been a significant amount of research on small GTP-binding proteins, their roles and functions in regulating maize kernel size remain elusive. Here, we identified ZmArf2 as a maize ADP-ribosylation factor-like family member that is highly conserved during evolution. Maize zmarf2 mutants showed a characteristic smaller kernel size. Conversely, ZmArf2 overexpression increased maize kernel size. Furthermore, heterologous expression of ZmArf2 dramatically elevated Arabidopsis and yeast growth by promoting cell division. Using expression quantitative trait loci (eQTL) analysis, we determined that ZmArf2 expression levels in various lines were mainly associated with variation at the gene locus. The promoters of ZmArf2 genes could be divided into two types, pS and pL, that were significantly associated with both ZmArf2 expression levels and kernel size. In yeast-one-hybrid screening, maize Auxin Response Factor 24 (ARF24) is directly bound to the ZmArf2 promoter region and negatively regulated ZmArf2 expression. Notably, the pS and pL promoter types each contained an ARF24 binding element: an auxin response element (AuxRE) in pS and an auxin response region (AuxRR) in pL, respectively. ARF24 binding affinity to AuxRR was much higher compared with AuxRE. Overall, our results establish that the small G-protein ZmArf2 positively regulates maize kernel size and reveals the mechanism of its expression regulation., (© 2023 Institute of Botany, Chinese Academy of Sciences.)

Accurate real-time classification of fluorescently labelled maize kernels is important for the industrial application of its advanced breeding techniques. Therefore, it is necessary to develop a real-time classification device and recognition algorithm for fluorescently labelled maize kernels. In this study, a machine vision (MV) system capable of identifying fluorescent maize kernels in real time was designed using a fluorescent protein excitation light source and a filter to achieve optimal detection. A high-precision method for identifying fluorescent maize kernels based on a YOLOv5s convolutional neural network (CNN) was developed. The kernel sorting effects of the improved YOLOv5s model, as well as other YOLO models, were analysed and compared. The results show that using a yellow LED light as an excitation light source combined with an industrial camera filter with a central wavelength of 645 nm achieves the best recognition effect for fluorescent maize kernels. Using the improved YOLOv5s algorithm can increase the recognition accuracy of fluorescent maize kernels to 96%. This study provides a feasible technical solution for the high-precision, real-time classification of fluorescent maize kernels and has universal technical value for the efficient identification and classification of various fluorescently labelled plant seeds.

The Zn content in cereal seeds is an important trait for crop production as well as for human health. However, little is known about how Zn is loaded to plant seeds. Here, through a genome-wide association study (GWAS), we identify the Zn-NA (nicotianamine) transporter gene ZmYSL2 that is responsible for loading Zn to maize kernels. High promoter sequence variation in ZmYSL2 most likely drives the natural variation in Zn concentrations in maize kernels. ZmYSL2 is specifically localized on the plasma membrane facing the maternal tissue of the basal endosperm transfer cell layer (BETL) and functions in loading Zn-NA into the BETL. Overexpression of ZmYSL2 increases the Zn concentration in the kernels by 31.6%, which achieves the goal of Zn biofortification of maize. These findings resolve the mystery underlying the loading of Zn into plant seeds, providing an efficient strategy for breeding or engineering maize varieties with enriched Zn nutrition., (© 2022 The Authors.)

Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for food industry. However, obtaining a large number of oil content reference values of maize kernels is time-consuming and expensive, and the limited data set also leads to low generalization ability of the model. Here, hyperspectral imaging technology and deep convolutional generative adversarial network (DCGAN) were combined to predict the oil content of single maize kernel. DCGAN was used to simultaneously expand their spectral data and oil content data. After many iterations, fake data that was very similar to the experimental data was generated. Partial least squares regression (PLSR) and support vector regression (SVR) models were established respectively, and their performance was compared before and after data augmentation. The results showed that this method not only improved the performance of two regression models, but also solved the problem of requiring a large amount of training data., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Previously, the complexity of folate accumulation in the early stages of maize kernel development has been reported, but the mechanisms of folate accumulation are unclear. Two maize inbred lines, DAN3130 and JI63, with different patterns of folate accumulation and different total folate contents in mature kernels were used to investigate the transcriptional regulation of folate metabolism during late stages of kernel formation by comparative transcriptome analysis. The folate accumulation during DAP 24 to mature kernels could be controlled by circumjacent pathways of folate biosynthesis, such as pyruvate metabolism, glutamate metabolism, and serine/glycine metabolism. In addition, the folate variation between these two inbred lines was related to those genes among folate metabolism, such as genes in the pteridine branch, para-aminobenzoate branch, serine/tetrahydrofolate (THF)/5-methyltetrahydrofolate cycle, and the conversion of THF monoglutamate to THF polyglutamate. The findings provided insight into folate accumulation mechanisms during maize kernel formation to promote folate biofortification.

Maize kernel is exposed to several fungal species, most notably Fusarium verticillioides, which can contaminate maize kernels with fumonisins. In an effort to increase genetic gains and avoid the laborious tasks of conventional breeding, the use of marker-assisted selection or genomic selection programs was proposed. To this end, in the present study a Genome Wide Association Study (GWAS) was performed on 339 RILs of a Multiparental Advanced Generation InterCross (MAGIC) population that had previously been used to locate Quantitative Trait Locus (QTL) for resistance to Fusarium Ear Rot (FER). Six QTLs for fumonisin content were detected in the bins 3.08, 4.07, 4.10, 7.03-7.04, 9.04-9.05 and 10.04-10.5. Five of the six QTLs collocate in regions where QTLs for FER were also found. However, the genetic variation for fumonisin content in kernel is conditioned by many other QTLs of small effect that could show QTL x environment interaction effects. Although a genomic selection approach to directly reduce fumonisin content in the kernel could be suitable, improving resistance to fumonisin content by genomic selection for FER would be more advisable., (© 2021. The Author(s).)

A rapid and nondestructive method is greatly important for the classification of aflatoxin B1 (AFB1) concentration of single maize kernel to satisfy the ever-growing needs of consumers for food safety. A novel method for classification of AFB1 concentration of single maize kernel was developed on the basis of the near-infrared (NIR) hyperspectral imaging (1100-2000 nm). Four groups of AFB1 samples with different concentrations (10, 20, 50, and 100 ppb) and one group of control samples were prepared, which were preprocessed with Savitzky-Golay (SG) smoothing and first derivative (FD) algorithms for their raw NIR spectra. A key wavelength selection method, combining the variance and order of average spectral intensity, was proposed on the basis of pretreated spectra. Moreover, principal component analysis (PCA) was conducted to reduce the dimensionality of hyperspectral data. Finally, a classification model for AFB1 concentrations was developed through linear discriminant analysis (LDA), combined with five key wavelengths and the first three PCs. The results show that the proposed method achieved an ideal performance for classifying AFB1 concentrations in a single maize kernel with overall accuracy, with an F1-score and Kappa values of 95.56%, 0.9554, and 0.9444, respectively, as well as the test accuracy yield of 88.67% for independent validation samples. The combinations of variance and order of average spectral intensity can be used for key wavelength selection which, combined with PCA, can achieve an ideal dimensionality reduction effect for model development. The findings of this study have positive significance for the classification of AFB1 concentration of maize kernels.

Aspergillus flavus is a saprophytic cosmopolitan fungus, capable of infecting crops both pre- and post-harvest and exploiting different secondary metabolites, including aflatoxins. Aflatoxins are known carcinogens to animals and humans, but display no clear effect in host plants such as maize. In a previous study, we mined the genome of A. flavus to identify secondary metabolite clusters putatively involving the pathogenesis process in maize. We now focus on cluster 32, encoding for fungal effectors such as salicylate hydroxylase ( SalOH ), and necrosis- and ethylene-inducing proteins (npp1 domain protein) whose expression is triggered upon kernel contact. In order to understand the role of this genetic cluster in maize kernel infection, mutants of A. flavus , impaired or enhanced in specific functions (e.g., cluster 32 overexpression), were studied for their ability to cause disease. Within this frame, we conducted histological and histochemical experiments to verify the expression of specific genes within the cluster (e.g., SalOH , npp1 ), the production of salicylate, and the presence of its dehydroxylated form. Results suggest that the initial phase of fungal infection (2 days) of the living tissues of maize kernels (e.g., aleuron) coincides with a significant increase of fungal effectors such as SalOH and Npp1 that appear to be instrumental in eluding host defences and colonising the starch-enriched tissues, and therefore suggest a role of cluster 32 to the onset of infection.

Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874⁻1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine-SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.

Developing the next plant generation within the seed requires the coordination of complex programs driving pattern formation, growth, and differentiation of the three main seed compartments: the embryo (future plant), the endosperm (storage compartment), representing the two filial tissues, and the surrounding maternal tissues. This review focuses on the signaling pathways and molecular players involved in early maize kernel development. In the 2 weeks following pollination, functional tissues are shaped from single cells, readying the kernel for filling with storage compounds. Although the overall picture of the signaling pathways regulating embryo and endosperm development remains fragmentary, several types of molecular actors, such as hormones, sugars, or peptides, have been shown to be involved in particular aspects of these developmental processes. These molecular actors are likely to be components of signaling pathways that lead to transcriptional programming mediated by transcriptional factors. Through the integrated action of these components, multiple types of information received by cells or tissues lead to the correct differentiation and patterning of kernel compartments. In this review, recent advances regarding the four types of molecular actors (hormones, sugars, peptides/receptors, and transcription factors) involved in early maize development are presented., (Copyright © 2017 The Author. Published by Elsevier Inc. All rights reserved.)