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Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Grain water content (GWC) is a key determinant for mechanical harvesting of maize (Zea mays). In our previous research, we identified a quantitative trait locus, qGWCl, associated with GWC in maize. Here, we examined near-isogenic lines (NILs) NILL and NILH that differed at the qGWC1 locus. Lower GWC in NILL was primarily attributed to reduced grain water weight (GWW) and smaller fresh grain size, rather than the accumulation of dry matter. The difference in GWC between the NILs became more pronounced approximately 35 d after pollination (DAP), arising from a faster dehydration rate in NILL. Through an integrated analysis of the transcriptome, proteome, and metabolome, coupled with an examination of hormones and their derivatives, we detected a marked decrease in JA, along with an increase in cytokinin, storage forms of IAA (IAA-Glu, IAA-ASP), and IAA precursor IPA in immature NILL kernels. During kernel development, genes associated with sucrose synthases, starch biosynthesis, and zein production in NILL, exhibited an initial up-regulation followed by a gradual down-regulation, compared to those in NILH. This discovery highlights the crucial role of phytohormone homeostasis and genes related to kernel development in balancing GWC and dry matter accumulation in maize kernels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Maize kernels were treated using two varieties of drying methodologies, namely combined hot air- and vacuum-drying (HAVD) and natural drying (ND). We performed frequency sweep tests, modified Cole–Cole (MCC) analysis, and frequency–temperature superposition (FTS) on these kernels. The kernels' elastic and viscous properties for ND were higher than those for HAVD. The heterogeneous nature of maize kernel may account for the curvature in MCC plot for the kernel treated by HAVD 75 °C and the failure of FTS. MCC analysis was more sensitive than FTS. The kernel treated by HAVD 75 °C demonstrated thermorheologically simple behavior across the entire temperature range (30–45 °C) in both MCC analysis and FTS. The frequency scale for the kernel treated using HAVD 75 °C was broadened by up to 70,000 Hz. The relaxation processes in the kernel treated by HAVD 75 °C were determined to be mainly associated with subunits of molecules or molecular strands. The data herein could be utilized for maize storage and processing. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Glass transition can cause the formation of drying cracks in different component of maize (Zea mays) at different drying stages, but the influence of the mechanism is unknown. This work aimed to study the effect of glass transition on crack formation in different components. It was found that the differences in moisture content, temperature distribution, and glass transition temperature of the components can cause different glass transition processes in the components at different drying stages with 55 °C hot air. The temperatures of the pericarp and germ were found to be higher and lower, respectively, than corresponding the glass transition temperature, so remained in a single state, resulting in the absence of cracks. Cracks generated in the hard endosperm and then extended to soft endosperm, which was consistent with the glass transition of the endosperm. At the preheating stage, the endosperm had no cracks. After the main drying stage, the stress crack index (SCI) of hard and soft endosperm were 160.0 and 65.3, respectively. After the cooling stage, the SCI of the hard and soft endosperm increased to 402.3 and 163.3, respectively. Cracks were mainly formed during the main drying and cooling stages, and more cracks formed in the cooling stage. The cracking in the components at different drying stages could be well explained by glass transition. • Crack formation in the components depended on the form and process of glass transition. • Pericarp and germ were in a single state resulted in the absence of cracks. • Occurrence and propagation of cracks in the endosperm consistent with its glass transition. • Glass transition could explain the crack degree in the components at different drying stages. [ABSTRACT FROM AUTHOR]

Global warming leads to frequent extreme weather, especially the extreme heat events, which threating the safety of maize production. Here we selected a pair of maize inbred lines, PF5411-1 and LH150, with significant differences in heat tolerance at kernel development stage. The two maize inbred lines were treated with heat stress at kernel development stage. Compared with the control groups, transcriptomic analysis identified 770 common up- and down-regulated genes between PF5411-1 and LH150 under heat stress conditions, and 41 putative TFs were predicted. Based on the interaction term of the two-factorial design, we also identified 6,744 differentially regulated genes between LH150 and PF5411-1, 111 common up-regulated and 141 common down-regulated genes were overlapped with the differentially regulated genes, respectively. Combined with proteins and metabolites data, several key pathways including seven differentially regulated genes were highly correlated with the heat tolerance of maize kernels. The first is the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway ko04141: protein processing in endoplasmic reticulum, four small heat shock protein (sHSP) genes were enriched in this pathway, participating with the process of ER-associated degradation (ERAD). The second one is the myricetin biosynthesis pathway, a differentially regulated protein, flavonoid 3',5'-hydroxylase [EC:1.14.14.81], catalyzed the synthesis of myricetin. The third one is the raffinose metabolic pathway, one differentially regulated gene encoded the raffinose synthase controlled the synthesis of raffinose, high level of raffinose enhances the heat tolerance of maize kernels. And the last one is the ethylene signaling pathway. Taken together, our work identifies many genes responded to heat stress in maize kernels, and finds out seven genes and four pathways highly correlated with heat tolerance of maize kernels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The structure of the maize kernels plays a critical role in determining maize yield and quality, and high-throughput, non-destructive microscope phenotypic characteristics acquisition and analysis are of great importance. In this study, Micro-CT technology was used to obtain images of maize kernels. An automatic CT image analysis pipeline was then developed to extract 20 traits related to the three-dimensional structure of kernel, embryo, endosperm, and cavity. The determination coefficients for five volume-related traits (embryo, endosperm, silty endosperm, embryo cavity, and endosperm cavity) were 0.95, 0.95, 0.77, 0.73, and 0.94, respectively. Further, we analyzed the phenotypic variations among a group of 303 inbred lines and conducted genome-wide association studies (GWAS). A total of 26 significant SNP loci were associated with these traits that are closely related to kernel volume, and 62 candidate genes were identified. Functional analysis revealed that most candidate genes corresponding to cavity traits encoded stress resistance proteins, while those corresponding to embryo and endosperm traits encoded proteins involved in regulating plant growth and development. These results will improve the understanding of the phenotypic traits of maize kernels and will provide new theoretical support for in-depth analysis of the genetic mechanism of kernel structure traits. [ABSTRACT FROM AUTHOR]

Maize kernels were treated using two varieties of drying methodologies, namely combined hot air- and vacuum-drying (HAVD) and natural drying (ND). We performed frequency sweep tests, modified Cole–Cole (MCC) analysis, and frequency–temperature superposition (FTS) on these kernels. The kernels’ elastic and viscous properties for ND were higher than those for HAVD. The heterogeneous nature of maize kernel may account for the curvature in MCC plot for the kernel treated by HAVD 75 °C and the failure of FTS. MCC analysis was more sensitive than FTS. The kernel treated by HAVD 75 °C demonstrated thermorheologically simple behavior across the entire temperature range (30–45 °C) in both MCC analysis and FTS. The frequency scale for the kernel treated using HAVD 75 °C was broadened by up to 70,000 Hz. The relaxation processes in the kernel treated by HAVD 75 °C were determined to be mainly associated with subunits of molecules or molecular strands. The data herein could be utilized for maize storage and processing.

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. [ABSTRACT FROM AUTHOR]

In this study, the rheological behavior of maize kernel was systematically investigated using a dynamic mechanical analyzer. The loss in toughness caused by drying resulted in a downward shift in the relaxation curve and an upward shift in the creep curve. The long relaxation behavior became obvious when the temperature was above 45 °C, resulting from the weakening of hydrogen bonds with temperature. The maize kernel relaxed more rapidly at high temperatures, caused by a reduction in the cell wall viscosity and polysaccharide tangles. The Deborah numbers were all much smaller than one, suggesting that the Maxwell elements showed viscous behavior. Maize kernel, as a viscoelastic material, showed a dominant viscous property at high temperatures. The decline in β with increasing drying temperature indicated an increase in the width of the relaxation spectrum. A Hookean spring elastic portion made up the majority of the maize kernel creep strain. The order–disorder transformation zone of maize kernel was about 50–60 °C. Due to the complexity of maize kernel, the William–Landel–Ferry constants differed from the universal values; these constants should be ascertained through experiments. Time-temperature superposition was successfully used to describe the rheological behavior. The results show that maize kernel is a thermorheologically simple material. The data acquired in this study can be used for maize processing and storage. [ABSTRACT FROM AUTHOR]

The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning. [ABSTRACT FROM AUTHOR]

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. Synopsis: The metal‐NA (Nicotianamine) transporter ZmYSL2 loads Zn from the maternal tissue into maize kernels. Overexpression of ZmYSL2 increases the amount of Zn in maize kernels, providing a potential approach for breeding Zn‐rich crop. Polymorphisms in basal endosperm transfer cell layer (BETL) localized ZmYSL2 correlate with natural variations in the Zn concentration of maize kernels.The opaque phenotype of ZmYSL2 mutants is due to deficiency of Zn rather than Fe in the kernels.Overexpression of ZmYSL2 increases the Zn concentration in the kernels by 31.6%, which achieves the goal of Zn biofortification of maize. [ABSTRACT FROM AUTHOR]

The change of temperature and moisture content during maize (Zea mays) kernel drying can cause glass transition, which can lead to crack formation. However, the influence mechanism of glass transition on crack formation is not clear. This work aimed to study the effect of form and process of glass transition on the crack formation. It was found that the form and process of glass transition of maize kernels were determined by the state diagram, moisture content, temperature and state distribution. Glass transition began at the corner of the kernel and extended from the surface to the centre, which was consistent with the development of cracks. The transition form, transition rate and duration of the transition state depended on drying stage and temperature, which could effectively explain the difference in the degree of cracking. This could be helpful for understanding the crack formation mechanism and optimising the operating conditions in maize drying. • Crack formation depended on the form and process of glass transition. • Moisture content and temperature of corn kernel affected the glass transition process. • The difference of crack degree could be explained effectively by the glass transition. • Occurrence and propagation of cracks were consistent with transition of glassy state. [ABSTRACT FROM AUTHOR]

Abstract 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.

Maize is one of the three worldwide cereal crops with the most outstanding production; however, its postharvest losses range from 2 to 40% due to inadequate harvesting, drying, and storage technologies. This study focuses on the Instant Controlled Pressure Drop technology (DIC) effect on maize kernels' drying and rehydration kinetics. In total, 19 different DIC treatments were carried out on maize kernels (~25% d.b.). The DIC parameters studied were steam pressure (0.1 to 0.4 MPa) and treatment time (10 to 90 s). After DIC treatment, drying kinetics were carried out by Convective Air Drying (CAD) at 50 °C and 0.4 ms−1 airflow. Rehydration kinetics and Water Holding Capacity (WHC) were evaluated at 20 °C. In comparison to CAD samples, DIC (0.4 MPa and 90 s) reduced the drying time from 180 min to ~108 min. Additionally, regarding the rehydration and WHC results, DIC achieved the same moisture content in only 3.5 min that controls achieved after 1 h of rehydration (0.40 g H2O/g dry matter). Moreover, DIC (0.4 MPa and nine cycles of 10 s) increased the WHC 2.3 times compared to the control. In this way, DIC could be a postharvest technology to improve maize kernels' drying operations and functional properties. [ABSTRACT FROM AUTHOR]

Abstract Background How to control the physical damage during maize kernel harvesting is a major problem for both mechanical designers and plant breeders. A limitation of addressing this problem is lacking a reliable method for assessing the relation between kernel damage susceptibility and threshing quality. The design, construction, and testing of a portable tool called “HANDY”, which can assess the resistance to mechanical crushing in maize kernel. HANDY can impact the kernel with a special accelerator at a given rotating speed and then cause measurable damage to the kernel. These factors are varied to determine the ideal parameters for operating the HANDY. Results Breakage index (BI, target index of HANDY), decreased as the moisture content of kernel increased or the rotating speed decreased within the tested range. Furthermore, the HANDY exhibited a greater sensitivity in testing kernels at higher moisture level influence on the susceptibility of damage kernel than that in Breakage Susceptibility tests, particularly when the centrifugation speed is about 1800 r/min and the centrifugal disc type is curved. Considering that the mechanical properties of kernels vary greatly as the moisture content changes, a subsection linear (average goodness of fit is 0.9) to predict the threshing quality is built by piecewise function analysis, which is divided by kernel moisture. Specifically, threshing quality is regarded as a function of the measured result of the HANDY. Five maize cultivars are identified with higher damage resistance among 21 tested candidate varieties. Conclusions The HANDY provides a quantitative assessment of the mechanical crushing resistance of maize kernel. The BI is demonstrated to be a more robust index than breakage susceptibility (BS) when evaluating threshing quality in harvesting in terms of both reliability and accuracy. This study also offers a new perspective for evaluating the mechanical crushing resistance of grains and provides technical support for breeding and screening maize varieties that are suitable for mechanical harvesting.

The structure of the maize kernels plays a critical role in determining maize yield and quality, and high-throughput, non-destructive microscope phenotypic characteristics acquisition and analysis are of great importance. In this study, Micro-CT technology was used to obtain images of maize kernels. An automatic CT image analysis pipeline was then developed to extract 20 traits related to the three-dimensional structure of kernel, embryo, endosperm, and cavity. The determination coefficients for five volume-related traits (embryo, endosperm, silty endosperm, embryo cavity, and endosperm cavity) were 0.95, 0.95, 0.77, 0.73, and 0.94, respectively. Further, we analyzed the phenotypic variations among a group of 303 inbred lines and conducted genome-wide association studies (GWAS). A total of 26 significant SNP loci were associated with these traits that are closely related to kernel volume, and 62 candidate genes were identified. Functional analysis revealed that most candidate genes corresponding to cavity traits encoded stress resistance proteins, while those corresponding to embryo and endosperm traits encoded proteins involved in regulating plant growth and development. These results will improve the understanding of the phenotypic traits of maize kernels and will provide new theoretical support for in-depth analysis of the genetic mechanism of kernel structure traits.

Fungal infection is a pre-harvest and post-harvest crisis for maize kernels and infection detection at early stage is still a difficult point. The feasibility of optical coherence tomography (OCT) for imaging mold-contamination on maize kernel at early stage is investigated. OCT technique provides high-resolution 2D tomographic images of micro-structure of maize and realizes immediate, automated, and non-destructive detection of mold by proposed method. After denoising, edge detection, smoothing, and flatten, thin pericarp layer is cropped and extracted. Then, the number of non-zero pixels on the upper boundary is calculated and the float threshold is used to classify whether there are mildew pixels in a certain column. All the experimental results suggest that mold contaminated maize kernel can be detected and marked by monitoring near surface layers combined with our proposed method. The OCT approach is suitable for fast, nondestructive diagnosis of mold at early stage, which may potentially lead to significant cost savings and better control of the spread of mold in the food industry. [ABSTRACT FROM AUTHOR]

Clarifying the maize kernel movement during the crushing process is critical for improving the design and optimization of the impact mill. Rather than through experiments, maize kernel movement can be quantitatively analyzed through the discrete element method (DEM), and this could contribute more to the study of the crushing mechanism and equipment optimization. However, having an accurate particle model and contact parameters are prerequisites to ensure the accuracy of the DEM simulation. In this study, we proposed a maize kernel model construction method for the Rocky DEM simulation and a calibration method to calibrate contact parameters. The three-axis size, volume, and shape information of real maize kernels were obtained by 3D scanning, and then the maize kernel model was constructed by the section method. The particle–low-carbon-plate (p–w) and particle–particle (p–p) restitution coefficients were calibrated by using the improved inclined surface drop method. In addition, the angle of repose (AoR) and discharging time were considered together to calibrate the dynamical friction coefficients of p–w and p–p through the funnel method. Additionally, the maize kernel model and calibrated parameter values were used in a DEM simulation of the inclined surface drop test and the funnel test. The maximum relative errors between the simulation values and the measured values of the inclined surface drop test and the funnel test were 4.38% and 6.98%, respectively, which further verified that the proposed maize kernel model construction and contact parameter calibration methods are feasible and accurate. The research method used in this study is a novel idea that can be applied for the construction of the particle model and calibration of the contact parameters of granular materials with complex geometric structures, as well as the maize kernel model, and shows that calibrated contact parameters can provide a reference for the maize kernel crushing simulation to optimize the impact mill. [ABSTRACT FROM AUTHOR]

Breakage can normally be induced by the impact of granular materials and mechanical equipment on agricultural products during seed discharging, harvesting, transportation, and crushing processes. Some breakage can damage the products, such as seed discharge and transportation, whereas, another breakage can be meaningful, such as crushing. Therefore, it is a high demand to investigate the breakage characteristics of product granules for the decision-making on the operational parameters of agricultural machinery, including the transportation, seeding, and crushing. Taking the maize kernel as the research object, the main purpose of this study is to clarify the influence of moisture content and impact speed on the impact breakage characteristics of products. Five types of maize kernel samples were first prepared with the variable moisture content. An impact testing device was designed for the single particle. A series of impact tests were performed on the maize kernels with five moisture contents. The breakage characteristics of the maize kernel were then characterized, in terms of breakage patterns, the extent of breakage, and the critical impact velocity. The experimental results revealed that the broken particles of maize kernels after breaking were divided into three levels of breakage patterns: the unbroken, rupture, and fragmentation, according to the size and quantity distribution characteristics. Among them, the fracture pattern included the overall and local rupture. The maximum particle mass ratio (W) was defined as the indicator for the breakage patterns of maize kernels, where the W was the mass of the big particle in the broken particles to the mass of the maize kernel. The regression equations were established between the W value and impact velocity of maize kernels with different moisture content, where the coefficients of determination (R2) were obtained all greater than 0.92. The critical rupture velocities of maize kernels with the moisture content from low to high were determined to be 11.45, 15.54, 19.84, 30.49, and 34.28 m/s, respectively, and the critical fragmentation velocities were 19.55, 23.75, 28.68, 42.07, and 46.79 m/s, respectively, according to the regression equations. It indicated that the critical rupture velocity and critical fragmentation velocity of the maize kernel increased with the increase of moisture content. The extent of breakage of maize kernels was characterized by the average particle size of broken particles. The 2FI model regression equation of average particle size was established with the impact velocity and moisture content using the response surface method (RSM), where the R2 was 0.96. In addition, the analysis of variance showed that there was an extremely significant influence on impact velocity, the moisture content of maize kernels, and the interaction on the extent of breakage (P<0.01). The extent of breakage of maize kernels increased with the increase of impact velocity, while decreasing with the increase of moisture content of maize kernels. These datasets can be used to determine the operational parameters of agricultural equipment during granule processing. The finding can also provide a theoretical basis for the impact breakage characteristics of agricultural products. [ABSTRACT FROM AUTHOR]

Plant micro-phenotype mainly refers to the phenotypic information at the tissue, cell, and subcellular levels, which is an important part of plant phenomics research. In view of the problems of low efficiency, large error, and few traits of traditional methods for detecting kernel microscopic traits, Micro-CT scanning technology was used to carry out precise identification of micro-phenotype on 11 varieties of maize kernels. A total of 34 microscopic traits were obtained based on CT sequence images of 7 tissues, including seed, embryo, endosperm, cavity, subcutaneous cavity, endosperm cavity and embryo cavity. Among the 34 microscopic traits, 4 traits, including endosperm cavity surface area, kernel volume, endosperm volume ratio and endosperm cavity specific surface area, were significantly different among maize types (P-value

The purpose of this study was to investigate the influence of the parameters of the grain air-sieve cleaner in laboratory conditions on the external quality of food maize (Zea mays L.) kernel in terms of the design for the selection of a suitable sieve mesh for cleaning procedures. The object of the research was maize kernel, variety Pionier P0216, year of cultivation 2019. The available laboratory equipment was used in the study. To evaluate the external quality of food maize kernel, indicators were determined, which were investigated before and after cleaning. An Asus notebook computer with software from Microsoft Windows XP and Ofice 2010 was used to evaluate the measurement results. These results were achieved: an average bulk density of 846.77 kg.m-3 was found in the input sample of food maize kernel after harvest, admixtures before cleaning reached an average of 19.1% and impurities of 2.76%, cleanliness of kernels before cleaning averaged 76.9%, the output after cleaning expressed in terms of bulk density reached an average value of 851.15 kg.m-3, admixtures after cleaning reached 0.07% and impurities 4.21%, clean kernels after cleaning reached 94.86% and damage kernels after cleaning decreased slightly by separation of fragments and chipped kernels. In conclusion, it was stated that the laboratory technique for post-harvest treatment of grain is at a high level worldwide. Currently, the issue of post-harvest processing of grain in Slovakia is addressed at an average level. Post-harvest processing and storage of grain in terms of enginery and technological and economic aspects is little researched in the Slovak Republic, so these issues are open to further research.

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.

In this study, the rheological behavior of maize kernel was systematically investigated using a dynamic mechanical analyzer. The loss in toughness caused by drying resulted in a downward shift in the relaxation curve and an upward shift in the creep curve. The long relaxation behavior became obvious when the temperature was above 45 °C, resulting from the weakening of hydrogen bonds with temperature. The maize kernel relaxed more rapidly at high temperatures, caused by a reduction in the cell wall viscosity and polysaccharide tangles. The Deborah numbers were all much smaller than one, suggesting that the Maxwell elements showed viscous behavior. Maize kernel, as a viscoelastic material, showed a dominant viscous property at high temperatures. The decline in β with increasing drying temperature indicated an increase in the width of the relaxation spectrum. A Hookean spring elastic portion made up the majority of the maize kernel creep strain. The order–disorder transformation zone of maize kernel was about 50–60 °C. Due to the complexity of maize kernel, the William–Landel–Ferry constants differed from the universal values; these constants should be ascertained through experiments. Time-temperature superposition was successfully used to describe the rheological behavior. The results show that maize kernel is a thermorheologically simple material. The data acquired in this study can be used for maize processing and storage.

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. [ABSTRACT FROM AUTHOR]

Fusarium verticillioides causes Fusarium ear rot (FER) of maize and contaminates the grain with mycotoxins called fumonisins. Structural barriers that prevent infection include a closed stylar canal of kernels, which has been suggested to contribute to resistance to FER. In this study, the stylar canal architecture of nine maize inbred lines, characterised for their response to F. verticillioides, was evaluated using scanning electron microscopy. The stylar canal of ovaries, sampled at 1–2 weeks before pollination, and maturing kernels sampled at approximately 1 month after self-pollination, were evaluated. Three inbred lines, CML 390 (highly resistant), RO 544 W (intermediately resistant) and R2565y (susceptible), had ovaries predominantly with closed stylar canals, with 92, 95 and 64.3% of the canals viewed as closed, respectively. Other highly resistant (CML 444, VO 617Y-2 and US 2540 W), intermediately resistant (RO 549 W) and susceptible (R119W and I137tnW) inbred lines displayed a mostly open stylar canal prior to pollination. The stylar canals of all inbred lines were mostly closed 1 month after pollination (90.3–100%). No significant association was found between stylar canal architecture and the FER-resistance status of the maize lines evaluated for ovaries and maturing maize kernels (P > 0.05). The results of the study suggest that the stylar canal architecture may facilitate fungal ingress but does not play a pivotal role in resistance to F. verticillioides. [ABSTRACT FROM AUTHOR]

To study the drying stress and predict cracking of the components of the maize kernel during drying, the viscoelastic stress-strain model of the maize kernel was developed considering the differences in viscoelastic properties of the components. The results showed that at the preheating and main drying stages, the stress was mainly caused by the uneven distribution of the moisture content and gradually increased with drying. At the cooling stage, the stress significantly increased in a short time due to the decrease in temperature. The stress of the hard endosperm was higher, the pericarp had the highest stress value, and the stress of the soft endosperm and germ was low. The crack volume percentage of the germ and pericarp was zero, which was consistent with the fact that there was no crack in the pericarp and germ, the cracks were mainly in the endosperm. The crack volume percentage of the endosperm gradually increased as drying progressed and significantly increased during the cooling stage, which was similar to the increasing trend in the crack degree of the endosperm at different drying stages. The viscoelastic stress-strain model combined with the von Mises stress criterion could be used to predict the stress crack formation of components at different drying stages. • Relaxation behavior of components could be described by a two-element Maxwell model. • A viscoelastic stress-strain model was developed considering the heterogeneity of components. • Stress was closely related to the distribution of moisture and temperature. • Crack formation in components could be predicted based on a viscoelastic stress-strain model. [ABSTRACT FROM AUTHOR]

Mechanical damage of maize kernel caused by external forces during processing is closely related to its internal mechanical properties. In this research, three main tissues’ (horny endosperm, floury endosperm and germ) mechanical properties of three maize varieties (Jinyu818, Zhengdan958 and Dafeng30) and five stages of moisture contents (11.78–34.73% w.b.) were evaluated using puncture test. Results showed that there was no (p > .05) or weak significant (0.01 0.80) with each other. This study can be considered as a contribution towards further studying and minimizing the mechanical damage of the maize kernel when applying external force during and post harvesting.

Maize is one of the three worldwide cereal crops with the most outstanding production; however, its postharvest losses range from 2 to 40% due to inadequate harvesting, drying, and storage technologies. This study focuses on the Instant Controlled Pressure Drop technology (DIC) effect on maize kernels’ drying and rehydration kinetics. In total, 19 different DIC treatments were carried out on maize kernels (~25% d.b.). The DIC parameters studied were steam pressure (0.1 to 0.4 MPa) and treatment time (10 to 90 s). After DIC treatment, drying kinetics were carried out by Convective Air Drying (CAD) at 50 °C and 0.4 ms−1 airflow. Rehydration kinetics and Water Holding Capacity (WHC) were evaluated at 20 °C. In comparison to CAD samples, DIC (0.4 MPa and 90 s) reduced the drying time from 180 min to ~108 min. Additionally, regarding the rehydration and WHC results, DIC achieved the same moisture content in only 3.5 min that controls achieved after 1 h of rehydration (0.40 g H2O/g dry matter). Moreover, DIC (0.4 MPa and nine cycles of 10 s) increased the WHC 2.3 times compared to the control. In this way, DIC could be a postharvest technology to improve maize kernels’ drying operations and functional properties.

Background: How to control the physical damage during maize kernel harvesting is a major problem for both mechanical designers and plant breeders. A limitation of addressing this problem is lacking a reliable method for assessing the relation between kernel damage susceptibility and threshing quality. The design, construction, and testing of a portable tool called "HANDY", which can assess the resistance to mechanical crushing in maize kernel. HANDY can impact the kernel with a special accelerator at a given rotating speed and then cause measurable damage to the kernel. These factors are varied to determine the ideal parameters for operating the HANDY. Results: Breakage index (BI, target index of HANDY), decreased as the moisture content of kernel increased or the rotating speed decreased within the tested range. Furthermore, the HANDY exhibited a greater sensitivity in testing kernels at higher moisture level influence on the susceptibility of damage kernel than that in Breakage Susceptibility tests, particularly when the centrifugation speed is about 1800 r/min and the centrifugal disc type is curved. Considering that the mechanical properties of kernels vary greatly as the moisture content changes, a subsection linear (average goodness of fit is 0.9) to predict the threshing quality is built by piecewise function analysis, which is divided by kernel moisture. Specifically, threshing quality is regarded as a function of the measured result of the HANDY. Five maize cultivars are identified with higher damage resistance among 21 tested candidate varieties. Conclusions: The HANDY provides a quantitative assessment of the mechanical crushing resistance of maize kernel. The BI is demonstrated to be a more robust index than breakage susceptibility (BS) when evaluating threshing quality in harvesting in terms of both reliability and accuracy. This study also offers a new perspective for evaluating the mechanical crushing resistance of grains and provides technical support for breeding and screening maize varieties that are suitable for mechanical harvesting. [ABSTRACT FROM AUTHOR]

Clarifying the maize kernel movement during the crushing process is critical for improving the design and optimization of the impact mill. Rather than through experiments, maize kernel movement can be quantitatively analyzed through the discrete element method (DEM), and this could contribute more to the study of the crushing mechanism and equipment optimization. However, having an accurate particle model and contact parameters are prerequisites to ensure the accuracy of the DEM simulation. In this study, we proposed a maize kernel model construction method for the Rocky DEM simulation and a calibration method to calibrate contact parameters. The three-axis size, volume, and shape information of real maize kernels were obtained by 3D scanning, and then the maize kernel model was constructed by the section method. The particle–low-carbon-plate (p–w) and particle–particle (p–p) restitution coefficients were calibrated by using the improved inclined surface drop method. In addition, the angle of repose (AoR) and discharging time were considered together to calibrate the dynamical friction coefficients of p–w and p–p through the funnel method. Additionally, the maize kernel model and calibrated parameter values were used in a DEM simulation of the inclined surface drop test and the funnel test. The maximum relative errors between the simulation values and the measured values of the inclined surface drop test and the funnel test were 4.38% and 6.98%, respectively, which further verified that the proposed maize kernel model construction and contact parameter calibration methods are feasible and accurate. The research method used in this study is a novel idea that can be applied for the construction of the particle model and calibration of the contact parameters of granular materials with complex geometric structures, as well as the maize kernel model, and shows that calibrated contact parameters can provide a reference for the maize kernel crushing simulation to optimize the impact mill.

Radio frequency (RF) heating has been expected as an alternative to traditional thermal treatments for postharvest pasteurization and disinfestation, due to its rapid heating, high thermal efficiency, volumetric heating， and selective heating. Corn is one of the most primary food crops in terms of both large yield and wide use in the world. Since a large amount of postharvest loss was caused by the invasion of mold and insects every year, especially in developing countries, it is highly urgent to develop RF heating for corn kernels. However, it needs to accurately predict the temperature when formulating parameters for the process of RF heating, where the corn temperature is the primary determinant of product quality. In this study, a three-dimensional geometric model of corn kernels was constructed using the simplified structure of germ and endosperm, and an RF heating mathematical model for corn kernels was developed using the heterogeneity of thermos-physical and dielectric properties in the COMSOL multi-physics software. A validated model was used to simulate the distribution of temperature and electric field, thereby exploring the changes of RF selective heating in corn kernels with different postures and moisture content. The results showed that the simulated and experimental temperatures were highly consistent, with a maximum relative error of 3.47%, indicating that the model could well predict the behavior of RF selective heating in corn kernels. The cold spots appeared in the center of the rectangular container, while the hot spots occurred in its corners and edges during RF heating, showing non-uniform heating. Specifically, the temperature difference between the corn kernels at the corner and center gradually increased as time proceeded. In surface temperature, the temperature of the germ was higher than that of endosperm, indicating the preferential heating of the germ. The geometric effect of the upright corn kernel was the largest, followed by the oblique and side-standing ones, where the preferential heating of germ was the greatest, as the enhanced intensity of electric field and power density of germ, whereas, that of the flat corn kernel was the smallest. Moreover, the temperature difference between germ and endosperm in corn kernels with the moisture content of 13.0% increased gradually during RF heating, while that in corn kernels with the moisture content of 16.5% and 20.0% increased rapidly first and then decreased. When the corn kernels were heated to 55 ℃, the preferential heating of germ in corn kernels with the moisture content of 16.5% was the largest, followed by those with the moisture content of 13.0%, and those with 20.0% moisture content was the smallest. Therefore, the posture and moisture content of corn kernels can be used to adjust RF heating and reduce the damage of RF selective heating on the germ quality. The current work can contribute to understanding the RF heating behavior in corn kernels, thereby serving as a useful guidance for the application of RF heating in low-moisture corn kernels. [ABSTRACT FROM AUTHOR]

Based on isobaric tags for relative and absolute quantification (iTRAQ) technology, the proteome of grains of a maize cultivar Huangzao 4 under drought stress at grain filling stage was analyzed. The results show that under drought stress, 438 proteins were differentially expressed in the maize grains during grain filling. Among them, 200 were up-regulated and 238 were down-regulated. The gene ontology (GO) analysis shows that the biological processes in which differential proteins are more involved are cellular processes, metabolic processes and single biological processes; proteins in the cell component category are mainly distributed in cells, cell parts and organelles; and the proteins the molecular function category mainly possess catalytic activity and binding function. Differentially expressed proteins classified by COG are mainly involved in protein post-translational modification and transport, molecular chaperones, general functional genes, translation, ribosomal structure, biosynthesis, energy production and transformation, carbohydrate transport and metabolism, amino acid transport and metabolism, etc. The subcellular structure of the differentially expressed proteins is mainly located in the cell chloroplast and cytosol. The proportions are 35. 01% and 30. 21% respectively. KEGG metabolic pathway enrichment analysis shows that the differentially expressed proteins are mostly involved in antibiotic biosynthesis, microbial metabolism in different environments, and endoplasmic reticulum protein processing; the metabolic pathways with higher enrichment are the carbon fixation pathway and estrogen signaling pathway of prokaryotes; and the higher enrichment and greater significance are in the tricarboxylic acid cycle, carbon fixation of photosynthetic organisms and proteasome. The results of this study preliminarily reveal the adaptive mechanism of maize grains in response to drought stress during grain filling, providing a theoretical reference for maize drought-resistant molecular breeding. [ABSTRACT FROM AUTHOR]

Near infrared hyperspectral imaging over the spectral range of 900–2500 nm was investigated for its potential to identify maize kernels inoculated with aflatoxigenic fungus (AF13) from healthy kernels and kernels inoculated with non-aflatoxigenic fungus (AF36). A total of 900 kernels were used with 3 treatments, namely, each 300 kernels inoculated with AF13, AF36 and sterile distilled water as control, separately. One hundred kernels from each treatment of 300 kernels were incubated for 3, 5 and 8 days, to obtain diverse samples. Based on the full mean spectra extracted from the same kernel side(s), the best mean overall prediction accuracies achieved were 96.3% for the 3-class (control, non-aflatoxigenic and aflatoxigenic) classification and 97.8% for the 2-class (aflatoxigenic-negative and -positive) classification using the partial least-squares discriminant analysis (PLS-DA) method. The 3-class and 2-class models using the full mean spectra extracted from different kernel sides had the best mean overall prediction accuracies of 91.5% and 95.1%. Using the most important 30, 55 and 100 variables determined by the random frog (RF) algorithm, the simplified type I-RF-PLSDA models achieved the mean overall prediction accuracies of 87.7%, 93.8% and 95.2% for the 2-class discrimination using different kernel sides' information. Among the most important 55 and 100 variables, a total of 25 and 67 variables were consistently selected in the 100 random runs and were therefore used further for establishing the type II-RF-PLSDA models. Using these 25 and 67 variables, the type II-RF-PLSDA models obtained the mean overall prediction accuracies of 82.3% and 94.9% separately. • NIR hyperspectral imaging to detect aflatoxigenic fungal infection on maize kernels. • Effect of maize kernel side on discrimination results was addressed. • Random frog algorithm was employed to optimise the discriminant analysis. • Both 3-class and 2-class models were established using different types of spectra. • Visualisation of predicted kernel class was proposed and demonstrated. [ABSTRACT FROM AUTHOR]

With the development of precision agriculture, the selection of maize kernels has gained more importance in scientific research and practical significance in agricultural production. In this study, the deep learning technology of machine vision was used to select maize kernels, solving the problems of previous maize kernel selection for specific sorting problems, the cumbersome process of artificial feature modeling, the problem of a small number of features, and the challenge of limited data. First, the maximum size of a model based on convolutional neural networks (CNNs) that could run under finite hardware conditions was determined by experiments. Four different network models (Faster R-CNN, Model 1.0, Model 2.0, and Model 3.0) were then designed and trained using a data set of maize kernels. Finally, the accuracy of the models was verified by comparison test, and the detection results of the models were analyzed according to their precision, recall, FPR, F1, precision-recall curve, average precision (AP), mean average precision (mAP), and detection speed. The results show that for the validation set not used for training, Model 1.0 had the highest average recall rate of 98.42% among the four models. Without taking into account the identification of the removed kernels, only excellent maize kernels were identified, and the mAP of Model 1.0 was as high as 97.27%. Moreover, Model 1.0 requires less computer resources, and its computer hardware requirement is lower. The precision, recall, and F1 value of Model 2.0 were increased by 3.73%, 3.55%, and 3.79%, respectively, and the false positive rate of Model 2.0 was reduced by 1.31% on average compared with the Faster R-CNN model. By comparing Model 1.0, Model 2.0, and Model 3.0, it was found that the overall performance of Model 2.0 was best. The size of the network model has an effect on the accurate selection of maize kernels, and a moderatesize model is the best. This study laid a good foundation for the further application of deep learning technology in the realtime sorting of maize kernels and additional applications in the field of agriculture. [ABSTRACT FROM AUTHOR]

[Display omitted] • Kernel properties and carotenoid in vitro digestion were analysed in 103 commercial dent hybrids. • Tested hybrids characterized higher zeaxanthin than lutein content. • Kernel hardness traits are related to the extent of the amount of digested carotenoids. • Fiber, protein and amylose correlated with the release of carotenoids during digestion. 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. [ABSTRACT FROM AUTHOR]

The study aimed at measuring changes in chemical composition of maize kernels due to Aspergillus flavus Link. and Fusarium verticillioides (Sacc.) Nirenberg infection. The samples of maize kernels were incubated at 28 °C for 7, 14, 21, and 28 days. The samples were analysed for mycotoxin, moisture, crude fat, crude protein, crude ash, and crude fibre. Maize kernels inoculated with A. flavus and F. verticillioides exhibited a significant decrease in crude fat. Aflatoxin B1 (AFB1) contamination increased in maize kernels inoculated with A. flavus, and fumonisin B1 (FB1) in kernels inoculated with F. verticillioides. Crude ash and crude fibre content showed no changes. Incubation time significantly affected AFB1 and FB1 contamination levels, moisture, crude fat, and crude protein contents. AFB1 and FB1 contamination were significantly correlated with crude fat degradation. The tested strains had similar deteriorative effects on maize kernels. The significant changes in the proximate composition were only observed in maize kernels with mycotoxin contamination above the regulatory limit of 10 µg kg−1, thus not fit for human consumption.

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.

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Mechanical damage of maize kernel caused by external forces during processing is closely related to its internal mechanical properties. In this research, three main tissues' (horny endosperm, floury endosperm and germ) mechanical properties of three maize varieties (Jinyu818, Zhengdan958 and Dafeng30) and five stages of moisture contents (11.78–34.73% w.b.) were evaluated using puncture test. Results showed that there was no (p >.05) or weak significant (0.01 < p <.05) difference between varieties, while significant moisture and tissue material effects were observed. For rupture force (Frp), ultimate strength (σb), toughness (Trp), initial firmness (Finf), average firmness (Favf), modulus of elastic (E), apparent modulus of elastic (AME), bioyeiled force (Fby), bioyeiled strength (σs) and penetration force (Fp), outer layer tissue (horny endosperm) of maize kernel at lower stage of moisture contents were higher than those of inner layer tissues (floury endosperm and germ) at higher moisture contents. While for bioyeiled deformation (Dby) and rupture deformation (Drp) values, results showed that inner layer tissues at with higher moisture content were the highest. Pearson's correlation analysis showed that most of the parameters had a significant correlation (p <.01, r > 0.80) with each other. This study can be considered as a contribution towards further studying and minimizing the mechanical damage of the maize kernel when applying external force during and post harvesting. [ABSTRACT FROM AUTHOR]

[Display omitted] • Epiphytic fungal community succession in maize kernels was influenced by storage temperature. • Eurotium was the dominant genus in all maize samples. • A. flavus exhibited a tendentious consumption of zein during maize storage. • The detoxification efficiency of AFB 1 was superior to that of ZEN during maize wet processing. Aspergillus flavus , a notorious saprobe and opportunistic plant pathogen, alters mycotoxin contamination and biochemical components in maize kernels during processing and storage, thereby reducing the possibilities of maize end use and compromising food safety. This study explored changes in mycotoxin production, fungal community succession and biochemical components in maize kernels stored at 20, 25 and 30 °C, exposed to A. flavus. Results showed that aflatoxin B 1 concentration increased over time, reaching 4.88 μg/kg at 20 °C, 167.23 μg/kg at 25 °C and 349.64 μg/kg at 30 °C after 15 days of storage, whereas the zearalenone production was characterized by an increase followed by a decrease. Correspondingly, the number of molds gradually increased and reached a stable stage after 10 days. High-throughput sequencing of the internal transcribed spacer (ITS) revealed that Eurotium dominated the fungal communities, with A. flavus reaching maximum abundance in maize kernels stored at 30 °C for 15 days. Correlation analysis indicated that the relative abundance of A. flavus was significantly negatively correlated with the content of zein and moisture (P < 0.05). Moreover, the wet milling process of maize effectively eliminated the concentration of aflatoxin B 1 and zearalenone from the starch. Pasting temperature and setback value of starch decreased while peak viscosity, final viscosity and breakdown value increased with storage. These findings indicate that interactions between the epiphytic fungal community and A. flavus at elevated storage temperatures aggravate both maize quality deterioration and mycotoxin contamination. Furthermore, they have a discernible impact on the pasting properties of starch. This insight informs strategies to control fungal infections during maize processing and storage. [ABSTRACT FROM AUTHOR]