Prediction of moisture content for a single maize kernel based on viscoelastic properties

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.

Dynamic Viscoelastic Behavior of Maize Kernel: Application of Frequency-Temperature Superposition

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.

Genes and pathways correlated with heat stress responses and heat tolerance in maize kernels

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.

Classification of Fluorescently Labelled Maize Kernels Using Convolutional Neural Networks

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.

An ARF24-ZmArf2 module influences kernel size in different maize haplotypes

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.

A Systematical Rheological Study of Maize Kernel

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.

A genome-wide association study identifies a transporter for zinc uploading to maize kernels

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.

Comparative Transcriptome Analysis Reveals Mechanisms of Folate Accumulation in Maize Grains

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.

Classification of Aflatoxin B1 Concentration of Single Maize Kernel Based on Near-Infrared Hyperspectral Imaging and Feature Selection

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 Exploits Maize Kernels Using an "Orphan" Secondary Metabolite Cluster

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.

Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging

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.

Signaling in Early Maize Kernel Development

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.

Comparative Histological and Transcriptional Analysis of Maize Kernels Infected with Aspergillus flavus and Fusarium verticillioides

Aspergillus flavus and Fusarium verticillioides infect maize kernels and contaminate them with the mycotoxins aflatoxin, and fumonisin, respectively. Genetic resistance in maize to these fungi and to mycotoxin contamination has been difficult to achieve due to lack of identified resistance genes. The objective of this study was to identify new candidate resistance genes by characterizing their temporal expression in response to infection and comparing expression of these genes with genes known to be associated with plant defense. Fungal colonization and transcriptional changes in kernels inoculated with each fungus were monitored at 4, 12, 24, 48, and 72 h post inoculation (hpi). Maize kernels responded by differential gene expression to each fungus within 4 hpi, before the fungi could be observed visually, but more genes were differentially expressed between 48 and 72 hpi, when fungal colonization was more extensive. Two-way hierarchal clustering analysis grouped the temporal expression profiles of the 5,863 differentially expressed maize genes over all time points into 12 clusters. Many clusters were enriched for genes previously associated with defense responses to either A. flavus or F. verticillioides. Also within these expression clusters were genes that lacked either annotation or assignment to functional categories. This study provided a comprehensive analysis of gene expression of each A. flavus and F. verticillioides during infection of maize kernels, it identified genes expressed early and late in the infection process, and it provided a grouping of genes of unknown function with similarly expressed defense related genes that could inform selection of new genes as targets in breeding strategies.

Expression of Genes for Si Uptake, Accumulation, and Correlation of Si with Other Elements in Ionome of Maize Kernel

The mineral composition of cells, tissues, and organs is decisive for the functioning of the organisms, and is at the same time an indicator for understanding of physiological processes. We measured the composition of the ionome in the different tissues of maize kernels by element microanalysis, with special emphasis on silicon (Si). We therefore also measured the expression levels of the Si transporter genes ZmLsi1, ZmLsi2 and ZmLsi6, responsible for Si uptake and accumulation. Two weeks after pollination ZmLsi1 and ZmLsi6 genes were expressed, and expression continued until the final developmental stage of the kernels, while ZmLsi2 was not expressed. These results suggest that exclusively ZmLsi1 and ZmLsi6 are responsible for Si transport in various stages of kernel development. Expression level of ZmLsi genes was consistent with Si accumulation within kernel tissues. Silicon was mainly accumulated in pericarp and embryo proper and the lowest Si content was detected in soft endosperm and the scutellum. Correlation linkages between the distribution of Si and some other elements (macroelements Mg, P, S, N, P, and Ca and microelements Cl, Zn, and Fe) were found. The relation of Si with Mg was detected in all kernel tissues. The Si linkage with other elements was rather specific and found only in certain kernel tissues of maize. These relations may have effect on nutrient uptake and accumulation.