Wheat Kernels Research Articles

An investigative study to utilise the Fusarium-damaged wheat as a feedstock for the black soldier fly larvae (Hermetia illucens)

Abstract This study investigated Fusarium-damaged wheat kernels (FDK) as a potential feeding substrate for black soldier fly larvae (Hermetia illucens; BSFL). Fusarium-damaged kernels are considered unsuitable for food and feed due to the presence of mycotoxins. Mycotoxins, like deoxynivalenol (DON), pose health risks when consumed by animals at concentrations exceeding the limits established by the Canadian Food Inspection Agency. However, BSFL have shown higher tolerance to mycotoxins, suggesting that FDK may be used as a feeding substrate for BSFL intended for animal consumption. To assess this, three FDK-based diets with varying concentrations of DON (4.49 ± 0.08 ppm, 6.04 ± 0.02 ppm, and 6.83 ± 0.04 ppm) and a healthy wheat-based diet (0 ppm DON; control diet) were formulated to grow the larvae. The diets were fed to larvae to assess their preference based on DON concentration. Concurrently, the accumulation of DON in BSFL biomass and its effects on growth parameters were evaluated. The larvae showed no preference for any DON concentration. The DON levels accumulated in the BSFL biomass were minimal, regardless of the DON concentration in the feed (), with the highest recorded at 0.87 ± 0.04 ppm compared to 6.83 ± 0.06 ppm in the diet. Despite the potential harm of FDK to animals, the growth parameters of BSFL improved, with larvae on FDK-based diets reaching a live body weight of 185.0 ± 3.2 mg compared to 177.6 ± 4.2 mg for the control on Day 15. The nutritional profile remained nearly identical across all DON concentrations (∼41% crude lipid and ∼39% crude protein in dried biomass). These findings suggest that BSFL raised on FDK-based diets can be used effectively for feed purposes.

Diagnosing Fungal Infection in Wheat Kernels by Integrating Spectroscopic Technology and Digital Color Imaging System: Artificial Neural Network, Principal Component Analysis and Correlation Feature Selection Techniques

ABSTRACTContamination of cereal grain, especially wheat, with fungal infections can cause significant economic impacts and it endangers the health of humans and livestock. This study aims to appraise the UV/VIS–NIR and digital color (RGB) imaging systems and spectroscopic methodology to detect wheat kernels infected by fungi such as Penicillium expansum and Fusarium graminearum. NIR spectra of 190–1100 nm at 10 nm intervals, visible color reflectance images and non‐visible reflectance images of wheat kernels in the ultraviolet and near‐infrared ranges were applied to develop the multi‐layer perceptron (MLP) artificial neural network model. The optimum wavelengths were selected by application of the principal component analysis (PCA) after preprocessing the raw spectra. A confusion matrix was used in the correlation feature selection method (CFS) for the decision tree classifier of selected features. The results showed that the four UV wavelengths of 310, 330, 400, and 410 nm were the best wavelengths using PCA to distinguish healthy and unhealthy wheat kernels. Considering the intensity of the wavelengths as the neural network inputs, samples were classified into healthy and unhealthy categories with an accuracy of 90.9%. Also, 18 features of color images in RGB, LAB, HSV, HSI, YCbCr, and YIQ spaces provided the highest average accuracy of 44.4% in classifying healthy and infected wheat kernels by using a CCD Proline camera in the ultraviolet range. In contrast, other cameras in the visible and invisible range showed low accuracy. Furthermore, the best classification accuracy of the healthy and infected samples by the use of the CFS method was obtained at 88.1%. Based on the findings, spectroscopic methodology proved to be highly effective for detecting, classifying and automatic cleaning of various agricultural seeds, with a particular emphasis on wheat kernals.

Reference-based image super-resolution of hyperspectral and red-green-blue image for determination of wheat kernel quality using deep learning networks

In the process of cultivation and harvest, wheat kernel quality is highly susceptible to various factors, such as disease, mildew, atrophy and impurities, and detection of kernel quality is essential to avoid hazard proliferation, facilitate product grading, and ensure food safety. Possessing abundant image and spectral characteristics, hyperspectral imaging (HSI) has gained impressive achievements in kernel quality analysis, but its low spatial resolution limits its detection accuracy. In this study, reference-based image super-resolution (RefSR) of HSI and Red-Green-Blue image was adopted to improve resolution to determine wheat kernel quality using deep learning networks. Firstly, RefSR was conducted by the improved transformer network with dual-branch feature extraction and weighted fusion operation and achieved excellent RefSR with significant resolution improvement, peak signal to noise ratio of 35.521 and structural similarity index of 0.97, outweighing the existing state-of-the-art networks. Then, the reflectance images (RIs) of effective wavelengths (EWs) from generated HSI images were combined with the residual network with a spatial, channel attention and multi-scale residual to determine wheat kernel quality. Precise analysis was achieved with the accuracy in calibration, validation and prediction sets of 100.00%, 95.26% and 92.78%. RefSR provides a novel and efficient approach for obtaining HSI images of high spatial resolution and facilitates the application of HSI in analysis of crop kernels. RIs of several sporadic EWs can be easily acquired and processed, achieving field and rapid kernel detection. Therefore, the proposed method furnishes the efficient, accurate and applicable determination of wheat kernel quality.

Wheat grain classification using hyperspectral imaging: Concatenating Vis-NIR and SWIR Data for single and bulk grains

Wheat variety identification is an essential component of the official inspection of wheat grains to determine their quality and trade value. Traditionally, wheat class identification requires deep knowledge about the appearance of plants and kernels, their history and distribution. Therefore, there is an urgent need for an automatic technology to standardize the nomenclature of wheat varieties to enable both growers and producers to identify their grains practically. The present study investigated the feasibility of Visible-Near Infrared (Vis-NIR) and Short-Wave Infrared (SWIR) spectral imaging to identify wheat classes. Both sides of the wheat kernel as single kernel measurements and bulk grains were used to acquire hyperspectral data and vertical and horizontal data concatenation was implemented to explore performance differences. In addition, the classification of bulk kernels from single kernel data was investigated. Spectral data was pre-treated by standard normal variate (SNV), Savitzky-Golay first (SG-1) and second derivatives (SG-2) and linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN) were applied as the classification algorithms. Furthermore, feature selection was utilised using the minimum redundancy maximum relevance (mRMR) algorithm to investigate the performance difference between data concatenation and feature selection. The results showed that ventral (up) side data showed better classification performances than reverse (down) side data, while Vis-NIR data achieved higher classification accuracies than SWIR data. However, the best classification performance for single kernels was obtained by LDA-SNV using up and down data in the Vis-NIR and SWIR regions vertically and horizontally concatenated with an accuracy of 93.72% for 10-fold cross-validation and 94.93% for test sets. Models based on a hundred features did not achieve the accuracy of models based on concatenated data. Moreover, classification performances of bulk samples were higher than single kernels, which achieved 100% accuracy for both cross-validation and test sets. The study demonstrates that spectral imaging has a high potential to identify wheat classes non-destructively.

3D reconstruction and morphological characterisation of single wheat grains by X‐ray μCT

SummaryThe intricate task of achieving three‐dimensional (3D) visual reconstruction of wheat kernels represents a notable challenge within the domain of digital grain analysis, playing a pivotal role in the realms of grain storage, processing, and breeding. However, existing investigations focused on individual kernels predominantly encompass dimensions such as length, width, height, and epidermal texture features, with a tendency to be invasive to the internal organisational structure of the kernel. Non‐local mean filtering algorithm is proposed to segment various tissues, and the 2D grey scale images are extracted from X‐ray micro‐computed tomography (μCT) to reconstruct a meticulous 3D visualisation model of individual wheat grains. Furthermore, building upon this foundation, an exhaustive assessment of morphological and structural parameters pertaining to each facet of the internal organisation of the wheat seed grain is conducted. Notably, these calculated parameters align with data generated by prior researchers，with 80% of the volume of the endosperm, 12% of the pericarp, about 2% of the endosperm and scutellum, and 4% of the pores. The established parameters and resultant 3D visual models serve as foundational components for subsequent in‐depth examinations into various physicochemical properties, including quality characteristics, heat, and mass transfer attributes, as well as variations in its morphological structure during breeding, which are pertinent to individual grains. This research contributes valuable insights and methodologies that can propel the advancement of studies in wheat kernel analysis.

Study on the Damage Characteristics of Wheat Kernels under Continuous Compression Conditions.

Peeling wheat yields higher-quality flour. During processing in a flaking machine, wheat kernels undergo continuous compression within the machine's chamber. As this compression persists, damage to the kernels intensifies and accumulates, eventually leading to kernel breakage. To study the damage characteristics of wheat kernels during peeling, this study established a continuous damage model based on Hertzian contact theory and continuous damage theory. The model's accuracy was validated through experiments, culminating in the calculation of critical parameters for wheat peeling. This study focused on different wheat varieties (Ningmai 22 and Jichun 1) and kernel sizes (the thicknesses of the small, medium, and large kernels were standardized as follows: Ningmai 22-2.67 ± 0.07 mm, 2.81 ± 0.07 mm, and 2.95 ± 0.07 mm; Jichun 1-2.98 ± 0.11 mm, 3.20 ± 0.11 mm, and 3.42 ± 0.11 mm). Continuous compression tests were conducted using a mass spectrometer, and critical damage parameters were analyzed and calculated by integrating the theoretical model with experimental data. The test results showed that the average maximum crushing force (Fc) for small, medium, and large-sized kernels of Ningmai 22 was 96.71 ± 2.27 N, 110.17 ± 2.68 N, and 128.41 ± 2.85 N, respectively. The average maximum crushing deformation (αc) was 0.65 ± 0.08 mm, 0.68 ± 0.13 mm, and 0.77 ± 0.17 mm, respectively. The average elastic-plastic critical pressure (Fs) was 50.21 N, 60.13 N, and 59.08 N, respectively, and the average critical values of elastic-plastic deformation (αs) were 0.37 mm, 0.38 mm, and 0.39 mm, respectively. For Jichun 1, the average maximum crushing force (Fc) for small-, medium-, and large-sized kernels was 113.34 ± 3.15 N, 125.28 ± 3.64 N, and 136.15 ± 3.29 N, respectively. The average maximum crushing deformation (αc) was 0.75 ± 0.11 mm, 0.83 ± 0.15 mm, and 0.88 ± 0.18 mm, respectively. The average elastic-plastic critical pressure (Fs) was 58.11 N, 64.17 N, and 85.05 N, respectively, and the average critical values of elastic-plastic deformation (αs) were 0.45 mm, 0.47 mm, and 0.52 mm, respectively. The test results indicated that during mechanical compression, if the deformation is less than αs, the continued application of the compression load will not result in kernel crushing. However, if the deformation exceeds αs, continued compression will lead to kernel crushing, with the required number of compressions decreasing as the deformation increases. If the deformation surpasses αc, a single compression load is sufficient to cause kernel crushing. Since smaller wheat kernels are more susceptible to breakage during processing, the peeling pressure (F) within the chamber should be controlled to remain below the average elastic-plastic critical pressure (Fs) of small-sized wheat kernels. Additionally, the kernel deformation (α) induced by the flow rate and loading in the chamber should be kept below the average elastic-plastic critical deformation (αs) of small-sized wheat kernels. This paper provides a theoretical foundation for the structural design and optimization of processing parameters for wheat peeling machines.

Risk Assessments and Risk Mitigation to Prevent the Introduction of African Swine Fever into the Danish Pig Population.

African Swine Fever poses a significant threat to pig production. An outbreak in Denmark would have severe economic consequences, potentially resulting in a loss between 335 million and 670 million euros. To mitigate the major risk factors, the Danish authorities and the Danish pig industry have implemented several risk-reducing measures. The small wild boar population in Danish nature has been culled, and a fence between Denmark and Germany has been constructed to stop or reduce the risk of migrating wild boar from Germany. All trucks arriving from abroad, intended for the transport of Danish pigs, are inspected, washed, and disinfected at facilities near the border before being allowed to transport pigs between herds or from herds out of Denmark. Cross-border trade of feedstuffs and potentially risky materials is continuously monitored. Based on risk assessments, feed types or bedding materials from countries with African Swine Fever that could contain parts of wild boar are banned from Danish pig herds. Certain types of fats and oils from countries with African Swine Fever can only be used after heat treatment. The import of whole kernels of corn, wheat, or barley is not considered a risk.

Carpological surveys of the Bronnitsky Settlement in the Novgorod Province

Identification of the diversity of cultivated plants of past historical epochs provides valuable information for clarifying the ways of formation of the cultural flora in a particular region. The medieval economy of ancient northern Russia was formed on the basis of a developed agrarian culture. The revealing of the composition and origin of its traditions is an important fundamental task. The purpose of this work was to study and determine the species identity of carbonized fossil remains of seeds and fruit crops that grew in the medieval northwestern Russia. Based on the results of morphological analysis of fossil plant remains, the presence of wheat, rye, barley and oat kernels was revealed.

Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing the interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth of applications of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, have been progressively integrated into NIRS. In this paper, two discriminant analysis problems, including wheat kernels and Yali pears as examples, and several representative calibration models were used to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which was based on the Gramian angular difference field method and coordinate attention convolutional neural networks (G-CACNNs). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results using the original spectrum. The accuracy of G-CACNNs in two discrimination tasks was 98.48% and 99.39%. Finally, this research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat

The quantitative nature of fusarium head blight (FHB) resistance requires further exploration of the wheat genome to identify regions conferring resistance. In this study, we explored the application of hyperspectral imaging of Fusarium-infected wheat kernels and identified regions of the wheat genome contributing significantly to the accumulation of Deoxynivalenol (DON) mycotoxin. Strong correlations were identified between hyperspectral reflectance values for 204 wavebands in the 397–673 nm range and DON mycotoxin. Dimensionality reduction using principal components was performed for all 204 wavebands and 38 sliding windows across the range of wavebands. The first principal component (PC1) of all 204 wavebands explained 70% of the total variation in waveband reflectance values and was highly correlated with DON mycotoxin. PC1 was used as a phenotype in a genome wide association study and a large effect QTL on chromosome 2D was identified for PC1 of all wavebands as well as nearly all 38 sliding windows. The allele contributing variation in PC1 values also led to a substantial reduction in DON. The 2D polymorphism affecting DON levels localized to the exon of TraesCS2D02G524600 which is upregulated in wheat spike and rachis tissues during FHB infection. This work demonstrates the value of hyperspectral imaging as a correlated trait for investigating the genetic basis of resistance and developing wheat varieties with enhanced resistance to FHB.

Mechanical properties and energy evolution mechanism of wheat grain under uniaxial compression

To determine the mechanical properties and energy evolution characteristics of wheat kernels during compression, the mechanical properties and energy evolution of wheat kernels with high-gluten, medium-gluten, and low-gluten were analyzed in uniaxial compression test. Based on the theory of Hertz's formula, the stress-strain laws of the three varieties of wheat kernels in the process of compression damage were investigated; Based on the law of energy conservation, the energy evolution characteristics of the three varieties of wheat kernels under the same compression conditions were analyzed. The results showed that the stress-strain process of wheat kernels with high-gluten, medium-gluten, and low-gluten under compressive stress followed a similar trend overall, with the corresponding minimum failure strengths at the peak being 51.79 MPa, 50.07 MPa, and 46.21 MPa, respectively. The energy evolution patterns of three varieties of wheat kernels under uniaxial compressive loading were approximately the same, indicating that the elastic energy was mainly accumulated before the peak and reached the maximum at the peak. In contrast, the proportion of dissipated energy increases after the peak and rapidly exceeds the elastic energy. In this paper, the damage characteristics of wheat kernels are described from the energy point of view, which is of great significance for the new conceptualization of the evolution of wheat grain damage.

Penalties in Granule Size Distribution and Viscosity Parameters of Starch Caused by Lodging in Winter Wheat

Granule size distribution of wheat starch is an important characteristic that could affect the functionality of wheat (Triticum aestivum L.) products. Lodging is a major limiting factor for wheat production. Few studies have been conducted to clarify how lodging influences the granule size distribution and viscosity parameters of starch in wheat grains. Two growing seasons, two high-yield winter wheat cultivars, and five artificial lodging treatments were imposed. The results indicated that lodging significantly reduced the content of starch and increased that of protein. Additionally, lodging caused a marked drop in both starch and protein yields. The relative loss of grain yield, starch yield, harvest index, and protein yield all differed remarkably among lodging treatments with a ranking of L2 > L1 > L4 > L3. Lodging also led to a reduction in the proportion (both by volume and by surface area) of B-type granules and a corresponding increase in that of A-type granules, and the more serious the lodging degree, the greater effect on the changes in these proportions. The smaller starch granules predominated in number, even though their collective contribution to the overall volume is was relatively minor. Meanwhile, it was found that the peak viscosity, hold viscosity, final viscosity, breakdown viscosity, and rebound value of wheat starch were significantly decreased by lodging. Correlation analysis showed that the peak and final viscosities were negatively correlated with volume percentages of A-type starch granules, but were positively correlated with B-type granules. This indicates that B-type granules have higher peak and final viscosities compared with A-type granules in wheat kernels. Lodging can reduce the proportion of B-type starch granules, and thus reduce the peak and the final viscosity in wheat grain.

Population growth of different stored product species on wheat, barley, and tritordeum

Stored product species can be developed in a variety of stored product grains. In this study, were compare three different grains for their suitability for population growth of primary and secondary stored product insects. In the first series, we evaluated the population growth and damage of Rhyzopertha dominica (F.) (Coleoptera: Bostrychidae), Sitophilus granarius (L.) (Coleoptera: Curculionidae) and Sitophilus oryzae (L.) (Coleoptera: Curculionidae) on whole wheat, barley and tritordeum kernels after 65 days. In the second series, three different percentages of whole and cracked kernels were tested: 100% cracked kernels, 100% whole kernels and 50% cracked +50% whole kernels, for Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae) and Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae). The results showed that tritordeum was suitable for the population growth of all species tested. In most of the cases, R. dominica showed higher frass production but S. granarius showed a higher number of damaged kernels. Cracked kernels were preferred by secondary pests than whole kernels and wheat showed the highest progeny production than tritordeum and barley. Overall, commodity, and grain type and percentage of cracked kernels can affect the population growth of stored product insect species. Results of this study showed that stored product insect species can develop in tritordeum and thus should be taken into consideration for further results as may be damaged during storage.

Potential of fructans as natural cryoprotectant agents in plant cryopreservation: concept validation on Arabidopsis thaliana L.

BACKGROUND: Today, synthetic chemicals are used in vitrification solutions for cryopreservation studies to mimic natural cryoprotectants that supply tolerance to organisms in nature against freezing stress. In the case of plants, PVS2, containing glycerol, dimethyl sulfoxide (Me2SO), ethylene glycol and sucrose, is considered as the golden standard for successful cryopreservation. However, Me2SO can generally cause toxicity to certain plant cells, adversely affecting viability after freezing and/or thawing. Hence, the replacement (or substantial reduction) of Me2SO by cheap, non-toxic and natural cryoprotectants became a matter of high priority to vitrification solutions or reducing their content gained escalating importance for the cryopreservation of plants. Fructans, sucrose derivatives mainly consisting of fructose residues, are candidate cryoprotectants. OBJECTIVE: Inspired by their protective role in nature, we here explored, for the first time, the potential of an array of 8 structurally different fructans as cryoprotectants in plant cryopreservation. MATERIALS AND METHODS: Arabidopsis thaliana L. seedlings were used as a model system with a one-step vitrification method. PVS2 solutions with different Me2SO and fructan contents were evaluated. RESULTS: It was found that branched low DP graminan, extracted from milky stage wheat kernels, led to the highest recovery (85%) among tested fructans with 12.5% Me2SO after cryopreservation, which was remarkably close to the viability (90%) observed with the original PVS2 containing 15% Me2SO. Moreover, its protective efficacy could be further optimized by addition of vitamin C acting as an antioxidant. CONCLUSION: Such novel formulations offer great perspectives for cryopreservation of various crop species.

Firefly Interval Selection Combined With Extreme Learning Machine for Spectral Quantification of Complex Samples

ABSTRACTFirefly algorithm (FA) combined with extreme learning machine (ELM) is developed for spectral interval selection and quantitative analysis of complex samples. The method firstly segments the spectra into a certain number of intervals. Vectors with 1 and 0, which represent the interval selected or not, are used as the inputs of the FA. The RMSEP value predicted by ELM model is used as the fitness function of the FA. The activation function and number of hidden layer nodes of ELM, number of spectral intervals, population number, environmental absorbance, and constant of FA are optimized. The predictive performance of FA‐ELM is compared with full‐spectrum PLS, ELM, genetic algorithm‐ELM (GA‐ELM), and particle swarm optimization‐ELM (PSO‐ELM) by one ultraviolet (UV) spectrum dataset of gasoil and three near‐infrared (NIR) spectral datasets of corn, wheat, and tablet samples. The results show that FA‐ELM has a better performance compared with its competitors in predicting monoaromatics, water, wheat kernel texture, and active pharmaceutical ingredients (APIs) in gasoil, corn, wheat, and tablet samples.

Influence of wheat content and origin on the volatilome of craft wheat beer: An investigation by combined multivariate statistical approaches

A deeper knowledge of the effect of wheat origin on the volatile organic compounds (VOCs) profile of craft wheat beer is crucial for its quality improvement and local product valorisation.The VOCs profile of 17 craft wheat beers obtained by common and durum, heritage and modern, wheat varieties grown in different fields sited at different altitudes was analysed.Data were processed by multivariate analysis using different approaches. Partial least square (PLS) analysis evidenced that wheat concentration was the highest source of VOCs variance, followed by, wheat species, wheat ancientness, and altitude of cultivation. An insight into the effect of wheat concentration was given by sparse PLS analysis (sPLS). The effect of wheat variety was explored by linear discriminant analysis (LDA), which permitted to correctly classify craft beers made with wheat of different origin (species and variety) on the basis of their VOCs profile. sPLS regression analysis permitted to find a combination of VOCs able to predict the altitude of wheat cultivation as well as to correctly classify wheat beers made with wheat cultivated at different altitudes. A further ‘one versus all’ approach by Soft Independent Modelling of Class Analogies (SIMCA) permitted to correctly authenticate beers made with different cereal species. Finally, shape analysis by generalized Procrustes analysis (GPA) revealed that the differences among samples were conserved and reflected from wheat kernels to wheat beers. This study suggests a promising use of volatiles fingerprinting with a combination of different statistical approaches to authenticate beer made with wheat of different origin and cultivated at different altitudes, thus stressing out the importance of territory in craft beer production, which, until now, was a neglected topic.

Natural Occurrence and Co-Occurrence of Beauvericin and Enniatins in Wheat Kernels from China.

A total of 769 wheat kernels collected from six provinces in China were analyzed for beauvericin (BEA) and four enniatins (ENNs), namely, ENA, ENA1, ENB and ENB1, using a solid phase extraction (SPE) technique with ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The results show that the predominant toxin was BEA, which had a maximum of 387.67 μg/kg and an average of 37.69 μg/kg. With regard to ENNs, the prevalence and average concentrations of ENB and ENB1 were higher than those of ENA and ENA1. The geographical distribution of BEA and ENNs varied. Hubei and Shandong exhibited the highest and lowest positive rates of BEA and ENNs (13.46% and 87.5%, respectively). However, no significant difference was observed among these six provinces. There was a co-occurrence of BEA and ENNs, and 42.26% of samples were simultaneously detected with two or more toxins. Moreover, a significant linear correlation in concentrations was observed between the four ENN analogs (r range: 0.75~0.96, p < 0.05). This survey reveals that the contamination and co-contamination of BEA and ENNs in Chinese wheat kernels were very common.

Climate Effects on Ergot and Ergot Alkaloids Occurrence in Italian Wheat.

In recent years, there has been an intensification of weather variability worldwide as a result of climate change. Some regions have been affected by drought, while others have experienced more intense rainfall. The incidence and severity of moldy grain and mycotoxin contamination during the growing and harvesting seasons have increased as a result of these weather conditions. Additionally, torrential rains and wet conditions may cause delays in grain drying, leading to mold growth in the field. In July 2023, a wheat field in Lecco (Lombardy, Italy) was affected by torrential rains that led to the development of the Claviceps fungi. In the field, dark sclerotia were identified on some ears. Wheat ears, kernels, and sclerotia were collected and analyzed by LC-MS/MS at IZSLER, Food Chemical Department, in Bologna. The wheat ears, kernels, and sclerotia were analyzed for 12 ergot alkaloids (EAs) according to (EU) Regulation 2023/915 (ergocornine/ergocorninine; ergocristine/ergocristinine; ergocryptine/ergocryptinine; ergometrine/ergometrinine; ergosine/ergosinine; ergotamine/ergotaminine), after QuEChERS (Z-Sep/C18) purification. The analyzed sclerotia showed significant differences in total alkaloid content that vary between 0.01 and 0.5% (w/w), according to the results of the 2017 EFSA scientific report. EAs detected in sclerotia were up to 4951 mg/kg, in wheat ears up to 33 mg/kg, and in kernels were 1 mg/kg. Additional mycotoxins, including ochratoxin A, deoxynivalenol, zearalenone, fumonisins, T2-HT2 toxins, and aflatoxins, were investigated in wheat kernels after purification with immunoaffinity columns (IAC). The analysis revealed the presence of deoxynivalenol in wheat kernels at a concentration of 2251 µg/kg. It is expected that climate change will increase the frequency of extreme weather events. In order to mitigate the potential risks associated with mycotoxin-producing fungi and to ensure the protection of human health, it is suggested that official controls be implemented in the field.

A pin-based probe for electronic moisture meters to determine moisture content in a single wheat kernel

BackgroundOptimum moisture in straw and grain at maturity is important for timely harvesting of wheat. Grain harvested at the right time has reduced chance of being affected by adverse weather conditions which is important to maintain grain quality and end use functionality. Wheat varieties with a short dry down period could help in timely harvest of the crop. However, measuring single kernel moisture in wheat and other small grain crops is a phenotyping bottleneck which requires characterising moisture content of the developing kernel at physiological maturity.ResultsHere we report developing a pin-based probe to detect moisture in a developing wheat kernel required for determining physiological maturity. An in-house designed pin-based probe was used with different commercially available electronic moisture meters to assess the moisture content of the individual kernels in spikes with high accuracy (R2 = 0.73 to 0.94, P < 0.001) compared with a reference method of oven drying. The average moisture values varied among different electronic moisture meters and the oven-dry method and differences in values were minimized at low kernel moisture content (< 50%). The single kernel moisture probe was evaluated in the field to predict the physiological maturity in wheat using 38% moisture content as the reference and visible notes on kernel stage.ConclusionThe pin-based moisture probe is a reliable tool for wheat physiologists and breeders to conveniently and accurately measure moisture content in developing grain that will aid in identifying wheat germplasm with fast dry-down characteristics.

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.

Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-usequality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predictDON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2=0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.