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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. [ABSTRACT FROM AUTHOR]

Wheat cultivation is important in Turkish agriculture, which ranks 10th among international wheat producers, and is an important wheat exporter, particularly to Europe. Fusarium-related threats, such as Fusarium Head Blight (FHB) and Fusarium Crown and Root Rots (FCR, FRR), and related mycotoxin seed contamination, jeopardize product quality. This study analysed 65 wheat seed samples for presence of Fusarium species, from cultivars of Triticum aestivum (bread wheat) and T. durum (durum wheat) collected from seven regions of Türkiye. PCR with specific primers, and phylogenetic analyses of TEF1-α segments, discriminated Fusarium species. Levels of the mycotoxin deoxynivalenol (DON) in flour samples were also evaluated. Out of 195 Fusarium isolates, the prominent species included F. graminearum (32% of isolates), F. proliferatum (16%), F. avenaceum (11%), F. clavum (11%), and F. verticillioides (7%). Less frequently isolated species were F. oxysporum (6%), F. acuminatum (3%), F. ramigenum (3%), F. culmorum (3%), F. poae (2%), F. sambucinum (2%), F. tricinctum (2%), Fusarium sp. FTSC12 (2%), F. andiyazi (1%), and F. equiseti, F. incarnatum, and F. fasciculatum (each 0.5%). Five of the 65 samples tested positive for DON, with two exceeding the European Commission threshold for mycotoxin contamination; one bread wheat from the Black Sea region, known for its annual rainfall, and a durum wheat sample from southeastern Anatolia, which had the highest detected DON level of 1730 μg kg-1. Among these samples F. graminearum was the predominant species. As F. andiyazi and F. ramigenum are not normally associated with wheat plants, a pathogenicity test was conducted with two isolates of each of these species, revealing no pathogenicity on the durum wheat cultivar ‘San Carlo’. These results provide a basis for managing fungal threats and mycotoxin contamination, safeguarding the quality of wheat grain as an essential agricultural product. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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Copyright of Agricultural Engineering is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Grain is one of the basic human necessities, and its quality and safety directly impact human dietary health. Various issues occur during grain storage, primarily mold and pest infestation. With the development of artificial intelligence, increasingly, more technologies are applied to grain detection and classification. Transformer-based models are becoming popular in grain detection. Although transformer models exhibit excellent performance, they are often large and cumbersome, limiting practical applications. We propose a framework named KD-ASF based on intermediate layer knowledge distillation and one-shot neural architecture search, to optimize the hyperparameters of vision transformer (ViT) for detecting and classifying molded wheat kernels (MDK), Insect-Damaged wheat kernels (IDK), and undamaged wheat kernels (UDK). In KD-ASF, we use the ViT model as our teacher network. Next, we design a search space containing adjustable hyperparameters of transformer building blocks. The super-network stacks maximum transformer building blocks and is trained under the guidance of the teacher network. Subsequently, the trained super-network undergoes evolutionary search, and the resulting networks are used for classifying different wheat kernels. We conducted experiments using a five-fold cross-validation approach and obtained an F1 score of 97.13%, and the last model parameter size is only 5.94M. The results demonstrate that this method not only outperforms the majority of neural networks in terms of performance but also has a significantly smaller model size than most network models. Its lightweight nature facilitates easy deployment and application. These findings indicate that the structure of KD-ASF is feasible and effective. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology / Zhongguo Shipin Xuebao is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Grain storage plays a vital part in social stability as well as people's daily lives. By virtue of its unique storage advantages, wheat has become one of the most important grain reserves in the world. Wheat kernels continue consuming their own energy to maintain lives during the whole process of storage, which will lead to deterioration of their edible qualities as well as may be infected by certain mold provided that the surrounding conditions are suitable. Thus, establishing an efficient and accurate method to assess wheat kernel's storage status is necessary for the sake of shaping a shelter to human beings as well as animals. For that reason, various wheat aging and wheat moldy detection methods have been summarized in this chapter. Up to now, study of aging and mold detection in wheat kernels can be divided into conventional chemical techniques and newly proposed physical detection methods. This review has analyzed in detail the limitations of conventional technologies, and then probes into some promising physical technologies for better detecting stored status of wheat kernels. [ABSTRACT FROM AUTHOR]

Quantity and quality of grain are both closely related to national development and social stability. Grain is lost during storage due to mildew and insects. Detection of damaged grain kernels not only can reduce the loss of grain, but also protect human beings from diseases caused by damaged grain. Therefore, research on the automatic detection of damaged grain is of continued urgency. In this paper, we propose a framework combining spectrogram generative adversarial network and progressive neural architecture search (SPGAN-PNAS) to detect and classify mildew-damaged wheat kernels (MDK), insect-damaged wheat kernels (IDK) and undamaged wheat kernels (UDK). First, the spectrogram generative adversarial network (SPGAN) is designed to enlarge the data set. Second, we apply progressive neural architecture search (PNAS) to generate network structure to classify three types of wheat kernels. An F1 of 96.2% is obtained using the proposed method with 5-fold cross-validation. The results are superior to the classical neural networks for detection and classification of damaged wheat kernels. Experimental results show that the structure of SPGAN-PNAS is feasible and effective. • A novel framework SPGAN-PNAS was proposed for detection of damaged wheat kernels. • Neural architecture search applied to detection and classification of damaged wheat. • Good and rapid results for detection and classification of damaged wheat kernels. • Method particularly suitable for large data sets. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mitigation of ZEA-infected wheat kernels by microorganism or enzyme was tested. • Effect of pre-treating wheat kernels by cold needle perforation was investigated. • (Un)targeted HPLC-MS/MS method to quantify mycotoxin and degradation products. • Two Bacillus strains and one enzyme were applied on contaminated wheat kernels. • ZEA-reduction by enzymatic treatment was statistically significant. With the aim of reintroducing wheat grains naturally contaminated with mycotoxins into the food value chain, a decontamination strategy was developed in this study. For this purpose, in a first step, the whole wheat kernels were pre-treated using cold needle perforation. The pore size was evaluated by scanning electron microscopy and the accessibility of enzymes and microorganisms determined using fluorescent markers in the size range of enzymes (5 nm) and microorganisms (10 μm), and fluorescent microscopy. The perforated wheat grains, as well as non-perforated grains as controls, were then incubated with selected microorganisms (Bacillus megaterium Myk145 and B. licheniformis MA572) or with the enzyme ZHD518. The two bacilli strains were not able to significantly reduce the amount of zearalenone (ZEA), neither in the perforated nor in the non-perforated wheat kernels in comparison with the controls. In contrast, the enzyme ZHD518 significantly reduced the initial concentration of ZEA in the perforated and non-perforated wheat kernels in comparison with controls. Moreover, in vitro incubation of ZHD518 with ZEA showed the presence of two non-estrogenic degradation products of ZEA: hydrolysed zearalenone (HZEA) and decarboxylated hydrolysed ZEA (DHZEA). In addition, the physical pre-treatment led to a reduction in detectable mycotoxin contents in a subset of samples. Overall, this study emphasizes the promising potential of combining physical pre-treatment approaches with biological decontamination solutions in order to address the associated problem of mycotoxin contamination and food waste reduction. [ABSTRACT FROM AUTHOR]

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

Summary: The marriage of metabolomic approaches with genetic design has proven a powerful tool in dissecting diversity in the metabolome and has additionally enhanced our understanding of complex traits. That said, such studies have rarely been carried out in wheat. In this study, we detected 805 metabolites from wheat kernels and profiled their relative contents among 182 wheat accessions, conducting a metabolite‐based genome‐wide association study (mGWAS) utilizing 14 646 previously described polymorphic SNP markers. A total of 1098 mGWAS associations were detected with large effects, within which 26 candidate genes were tentatively designated for 42 loci. Enzymatic assay of two candidates indicated they could catalyse glucosylation and subsequent malonylation of various flavonoids and thereby the major flavonoid decoration pathway of wheat kernel was dissected. Moreover, numerous high‐confidence genes associated with metabolite contents have been provided, as well as more subdivided metabolite networks which are yet to be explored within our data. These combined efforts presented the first step towards realizing metabolomics‐associated breeding of wheat. [ABSTRACT FROM AUTHOR]

Background: The sustainable management of agricultural resources requires the integration of cutting‐edge science with the observation and identification of crops. This assists experts to make correct decisions. The aim of this study is to assess the robustness of a commonly used deep learning tool, VGG16, in improving the categorization of wheat kernels. Two fusion methodologies were considered simultaneously. We performed experiments on visible light (RGB), short wave infrared (SWIR), and visible‐near infrared (VNIR) datasets, including 40 classes, with 200 samples in each class, giving 8000 samples in total. Results: After making simulations with 6400 training and 1600 testing samples, we achieved excellent performance scores, with 98.19% and 100% accuracy rates, respectively. Conclusion: The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the relationship between the surface textural features of wheat kernels and the quantity of DNA of fungi of the genus Fusarium. The images of kernels of four wheat cultivars were acquired on the ventral side and dorsal side with a flatbed scanner. Fungal DNA was quantified with specific TaqMan probes for F. culmorum and F. graminearum sensu stricto. In images acquired on the ventral side, the quantity of fungal DNA was most highly correlated with texture aHVariance from channel a in cultivar 1 (0.86), with one texture from channel R in cultivar 2 (0.80), with one texture from channel L (0.69) and one texture from channel X (− 0.69) in cultivar 3, and with one texture from channel b in cultivar 4 (0.70). In the images acquired on the dorsal side, the quantity of fungal DNA was most highly correlated with one texture from channel V in cultivar 3 (0.89), two textures from channel B in cultivar 1 (− 0.74), one texture from channel R (− 0.77), and two textures from channel Z (0.77) in cultivar 2, and one texture from channel a and textures from channel S (0.74) in cultivar 4. The results of this study indicate the usefulness of image analysis for detecting fungal infections in grain. The findings make a valuable contribution to the development of rapid, inexpensive, non-invasive, and effective methods for preliminary screening of infected and healthy kernels and for evaluating the severity of fungal infections in kernels. [ABSTRACT FROM AUTHOR]

The effects of region, genotype, harvest year, and their interaction were evaluated in relation to volatile organic compounds (VOCs) in wheat kernels. The study provides a theoretical basis for wheat authentication. A total of 148 wheat samples with 10 genotypes (G) from three regions (R) across 2011/2012, 2012/2013, 2013/2014, 2014/2015, 2015/2016 (Y) were collected, and the VOCs were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS). The results showed that R, G, and Y all significantly affected the VOCs of wheat. Y significantly influenced 23 VOCs such as Dihydro-2(3h)-furanone (78.41%) and Pentan-1-ol (66.06%), and G significantly influenced Butanal (23.59%). Additionally, the linear discriminant analysis (LDA) analysis method results showed that the overall correct classification of the five harvest years was 98%, whereas the correct rate of distinguishing ten genotypes was only 27.00%. Under the interaction effects of multiple factors, the overall correct classification rate for identifying wheat from three regions was 54.10%. [Display omitted] • A total of 41 VOCs were identified in 148 wheat samples by GC-IMS. • R, G, Y, and their interactions significantly affected the VOCs of wheat kernels. • Dihydro-2(3h)-furanone (78.41%) and other 22 VOCs were mainly influenced by Y. • The discrimination rates for R, G, and Y were 54.1%, 27%, and 98%, respectively. [ABSTRACT FROM AUTHOR]

The aim of this work was to characterize visual properties, instrumentally measured colour properties, field fungi presence and Alternaria toxins levels in wheat samples grown under conditions aimed at inhibition and stimulation of wheat infection with fungi from the Alternaria genus. Experiment was carried out on the wheat treated by fungicide and wheat inoculated by Alternaria spp., while non treated wheat was used as a control. Statistically significant difference was observed between all three treatments using visual scale. Protected wheat samples were significantly different from other samples in terms of all measured colour parameters while inoculated and control wheat samples were significantly different in terms of lightness and dominant wavelength. Identification of field fungi in the all examined wheat samples showed that the dominant mycotoxigenic fungus was Alternaria spp., followed by Fusarium spp. The content of Alternaria toxins in samples of wheat hulls and dehulled kernels point out at higher concentrations of Alternaria toxins in hulls than in dehulled kernels. [Projekat Ministarstvo nauke Republike Srbije, br. III 46001 i br. III 46005]

Fusarium head blight (FHB) compromises the processing suitability and nutritional value of grain, and it causes significant crop losses. The aim of the study was to develop models for the classification of wheat (Triticum aestivum L.) kernels infected with fungi and healthy wheat kernels. Wheat kernels were classified with the use of Decision Tree, Rule-based, Bayes, Lazy, Meta and Function classifiers, as well as multilayer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN). Twenty textures were selected from RGB, Lab, XYZ colour spaces each, for every wheat variety and each kernel side. Accuracy ranged from 82% for the dorsal side of kernels for Naive Bayes and IBk classifiers to 100% for the ventral side of kernels for IBk, FLDA and Naive Bayes classifiers. Classification accuracy was highest in the model based on texture attributes from Lab colour space. The final model of 20 attributes from Lab colour space was applied to a set of kernels from all wheat varieties, analysed on the ventral side. The accuracy of the classification model ranged from 94% to 98%, depending on the applied classifier. The models developed with the use of neural networks were characterised by overall classification accuracy of above 99% for MLP networks, above 96% for RBF networks and above 97% for PNN. The developed models indicate that analyses should be performed on the ventral side of kernels based on textures from Lab colour space. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the correlations between the external friction angle of wheat kernels vs. the roughness of the friction plate and the basic physical properties of kernels. The average external friction angle ranged from 17° to 24°, and its value increased with an increase in surface roughness. The analyzed relationship was well described by a logarithmic function. The dimensions, mass, geometric mean diameter, aspect ratio, sphericity index and density of wheat kernels had practically no influence on the angle of external friction, which indicates that wheat kernels cannot be reliably sorted based on their frictional properties. [ABSTRACT FROM AUTHOR]

Wheat infections caused by fungi of the genus Fusarium decrease yields and have serious economic consequences. The produced mycotoxins have harmful effects on human and animal health. The aim of this study was to develop classification models based on selected textural parameters to distinguish between infected and healthy wheat kernels. The classification accuracy of kernels positioned on the ventral side was determined at 78-100% in the model based on textural parameters from hyperspectral images, and at 95-100% based on images generated by a flatbed scanner. Kernels positioned on the dorsal side were correctly classified in 78-98% based on hyperspectral images, and in 92-100% based on colour images. In the models combining textural parameters from the ventral and dorsal sides of wheat kernels, classification accuracy reached 76-98% in hyperspectral images, and 94-100% in images generated by a flatbed scanner. The imaging technique—flatbed scanner and the ventral side of the kernels provided higher classification accuracy results. The results will contribute to further research aiming to develop models for the determination of fungal chemotypes and/or fungal species based on selected textural features of wheat kernels. [ABSTRACT FROM AUTHOR]

Copyright of Journal of College of Education for Pure Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a hyperspectral imaging system of 866.4−1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky−Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC−NCA−SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC−NCA−SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.

The frictional properties of seeds have been extensively studied, but most researchers have identified only the type of tested friction materials (e.g. concrete, steel, wood) without describing their manufacturing characteristics. The aim of this study was to determine the correlations between the external friction angle of wheat kernels vs. the roughness of the friction plate and the basic physical properties of kernels. The experiment was performed on the kernels of winter wheat var. Jensen obtained directly from spikes and threshed kernels. The external friction angle of every kernel was measured on six friction plates characterized by different surface geometry. Measurements were performed in five replications with a photosensor device which registered the external friction angle of kernels. The basic dimensions and the mass of every kernel were measured, and the results were used to calculate four shape factors for the analyzed kernels. The correlations between the evaluated attributes were determined by one-way analysis of variance (ANOVA) and linear correlation analysis. The roughness of the friction plate significantly influenced the external friction angle of wheat kernels. The average values of the angle of external friction ranged from around 16° to around 24°. The lowest value of the angle of external friction was noted for surface roughness of Ra = 0.93 μm, and the highest value - for surface roughness of Ra = 5.86 μm. Higher values of the angle of external friction were observed in kernels obtained directly from spikes than in threshed kernels. [ABSTRACT FROM AUTHOR]

Purpose: Underlying mechanisms of the beneficial health effects of low glycemic index starchy foods are not fully elucidated yet. We varied the wheat particle size to obtain fiber-rich breads with a high and low glycemic response and investigated the differences in postprandial glucose kinetics and metabolic response after their consumption. Methods: Ten healthy male volunteers participated in a randomized, crossover study, consuming C-enriched breads with different structures; a control bread (CB) made from wheat flour combined with wheat bran, and a kernel bread (KB) where 85 % of flour was substituted with broken wheat kernels. The structure of the breads was characterized extensively. The use of stable isotopes enabled calculation of glucose kinetics: rate of appearance of exogenous glucose, endogenous glucose production, and glucose clearance rate. Additionally, postprandial plasma concentrations of glucose, insulin, glucagon, incretins, cholecystokinin, and bile acids were analyzed. Results: Despite the attempt to obtain a bread with a low glycemic response by replacing flour by broken kernels, the glycemic response and glucose kinetics were quite similar after consumption of CB and KB. Interestingly, the glucagon-like peptide-1 (GLP-1) response was much lower after KB compared to CB (iAUC, P < 0.005). A clear postprandial increase in plasma conjugated bile acids was observed after both meals. Conclusions: Substitution of 85 % wheat flour by broken kernels in bread did not result in a difference in glucose response and kinetics, but in a pronounced difference in GLP-1 response. Thus, changing the processing conditions of wheat for baking bread can influence the metabolic response beyond glycemia and may therefore influence health. [ABSTRACT FROM AUTHOR]

Copyright of Chemical Industry / Hemijska Industrija is the property of Association of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study was to explore the potential of near infrared hyperspectral imaging combined with statistical regression models and neural networks for nondestructive prediction of protein content of wheat kernels. Seventy-nine samples from 11 breeds of wheat kernels were collected. The protein percentage of each sample measured by semimicro-Kjeldahl method was taken as the reference value. After comparing the prediction models of principal components regression (PCR) and partial least squares regression (PLSR) with various pretreatment methods, PLSR preprocessed by zero mean normalization (z score) function of MATLAB was found to obtain better prediction results than other regression models. Based on 10 latent variables of PLSR, the radial basis function (RBF) neural network was applied to improve the prediction, in which the coefficients of determination (R2) were greater than 0.92 for both the calibration set and validation set, while the corresponding RMSE values were 0.3496 and 0.4005, respectively. Therefore, hyperspectral imaging can provide a fast and non-destructive method for predicting the wheat kernels' protein content. [ABSTRACT FROM AUTHOR]

Food contamination may contribute to serious human health problems. In this work, the effect of γ-irradiation 0, 5, 10, and 15 kGy doses on the removal of polycyclic aromatic hydrocarbons from wheat kernels was investigated. Additionally, variations in the contents of 16 congeners on bacterial load and other chemical parameters were studied. Polycyclic aromatic hydrocarbons concentrations were determined by high-performance liquid chromatography followed by fluorescence detection. The effects of γ-irradiation on the concentrations varied for each congener, with the highest irradiation dose being the most effective. Benzo[a]pyrene was reduced by ∼50%, and the total concentration of all congeners decreased dramatically from 154 to 21 µg.kg−1upon 15 kGy irradiation at a dose rate of 8.49 kGy h−1. The total bacterial load was reduced from 2.4 to < 1 log10cfu g−1, while wheat chemical properties were not affected. [ABSTRACT FROM PUBLISHER]

Food micro-structure plays a crucial role in determining the properties of its end products. Micro X-ray Computer Tomography (CT) provides an excellent tool to assess the internal components of food products non-destructively. This paper describes the 3D visualization and quantitative analysis of the internal features of Canadian Amber Durum Wheat kernel after damage caused by sprouting and insect infestation with Cryptolestes ferrugineus . Micro X-ray CT images were reconstructed and processed using image processing algorithms. Imaging techniques such as segmentation and thresholding were used to render visualization modules such as volume rendering, geometric reconstruction, skeletonization and absolute permeability experiment simulation. In addition to visualization, morphometric parameters such as porosity, anisotropy and absolute permeability were determined in a selected region of interest of the sprout damaged and insect infested wheat kernel. Our data revealed that sprouting damage is first evident in the middle section of the sprouted wheat kernel, and associated with greater porosity than the insect damaged kernel. Insect damage occurred on the bottom of the wheat kernel. Compared to sprout damaged kernels, insect damaged kernels had similar anisotropy. X-ray micro CT is a powerful tool for the non-invasive investigation of damage, and microstructure organization in biological materials such as single wheat kernels. [ABSTRACT FROM AUTHOR]

To explore the potential of using radio frequency (RF) energy to control insects or microorganisms in low-moisture granular food crops, wheat kernels were selected as a sample product, and a computer simulation model was developed to study the influence of sample size (wheat kernels filled in plastic container in cuboid shape) and vertical position on RF heating uniformity and to determine the cold and hot spots in the samples. The simulated temperature profile of RF heating was compared with the experimental results to validate the computer model. Temperature uniformity index (TUI) was selected as a criterion to evaluate RF heating uniformity. Results suggested that temperature uniformity of sample depended on sample size and location, where for small sample size, sample placed on either bottom electrode or closed to top electrode would achieve better RF heating uniformity; while for sample in large size, placed in the middle or slightly lower than middle position between the two parallel electrodes achieved better temperature uniformity. Also, a larger sample size had better RF heating uniformity, especially when sample size equaled to the size of the top electrode, indicating that RF energy is more suitable for treating bulk materials. Corner heating was also observed where hot spots located at the corner of samples in cuboid shape, and cold spots depended on sample vertical position. In this study, cold spots were at the center of the top and bottom layers when the sample was placed at the middle position between the two parallel electrodes, at the center of top layer when sample was placed on the bottom electrode, and at the center of bottom layer when sample was placed close to top electrode. Information generated from this study would be essential for developing RF treatment protocol to control insects or microorganisms in low-moisture granular agricultural products. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the influences of region, genotype, harvest year and their interactions on stable carbon, nitrogen and hydrogen isotopic ratio (δ 13 C, δ 15 N and δD) fingerprints in wheat kernels. A total of 270 wheat kernel samples including ten genotypes were collected from three different regions of China during 2011–2013 harvest. Analysis of variance was employed to investigate the effects of region, genotype, harvest year and their interactions on the δ 13 C, δ 15 N and δD. The results showed that the δ 13 C and δ 15 N values in wheat kernels were significantly influenced by the region, genotype, harvest year and their interactions (region × genotype, genotype × year, region × year and region × genotype × year), δD was significantly affected by region, genotype, harvest year and region × year. Region accounted for the largest proportion of the total variation and explained 47.57%, 58.02% and 27.96% for δ 13 C, δ 15 N and δD, respectively. [ABSTRACT FROM AUTHOR]

The insects in internal kernels not only harm the grain directly but also downgrade the market value of grains. It is very important to detect insects inside grain kernels accurately. The image features extracted from the kernel with different postures was a large difference. The automatic posture recognition of wheat kernels was primary to automatically detect insects inside kernels based on the near-infrared hyperspectral imaging technology. The five kernels were placed in a black plastic plate, and were not touched at the same posture. The hyperspectral data cubes of the wheat kernel with three postures were separately acquired at the first 14, 18, and 23 days after the rice weevil oviposition. The three postures of the kernel were crease down, crease up, and crease side. The hyperspectral data cube of the kernel was analyzed by the principal component analysis, and the optimal wavelength was 927.61 nm. According to the coordinate, center of gravity, and other information of the kernel in the optimal wavelength images, the sub-image of a single wheat kernel was automatically segmented from the hyperspectral data cube. There were two possible germ regions at both ends of the kernel, and the roughness degree of the germ region was larger. Thirteen features of the possible germ region, such as texture and invariant moments, were extracted to characterize the roughness degree. Each of the 13 features was used to discriminate the germ region by the principle of the germ region having a larger roughness degree. The recognition accuracy rate was 100% using the fourth invariant moment to identify the germ region of kernels, so the fourth invariant moment was the optimal feature because of the highest recognition accuracy rate. The six texture features and the seven invariant moment features were extracted from the gradient image of the kernel germ region. The thirteen features such as extension ration and symmetry degree of wheat germ region were extracted from the binary image of kernel germ region. The twenty six features were extracted in order to character the differences of the three postures of kernels. The evaluation principle of the feature subset was proposed based on the recognition accuracy of the v-fold cross-validation training model and the number of the selected features. The artificial fish swarm algorithm was applied to the feature selection of the kernel postures. The algorithm selected 13 features that composed the optimal feature space from the 26 features, such as symmetry degree of wheat germ region and complexity. The recognition accuracy of the validation set was up to 93.33%. According to the three-fold standard deviation principle of normal distribution, the classical matter-element matrix and the extensional matter-element matrix were constructed with the feature mean values and the standard deviations. The quantitative fuzzy feature weights were automatically determined based on the maximum deviation fuzzy analysis. The 1 200 samples were selected to train, and the 600 samples were used to validate. The three postures of wheat kernels were recognized by the extension classifier based on the fuzzy weights. The 567 samples in validation set were correctly identified, and the recognition accuracy of the classifier was 94.5%. The experiment showed that the automatic posture recognition of wheat kernels based on the local region features was feasible. [ABSTRACT FROM AUTHOR]

Traditional methods used to detect fungi or mycotoxins are time-consuming and prevent real-time monitoring. In this study, solid-phase microextraction combined with full two-dimensional gas chromatography time-of-flight mass spectrometry was utilized to detect volatile organic compounds (VOCs) produced by fungi during grain infestation predictive F. graminearum PH-1 infestation in wheat. The results show that the VOCs emitted by F. graminearum can distinguish strains at different growth stages. The growth matrices (potato dextrose agar medium and wheat kernels) play a large role in VOC production. The infection of wheat sample F. graminearum showed that a specific relationship between VOCs and the composition of fungal flora, for example, 5-pentyl-cyclohexa-1,3-diene, 3-hexanone, and 1,3-octadiene, was positively correlated with the infection rate of PH-1. In the correlation study of fungal mycotoxins and VOCs, zearalenone produced by F. graminearum was predicted based on the VOCs released. Further analysis determined the correlation of three VOCs, 6-butyl-1,4-cycloheptadiene, hexahydro-3-methylenebenzofuran-2(3 H )-one, and ( E , E )-3,5-octadien-2-one, with zearalenone production, confirming the ability of VOCs as characteristic markers of mycotoxins.

Wheat is one of the main crops in Mediterranean countries, and its cultivation has an important role in the Syrian economy. In Syria, Fusarium head blight (FHB) has not been reported so far. Mycological analysis of 48 samples of wheat kernels collected from cultivation areas with different climatic conditions were performed in 2009 and 2010. Fungal isolates were identified at the genus level morphologically; Fusarium species were characterized morphologically and by species-specific PCR. The most frequent fungal genera found were Alternaria spp. and Cladosporium spp., with frequencies of 24.7% and 8.1%, respectively, while the frequency of Fusarium spp. was 1.5% of kernels. Most frequent Fusarium species were F. tricinctum (30% of all Fusarium isolates), F. culmorum (18%), F. equiseti (14%) and F. graminearum (13%). The mycotoxin production potential of selected Fusarium isolates was assessed by HPLC-MS analysis of rice cultures; chemotyping by PCR was carried out for comparison. All six F. graminearum strains tested produced small amounts (<3 mg/kg) of nivalenol (NIV). All ten F. culmorum strains tested produced large amounts of trichothecenes (>100 mg/kg); four strains produced NIV and six strains produced deoxynivalenol (DON) and 3-acetyl-deoxynivalenol (3Ac-DON). PCR chemotyping lead to an oversimplified picture, because all 3Ac-DON chemotype strains produced more DON than 3Ac-DON; furthermore, the strongest NIV producers produced significant amounts of DON. All tested strains of F. culmorum, F. graminearum, F. pseudograminearum (two strains) and most F. equiseti strains (five of six strains) produced zearalenone. Grains of durum wheat were more frequently colonized by Fusarium spp. than grains of soft wheat. Incidence of Fusarium spp. in irrigated fields was higher than in rainfed fields. The incidence of Fusarium strains producing mycotoxins raises concerns about the risk of Fusarium head blight to Syria and its consequences for public health. [ABSTRACT FROM AUTHOR]

Unground wheat kernels contaminated with 2.09 mg deoxynivalenol (DON) per kg dry matter were stored for up to 56 days at moisture contents of 15, 17.5 and 20% to study the alterations of DON concentration when the wheat was stored either unsupplemented or supplemented with 5 g sodium metabisulfite (Na2S2O5, SBS), 10 g propionic acid (PA) or 5 g SBS plus 10 g PA per kg. SBS addition alone or in combination with PA reduced the DON contamination to 1.2-4.3% of the initial DON concentration while DON concentration of unsupplemented and wheat batches supplemented only with PA varied inconsistently or remained unchanged. The SBS-related DON reduction was paralleled by a concomitant increase in the concentration of the non-toxic reaction product DON sulfonate. In contrast to the unsupplemented wet-stored controls, SBS addition prevented the growth of moulds and yeasts when added alone or in combination with PA. In conclusion, for the conditions examined, the wet preservation of DON-contaminated wheat with SBS seems to be promising as an on-farm detoxification measure. [ABSTRACT FROM AUTHOR]

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops. [ABSTRACT FROM AUTHOR]

Abstract: Physical appearance and kernel morphology significantly affect the grade of a harvested crop in addition to other factors such as test weight, percentage of foreign matter and constituent components. Moisture content of grain can potentially affect the physical appearance and kernel morphology. The objective of this study was to evaluate the effect of moisture content on the classification capability of colour, morphology and textural features of imaged grains. Colour images of individual kernels and bulk samples of three grain types, namely Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat and barley were acquired using a machine vision system. The grain kernels were conditioned to 12%, 14%, 16%, 18% and 20% moisture contents before imaging. Previously developed algorithms were used to extract 123 colour, 56 textural features from bulk sample images and 123 colour, 56 textural, 51 morphological features from individual kernel images. The extracted features were analysed for the effect of moisture content. Statistical classifiers and a back propagation neural network model were used for classifying the grain bulk at different moisture contents. The colour and textural features of bulk grain images were affected by the moisture content more than that of the single kernel images. [Copyright &y& Elsevier]

In 1998–99 and 1999–2000 six trials were conducted to evaluate the effect of fungicides on Fusarium head blight in the field, on infected kernels and deoxynivalenol (DON) concentration in grain. A single application of prochloraz, tebuconazole, epoxiconazole or bromuconazole, applied to durum wheat varieties at the manufacturer's recommended dose at the beginning of anthesis stage, provided good control of the disease when infective pressure in the field was low to medium, and when the main pathogens were F. graminearum and F. culmorum. Kresoxim-methyl showed a low efficacy at controlling the disease. Tebuconazole, prochloraz and bromuconazole were effective at controlling F. graminearum and F. culmorum, while kresoxim-methyl was not effective in reducing Fusarium infected kernels. DON concentration in grain of cultivars inoculated with F. graminearum and F. culmorum was high, averaging 4.2 mg kg−1 (untreated control). Tebuconazole, prochloraz and bromuconazole reduced DON concentration by 43%, while epoxiconazole was ineffective. DON concentration in kernels of naturally infected cultivars was 1.95 mg kg−1, a concentration which exceeds the 1 mg kg−1 maximum level of contamination allowed in the United States. Furthermore prochloraz, bromuconazole and tebuconazole applications, in the naturally inoculated trials, reduced DON concentration from 73% to 96%, while epoxiconazole showed the lowest effectiveness. Moreover, a positive linear correlation between Fusarium infected grains and the DON concentration was observed. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the feasibility of using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics for quantifying deoxynivalenol (DON) in individual wheat kernels. In total, 120 wheat kernels of severely damaged kernels, moderately damaged kernels and asymptomatic kernels (SDKs, MDKs and AKs, respectively) were collected, and the DON content in the individual wheat kernels was analyzed by HPLC-MS/MS. Partial least squares (PLS), support vector machine (SVM) and local PLS based on global PLS scores (LPLS-S) algorithms were employed for building quantification models of DON. The results showed that SDKs and MDKs might contain low or no DON, while AKs could have a high DON content. Comparing the three modeling strategies, LPLS-S using mixed spectra achieved the best performance for kernels with RMSEP of 40.25 mg/kg and RPD of 2.24, which confirmed that NIR-HSI could be a feasible method for monitoring DON in individual kernels and removing highly contaminated kernels prior to food chain entry. • The impact of the kernel orientation (crease facing down or up) on the model results was investigated. • Wheat kernels were classified according to the degree of Fusarium infection by NIR-HSI and PLS-DA. • NIR-HSI combined with chemometrics was employed to quantify DON content in individual kernels. [ABSTRACT FROM AUTHOR]

Rice-weevil (RW) has great harm to wheat kernels, when the RW-damaged wheat kernels are mixed into the sound wheat kernels, the quality of wheat products will be seriously reduced. In this work, a novel method of calibrating the model based on multi-angle near-infrared(NIR) hyperspectral data was proposed to identify sound wheat kernels and RW-damaged wheat kernels. Hyperspectral images on four sides of each wheat kernel were collected. Some commonly used spectral preprocessing methods and two feature extraction algorithms (successive projections algorithm (SPA), and random frog (RF)) were used to combine three machine learning models (partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM))) for modeling analysis. After multivariate data analysis, it was found that standard normal variate (SNV)-SPA-LDA was the best hybrid model. Finally, considering the actual situation, the reliability of the calibrated model was verified by external validation, and the classification results were visualized, in which the accuracy, sensitivity and specificity of the model were 97%, 98% and 96%, respectively. The results indicated that the model calibrated by hyperspectral data from four sides of wheat kernel was reliable. [Display omitted] • NIR-HSI technology was used to classify rice-weevil damaged wheat kernels. • The HSI data on four sides of wheat kernels were analyzed. • The external validation results showed that the calibrated model was reliable. • This work provided a reference for the development of multispectral online detection system. [ABSTRACT FROM AUTHOR]

This work was performed to investigate on the yeast ecology of durum wheat to evaluate the interaction between kernel yeasts and the commercial baker's yeast Saccharomyces cerevisiae during dough leavening. Yeast populations were studied in 39 genotypes of durum wheat cultivated in Sicily. The highest level of kernel yeasts was 2.9 Log CFU/g. A total of 413 isolates was collected and subjected to phenotypic and genotypic characterization. Twenty-three yeast species belonging to 11 genera have been identified. Filobasidium oeirense , Sporobolomyces roseus and Aureobasidium pullulans were the species most commonly found in durum wheat kernels. Doughs were co-inoculated with yeasts isolated from wheat kernels and commercial Saccharomyces cerevisiae , in order to evaluate the interactions between yeasts and the leavening performance. Yeast populations of all doughs have been monitored as well as dough volume increase and weight loss (as CO 2) measured after 2 h of fermentation. The doughs whose final volume was higher than control dough (inoculated exclusively with S. cerevisiae) were those inoculated with Naganishia albida , Vishniacozyma dimennae (118 mL each), and Candida parapsilosis (102 mL). The weight losses were variable, depending on the co-culture used with S. cerevisiae and the values were in the range of 0.08–1.00 g CO 2 /100 g. The kernel yeasts species C. parapsilosis , N. albida , P. terrestris , R. mucilaginosa and V. dimennae deserves future attention to be co-inoculated with the commercial starter S. cerevisiae in order to improve the sensory characteristics of bread. • Ecology of yeast populations associated to Triticum turgidum subsp. durum. • Identification of the yeast species isolated to modern genotypes and old lantraces • Interaction between kernel yeast species and Saccharomyces cerevisiae. • Different volume increases in co-inoculated doughs. [ABSTRACT FROM AUTHOR]

Conventional methods for detecting fungal contamination are generally time-consuming and sample-destructive, making them impossible for large-scale nondestructive detection and real-time analysis. Therefore, the potential of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was examined for the rapid determination of fungal infection on wheat samples in a rapid and nondestructive manner. In addition, the validation experiment of detecting the percent A. flavus infection presented in simulated field samples was carried out. Because the dual separation of HS-GC-IMS could generate massive amounts of three-dimensional data, proper chemometric processing was required. In this study, two chemometric strategies including: (i) nontargeted spectral fingerprinting and (ii) targeted specific markers were introduced to evaluate the performances of classification and prediction models. Results showed that satisfying results for the differentiation of fungal species were obtained based on both strategies (>80%) by the genetic algorithm optimized support vector machine (GA-SVM), and better values were obtained based on the first strategy (100%). Likewise, the GA-SVM model based on the first strategy achieved the best prediction performances ( R 2 = 0.979-0.998) of colony counts in fungal infected samples. The results of validation experiment showed that GA-SVM models based on the first strategy could still provide satisfactory classification (86.67%) and prediction ( R infection presented in simulated field samples at day 4. This study indicated the feasibility of HS-GC-IMS-based approaches for the early detection of fungal contamination in wheat kernels.2 = 0.889) performances for percent A. flavus infection presented in simulated field samples at day 4. This study indicated the feasibility of HS-GC-IMS-based approaches for the early detection of fungal contamination in wheat kernels.

In this study, a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky–Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC–NCA–SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC–NCA–SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable. [ABSTRACT FROM AUTHOR]