Sprouting detection at early stages in individual CWAD and CWRS wheat kernels using SWIR spectroscopy

Reflectance spectra (1,250–2,390 nm) of two classes of western Canadian wheat (CWAD and CWRS) were studied to detect sprouting damage in individual kernels at the early stages of germination. Alpha-amylase activity levels were used as an indicator for the sprouting stage. Partial least squared discriminant analysis (PLSDA) and Logistic regression methods were used to build classification models. The optimal threshold α-amylase activity value for the separation of sprout damage classes was determined according to the area under the ROC curves. The results show that both PLSDA and logistic regression could distinguish kernels with an α-amylase activity larger than 1 SKB unit of activity from those with less enzyme activity. A total classification accuracy of over 91 and 86% was obtained for CWRS and CWAD, respectively.     

Evaluation of the effect of moisture content on cereal grains by digital image analysis

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.     

Phenolic acid composition of sprouted wheats by ultra-performance liquid chromatography (UPLC) and their antioxidant activities

The phenolic acid profiles of flours from two Canadian wheat classes, Canadian Western Red Spring (CWRS) and Canadian Western Amber Durum (CWAD), were investigated using two different extraction mediums and analysed on an ultra-performance liquid chromatography (UPLC) system at different degrees of sprout damage. A sound (non-sprouted) control sample as well as two different sprouted sub-samples, derived from different germination protocols of the control, were prepared for both the CWAD and CWRS. Free phenolic acids were extracted from the ground whole wheat meal using three repetitive 80% ethanol extractions. Bound phenolic compounds were subsequently released from the residue by alkaline hydrolysis followed by triplicate extraction with diethyl ether:ethyl acetate (1:1, v/v). Twelve phenolic acid standards were clearly resolved and quantified using a short 5 min elution gradient. Seven phenolic acids (4-hydroxybenzoic, vanillic, caffeic, syringic, p-coumaric, ferulic and sinapic) were detected in the CWRS and CWAD alcoholic and alkaline extracts. Syringic acid was the main compound in the free phenolic alcoholic extracts of the wheat meal representing 77.0% and 75.3% of the total amount of detected free phenolic compounds for CWRS and CWAD, respectively. However, the major released phenolic compound detected in the alkaline hydrolysed extracts was ferulic acid accounting for 72.3% and 71.0% for CWRS and CWAD respectively total bound phenolics. During germination, syringic acid levels rose as the length of germination time increased, resulting in the increase in total phenolic compound and antioxidant activity of the sprouted wheat flours. There was an increase in total phenolic compounds and the antioxidant activity of the alcoholic extracts from the CWRS and CWAD wheat flours as the germination time was extended. As a result, the sprouted wheats exhibits better nutritional properties than un-germinated wheat and could be used to improve the nutrition value in food products.     

Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples

Wheat classes at different moisture levels need to be identified to accurately segregate, properly dry, and safely store before processing. This paper introduces a new method using a near infrared (NIR) hyperspectral imaging system (960–1,700 nm) to identify five western Canadian wheat classes (Canada Western Red Spring (CWRS), Canada Western Extra Strong (CWES), Canada Western Red Winter (CWRW), Canada Western Soft White Spring (CWSWS), and Canada Western Hard White Spring (CWHWS)) and moisture levels, independent of each other. The objectives of this research also included identification of each wheat class at specific moisture levels of 12, 14, 16, 18, and 20%. Bulk samples of wheat were scanned in the 960–1,700 nm wavelength region at 10 nm intervals using an Indium Gallium Arsenide (InGaAs) NIR camera. Spectral feature data sets were developed by calculating relative reflectance intensities of the scanned images. Principal components analysis was used to generate scores images and loadings plots. The NIR wavelengths in the region of 1,260–1,360 nm were important based on the loadings plot of first principal component. In statistical classification, the linear and quadratic discriminant analyses were used to classify wheat classes giving accuracies of 61–97 and 82–99%, respectively, independent of moisture contents. It was also found that the linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) could classify moisture contents with classification accuracies of 89–91 and 91–99%, respectively, independent of wheat classes. Once wheat classes were identified, classification accuracies of 90–100 and 72–99% were observed using LDA and QDA, respectively, when identifying specific moisture levels. Spectral features at key wavelengths of 1,060, 1,090, 1,340, and 1,450 nm were ranked at top in classifying wheat classes with different moisture contents. This work shows that hyperspectral imaging techniques can be used for rapidly identifying the wheat classes even at varying moisture levels.     

Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging

Insect damage in wheat adversely affects its quality and is considered one of the most important degrading factors in Canada. The potential of near-infrared (NIR) hyperspectral imaging for the detection of insect-damaged wheat kernels was investigated. Healthy wheat kernels and wheat kernels visibly damaged by Sitophilus oryzae, Rhyzopertha dominica, Cryptolestes ferrugineus, and Tribolium castaneum were scanned in the 1000–1600 nm wavelength range using an NIR hyperspectral imaging system. Dimensionality of the acquired hyperspectral data was reduced using multivariate image analysis. Six statistical image features (maximum, minimum, mean, median, standard deviation, and variance) and 10 histogram features were extracted from images at 1101.69 and 1305.05 nm and given as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis) for classification. Linear discriminant analysis and quadratic discriminant analysis classifiers correctly classified 85–100% healthy and insect-damaged wheat kernels.     

Antioxidant and free radical scavenging activities of whole wheat and milling fractions

The effects of milling on the phenolic content and antioxidant capacity of two wheat cultivars, namely CWAD (Canadian Western Amber Durum; Triticum turgidum L. var. durum) and CWRS (Canadian Western hard red spring; Triticum aestivum L.) were studied. The milling of wheat afforded several fractions, namely bran, flour, shorts and feed flour. In addition, semolina was the end-product of durum wheat milling. Among different milling fractions the bran had the highest phenolic content while the endosperm possessed the lowest amount and this was also reflected in free radical and reactive oxygen species (ROS) scavenging capacity, reducing power and iron (II) chelation capacity of different milling fractions in the two cultivars. This study demonstrated the importance of bran in the antioxidant activity of wheat, hence consumption of whole wheat grain may render beneficial health effects.     

Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging

Fusarium damage in wheat may reduce the quality and safety of food and feed products. In this study, the use of hyperspectral imaging was investigated to detect fusarium damaged kernels (FDK) in Canadian wheat samples. More than 5,200 kernels, representing seven major Canadian wheat classes, with varying degree of infection symptoms ranging from sound through mild to severe were imaged in the visible-NIR (400–1,000 nm) wavelength range. Partial least squares discriminant analysis (PLS-DA) was used to segregate kernels into sound and damaged categories based on kernel mean spectra. A universal PLS-DA model based on four wavelengths was able to detect FDK in all seven classes with an overall accuracy of 90 % and false positives of 9 %.     

Wheat-Legume Composite Flour Quality

Wheat-legume composite flours were produced by blending Canada Western Extra Strong (CWES) and Canada Western Red Spring (CWRS) wheat with varying amounts of three legume proteins. Legume protein addition produced breads with lower specific loaf volume, coarser crumb and firmer texture, and cooked white-salted noodles with greater compression stress and less cutting stress than the wheat controls. The CWES wheat compensated for the negative baking effects of the legume proteins as much as the CWRS wheat. End-use quality did not change at 2% soybean protein addition. Yellow pea protein produced the greatest quality changes, followed by chickpea and soybean protein.     

Wheat Class Identification Using Thermal Imaging

Wheat classes and varieties are determined by trained professionals in the laboratory. Several approaches have been made using machine vision technology for nondestructive and online identification of wheat classes, but the performance has been poor and inconsistent. An infrared thermal imaging system was developed to identify eight western Canadian wheat classes. Samples of 20 g each of wheat at 14% moisture content (wet basis) spread in a 100 × 100 mm monolayer were heated by a plate heater (maintained at 90 °C) placed at a distance of 10 mm from the grain layer. The surface temperatures of the top surface of the grain bulk were imaged before heating, after heating for 180 s, and after cooling for 30 s using an infrared thermal camera (n = 100). Temperature rise (after heating) and drop (after cooling) were significantly different for wheat classes (α = 0.05). The temperature rise ranged from 14.94 (Canada Western Red Spring) to 17.80 °C (Canada Prairie Spring Red), and the drop ranged from 3.67 (Canada Western Extra Strong) to 4.42 °C (Canada Prairie Spring Red) after heating for 180 s and cooling for 30 s, respectively. The rate of heating and cooling was negatively correlated with protein content of wheat (r = −0.63 for heating, r = −0.65 for cooling) and true density (r = −0.67 for heating, r = −0.71 for cooling), and positively correlated with grain hardness (r = +0.41 for heating, r = +0.53 for cooling). Overall classification accuracies of an eight-class model, red-class model (four classes), white-class model (four classes), and pairwise (two-class model) comparisons using a quadratic discriminant method were 76%, 87%, 79%, and 95%, and 64%, 87%, 77%, and 91% using bootstrap and leave-one-out validation methods, respectively. There were several misclassifications in the four and eight-class models. Thermal imaging approach may have potential to develop classification methods for two classes, which are similar and difficult to distinguish by visual inspection; however, the effect of growing season, defects, and kernel size must be considered while developing such methods. The temperature rise after heating and drop after cooling were tested for Canada Western Red Spring wheat at three moisture levels (11%, 14%, and 17% wet basis; n = 20). There were no significant differences (α = 0.05) in the mean temperature rise and temperature drop between 11%, 14%, and 17% moisture samples.     

Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging

Fusarium head blight is a fungal disease that affects the world’s small grains, such as wheat and barley. Attacking the spikelets during development, the fungus causes a reduction of yield and grain of poorer processing quality. Secondary metabolites that often accompany the fungus, such as deoxynivalenol (DON), are health concerns to humans and livestock. Conventional grain inspection procedures for Fusarium damage are heavily reliant on human visual analysis. As an inspection alternative, a near-infrared (NIR) hyperspectral image system (1000–1700 nm) was fabricated and applied to Fusarium-damaged kernel recognition. An existing extended visible (400–1000 nm) system was similarly used. Exhaustive searches were performed on the 144 and 125 wavelength pair images that, respectively, comprised the NIR and visible systems to determine accuracy of classification using a linear discriminant analysis (LDA) classifier. On a limited set of wheat samples the best wavelength pairs, either with visible or NIR wavelengths, were able to discriminate Fusarium-damaged kernels from sound kernels, both based on visual assessment, at an average accuracy of approximately 95%. Accuracy dropped off substantially when the visual contrast between the two kernel conditions became imperceptible. The NIR region was slightly better than the visible region in its broader array of acceptable wavelength pairs. Further, the region of interest (ROI) defined as the whole kernel was slightly better than ROIs limited to either a portion of the endosperm or the germ tip. For the NIR region, the spectral absorption near 1200 nm, attributed to ergosterol (a primary constituent in fungi cell membranes), was shown to be useful in spectral recognition of Fusarium damage.     

基于差分图像边界距离的粮粒孔洞自动检测

粮粒孔洞的自动检测是近红外高光谱图像技术检测粮粒内部害虫中的一个关键问题。提出基于差分图像边界距离的粮粒孔洞自动检测方法,该方法通过求取粮粒(内部)轮廓与阈值分割后二值图像的差分,若差分图像中的目标与粮粒边界的最远距离大于某个阈值时,则该目标应判别为边界(内部)孔洞。用米象的幼虫、蛹和成虫3个侵染阶段粮粒的900帧近红外图像进行训练,用450帧近红外图像进行检验,结果表明该方法不仅可以判断粮粒是否存在孔洞,还能检测出孔洞的数量及形态,其中边界孔洞和内部孔洞的识别率分别为97.33%和95.56%,证实了基于差分图像边界距离的粮粒孔洞检测方法是可行的。     

Total phenolic content,antioxidant activity,and type II diabetes related functionality of traditionally processed ox-eye bean [Mucuna gigantea (Willd) DC.] seeds: An Indian underutilized food legume

The methanolic extract of ox-eye bean [Mucuna gigantea (Willd) DC.] contained total free phenolic content of 14.80±1.28 g catechin equivalent/100 g extract dry matter. Encouraging levels of ferric reducing/antioxidant power (FRAP, 1,023 mmol Fe[II]/mg extract), inhibition of β-carotene degradation (59.35%) and radical scavenging activity against DPPH (72.12%) and superoxide (43.11%) were exhibited by the raw samples. Further, it also recorded 82.17% of α-amylase and 91.26% of α-glucosidase enzyme inhibition characteristics. Sprouting+oil-frying caused a apparent increase on the total free phenolic content and also significant improvement on the antioxidant and free radical scavenging capacity of methanolic extract, while soaking+cooking as well as open-pan roasting treatments showed diminishing effects. Moreover, inhibition of α-amylase and α-glucosidase enzyme activities was declined to 22.82 and 45.47%, respectively during sprouting+oilfrying treatment, which are more desirable for the dietary management of type II diabetic patients.     

QUALITY ATTRIBUTES OF CANADIAN HARD WHITE SPRING WHEAT

Quality characteristics of five pilot‐scale milled Canadian hard white spring wheats were compared to a No.1 grade commercial composite Canada Western Red Spring (1CWRS) wheat. One metric ton of samples was milled on the Canadian International Grains Institute pilot Buhler mill (Buhler AG, Uzwil, Switzerland) into straight‐grade (SG), 85% and whole wheat flours. At the SG extraction level, the white wheats with their lighter colored seed coats had improved milling yields (up to 2.6%) and lower ash (0.01–0.09%) than the 1CWRS control wheat. Majority of white wheat flours had higher protein contents than the 1CWRS control flours for all flour extractions. Based on dough rheological properties of the flours, three of the white wheats (Kanata, Snowbird and BW 275 ) were equal to or better than the red 1CWRS control for nearly all farinograph and mixograph parameters at all flour extractions other than farinograph absorption. Two of the white wheat lines (RL 4863 and RL 4858 ) had excessively weak and overly strong dough properties, respectively. Evaluation of pan bread, bagels and tortillas showed that white wheats generally produced end‐products that were comparable or superior to 1CWRS and that their most significantly positive quality compared to 1CWRS was their substantially lighter colored end‐products.     

基于高光谱成像技术的小麦籽粒品种鉴别方法研究

为了更精确地鉴别小麦品种,实现小麦品种快速、无损、有效、稳定的鉴别。利用高光谱成像系统采集6个小麦品种籽粒光谱和图像信息,提取小麦籽粒胚、胚乳、胚和胚乳混合部位的光谱,采用不同的预处理方法对原始光谱进行处理,利用竞争性自适应重加权算法(CARS)和连续投影算法(SPA)提取特征波长,基于全波长和特征波长建立线性判别分析(LDA)、支持向量机(SVM)和K最邻近(KNN)模型,筛选出最佳的籽粒部位光谱、预处理方法和特征波长提取方法;在此基础上,分析光谱信息、形态特征及二者结合信息对小麦品种的鉴别效果。结果表明,基于34个特征波长光谱信息结合形态特征建立的LDA模型效果最佳,其训练集和预测集的正确判别率分别为91.3%和86.0%。基于高光谱成像技术进行小麦品种鉴别是可行和有效的。     

Improving wheat for bread and tortilla production by manipulating glutenin‐to‐gliadin ratio

Gluten, starch, water soluble material, and glutenin‐rich and gliadin‐rich proteins were extracted from three Canadian wheat cultivars representing the Canada Western Red Spring (CWRS) (cv Roblin), Canada Western Extra Strong (CWES) (cv Glenlea) and Canada Prairie Spring (CPS) (cv AC Crystal) classes having glutenin‐to‐gliadin (Glu:Gli) ratios of 0.70, 0.75 and 0.85 respectively, all giving the same high‐molecular‐weight glutenin subunit score (Glu‐1 score) of 10. The resulting fractions were reconstituted to produce 18 mixtures of flour components, representing all combinations of Glu:Gli ratio and protein content observed in the original three flours. Dough rheological properties and baking (bread and tortilla) performance were determined using small‐scale techniques. Within any of the cultivars, increasing the Glu:Gli ratio in a reconstituted dough system had significant effects on dough and end‐use properties, causing increases in mixograph development time (MDT), maximum resistance (Rmax), pan bread loaf volume, tortilla dough maximum resistance and cooked tortilla puncture force. The CWRS wheat Roblin, proved to be best suited for pan bread at higher protein content and higher Glu:Gli ratio, and also produced a high protein tortilla of large diameter at a Glu:Gli ratio of 0.70. The CPS flour, AC Crystal, was good for making tortillas at protein contents of 110–130 g kg⁻¹ and at its original ratio of 0.85. The CWES wheat Glenlea, did not perform as well in bread or tortilla‐making but in its role as a blending wheat, altering the Glu:Gli ratio of Glenlea to 0.70 may have an advantage by lowering mixing time without compromising baking properties. Manipulating the Glu:Gli ratio may make a wheat cultivar suitable for a particular end‐product. For the Department of Agriculture and Agri‐Food, Government of Canada, © Minister of Public Works and Government Services Canada 2005. Published for SCI by John Wiley & Sons, Ltd.     

基于CNN神经网络的小麦不完善粒高光谱检测

利用高光谱成像技术对小麦不完善粒进行无损检测。以932个小麦为样本,其中正常粒样本486个、破损粒样本170个、虫蚀粒样本149个及黑胚粒样本127个为研究对象,通过高光谱图像采集系统采集样本的光谱信息,然后从每个样本的116个波段中选取30个波段,建立基于深度学习的卷积神经网络(convolutional neural networks,CNN)模型。实验中的CNN采用2个卷积层,第1层采用大小为3×3的32个卷积核,第2层采用大小为5×5的64个卷积核,池化层采用最大池,激活函数采用修正线性单元,为避免过拟合,在全连接层后面接入dropout层,参数设置为0.5,其他卷积参数均为默认值,得到校正集总识别率为100.00%,测试集总识别率为99.98%。最后,以支持向量机(support vector machine,SVM)为基线模型进行对比,从116个波段中选取90个波段进行建模,测试集总识别率为94.73%。通过实验对比可以看出,CNN模型比SVM模型识别率高。研究表明CNN模型能够实现对小麦不完善粒的准确、快速、无损检测。     

As the number falls,alternatives to the Hagberg–Perten falling number method: A review

This review examines the application, limitations, and potential alternatives to the Hagberg–Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.     

Control of wheat α-amylase using inhibitors from cereals

A survey of 46 varieties of cereals and related species (including 27 different species from the Poaceae) indicated the presence of a strong inhibitor of wheat α-amylase in all seven Hordeum species tested. Rye contained a lower level of inhibitor activity, but the other species contained insignificant amounts of wheat α-amylase inhibitor activity. The partially purified barley inhibitor was most effective in inhibiting wheat α-amylase activity at high pH. The addition of chromosome 2 of barley to wheat (Chinese Spring addition line 2H) resulted in an apparent increase in the molecular weight of the α-amylase produced during germination. This was probably due to the formation of a complex between the inhibitor encoded by the asi gene on chromosome 2 of barley and wheat α-amylase 2. Breeding of wheat with the barley inhibitor gene may reduce the impact of the high α-amylase levels that result from pre-harvest sprouting in wheat.     

Synchrotron based infrared imaging study of compositional changes in stored wheat due to infection with Aspergillus glaucus

Fungi are one of the serious causes of spoilage in stored grain including wheat. Aspergillus spp. is one of the most common storage fungi that spoils stored wheat. The damage caused by fungi adversely affects the quality of wheat and reduces its nutritional composition. Present methods of analysing chemical composition of wheat and other cereals using wet chemistry are destructive and use bulk grain and thus rely on bulk analysis. Grains, similar to other biological materials, are highly non-homogenous, hence, bulk analysis which causes damage to intrinsic structure of kernels, cannot be used for characterization of single kernels and studying the compositional distribution within a single kernel. In the present work, synchrotron based high resolution infrared imaging was used to study the compositional changes in stored wheat due to fungal damage. Clear differences between healthy and damaged wheat endosperm spectra were observed at peaks around 1740, 1595, and 1250 cm⁻¹. The difference in the absorption of infrared radiation was likely caused due to reduced lipid (1740 cm⁻¹), lignin (1595 cm⁻¹) and cellulose (1250 cm⁻¹) content in damaged wheat endosperm.