Western Canadian Wheat Classes Research Articles

In vitro assessment of the starch digestibility of western Canadian wheat market classes and cultivars

The objective of the study was to measure the effect of wheat market class and cultivar on starch digestibility using an in vitro model that mimics the chicken digestive tract and relate it to grain characteristics. The study evaluated 18 wheat cultivars from eight western Canadian wheat classes and, each cultivar was replicated four times. Samples were subjected to gastric and small intestine (SI) digestion phases and each sample was assayed in triplicate; glucose release was measured in SI phase. Starch granule distribution, amylose, total starch, crude protein (CP), ash, and non-starch polysaccharides (NSP) were analyzed in all wheat samples. Small intestinal phase times of 15, 60, and 120 min were chosen to approximate digestion in the terminal duodenum, jejunum, and ileum. Starch digestibility of wheat classes ranged as follows: 15 min — 33.1% to 49.1%, 60 min — 80.2% to 93.3%, and 120 min — 92.4% to 97.6%. Starch digestibility positively correlated with CP, ash, NSP, and proportion of large granules, whereas it negatively correlated with total starch, and proportion of small and medium granules. In conclusion, market class and cultivar of western Canadian wheat affects both rate and extent of starch digestibility and it is related to various grain characteristics.

Starch digestibility and apparent metabolizable energy of western Canadian wheat market classes in broiler chickens.

Wheat is the primary grain fed to poultry in western Canada, but its nutritional quality, including the nature of its starch digestibility, may be affected by wheat market class. The objectives of this study were to determine the rate and extent of starch digestibility of wheat market classes in broiler chickens, and to determine the relationship between starch digestibility and wheat apparent metabolizable energy (AME). In vitro starch digestion was assessed using gastric and small intestinal phases mimicking the chicken digestive tract, while in vivo evaluation used 468 male broiler chickens randomly assigned to dietary treatments from 0 to 21 d of age. The study evaluated 2 wheat cultivars from each of 6 western Canadian wheat classes: Canadian Prairie Spring (CPS), Canadian Western Amber Durum (CWAD), CW General Purpose (CWGP), CW Hard White Spring (CWHWS), CW Red Spring (CWRS), and CW Soft White Spring (CWSWS). All samples were analyzed for relevant grain characteristics. Data were analyzed as a randomized complete block design and cultivars were nested within market class. Pearson correlation was used to determine relationships between measured characteristics. Significance level was P ≤ 0.05. The starch digestibility range and wheat class rankings were: proximal jejunum - 23.7 to 50.6% (CWHWSc, CPSbc, CWSWSbc, CWRSab, CWGPa, CWADa); distal jejunum - 63.5 to 76.4% (CWHWSc, CPSbc, CWSWSbc, CWRSab, CWGPa, CWADa); proximal ileum - 88.7 to 96.9% (CWSWSc, CPSbc, CWHWSbc, CWRSb, CWGPb, CWADa); distal ileum - 94.4 to 98.5% (CWSWSb, CWHWSb, CPSb, CWRSab, CWGPab, CWADa); excreta - 98.4 to 99.3% (CPSb, CWRSb, CWHWSb, CWSWSab, CWGPab, CWADa). Wheat class affected wheat AMEn with levels ranging from 3,203 to 3,411 kcal/kg at 90% DM (CWRSc, CWSWSc, CPSb, CWGPb, CWADa, CWHWSa). Significant and moderately strong positive correlations were observed between in vitro and in vivo starch digestibility, but no correlations were found between AME and starch digestibility. In conclusion, rate and extent of starch digestibility and AME were affected by western Canadian wheat class, but starch digestibility did not predict AME.

Characterization of the Influence of Moisture Content on the Morphological Features of Single Wheat Kernels Using Machine Vision

The objective of this study was to quantify changes in morphological features of kernels of western Canadian wheat classes caused by moisture increase using a machine vision system. One hundred single wheat kernels for each of eight western Canadian wheat classes were successively conditioned from 12% to 20% (wet basis) moisture contents using potassium hydroxide (KOH) concentrations which regulated relative humidity. A digital camera of 7.4 7.4-m pixel resolution with an inter-line transfer charge-coupled device (CCD) image sensor was used to acquire images of single kernels. A machine vision algorithm developed at the Canadian Wheat Board Centre for Grain Storage Research, University of Manitoba, was implemented to extract seven morphological features (area, perimeter, major axis length, minor axis length, maximum radius, minimum radius, and mean radius) from the wheat kernel images. All the seven features of Canada Western Red Spring, Canada Western Amber Durum, Canada Prairie Spring White, Canada Prairie Spring Red, Canada Western Extra Strong, Canada Western Red Winter, Canada Western Hard White Spring, and Canada Western Soft White Spring wheat kernels were significantly (a = 0.05) different as the moisture content increased from 12% to 20%. All seven features showed a linearly increasing trend with an increase in moisture content.

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.

Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples

Wavelet texture analysis was used for classification of eight Western Canadian wheat classes using near infrared hyperspectral imaging of bulk samples. Hyperspectral images (slices) at 10 nm interval were acquired in the wavelength range 960–1700 nm. From each slice of hyperspectral data, central 256 × 256 pixels were analyzed using a wavelet transformation at five levels (resolutions) employing Daubechies-4 wavelets. Energy and entropy features were computed at each level in the horizontal, vertical, and diagonal orientations. Additionally, rotational invariant features were obtained by adding features from all three orientations. Based on a stepwise linear discriminant analysis, top 100 features were selected and used for classification of wheat classes. Linear and quadratic statistical classifiers and a standard back propagation neural network (BPNN) classifier were used for classification using top 10–100 features. In another approach, principal component (PC) score images obtained from hypercubes were used for wavelet analysis and classification. The wavelet energy features contributed more than the entropy features in class discrimination. The rotational invariant features were more important than the features at any individual orientation. The wavelet texture features at finer resolutions were more important than those at the coarser resolutions. The highest average classification accuracy of eight classes was 99.1% when top 90 features were used for classification in a linear discriminant classifier. The BPNN had the highest average classification accuracy of 92.1% using the top 70 features. Using wavelet features from score images, the PC2 features gave the highest classification accuracy (79.9%). The wavelet texture features of hyperspectral images can be used effectively for classification of wheat classes of Western Canada.

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.

Comparison of illuminations to identify wheat classes using monochrome images

Wheat class identification using machine vision is an objective method which can be used for online testing to automate handling, binning and shipping operations in grain industry. The efficiencies of a monochrome camera-based vision system with three different illuminations (incandescent light (IL), fluorescent ring light (FRL), fluorescent tube light (FTL)) were determined to identify eight western Canadian wheat classes at four moisture levels (11%, 14%, 17% and 20%). The monochrome images of the bulk wheat samples were acquired at each moisture level (3 illuminations × 8 classes × 4 moisture contents × 100 replications = 9600 images). A linear discriminant function was used for the classification of wheat samples using 32 gray level textural features extracted from the monochrome images. The mean gray values of the wheat classes were in the ranges of 75–103, 73–115, and 107–143 for IL, FRL and FTL, respectively. The mean gray values of wheat classes were significantly different within each illumination and between different illuminations ( α = 0.05). Mean gray value was the highest for FTL and the lowest for IL illumination. The moisture content of the wheat samples had significant effect on the mean gray values. The overall classification accuracies were 90%, 81% and 96% for IL, FRL and FTL, respectively, when all the wheat classes were at the same moisture levels. It was 66%, 53% and 85% for IL, FRL and FTL, respectively, when the wheat classes were at different moisture levels. The classification accuracies of a 2-stage classification system for the classes with different moisture levels were 68%, 56% and 90% for IL, FRL and FTL, respectively.

Technical Note: Wheat Class Identification Using Thermal Imaging: A Potential Innovative Technique

An infrared thermal imaging system was assessed for its capability to identify eight western Canadian wheat classes. Twenty-gram wheat samples at 14% moisture content (wet basis) were spread in a 100 100 mm monolayer and heated by a plate heater (maintained at 90C) 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 a thermal camera (n = 100 samples in each class). Temperature rise (after heating) and drop (after cooling) were significantly different for wheat classes (a = 0.05). Wheat samples were classified with twelve derived thermal features using statistical classifiers. Overall classification accuracies of an eight-class model, red class model (four classes), white class model (four classes), and pairwise comparison (two-class model) using a quadratic discriminant method were 76%, 87%, 79%, and 95%, respectively. The thermal imaging approach may have potential to develop class identification methods in wheat handling facilities. However, further investigations are required to determine the performance of this system for wheat samples mixed with defects, with different moisture contents, and grown in different locations and years.

Wheat class identification using monochrome images

Wheat class identification by bulk sample analysis using a machine vision method would be helpful for automation of grain handling, binning and shipping operations in grain elevators. A machine vision system with a monochrome camera was used to identify eight western Canadian wheat classes at four moisture levels (11%, 14%, 17% and 20% wet basis) by bulk sample analysis ( n=100 images for each group of samples). Grayscale images (1024×768 pixels) of the grain bulk were captured by the monochrome camera, and stored on a data acquisition system. Algorithms were developed to extract 32 textural features automatically from the grayscale images. The mean gray values of the western Canadian wheat classes ranged between 106 and 143, and it was the highest for Canada Prairie Spring Red and the lowest for Canada Western Extra Strong and Canada Western Red Winter. The mean gray values of the wheat samples were significantly higher at 17% moisture content and lower at 11% moisture content among the tested moisture levels ( α=0.05). The overall classification accuracies of a quadratic discriminant function were 93.8%, 92.5%, 92.0% and 94.4% when the wheat classes were at 11%, 14%, 17% and 20% moisture contents, respectively. Similarly, the accuracies of a linear discriminant function were 96.1%, 95.0%, 95.4% and 96.3% at 11%, 14%, 17% and 20% moisture contents, respectively. When the wheat classes were identified irrespective of moisture levels (images of the four moisture level grains in each class were mixed together), the accuracy was 89.8% and 85.4% for quadratic and linear discriminant functions, respectively. A monochrome image analysis system has the potential to use for online identification of classes in wheat handling facilities. However, further research is required to determine the performance of the developed method for impurities in bulk grain such as foreign material and dockage.