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Healthy and fungal-damaged wheat kernels infected by the species of storage fungi, namely Penicillium spp., Aspergillus glaucus, and A. niger, were scanned using a short-wave near-infrared hyperspectral imaging system in the 700–1100 nm wavelength range and an area scan colour camera. A multivariate image analysis was used to reduce the dimensionality of the hyperspectral data and to select the significant wavelength using principal component analysis. Wavelength 870 nm, which corresponded to the highest factor loading of first principal component, was considered to be significant. Statistical and histogram features from the 870 nm wavelength image were selected and used as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis). From the colour images, a total of 179 features (123 colour and 56 textural) were extracted and the top features selected from these features were used as input to the statistical classifiers. The linear discriminant analysis classifier correctly classified 97.3–100.0% healthy and fungal-infected wheat kernels, using the combined hyperspectral image features and the top ten features selected from 179 colour and textural features of the colour images as input. [ABSTRACT FROM PUBLISHER]

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns. [ABSTRACT FROM AUTHOR]

Abstract: Quantitative characterization of pore topology inside grain bulks is necessary to predict the air traverse time and the cooling or fumigation pattern for the design of storage management strategies. Quantification of 3D microarchitecture of the inter-granular airspace in the grain bulks can also contribute to the development of predictive models of insect movement and for designing acoustic systems for insect infestation detection. In this study, inter-connected 3D array of void spaces was characterized by geometrical quantities such as specific surface area, pore throat size and nodal pore volume. These features were obtained from a 13.1cm×13.1cm×5cm volume of wheat and pea bulks. The grain bulks were imaged using a high resolution X-ray computed tomography system at 200μm resolution. The spatial distributions were computed based on 3D medial axis analysis of the void space in the images using 3DMA-Rock software and a high performance Polaris computer. The other features calculated were medial axis tortuosity, throat surface area and porosity from the 3D images. Characterization of pore throat network provides reliable observation for facilitating realistic prediction of permeability and the nature of air and gas distribution inside grain bulks. [Copyright &y& Elsevier]

Abstract: Sprouted wheat kernels adversely affect bread and pasta making quality, thus lowering the grade and value to millers, bakers and grain dealers. In this study, the potential of using soft X-ray system in detecting the sprouted wheat kernels was evaluated. Sprouted kernels were produced by germinating seeds. Both the sprouted and healthy samples were X-rayed using a soft X-ray system. White specks were observed in all the sprouted kernel X-ray images. Algorithms were written to extract 55 image features including gray level modeling and histogram from the scanned images. Identification of sprouted and healthy kernels was determined using statistical and neural network classifiers. A four-layer back propagation neural network model correctly classified 90% and 95% of the sprouted and healthy kernels, respectively. Statistical classifier correctly identified 87% and 92% of the sprouted and healthy kernels, respectively. [Copyright &y& Elsevier]

Differentiation of wheat classes is one of the important challenges to the Canadian grain industry. Even though some wheat classes may look similar, their chemical composition and consequently the end-product quality can vary significantly. Visual differentiation of wheat classes suffers from disadvantages such as inconsistency, low throughput, and labour intensiveness. A near-infrared (NIR) hyperspectral imaging system was used to develop classification models to differentiate wheat classes grown in western Canada. Wheat bulk samples were scanned in the wavelength region of 960–1700nm at 10nm intervals using an InGaAs NIR camera. Seventy-five relative reflectance intensities were extracted from the scanned images and used for the differentiation of wheat classes using a statistical classifier and an artificial neural network (ANN) classifier. Classification accuracies were 100% in classifying Canada Prairie Spring Red (CPSR), Canada Western Red Winter (CWRW), and Canada Western Soft White Spring (CWSWS) wheat classes and >94% for the other wheat classes (Canada Western Extra Strong (CWES), Canada Western Hard White Spring (CWHWS), Canada Western Red Spring (CWRS), Canada Prairie Spring White (CPSW) and Canada Western Amber Durum (CWAD)) using Linear Discriminant Analysis (LDA) with a leave-one-out cross-validation method. In Quadratic Discriminant Analysis (QDA) with a leave-one-out cross-validation method, the classification accuracies were >86% for all wheat classes. The overall classification accuracies of 60% training–30% testing–10% validation (referred to as 60-30-10) and 70% training–20% testing–10% validation (referred to as 70-20-10) ANN models were above 90% for independent validation sets using three-layer standard and Wardnet back-propagation neural network architectures. [Copyright &y& Elsevier]

The predicted moisture data, using the three-dimensional (3D) single wheat drying model (Part I) considering actual kernel geometry, were compared with two published semi-empirical thin-layer wheat drying models, and one published axisymmetric heat and mass transfer model for a single wheat kernel. The published axisymmetric model assumed a uniform initial moisture distribution within the whole kernel of ellipsoidal geometry and a single value of the water diffusion coefficient. Our model predictions agreed well with the observations obtained from the published models. Further, our model can predict the moisture distribution pattern within individual wheat components with drying time. Moisture and temperature distributions inside the wheat kernel and the effect of drying temperature on wheat quality were explained by calculating the Biot and Lewis numbers. High Biot numbers for moisture transfer (0.89×106–3.04×106 for endosperm and 1.98×106–3.37×106 for germ) indicated that moisture transfer inside the wheat structural components was governed by high internal resistance, whereas low Biot numbers for heat transfer (0.12–0.20 for endosperm and 0.02–0.10 for germ) indicated that heat transfer was controlled by external resistance and, hence, temperature within the wheat kernels was uniform at any given time during drying. Lewis numbers indicated that the rate of heat transfer was very high compared to moisture transfer in both the endosperm and the germ. Sensitivity analysis was performed for selecting proper model parameters which indicated that values of water diffusion coefficients in the germ and endosperm are the most sensitive parameters for a grain drying process. [Copyright &y& Elsevier]

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]

X-ray computed tomography (CT) is a technique that uses X-ray images to reconstruct the internal microstructure of objects. A high-resolution X-ray CT system with a resolution of 120μm was used to scan slices of grain bulks. Grain bulks of wheat, barley, flax seed, peas and mustard were scanned along horizontal and vertical directions. The X-ray CT images were analysed to explain the airflow resistance difference along the horizontal and vertical directions of grain bulks. Total airspace, airpath distribution and size of airpaths were determined from the images. Morphological information of the airpaths from the tomographic images showed that the size and number of airpaths vary between horizontal and vertical directions of many grain bulks. Airpath area and airpath lengths along the horizontal direction were 100% higher than in the vertical direction for wheat, barley and flax seed bulks, where as for pea and mustard bulks, they were only 30% higher in the horizontal direction than in the vertical direction. The numbers of airpaths along the horizontal direction for wheat, barley and flax seed, respectively, were 92%, 145% and 187% more than along the vertical direction. In pea and mustard bulks, however the increase in the number of airpaths was only 28% and 17%. The ratio of total airspace area to the total number of airpaths in the grain bulk is the best predictor for the difference in the airflow resistance in horizontal and vertical directions in the grain bulk. [Copyright &y& Elsevier]

The performances of a four-layer backpropagation neural network and a non-parametric statistical classifier were compared for classification of barley, Canada Western Amber Durum wheat, Canada Western Red Spring wheat, oats, and rye. A total of 230 features (51 morphological, 123 colour, and 56 textural) from the high-resolution images of kernels of the five grain types were extracted and used as input features for classification. Different feature models, viz. morphological, colour, texture, and a combination of the three, were tested for their ability to classify these cereal grains. To make the classification process fast, the number of input features were reduced to 60 and 30. A set of features consisting of an equal number of morphological, colour, and textural features gave the best classification accuracies. The neural network classifier outperformed the non-parametric classifier in almost all the instances of classification. [Copyright &y& Elsevier]