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On the diffusion constant of water in wheat 总被引：1，自引：0，他引：1
Diffusion-weighted magnetic resonance imaging (MRI) was used to obtain diffusion constants for water in the embryo and endosperm of wheat. Our experiments showed a significant difference between the diffusion constant for the two components. It was also shown that water diffusion in both the endosperm and embryo deviates from the typically observed Gaussian behavior in bulk fluids, showing a time-dependent diffusion constant. Diffusion constants for the embryo and endosperm were shown to differ by an order of magnitude. Using a model for restricted diffusion, information on the endosperm pore size and the embryo cell dimensions could be obtained. 相似文献
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Chandra B. Singh Digvir S. Jayas Jitendra Paliwal Noel D. G. White 《Cereal Chemistry》2009,86(3):256-260
Sprout damage which results in poor breadmaking quality due to enzymatic activity of α‐amylase is one of the important grading factors of wheat in Canada. Potential of near‐infrared (NIR) hyperspectral imaging was investigated to detect sprouting of wheat kernels. Artificially sprouted, midge‐damaged, and healthy wheat kernels were scanned using NIR hyperspectral imaging system in the range of 1000–1600 nm at 60 evenly distributed wavelengths. Multivariate image analysis (MVI) technique based on principal components analysis (PCA) was applied to reduce the dimensionality of the hyperspectral data. Three wavelengths 1101.7, 1132.2, and 1305.1 nm were identified as significant and used in analysis. Statistical discriminant classifiers (linear, quadratic, and Mahalanobis) were used to classify sprouted, midge‐damaged, and healthy wheat kernels. The discriminant classifiers gave maximum accuracy of 98.3 and 100% for classifying healthy and damaged kernels, respectively. 相似文献
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Knowledge on three-dimensional (3D) movement and distribution of Cryptolestes ferrugineus (Stephens) (Coleoptera: Laemophloeidae) in grain bulks assists in the prediction of their distribution inside a bin. The following experiments were conducted to determine the 3D dispersal patterns of adult C. ferrugineus in wheat with 14.5% moisture content: 1) at various insect densities (0.35, 1.77 and 3.53 A/kg (adults/kg) at 20°C and in 24 h movement period; 2) in different movement periods (6, 24, and 72 h) at 20°C and 0.35 A/kg insect density; and 3) at different temperatures (20, 30 and 35°C) at 0.35 A/kg density in 24 h movement period. To create the densities of 0.35, 1.77, and 3.53 A/kg, 100, 500, and 1,000 adults were introduced in about 285 kg wheat, respectively. The 285 kg of wheat was kept in 343 mesh cubes, which in turn were packed in a wooden box. The introduced adults were counted at the end of the movement periods. Adult C. ferrugineus tended to move downward from the point of introduction, and then diffused throughout the grain bulk. The effects of insect densities, movement periods, and temperatures on the dispersion pattern of insects were similar in 1D columns, 2D chambers, and 3D grain bulk. 相似文献
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Fabrication and optimization of a conducting polymer sensor array using stored grain model volatiles
Hossain ME Rahman GM Freund MS Jayas DS White ND Shafai C Thomson DJ 《Journal of agricultural and food chemistry》2012,60(11):2863-2873
During storage, grain can experience significant degradation in quality due to a variety of physical, chemical, and biological interactions. Most commonly, these losses are associated with insects or fungi. Continuous monitoring and an ability to differentiate between sources of spoilage are critical for rapid and effective intervention to minimize deterioration or losses. Therefore, there is a keen interest in developing a straightforward, cost-effective, and efficient method for monitoring of stored grain. Sensor arrays are currently used for classifying liquors, perfumes, and the quality of food products by mimicking the mammalian olfactory system. The use of this technology for monitoring of stored grain and identification of the source of spoilage is a new application, which has the potential for broad impact. The main focus of the work described herein is on the fabrication and optimization of a carbon black (CB) polymer sensor array to monitor stored grain model volatiles associated with insect secretions (benzene derivatives) and fungi (aliphatic hydrocarbon derivatives). Various methods of statistical analysis (RSD, PCA, LDA, t test) were used to select polymers for the array that were optimum for distinguishing between important compound classes (quinones, alcohols) and to minimize the sensitivity for other parameters such as humidity. The performance of the developed sensor array was satisfactory to demonstrate identification and separation of stored grain model volatiles at ambient conditions. 相似文献
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