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Abstract: Polymer-based sensors have several advantages for gas detection. They can utilize different polymer composite films as their sensing materials, which results in their ability to respond to a wide range of volatiles. However, it is shown that polymer-based sensors suffer from a large temperature sensitivity that causes shift in their volatile response. To overcome this disadvantage, a new design of interdigitated electrodes on a micro-hotplate is proposed. This detector employs a micromachined sensor structure on silicon wafer that maintains a constant temperature throughout the device. Different micromachined sensor structures and substrates were investigated as candidates for constant temperature platforms. COMSOL simulations were performed to investigate the heat transfer profile. Sensor structure and dimensions together with the heater structure were optimized based on the simulation results. [ABSTRACT FROM AUTHOR]

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]

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]

Abstract: Cereal grains are the major source of food for humans and most domesticated animals. In many developing countries, overall post-harvest losses of cereals and legumes of about 10–15% are fairly common. Consumption of cereals and legumes by pests such as insects during storage and microbial spoilage or contamination may make these totally inedible. On farms, manual samples, traps, and probes have been used to determine the presence of insects. Manual inspection, sieving, cracking-floatation and Berlese funnels are being used at present to detect insects in grain handling facilities. These methods are not efficient and are time consuming. Acoustic detection, carbon dioxide measurement, uric acid measurement, near-infrared spectroscopy, and soft X-ray method have the potential for use at the industry level to detect insects in grain samples as their usefulness has been demonstrated in the research laboratories. Researchers have developed image analysis programs to automatically scan X-ray images to detect insect infestations. The use of near-infrared (NIR) spectroscopy has been investigated to detect hidden insects in wheat kernels. X-ray and NIR spectroscopy methods are cost prohibitive and current NIR instrumentation requires complex operating procedures and calibrations. The advantages and limitations of these insect detection methods are evaluated and the advantages of one technique over the other are described in this paper. [Copyright &y& Elsevier]