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Moisture content of wood chips is an important factor to be known in their utilization as a biomass material. Several moisture measuring instruments are available in the market, but for most of these instruments, some sort of sample preparation is needed that involves sizing, grinding, and weighing. The samples in this process are usually destroyed, and the measurement involves considerable time and labor. The standard methods of oven drying and Karl Fisher also fall in the destructive and laborious category. In this paper, estimation of moisture content of hardwood chips, from the measurement of certain electrical properties of a parallel-plate capacitor, holding the samples of these materials between them at two radio frequencies, is presented. The wood chip samples tested were in the moisture range of 3%–50%. The standard error of prediction was 2.0, and the prediction accuracies were better for the samples with moisture contents below 25%. The construction and performance of a prototype moisture sensing instrument working on these principles is briefly described. This method being nondestructive and rapid would be useful in the biofuel industry. [ABSTRACT FROM PUBLISHER]

It would be useful to know the total kernel mass within a given mass of peanuts (mass ratio) while the peanuts are bought or being processed. In this work, the possibility of finding the mass ratio while the peanuts were in their shells was investigated. Capacitance, phase angle, and dissipation factor measurements on a parallel-plate capacitor holding in-shell peanut samples were made at frequencies from 1 to 10MHz insteps of 1MHz. A calibration equation was developed by multilinear regression analysis correlating the percentage ratio of the kernel weight with the measured capacitance, dissipation factor, and phase angle values of in-shell peanut samples with known kernel weights. The equation was used to predict the percentage mass ratio in the validation groups. Fitness of calibration model was verified using standard error of calibration, root mean square error of calibration, and leverage and influence plots. The predictability percentage, within 1% and 2% of the visual determination, was calculated by comparing the kernel mass ratio, obtained by the model equation and the reference value obtained by visual determination. Cross-validation gave 96% and 100% predictability, and external validation gave 87% and 98% predictability within 1% and 2% difference, respectively. [ABSTRACT FROM AUTHOR]