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Title: Estimation of mass ratio of the total kernels within a sample of in-shell peanuts using RF Impedance Method

Authors
 Kandala, Chari Sundaram, Jaya

 Submitted to: Electronics Letters Publication Type: Peer Reviewed Journal Publication Acceptance Date: November 15, 2009 Publication Date: January 26, 2010 Repository URL: http://DOI: 10.1155/2010/375430 Citation: Kandala, C., Sundaram, J. 2010. Estimation of mass ratio of the total kernels within a sample of in-shell peanuts using RF Impedance Method. Reseach Letters in Electronics, Article ID 375430, Vol. 2010. Interpretive Summary: Peanuts harvested and dried are sold by weight, while they are in their shells. It would be very useful if we can estimate at the time of buying a load of peanuts, the weight of the kernels we may obtain when this load of peanuts is shelled, without the need to shell the peanuts. Knowledge of the ratio of kernel mass to the total peanut pod mass could be a good indicator of the quality of the peanuts that are being purchased. Up front, it would indicate to the buyer what portion of the peanuts he is planning to buy has useful kernels and how much is waste. In the traditional way of determining the mass ratio, about 500 g of peanut sample is shelled and the kernels are separated and weighed. The kernel mass ratio is then calculated as the ratio of the kernel weight to the sample weight. This method is destructive, time and labor intensive. A device which can estimate the mass ratio without shelling the peanuts will be very useful for peanut industry. An attempt was made here, to develop an RF Impedance method for this purpose. The conduction of RF signals through a medium depends on the density of the medium and some other electrical properties. In the present work, variation of certain electrical properties of a sample of peanuts at different radio frequencies was used to estimate the relative compactness of the medium. For this the ratio of the weight of total kernels to the weight of the in-shell peanuts was correlated to the capacitance and phase angle values of a parallel-plate system holding a sample of in-shell peanuts. A calibration equation was developed to predict the total kernel weight and the equation was validated. . The equation was used to predict the percentage mass ratio in the validation groups. Fitness of calibration model was verified using the parameters, standard error of calibration (SEC) and root mean square error of calibration (RMSEC). The predictability percentage, within 1% and 2% was calculated by comparing the kernel mass ratio, obtained by the model equation and the reference value obtained by visual determination. With the validation groups cross validation gave 100 % and 96 % predictability, and external validation gave 87 % and 98% predictability within 1 % and 2% difference respectively. This method is rapid and non destructive. Technical Abstract: 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 this 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 10 MHz insteps of 1 MHz. A calibration equation was developed by multi linear regression (MLR) 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 the parameters, standard error of calibration (SEC) and root mean square error of calibration (RMSEC). The predictability percentage, within 1% and 2% was calculated by comparing the kernel mass ratio, obtained by the model equation and the reference value obtained by visual determination. With the validation groups cross validation gave 100 % and 96 % predictability, and external validation gave 87 % and 98% predictability within 1 % and 2% difference respectively.