Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2003
Publication Date: 6/1/2004
Citation: Boldor, D., Sanders, T.H., Simunovic, J. 2004. Dielectric properties of in-shell and shelled peanuts at microwave frequencies. Transactions of the ASAE 47:1159-1169. Interpretive Summary: Microwave energy offers a less costly and more controllable source of energy than gas heat currently used to dry peanuts. Studies to examine the potential use of microwave energy to dry peanuts are dependent on understanding the characteristics of the interaction of peanuts with electric fields and implicitly with electromagnetic waves, including those in the microwave region. These characteristics are referred to as the dielectric properties of peanuts. This study determined the dielectric properties of peanuts by using methods previously used for wheat, corn, and other agricultural commodities. Data for dielectric properties of peanuts were determined for a range of moisture contents and temperatures at the microwave frequencies used in food processing. These data will be utilized in future studies to examine the potential for development of a continuous flow, temperature feed back controlled, microwave energy peanut dryer.
Technical Abstract: Dielectric properties ( ', '') of ground samples of peanut (Arachis hypogaea L.) pods and kernels were measured for several densities, temperatures, and moisture contents, in the range of 300 to 3000 MHz. Dielectric mixture equations were used to correlate the dielectric properties with density. The coefficients of quadratic and linear dielectric mixture equtions are tabulated for 915 and 2450 MHz, for different temperatures and moisture contents. The values of the dielectric constants ( ') and loss factors ( '') of bulk peanut pods and kernels were determined by extrapolation of the first and second-order polynomials that relate ( ') and ( '') with denisty. An equation that determines the dielectric properties of bulk peanut pods and kernels as a function of their temperature and moisture content was determined using multiple linear regression.