Moisture and oil contents are important quality factors often measured and monitored in the processing and storage
of food products such as corn and peanuts. For estimating these parameters for peanuts nondestructively a parallel-plate
capacitance sensor was used in conjunction with an impedance analyzer. Impedance, phase angle and dissipation factor
were measured for the parallel-plate system, holding the in-shell peanut samples between its plates, at frequencies
ranging between 1MHz and 30 MHz in intervals of 0.5 MHz. The acquired signals were analyzed with discrete wavelet
analysis. The signals were decomposed to 6 levels using Daubechies mother wavelet. The decomposition coefficients
of the sixth level were passed onto a stepwise variable selection routine to select significant variables. A linear
regression was developed using only the significant variables to predict the moisture and oil content of peanut pods (inshell
peanuts) from the impedance measurements. The wavelet analysis yielded similar R<sup>2</sup> values with fewer variables
as compared to multiple linear and partial least squares regressions. The estimated values were found to be in good
agreement with the standard values for the samples tested. Ability to estimate the moisture and oil contents in peanuts
without shelling them will be of considerable help to the peanut industry.
Moisture content (MC) is an important quality factor that is measured and monitored, at various stages of processing and storage, in the food industry. There are some commercial instruments available that use near infrared (NIR) radiation measurements to determine the moisture content of a variety of grain products, such as wheat and corn, with out the need of any sample grinding or preparation. However, to measure the MC of peanuts with these instruments the peanut kernels have to be chopped into smaller pieces and filled into the measuring cell. This is cumbersome, time consuming and destructive. An NIR reflectance method is presented here by which the average MC of about 100 g of whole kernels could be determined rapidly and nondestructively. The MC range of the peanut kernels tested was between 8% and 26%. Initially, NIR reflectance measurements were made at 1 nm intervals in the wave length range of 1000 nm to 1800 nm and the data was modeled using partial least squares regression (PLSR). The predicted values of the samples tested in the above range were compared with the values determined by the standard air-oven method. The predicted values agreed well with the air-oven values with an R2 value of 0.96 and a standard error of prediction (SEP) of 0.83. Using the PLSR beta coefficients, five key wavelengths were identified and using multiple linear regression (MLR) method MC predictions were made. The R2 and SEP values of the MLR model were 0.84 and 1.62, respectively. Both methods performed satisfactorily and being rapid, nondestructive, and non-contact, may be suitable for continuous monitoring of MC of grain and peanuts as they move on conveyor belts during their processing.