|Naganathan, Konda - UNIVERSITY OF NEBRASKA|
|Subbiah, J. - UNIVERSITY OF NEBRASKA|
Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 17, 2009
Publication Date: November 21, 2009
Citation: Naganathan, K., Kandala, C., Subbiah, J. 2009. NIR Reflectance Spectroscopy for nondestructive moisture content determination of peanut kernels. Transactions of the ASABE. 52(5):1661-1665. Interpretive Summary: There are some commercial instruments such as Zeltex ZX 800 that use the NIR transmission method to determine the moisture, oil and protein in agricultural products. These are useful for whole grain samples with relatively small kernel size such as wheat, corn and barley but not suitable for measurements on peanuts because of their large kernel size. An NIR reflectance method is described here by which the average moisture content of a sample of 100 g of peanut kernels could be measured within acceptable accuracies rapidly and without the need for any sample preparation. The ability to predict the moisture content of whole peanut kernels by this method would be useful in the development of a low-cost commercial NIR instrument useful for the peanut industry.
Technical Abstract: There are some commercial instruments available that use near infrared (NIR) radiation measurements to determine the moisture content (MC) 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.