NIR reflectance method to determine moisture content in food products

Abstract

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.