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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality & Safety Assessment Research » Research » Publications at this Location » Publication #368713

Research Project: Assessment and Improvement of Poultry Meat, Egg, and Feed Quality

Location: Quality & Safety Assessment Research

Title: Calibration Algorithm for Rapid and Nondestructive Moisture Sensing in In-Shell Nuts

item Trabelsi, Samir
item Lewis, Micah

Submitted to: IEEE Sensors Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/4/2019
Publication Date: 12/3/2019
Citation: Trabelsi, S., Lewis, M.A. 2019. Calibration Algorithm for Rapid and Nondestructive Moisture Sensing in In-Shell Nuts. IEEE Sensors Letters. 10.1109/LSENS.2019.2952026.

Interpretive Summary: Rapid, nondestructive sensing of product moisture content is critical in many industries including pharmaceutical, mining, agriculture and food. In agriculture, moisture content is used in pre-harvest and post-harvest operations to assess the quality of a given commodity, its fair value, and safe storage conditions. For instance, moisture content is used in the grading of peanuts, almonds and other nuts. It is also used to determine the shelf life of these commodities and to monitor the drying processes. Currently used electronic moisture meters require that the nuts be shelled before the kernel moisture can be determined. This is tedious, time consuming, and impractical in dynamic situations where kernel moisture is needed in real time. Recently, a low-cost microwave moisture meter, operating at a single frequency of 5.8 GHz, allowed the determination of peanut kernel moisture content while still in the shell. The measurement principle relies on measurement of the dielectric properties of in-shell-peanuts and peanut kernels and correlating those properties with the peanut kernel moisture content. The microwave moisture meter was calibrated against the moisture determined by the standard oven-drying technique. In this research, in-shell peanuts and in-shell almonds were conditioned to several different moisture contents in the range from about 8% to 20% for peanuts and 5% to 15% for almonds and to several different temperatures ranging from about 15 to 35 degrees C for measurement of the dielectric properties at 5.8 GHz. The resulting dielectric properties were then analyzed in association with a density-independent function for moisture-content prediction, and relationships were developed for prediction of kernel moisture content in in-shell peanuts and almonds. The novel calibration algorithm for simultaneous and nondestructive determination of in-shell nut moisture content and kernel moisture content from the dielectric properties of the in-shell nuts at a single microwave frequency, 5.8 GHz, is presented. The new calibration requires only calibration for in-shell nuts against the standard oven reference technique and therefore reduces the calibration procedure by fifty percent. Also, the new algorithm limits the propagation of errors resulting from multiple calibrations, as previously two separate calibrations were needed, one for the in-shell nuts and one for the kernels. The algorithm relied on the use of a density-independent function, expressed in terms of the dielectric properties, and the existence of a linear relationship between the in-shell nut moisture content and the kernel moisture content. The algorithm for peanuts and almonds can be used for other commodities. The new algorithm can be implemented in microwave moisture sensors for in-shell nuts in static or dynamic situations, so it should be highly useful for rapid moisture monitoring in the peanut and almond industries, and provide means for maintaining product quality for the benefit of the industries and consumers as well.

Technical Abstract: A new calibration algorithm is presented for instantaneous and nondestructive determination of moisture content in in-shell nuts from measurement of their dielectric properties at a single microwave frequency. The new calibration algorithm relies on the use of a density-independent function, expressed in terms of the dielectric properties, and the existence of a direct relationship between the in-shell moisture content and kernel moisture content. Results of measurement by using a microwave moisture sensor operating at 5.8 GHz are presented and kernel moisture calibration equations are established for peanuts and almonds.