Utilization of a Resonant Cavity for Characterization of Single In-Shell Peanuts

Abstract

Peanuts are taken through routine postharvest procedures to assess quality, establish the grade, and determine the amount of revenue that the farmer will be allotted. Moisture content and meat content are examples of parameters determined to assess the grade of the peanuts. Such parameters are assessed for bulk samples usually with mass > 1 kg, and the resulting values are averages of peanuts within the bulk sample. Such processes are destructive and lack provision for assessment of single, in-shell peanuts. Thus, the characterization of single, in-shell peanuts was investigated by measuring the shift in resonant frequency and the change in cavity transmission characteristics caused by each peanut once inserted into a resonant cavity connected to a vector network analyzer (VNA). Such microwave measurements were taken for over 300 in-shell peanuts at 22 °C. The peanuts were divided into seven categories based on custom fillings to simulate diseased and damaged peanuts. One category, serving as the control, consisted of intact peanuts, while the other categories consisted of peanuts filled with coffee, cornstarch, and/or a single kernel. The measured resonant-cavity parameters and the calculated dielectric properties provided a means to characterize each in-shell peanut. Statistical analysis was performed to assess differentiation between the seven categories using those parameters. Kruskal–Wallis One-Way ANOVA on Ranks, Tukey test, and Dunn’s Method were used to determine which categories were statistically significantly different from each other for each parameter at the 95% confidence interval. Lastly, artificial intelligence was used to investigate the creation of models to accurately classify the peanuts.