Quantitative detection of moisture content of corn by olfactory visualization technology

Abstract

In this study, a novel method for quantitatively detecting the moisture content of corn by olfactory visualization technology is proposed. First of all, an olfactory visualization system was developed to obtain odor information of corn samples with varying moisture contents using sensor arrays. The sensor array was constructed with twelve chemical dyes, which helped assemble the system. Then, the RreliefF algorithm was utilized to sort the characteristic variables of the sensor image by weight, and the analytical model based on different combinations of input features was built using support vector regression (SVR). Moreover, the ability of three optimization algorithms, namely grid search (GS), particle swarm optimization (PSO) and dung beetle optimizer (DBO), was compared to determine their effectiveness in optimizing the parameters of the SVR model. The results reveal that the RreliefF-GS-SVR model demonstrates an enhanced correlation coefficient of prediction (RP), with an increase from 0.97 to 0.98, as well as the root mean square error of prediction (RMSEP), is reduced from 0.66% to 0.53% when compared to the GS-SVR model. Similarly, in comparison to the RreliefF-GS-SVR model and RreliefF-PSO-SVR model, the RreliefF-DBO-SVR model shows better predictive performance, with an increase in RP to 0.99 and a decrease in RMSEP to 0.25%. The comprehensive findings indicate that integrating olfactory visualization technology and multivariate analysis is feasible for conducting quantitative analysis of grain moisture content.