Nondestructive detection of water status and distribution in corn kernels during hot air drying using multispectral imaging

Abstract

BACKGROUND The characteristics of corn kernels are strongly connected with the content of three statuses of water: bound water, immobilized water, and free water. Monitoring different water contents is very important to optimize the drying process, improve corn quality, and reduce energy consumption. The feasibility of nondestructive detection of water status and its distribution in corn kernels during the hot‐air drying process using multispectral imaging was investigated. RESULTS The chemometric methods used to develop prediction models were back propagation neural network, least‐squares support vector machine, and partial least squares. The back propagation neural network achieved the best prediction performance for total and free water contents, with correlation coefficient of prediction (Rp) of 0.9717 and 0.9782 respectively, root‐mean‐square error of prediction (RMSEP) of 4.48% and 2.54% respectively, and ratio of prediction to deviation (RPD) of 4.87 and 4.29 respectively. And partial least squares was better for the prediction of immobilized and bound water contents, with Rp of 0.9612 and 0.9798 respectively, RMSEP of 0.57% and 0.06% respectively, and RPD of 4.78 and 4.42 respectively. CONCLUSION It could be concluded that multispectral imaging combined with chemometric methods would be a promising technique for rapid and nondestructive detection of water status and its distribution in corn kernels. © 2023 Society of Chemical Industry.

Comparison of water mobility and distribution in Dioscorea opposita Thunb. cv. Tiegun during hot‐air and microwave drying processes

“…To upgrade the dehydrated product, particularly in terms of its quality, it is critical to monitor water change during the drying process. Low‐field nuclear magnetic resonance (LF‐NMR) has been characterised as a sensitive, fast, convenient, and non‐destructive method for detecting moisture dynamic characteristics during the drying of agricultural products (Fan & Zhang, 2019; Kamal et al ., 2019a; Cheng et al ., 2021), and has been effectively applied for monitoring variations in water mobility and distribution in dried fruits and vegetables such as carrot slices (Xu et al ., 2017a), broccoli (Xu et al ., 2017b), shiitake mushroom (Zhao et al ., 2019), apple slices (Kamal et al ., 2019b), garlic slices (Chen et al ., 2020), burdock slices (Zhang et al ., 2020), ginger slices (Zeng et al ., 2022), and corn kernel (Ren et al ., 2023).…”