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

• Published in: Journal of the science of food and agriculture 2023-04, Vol.103 (6), p.3139-3145
• Main Authors: Ren, Lin, Liu, Wei, Liu, Changhong, Zheng, Lei
• Format: Article
• Language: English
• Subjects: Air drying; Back propagation; Back propagation networks; Bound water; Chemometrics; Corn; Correlation coefficient; Correlation coefficients; Drying; Energy consumption; Food preservation; Imaging; Kernels; Least squares; Least-Squares Analysis; low‐field nuclear magnetic resonance; multispectral imaging; Neural networks; Neural Networks, Computer; nondestructive detection; Nondestructive testing; Prediction models; Support Vector Machine; Support vector machines; Vegetables; Water; water content; Zea mays - chemistry
• ISSN: 0022-5142; 1097-0010
• DOI: 10.1002/jsfa.12467

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.