Investigation on relaxation properties of maize kernels based on the multicomponent structure

Zheng, Zhaohui; Ren, Liuyang; Fu, Hanyu; Yang, Pei; Lv, Lanlan; Xu, Jingshen; Yang, Deyong

doi:10.1016/j.powtec.2023.118905

Cited by 5 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the presence of moisture, maize kernels are neither a pure solid nor a pure liquid, but a typical biological material with viscoelastic characteristics 10 . When the grain is deformed by an external force, it takes a certain amount of time to reach a new size.…”

Section: Introductionmentioning

confidence: 99%

Prediction of moisture content for a single maize kernel based on viscoelastic properties

Qiao,

Xia,

et al. 2024

J Sci Food Agric

View full text Add to dashboard Cite

BackgroundRapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from costly equipment, stringent environmental requirements, or limited accuracy. To bridge this research gap, this study proposes a simple and effective method for detecting moisture content of single maize kernels based on viscoelastic properties.ResultsTwo types of viscoelastic experiments were conducted, involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force‐time graphs and viscoelastic parameters were extracted based on the constitutive model. Then, viscoelastic parameters and data of force‐time graphs are employed as input variables to explore the relationships with moisture content separately. Furthermore, the impact of different preprocessing methods and feature variables on model accuracy is explored based on force‐time graphs. The results indicate that models utilizing the force‐time data have superior accuracy than those utilizing viscoelastic parameters. The best model is established by partial least squares regression based on S‐G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set are 0.954 and 0.021, respectively. The corresponding values of the prediction set are 0.905 and 0.029, respectively.ConclusionsThis study confirms the potential for accurate and fast detection of moisture content in single maize kernel using viscoelastic properties, which provides a novel approach for the detection of various components in cereals.This article is protected by copyright. All rights reserved.

show abstract

Section: Introductionmentioning

confidence: 99%