Early detection of germinated wheat grains using terahertz image and chemometrics

Scientific Reports
Yuying JiangShanhong Xia

Abstract

In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtained by THz time spectroscopy, were distinctly different. Used for original data compression and feature extraction, principal component analysis (PCA) revealed the changes that occurred in the inner chemical structure during germination. Two thresholds, one indicating the start of the release of α-amylase and the second when it reaches the steady state, were obtained through the first five score images. Thus, the first five PCs were input for the partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) models, which were used to classify seven different germination times between 0 and 48 h, with a prediction accuracy of 92.85%, 93.57%, and 90.71%, respectively. The experimental results indicated that the combination of THz imaging technology and chemometrics could be a new effective way to discriminate wheat grains at the early germination stage of approximately 6 h.

References

Mar 6, 2003·Nature Materials·Bradley Ferguson, Xi-Cheng Zhang
Jul 22, 2010·Applied Optics·Christy Fernandez CullDavid J Brady
Feb 23, 2012·Journal of Biomedical Optics·Anthony J FitzgeraldVincent P Wallace
Jun 13, 2014·Optics Express·Hongyi GeShanhong Xia
Jul 9, 2014·Journal of Biomedical Optics·Kun MengJian-heng Zhao

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Citations

Sep 4, 2020·Scientific Reports·Pedro Martín-MateosPablo Acedo
Dec 24, 2018·Scientific Reports·Mindaugas KaraliūnasGintaras Valušis

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Methods Mentioned

BETA
X-ray
nuclear magnetic resonance
feature extraction
PCA
imaging technology
irradiate

Software Mentioned

MATLAB
Origin
Terahertz
PLSR

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