Early detection of germinated wheat grains using terahertz image and chemometrics.

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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Jiang Y,Ge H,Lian F,Zhang Y,Xia S

doi

10.1038/srep21299

subject

Has Abstract

pub_date

2016-02-19 00:00:00

pages

21299

issn

2045-2322

pii

srep21299

journal_volume

6

pub_type

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