Abstract-Early detection of germinated wheat grains using terahertz image and chemometrics

• Yuying Jiang
• , Hongyi Ge
• , Feiyu Lian
• , Yuan Zhang
•  & Shanhong Xia

• http://www.nature.com/articles/srep21299

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.

Abstract-Characterization of Wheat Varieties Using Terahertz Time-Domain Spectroscopy

Hongyi Ge ^1,2,* , Yuying Jiang ^1,2

, Feiyu Lian ³

, Yuan Zhang ³

 and Shanhong Xia ^1,2

http://www.mdpi.com/1424-8220/15/6/12560

¹ State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100080, China² University of the Chinese Academy of Sciences, Beijing 100080, China³ Key Laboratory of Grain Information Processing & Control, Ministry of Education, Zhengzhou 450001, China

* Author to whom correspondence should be addressed.

Received: 19 April 2015 / Accepted: 21 May 2015 / Published: 27 May 2015

Terahertz (THz) spectroscopy and multivariate data analysis were explored to discriminate eight wheat varieties. The absorption spectra were measured using THz time-domain spectroscopy from 0.2 to 2.0 THz. Using partial least squares (PLS), a regression model for discriminating wheat varieties was developed. The coefficient of correlation in cross validation (R) and root-mean-square error of cross validation (RMSECV) were 0.985 and 1.162, respectively. In addition, interval PLS was applied to optimize the models by selecting the most appropriate regions in the spectra, improving the prediction accuracy (R = 0.992 and RMSECV = 0.967). Results demonstrate that THz spectroscopy combined with multivariate analysis can provide rapid, nondestructive discrimination of wheat varieties.