Abstract-Identification of Transgenic Ingredients in Maize Using Terahertz Spectra

Feiyu Lian ;  Degang Xu ;  Maixia Fu ;  Hongyi Ge ;  Yuying Jiang ;  Yuan Zhang

http://ieeexplore.ieee.org/document/7942015/

The terahertz (THz) spectra in the 0.2–1.6 THz (6.6–52.8 cm−1) range of various strains of maize grains (MIR162, Bt-11, Mon810, and Jinboshi781) were investigated using a THz time-domain spectroscopy system. Principal component analysis (PCA) was used to extract the feature data based on the cumulative contribution rates (above 95%); the top four principal components were selected, and a support vector machine (SVM) method was then applied. Several selection kernels (linear, polynomial, and radial basis functions) were used to identify the four maize grain types. The results showed that the samples were identified with accuracy of nearly 92.08%; additionally, total positive identification was more than 91.67%, and negative identification reached 93.33%. The proposed approach was then compared with other methods, including principal component regression, partial least squares regression, and backpropagation neural networks. These comparisons showed that the PCA-SVM approach outperformed the other methods and also indicated that the proposed method that combines THz spectroscopy technology with PCA-SVM is efficient and practical for transgenic ingredient identification in maize

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-Discrimination of Moldy Wheat Using Terahertz Imaging Combined with Multivariate Classification

Yuying Jiang,   Hongyi Ge,   Feiyu Lian,   Yuan Zhang and  Shanhong Xia  

RSC Adv., 2015, Accepted Manuscript

DOI: 10.1039/C5RA15377H
Received 02 Aug 2015, Accepted 17 Oct 2015
First published online 19 Oct 2015
http://pubs.rsc.org/en/content/articlelanding/2015/ra/c5ra15377h#!divAbstract

Terahertz (THz) imaging was employed to develop a novel method for discriminating wheat of varying states of moldiness. Spectral data, in the range of 0.1–1.6 THz, were extracted from regions of interest (ROIs) in the THz images. Principal component analysis (PCA) was used to evaluate the spectral data and determine the cluster trend. Six optimal frequencies were selected by implementing PCA directly for each image’s ROI. Classification models for moldy wheat identification were established using the support vector machine (SVM) method, a partial least-squares regression analysis, and the back propagation neural network method. The models developed from these methods were based on the full and optimal frequencies, using the top three principal components as input variables. The PCA-SVM method achieved a prediction accuracy of over 95%, and was implemented at every pixel in the images to visually demonstrate the moldy wheat classification method. Our results indicate that THz imaging combined with chemometric algorithms is efficient and practical for the discrimination of moldy wheat.

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.