Abstract-Analytical-performance improvement of laser-induced breakdown spectroscopy for the processing degree of wheat flour using a continuous wavelet transform

Ping Yang, Yining Zhu, Shisong Tang, Zhongqi Hao, Lianbo Guo, Xiangyou Li, Yongfeng Lu, and Xiaoyan Zeng

https://www.osapublishing.org/ao/abstract.cfm?uri=ao-57-14-3730

Quality and safety of food are two of the most important matters in our lives. Wheat is one of the most important products in the modern agricultural processing industry. Issues of mislabeling and adulteration are of increasingly serious concern in the grain market. They threaten the credibility of producers and traders and the rights of the consumers. Therefore, it is very significant to guarantee the processing degree of wheat flour. In this work, two different spectral peak recognition methods, i.e., artificial spectral peak recognition and automatic spectral peak recognition, are carried out to study the adulteration problem in the food industry. Three grades of the processing degree of wheat flour from northern China are classified by laser-induced breakdown spectroscopy (LIBS). To search for an automatic classification model, continuous wavelet transform is used for the automatic recognition of the LIBS spectrum peak. Principal component analysis is used to reduce the collinearity of LIBS spectra data. First, 20 principal components were selected to represent the spectral data for the following discrimination analysis by a support vector machine. The results showed that the classification accuracies of automatic spectral peak recognition are better than those of artificial spectral peak recognition. The classification accuracies of artificial spectral peak recognition and automatic spectral peak recognition are 95.33% and 98.67%; the fivefold cross-validation classification accuracies are 94.67% and 96.67%; and the operation times were 240 min and 2 min, respectively. It can be concluded that LIBS can provide simpler and faster classification without the use of any chemical reagent, which represents a decisive advantage for applications dedicated to rapidly detecting the processing degree of wheat flour and other cereals.

© 2018 Optical Society of America

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.