Next Article in Journal
Co-Cultured Continuously Bioluminescent Human Cells as Bioreporters for the Detection of Prodrug Therapeutic Impact Pre- and Post-Metabolism
Next Article in Special Issue
Remotely Exploring Deeper-Into-Matter by Non-Contact Detection of Audible Transients Excited by Laser Radiation
Previous Article in Journal
Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm
Previous Article in Special Issue
Monitoring the Wobbe Index of Natural Gas Using Fiber-Enhanced Raman Spectroscopy
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(12), 2830;

Detection of Water Content in Rapeseed Leaves Using Terahertz Spectroscopy

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
State Key Laboratory of Modern Optical Instruments, Zhejiang University, Hangzhou 310027, China
Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology No. 516, Jungong Road, Shanghai 200093, China
Daheng Scitech Mansion, No. 3 Suzhou Street, Haidian District, Beijing 100080, China
Author to whom correspondence should be addressed.
Received: 17 October 2017 / Revised: 24 November 2017 / Accepted: 29 November 2017 / Published: 6 December 2017
(This article belongs to the Special Issue Spectroscopy Based Sensors)
Full-Text   |   PDF [1510 KB, uploaded 8 December 2017]   |  


The terahertz (THz) spectra of rapeseed leaves with different water content (WC) were investigated. The transmission and absorption spectra in the range of 0.3–2 THz were measured by using THz time-domain spectroscopy. The mean transmittance and absorption coefficients were applied to analyze the change regulation of WC. In addition, the Savitzky-Golay method was performed to preprocess the spectra. Then, the partial least squares (PLS), kernel PLS (KPLS), and Boosting-PLS were conducted to establish models for predicting WC based on the processed transmission and absorption spectra. Reliable results were obtained by these three methods. KPLS generated the best prediction accuracy of WC. The prediction coefficient correlation (Rval) and root mean square error (RMSEP) of KPLS based on transmission were Rval = 0.8508, RMSEP = 0.1015, and that based on absorption were Rval = 0.8574, RMSEP = 0.1009. Results demonstrated that THz spectroscopy combined with modeling methods provided an efficient and feasible technique for detecting plant physiological information. View Full-Text
Keywords: terahertz spectroscopy; rapeseed leaf; water content; kernel PLS; Boosting-PLS terahertz spectroscopy; rapeseed leaf; water content; kernel PLS; Boosting-PLS

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Nie, P.; Qu, F.; Lin, L.; Dong, T.; He, Y.; Shao, Y.; Zhang, Y. Detection of Water Content in Rapeseed Leaves Using Terahertz Spectroscopy. Sensors 2017, 17, 2830.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top