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Sensors 2012, 12(10), 13393-13401; doi:10.3390/s121013393
Article

Quantitative Analysis of Total Amino Acid in Barley Leaves under Herbicide Stress Using Spectroscopic Technology and Chemometrics

1,†
,
1,†
,
1,3
,
1,* , 2
 and
2
1 College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 2 College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China 3 Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University, Hangzhou 310058, China These authors contributed equally to this work.
* Author to whom correspondence should be addressed.
Received: 13 August 2012 / Revised: 21 September 2012 / Accepted: 24 September 2012 / Published: 1 October 2012
(This article belongs to the Section Physical Sensors)
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Abstract

Visible and near infrared (Vis/NIR) spectroscopy were employed for the fast and nondestructive estimation of the total amino acid (TAA) content in barley (Hordeum vulgare L.) leaves. The calibration set was composed of 50 samples; and the remaining 25 samples were used for the validation set. Seven different spectral preprocessing methods and six different calibration methods (linear and nonlinear) were applied for a comprehensive prediction performance comparison. Successive projections algorithm (SPA) and regression coefficients (RC) were applied to select effective wavelengths (EWs). The results indicated that the latent variables-least-squares-support vector machine (LV-LS-SVM) model achieved the optimal performance. The prediction results by LV-LS-SVM with raw spectra were achieved with a correlation coefficients (r) = 0.937 and root mean squares error of prediction (RMSEP) = 0.530. The overall results showed that the NIR spectroscopy could be used for determination of TAA content in barley leaves with an excellent prediction precision; and the results were also helpful for on-field monitoring of barley growing status under herbicide stress during different growth stages.
Keywords: visible and near infrared spectroscopy; barley; total amino acid; variable selection; successive projections algorithm; least squares-support vector machine visible and near infrared spectroscopy; barley; total amino acid; variable selection; successive projections algorithm; least squares-support vector machine
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.

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Bao, Y.; Kong, W.; He, Y.; Liu, F.; Tian, T.; Zhou, W. Quantitative Analysis of Total Amino Acid in Barley Leaves under Herbicide Stress Using Spectroscopic Technology and Chemometrics. Sensors 2012, 12, 13393-13401.

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