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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 Chemical 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 (CC BY 3.0).
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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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