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Sensors 2012, 12(8), 10871-10880; doi:10.3390/s120810871

Fast Analysis of Superoxide Dismutase (SOD) Activity in Barley Leaves Using Visible and Near Infrared Spectroscopy

1
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
3
College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Received: 14 May 2012 / Revised: 20 June 2012 / Accepted: 31 July 2012 / Published: 7 August 2012
(This article belongs to the Section Chemical Sensors)
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Abstract

Visible and near infrared (Vis/NIR) spectroscopy was investigated for the fast analysis of superoxide dismutase (SOD) activity in barley (Hordeum vulgare L.) leaves. Seven different spectra preprocessing methods were compared. Four regression methods were used for comparison of prediction performance, including partial least squares (PLS), multiple linear regression (MLR), least squares-support vector machine (LS-SVM) and Gaussian process regress (GPR). Successive projections algorithm (SPA) and regression coefficients (RC) were applied to select effective wavelengths (EWs) to develop more parsimonious models. The results indicated that Savitzky-Golay smoothing (SG) and multiplicative scatter correction (MSC) should be selected as the optimum preprocessing methods. The best prediction performance was achieved by the LV-LS-SVM model on SG spectra, and the correlation coefficients (r) and root mean square error of prediction (RMSEP) were 0.9064 and 0.5336, respectively. The conclusion was that Vis/NIR spectroscopy combined with multivariate analysis could be successfully applied for the fast estimation of SOD activity in barley leaves.
Keywords: visible and near infrared spectroscopy; barley; superoxide dismutase; variable selection; least squares-support vector machine; Gaussian process regression visible and near infrared spectroscopy; barley; superoxide dismutase; variable selection; least squares-support vector machine; Gaussian process regression
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Kong, W.; Zhao, Y.; Liu, F.; He, Y.; Tian, T.; Zhou, W. Fast Analysis of Superoxide Dismutase (SOD) Activity in Barley Leaves Using Visible and Near Infrared Spectroscopy. Sensors 2012, 12, 10871-10880.

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