Paper Info
Title
Fast analysis of superoxide dismutase (SOD) activity in barley leaves using visible and near infrared spectroscopy.
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.
Year
DOI
Venue
2012
10.3390/s120810871
SENSORS
Keywords
Field
DocType
visible and near infrared spectroscopy,barley,superoxide dismutase,variable selection,least squares-support vector machine,Gaussian process regression
Least squares,Analytical chemistry,Least squares support vector machine,Partial least squares regression,Near-infrared spectroscopy,Mean squared error,Smoothing,Engineering,Spectroscopy,Linear regression
Journal
Volume
Issue
ISSN
12
8
1424-8220
Citations 
PageRank 
References 
1
0.39
3
Authors
6
Name
Order
Citations
PageRank
Wenwen Kong1176.23
Yun Zhao210.39
Fei Liu3206.06
Yong He448765.25
Tian Tian530.96
Wei-Jun Zhou620616.00