期刊
SENSORS
卷 12, 期 8, 页码 10871-10880出版社
MDPI AG
DOI: 10.3390/s120810871
关键词
visible and near infrared spectroscopy; barley; superoxide dismutase; variable selection; least squares-support vector machine; Gaussian process regression
资金
- 863 National High Technology Research and Development Program of China [2011AA100705, 2012AA101903]
- Natural Science Foundation of China [31071332]
- Zhejiang Provincial Natural Science Foundation of China [Z3090295]
- China Postdoctoral Science Foundation [2011M501009]
- Science and Technology Department of Zhejiang Province [2011C32G2130011]
- Fundamental Research Funds for the Central Universities [2012FZA6005]
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.
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