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.
Highlights
Barley (Hordeum vulgare L.) is one of the most important cultivated crops in the World [1]
The best prediction performance was achieved by the latent variables (LVs)-least squares-support vector machine (LS-SVM) model with SG spectra, and correlation coefficients (r) = 0.9064 and root mean square error of prediction (RMSEP) = 0.5336
Visible and near infrared (Vis/Near infrared (NIR)) spectroscopy combined with multivariate analysis was successfully applied for the fast estimation of superoxide dismutase (SOD) activity in barley leaves
Summary
Barley (Hordeum vulgare L.) is one of the most important cultivated crops in the World [1]. The traditional methods require destruction of the plants for SOD detection, which prevents the further use of the leaf samples. The correlation coefficients should be over 0.8 in the final prediction model for agriculture applications, which means the model could be considered as an effective and quantitative determination. The correlation between NIR data and SOD activity in barley leaves has not been studied in detail. The objectives of this experiment were to study the feasibility of using NIR spectroscopy to predict the activity of SOD in barley leaves, and compare the performance of different spectral preprocessing methods, different effective selection methods and calibration methods (partial least squares, least squares-support vector machine and Gaussian process)
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