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Fast analysis of superoxide dismutase (SOD) activity in barley leaves using visible and near infrared spectroscopy.

Kong W, Zhao Y, Liu F, He Y, Tian T, Zhou W - Sensors (Basel) (2012)

Bottom Line: Seven different spectra preprocessing methods were compared.The results indicated that Savitzky-Golay smoothing (SG) and multiplicative scatter correction (MSC) should be selected as the optimum preprocessing methods.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.

View Article: PubMed Central - PubMed

Affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. zjukww@163.com

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.

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Selected EWs by SPA according to SG spectra.
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f2-sensors-12-10871: Selected EWs by SPA according to SG spectra.

Mentions: Table 3 shows the selected EWs by SPA and RC with the optimized preprocessing spectra. In the SPA, the maximum number of selected variable was set as 30 and cross-validation was applied. In SPA, the cross-validation was carried out to the training set (calibration set) to make sure that the selection of relevant variables were stable and robust, and avoiding the possible over-fitting problems. The EWs selected by SPA were ranked in the order of importance in Table 3. The locations of the selected EWs by SPA according to SG spectra were shown in Figure 2 and the regression coefficient plot is shown in Figure 3.


Fast analysis of superoxide dismutase (SOD) activity in barley leaves using visible and near infrared spectroscopy.

Kong W, Zhao Y, Liu F, He Y, Tian T, Zhou W - Sensors (Basel) (2012)

Selected EWs by SPA according to SG spectra.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472862&req=5

f2-sensors-12-10871: Selected EWs by SPA according to SG spectra.
Mentions: Table 3 shows the selected EWs by SPA and RC with the optimized preprocessing spectra. In the SPA, the maximum number of selected variable was set as 30 and cross-validation was applied. In SPA, the cross-validation was carried out to the training set (calibration set) to make sure that the selection of relevant variables were stable and robust, and avoiding the possible over-fitting problems. The EWs selected by SPA were ranked in the order of importance in Table 3. The locations of the selected EWs by SPA according to SG spectra were shown in Figure 2 and the regression coefficient plot is shown in Figure 3.

Bottom Line: Seven different spectra preprocessing methods were compared.The results indicated that Savitzky-Golay smoothing (SG) and multiplicative scatter correction (MSC) should be selected as the optimum preprocessing methods.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.

View Article: PubMed Central - PubMed

Affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. zjukww@163.com

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.

Show MeSH
Related in: MedlinePlus