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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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Predicted vs. reference activity of SOD by SPA-LS-SVM (SG) in prediction set.
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f5-sensors-12-10871: Predicted vs. reference activity of SOD by SPA-LS-SVM (SG) in prediction set.

Mentions: Comparing the eight different models, they all achieved acceptable results. Table 4 lists the prediction results by different models. The best prediction performance was achieved by the LV-LS-SVM model with SG spectra, and correlation coefficients (r) = 0.9064 and root mean square error of prediction (RMSEP) = 0.5336. The LS-SVM and GPR models gave better prediction results than PLS and MLR models, which indicated that nonlinear calibration methods were more suitable for predicting activity of SOD in barley leaves. In this study, the performance of EWs selected by SPA was better than regression coefficients analysis. The possible reason was that SPA selected the relevant variables with least collinearity. However, both SPA and RC were considered useful methods for the selection of EWs, they selected just 2%–3% of the number of wavelengths as input for calibration models and gave acceptable results. The least EWs was 7, which selected by SPA according to SG spectra and the SPA-LS-SVM model gave the optimal result with r = 0.8267 and RMSEP = 0.7330, it was important for instrument development. The scatter plot for prediction sets by LV-LS-SVM(SG) and SPA-LS-SVM(SG) were shown in Figures 4 and 5.


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)

Predicted vs. reference activity of SOD by SPA-LS-SVM (SG) in prediction set.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-12-10871: Predicted vs. reference activity of SOD by SPA-LS-SVM (SG) in prediction set.
Mentions: Comparing the eight different models, they all achieved acceptable results. Table 4 lists the prediction results by different models. The best prediction performance was achieved by the LV-LS-SVM model with SG spectra, and correlation coefficients (r) = 0.9064 and root mean square error of prediction (RMSEP) = 0.5336. The LS-SVM and GPR models gave better prediction results than PLS and MLR models, which indicated that nonlinear calibration methods were more suitable for predicting activity of SOD in barley leaves. In this study, the performance of EWs selected by SPA was better than regression coefficients analysis. The possible reason was that SPA selected the relevant variables with least collinearity. However, both SPA and RC were considered useful methods for the selection of EWs, they selected just 2%–3% of the number of wavelengths as input for calibration models and gave acceptable results. The least EWs was 7, which selected by SPA according to SG spectra and the SPA-LS-SVM model gave the optimal result with r = 0.8267 and RMSEP = 0.7330, it was important for instrument development. The scatter plot for prediction sets by LV-LS-SVM(SG) and SPA-LS-SVM(SG) were shown in Figures 4 and 5.

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