Fast Analysis of Superoxide Dismutase (SOD) Activity in Barley Leaves Using Visible and Near Infrared Spectroscopy

Sensors

Wenwen Kong¹  Yun Zhao¹  Fei Liu¹  Yong He¹  Tian Tian² 

[1]College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

[2] E-Mails:

[3]College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China

[4] E-Mails:

关键词: visible and near infrared spectroscopy;    barley;    superoxide dismutase;    variable selection;    least squares-support vector machine;    Gaussian process regression;   

DOI  :  10.3390/s120810871

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.