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

• Main Authors: Fei Liu, Yun Zhao, Wenwen Kong, Yong He, Tian Tian, Weijun Zhou
• Format: Article
• Language: English
• Published: MDPI AG 2012-08-01
• Series: Sensors
• Subjects: visible and near infrared spectroscopy; barley; superoxide dismutase; variable selection; least squares-support vector machine; Gaussian process regression
• Online Access: http://www.mdpi.com/1424-8220/12/8/10871

Visible and near infrared (Vis/NIR) spectroscopy was investigated for the fast analysis of superoxide dismutase (SOD) activity in barley (<em>Hordeum</em> <em>vulgare</em> 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 (<em>r</em>) 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.