title: Visible/near infrared spectroscopy and chemometrics for the prediction of trace element (Fe and Zn) levels in rice leaf.
abstract: Two sensitive wavelength (SW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the levels of some trace elements (Fe, Zn) in rice leaf. A total of 90 samples were prepared for the calibration (n = 70) and validation (n = 20) sets. Calibration models using SWs selected by LVA and ICA were developed and nonlinear regression of a least squares-support vector machine (LS-SVM) was built. In the nonlinear models, six SWs selected by ICA can provide the optimal ICA-LS-SVM model when compared with LV-LS-SVM. The coefficients of determination (R2), root mean square error of prediction (RMSEP) and bias by ICA-LS-SVM were 0.6189, 20.6510 ppm and -12.1549 ppm, respectively, for Fe, and 0.6731, 5.5919 ppm and  1.5232 ppm, respectively, for Zn. The overall results indicated that ICA was a powerful way for the selection of SWs, and Vis/NIR spectroscopy combined with ICA-LS-SVM was very efficient in terms of accurate determination of trace elements in rice leaf.
authors: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. yhe@zju.edu.cn
authors: Yongni
authors: Y
authors: Shao
authors: Yong
authors: Y
authors: He
mesh_terms: D002138:Calibration; D019992:Databases as Topic; D007501:Iron / Q000032:analysis*; D016018:Least-Squares Analysis; D012275:Oryza / Q000737:chemistry*; D018515:Plant Leaves / Q000737:chemistry*; D025341:Principal Component Analysis; D012015:Reference Standards; D015203:Reproducibility of Results; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D060388:Support Vector Machine; D015032:Zinc / Q000032:analysis*
chemical_list: D007501:Iron; D015032:Zinc
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