title: [Measurement of soil organic matter and available K based on SPA-LS-SVM].
abstract: Visible and short wave infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement of soil organic matter (OM) and available potassium (K). Four types of pretreatments including smoothing, SNV, MSC and SG smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were implemented for calibration models. The LS-SVM model was built by using characteristic wavelength based on successive projections algorithm (SPA). Simultaneously, the performance of LSSVM models was compared with PLSR models. The results indicated that LS-SVM models using characteristic wavelength as inputs based on SPA outperformed PLSR models. The optimal SPA-LS-SVM models were achieved, and the correlation coefficient (r), and RMSEP were 0. 860 2 and 2. 98 for OM and 0. 730 5 and 15. 78 for K, respectively. The results indicated that visible and short wave near infrared spectroscopy (Vis/SW-NIRS) (325 approximately 1 075 nm) combined with LS-SVM based on SPA could be utilized as a precision method for the determination of soil properties.
authors.forename: Hai-Liang
authors.initials: HL
authors.lastname: Zhang
authors.forename: Xue-Mei
authors.initials: XM
authors.lastname: Liu
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D009930:Organic Chemicals / Q000032:analysis*; D011188:Potassium / Q000737:chemistry*; D012987:Soil / Q000737:chemistry*; D013055:Spectrophotometry, Infrared; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine
chemical_list: D009930:Organic Chemicals; D012987:Soil; D011188:Potassium