Abstract
For fast detection of inorganic phosphorus fractions and their phosphorus contents in soil, a method employing near-infrared spectroscopy (NIRS) combined with partial least squares (PLS) and least squares support vector machine (LS-SVM) was proposed. Fifty soil samples for each of iron phosphate, magnesium phosphate, calcium phosphate and aluminum phosphate, with application rates of phosphate being respectively 0, 60, 200 and 500 mgkg-1, were taken to acquire the near infrared reflectance spectroscopy of the soil samples. PLS model regression coefficients were used to obtain the effective wavelengths (EWs) of the four phosphates as the input of LS-SVM to create a prediction model for detection of these phosphates and their phosphorus contents in the soil samples. The result showed that LS-SVM model using EWs as input had an advantage over PLS model. The correlation coefficient R2 of EWs-LS-SVM model was respectively 0.90, 0.86, 0.88 and 0.85, and the mean square error of prediction (RMSEP) was respectively 12, 10, 15 and18. For classification of various phosphates, a number of wavelengths equal to 12 yielded the optimal result with a 2% minimum classification error. The result showed that the method of NIRS combined with PLS and LS-SVM offered high accuracy and allowed for fast detection of inorganic phosphorus fractions and their phosphorus contents in soil.
