Selection of Preprocessing Parameters for PCA of Soil Classification Affected by Particle Sizes Based on Vis/NIR Spectroscopy

Abstract:

. Instrumental measurement of soil properties are affected by several factors including soil texture. The classification of soil texture can help improve the accuracy of calibration models for soil measurement. In the study, the effect of soil particle size on the selection of preprocessing methods for principle component analysis (PCA) of soil classification was examined. Total 178 soil samples with five textures were collected from Silsoe Farm, Cranfield, England. After dried and ground, soil samples sieved by a 2mm sieve were named by Mixed Group. A Vis/NIR spectrophotometer (LabSpec2500, spectral range 350-2200nm, ASD) was used for spectral scanning of soil samples. After that, all samples were sieved by a 1mm sieve and divided into two groups: one with particle size less than 1mm named by Thin Group and another with particle size between 1mm and 2mm named by Thick Group. Preprocessing methods of moving average with segment size of 5(MA5), standard normal variation (SNV) and 1st Savitzky-Golay derivatives with smoothing points of 3(Der1(3)) were examined. PCA was applied to evaluate the discriminative capacity of MA5, MA5+SNV and MA5+Der1(3). The score plots of 1st~2nd and 2nd~3rd PCs show that MA5+Der1(3) is the best preprocessing method not only for Thick Group and Thin Group, but also for Mixed Group. MA5+SNV is suitable for Thick Group and Thin Group but does not perform well for Mixed Group. Only MA5 does not perform well in any of three groups. The study suggests that pre-processing with 1st derivative is an essential step for soil classification with various particle sizes using Vis/NIR spectroscopy.

• Periodical: Key Engineering Materials (Volumes 467-469)
• Pages: 725-730
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.467-469.725
• Online since: February 2011
• Authors: Hai Qing Yang, Bo Yan Kuang, Abdul M. Mouazen
• Keywords: Derivative, Particle Size, Principal Component Analysis (PCA), Soil Classification, Standard Normal Variation

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