01674naa a2200253 a 450000100080000000500110000800800410001902400540006010000220011424501360013626000090027252008710028165000240115265000170117665000200119365300300121365300210124365300270126465300260129170000170131770000200133470000170135477300490137121339852021-11-08       2021    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1016/j.geodrs.2021.e004362DOI1 aSANTANA, F. B. de  aComparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries.h[electronic resource]  c2021  aIn this study a systematic comparison was carried out to assess differences on the accuracy between partial least squares (PLS) and support vector machine (SVM) regression algorithms in soil organic matter and particle size determinations using vis-NIR spectroscopy. The comparison consisted in investigating the influence on the size of calibration set on the external validation set accuracy. For this purpose, three vis-NIR soil libraries containing 14,212, 15,330 and 42,471 soil samples were used to determine sand, clay, and SOM content, respectively. To increase the variability of the results obtained, each calibration subset was randomly generated 49 times and for each iteration a PLS, SVM-Linear and SVM-RBF (radial basis function) regression models were built. These calibration subsets were composed by 250, 1000, 2000, 5000 and 8000 or 10,000 samples.  aSoil organic carbon  aSoil texture  aTextura do Solo  aCarbono orgânico do solo  aMachine learning  aMolecular spectroscopy  aSoil spectral library1 aOTANI, S. K.1 aSOUZA, A. M. de1 aPOPPI, R. J.  tGeoderma Regionalgv. 27, e00436, Dec. 2021.