01785naa a2200253 a 450000100080000000500110000800800410001902200140006002400380007410000240011224500930013626000090022952010640023865000170130265000200131965000210133965000120136065000190137270000200139170000180141170000210142970000230145077300580147321084162020-02-12       2019    bl uuuu     u00u1 u    #d  a0026-265X7 a10.1016/j.microc.2019.03.0702DOI1 aMORAIS, P. A. de O.  aUsing image analysis to estimate the soil organic carbon content.h[electronic resource]  c2019  aSoil test for organic carbon content using the Walkley-Black standard method is a laborious task that takes almost 5 working days to report the results. In order to improve lab analysis efficiency, a new analytical method using digital images is proposed. Using multivariate image analysis (MIA) soil organic carbon (SOC) contents can be determined in&lt3 days. The MIA method for SOC was developed using 177 soil samples collected from 3 regions of Brazil (North, West Central, and Northeast). Digital images of soil samples were correlated with the organic carbon contents determined by the Walkley-Black standard method using multivariate regression algorithms. In the end, MIA model employing a machine learn approach using least squares support vector machine (LS-SVM), presented an excellent correlation, r2 &gt 0.93, between image data and SOC contents measured by the standard analytical method. The proposed MIA method is eco-friendly, cheap, fast, and a clean alternative that can be employed by soil testing laboratories for measuring SOC contents.  aChemometrics  aGreen chemistry  aSoil test values  aCarbono  aSolo Orgânico1 aSOUZA, D. M. de1 aMADARI, B. E.1 aSOARES, A. da S.1 aOLIVEIRA, A. E. de  tMicrochemical Journalgv. 147, p. 775-781, June 2019.