Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site

Abstract The potential of mid-infrared spectroscopy in combination with partial least-squares regression was investigated to estimate total and phosphate-extractable arsenic contents in soil samples collected from a highly variable arsenic-contaminated disused cattle-dip site. Principal component analysis was performed prior to mid-infrared partial least-squares analysis to identify spectral outliers in the absorbance spectra of soil samples. The mid-infrared partial least-squares calibration model (n = 149) excluding spectral outliers showed an acceptable reliability (coefficient of determination, $$R_{\text{c}}^{2}$$Rc2 = 0.75 (P < 0.01); ratio of performance to interquartile distance, RPIQc = 2.20) to estimate total soil arsenic. For total soil arsenic, the validation of final calibration model using 149 unknown samples also resulted in a good acceptability with $$R_{\text{v}}^{2}$$Rv2 = 0.67 (P < 0.05) and RPIQv = 2.01. However, the mid-infrared partial least-squares calibration model based on phosphate-extractable arsenic was not acceptable to estimate the extractable (bioavailable) arsenic content in soil ($$R_{\text{c}}^{2}$$Rc2 = 0.13 (P > 0.05); RPIQc = 1.37; n = 149). The results show that the mid-infrared partial least-squares prediction model based on total arsenic can provide a rapid estimate of soil arsenic content by taking into account the integrated effects of adsorbed arsenic, arsenic-bearing minerals and arsenic associated with organic components in the soils. This approach can be useful to estimate total soil arsenic in situations, where analysis of a large number of samples is required for a single soil type and/or to monitor changes in soil arsenic content following (phyto)remediation at a particular site.