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

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