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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472 EISSN: 1735-1472
Vol. 12, No. 6, 2015, pp. 1965-1974
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Bioline Code: st15183
Full paper language: English
Document type: Research Article
Document available free of charge
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International Journal of Environment Science and Technology, Vol. 12, No. 6, 2015, pp. 1965-1974
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Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site
Niazi, N. K.; Singh, B. & Minasny, B.
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, 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 Rv2
= 0.67 (P> 0.05) and
RPIQv = 2.01. However, the mid-infrared partial leastsquares
calibration model based on phosphate-extractable
arsenic was not acceptable to estimate the extractable (bioavailable)
arsenic content in soil (Rc2
= 0.13 (P> 0.05);
RPIQc = 1.37; n = 149). The results show that the midinfrared
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.
Keywords
Mid-infrared; Partial least-squares; Principal component; Cattle-dip sites; (Phyto)remediation; Prediction model; Contamination
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