Analysis of petroleum contaminated soils by spectral modeling and pure response profile recovery of n-hexane

Somsubhra Chakraborty, David C. Weindorf, Bin Li, Md Nasim Ali, K. Majumdar, D. P. Ray

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

This pilot study compared penalized spline regression (PSR) and random forest (RF) regression using visible and near-infrared diffuse reflectance spectroscopy (VisNIR DRS) derived spectra of 164 petroleum contaminated soils after two different spectral pretreatments [first derivative (FD) and standard normal variate (SNV) followed by detrending] for rapid quantification of soil petroleum contamination. Additionally, a new analytical approach was proposed for the recovery of the pure spectral and concentration profiles of n-hexane present in the unresolved mixture of petroleum contaminated soils using multivariate curve resolution alternating least squares (MCR-ALS). The PSR model using FD spectra (r2 = 0.87, RMSE = 0.580 log10 mg kg-1, and residual prediction deviation = 2.78) outperformed all other models tested. Quantitative results obtained by MCR-ALS for n-hexane in presence of interferences (r2 = 0.65 and RMSE 0.261 log10 mg kg-1) were comparable to those obtained using FD (PSR) model. Furthermore, MCR ALS was able to recover pure spectra of n-hexane.

Original languageEnglish
Pages (from-to)10-18
Number of pages9
JournalEnvironmental Pollution
Volume190
DOIs
StatePublished - Jul 2014

Keywords

  • Penalized spline
  • Petroleum
  • Random forest
  • Standard normal variate
  • Visible near infrared diffuse reflectance spectroscopy
  • n-hexane

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