Evaluation of near-infrared chemical imaging for the prediction of surface water quality parameters

Abstract

Near-infrared (NIR) chemical imaging is an emerging technique with the potential for the detection of contaminants in the environmental field. In this study the potential of NIR chemical imaging (NIR-CI) to predict concentrations of nutrients (total nitrogen, total phosphorus) and indicator microorganisms (Escherichia coli) in surface water was investigated. Chemical images of multiple samples were obtained simultaneously using a pushbroom imaging system operating in the 950–1650 nm wavelength range with spectral resolution of 7 nm. Using partial least squares regression models, the relationship between these pollutants and NIR spectral data extracted from the chemical images in samples of aqueous surface water and filtered residue from surface water was assessed. When calibration models were tested on an independent data set, it was found that models developed on filtered residue spectra outperformed those developed on aqueous samples. For samples of filtered residue, the performance of the calibrations achieved for total nitrogen was reasonable (R² > 0.75); however, performance for total phosphorus and E. coli was poor (R² < 0.5). Lower concentrations of these parameters were detected in the surface water samples included in the study (<1 mg L⁻¹ and <20 colony-forming units per 100 mL, respectively), a likely reason for the poor performance. The results indicate that NIR-CI has the potential for screening samples in which the contaminant concentration exceeds 1 mg L⁻¹.

Keywords:

• near-infrared
• spectroscopy
• water quality
• partial least squares regression

Additional information

Funding

This work was supported by the Irish Research Council (IRC) under the ‘EMBARK Initiative’ Postgraduate Scholarship Scheme. Aoife Gowen, Rory Coffey and Enda Cummins acknowledge funding from the EU FP7 Marie Sklodowska-Curie Actions. Aoife Gowen also acknowledges funding from the European Research Council.