Electronic nose and SPME techniques to monitor phenanthrene biodegradation in soil

Abstract

Phenanthrene is a hydrophobic organic pollutant, composed of three--fused benzene rings belonging to polycyclic aromatic hydrocarbons (PAHs). PAHs are produced by incomplete combustion of organic matter due to natural events, i.e. fires, and anthropogenic actions, i.e. C--containing fuels combustion. The presence of PAHs in contaminated soils is usually detected by classical extraction techniques, such as solvent extractions through Soxhlet extractor. In this work two recently developed techniques (solid phase microextraction-SPME and electronic nose-EN) able to analyse the headspace of solid or liquid samples were used to monitor the possible degradation of phenanthrene in an artificially contaminated soil. The analysis by SPME showed a drastic decrease of phenanthrene content after 30 days of incubation (−92%) and different treatments with nutrient solutions and/or surfactant improve this rate up to 97%. Differently, the analysis of soil headspaces by EN, processed by principal component analysis (PCA), showed that contaminated and uncontaminated soil samples (controls) might be distinguished on a temporal scale. Furthermore, PCA showed that phenanthrene--contaminated soil samples produced chemical images, which were delayed relative to controls at the same period of incubation. The application of partial least square--discriminant analysis (PLS DA) to chromatograms obtained by SPME pointed out the presence, in the headspace of phenanthrene--treated soils, of a series of possible indicators involved in phenanthrene degradation, which were completely absent in relative unpolluted controls. Results suggest that the two techniques do not necessarily represent mutually exclusive alternatives, but giving different information, they may be considered as complementary.

A Suppression Method of Concentration Background Noise by Transductive Transfer Learning for a Metal Oxide Semiconductor-Based Electronic Nose

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Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection

“…It is able to imitate the olfaction system of humans and mammals, and to recognize odorant gases (Sohn et al, 2008;Zhang et al, 2011). In the past 20 years, a number of studies on E-nose have been presented for rapid detection and classification of chemicals in many fields, that is, food industry (Brudzewski et al, 2012a(Brudzewski et al, , 2012bConcina et al, 2012;Bhattacharyya et al, 2008;Tudu et al, 2009), environmental monitor (Arnold et al, 2012;Cesare et al, 2008;Romain and Nicolas, 2010), medical diagnosis (Tran et al, 2010;Guo et al, 2010;Trincavelli et al, 2010;Adiguzel and Kulah, 2015;Bruins et al, 2013), public security (Brudzewski et al, 2012a(Brudzewski et al, , 2012b, etc.…”

On-line Calibration of Semiconductor Gas Sensors Based on Prediction Model

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