- Title
- Spectroscopy-based chemometric approaches for the determination of antibiotics in soils
- Creator
- Bahremand Abrasi, Shabnam
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2024
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Detecting and quantifying organic contaminants, including pesticides, polycyclic aromatic hydrocarbons (PAHs), and pharmaceuticals usually present in the environment, are challenging tasks, primarily because of the complex analytical difficulties associated with these compounds. Traditional methods are labour-intensive and generally use toxic solvents. Non-destructive spectroscopy techniques, besides promoting sustainable analytical practices, offer opportunities to reduce toxic chemicals' environmental footprint. This thesis addresses developing the quantification procedure using chemometrics models and also investigates the non-destructive spectroscopy method, FTIR (Fourier transform infrared), which is more eco-friendly, combined with chemometrics. It aims to provide faster and greener methods for detecting antibiotics in soil matrices. The core focus of this research is a comprehensive Partial Least Squares (PLS) analysis of three quinolone antibiotics, Gatifloxacin, Ofloxacin, and Lomefloxacin (G, O, and L), within a diverse spectrum of soil matrices, ranging from the simple (KBr) to complex combinations (sand, clay, sand/clay, and clay/humic acid). Systemic FTIR-DRIFTS data collection has been done from individual quinolones and their mixtures across each matrix. The specific objectives involve gathering FTIR data for individual and mixed antibiotics in various soil matrices, creating and validating chemometric models tailored to each soil matrix, analysing experimental results, assessing model performance with different soil matrix calibrations, and understanding its behaviour with combined and comprehensive soil matrix calibrations. The first study comprises five separate experiments conducted in five different soil matrices (KBr, Sand, Clay, Sand & Clay and Clay & humic acid). For each experiment, a total of 54 spectral data sets were collected. These data sets were obtained from varying concentrations (ranging from 0.1% to 10%) of individual antibiotics (G, O, and L), as well as mixtures of these antibiotics using FTIR spectroscopy. These data sets have been subjected to PLS analysis. The PLS model shows promising performance in detecting G, O, and L in various soil matrices, with high R-squared (>0.95) indicating strong fits. The second study included applying sets of spectra from each sample soil matrix to all soil matrix calibrations from previous experiments. Therefore, 75 PLS analyses have been done for G, O, and L in five soil matrix samples (KBr, Sand, Clay, Sand & Clay, and Clay & humic acid). This comprehensive approach made it possible to thoroughly examine how different calibrations influenced the results. The study revealed a clear trend: when the sample and calibration matrices matched like both KBr (KBr/KBr) and both sand (S100/S100), the prediction models functioned exceptionally well with high R-squared and low RMSE values. In contrast, when the sample and calibration matrices differed (e.g., KBr/S100), the models struggled, resulting in lower R-squared values. The final study aimed to enhance analyte detection accuracy within diverse soil matrices. The first part introduced innovative calibration methods involving a combination of spectral data from different soil matrix sample sets. In the second part, a comprehensive calibration model was created by merging data from all five soil matrices. This comprehensive approach enabled the analysis of samples across different soil types and compositions, enhancing the model's versatility. The study's results emphasised that PLS models performed best when the calibration matrices closely matched the analysed soil matrices. In many cases, the comprehensive calibration model achieved impressive R-squared and RMSE values, indicating its effectiveness in detecting quinolone compounds.
- Subject
- chemometric; antibiotics; spectroscopy; soil
- Identifier
- http://hdl.handle.net/1959.13/1510180
- Identifier
- uon:56353
- Rights
- Copyright 2024 Shabnam Bahremand Abrasi
- Language
- eng
- Full Text
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