Fingerprinting Technique Of Oil Species Based On Concentration Parameter Fluorescence Spectra

Fingerprinting Technique of Oil Species Based on Concentration Parameter Fluorescence SpectraThere has been a growing concern in recent years about increasing occurrence of spilled oils to the environment and proven toxic potential of these pollutants on human health and wildlife. The existence of these harmful substances in the environment has disrupted the natural cycles and processes and caused great economic loss to nations rich or poor. Precisely determining the sources of spilled oils can provide scientific evidence for the investigation and handling of spilled oils accidents.The development and implement of a method that is efficient, economic and easy to use routinely could offer decision-makers and model developers preliminary information of spilled oils in a short period, while the complicated approaches could provide more detailed information afterwards.The oil-bearing samples contain traces of polycyclic aromatic hydrocarbons (PAHs) that are highly fluorescent. Fluorescence-based techniques feature high sensitivity, good diagnostic potential, relatively simple instrumentation and suitability for portable instrumentation. Unfortunately, the chemical and physical complexities of crude oils and petroleum products lead to broad spectra without fine structures.Two main areas of interest distinguished themselves in fluorescence techniques over the last two decades:Multi-dimensional Fluorescence techniques to obtain more fluorescence information of multi-fluorehore mixtures, and the applications of chemometrics in spectroscopic study.The thesis begins with an overview of spilled oils fingerprinting technique and the development of Enviromental Forensics, followed by a detailed review of relevant studies, including (i) application of fluorescence techniques to petroleum-related samples and the concentration-depended fluorescence studies of PAHs and (ii) application of chemometrics to enviromental analysis of PAHs. In this thesis, two recent versions of this technique, Multi-dimensional Fluorescence techniques and data mining methods, have been applied to the analysis of spilled oils samples to improve identification accuracy.In Chapter 3, concentration-dependent fluorescence study of single PAH molecules and petroleum-related samples were presented first. Based on a detailed discussion on different spectrum approaches and extractants, the author described a novel method she developed for species identification of petroleum-related samples using the concentration auxiliary parameter synchronous fluorescence technique. By introducing concentration value as a parameter, a new Concentration-Synchronous-Matrix-Fluorescence (CSMF) spectrum was formed with a series of synchronous fluorescence spectra (SFS) at different levels of concentration. It was observed that the SFS varied with concentration level and the profiles of CSMF spectra changed from species to species. Therefore, CSMF spectra can be used for species identification.A detailed experimental investigation on different levels of petroleum-related samples is given in Chapter 4, along with the consideration of various disturbances, such as weathering, adulation of seawater, change of light source intensity, mixture of different oils, among others. The CSMF spectra of 36 petroleum-related samples from different oil-spill types had been obtained and three data sets were chosen to assess the feasibility and performance of the feature extraction and pattern recognition methods used in this thesis work.The author's main work is to perform data mining of the CSFM spectra, which is described in Chapters 5 and 6. Effective feature extraction is the key to accurate pattern recognition. In Chapter 5, principal components analysis (PCA) and partial least square analysis (PLS) are used to extract the main orthogonal contributions, which explain most of the variance of the spectra measurement matrix. The results show that the PCA can divide the samples to different oil types in the principal components space, while PLS can give a better classification of the closely-related source of petroleum-related samples due to its ability to find the multi-dimensional direction in the measurement matrix space that explains the maximum direction in the response vector space. The CSMF images transformed by Gabor wavelet exhibit strong characteristics of spatial locality, and scale and orientation selectivity, and Gabor is shown to be the best feature extraction method to the pattern recognition.The work presented in Chapter 6 was carried out to measure the effectiveness, computing speed, and accuracy of the classification methods used in this thesis. The partial surface fitting to CSFM with interpolation was introduced first. With surface fitting, CSMF spectra of the closely-related source crude oil samples were successfully discriminated, and the initial concentration of the test samples was also obtained. Large disturbances, however, result in low accuracy of discrimination.The feature extraction methods, such as PCA, PLS and Gabor wavelet, combining with the pattern recognition methods, such as artificial neural network (ANN) and supported vector machine (SVM), were used to identify the CSFMs of the data sets introduced in Chapter 4. An ideal result of closely-related oil source samples with the 92% of the correct rate of oil species recognition is achieved by combining Gabor wavelet with SVM. The obtained results suggest that the newly-developed method may become a more specifically applicable means in spilled oils identification.In the last chapter of the thesis, a thorough discussion of the thesis work is given. In addition, suggestions for future work are provided, including direction of data mining, concept of new instrumentation design, and several additional experiments, which should lead to a better understanding of the mechanisms involved in concentration-depended fluorescence spectra.