Oil pollutant identification based on excitation-emission matrix of UV-induced fluorescence and deep convolutional neural network

The application of laser‑induced fluorescence in oil spill detection

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

Oil species identification based on fluorescence excitation-emission matrix and transformer-based deep learning

After oil spills are found at sea, the identification on oil species can help determine the source of leakage and form the plan of post-accident treatment. Since the fluorometric characteristics of petroleum hydrocarbon reflect its molecular structure, the composition of oil spills could potentially be inferred using the fluorescence spectroscopy method. The excitation-emission matrix (EEM) includes additional fluorescence information in the dimension of excitation wavelength, which could be useful to identify oil species. This study proposed an oil species identification model using transformer network. The EEMs of oil pollutants are reconstructed into sequenced patch input that consists of the fluorometric spectra obtained under the different excitation wavelengths. The comparative experiments show that the proposed model can reduce the incorrect predictions and achieve higher identification accuracies than the regular convolutional neural networks that have been used in the previous studies. According to the structure of transformer network, an ablation experiment is also designed to evaluate the contributions of different input patches and seek for the optimal excitation wavelengths for oil species identification. The proposed model is expected to identify oil species, and even other fluorescent materials, based on the fluorometric spectra collected under multiple excitation wavelengths.

Objective interpretation of ultraviolet-induced luminescence for characterizing pictorial materials

Ultraviolet-induced Luminescence (UVL) is the property of some materials of emitting light once illuminated by a source of UV radiation. This feature is characteristic of some mediums and pigments, such as some red lakes, widely used for the realisation of works of art. On the one hand, UVL represents a like strike for a researcher in the cultural heritage field: in fact, UVL allows to characterise the state of conservation of the paintings and, in some cases, to recognize at glance some of the materials used by the artists. On the other hand, the contribution of UVL to the study of the artefacts is almost always limited to qualitative observation, while any speculation about the cause of the luminescence emission relies on the observer's expertise. The aim of this paper is to overcome this paradigm, moving a step toward a more quantitative interpretation of the luminescence signal. The obtained results concern the case study of pictorial materials by Giuseppe Pellizza da Volpedo (1868-1907, Volpedo, AL, Italy) including his iconic masterpiece Quarto Stato (1889-1901), but the method has general validity and can be applied whenever the appropriate experimental conditions occur. Once designed an appropriate set-up, the statistical comparison between the acquisitions performed on Quarto Stato, on a palette belonged to the master, on drafts made by the author himself and on a set of ad hoc prepared samples both with commercial contemporary pigments and prepared with the traditional recipe, shed some light on which materials have been employed by the artist, where they have been applied and support some intriguing speculations on the use of the industrial lakes in the Quarto Stato painting.

Deep or Shallow? A Comparative Analysis on the Oil Species Identification Based on Excitation-Emission Matrix and Multiple Machine Learning Algorithms

With the continuous expansion of petroleum extraction, transportation, and storage, the risk of oil spills at sea has also increased, posing a serious threat to marine safety. The excitation-emission matrix (EEM), which is composed of the fluorometric spectra under multiple excitation wavelengths, becomes a feasible approach to identify oil species. Despite the fact that various machine learning models have been applied to analyse EEMs of oil pollutants, it is unclear how much improvements the deep learning models have achieved, especially comparing with the shallow learning models. This paper presents a comparative analysis on the oil species identification using four types of machine learning models: random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and deep convolutional neural network (DCNN). The fluorescence of some common oils was excited using a tuneable xenon lamp and collected with a high-resolution spectrometer to form the EEMs for model training and testing.The results show that SVM, BPNN, and DCNN achieved high identification accuracies that are more than 93% on all types of oils tested in the study. The two deep learning models didn't have significant improvement over the SVM model. Considering the fact that the deep learning models require much larger number of calculations and longer running time, the SVM tends to be more suitable for oil species identification when considering the balance between the model accuracy and efficiency. This study provides some guidance on the choices of oil species identification model in the cases of oil spills.

Establishment and optimization of the three-band fluorometric indices for oil species identification: Implications on the optimal excitation wavelengths and the detection band combinations

BACKGROUND

The oil pollutants have become a major threat to the ocean environment with the rapid development of social economy and offshore production activities. The detection and identification of petroleum pollutants is the foundation and pre-requisite for controlling offshore oil pollution. Spectroscopic analysis using the ultraviolet-induced fluorescence of oil pollutants is a potential way for oil species identification. Nevertheless, the current fluorescence spectroscopic analysis methods, such as excitation-emission matrix, has high requirements on the detection equipment. There is a great need for rapid oil species identification method when it comes to practical applications. (93) RESULTS: This study established the three-band fluorometric index (TBFI) method and examined its capability on the oil species identification. The fluorescence of five different types of oil samples under various excitation wavelengths were excited using a tuneable xenon lamp, and measured using a high-resolution spectrometer. The optimal excitation wavelengths and the corresponding detection band combinations were explored through the iterations of an optimal band combination algorithm through different excitation wavelengths. According to the results, three out of the four TBFIs tested in this study are suitable for oil species identification. They share the same sets of the excitation wavelengths under 365 nm, 375 nm, and 435 nm. The detection band combinations corresponding to each TBFI under each feasible excitation wavelength were also determined in this study. (125) SIGNIFICANCE: The three-band fluorometric index method proposed in this study provides a feasible way for the rapid oil species identification using fluorescence spectroscopic analysis. The optimal excitation and detection wavelengths determined in this study also provide the theoretical basis for the designs of fluorescence sensors for rapid oil spill detection and identification. (51).

A Comprehensive Review on the Excitation-Emission Matrix Fluorescence Spectroscopic Characterization of Petroleum-Containing Substances: Principles, Methods, and Applications

Petroleum-containing substance (PCS) is a general term used for petroleum and its derivatives. A comprehensive characterization of PCSs is crucial for resource exploitation, economic development and environmental protection. Fluorescence spectroscopy, especially excitation-emission matrix fluorescence (EEMF) spectroscopy, has been proved to be a powerful tool to characterize PCSs since its remarkable sensitivity, selectivity, simplicity and high efficiency. However, there is a lack of systematic review focusing on this field in the literature. This paper reviews the fundamental principles and measurements of EEMF for characterizing PCSs, and makes a systematic introduction to various information mining methods including basic peak information extraction, spectral parameterization and some commonly used chemometric methods. In addition, recent advances in applying EEMF to characterize PCSs during the whole life-cycle process of petroleum are also revisited. Furthermore, the current limitations of EEMF in the measurement and characterization of PCSs are discussed and corresponding solutions are provided. For promoting the future development of this field, the urgent need to build a relatively complete EEMF fingerprint library to trace PCSs, not only pollutants but also crude oil and petroleum products, is proposed. Finally, the extensions of EEMF to high-dimensional chemometrics and deep learning are prospected, with the expectation of solving more complex systems and problems.