Oil species identification technique developed by Gabor wavelet analysis and support vector machine based on concentration-synchronous-matrix-fluorescence spectroscopy

Online detection of alkanes by a biological-phase microextraction and biosensing (BPME-BS) device

Oil spill incidents occur frequently and threaten ecosystems and human health. Solid-phase microextraction allows direct alkane extraction from environmental matrices to improve the limit of detection but is unable to measure alkanes on site. A biological-phase microextraction and biosensing (BPME-BS) device was developed by immobilising an alkane chemotactic Acinetobacter bioreporter ADPWH_alk in agarose gel to achieve online alkane quantification with the aid of a photomultiplier. The BPME-BS device had a high enrichment factor (average 7.07) and a satisfactory limit of detection (0.075 mg/L) for alkanes. The quantification range was 0.1-100 mg/L, comparable to a gas chromatography flame ionisation detector and better than a bioreporter without immobilisation. ADPWH_alk cells in the BPME-BS device maintained good sensitivity under a wide range of environmental conditions, including pH (4.0-9.0), temperature (20-40 °C), and salinity (0.0-3.0%), and its response remained stable within 30 days at 4 °C. In a 7-day continual measurement, the BPME-BS device successfully visualised the dynamic concentration of alkanes, and a 7-day field test successfully captured an oil spill event, helping in source apportionment and on-scene law enforcement. Our work proved that the BPME-BS device is a powerful tool for online alkane measurement, showing substantial potential for fast detection and rapid response to oil spills on site and in situ.

Emerging Chemical Methods for Petroleum and Petroleum-Derived Dissolved Organic Matter Following the Deepwater Horizon Oil Spill

Despite the fact that oil chemistry and oils spills have been studied for many years, there are still emerging techniques and unknown processes to be explored. The 2010 Deepwater Horizon oil spill in the Gulf of Mexico resulted in a revival of oil spill research across a wide range of fields. These studies provided many new insights, but unanswered questions remain. Over 1,000 journal articles related to the Deepwater Horizon spill are indexed by the Chemical Abstract Service. Numerous ecological, human health, and organismal studies were published. Analytical tools applied to the spill include mass spectrometry, chromatography, and optical spectroscopy. Owing to the large scale of studies, this review focuses on three emerging areas that have been explored but remain underutilized in oil spill characterization: excitation-emission matrix spectroscopy, black carbon analysis, and trace metal analysis using inductively coupled plasma mass spectrometry.

Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion-emission matrices

In recent years, marine oil spill accidents have been occurring frequently during extraction and transportation, and seriously damage the ecological balance. Accurate monitoring of oil spills plays a vital role in estimating oil spill volume, determination of liability, and clean-up. The oil that leaks into natural environments is not a single type of oil, but a mixture of various oil products, and the oil film thickness on the sea surface is uneven under the influence of wind and waves. Increasing the mixed oil film thickness dimension and the mix proportion dimension has been proposed to weaken the effect of the detection environment on the fluorescence measurement results. To preserve the relationships between the data of oil films with different thicknesses and the relationships between the data of oil films with different mixing proportions, the three-dimensional fluorescence spectral data of mixed oil films on a seawater surface were measured in the laboratory, producing a thickness-fluorescence matrix and a proportion-fluorescence matrix. The nonlinear variation of the fluorescence spectra was investigated according to the fluorescence lidar equation. This work pre-processes the data by sum normalization and two-dimensional principal component analysis (2DPCA) and uses the dimensionality reduction results as two feature-point views. Then, semi-supervised classification of collaborative training (co-training) with K-nearest neighbors (KNN) and a decision tree (DT) is used to identify the samples. The results show that the average overall accuracy of this coupling model can reach 100%, which is 20.49% higher than that of the thickness-only view. Using unlabeled data can reduce the cost of data acquisition, improve the classification accuracy and generalization ability, and provide theoretical significance and application prospects for discrimination of spectrally similar oil species in natural marine environments.

Identification of a detection panel for post-transplant virus infection through integrated analysis of non-coding RNAs in peripheral blood

Viral infection seriously affects the survival and life quality of transplanted patients without an accurate diagnosis during the early stage. Herein, we aimed to develop a novel diagnostic method based on non-coding RNAs expression in peripheral blood. An immunosuppressive mouse model of viral infection after transplantation was established. Differentially expressed non-coding RNAs were distinguished by microarray analyses in the virus-infected group. After homology analysis, 46 miRNAs and 24 lncRNAs were further verified by qRT-PCR in the peripheral blood samples of transplanted patients. Compared with normal transplanted patients, miR-29b, miR-185, and NR_073415.2 were significantly downregulated in the PBMC of post-transplant patients with viral infection. Based on the expression of the above three RNAs, principal component analysis (PCA) identified a slight overlap between the two groups. A 3-non-coding-RNA detection panel was constructed by the support vector machine analysis (SVM), whose loss rate was 14.71%. The area under the curve of it was 0.909. With the optimal cut-off value (Y = 0.328), the sensitivity was 0.929 and the specificity was 0.781. Therefore, based on non-coding RNAs expressions, a detection panel for viral infection after organ transplantation was formed with high diagnostic specificity and sensitivity.

Decision support tools for oil spill response (OSR-DSTs): Approaches, challenges, and future research perspectives

Marine oil spills pose a significant threat to ocean and coastal ecosystems. In addition to costs incurred by response activities, an economic burden could be experienced by stakeholders dependent on coastal resources. Decision support tools for oil spill response (OSR-DSTs) have been playing an important role during oil spill response operations. This paper aims to provide an insight into the status of research on OSR-DSTs and identify future directions. Specifically, a systematic review is conducted including an examination of the advantages and limitations of currently applied and emerging decision support techniques for oil spill response. In response to elevated environmental concerns for protecting the polar ecosystem, the review includes a discussion on the use of OSR-DSTs in cold regions. Based on the analysis of information acquired, recommendations for future work on the development of OSR-DSTs to support the selection and implementation of spill response options are presented.

Rapid fingerprinting technology of heavy oil spill by mid-infrared spectroscopy

With the increase of unconventional oil production and transportation, the detection methods of light crude oil have been challenged. Mid-Infrared spectroscopy can reflect the functional group of the oil related samples, which has strong absorption signals with distinguishable peaks featured as a fast, economy, and robust technique. Nevertheless, the previous study and application of oil relevant samples, such as petroleum chemical industry online monitoring, are mainly based on Near-infrared spectroscopy. Recently, the rapid development of the spectral instrument manufacturing and the data analysis methods provides a more comprehensive technical support for the rapid and accurate identification of marine oil spill by Mid-infrared spectroscopy. In this paper, 10 crude oil samples were selected for infrared spectroscopy detection, and the results were analysed and compared with those of gas chromatography flame ionization detection method. The character information of the IR spectra and GC/FID chromatograms were extracted and classified both by principal component analysis and partial least squares regression. Under the condition of small sample size, the recognition accuracy was up to 100%. The results show that the mid-infrared method combined with chemometrics can be expected to achieve rapid, accurate and economical identification of heavy oil species.

Applying Raman Microspectroscopy to Evaluate the Effects of Nutrient Cations on Alkane Bioavailability to Acinetobacter baylyi ADP1

Contamination with petroleum hydrocarbons causes extensive damage to ecological systems. On oil-contaminated sites, alkanes are major components; many indigenous bacteria can access and/or degrade alkanes. However, their ability to do so is affected by external properties of the soil, including nutrient cations. This study used Raman microspectroscopy to study how nutrient cations affect alkanes' bioavailability to Acinetobacter baylyi ADP1 (a known degrader). Treated with Na, K, Mg, and Ca at 10 mM, A. baylyi was exposed to seven n-alkanes (decane, dodecane, tetradecane, hexadecane, nonadecane, eicosane, and tetracosane) and one alkane mixture (mineral oil). Raman spectral analysis indicated that bioavailability of alkanes varied with carbon chain lengths, and additional cations altered the bacterial response to n-alkanes. Sodium significantly increased the bacterial affinity toward decane and dodecane, and K and Mg enhanced the bioavailability of tetradecane and hexadecane. In contrast, the bacterial response was inhibited by Ca for all alkanes. Similar results were observed in mineral oil exposure. Our study employed Raman spectral assay to offer a deep insight into how nutrient cations affect the bioavailability of alkanes, suggesting that nutrient cations can play a key role in influencing the harmful effects of hydrocarbons and could be optimized to enhance the bioremediation strategy.

Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine

The establishment and development of a set of methods of oil accurate recognition in a different environment are of great significance to the effective management of oil spill pollution. In this work, the concentration-emission matrix (CEM) is formed by introducing the concentration dimension. The principal component analysis (PCA) is applied to extract the spectral feature. The classification methods, such as Probabilistic Neural Networks (PNNs) and Genic Algorithm optimization Support Vector Machine (SVM) parameters (GA-SVM), are used for oil identification and the recognition accuracies of the two classification methods are compared. The results show that the GA-SVM combined with PCA has the highest recognition accuracy for different oils. The proposed approach has great potential in rapid and accurate oil source identification.

The application of key feature extraction algorithm based on Gabor wavelet transformation in the diagnosis of lumbar intervertebral disc degenerative changes

OBJECTIVE

Based on the theoretical basis of Gabor wavelet transformation, the application effects of feature extraction algorithm in Magnetic Resonance Imaging (MRI) and the role of feature extraction algorithm in the diagnosis of lumbar vertebra degenerative diseases were explored.

METHOD

The structure of lumbar vertebra and degenerative changes were respectively introduced to clarify the onset mechanism and pathological changes of lumbar vertebra degenerative changes. Most importantly, the theoretical basis of Gabor wavelet transformation and the extraction effect of feature information in lumbar vertebra MRI images were introduced. The differentiation effects of feature information extraction algorithm on annulus fibrosus and nucleus pulposus were analyzed. In this study, the data of lumbar spine MRI was randomly selected from the Wenzhou Lumbar Spine Research Database as research objects. A total of 130 discs were successfully fitted, and 109 images were graded by a doctor after observation, which was compared with the results of the artificial diagnosis. Through the comparison with the results of observation and diagnosis by professional doctors, the accuracy of feature extraction algorithm based on Gabor wavelet transformation in the diagnosis of lumbar vertebra degenerative changes was analyzed.

RESULTS

1. Compared with the results of the manual diagnosis, the accuracy of the classification method was 88.3%. In addition, the specificity (SPE), accuracy (ACC), and sensitivity (SEN) of the classification method were respectively 89.5%, 92.4%, and 87.6%. 2. The mutual information method and the KLT algorithm were utilized for vertebral body tracking. The maximum mutual information method was more effective in the case of fewer image sequences; however, with the increase of image frames, the accumulation of errors would make the tracking effects of images get worse. Based on the KLT algorithm, the enhanced vertebral boundary information was selected; the soft tissues showed in the obtained images were smooth, the boundary information of vertebral body was enhanced, and the results were more accurate.

CONCLUSION

The feature extraction algorithm based on Gabor wavelet transformation could easily and quickly realize the localization of the lumbar intervertebral disc, and the accuracy of the results was ensured. In addition, from the aspect of vertebral body tracking, the tracking effects based on the KLT algorithm were better and faster than those based on the maximum mutual information method.

Classification of water contamination developed by 2-D Gabor wavelet analysis and support vector machine based on fluorescence spectroscopy

The identification of the specific categories of pollutants in the urban water supply system is necessary. Traditional detection methods are based mainly on common water quality indicators. However, inspecting these water quality indicators is made difficult by issues such as long analysis time, insufficient sensitivity, need for reagents, and generation of waste liquid. These problems hinder high-frequency water detection and monitoring. In this study, three-dimensional (3D) fluorescence spectroscopy is adopted as a monitoring method for water quality. An identification method based on two-dimensional (2D) Gabor wavelets and support vector machine (SVM) multi-classification is also proposed. The Delaunay triangulation method for interpolation is used to pre-process 3D fluorescence spectra and thereby eliminate Rayleigh scattering and Raman scattering. A 2D Gabor wavelet function generated by filters of different scales and rotation angles is proposed to extract the features of the spectra. The block statistics method, based on Gabor feature description, is employed to enhance the efficiency in describing spectra features. Then, multiple SVM classifiers are used in pollutant classification and recognition. By comparing the proposed method with principal component analysis, which is a commonly used feature extraction method, this study finds that the application of 2D Gabor wavelets and block statistics can effectively describe the characteristics of 3D fluorescence spectra. Moreover, 2D Gabor wavelets achieve high classification accuracy, especially for substances with closely positioned or overlapping characteristic peaks.

Oil source recognition technology using concentration-synchronous-matrix-fluorescence spectroscopy combined with 2D wavelet packet and probabilistic neural network

Developing an accurate, rapid and economic oil source recognition method is essential for water recourses protection. Concentration-synchronous-matrix-fluorescence (CSMF) spectroscopy combined with 2D wavelet packet and probabilistic neural network (PNN) was proposed for source recognition of crude oil and petroleum products samples in this study. 2D wavelet packet was used to extract wavelet packet coefficients as the feature vectors from CSMF contour image and four algorithms, Back-propagation (BP) neural network, Radial based function neural network (RBFNN), Support vector Machine (SVM) and probabilistic neural network (PNN) were carried out for pattern recognition. With the introduction of interference factors such as weathering and sea water adulteration to the three samples from Bohai bay territory of China, the comparison about accuracy and recognition time of the four methods was discussed and the results showed that PNN network maintain the highest recognition accuracy and speed. These findings may offer potential application for oil spill recognition for unconventional oil.

Design and Implementation of a Coastal-Mounted Sensor for Oil Film Detection on Seawater

The routine surveillance of oil spills in major ports is important. However, existing techniques and sensors are unable to trace oil and micron-thin oil films on the surface of seawater. Therefore, we designed and studied a coastal-mounted sensor, using ultraviolet-induced fluorescence and fluorescence-filter systems (FFSs), to monitor oil spills and overcome the disadvantages of traditional surveillance systems. Using seawater from the port of Lingshui (Yellow Sea, China) and six oil samples of different types, we found that diesel oil’s relative fluorescence intensity (RFI) was significantly higher than those of heavy fuel and crude oils in the 180–300 nm range—in the 300–400 nm range, the RFI value of diesel is far lower. The heavy fuel and crude oils exhibited an opposite trend in their fluorescence spectra. A photomultiplier tube, employed as the fluorescence detection unit, efficiently monitored different oils on seawater in field experiments. On-site tests indicated that this sensor system could be used as a coastal-mounted early-warning detection system for oil spills.

Performance of fluorescence spectroscopy for beef meat authentication: Effect of excitation mode and discriminant algorithms

This study evaluated the performance of classical front face (FFFS) and synchronous (SFS) fluorescence spectroscopy combined with Partial Least Square Discriminant Analysis (PLSDA), Support Vector Machine associated with PLS (PLS-SVM) and Principal Components Analysis (PCA-SVM) to discriminate three beef muscles (Longissimus thoracis, Rectus abdominis and Semitendinosus). For the FFFS, 5 excitation wavelengths were investigated, while 6 offsets were studied for SFS. Globally, the results showed a good discrimination between muscles with Recall and Precision between 47.82 and 94.34% and Error ranging from 6.03 to 32.39%. For the FFFS, the PLS-SVM with the 382nm excitation wavelength gave the best discrimination results (Recall, Precision and Error of 94.34%, 89.53% and 6.03% respectively). For SFS, when performing discrimination of the three muscles, the 120nm offset gave the highest Recall and Precision (from 57.66% to 94.99%) and the lowest Error values (from 6.78 to 8.66%) whatever the algorithm (PLSDA, PLS-SVM and PCA-SVM).