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Monitoring of polycyclic aromatic hydrocarbon contamination at four oil spill sites using fluorescence spectroscopy coupled with parallel factor-principal component analysis
摘要： Fluorescence spectroscopy analysis of oil and environmental samples collected from four oil spill incidents in Canada—a 2016 pipeline spill into the North Saskatchewan River (NSR), Saskatchewan; a 2015 train derailment in Gogama, Ontario; the 1970 sinking of the SS Arrow ship in Chedabucto Bay, Nova Scotia; and the 1970 sinking of the Irving Whale barge in the Gulf of St. Lawrence—permitted assessment of the PAH content of environmentally weathered samples. A recently developed fluorescence fingerprinting model based on excitation–emission matrix-parallel factor analysis-principal component analysis (EEM-PARAFAC-PCA) was applied to (i) evaluate the intensity of the abundant PAH groups in the samples, (ii) investigate changes in the PAH composition of environmental samples over time due to weathering, and (iii) classify the original spilled oil and environmental samples within the already established classes of the fingerprinting PCA model. The environmental sediment samples collected from the Husky Energy spill site show loss of PAHs occurring over the course of 15 months post-spill. However, the extent of weathering depends on several environmental factors rather than solely the time of weathering, the PAH loss was maximum at 15 months. There was a decrease in the PAH content of the environmental samples of Gogama spill collected 20 months post-spill. Almost all of Gogama environmental sediment samples underwent substantial weathering, making PCA classification impractical. The SS Arrow and Irving Whale samples fell within adjacent PCA groups, as they both had a similar type of spilled oil (Bunker C) with similarity in chemical composition.
关键词： EEM-PARAFAC-PCA,fluorescence spectroscopy,environmental monitoring,oil spill,PAH contamination
更新于2025-11-19 16:56:42
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Rapid Scanning of the Origin and Antioxidant Potential of Chilean Native Honey Through Infrared Spectroscopy and Chemometrics
摘要： Antioxidant compounds have the ability to trap free radicals; in honey, this capacity is related to the botanical origin of the sample, and therefore, there has been a growing interest in verifying the floral origin of beehive products and its relation with the polyphenolic compounds with potential antioxidant activity. A FTIR spectrum has been use to discriminate floral origin in Chilean monofloral samples and to predict their antioxidant capacity. Forty-nine honey samples from different geographical zones and botanical origin were classified according to melissopalynology analysis, and total phenolic and flavonoid contents were quantified by spectrophotometric methods. Discriminant analysis showed that Quillay (Quillaja saponaria), Corcolén (Azara petiolaris), and Tebo (Retanilla trinervia) honeys showed similarities related to their common geographical origin, while Ulmo (Eucryphia cordifolia) presents a differentiate behavior. The FTIR spectra were able to predict phenolic and flavonoid content, establishing the potential of spectroscopic tools for quality control in Chilean beehive industry.
关键词： PCA,Honeybee,Antioxidant,Melissopalynology,FTIR
更新于2025-11-14 15:18:02
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Investigation of Remote Sensing Image Fusion Strategy Applying PCA to Wavelet Packet Analysis Based on IHS Transform
摘要： Further exploration of wavelet packet analysis (WPA) in the area of image fusion has been a hot topic. It is a strategy to combine WPA with such other transforms as intensity–hue–saturation (IHS), principle component analysis (PCA) for image fusion between the panchromatic (PAN) and the multispectral (MS) image. The paper puts forward a distinct fusion method. Its main idea can be stated as three steps. Firstly, intensity component is derived from IHS model of the image after an MS image is transformed from RGB to IHS. Secondly, intensity component and a matched PAN image are decomposed by WPA at the second scale, respectively. The innovational concept with two aspects is applying PCA theory to merge wavelet packet coefficients. One is to detect edge and produce self-adaptive weighted ratios for low-frequency band. The other is to yield another weighted coefficients for high-frequency bands based on standard deviation. Lastly, the new intensity component created by implementing inverse WPA, matching with hue and saturation reserved, makes up a color composition. A fused image is produced when carrying out transformation from IHS to RGB for the composition. It turns out that the presented fusion strategy is effective with experiments.
关键词： Intensity–hue–saturation (IHS),Image fusion,PCA-based fusion rule,Principle component analysis (PCA),Wavelet packet analysis (WPA)
更新于2025-09-23 15:23:52
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Imbalanced Learning-Based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
摘要： Change detection is a quite challenging task due to the imbalance between unchanged and changed class. In addition, the traditional difference map generated by log-ratio is subject to the speckle, which will reduce the accuracy. In this letter, an imbalanced learning-based change detection is proposed based on PCA network (PCA-Net), where a supervised PCA-Net is designed to obtain the robust features directly from given multitemporal synthetic aperture radar (SAR) images instead of a difference map. Furthermore, to tackle with the imbalance between changed and unchanged classes, we propose a morphologically supervised learning method, where the knowledge in the pixels near the boundary between two classes is exploited to guide network training. Finally, our proposed PCA-Net can be trained by the data sets with available reference maps and applied to a new data set, which is quite practical in change detection projects. Our proposed method is veri?ed on ?ve sets of multiple temporal SAR images. It is demonstrated from the experiment results that with the knowledge in training samples from the boundary, the learned features bene?t change detection and make the proposed method outperform than supervised methods trained by randomly drawing samples.
关键词： Change detection,imbalance learning,synthetic aperture radar (SAR) images,PCA network (PCA-Net)
更新于2025-09-23 15:23:52
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Enhancing multispectral remote sensing data interpretation for historical gold mines in Egypt: a case study from Madari gold mine
摘要： In the last decade, most of the outcrops around the historic gold mines in Egypt had been damaged by the local miners, a situation that complicated remote sensing-based exploration research activities. Madari gold mine area was no more fortunate than other mines in the region. This study identifies a new integrated remote sensing workflow that emphasizes the spectral variations related to differences in chemical and mineralogical compositions of the investigated rock units and deemphasizes the spectral variations introduced by the local miners. All combinations of ratio images are first generated from Landsat 8 Operational Land Imager (OLI) data, then a suite of ratio images that best differentiates between the investigated units is selected, and finally the selected ratio images were stacked to substitute the original image bands in the further processing techniques. The PCA was then applied to the selected ratio images within the stack. Subsequently, a statistical analysis of the eigenvector matrix for each of the PC bands was conducted to select the optimum PC bands and a Principal Component False Color Composite image (PC-FCC) was created from the three selected PC bands. The PC-FCC image (PC3, PC11, PC4 in RGB) was chosen as a result of subtracting the average PC eigenvector negative weights from the average positive eigenvectors weights. Not only was the PC-FCC image used to distinguish the main rock units in the damaged area, but also, to identify the areas with intense alteration zones.
关键词： Eastern Desert,Principal component analysis (PCA),Landsat 8 (OLI),Madari gold mine,Egypt,Ratio images
更新于2025-09-23 15:23:52
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[Lecture Notes in Computational Vision and Biomechanics] Computer Aided Intervention and Diagnostics in Clinical and Medical Images Volume 31 || A Hybrid Fusion of Multimodal Medical Images for the Enhancement of Visual Quality in Medical Diagnosis
摘要： In the ?eld of medical imaging, Multimodal Medical Image Fusion (MIF) is a method of extracting complementary information from diverse source images from different modalities such as Magnetic Resonance Imaging, Computed Tomography, Single Photon Emission Computed Tomography, and Positron Emission Tomography and coalescing them into a resultant image. Image fusion of multimodal medical images is the present-day studies in the ?eld of medical imaging, biomedical research, and radiation medicine and is widely familiar by medical and engineering ?elds. In medical image fusion, single method of fusion is not pro?cient as it always lags in information while comparing with other available techniques. Hence, fusion for hybrid image is used to perform the image processing by applying multiple fusion rules. The integration of these results was obtained together as a single image. In proposed system, Shearlet Transform (ST) and Principal Component Analysis (PCA) are used to apply integrated fusion. The fusion technique is applied for CT that is Computed Tomography and Magnetic Resonance Imaging (MRI) images, where these images are ?rst transformed using the Shearlet Transform and PCA is applied to the transformed images. Finally, the fusion image is acquired using Inverse Shearlet transform (IST). The proposed system performance is evaluated by using speci?c metrics, and it is demonstrated that the outcome of proposed integrated fusion performs better when compared to existing fusion techniques.
关键词： Image fusion,Medical image,Shearlet Transform (ST),Principal Component Analysis (PCA)
更新于2025-09-23 15:23:52
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Development of a new algorithm based on FDT Matrix perimetry and SD-OCT to improve early glaucoma detection in primary care
摘要： Purpose: The purpose of this study was to develop an objective algorithm to discriminate the earliest stages of glaucoma using frequency doubling technology (FDT) Matrix perimetry and spectral domain-optical coherence tomography (OCT) technology to improve primary care detection. Materials and methods: Three hundred six eyes (mean age 58.67±15.12) from 161 patients were included and classified in the following three groups: 101 nonglaucoma (GI-NG), 100 glaucoma suspect (GII-SG), and 105 open-angle glaucoma (GIII-OAG). All participants underwent a visual field exploration using the Humphrey Matrix visual field instrument and retinal nerve fiber layer evaluation using the Topcon 3D OCT-2000. Pattern deviation plot was divided into 19 areas and five aggrupation or quadrants and ranked with a value between 0 and 4 according to its likelihood of normality, and differences among three groups were analyzed. Principal component analysis (PCA) was also used to extract the most notable features of FDT and OCT, and a logistic regression analysis was applied to obtain the classification rules. Results: Only area numbers 7 and 12 and the central zone of FDT Matrix showed statistical differences (P<0.05) between GI-NG and GII-SG. The classification rules were estimated by the four PCA obtained from FDT Matrix and 3D OCT-2000 in a separate and combined use. Area under the receiver operating characteristic curve was 78.88% with FDT-PCA, 82.09% with OCT-PCA, and 94.27% with combined use of FDT and OCT-PCA to discriminate GI-NG and GII-SG. Conclusion: The predictive rules based on FDT-PCA or OCT-PCA provide a high sensitivity and specificity to detect the earliest stages of glaucoma and even better in combined use. These predictive rules may help the future development of software for FDT Matrix perimetry and 3D OCT-2000, which will greatly improve their diagnostic ability, making them useful in daily practice in a primary care setting.
关键词： 3D OCT-2000,PCA,glaucoma,primary care,FDT Matrix
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) - Bangkok, Thailand (2018.10.21-2018.10.24)] 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) - Optimal PCA-EOC-KNN Model for Detection of NS1 from Salivary SERS Spectra
摘要： Non Structural Protein 1 (NS1) has recently been known as an alternative biomarker for diseases caused by flavivirus. It has been clinically acknowledged for early detection of dengue infection, since NS1 presence in blood can be as early as the first day of infection. Surface Enhanced Raman Spectroscopy (SERS) is an improvement to Raman spectroscopy, which amplifies the intensity of Raman scattering so to be usable. This also enables SERS to detect molecular structure up to a single molecule. As such, it is favorable amongst researchers investigating disease biomarker. Algorithm k-nearest neighbor (kNN) is a strategy to classify an unknown based on learning data, nearest to the class. Our work here intends to determine the optimal nearest neighbor number, distance rule and classifier rule for PCA-EOC-KNN model for automated detection of NS1 fingerprint from SERS spectra of adulterated saliva. Results show that PCA-EOC-KNN classifier performs with accuracy, precision, sensitivity and specificity above 90%, using Consensus classifier rule, Euclidean or Correlation or Cosine distance rule and k-value of 1, 3 and 5.
关键词： k-Nearest Neighbour (kNN),Nonstructural Protein 1 (NS1),Principal Component Analysis (PCA)
更新于2025-09-23 15:22:29
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[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - 3D Point Cloud Coarse Registration based on Convex Hull Refined by ICP and NDT
摘要： Non-rigid registration is a crucial step for many applications such as motion tracking, model retrieval, and object recognition. The accuracy of these applications is highly dependent on the initial position used in registration step. In this paper we propose a novel Convex Hull Aided Coarse Registration refined by two algorithms applied on projected points.Firstly,the proposed approach uses a statistical method to find the best plane that represents each point cloud. Secondly, all the points of each cloud are projected onto the corresponding planes. Then, two convex hulls are extracted from the two projected point sets and then matched optimally. Next, the non-rigid transformation from the reference to the model is robustly estimated through minimizing the distance between the matched point's pairs of the two convex hulls.Finally, this transformation estimation is refined by two methods. The first one is the refinement of coarse registration by Iterative Closest Point (ICP). The second one consists of the refinement of coarse registration by the Normal Distribution Transform (NDT). An experimental study ,carried out on several clouds, shows that the refinement of coarse registration with ICP gives, in the most cases, a better result than refinement with NDT.
关键词： Iterative Closest Point (ICP),Convex Hull,Normal Distribution Transform (NDT),Non rigid registration,3D point cloud,Principal Component Analysis (PCA)
更新于2025-09-23 15:22:29
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Quaternion-Based Multiscale Analysis for Feature Extraction of Hyperspectral Images
摘要： This paper proposes a new method called multiscale quaternion Weber local descriptor histogram (MQWLDH) for feature extraction of hyperspectral images (HSIs), which is used to model spatial information based on the corresponding spectral features. The proposed method first transforms spectral data into an orthogonal space using principal component analysis, and extracts the first three principal components (PCs) based on the maximum variance theory. Then construct the MQWLDH to extract spatial features based on those first three PCs. The proposed method uses the algebraic structure of quaternions to unify the process of processing the first three PCs, which reduces the computational cost and the dimensionality of the extracted spatial feature vector. Moreover, the constructed quaternion Weber local descriptor effectively characterizes the variations of each pixel neighborhood and detects the edges of HSIs. To capture more intrinsic spatial information contained in homogeneous regions of different sizes and shapes, multiscale feature histograms are constructed. Finally, a feature fusion framework is proposed to fuse spectral and spatial features, so that spectral information can be fully utilized. The experimental results on three HSI data sets demonstrate that the proposed method provides effective features to different classifiers and achieves excellent classification performance.
关键词： multiscale feature histograms.,principal component analysis (PCA),Feature extraction,quaternion Weber local descriptor (QWLD)
更新于2025-09-23 15:22:29