Traditional Extraction Methods Research Articles

Comparative Studies on the Extraction of Lineaments and Its Variability Based on an Improved Line Segment Tracking Method—Taking the Gaosong Ore Field in the Gejiu Gejiu Tin Ore Deposit as an Example

Remote sensing technology is widely used in the application of the extraction of lineaments in geological research. However, most of the current traditional lineament extraction methods are based on edge detection technology using a digital filter to extract the mitochondrial features in the image. These previously used algorithms are generally unable to extract the mitochondrial features in the shadow area on the mountain and the area parallel to the direction of the solar incident angle. In this paper, we have improved the STA algorithm (Line Segment Tracking method). The line segmentation and connection methods were firstly proposed, and then 32 search directions were set with one pixel as the center to find the best continuity direction of the lineament. A threshold was then set in this direction to determine whether it was a line element. Finally, line elements were connected into lineaments for further lineament interpretation. The lineament data from three different directions were collected in the Gaosong ore field in the Gejiu mining area, namely at the first place the traditional Canny algorithm, then the improved line segment tracking method, and finally the field measurement. The coefficients of the three-type lineament variations were compared by calculating the lineaments’ strength and their density attributes. It shows that the lineament variability extracted based on the improved line segment tracking method is closer to the measured lineament variability. The results show that the lineaments extracted by the improved algorithm are closer to the measured data. The improved algorithm has higher precision than the traditional Canny algorithm. More realistic and comprehensive lineaments can be attained by using the improved method to extract lineaments in the mining area, which is helpful to the study of the geology of the mining area and helps to construct a more accurate structure model to serve the needs of the mine production.

A Two-Step Simulated Annealing Algorithm for Spectral Data Feature Extraction

To address the shortcomings in many traditional spectral feature extraction algorithms in practical application of low modeling accuracy and poor stability, this paper introduces the "Boruta algorithm-based local optimization process" based on the traditional simulated annealing algorithm and proposes the "two-step simulated annealing algorithm (TSSA)". This algorithm combines global optimization and local optimization. The Boruta algorithm ensures that the feature extraction results are all strongly correlated with the dependent variable, reducing data redundancy. The accuracy and stability of the algorithm model are significantly improved. The experimental results show that compared with the traditional feature extraction method, the accuracy indexes of the inversion model established by using the TSSA algorithm for feature extraction were significantly improved, with the determination coefficient R2 of 0.9654, the root mean square error (RMSE) of 3.6723 μg/L, and the mean absolute error (MAE) of 3.1461 μg/L.

An intelligent crash recognition method based on 1DResNet-SVM with distributed vibration sensors

The vibration recognition along the fiber is still a challenge in highway accident monitoring with distributed optical fiber vibration sensing system (DVS). In this paper, a method named 1DResNet-SVM combining 1D residual Neural Network (1DResNet) and support vector machines (SVM) is proposed. One-dimensional raw vibration signals are used to avoid information loss of data. One-dimensional convolution is combined with residual block to solve the degradation of deep network models. Experiments show that the proposed 1DResNet can extract the distinguishable characteristics of DVS vibration signals better than traditional manual feature extraction methods. Then SVM is selected to replace the fully-connected layer in the network which can effectively recognize crash. The proposed 1DResNet-SVM model can achieve an average recognition rate of 96.2%. Three typical events in highway accidents are carried out in field tests, which can reach 100% especially for crash recognition. This paper provides a new method for the identification of crash accidents.

Identifying Alpine Lakes in the Eastern Himalayas Using Deep Learning

Alpine lakes, which include glacial and nonglacial lakes, are widely distributed in high mountain areas and are sensitive to climate and environmental changes. Remote sensing is an effective tool for identifying alpine lakes over large regions, but in the case of small lakes, the complex terrain and extreme weather make their accurate identification extremely challenging. This paper presents an automated method for alpine lake identification developed by leveraging deep learning algorithms and multi-source high-resolution satellite data. The method is able to detect the outlines and types of alpine lakes from high-resolution optical and Synthetic Aperture Radar (SAR) satellite data. In this study, a total of 4584 alpine lakes (including 2795 glacial lakes) were identified in the Eastern Himalayas from Sentinel-1 and Sentinel-2 data acquired during 2016–2020. The average area of the lakes was 0.038 km2, and the average elevation was 4974 m. High accuracy was reported for the dataset for both segmentation (mean Intersection Over Union (MIoU) > 72%) and classification (Overall Accuracy, User’s and Producer’s Accuracies, and F1-Score are all higher than 85%). A higher accuracy was found for the combination of optical and SAR data than relying on single-sourced data, for which the MIoU increased by at least 12%, suggesting that the combination of optical and SAR data is critical for improving the identification of alpine lakes. The deep learning-based method demonstrated a significant improvement over traditional spectral extraction methods.

Effect of girth of Acacia catechu trees on recovery of katha through traditional method of extraction

Effect of girth of Acacia catechu trees on recovery of katha through traditional method of extraction

Research on Joint Extraction Method of Entity and Relation Triples Based on Hierarchical Cascade Labeling

As an important research field of artificial intelligence, knowledge graph develops rapidly, and triplet extraction is the key to the construction of a knowledge graph. The traditional pipeline extraction method will bring the error of entity recognition into the relationship extraction and affects the extraction effect. Besides, the traditional pipeline extraction method cannot solve the SEO (Single Entity Overlap) and EPO (Entity Pair Overlap) problems. Inspired by this, we compare the advantages and disadvantages of the mainstream methods of entity and relationship triples joint extraction, propose a new joint extraction method of entity relation triples based on a hierarchical cascade labeling model (named HCL model), and the HCL model is based on multi neural network cooperation. Further, we construct a balanced sampling Chinese dataset about the entity and relational triplet extraction which contains SEO and EPO. We carry out the experiments on the balanced data set, and the F1 value of the HCL model reaches 65.4% better than other baseline models.

Optimization of green deep eutectic solvent (DES) extraction of Chenopodium quinoa Willd. husks saponins by response surface methodology and their antioxidant activities.

Quinoa saponins have outstanding activity, and there are an increasing number of extraction methods, but there are few research programs on green preparation technology. The extraction conditions of quinoa saponins with deep eutectic solvents (DESs) were optimized by single-factor experiments combined with response surface methodology. The antioxidant capacity of saponins extracted by DESs and traditional methods was evaluated by the DPPH clearance rate, iron ion chelation rate and potassium ferricyanide reducing power. The results show that the optimal DES is choline chloride: 1,2-propylene glycol (1 : 1), and its water content is 40%. The optimal extraction conditions were as follows: the solid-to-solvent ratio was 0.05 g mL-1, the extraction time was 89 min, and the extraction temperature was 75 °C. Under these conditions, the extraction of quinoa saponins by DES was more effective than the traditional extraction methods. The saponins extracted by DES and traditional methods were analyzed by UPLC-MS, and five main saponins were identified. Quantitative analysis by HPLC-UV showed that Q1 (m/z = 971) and Q2 (m/z = 809) had higher contents of saponins. In vitro antioxidant experiments showed that all DES saponin extracts showed good antioxidant capacity. This study provides new insight into the development and utilization of quinoa saponins.

Continuous extraction of coronary artery centerline from cardiac CTA images using a regression-based method.

Coronary artery centerline extraction in cardiac computed tomography angiography (CTA) is an effectively non-invasive method to diagnose and evaluate coronary artery disease (CAD). The traditional method of manual centerline extraction is time-consuming and tedious. In this study, we propose a deep learning algorithm that continuously extracts coronary artery centerlines from CTA images using a regression method. In the proposed method, a CNN module is trained to extract the features of CTA images, and then the branch classifier and direction predictor are designed to predict the most possible direction and lumen radius at the given centerline point. Besides, a new loss function is developed for associating the direction vector with the lumen radius. The whole process starts from a point manually placed at the coronary artery ostia, and terminates until tracking the vessel endpoint. The network was trained using a training set consisting of 12 CTA images and the evaluation was performed using a testing set consisting of 6 CTA images. The extracted centerlines had an average overlap (OV) of 89.19%, overlap until first error (OF) of 82.30%, and overlap with clinically relevant vessel (OT) of 91.42% with manually annotated reference. Our proposed method can efficiently deal with multi-branch problems and accurately detect distal coronary arteries, thereby providing potential help in assisting CAD diagnosis.

Extraction Optimization, Preliminary Identification, and Bioactivities in Corn Silk.

For thousands of years, corn silk has been widely used as an antidiabetic, antioxidant, and antihyperlipidemic and for other effects, but there is a lack of studies that correlate the extracts of flavonoid composition with their biological activities. Thus, the objectives of this study were to optimize the conditions for extracting flavonoids, identify flavonoids, and correlate the flavonoid composition with the biological activities in corn silk. The response surface experiments showed that the highest flavonoid content was predicted at 45.321 min, 57.349°C, 26.089 mL/g, and 71.269%, respectively. The verification experiment results under these optimized conditions showed an ultrasonic time of 45 min, an ultrasonic temperature of 57°C, a liquid-to-material ratio of 26, and an ethanol volume fraction of 70%. No significant differences (the relative error is 4.378%) were observed between the theoretical and experimental TFC values, indicating that the developed models were accurate. Under these optimum extraction conditions, 20 major compounds were identified and quantified by UPLC-LTQ/Orbitrap MS. Furthermore, these optimum ethanol extracts of corn silk are effective against Bacillus subtilis and hypoglycemic activity compared with the traditional heating reflux extraction method. Six corn silk components seem to be the main contributors to the inhibitory effect against Bacillus subtilis and hyperglycemia activities. These results are useful for the application of corn silk in the food or pharmaceutical industry.

Comparison of different technologies using organic acid and mineral acid solvents on pectin extraction from apple pomace

Pectin is one of the most widely used functional polysaccharide, which is extensively used in food, biotechnological, chemical, cosmetic, and pharmaceutical industries. Pectin can be suitably extracted from apple pomace which is the main by-product of apple juice industry. In this study, pectin obtained from microwave assisted extraction (MAE) and conventional extraction (CE) were compared in terms of extraction yield, extraction time and extraction temperature. The type of acids namely organic acids and mineral acids were also compared, in which pectin from apple pomace was extracted using different concentration of organic acid (citric acid, 0.1 M and 0.05 M) and mineral acid (hydrochloric acid, 0.1 M and 0.05 M). The best extraction condition of the extraction process was found to be higher in yield by using citric acid at microwave power of 180 W, solid-to-liquid ratio of (1:25), concentration of citric acid of 0.1 M for 90 s. Under these conditions, the extraction yield of pectin reached 19.1 ± 0.2%, methoxyl content (5.27%), anhydroglacturonic acid (43.38%) and degree of esterification (68.97%). The current work showed that critic acid is a ‘green’ and safe acidic solvent that could be used to extract pectin from apple pomace that was highly competitive and environmentally friendly process. The microwave Assisted extraction method is considered to be a potential alternative conventional (traditional) extraction method. The obtained data undertakes that apple pomace obtained by the green extraction has a great potential to be a valuable source of pectin, while preserving similar quality to conventional sources of pectin.

Developing Loop Mediated Isothermal Amplification (LAMP) assays for rapid, presumptive DNA detection of an invasive reptile (Boa constrictor)

Context Wildlife trade is a prominent pathway for invasive species introductions into novel environments. Deliberate or accidental release of exotic pets can result in the establishment of alien populations, with damaging impacts for native species and environmental assets. This process is well documented for reptiles globally and is of considerable biosecurity concern in Australia. Boa constrictor is one species at high risk of establishment in Australia, and has insufficient biosecurity detection and post-border control capacity. Aims We aimed to develop rapid DNA-based presumptive testing capacity for detecting B. constrictor, with appropriate sensitivity and specificity to operate in a trace DNA biosecurity context. Methods Loop Mediated Isothermal Amplification (LAMP) is an emerging biosecurity tool that provides highly specific, sensitive, low-resource methods for detection of trace DNA in the absence of physical evidence. We developed colourimetric and fluorescent LAMP assays targeting the mitochondrial DNA control region of B. constrictor. We tested and validated these assays against synthetic DNA fragments, as well as DNA extracted from: (1) vouchered museum B. constrictor tissue; (2) shed B. constrictor skin samples; (3) a range of non-target species to test specificity; and (4) trace DNA recovered from glass tanks post B. constrictor presence. Key results We successfully detected synthetic target DNA down to 1 fg and genomic B. constrictor DNA from tissue and shed skins down to <10 pg in under 30 minutes with our fluorescence-based LAMP assay. Additionally, we were able to detect B. constrictor trace DNA following 24 h of presence utilising a traditional laboratory-based DNA extraction method (approximately 180 min) and a rapid lysis step (approximately 8 min). Conclusions Both colourimetric and fluorescent assays show promise for the specific detection of B. constrictor in biosecurity contexts, including post-border enforcement and compliance checks in the domestic illicit wildlife trade. Implications Our findings greatly strengthen the ongoing development of biosecurity tools for trace DNA detection of commonly traded and trafficked species (i.e. reptiles) in wildlife enforcement contexts, advancing both preparedness and surveillance.

Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection.

Electroencephalogram (EEG) signals are an essential tool for the detection of epilepsy. Because of the complex time series and frequency features of EEG signals, traditional feature extraction methods have difficulty meeting the requirements of recognition performance. The tunable Q-factor wavelet transform (TQWT), which is a constant-Q transform that is easily invertible and modestly oversampled, has been successfully used for feature extraction of EEG signals. Because the constant-Q is set in advance and cannot be optimized, further applications of the TQWT are restricted. To solve this problem, the revised tunable Q-factor wavelet transform (RTQWT) is proposed in this paper. RTQWT is based on the weighted normalized entropy and overcomes the problems of a nontunable Q-factor and the lack of an optimized tunable criterion. In contrast to the continuous wavelet transform and the raw tunable Q-factor wavelet transform, the wavelet transform corresponding to the revised Q-factor, i.e., RTQWT, is sufficiently better adapted to the nonstationary nature of EEG signals. Therefore, the precise and specific characteristic subspaces obtained can improve the classification accuracy of EEG signals. The classification of the extracted features was performed using the decision tree, linear discriminant, naive Bayes, SVM and KNN classifiers. The performance of the new approach was tested by evaluating the accuracies of five time-frequency distributions: FT, EMD, DWT, CWT and TQWT. The experiments showed that the RTQWT proposed in this paper can be used to extract detailed features more effectively and improve the classification accuracy of EEG signals.

Hyperspectral Meets Optical Flow: Spectral Flow Extraction for Hyperspectral Image Classification.

Hyperspectral image (HSI) classification has always been recognised as a difficult task. It is therefore a research hotspot in remote sensing image processing and analysis, and a number of studies have been conducted to better extract spectral and spatial features. This study aimed to track the variation of the spectrum in hyperspectral images from a sequential data perspective to obtain more distinguishable features. Based on the characteristics of optical flow, this study introduces an optical flow technique for the extraction of spectral flow that denotes the spectral variation and implements a dense optical flow extraction method based on deep matching. Lastly, the extracted spectral flow are combined with the original spectral features and input into a commonly used support vector machine (SVM) classifier to complete the classification. Extensive classification experiments on three benchmark HSI test sets show that the classification accuracy obtained by the spectral flow extracted in this study (SpectralFlow) is higher than traditional spatial feature extraction methods, texture feature extraction methods, and the latest deep-learning-based methods. Furthermore, the proposed method can produce finer classification thematic maps, thereby demonstrating strong practical application potential.

A CNN-OSELM Multi-Layer Fusion Network With Attention Mechanism for Fish Disease Recognition in Aquaculture

The increasing global population has escalated the demand for fish products. This calls for a stable supply which can only be met through improved aquaculture practices. The automatic recognition of fish diseases from diseased underwater images is one of such practices that aims to control disease, improve fish production and optimize profits. However, due to lack of public fish disease dataset, there has been limited research towards fish disease recognition. Moreover, due to low quality of underwater images and complex underwater environments, traditional hand-designed feature extraction methods or convolutional neural networks (CNNs)-based classifiers cannot adequately recognize fish diseases in real underwater scenes. Therefore, this paper proposes a novel hybrid approach based on multilayer fusion, attention mechanism and online sequential extreme learning machine (OSELM) to recognize fish diseases in aquaculture. We further compare the classification performance of the proposed model with baseline, attention-based, ConvNeXt and Swin transformer models. Feature extraction is enhanced by integrating same level features and focusing on salient features for fish disease recognition. The characteristic information of fish disease is refined by using strongly discriminative features of the infected fish regions and weakening regions of low interest using convolutional block attention module (CBAM). The module is added to the multilayer fusion network to sequentially infer attention maps along the channel and spatial dimensions for every intermediate feature map. Fish disease recognition is then done using OSELM for faster learning and improved classification performance. The models are trained, validated and tested on a custom dataset with image samples collected from various internet sources. The proposed method achieves 94.28% of accuracy, precision of 92.67%, recall of 92.17% and 92.42% of F1- score on dataset with background elimination. The proposed method can be used for fish disease identification in complex underwater environments in aquaculture.

Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques

Abstract Content Based Image Retrieval is ever growing technology for many applications such as medical, remote sensing, social media search engines and surveillance monitoring, etc,. Representing image content with appropriate features is tedious task using traditional low level feature extraction methods. Deep learning models achieved high precision in classification and object detection algorithms by extracting automated high level feature extraction process. This paper proposed a hybrid feature extraction technique by combining the high level features and low level features to improve the robustness of the feature vector. The proposed model used pre-trained Googlenet model as feature extractor and combined with Gabor multiscale texture features. The final feature vector will be used for retrieving the relevant image data from the large scale image dataset. It has achieved the precision of 91 percent which shows better than state of art methods.

Evolving LSTM Networks for Time‐Series Classification in EdgeIoT

We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time‐series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC‐optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing.

An all-in-one platform to deplete pathogenic bacteria for rapid and safe enrichment of plant-derived extracellular vesicles.

Plant-derived extracellular vesicles (PDEVs) have exhibited several advantages, such as high biocompatibility, improvement of skin conditions, and the prevention of skin aging. However, traditional methods of extraction for plant substances, such as heating under reflux or solvent extraction, are complicated, time-consuming, and low in purity. Accordingly, a simple and efficient platform is necessary for purely isolating natural substances from plants. In this study, we report a newly designed platform for removing impurities to purify PDEVs. The proposed platform comprises three parts: (i) inflow of samples, (ii) depletion of impurities, and (iii) collection of PDEVs. The platform is designed to flow from top to bottom using gravity without the need for electric components. The platform allows the delimitation of impurities, such as the pathogenic bacteria in PDEVs, by capturing magnetic beads coated with Concanavalin A (Con A). We validate the practicality of our platform using extracellular vesicles derived from liquorice (LdEVs). Notably, the LdEVs purified using the Con A-coated magnetic beads provide better cell uptake and wound recovery than the commercialized extract LdEVs. This highlights the therapeutic potential of fresh LdEVs purified using our platform, particularly in preventing skin aging. The findings of this study hold significant practical implications for the cosmeceutical and therapeutic field, providing a promising approach for the extraction and purification of natural substances from plants to harness their benefits effectively.

Sodium hexametaphosphate-treated halloysite based solid-phase extraction of biguanides from dietary supplements.

Raw halloysite was purified by using sodium hexametaphosphate and utilized as the solid-phase extraction sorbent for the determination of biguanides from dietary supplements. The purified halloysite was characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction. The purified halloysite interacted with biguanides through hydrophilic interaction and ion exchange on account of its abundant hydroxyl groups and negative charge. Compared with traditional extraction methods based on hydrophobic interaction and/or ion exchange, the purified halloysite adsorbed more biguanides due to hydrophilicity and ion exchange, with a sample loading volume of up to 100 mL at least. Excellent reproducibility of halloysite purification was achieved, with within-batch (n = 3) and batch-to-batch (n = 3) relative standard deviations in the ranges of 1.5-4.2% and 5.6-8.8%, respectively. Coupled with reversed-phase liquid chromatography-tandem mass spectrometry, a low limit of detection of 0.3 μg kg-1 was obtained. The intra- and inter-day mean recoveries of the biguanides spiked at three levels in dietary supplements were within the ranges of 88.5-107.2% and 86.4-102.0%, respectively. The intra- and inter-day precisions were within the ranges of 1.5-6.4% and 5.4-9.9%, respectively. These results indicated that the developed method is efficient for the determination of trace biguanides in dietary supplements.

Ekstrakcija sporednih proizvoda prerade biljnih čajeva uz pretretman gasnom plazmom

Traditional extraction methods involve the use of polar solvents and/or high temperatures. Both are often energy or timeconsuming and require additional purification steps. This compromises the quality of the extracts obtained. Non-thermal plasma pretreatment for extraction is a promising technique that could increase the sustainability of the process while following the principles of hurdle technology. In this work, we investigated the effects of plasma treatment on the aqueous extraction of antioxidant compounds from herbal tea by-products. Stinging nettle, hoary willowherb, and St. John's Wort extracts were treated with plasma at different time intervals. The extracts obtained were analyzed for their antioxidant capacity and total phenolic content. Their potential use as growth media for Ligilactobacillus sp. was also investigated. Hoary willowherb extracts showed the highest total phenolic content and antioxidant activity. While plasma treatment slightly decreased the antioxidant capacity of these extracts, it improved microbial growth. In contrast to hoary willowherb, the antioxidant capacity of stinging nettle extracts' was not affected by plasma treatment. These extracts were also better for bacterial growth. When added to 25% v/v Mrs broth, stinging nettle extracts achieved a growth rate similar to that of pure Mrs broth (~10⁹ CFU/ml), demonstrating their potential for microbial biomass production and food and feed supplementation.

A novel microextraction technique aided by air agitation using a natural hydrophobic deep eutectic solvent for the extraction of fluvastatin and empagliflozin from plasma samples: application to pharmacokinetic and drug-drug interaction study.

This study focuses on the interaction between the antihyperlipidemic drug fluvastatin (FLV) and the antidiabetic drug empagliflozin (EMP), which are commonly co-administered medications. EMP's impact on FLV levels is attributed to its inhibition of organic anion transporting polypeptide 1B1 (OATP1B1), responsible for FLV liver uptake, consequently elevating FLV concentrations in blood. Traditional extraction methods for FLV faced difficulties due to its high hydrophobicity. In this study, a hydrophobic natural deep eutectic solvent (NDES) using air assisted dispersive liquid-liquid microextraction (AA-DLLME) was utilized as an excellent choice for achieving the highest extraction recovery, reaching 96% for FLV and 92% for EMP. The NDES was created through the combination of menthol and hippuric acid in a 4 : 1 ratio, making it a green and cost-effective pathway. Liquid phase microextraction followed by spectrofluorometric measurements of FLV at λem = 395 nm and EMP at λem = 303 nm, with excitation at a single wavelength of 275 nm was carried out. Response surface methodology (RSM) relying on central composite design (CCD) was used to optimize the variables affecting the AA-NDES-DLLME. The optimized conditions for extraction are: NDES volume of 200 μL, centrifugation time of 15 minutes, air-agitation cycle of 6 cycles, and sample pH of 4.0. Under these optimized conditions, the developed method exhibited good linearity and precision. The method showed good recoveries from rabbit plasma samples spiked at varying concentrations of the analyzed compounds. To assess the applicability and effectiveness of the hydrophobic DES, the validated method was applied to extract the studied drugs from rabbit plasma samples after oral administration of FLV alone and in combination with EMP. The pharmacokinetic parameters of FLV were calculated in both cases to investigate any changes and determine the need for dose adjustment.