Radar false alarm plots elimination based on multi-feature extraction and classification

Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.

Research on Extraction Method of Surface Information Based on Multi-Feature Combination Such as Fractal Texture

Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.

A Review on Sources,Extractions and Analysis Methods of a Sustainable Biomaterial:Tannins

Condensed and hydrolysable tannins are non-toxic natural polyphenols that are a commercial commodity industrialized for tanning hides to obtain leather and for a growing number of other industrial applications mainly to substitute petroleum-based products.They are a definite class of sustainable materials of the forestry industry.They have been in operation for hundreds of years to manufacture leather and now for a growing number of applications in a variety of other industries,such as wood adhesives,metal coating,pharmaceutical/medical applications and several others.This review presents the main sources,either already or potentially commercial of this forestry by-materials,their industrial and laboratory extraction systems,their systems of analysis with their advantages and drawbacks,be these methods so simple to even appear primitive but nonetheless of proven effectiveness,or very modern and instrumental.It constitutes a basic but essential summary of what is necessary to know of these sustainable materials.In doing so,the review highlights some of the main challenges that remain to be addressed to deliver the quality and economics of tannin supply necessary to fulfill the industrial production requirements for some materials-based uses.

Automatic Extraction Method of 3D Feature Guidelines for Complex Cultural Relic Surfaces Based on Point Cloud

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.

Preferentially selective extraction of lithium from spent LiCoO_(2)cathodes by medium-temperature carbon reduction roasting

Lithium recovery from spent lithium-ion batteries(LIBs)have attracted extensive attention due to the skyrocketing price of lithium.The medium-temperature carbon reduction roasting was proposed to preferential selective extraction of lithium from spent Li-CoO_(2)(LCO)cathodes to overcome the incomplete recovery and loss of lithium during the recycling process.The LCO layered structure was destroyed and lithium was completely converted into water-soluble Li2CO_(3)under a suitable temperature to control the reduced state of the cobalt oxide.The Co metal agglomerates generated during medium-temperature carbon reduction roasting were broken by wet grinding and ultrasonic crushing to release the entrained lithium.The results showed that 99.10%of the whole lithium could be recovered as Li2CO_(3)with a purity of 99.55%.This work provided a new perspective on the preferentially selective extraction of lithium from spent lithium batteries.

Chinese Clinical Named Entity Recognition Using Multi-Feature Fusion and Multi-Scale Local Context Enhancement

Chinese Clinical Named Entity Recognition(CNER)is a crucial step in extracting medical information and is of great significance in promoting medical informatization.However,CNER poses challenges due to the specificity of clinical terminology,the complexity of Chinese text semantics,and the uncertainty of Chinese entity boundaries.To address these issues,we propose an improved CNER model,which is based on multi-feature fusion and multi-scale local context enhancement.The model simultaneously fuses multi-feature representations of pinyin,radical,Part of Speech(POS),word boundary with BERT deep contextual representations to enhance the semantic representation of text for more effective entity recognition.Furthermore,to address the model’s limitation of focusing just on global features,we incorporate Convolutional Neural Networks(CNNs)with various kernel sizes to capture multi-scale local features of the text and enhance the model’s comprehension of the text.Finally,we integrate the obtained global and local features,and employ multi-head attention mechanism(MHA)extraction to enhance the model’s focus on characters associated with medical entities,hence boosting the model’s performance.We obtained 92.74%,and 87.80%F1 scores on the two CNER benchmark datasets,CCKS2017 and CCKS2019,respectively.The results demonstrate that our model outperforms the latest models in CNER,showcasing its outstanding overall performance.It can be seen that the CNER model proposed in this study has an important application value in constructing clinical medical knowledge graph and intelligent Q&A system.

Effects of conductive agent type on lithium extraction from salt lake brine with LiFePO_(4) electrodes

Electrochemical lithium extraction from salt lakes is an effective strategy for obtaining lithium at a low cost.Nevertheless,the elevated Mg:Li ratio and the presence of numerous coexisting ions in salt lake brines give rise to challenges,such as prolonged lithium extraction periods,diminished lithium extraction efficiency,and considerable environmental pollution.In this work,Li FePO4(LFP)served as the electrode material for electrochemical lithium extraction.The conductive network in the LFP electrode was optimized by adjusting the type of conductive agent.This approach resulted in high lithium extraction efficiency and extended cycle life.When the single conductive agent of acetylene black(AB)or multiwalled carbon nanotubes(MWCNTs)was replaced with the mixed conductive agent of AB/MWCNTs,the average diffusion coefficient of Li+in the electrode increased from 2.35×10^(-9)or 1.77×10^(-9)to 4.21×10^(-9)cm^(2)·s^(-1).At the current density of 20 mA·g^(-1),the average lithium extraction capacity per gram of LFP electrode increased from 30.36 mg with the single conductive agent(AB)to 35.62 mg with the mixed conductive agent(AB/MWCNTs).When the mixed conductive agent was used,the capacity retention of the electrode after 30 cycles reached 82.9%,which was considerably higher than the capacity retention of 65.8%obtained when the single AB was utilized.Meanwhile,the electrode with mixed conductive agent of AB/MWCNTs provided good cycling performance.When the conductive agent content decreased or the loading capacity increased,the electrode containing the mixed conductive agent continued to show excellent electrochemical performance.Furthermore,a self-designed,highly efficient,continuous lithium extraction device was constructed.The electrode utilizing the AB/MWCNT mixed conductive agent maintained excellent adsorption capacity and cycling performance in this device.This work provides a new perspective for the electrochemical extraction of lithium using LFP electrodes.

Influence of the Extraction Process on the Characteristics of Romanian Mountain Walnut Oil

The objective of this work is to extract walnut oil using various processes in order to compare the influence on the nature of the components extracted, and thus identify the areas of potential use. We carried out the extractions by mechanical process, thanks to a press in reduced model provided with a worm. We obtained cold extracted oil whose characteristics slightly diverge from extra virgin oil found in shops in Romania, but its composition is similar. We were also able to extract by chemical process using two methods, Folch and Soxhlet. Commercially available table walnut oils are only cold extracted to avoid the presence of solvents. Those are difficult to remove and strongly oxidize the oil. Currently, consumers appreciate walnut oil for its taste and nutritional qualities. In nutrition, this oil is put forward for its composition rich in polyunsaturated fatty acids, which are needed for human body. Food supplements made from walnut oil are available today. For the moment, this is the only use of walnut oil. Indeed, there are some studies on other fields of application, but they remain in the field of research and nothing has yet been commercialized. In this present study, we compared the chemical and physical properties of cold-extracted oil with the solvent extraction of walnut kernel originating from the mountain region of Rumania. The cold extracted oil has a high content of polyunsaturated fatty acids (63%) and monounsaturated fatty acids (30%), a very low level of saturated fatty acid (7%) and no content of linolenic acid. The Soxhlet and Folch methods produced slightly different oils with increased amounts of minor components, which changes their characteristic. Even when solvent-extracted oils do not meet the standard criteria imposed by the Codex Alimentarius, they offer a possible use in the fields of food, cosmetics industries and biomedicine.

Development of a Simple Extraction Method for Tetracycline Analogues from Bovine Milk

Tetracycline and analogues are among the most used antibiotics in the dairy industry. Besides the therapeutic uses, tetracyclines are often incorporated into livestock feed as growth promoters. A considerable amount of antibiotics is released unaltered through milk from dairy animals. The presence of antibiotic residues in milk and their subsequent consumption can lead to potential health impacts, including cancer, hypersensitivity reactions, and the development of antibiotic resistance. Thus, it is important to monitor residual levels of tetracyclines in milk. The purpose of this study is to develop a quick and simple method for simultaneously extracting five tetracycline analogues from bovine milk. Specifically, five tetracycline analogues: Chlortetracycline (CTC), demeclocycline (DEM), doxycycline (DC), minocycline (MC), and tetracycline (TC) were simultaneously extracted from milk using trifluoroacetic acid. Subsequently, the extracted analogues were separated by reverse-phase high-performance liquid chromatography (RP-HPLC) and detected at 355 nm using UV/Vis. Calibration curves for all five tetracycline analogues show excellent linearity (r2 value > 0.99). Percent recovery for MC, TC, DEM, CTC, and DC were: 31.88%, 96.91%, 151.29, 99.20%, and 85.58% respectively. The developed extraction method has good precision (RSD < 9.9% for 4 of the 5 analogues). The developed method with minimal sample preparation and pretreatment has the potential to serve as an initial screening test.

A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities,and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation.However,this method has some problems,such as relying on expert experience and poor portability.Inspired by the rule-based entity relation extraction method,this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed,which is abbreviated as RSTAC.This model refines the extraction rules of relation semantic templates from relation corpus through dependency parsing and realizes the automatic construction of relation semantic templates.Based on the relation semantic template,the process of relation classification and triplet extraction is constrained,and finally,the entity relation triplet is obtained.The experimental results on the three major Chinese datasets of DuIE,SanWen,and FinRE showthat the RSTAC model successfully obtains rich deep semantics of relation,improves the extraction effect of entity relation triples,and the F1 scores are increased by an average of 0.96% compared with classical joint extraction models such as CasRel,TPLinker,and RFBFN.

Development of Extraction and Detection Method for a Chemotherapeutic Drug with Phenytoin in Biological Samples

Dexamethasone is classified as a corticosteroid and is commonly used among cancer patients to decrease the amount of swelling around the tumor. Among patients with cancer, in particular brain tumors, seizures can become a daily routine in their everyday lives. To counteract the seizures, an antiepileptic drug such as phenytoin is administered to act as an anticonvulsant. Phenytoin and dexamethasone are frequently administrated concurrently to brain cancer patients. A previous study has shown that phenytoin serum concentration decreases when administrated concurrently with dexamethasone. Thus, it is important to monitor the concentration of these two drugs in biological samples to ensure that the proper dosages are administrated to the patients. This study aims to develop an effective extraction and detection method for dexamethasone and phenytoin. A reverse-phase high-performance liquid chromatography (HPLC) method with UV/Vis detection has been developed to separate phenytoin and dexamethasone at 219 nm and 241 nm respectively from urine samples. The mobile phase consists of a mixture of 0.01 M KH2PO4, acetonitrile, and methanol adjusted to pH 5.6 (48:32:20) and is pumped at a flow rate of 1.0 mL/min. Calibration curves were prepared for phenytoin and dexamethasone (r2 > 0.99). An efficient solid-phase extraction (SPE) method for the extraction of dexamethasone and phenytoin from urine samples was developed with the use of C-18 cartridges. The percent recovery for phenytoin and dexamethasone is 95.4% (RSD = 1.15%) and 81.1% (RSD = 3.56%) respectively.

A Situational Awareness Method for Initial Insulation Fault of Distribution Network Based on Multi-Feature Index Comprehensive Evaluation

Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.

Highly selective extraction of aromatics from aliphatics by using metal chloride-based ionic liquids

The separation of aromatics from aliphatics is essential for achieving maximum exploitation of oil resources in the petrochemical industry.In this study,a series of metal chloride-based ionic liquids were prepared and their performances in the separation of 1,2,3,4-tetrahydronaphthalene(tetralin)/dodecane and tetralin/decalin systems were studied.Among these ionic liquids,1-ethyl-3-methylimidazolium tetrachloroferrate([EMIM][FeCl_(4)])with the highest selectivity was used as the extractant.Density functional theory calculations showed that[EMIM][FeCl_(4)]interacted more strongly with tetralin than with dodecane and decalin.Energy decomposition analysis of[EMIM][FeCl_(4)]-tetralin indicated that electrostatics and dispersion played essential roles,and induction cannot be neglected.The van der Waals forces was a main effect in[EMIM][FeCl_(4)]-tetralin by independent gradient model analysis.The tetralin distribution coefficient and selectivity were 0.8 and 110,respectively,with 10%(mol)tetralin in the initial tetralin/dodecane system,and 0.67 and 19.5,respectively,with 10%(mol)tetralin in the initial tetralin/decalin system.The selectivity increased with decreasing alkyl chain length of the extractant.The influence of the extraction temperature,extractant dosage,and initial concentrations of the system components on the separation performance were studied.Recycling experiments showed that the regenerated[EMIM][FeCl_(4)]could be used repeatedly.

Influence of extraction voltage on electron and ion behavior characteristics

The characteristics of the extracted ion current have a significant impact on the design and testing of ion source performance.In this paper,a 2D in space and 3D in velocity space particle in cell(2D3V PIC)method is utilized to simulate plasma motion and ion extraction characteristics under various initial plasma velocity distributions and extraction voltages in a Cartesian coordinate system.The plasma density is of the order of 10^(15)m^(-3)-10^(16)m^(-3)and the extraction voltage is of the order of 100 V-1000 V.The study investigates the impact of various extraction voltages on the velocity and density distributions of electrons and positive ions,and analyzes the influence of different initial plasma velocity distributions on the extraction current.The simulation results reveal that the main reason for the variation of extraction current is the spacecharge force formed by the relative aggregation of positive and negative net charges.This lays the foundation for a deeper understanding of extraction beam characteristics.

Feature extraction and learning approaches for cancellable biometrics:A survey

Biometric recognition is a widely used technology for user authentication.In the application of this technology,biometric security and recognition accuracy are two important issues that should be considered.In terms of biometric security,cancellable biometrics is an effective technique for protecting biometric data.Regarding recognition accuracy,feature representation plays a significant role in the performance and reliability of cancellable biometric systems.How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community,especially from researchers of cancellable biometrics.Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance,while the privacy of biometric data is protected.This survey informs the progress,trend and challenges of feature extraction and learning for cancellable biometrics,thus shedding light on the latest developments and future research of this area.

A bulk extraction method to determine the stable isotope ratios of iron,nickel,copper,zinc,and cadmium in seawater using multi-collector inductively coupled plasma mass spectrometry

The oceanic trace metals iron(Fe),nickel(Ni),copper(Cu),zinc(Zn),and cadmium(Cd)are crucial to marine phytoplankton growth and global carbon cycle,and the analysis of their stable isotopes can provide valuable insights into their biogeochemical cycles within the ocean.However,the simultaneous isotopic analysis of multiple elements present in seawater is challenging because of their low concentrations,limited volumes of the test samples,and high salt matrix.In this study,we present the novel method developed for the simultaneous analysis of five isotope systems by 1 L seawater sample.In the developed method,the NOBIAS Chelate-PA1 resin was used to extract metals from seawater,the AG MP-1M anion-exchange resin to purify Cu,Fe,Zn,Cd,and the NOBIAS Chelate-PA1 resin to further extract Ni from the matrix elements.Finally,a multi-collector inductively coupled plasma mass spectroscope(MC-ICPMS)was employed for the isotopic measurements using a doublespike technique or sample-standard bracketing combined with internal normalization.This method exhibited low total procedural blanks(0.04 pg,0.04 pg,0.21 pg,0.15 pg,and 3 pg for Ni,Cu,Fe,Zn,and Cd,respectively)and high extraction efficiencies(100.5%±0.3%,100.2%±0.5%,97.8%±1.4%,99.9%±0.8%,and 100.1%±0.2%for Ni,Cu,Fe,Zn,and Cd,respectively).The external errors and external precisions of this method could be considered negligible.The proposed method was further tested on the seawater samples obtained from the whole vertical profile of a water column during the Chinese GEOTRACES GP09 cruise in the Northwest Pacific,and the results showed good agreement with previous related data.This innovative method will contribute to the advancement of isotope research and enhance our understanding of the marine biogeochemical cycling of Fe,Ni,Cu,Zn,and Cd.

Extraction of Acoustic Normal Mode Depth Functions Using Range-Difference Method with Vertical Linear Array Data

Data-derived normal mode extraction is an effective method for extracting normal mode depth functions in the absence of marine environmental data.However,when the corresponding singular vectors become nonunique when two or more singular values obtained from the cross-spectral density matrix diagonalization are nearly equal,this results in unsatisfactory extraction outcomes for the normal mode depth functions.To address this issue,we introduced in this paper a range-difference singular value decomposition method for the extraction of normal mode depth functions.We performed the mode extraction by conducting singular value decomposition on the individual frequency components of the signal's cross-spectral density matrix.This was achieved by using pressure and its range-difference matrices constructed from vertical line array data.The proposed method was validated using simulated data.In addition,modes were successfully extracted from ambient noise.

Insight into the experiment and extraction mechanism for separating carbazole from anthracene oil with quaternary ammonium-based deep eutectic solvents

Carbazole is an irreplaceable basic organic chemical raw material and intermediate in industry.The separation of carbazole from anthracene oil by environmental benign solvents is important but still a challenge in chemical engineering.Deep eutectic solvents (DESs) as a sustainable green separation solvent have been proposed for the separation of carbazole from model anthracene oil.In this research,three quaternary ammonium-based DESs were prepared using ethylene glycol (EG) as hydrogen bond donor and tetrabutylammonium chloride (TBAC),tetrabutylammonium bromide or choline chloride as hydrogen bond acceptors.To explore their extraction performance of carbazole,the conductor-like screening model for real solvents (COSMO-RS) model was used to predict the activity coefficient at infinite dilution (γ^(∞)) of carbazole in DESs,and the result indicated TBAC:EG (1:2) had the stronger extraction ability for carbazole due to the higher capacity at infinite dilution (C^(∞)) value.Then,the separation performance of these three DESs was evaluated by experiments,and the experimental results were in good agreement with the COSMO-RS prediction results.The TBAC:EG (1:2) was determined as the most promising solvent.Additionally,the extraction conditions of TBAC:EG (1:2) were optimized,and the extraction efficiency,distribution coefficient and selectivity of carbazole could reach up to 85.74%,30.18 and 66.10%,respectively.Moreover,the TBAC:EG (1:2) could be recycled by using environmentally friendly water as antisolvent.In addition,the separation performance of TBAC:EG (1:2) was also evaluated by real crude anthracene,the carbazole was obtained with purity and yield of 85.32%,60.27%,respectively.Lastly,the extraction mechanism was elucidated byσ-profiles and interaction energy analysis.Theoretical calculation results showed that the main driving force for the extraction process was the hydrogen bonding ((N–H...Cl) and van der Waals interactions (C–H...O and C–H...π),which corresponding to the blue and green isosurfaces in IGMH analysis.This work presented a novel method for separating carbazole from crude anthracene oil,and will provide an important reference for the separation of other high value-added products from coal tar.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.

Extraction,chemical components,bioactive functions and adulteration identification of walnut oils:A review

Walnut oil is a functional wood oil known to researchers that may potentially be a large source of Chinese edible oils.There are various extraction methods for walnut oil,including traditional(pressing,solvent-and enzymeassisted extraction)and novel methods(microwave,ultrasound,supercritical CO_(2),subcritical and other extraction technologies).Walnut oil is rich in nutrients,including phytosterols,tocopherols,polyphenols,squalene and minerals.It provides many health benefits,such as antioxidant,antitumor,anti-inflammatory,antidiabetic and lipid metabolism-related functions.In addition,the authentication of walnut oil has received much research attention.The present review provides detailed research on walnut oil extraction,composition,health benefits and adulteration identification methods.The path toward further walnut oil improvement in the context of the market value of walnut oil is also discussed.