Sequential Extraction of Natural Surface Coatings: Comparison with Single Stage Extraction and Its Application in Investigating Adsorption Characteristics of Surface Coating Components for Metals

Multistage sequential extraction with different sequences was applied to extracting natural surface coa tings（NSCs）, and the results were compared with those by single stage extraction technique. The optimal sequential extraction scheme with the best performance was selected and incorporated into the method of selective extraction followed by adsorption and statistic analyses to study the adsorption parameters of Fe oxides, Mn oxides and organic matters in NSCs for Pb and Cd, to validate the applicability of the sequential extraction scheme. The Langmuir ad sorption parameters of each component of surface coating estimated according to the adsorption data of single stage extraction of NSCs were all similar to those of sequential extraction of NSCs, and the maximum difference between the metal adsorption abilities of the components estimated via the two extraction techniques was only about 20%, in dicating that either extraction techniques could be used. The optimal sequential extraction scheme obtained was first extracted with NH2OH·HCl, then with Na2S2O4, and finally with oxalic acid.

Effects of ultrasonic-assisted extraction on bioactive compounds,volatile flavors and antioxidant activities of vine tea water extracts

Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigates the influence of different extraction methods(room temperature water extraction,boiling water extraction,ultrasonic-assisted room temperature water extraction,and ultrasonic-assisted boiling water extraction,referred to as room temperature water extraction(RE),boiling water extraction(BE),ultrasonic assistance at room temperature water extraction(URE),and ultrasonic assistance in boiling water extraction(UBE))on the yield,dihydromyricetin(DMY)content,free amino acid composition,volatile aroma components,and antioxidant properties of vine tea extracts.Results:A notable influence of extraction temperature on the yield of vine tea extracts(P<0.05),with BE yielding the highest at 43.13±0.26%,higher than that of RE(34.29±0.81%).Ultrasound-assisted extraction significantly increased the DMY content of the extracts(P<0.05),whereas DMY content in the RE extracts was 59.94±1.70%,that of URE reached 66.14±2.78%.Analysis revealed 17 amino acids,with L-serine and aspartic acid being the most abundant in the extracts,nevertheless ultrasound-assisted extraction reduced total free amino acid content.Gas chromatography-mass spectrometry analysis demonstrated an increase in the diversity and quantity of compounds in the vine tea water extracts obtained through ultrasonic-assisted extraction.Specifically,69 and 68 volatile compounds were found in URE and UBE extracts,which were higher than the number found in RE and BE extracts.In vitro,antioxidant activity assessments revealed varying antioxidant capacities among different extraction methods,with RE exhibiting the highest DPPH scavenging rate,URE leading in ABTS•+free radical scavenging,and BE demonstrating superior ferric ion reducing antioxidant activity.Conclusion:The findings suggest that extraction methods significantly influence the chemical composition and antioxidant properties of vine tea extracts.Ultrasonic-assisted extraction proved instrumental in elevating the DMY content in vine tea extracts,thereby enriching its flavor profile while maintaining its antioxidant properties.

SIGNAL FEATURE EXTRACTION BASED UPON INDEPENDENT COMPONENT ANALYSIS AND WAVELET TRANSFORM

It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.

Volatile components of Rhizoma Alpiniae Officinarum using three different extraction methods combined with gas chromatography-mass spectrometry

Volatile components from Rhizoma Alpiniae Officinarum were respectively extracted by three methods including hydrodistillation, headspace solid-phase microextraction （HS-SPME） and diethyl ether extraction. A total of 40 （hydrodistillation）, 32 （HS-SPME） and 37 （diethyl ether extraction） compounds were respectively identified by gas chromatography-mass spectrometry （GC/MS） and 22 compounds were overlapped, including β-farnesene, 7-muurolene, 2,6-dimethyl-6- （4-methyl-3-pentenyl）bicyclo[3.1.1]hept-2-ene, eucalyptol and cadina-1（10）, 4-diene and so forth, varying in relative contents. HS-SPME is fast, sample saving and solvent-free and it also can achieve similar profiles as those from hydrodistillation and solvent extraction. Therefore, it can be the priority for extracting volatile components from medicinal plants.

Small Target Extraction Based on Independent Component Analysis for Hyperspectral Imagery

A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target information hided in high-dimensional data and projects them into low-dimensional space.Secondly, the feature images are selected with kurtosis .At last, small targets are extracted with histogram image segmentation which has been labeled by skewness.

Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis

The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.

Ultrasonic Nebulization Extraction Coupled with Gas Chromatography-Mass Spectrometry for Analysis of Volatile Components in Traditional Chinese Patent Medicine

The ultrasonic nebulization extraction（UNE） was developed and applied to the extraction of volatile components from traditional Chinese patent medicine Xiaoyao Pills. Several parameters of ultrasonic nebulization extraction including the sample particle size, solvent volume, extraction time and ultrasonic power were studied and selected. As a result, 2.4 g of sample with particle size of 80 mesh was extracted with 15 mL of n-hexane for 20 min at an ultrasonic power of 35 W. The volatile components were analyzed by gas chromatography-mass spectrometry （GC-MS） under the optimal conditions and 57 compounds were identified. The precision, repeatability and stability of the proposed method were also studied. Compared with ultrasonic-assisted extraction（UAE） and hydrodistillation（HD） extraction, the proposed method is more efficient, faster and easier to be operated at room temperature with smaller sample and energy consumption. It is suggested that the ultrasonic nebulization extraction can be used as a novel alternative method for the extraction of volatile components from traditional Chinese patent medicine.

Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain

Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.

Feature Extraction of Fabric Defects Based on Complex Contourlet Transform and Principal Component Analysis

To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.

Research on weak signal extraction and noise removal for GPR data based on principal component analysis

The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.

Optimization of Extraction Process for Antioxidant Active Components in Seeds of Firmiana platanifolia

[Objectives]To optimize the extraction process of antioxidant active components from seeds of Firmiana platanifolia.[Methods]The seeds of F.platanifolia were taken as the research object.The primary samples of seeds of F.platanifolia were prepared following the procedure of selection,cleaning,shelling,drying and pulverization.The DPPH·(1,1-diphenyl-2-picrylhydrazyl)scavenging rate of the extract of seeds of F.platanifolia was used as an indicator,to conduct the single factor experiment for determining optimum single factors such as extraction solvent,extraction time,solid-to-liquid ratio,and extraction temperature.Then the experimental design was optimized by the response surface methodology.[Results]Single factor experiment showed that the optimal extraction solvent for the antioxidant active components of F.platanifolia seeds was ethyl acetate,the optimal extraction time was 50 min,the optimum solid-to-liquid ratio was 1∶6 g/mL,and the optimal extraction temperature was 60℃.The response surface optimization experiment showed that the optimal extraction process of the antioxidant active component extract of F.platanifolia seeds was:the extraction solvent was ethyl acetate,the extraction time was 45 min,the solid-to-liquid ratio was 1∶5 g/mL,and the extraction temperature was 55℃,which was basically consistent with the results of single factor experiment.Under the optimal extraction conditions,the DPPH·scavenging rate of ethyl acetate extract(200 mg/mL)of F.platanifolia seeds was 94.25%,which was equivalent to that of vitamin E(V E)solution(147.39 mg/L)dissolved with ethyl acetate.[Conclusions]The ethyl acetate extract of F.platanifolia seeds has a strong antioxidant capacity of eliminating DPPH·in vitro.

Feature Extraction of Sectorial Scan Image of Thick-Walled Electron Beam Welding Seam Based on Principal Component Analysis

A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in defect recognition. Seven features were extracted from the image and represented 87. 3% information of the original data. Both the extracted features and the original data were used to train support vector machine model to assess the feature extraction performance in two aspects: recognition accuracy and training time. The results show that using the extracted features the recognition accuracy of pore,crack,lack of fusion and lack of penetration are 93%,90.7%,94.7% and 89.3%,respectively,which is slightly higher than those using the original data. The training time of the models using the extracted features is extremely reduced comparing with those using the original data.

Study on Extraction of Effective Aroma Components from Formula Tobacco by the Supercritical CO2 Extraction Method and Its Aroma Composition

[Objectives]This study was conducted to improve the sensory quality and industrial availability of tobacco extracts.[Methods]The L9(34)design was adopted to carried out an extraction experiment,in which formula tobacco was extracted using supercritical CO2,and the extract was concentrated by vacuum distillation.Through sensory evaluation and chemical analysis,the function determination and chemical composition analysis of the tobacco extracts were carried out,and the optimal supercritical fluid extraction process was finally determined.[Results]The obtained optimal supercritical fluid extraction conditions were as follows:extraction temperature 55℃,extraction pressure 25 MPa,CO2 flow rate of 20 L/h,and entrainer of 95%ethanol.The tobacco extract obtained under the optimal conditions endowed the cigarettes with full and delicate aroma,less irritation and clean aftertaste and made the flavor of the cigarettes overall coordinated and softer,so the sensory quality was significantly improved.[Conclusions]The tobacco extract obtained by the supercritical CO2 extraction method from formula tobacco can effectively improve the quality of cigarettes.

Calculation of Extraction Equilibrium in Multi-Component System by Newton-Raphson Algorithm

Based on the extraction equilibrium and mass balances in countercurrent extraction systems, a novel method was studied for dealing with the extraction equilibrium and the mass distribution in a multi-component(gamma-component) system. The relationships of mass distribution (x(i), y(i), i = 1, ..., lambda) between two phases were expressed by 2 lambda dimensional simultaneous equations. These simultaneous equations can be converted to a one-dimension nonlinear equation, then it was solved by Newton-Raphson algorithm within a few number of iteration. Compared with the regular calculation method for the 2 lambda dimensional simultaneous equations, Newton-Raphson algorithm can decrease the number of iteration, increase the convergence of the equations and accelerate the speed of simulation. It was verified in many multi-component systems with satisfactory results. As an example, a five-component system is demonstrated in this paper.

Analysis of Active Components of Hexane Extractives of Morus alba Leaves

[Objective]The aim was to explore the active compound of Morus alba leaves with the analysis of active components of hexane extractives of M.alba leaves.[Method]Antibacterial assays of hexane extractives of M.alba leaves were done and the volatile components of hexane extractives of M.alba leaves with the strongest antibacterial activity were analyzed by gas chromatography-mass spectrometry（GC-MS）.[Result]The results of inhibitory effect of hexane extractives of M.alba leaves on Staphylococcus aureus and Escherichia coli showed that hexane extractives of M.alba leaves collected in June,July and August had antibacterial activity against S.aureus and had no antibacterial effect against E.coli.Furthermore,hexane extractives of M.alba leaves collected in June had the strongest antibacterial activity against S.aureus（inhibition diameter 10.95 mm）.The hexane extractives of M.alba leaves collected in June was analyzed by GC-MS,the main volatile components were tetradecane（16.76%）,dodecane（13.20%）,diisobutyl phthalate（10.26%）,decane（9.10%）,hexadecane（8.71%）,linolenyl alcohol（7.25%）,octadecane（5.88%）,eicosane（3.26%）,dibutyl phthalate（2.59%）.[Conclusion]Linolenyl alcohol was the potential antibacterial compound.

Minimum Amount of Extracting Solvent of a Separation of Two Rare Earth Components

A significant development in the theory of countercurrent extraction will be presented in this article. New expressions of the term in countercurrent extraction process analysis, “Adjacent Stage Impurity Ratio” (ASIR), are deduced. Furthermore, based on the term together with mass balance and extraction equilibrium, the conditions where a given countercurrent extraction separation operation can have minimum amounts of both extracting solvent and scrubbing agent solution can be estimated, and the equations of the two minimum amounts can be deduced. It was found that the equations for a two-component separation using a single aqueous or organic feed are exactly the same as they appeared in the theory initially established in 1970s. Unlike its earlier version, the present derivation does not involve feed-stage-composition hypothesis, and also has the advantage of dealing with a double-feed system where both aqueous and organic feeds are simultaneously employed whereas the earlier theory can only analyze a separation using a single aqueous or organic feed.

Sub-pixel extraction of laser stripeand itsapplication in laser plane calibration

For calibrating the laser plane to implement 3D shape measurement, an algorithm for extracting the laser stripe with sub-pixel accuracy is proposed. The proposed algorithm mainly consists of two stages： two-side edge detection and center line extraction. First, the two-side edge of laser stripe is detected using the principal component angle-based progressive probabilistic Hough transform and its width is calculated through the distance between these two edges. Secondly, the center line of laser strip is extracted with 2D Taylor expansion at a sub-pixel level and the laser plane is calibrated with the 3D reconstructed coordinates from the extracted 2D sub-pixel ones. Experimental results demonstrate that the proposed method can not only extract the laser stripe at a high speed, nearly average 78 ms/frame, but also calibrate the coplanar laser stripes at a low error, limited to 0.3 mm. The proposed algorithm can satisfy the system requirement of two-side edge detection and center line extraction, and rapid speed, high precision, as well as strong anti-jamming.

Principal Component Feature for ANN-Based Speech Recognition

Using function approximation technology and principal component analysis method, this paper presents a principal component feature to solve the time alignment problem and to simplify the structure of neural network. Its extraction simulates the processing of speech information in human auditory system. The experimental results show that the principal component feature based recognition system outperforms the standard CDHMM and GMDS method in many aspects.

A Comparative Study on Chemical Composition of Supercritical CO_2 Extraction Products in Peels of Trichosanthes kirilowii Maxim. from Changqing District

[Objective] This study aimed to investigate the differences in chemical composition of supercritical CO2 extraction products in peels of Trichosanthes kirilowii Maxim. from Changqing district. [Method] Supercritical fluidextraction （SFE） and GCMS method were applied to determine and analyze the chemical components of the extracts in peels of three strains of Trichosanthes kirilowii Maxim. [Result] The chemical components of supercritical CO2 extraction products in peels of three strains of Trichosanthes kirilowii Maxim. varied., and the number of chemical components with normalized percentage content higher than 1% was 5, 7 and 8, respectively. There are 14 kinds of common components, and the relative content of hexadecanoic acid was the highest. [Conclusion] Supercritical CO2 extracts in peels of different strains of Trichosanthes kirilowii Maxim. contain different chemical components, providing scientific basis for breeding excellent varieties and the development and utilization of Trichosanthes kirilowii Maxim.

Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.