Selective extraction of catecholamines by packed fiber solid‐phase using composite nanofibers composing of polymeric crown ether with polystyrene

For the first time, electrospun composite nanofibers comprising polymeric crown ether with polystyrene (PCE‐PS) have been used for the selective extraction of catecholamines – dopamine (DA), norepinephrine (NE) and epinephrine (E) – prior to their analysis by high‐performance liquid chromatography–electrochemical detection. Using a minicartridge packed with PCE‐PS composite nanofibers, the target compounds were extracted effectively from urine samples to which diphenylborinic acid 2‐aminoethyl ester was added as a complexing reagent. The extracted catecholamines could be liberated from the fiber by the addition of acetic acid. A good linearity was observed for catecholamines in the range of 2.0–200 ng mL^?1 (NE, E and DA). The detection limits of catecholamines (signal‐to‐noise ratio = 3) were 0.5 ng mL^?1 (NE), 0.2 ng mL^?1 (E) and 0.2 ng mL^?1 (DA), respectively. Under the optimized conditions, the absolute recoveries of the above three catecholamines were 90.6% (NE), 88.5% (E) and 94.5% (DA). The repeatability of extraction performance was from 5.4 to 9.2% (expressed as relative standard deviation). Our results indicate that the proposed method could be used for the determination of NE, E and DA in urine. Copyright © 2014 John Wiley & Sons, Ltd.     

Coordination and extraction properties of new bis- and tetrakis(diphenylphosphoryl)-substituted pyrazines towards f-block elements

2,3-Bis- and 2,3,5,6-tetrakis(diphenylphosphoryl)-substituted pyrazines have been synthesized from the corresponding polychloropyrazines and ethyl diphenylphosphinite. They may serve as new N,O-bidentate organophosphorus ligands for extraction and recovery of f-block metal ions from nitric acid solutions.     

Modified layered bimetallic compound used as a novel adsorbent for the solid-phase extraction of monoaromatic hydrocarbons from water samples prior to gas chromatography–mass spectrometry

This study reports a promising method of solid-phase extraction for determining the toluene, ethylbenzene, p-xylene, m-xylene, o-xylene, 1,3,5-trimethylbenzene, and 1,2,4-trimethylbenzene in water samples by gas chromatography–mass spectrometry (GC–MS). Prior to this procedure, the magnesium–aluminum bimetallic hydroxides modified with sodium dodecylbenzenesulfonate (Mg/Al-SDBS-LDH) were prepared and served as the novel solid-phase extractant. The Mg/Al-SDBS-LDH has advantage of good hydrophobicity and larger spacing which facilitates the monoaromatic hydrocarbons (MAHCs) into the interlayer for adsorption. As a result, the seven MAHCs in 500 mL water samples were enriched greatly, and the theoretical enrichment factor reached to 125 times. Under the optimized conditions of solid-phase extraction (SPE) and GC–MS, the mass concentration of each MAHC (0.005–10, 0.01–10, or 0.05–10 ng/mL) had a fine linear relationship with peak area. The correlation coefficients were more than 0.995. The detection limits were between 0.001 and 0.01 ng/mL, and the RSD were between 3.1% and 6.6%. The method had been applied to determine the seven MAHCs in the Dongfengqu river water and laboratory wastewater of Chengdu University of Technology successfully.     

Improved neural networks based on genetic algorithm for pulse recognition

Pulse diagnosis is an important part of Chinese medicine and has played an important role in the development of Chinese medical science. However, the pulse is traditionally determined by cutting it off, which leads to a lack of objective standard pulse identification methods and affects their accuracy and feasibility. This research has studied and discussed the processing and identification of four kinds of pulse: normal pulse, wiry pulse, smooth pulse, and thready pulse. Four frequency-domain characteristics of the pulse wave and six kinds of wavelet scale energy characteristic information were extracted, and a three-layer BP (backprocessing) neural network was established. The LM (Levenberg–Marquard) algorithm and a genetic algorithm were used to improve the BP neural network, to train on and predict experimental samples, and to obtain classification accuracies of 90% and 95% respectively. Moreover, improved BP neural network based on a genetic algorithm has shown highly superior performance in terms of convergence speed and low error rate.     

FFT动态相位重建算法的滤波窗影响分析

为了优化相位重建算法，针对波面干涉图的傅里叶频谱，分析了不同滤波窗口的分布特征和频谱响应，通过计算机仿真和实验测试，确定了FFT动态相位重建算法的最佳滤波窗口类型。其中处理仿真干涉图重建的波面与原始波面的波面峰谷值残差为0.008 5λ，波面均方根值残差为0.000 1λ；处理实验干涉图获得的波面与移相干涉测量法获得的波面峰谷值残差为0.009 3λ，波面均方根值残差为0.000 5λ。结果表明:选取Hamming窗进行滤波处理并重建的相位经拟合后得到的波面较参考波面的面形残差最小，相位重建精度优于0.01λ，可进一步应用于大口径光学元件的测量中。     

吸收峰混叠的太赫兹光谱区间组合特征提取算法

太赫兹光谱是物质识别的前沿方法之一。由于不同物质的分子组成或结构各异,许多物质的太赫兹吸收谱会在特定频率上出现吸收峰,可以作为混合物成分检测的重要特征。有效准确地提取这些吸收峰的参数,是提高识别率的关键。多峰拟合算法将光谱曲线拟合成若干个标准峰函数之和,能够同时提取到吸收峰的频率、峰高、峰宽等信息。但是该算法以寻峰算法结果为基础确定吸收峰的大致位置和数量,寻峰结果不一定是最优的拟合结果,而且很难准确识别定位混叠状态的吸收峰。为了提高混叠光谱中吸收峰的识别定位精度,提出以大幅度平滑后的曲线波谷为分界点,将预处理后的光谱分成若干个子区间。然后将子区间组合起来进行多峰拟合,通过遗传算法得到最优的拟合子区间组合和吸收峰频率近似值,拟合时每个子区间中通过峰数递增最优化方法确定拟合的吸收峰数,最后微调优化得到最优的吸收峰频率、峰高值。为了实现物质的识别,通过密度聚类算法得到同一类纯净物在多次测量中的共同吸收峰,以此作为标准数据,通过提出的基于吸收峰特征的光谱匹配算法实现了纯净物和不同含量混合物的快速识别。对10类纯净物的实际光谱数据进行拟合聚类,得到其吸收峰参数,结果与太赫兹光谱数据库一致。通过识别算法对纯净物测试集进行识别的识别率为100%,证明了特征提取和物质识别算法的有效性。对于含有混叠峰的混合物光谱,二阶导数法对葡萄糖-乳糖混合物光谱中被掩盖吸收峰(1.280 THz)的识别率仅为70%,提取到的频率平均值为1.316 THz;而该算法提高识别率至95%,频率平均值为1.281 THz,该算法提高了对混叠峰的分辨能力,能够精确定位混叠峰。对10类纯净物构成的6类不同程度混叠的二元混合物前二、三识别率分别达到90.8%和98.3%,提取到的特征能够有效应用于混合物的成分检测。该算法能够以纯净物数据为标准数据实现成分各异的混合物成分检测,对于太赫兹光谱混合物成分检测有重要意义。     

An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.     

Solubility study and intensification of extraction of phenolic and anthocyanin compounds from Oryza sativa L. ‘Violet Nori’

Oryza sativa L. ‘Violet Nori’ is an Italian cultivar of spontaneous growing aromatic purple rice which is particularly rich in polyphenolic compounds, especially anthocyanins, conferring it an excellent antioxidant capacity. The present study aimed at increasing the extraction yields of its antioxidant compounds with green strategies and it is divided into two steps. The first step concerned a solubility study of the targeted polyphenols in different ethanol:water mixtures by means of a theoretical prediction method, using the simulation program COSMO-RS, and the subsequently confirmation of the computational results by practical experiments. Once the best extraction mixture was identified, the second step of the work was performed, with the purpose of intensifying the extraction yield. Therefore, various innovative green extraction techniques, including ultrasound, using both the probe system and the ultrasonic bath, bead milling, microwave and accelerated solvent extractions were tested and compared to conventional maceration.Results, expressed in terms of total phenolic and total monomeric anthocyanin contents, showed that the best extracting solvent for ‘Violet Nori’ rice was the mixture ethanol:water (60:40 v/v), being COSMO-RS computational predictions in good correlation with the experimental results. Moreover, the most efficient techniques to extract the antioxidant compounds resulted to be both ultrasound-assisted extraction probe and bead milling, that in only 5 min got the same extractive efficiency obtained after 3 h of conventional maceration.     

Enrichment of HDL proteome and phospholipidome from human serum via IMAC/MOAC affinity

High-density lipoproteins (HDLs) have anti-inflammatory and antioxidant properties and are potentially cardio-protective. Defective HDL function is caused by alterations in both the proteome and lipidome of HDL particles. As potential biomarkers, the development of analytical methods is necessary for the enrichment of HDLs. Therefore, a method for selective enrichment of HDLs using immobilized metal ion affinity chromatography (IMAC) and metal oxide affinity chromatography (MOAC) is presented. SPE-based isolation of HDLs from whole serum is adopted as an alternative to traditional ultracentrifugation methods followed by SDS–PAGE. The enrichment mechanism relies on isoelectric points of lipoproteins and metal oxide. Negatively charged lipoprotein particles interact with positively charged metal oxides and IMAC affinity, which acts as a cation. Identified proteins from HDL through MALDI–MS analysis are apo AI, AII, AIV, CI, CIII, E, J, M, H, serum amyloid A and other nonapoproteins that are part of HDL particles and perform cellular functions. This serum-based proteomics approach gives insight into the functional role of HDL. HDL-associated phospholipids have also been analyzed by LDI–MS. Results suggest that the adopted analytical strategy is a feasible idea to extract lipoproteins from serum. A comparative study of healthy and diseased samples using this approach will provide valuable information in future.     

Cost‐efficient magnetic nanoporous carbon derived from citrus peel for the selective adsorption of seven insecticides

A magnetic solid‐phase extraction adsorbent that consisted of citrus peel‐derived nanoporous carbon and silica‐coated Fe₃O₄ microspheres (C/SiO₂@Fe₃O₄) was successfully fabricated by co‐precipitation. As a modifier for magnetic microspheres, citrus peel‐derived nanoporous carbon was not only economical and renewable for its raw material, but exerted enormous nanosized pore structure, which could directly influence the type of adsorbed analytes. The C/SiO₂@Fe₃O₄ also possessed the advantages of Fe₃O₄ microspheres like superparamagnetism, which could be easily separated magnetically after adsorption. Integrating the superior of biomass‐derived nanoporous carbon and Fe₃O₄ microspheres, the as‐prepared C/SiO₂@Fe₃O₄ showed high extraction efficiency for target analytes. The obtained material was characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, X‐ray photoelectron spectroscopy, and the Brunauer–Emmett–Teller method, which demonstrated that C/SiO₂@Fe₃O₄ was successfully synthesized. Under the optimal conditions, the adsorbent was selected for the selective adsorption of seven insecticides before gas chromatography with mass spectrometry detection, and good linearity was obtained in the concentration range of 2–200 μg/kg with the correlation coefficient ranging from 0.9952 to 0.9997. The limits of detection were in the range of 0.03–0.39 μg/kg. The proposed method has been successfully applied to the enrichment and detection of seven insecticides in real vegetable samples.