一种基于光谱信息计算解析的液相色谱滤噪新方法

提出一种基于光谱信息计算解析的高效液相色谱滤噪新方法.对于HPLC/DAD所获得的二维量测数据,取半峰高以上色谱数据点所对应的各列光谱数据进行计算处理,求得在检测波长下的单位标准光谱,并根据光谱维数据与单位标准光谱的关系,采用最小二乘法重构计算求出色谱维数据,从而去除色谱噪声.仿真实验和实测谱图的滤噪结果表明,该方法能有效滤除常见噪声,与中值滤波算法相比较,滤噪性能明显优于后者,是提高色谱定量分析准确度的有力工具.     

离线二维液相色谱法分离巴天酸模根的化学成分

建立了离线二维反相/反相液相色谱分离体系（2D-RPLC/RPLC）,对巴天酸模中的化学成分进行分离。通过比较巴天酸模乙酸乙酯萃取液在环氧四氮唑和Unitary C18色谱柱上的高效液相色谱图,确定以环氧四氮唑色谱柱为第一维色谱柱,以Unitary C18色谱柱为第二维色谱柱。流动相均采用0.1%（v/v）甲酸水溶液和甲醇,梯度洗脱。经一维色谱分离后,共收集18个流分,采用二维色谱对这18个流分进行了进一步的分离分析。实验结果表明,该二维色谱分离方法高效、可行,为巴天酸模药材的微量组分的分离以及活性化合物的筛选提供了分离方法。     

免疫算法用于多组分二维色谱数据的解析

通过对免疫系统抗体对抗原消除作用及其记忆功能的模拟,提出了解析二维数据矩阵的新方法。采用模拟的EMG公式作为抗体输入,对二维信号逐行进行迭代消除,从重叠峰中提取单一组分的色谱信息和光谱信息。通过对二维色谱数据的解析,结果表明,该方法可方便地用于多组分重叠二维色谱的解析。     

二维材料及其量子点在分离科学领域的研究进展

二维材料是一种新型的分离材料,具有原子尺寸、机械强度优异、比表面积大、表面化学丰富以及 物理、化学稳定性良好等特性,引起了分离科学领域研究人员的广泛关注,其中以石墨烯为典型代表。随着 对石墨烯材料的广泛研究,相继发展了二维过渡金属硫化物（TMDs）、层状双氢氧化物（LDHs）、金属有机框 架（MOFs）、共价有机骨架（COFs）、二维过渡金属碳化物或碳氮化物（MXene）、六方氮化硼（h-BN）等多种新 兴二维材料。该文介绍并讨论了二维材料及其量子点的特点及应用,重点介绍了二维材料及其量子点在膜分 离、固相萃取/固相微萃取、液相色谱、气相色谱、毛细管电色谱等分离科学领域中的应用。此外,还探讨了 二维材料在分离科学领域中面临的挑战及应用前景。     

在线单柱二维液相色谱法快速纯化牛胰腺中细胞色素C

用二维（弱阳,疏水）色谱柱首次完成了在线单柱二维液相色谱法快速纯化牛胰腺中的细胞色素C.在将牛胰腺粗提液进样到该二维色谱柱后,在弱阳离子交换模式下,以梯度洗脱方式进行一维色谱分离,并将分离得到的细胞色素C样品液收集到色谱仪的附加样品储液管内.然后将储液管中样品液全部排出,并二次进样到同一根二维色谱柱中,与此同时也完成了对该样品液的缓冲溶液交换,按疏水色谱（HIC）分离模式进行分离.最终对细胞色素C完成了第二维的HIC纯化.上述全部操作均为在线,在一具有正压的封闭体系中进行并可在52分钟内完成.细胞色素C的最终产品纯度高达94.7%（RSD=1.91%）,质量回收率为80.5%（RSD=2.20%）.预计此在线单柱二维液相色谱法也可能用于牛胰腺中其他功能蛋白的快速纯化,并可能将其放大到制备和生产规模.     

多维液相色谱-质谱用于酶解猪血蛋白中活性肽的研究

以强阳离子交换柱（SCX）为一维色谱柱,反相柱（RP）为二维色谱柱,采用在线捕集接口形式,通过10通阀连接一、二维色谱柱,构建了二维液相色谱分离系统。将该系统用于酶解猪血蛋白中对血管紧缩素Ⅰ转移酶（ACE）具有活性抑制作用的肽进行分离、鉴定,共检测出104个组分。收集一维馏分,离线注入LC—MS,鉴定出其中含有SAL、DKF、ESF、STVL及FESF5个小肽。     

高分辨采样二维液相色谱法同时测定金银花中绿原酸和木犀草苷含量

采用高分辨采样二维液相色谱法（HiRes 2D-LC）对金银花中绿原酸和木犀草苷进行准确定量分析。第一维液相色谱采用C18色谱柱，以乙腈和0.4%（v/v）磷酸水溶液为流动相进行梯度洗脱；第二维液相色谱采用SB-Phenyl色谱柱，以乙腈和0.5%（v/v）乙酸水溶液为流动相进行梯度洗脱；二维接口采用五位十通阀，并配置2个多中心切割阀，对绿原酸组分和木犀草苷组分进行多次连续切割。实验结果表明，二维液相色谱分析提高了绿原酸和木犀草苷色谱峰的确认能力，可揭示一维液相色谱分析中共洗脱或隐藏峰的信息；高分辨采样模式实现了一维目标组分的片段式整峰切割，提高了二维液相色谱分析的准确定量能力；通过线性关系、基质加标回收和重复性等考察结果，表明高分辨采样二维液相色谱具有优异的定量准确性和重复性，为中药等复杂基质组分样品的分离和准确定量提供了新方法。     

联用色谱数据的局部分辨

提出了一种对二维数据严重重叠峰中待测组份进行分辨的新方法：子窗口分析法（ｓｕｂｗｉｎｄｏｗ ａｎａｌｙｓｉｓ，ＳＡ）。该方法充分利用重叠区信息，成功地解析出严重重叠峰中待测组份的光谱，进而利用正交投影求得待测组份色谱曲线．这种对二维数据进行局部分辨的方法，降低了对色谱分离条件的要求，可直接应用于未知组份的定性定量分析。     

基于不同提取方法的水稻叶片蛋白质组的二维液相色谱分离及高分辨质谱分析

建立了酚法提取-二维液相色谱分离-高分辨质谱分析水稻叶片蛋白质组的方法。水稻叶片蛋白质经过酚法提取,酶解肽段脱盐后用离线反相-反相二维液相色谱分离,然后用线性离子阱/静电场轨道阱组合式高分辨质谱分析,共鉴定到2712种蛋白质。比较了液相色谱分离系统（一维液相色谱与二维液相色谱）和水稻叶片蛋白质提取方法（酚法、十二烷基硫酸钠法（SDS法）和三氯乙酸/丙酮法（TCA/丙酮法））对鉴定蛋白质数量的影响,结果表明：在二维液相色谱条件下,酚法、SDS法和TCA/丙酮法鉴定到的蛋白质数目为2712、2415和1914,分别是一维液相色谱条件下鉴定到的蛋白质数目的2.7、2.5和1.9倍。二维液相色谱条件下,酚法鉴定到的蛋白质数目比SDS法和TCA/丙酮法分别多297和798。与SDS法和TCA/丙酮法相比,酚法不但鉴定到的蛋白质数量多,而且能够鉴定到一些极端蛋白质,如酸性、碱性及高等电点的蛋白质。此外,对二维液相色谱条件下3种蛋白质提取方法提取到的蛋白质进行生物学功能分类,发现3种方法鉴定到的蛋白质的功能存在互补性,但酚法鉴定到的蛋白质功能种类最多。该法为水稻蛋白质组学研究提供了技术支撑,同时也为其他作物的蛋白质组学研究技术提供重要的借鉴。     

蛋白质/多肽的微流控二维芯片电泳*

在微流控芯片上构建多维分离系统,为蛋白质组学研究提供了一个有发展前景的高效分离分析技术平台。本文介绍了二维芯片电泳系统耦联模式选取及正交性评价的方法;综述了针对蛋白质/多肽分离分析的各种耦联模式微流控二维芯片电泳分析系统,如胶束电动力学色谱（MEKC）与毛细管区带电泳（CZE）,开管电色谱（OECE）与CZE,等电聚焦（IEF）与CZE, IEF与SDS毛细管凝胶电泳（CGE）, SDS-CGE与MEKC等。特别对二维电泳芯片切换接口的类型进行了分类,探讨了用于微流控二维芯片电泳系统的检测技术,并展望了微流控二维电泳芯片在蛋白质组学研究中的应用前景和发展方向。     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.     

GA-ACE: Alternating conditional expectations regression with selection of significant predictors by genetic algorithms

The non-linear regression technique known as alternating conditional expectations (ACE) method is only applicable when the number of objects available for calibration is considerably greater than the number of considered predictors. Alternating conditional expectations regression with selection of significant predictors by genetic algorithms (GA-ACE), the non-linear regression technique presented here, is based on the ACE algorithm but introducing several modifications to resolve the applicability limitations of the original ACE method, thus facilitating the practical implementation of a very interesting calibration tool. In order to overcome the lack of reliability displayed by the original ACE algorithm when working on data sets characterized by a too large number of variables and prior to the development of the non-linear regression model, GA-ACE applies genetic algorithms as a variable selection technique to select a reduced subset of significant predictors able to accurately model and predict a considered variable response. Furthermore, GA-ACE actually provides two alternative application approaches, since it allows either the performance of prior data compression computing a number of principal components to be subsequently subjected to GA-selection, or working directly on original variables.In this study, GA-ACE was applied to two real calibration problems, with a very low observation/variable ratio (NIR data), and the results were compared with those obtained by several linear regression techniques usually employed. When using the GA-ACE non-linear method, notably improved regression models were developed for the two response variables modeled, with root mean square errors of the residuals in external prediction (RMSEP) equal to 11.51 and 6.03% for moisture and lipid contents of roasted coffee samples, respectively. The improvement achieved by applying the new non-linear method introduced is even more remarkable taking into account the results obtained with the best performance linear method (IPW-PLS) applied to predict the studied responses (14.61 and 7.74% RMSEP, respectively).     

Fabrication, structural characterization, and applications of langmuir and langmuir-blodgett films of a poly(azo)urethane

The synthesis of a poly(azo)urethane by fixing CO(2) in bis-epoxide followed by a polymerization reaction with an azodiamine is presented. Since isocyanate is not used in the process, it is termed "clean method" and the polymers obtained are named "NIPUs" (non-isocyanate polyurethanes). Langmuir films were formed at the air-water interface and were characterized by surface pressure vs mean molecular area per mer unit (Pi-A) isotherms. The Langmuir monolayers were further studied by running stability tests and cycles of compression/expansion (possible hysteresis) and by varying the compression speed of the monolayer formation, the subphase temperature, and the solvents used to prepare the spreading polymer solutions. The Langmuir-Blodgett (LB) technique was used to fabricate ultrathin films of a particular polymer (PAzoU). It is possible to grow homogeneous LB films of up to 15 layers as monitored using UV-vis absorption spectroscopy. Higher number of layers can be deposited when PAzoU is mixed with stearic acid, producing mixed LB films. Fourier transform infrared (FTIR) absorption spectroscopy and Raman scattering showed that the materials do not interact chemically in the mixed LB films. The atomic force microscopy (AFM) and micro-Raman technique (optical microscopy coupled to Raman spectrograph) revealed that mixed LB films present a phase separation distinguishable at micrometer or nanometer scale. Finally, mixed and neat LB films were successfully characterized using impedance spectroscopy at different temperatures, a property that may lead to future application as temperature sensors. Principal component analysis (PCA) was used to correlate the data.     

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.     

Left or Right? The Direction of Compression‐Generated Vortex‐Like Flow Selects the Macroscopic Chirality of Interfacial Molecular Assemblies

A new method is described through which the macroscopic chirality of interfacial molecular assemblies of an achiral porphyrin can be mechanically controlled using an original yet efficient Langmuir-Blodgett (LB) technique. By using the unilateral compression geometry, we find that the assemblies deposited from the mirror regions of the LB trough display mirror macroscopic chirality. It is indicated that vortex-like flows could be generated during compression, and that it is the direction of this compression-generated vortex-like flow that determine the macroscopic chirality of the formed assemblies. Moreover, the standard sample-fabrication method with bilateral compression geometry is reformed, and we find that the samples formulated around the left-hand- and right-hand-side Langmuir barriers display opposite macroscopic chiralities. The results suggest that mechanically controlled supramolecular chirogenesis could be efficiently realized through such an LB technique. The investigation establishes a new forum for further investigation of the mechanically induced preferred supramolecular chirality in terms of interfacial organization, and provides the old LB technique with new opportunities for controlling the macroscopic chirality of a supramolecular system that is wholly composed of achiral units.     

Locally linear embedding method for dimensionality reduction of tissue sections of endometrial carcinoma by near infrared spectroscopy

Locally linear embedding (LLE) is introduced here as a nonlinear compression method for near infrared reflectance spectra of endometrial tissue sections. The LLE has been evaluated by using support vector machine (SVM) classifiers and the projected difference resolution (PDR) method. Synthetic data sets devised to resemble near-infrared spectra of tissue samples were used to characterize the performance of the LLE. The LLE was compared using principal component compression (PCC) method to evaluate nonlinear and linear compression. For a set of real tissue samples, if the compressed data were not range-scaled prior to SVM classification, the principal component compressed data gave an average prediction rate of 39 ± 2% while the LLE 94 ± 2%; if range-scaled after compression, the LLE and PCC performed evenly, with maximum average prediction values of 94 ± 2% and 93 ± 2%, respectively. The SVM without compression yielded a classification rate of 92 ± 2%. The prediction accuracy was consistent with PDR results. Without the second derivative preprocessing, the classification rates were 90 ± 3%, 89 ± 2%, and 78 ± 2% for the LLE compressed, the PCC, and no compression classifications by the SVM, respectively.     

Determination of an acceptable level of spectral data compression by Discrete Wavelet Transforms

Today, due to the ever increasing amount of data generated by analytical instruments, good compression methods are desired to keep computation time acceptable. The lower the volume and noise content of data, the easier it becomes to investigate and interpret the modeling results. Discrete Wavelet Transform (DWT) is an effective data compression and noise suppression tool. Compression can be performed at different levels, in each, the size of signal part of the data reduces to half the size. This work includes an approach for determining an acceptable level of compression of data where the aim is to achieve minimal loss of information and no significant change in the structure of data, which could mean, e.g. no loss in predictive ability or the effective rank of the data-set. The method is based on estimation of the Singular Values (SVs) from a data matrix and the Singular Values at each level of compression followed by the application of Median Absolute Deviation (MAD) of the correlation between original SVs and compression SVs as a simple statistical test for the determination of the optimum level of compression. We illustrate the method using FT-Raman data from aqueous solutions of three sugars (glucose, trehalose and sucrose) and NMR data from mixtures of three alcohols. A sudden change in prediction error sum of square plots from Partial Least Squares (PLS) modeling confirms the results from MAD statistics.     

SDAT: analysis of <Superscript>131m</Superscript>Xe with <Superscript>133</Superscript>Xe interference

The Spectral Deconvolution Analysis Tool (SDAT) software was developed at The University of Texas at Austin. SDAT utilizes a standard spectrum technique for the analysis of β–γ coincidence spectra. Testing was performed on the software to compare the standard spectrum analysis technique with a region of interest (ROI) analysis technique. Experimentally produced standard spectra and sample data were produced at the Nuclear Engineering Teaching Laboratory (NETL) TRIGA reactor. The results of the testing showed that the standard spectrum technique had lower errors than the ROI analysis technique for samples with low counting statistics. In contrast, the ROI analysis technique outperformed the standard spectrum technique in high counting statistics samples. It was also shown that the standard spectrum technique benefitted from a compression of the number of channels within the spectra.     

Fabrication and characterization of functional graded polyurethane foam (FGPUF)

In this study, functionally graded polyurethane foams (FGPUFs) were produced using a layer‐by‐layer casting technique. Discontinuous FGPUFs were fabricated by this method. The scanning electron microscopy was used to study the morphology of all specimens. The mechanical properties of the polyurethane foams (PUFs) were evaluated by compression, indentation force deflection, drop weight tests, and dynamic mechanical thermal analysis. Scanning electron microscopy micrographs taken from different zones of functionally graded material showed the variation of the morphology of cells as well as the suitable interfaces between the layers of PUF. Investigation of mechanical properties suggested that FGPUF specimens have an optimum behavior between other specimens in compression and indentation force deflection tests. The results of drop weight test showed that FGPUF samples behaved like an energy absorber (14.31 KN) in comparison to other PUFs. The results of dynamic mechanical thermal analysis data showed an improvement in glass transition temperature (T_g) to −47.2°C and stability of modulus of FGPUFs as temperature increases.     

New Design Method for Fabricating Multilayer Membranes Using CO2-Assisted Polymer Compression Process

It was verified that deep learning can be used in creating multilayer membranes with multiple porosities using the CO₂-assisted polymer compression (CAPC) method. To perform training while reducing the number of experimental data as much as possible, the experimental data of the compression behavior of two layers were expanded to three layers for training, but sufficient accuracy could not be obtained. However, the accuracy was dramatically improved by adding the experimental data of the three layers. The possibility of only simulating process results without the necessity for a model is a merit unique to deep learning. Overall, in this study, the results show that by devising learning data, deep learning is extremely effective in designing multilayer membranes using the CAPC method.