Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis

An extensive study was carried out in coal samples coming from several origins trying to establish a relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding near-infrared spectral data. This research was developed by applying both quantitative (partial least squares regression, PLS) and qualitative multivariate analysis techniques (hierarchical cluster analysis, HCA; linear discriminant analysis, LDA), to determine a methodology able to estimate property values for a new coal sample. For that, it was necessary to define homogeneous clusters, whose calibration equations could be obtained with accuracy and precision levels comparable to those provided by commercial online analysers and, study the discrimination level between these groups of samples attending only to the instrumental variables. These two steps were performed in three different situations depending on the variables used for the pattern recognition: property values, spectral data (principal component analysis, PCA) or a combination of both. The results indicated that it was the last situation what offered the best results in both two steps previously described, with the added benefit of outlier detection and removal.     

CRYSTAF Analysis of Polyethylene Synthesized with Phillips Catalyst

Crystallization analysis fractionation (CRYSTAF) was used for the first time to investigate the solution crystallization behavior of ethylene homopolymers and copolymers made with Phillips CrO_x/SiO₂ catalyst. Interestingly, the crystallization peak temperatures (T_p) of copolymers of ethylene and cyclopentene increased with increasing cyclopentene molar fraction in the copolymer. Comparing two factors (short chain branches (SCBs) and cyclopentene incorporation), decreasing SCB frequency is proposed as the dominant factor to explain the increase of crystallization peak temperatures with increasing cyclopentene incorporation. In addition, SCB frequency and molecular weight might be the two significant factors determining the crystallization temperature of polyethylene made with Phillips CrO_x/SiO₂ catalyst with different cocatalysts (triethylaluminum and diethylaluminum ethoxide).     

Simultaneous measurement of glucose and glutamine in aqueous solutions by near infrared spectroscopy

A method is described for measuring the concentrations of both glucose and glutamine in binary mixtures from near infrared (NIR) absorption spectra. Spectra are collected over the range from 5000–4000/cm (2.0–2.5μm) with a 1-mm optical path length. Glucose absorbance features at 4710, 4400, and 4300/cm and glutamine features at 4700, 4580, and 4390/cm provide the analytical information required for the measurement. Multivariate calibration models are generated by using partial least squares (PLS) regression alone and PLS regression combined with a preprocessing digital Fourier filtering step. The ideal number of PLS factors and spectral range are identified separately for each analyte. In addition, the optimum Fourier filter parameters are established for both compounds. The best overall analytical performance is obtained by combining Fourier filtering and PLS regression. Glucose measurements are established over the concentration range from 1.66–59.91 mM, with a standard error of prediction (SEP) of 0.32 mM and a mean percent error of 1.84%. Glutamine can be measured over the concentration range from 1.10–30.65 mM with a SEP of 0.75 mM and a mean percent error of 6.67%. These results demonstrate the analytical utility of NIR spectroscopy for monitoring glucose and glutamine levels in mammalian and insect cell cultures.     

Improved peak selection strategy for automatically determining minute compositional changes in fuels by gas chromatography-mass spectrometry

During the development of automated computational methods to detect minute compositional changes in fuels, it became apparent that peak selection through the spectral deconvolution of gas chromatography-mass spectrometry (GC-MS) data is limited by the complexity and noise levels inherent in the data. Specifically, current techniques are not capable of detecting minute, chemically relevant compositional differences with sufficient sensitivity. Therefore, an alternative peak selection strategy was developed based on spectral interpretation through interval-oriented parallel factor analysis (PARAFAC). It will be shown that this strategy outperforms the deconvolution-based peak selection strategy as well as two control strategies. Successful application of the PARAFAC-based method to detect minute chemical changes produced during microbiological growth in four different inoculated diesel fuels will be discussed.     

Re-weighted random series path integral simulations of molecular clusters: Applications to lithium solvated by a mixed Stockmayer cluster

Nuclear quantum effects in finite temperature simulations of molecular clusters are determined by taking advantage of a recently developed method based on the Feynman Path Integral. The structural and thermodynamic properties, including the nuclear quantum effects are determined for three Stockmayer clusters. The ionic system contain a lithium ion solvated by six strong dipoles and 12 weaker ones. The presence of the ion in the mixed Stockmayer cluster drastically enhances the fluxional nature of the less polar components which occupy the second solvation layer, whereas the neutral counterpart has the effect of reducing it. The nuclear quantum effects are significant at room temperature and above for the solvated ionic system. These are attributable to two factors: (a) the lightness of the lithium ion and (b) the stiffness of the ion-dipole interactions. At 300 K, the difference between the fully converged quantum and the classical heat capacities is about 1.3 K_B for the ionic cluster. This difference is about 10 SDs obtained from 95% confidence estimates of the statistical fluctuations. Cubic convergence is confirmed for temperatures as low as 50 K by regression analysis. The nuclear quantum effects do not change the peak melting temperature of the cluster.     

A numerical study of expected accuracy and precision in Calibration-Free Laser-Induced Breakdown Spectroscopy in the assumption of ideal analytical plasma

Calibration-Free Laser-Induced Breakdown Spectroscopy (CF-LIBS) has been proposed several years ago as an approach for quantitative analysis of Laser-Induced Breakdown Spectroscopy spectra. Recently developed refinement of the spectral processing method is described in the present work. Accurate quantitative results have been demonstrated for several metallic alloys. However, the degree of accuracy that can be achieved with Calibration-Free Laser-Induced Breakdown Spectroscopy analysis of generic samples still needs to be thoroughly investigated. The authors have undertaken a systematic study of errors and biasing factors affecting the calculation in the Calibration-Free Laser-Induced Breakdown Spectroscopy spectra processing. These factors may be classified in three main groups: 1) experimental aberrations (intensity fluctuations and inaccuracy in the correction for spectral efficiency of a detection system), 2) inaccuracy in theoretical parameters used for calculations (Stark broadening coefficients and partition functions) and 3) plasma non-ideality (departure from thermal equilibrium, spatial and temporal inhomogeneities, optical thickness, etc.). In this study, the effects of experimental aberrations and accuracy of spectral data were investigated, assuming that the analytical plasma is ideal. Departure of the plasma conditions from ideality will be the object of future work. The current study was based on numerical simulation. Two kinds of metallic alloys, iron-based and aluminum-based, were studied. The relative weight of the error contributions was found to depend on the sample composition. For the here-investigated samples, the experimental aberrations contribute to the overall uncertainty on the quantitative results more than theoretical parameters. The described simulation method can be applied to the Calibration-Free Laser-Induced Breakdown Spectroscopy analysis of any other kind of sample.     

HPAEC‐PAD method for the analysis of alkaline hydrolyzates of Haemophilus influenzae type b capsular polysaccharide

A gradient method has been devised for the rapid analysis of alkaline hydrolyzates of Haemophilus influenzae type b (Hib) capsular polysaccharide‐based vaccines by high‐performance anion exchange chromatography with pulsed amperometric detection (HPAEC‐PAD). As compared with published procedures, peak shape and sensitivity were significantly improved with this approach, analysis time was short and there was little interference from impurities. The limits of detection and quantification were established with a purified reference polysaccharide. We propose this method as a practical alternative for the analysis of minute amounts of Hib polysaccharide, which can be lower than with the conventional approaches. Copyright © 2013 John Wiley & Sons, Ltd.     

Isotope pattern vector based tandem mass spectral data calibration for improved peptide and protein identification

Tandem mass spectra contain noisy peaks which make peak picking for peptide identification difficult. Moreover, all spectral peaks can be shifted due to systematic measurement errors. In this paper, a novel use of an isotope pattern vector (IPV) is proposed for denoising and systematic measurement error prediction. By matching the experimental IPVs with the theoretical IPVs of candidate fragment ions, true ionic peaks can be identified. Furthermore, these identified experimental IPVs and their corresponding theoretical IPVs are used in an optimization process to predict the systematic measurement error associated with the target spectrum. In return, the subsequent spectral data calibration based on the predicted systematic measurement error enhances the data quality. We show that such an integrated denoising and calibration process leads to significantly improved peptide and protein identification. Different from the commonly employed chemical calibration methods, our IPV‐based method is a purely computational method for individual spectra analysis and globally optimizes the use of spectral data. Copyright © 2009 John Wiley & Sons, Ltd.     

Real-Time Distinguishing of the Xylene Isomers Using Photoionization and Dissociation Mass Spectra Obtained by Femtosecond Laser Mass Spectrometry (FLMS)

Abstract

Distinguishing chemicals and improvement on analytical methods has a direct impact on modern chemical analysis. In this work, the dissociative ionization of xylene isomers was investigated using a femtosecond laser mass spectrometry (FLMS) method with a custom-built linear time-of-flight (TOF) instrument. Laser beams at 800?nm and 400?nm were used and intensity-dependent analysis of the obtained mass spectra was performed using principal component analysis (PCA) to distinguish the xylene isomers, which give identical mass spectra in appearance that cannot be distinguished using normal mass spectrometry methods. The results show that there is a statistically highly significant difference between the xylene isomers for two principal components (1 ? α?>?99.99%) and minimal information loss (<5%) took place during the PCA procedure. Also, the use of the k-medoid clustering method showed that the isomers may be distinguished in real-time for a wide range of ionization laser pulse powers with approximately 99% accuracy. The results suggest that real-time isomer analysis by the FLMS method is suitable for mass spectral identification applications. The FLMS method has been shown to be an important alternative to other mass spectrometric methods that use different ionization mechanisms.     

Determination of GDC‐0980 (apitolisib), a small molecule dual phosphatidylinositide 3‐kinase/mammalian target of rapamycin inhibitor in dog plasma by LC‐MS/MS to support a GLP toxicology study

An LC‐MS/MS method for the determination of GDC‐0980 (apitolisib) concentrations in dog plasma has been developed and validated for the first time to support pre‐clinical drug development. Following protein precipitation with acetonitrile, the resulting samples were analyzed using reverse‐phase chromatography on a Metasil AQ column. The mass analysis was performed on a triple quadruple mass spectrometer coupled with an electrospray interface in positive ionization mode. The selected reaction monitoring transitions monitored were m/z 499.3 → 341.1 for GDC‐0980 and m/z 507.3 → 341.1 for IS. The method was validated over the calibration curve range 0.250–250 ng/mL with linear regression and 1/x² weighting. Relative standard deviation (RSD) ranged from 0.0 to 10.9% and accuracy ranged from 93.4 to 113.6% of nominal. Stable‐labeled internal standard GDC‐0980‐d₈ was used to minimize matrix effects. This assay was used for the measurement of GDC‐0980 dog plasma concentrations to determine toxicokinetic parameters after oral administration of GDC‐0980 (0.03, 0.1 and 0.3 mg/kg) to beagle dogs in a GLP toxicology study. Peak concentration ranged from 3.23 to 84.9 ng/mL. GDC‐0980 was rapidly absorbed with a mean time to peak concentration ranging from 1.3 to 2.4 h. Mean area under the concentration–time curve from 0 to 24 hours ranged from 54.4 to 542 ng h/mL. Copyright © 2015 John Wiley & Sons, Ltd.     

Synthesis and Characterization of Oligo(2,7-biphenylenylene-(E)-vinylene)s: Conjugated Oligomers Containing Antiaromatic 12-π Electron Units

The syntheses and the electronic properties of oligo(2,7-biphenylenylene-(E)-vinylene)s are reported. According to our spectral analysis, a limit of 2.5 ev for the peak band-gap of poly(2,7-biphenylenylene-(E)-vinylene)s is estimated. In addition, we discovered that oligo(2,7-biphenylenylene-(E)-vinylene)s are amphoteric and can be either oxidized or reduced electrochemically.     

Development of near infrared sensors: Detection of influential factors by the AComDim method

The development of near infrared (NIR) sensors has to go through different steps of testing. Once a prototype is ready to be used, it is necessary to evaluate and optimize the experimental conditions and the data collection, in terms of accuracy, repeatability, reproducibility and speed. This paper studies the effects of controllable experimental factors on the quality of the spectral response, to determine the influence of each instrumental parameter and to improve the predictions obtained from the collected data. The AComDim method, based on the multi-block analysis of ANOVA matrices, was used here to evaluate the impact of experimental factors on the responses from the different sensors tested.     

Error analysis and improvements of coupled-cluster theory

Summary The error in the energy of the traditional coupled-cluster (TCC) approach and of several variants is analyzed in terms of the error of the cluster operatorS. A key feature of this analysis is that TCC can be based on an energy functional (asymmetric inS andS ) that is made stationary with respect to variation ofS . The error of TCC scales with the particle numbern, but it is not quadratic in . An improved coupled-cluster method (ICC) is presented that is the next step in a hierarchy from TCC to an exact variational theory. An alternative hierarchy is possible that leads to the extended coupled-cluster (ECC) method of Arponen. Variational (VCC) and unitary (UCC) coupled cluster theories and their stationary conditions and errors are analyzed along similar lines and practicable VCC or UCC approaches are presented. An infinite summation of certain terms in the VCC expectation value is shown to lead to a coupled-pair functional of the type proposed by Ahlrichs. The various CC schemes discussed here are compared on the CC-D, CC-SD and CC-SDT levels and beyond this. Special aspects referring to properties are also discussed.     

因子分析-电感耦合等离子体原子发射光谱分析中的峰位优化及光谱干扰校正

将目标因子分析用于电感耦合等离子体原子发射光谱分析(ICP-AES)对高纯氧化铥中稀土杂质的测定及光谱重叠干扰校正.数据矩阵由纯光谱矢量、混合物光谱矢量及模型背景矢量构成.提出利用各目标矢量的真误差的平方和为评价函数优化扫描光谱峰位,从而消除扫描式ICP光谱仪发射光谱线的峰位移动给测定带来的影响.     

Characterization of Sulfur-Compounds as Chemotaxonomic Markers in the Essential Oils of Allium Species by Solvent-Free Microwave Extraction and Gas Chromatography–Mass Spectrometry

Allium species were examined to authenticate the chemotaxonomic controversy about these plants by analyzing their extracted compound profiles. Essential oils of various species were isolated using conventional hydro-distillation and solvent-free microwave extraction (SFME). A comparison of the isolation procedures was performed. The presence of sulfur compounds in the Allium genus is a prominent characteristic for their medicinal uses. These components were characterized using two-way hierarchical cluster analysis (HCA) and principal component analysis (PCA). The variation of sulfur-compounds was performed by qualitative analysis of Allium species by gas chromatography–mass spectrometry (GC–MS). 2,4-Dimethyl-5,6-dithia-2,7-nonadienal, 4,6-diethyl-1,2,3,5-tetrathiolane, and 5,7-diethyl-1,2,3,4,6-pentathiepane were revealed as potential chemotaxonomic markers for all of the Alliums examined in this study. These markers may be used to provide improved systematics for other Allium species.     

铁皮石斛的裂解气相色谱指纹图谱及其系统聚类分析

采用裂解气相色谱/质谱法(Py-GC/MS)测定了10种不同产地的铁皮石斛并结合系统聚类分析法比较了这些铁皮石斛的指纹图谱,采用释放气体分析法考察了裂解温度对指纹图谱的影响。结果表明,0.4 mg样品在450 ℃下可瞬间裂解,10种样品的指纹图谱具有相似性,且重现性好;采用系统聚类分析能区别不同产地的样品。本法快速、简便、准确,不失为药材质量控制的良好方法。     

Application of genetic algorithm‐support vector regression (GA‐SVR) for quantitative analysis of herbal medicines

In this paper, a genetic algorithm‐support vector regression (GA‐SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used approaches, Random Forests (RF) and partial least squares regression (PLSR) combined with GA (namely GA‐RF and GA‐PLSR, respectively), were also employed and compared with the GA‐SVR method. The models were evaluated in terms of the correlation coefficient between the measured and predicted values (Rp), root mean square error of prediction, and root mean square error of leave‐one‐out cross‐validation. The performance has been tested on a simulated system, a chromatographic data set, and a near‐infrared spectroscopic data set. The obtained results indicate that the GA‐SVR model provides a more accurate answer, with higher Rp and lower root mean square error. The proposed method is suitable for the quantitative analysis and quality control of herbal medicines. Copyright © 2012 John Wiley & Sons, Ltd.     

聚类分析辅助中药寡糖电泳分析鉴定中药

基于中药多糖结构的复杂性和特征性,针对多糖部分降解后的寡糖片段,建立了一种采用毛细管区带电泳法(CZE)分离分析中药寡糖,并利用其特征性电泳谱图信息,结合聚类分析(CA)进行中药鉴定的方法。该方法以1-苯基-3-甲基-5-吡唑啉酮(PMP)为寡糖柱前衍生化试剂,对3个科属的6种中药如黄精、玉竹等同时进行鉴定。采用的电泳条件:未涂层熔融石英毛细管柱(49 cm(有效长度40 cm)×50 μm),以50 mmol/L磷酸盐缓冲液(pH 2.5)为运行缓冲液,检测波长为245 nm,运行电压为15 kV,虹吸进样10 cm×4 s,柱温为室温。结果表明聚类分析辅助中药寡糖电泳分析法可有效用于3个科属6种中药的鉴定。本方法结果可靠,重现性好,可以作为中药鉴定的一种有效手段。     

Blind image analysis for the compositional and structural characterization of plant cell walls

A new image analysis strategy is introduced to determine the composition and the structural characteristics of plant cell walls by combining Raman microspectroscopy and unsupervised data mining methods. The proposed method consists of three main steps: spectral preprocessing, spatial clustering of the image and finally estimation of spectral profiles of pure components and their weights. Point spectra of Raman maps of cell walls were preprocessed to remove noise and fluorescence contributions and compressed with PCA. Processed spectra were then subjected to k-means clustering to identify spatial segregations in the images. Cell wall images were reconstructed with cluster identities and each cluster was represented by the average spectrum of all the pixels in the cluster. Pure components spectra were estimated by spectral entropy minimization criteria with simulated annealing optimization. Two pure spectral estimates that represent lignin and carbohydrates were recovered and their spatial distributions were calculated. Our approach partitioned the cell walls into many sublayers, based on their composition, thus enabling composition analysis at subcellular levels. It also overcame the well known problem that native lignin spectra in lignocellulosics have high spectral overlap with contributions from cellulose and hemicelluloses, thus opening up new avenues for microanalyses of monolignol composition of native lignin and carbohydrates without chemical or mechanical extraction of the cell wall materials.