Hyphenation of column liquid chromatography and Raman spectroscopy via a liquid-core waveguide: chemometrical elimination of spectral eluent background

In column liquid chromatography (LC) coupled to conventional Raman spectroscopy (RS) removal of the spectral background of the eluent is often demanding, because of the strong signals of the organic modifier. A new chemometrical method is proposed, called the eluent background subtraction (EBS) method, which can correct for small shape and intensity differences of the eluent spectra. The variations in the eluent spectra are modelled using principal component analysis (PCA). The PCA loading vectors are subsequently used for eluent background correction of the elution spectra of the analyte. The loading vectors are fitted under these spectra by an asymmetric least-squares method. This method was successfully applied under various experimental conditions and performed much better than conventional background correction methods. Analyte detectability was improved by (weighted) averaging of all elution spectra and smoothing via a p-spline function.     

Identification of a lipid‐related peak set to enhance the interpretation of TOF‐SIMS data from model and cellular membranes

Principal component analysis (PCA) of time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) data enables differentiating structurally similar molecules according to linear combinations of multiple peaks in their spectra. However, in order to use PCA to correctly identify variations in lipid composition between samples, the discrimination achieved must be based on chemical differences that are related to the lipid species, and not sample‐associated contamination. Here, we identify the positive‐ion TOF‐SIMS peaks that are related to phosphatidylcholine lipid headgroups and tail groups by PCA of spectra acquired from lipid isotopologs. We demonstrate that restricting PCA to a contaminant‐free lipid‐related peak set reduces the variability in the spectra acquired from lipid samples that is due to contaminants, which enhanced differentiating different lipid standards, but adversely affected the contrast in PC scores images of phase‐separated lipid membranes. We also show that PCA of a restricted data set consisting of the peaks related to lipids and amino acids increases the likelihood that the discrimination of TOF‐SIMS data acquired from intact cells is based on differences in the lipids and proteins on the cell surface, and not sample‐specific contamination without compromising sample discrimination. We expect that the lipid‐related peak database established herein will facilitate interpreting the TOF‐SIMS data and PCA results from studies of both model and cellular membranes, and enhance identifying the origins of the peaks that contribute to discriminating different types of cells. Copyright © 2011 John Wiley & Sons, Ltd.     

Software-induced variance in two-dimensional gel electrophoresis image analysis

Experimental variability in 2-DE is well documented, but little attention has been paid to variability arising from postexperimental quantitative analyses using various 2-DE software packages. The performance of two 2-DE analysis software programs, Phoretix 2D Expression v2004 (Expression) and PDQuest 7.2 (PDQuest), was evaluated in this study. All available background subtraction and smoothing algorithms were tested using both data generated from one single 2-DE gel image, thus excluding experimental variance, and with authentic sets of replicate gels (n = 5). A slight shift of the image boundaries (the "cropping area") caused both programs to induce variance in protein spot quantification of otherwise identical gel images. The resulting variance for PDQuest (CV(mean) = 8%) was approximately twice that for Expression (CV(mean) = 4%). In authentic sets of replicate 2-DE gels (n = 5), the experimental variance confounded the software-induced variance to some extent. However, Expression still outperformed PDQuest, which exhibited software-induced variance as high as 25% of the total observed variance. Surprisingly, the complete omission of background subtraction algorithms resulted in the least amount of software-based variance. These data indicate that 2-DE gel analysis software constitutes a significant source of the variance observed in quantitative proteomics, and that the use of background subtraction algorithms can further increase the variance.     

Collagen‐based proteinaceous binder‐pigment interaction study under UV ageing conditions by MALDI‐TOF‐MS and principal component analysis

This study focuses on acquiring information on the degradation process of proteinaceous binders due to ultra violet (UV) radiation and possible interactions owing to the presence of historical mineral pigments. With this aim, three different paint model samples were prepared according to medieval recipes, using rabbit glue as proteinaceus binders. One of these model samples contained only the binder, and the other two were prepared by mixing each of the pigments (cinnabar or azurite) with the binder (glue tempera model samples). The model samples were studied by applying Principal Component Analysis (PCA) to their mass spectra obtained with Matrix‐Assisted Laser Desorption/Ionization‐Time of Flight Mass Spectrometry (MALDI‐TOF‐MS). The complementary use of Fourier Transform Infrared Spectroscopy to study conformational changes of secondary structure of the proteinaceous binder is also proposed. Ageing effects on the model samples after up to 3000 h of UV irradiation were periodically analyzed by the proposed approach. PCA on MS data proved capable of identifying significant changes in the model samples, and the results suggested different aging behavior based on the pigment present. This research represents the first attempt to use this approach (PCA on MALDI‐TOF‐MS data) in the field of Cultural Heritage and demonstrates the potential benefits in the study of proteinaceous artistic materials for purposes of conservation and restoration. Copyright © 2012 John Wiley & Sons, Ltd.     

Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth profiles in X‐ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES) and secondary ion mass spectrometry (SIMS). These methods have proved equally useful in fundamental studies as in applied work where speed of interpretation is very valuable. Until now these methods have been difficult to apply to very large datasets such as spectra associated with 2D images or 3D depth‐profiles. Existing algorithms for computing PCA matrices have been either too slow or demanded more memory than is available on desktop PCs. This often forces analysts to ‘bin’ spectra on much more coarse a grid than they would like, perhaps even to unity mass bins even though much higher resolution is available, or select only part of an image for PCA analysis, even though PCA of the full data would be preferred. We apply the new ‘random vectors’ method of singular value decomposition proposed by Halko and co‐authors to time‐of‐flight (ToF)SIMS data for the first time. This increases the speed of calculation by a factor of several hundred, making PCA of these datasets practical on desktop PCs for the first time. For large images or 3D depth profiles we have implemented a version of this algorithm which minimises memory needs, so that even datasets too large to store in memory can be processed into PCA results on an ordinary PC with a few gigabytes of memory in a few hours. We present results from ToFSIMS imaging of a citrate crystal and a basalt rock sample, the largest of which is 134GB in file size corresponding to 67 111 mass values at each of 512 × 512 pixels. This was processed into 100 PCA components in six hours on a conventional Windows desktop PC. © 2015 The Authors. Surface and Interface Analysis published by John Wiley & Sons Ltd.     

Simultaneous quantitative analysis of overlapping spectrophotometric signals using wavelet multiresolution analysis and partial least squares

The mathematical bases and program algorithms of discrete wavelet transform (DWT), multiresolution and Mallat’s pyramid algorithm were described. The multiresolution analysis (MRA) based on Daubechies orthogonal wavelet basis was studied as a tool for removing noise and irrelevant information from spectrophotometric spectra. After wavelet MRA pre-treatment, eight error functions were calculated for deducing the number of factors. A partial least squares based on wavelet MRA (WPLS) method was developed to perform simultaneous spectrophotometric determination of Fe(II) and Fe(III) with overlapping peaks. Data reduction was performed using wavelet MRA and principal component analysis (PCA) algorithm. Two programs, SPWMRA and SPWPLS, were designed to perform wavelet MRA and simultaneous multicomponent determination. Experimental results showed the WPLS method to be successful even where there was severe overlap of spectra.     

Classification of single-molecule surface-enhanced resonance Raman spectra of Rhodamine 6G from isolated Ag colloidal particles by principal component analysis

Surface enhanced resonance Raman (SERR) spectra of Rhodamine 6G are measured from single isolated Ag particles and analyzed by using a chemometrics technique, principal component analysis (PCA). The Ag particles are incubated with various amounts of R6G yielding the ratio of Ag particles to R6G molecules from 1:1 to 1:1000. Acquired SERR spectra are considered due to a single or very few R6G molecules. PCA is used to determine the number of chemically distinguishable species that contribute to the measured SERR spectra. A simple clustering tool, score bi-plot, is then inspected on grouping of the SERR spectra. The spectra are found to be largely similar except for the variability in the intensity and position of the bands that is believed to be correlated with the lifetime of the strong enhancement at specific places on an Ag surface. The spectra from four different Ag particles carrying more than 1000 R6G molecules are, however, unambiguously separated. Different aspects of the applied data analysis method and physicochemical perspective of the results are discussed.     

基于小波变换平滑主成分分析

小波变换具有很强的信号分离能力,很容易把随机噪音从信号中分离出来,从而提高信号的信噪比。本文把小波变换引入到因子分析中,提出了基于小波变换平滑主成分分析,该算法既保留普通主成分分析的正交分解,又具备了小波变换的信号分离能力。模拟数据和实验数据的结果表明,该算法具有从低信噪比的数据中提取出有用信息,并提高信号的信噪比。迭代目标变换因子分析处理实验数据的结果表明,基于小波变换平滑主成分分析的处理结果优     

Application of HR-MAS NMR spectroscopy for studying chemotype variations of Withania somnifera (L.) Dunal

Withania somnifera (L.) Dunal (Solanaceae), commonly known as Ashwagandha, is one of the most valued Indian medicinal plants with a number of pharmaceutical and nutraceutical applications. Metabolic profiling has been performed by HR-MAS NMR spectroscopy on fresh leaf and root tissue specimens from four chemotypes of W. somnifera. The HR-MAS NMR spectroscopy of lyophilized defatted leaf tissue specimens clearly distinguishes resonances of medicinally important secondary metabolites (withaferin A and withanone) and its distinctive quantitative variability among the chemotypes. A total of 41 metabolites were identified from both the leaf and root tissues of the chemotypes. The presence of methanol in leaf and root tissues of W. somnifera was detected by HR-MAS NMR spectroscopy. Multivariate principal component analysis (PCA) on HR-MAS (1) H NMR spectra of leaves revealed clear variations in primary metabolites among the chemotypes. The results of the present study demonstrated an efficient method, which can be utilized for metabolite profiling of primary and secondary metabolites in medicinally important plants.     

主成分分析-支持向量回归建模方法及应用研究

将主成分分析(PCA)用于近红外光谱的特征提取,并与支持向量回归(SVR)相结合,实现了主成分分析-支持向量回归(PCA-SVR)用于近红外光谱定量分析的建模方法。与单纯的SVR方法相比,不仅提高了运算速度,而且提高了模型的预测准确度。将PCA-SVR方法用于烟草样品中总糖和总挥发碱含量的测定,所得结果的预测均方根误差分别为1.323和0.0477;回收率分别为91.8%~112.6%和88.9%~120.2%。     

ToF‐SIMS analysis of a polymer microarray composed of poly(meth)acrylates with C6 derivative pendant groups

Surface analysis plays a key role in understanding the function of materials, particularly in biological environments. Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides highly surface sensitive chemical information that can readily be acquired over large areas and has, thus, become an important surface analysis tool. However, the information‐rich nature of ToF‐SIMS complicates the interpretation and comparison of spectra, particularly in cases where multicomponent samples are being assessed. In this study, a method is presented to assess the chemical variance across 16 poly(meth)acrylates. Materials are selected to contain C₆ pendant groups, and ten replicates of each are printed as a polymer microarray. SIMS spectra are acquired for each material with the most intense and unique ions assessed for each material to identify the predominant and distinctive fragmentation pathways within the materials studied. Differentiating acrylate/methacrylate pairs is readily achieved using secondary ions derived from both the polymer backbone and pendant groups. Principal component analysis (PCA) is performed on the SIMS spectra of the 16 polymers, whereby the resulting principal components are able to distinguish phenyl from benzyl groups, mono‐functional from multi‐functional monomers and acrylates from methacrylates. The principal components are applied to copolymer series to assess the predictive capabilities of the PCA. Beyond being able to predict the copolymer ratio, in some cases, the SIMS analysis is able to provide insight into the molecular sequence of a copolymer. The insight gained in this study will be beneficial for developing structure–function relationships based upon ToF‐SIMS data of polymer libraries. © 2016 The Authors Surface and Interface Analysis Published by John Wiley & Sons Ltd.     

Metabolic profiling of normal and hypertensive rat kidney tissues by hrMAS-NMR spectroscopy

Intact kidney tissue samples of normal and spontaneously hypertensive rats (SHRs) were analyzed by hrMAS-NMR spectroscopy and principal component analysis (PCA). Radial components (cortex, outer stripe of the outer medulla, inner stripe of the outer medulla, and papilla) were sampled from various regions across the kidney from multiple animals in order to establish inter- and intra-animal variability. The effects of temperature were also measured. Papilla was differentiated from the other tissue types, and this variation by tissue type was greater than the effect of temperature on the samples (spectra were compared from samples at 2 and 30 °C). This study also revealed long term stability issues of tissue storage at -80 °C. The PCA showed that the greatest differentiation between normal rats and SHRs was found in the cortex and the regions in the NMR spectra that were correlated with this variation were identified.Abbreviations hrMAS High-resolution magic angle spinning - NMR Nuclear magnetic resonance - PCA Principal component analysis - CSA Chemical shift anisotropy - DD Dipolar coupling - SHR Spontaneously hypertensive rat     

自适应拉曼光谱成像数据去噪及其在植物细胞壁光谱分析中的应用

拉曼光谱成像数据存在基线漂移与宇宙射线干扰峰两类噪声信号,无法直接用于光谱分析研究,必须去除。现有单光谱去噪方法处理结果不稳定、可重复性差。针对这一问题,本研究提出了一种自适应拉曼光谱成像数据新型去噪法,采用优化的自适应迭代惩罚最小二乘法( Adaptive iteratively reweighted penalized least-squares,airPLS)和基于主成分分析( PCA)的干扰峰消除算法修正光谱基线漂移和宇宙射线干扰峰,具有输入参数少、光谱失真小、处理速度快、去噪结果稳定等优点。利用本方法去除了芒草( Miscanthus sinensis)细胞壁拉曼光谱成像数据(9010条光谱)中的噪声信号,并对去噪后数据进行PCA和聚类分析(CA),成功区分非植物光谱与植物光谱,分类结果优于未去噪数据。预期本方法可应用于其它光谱成像数据去噪,为光谱的解译和定量分析提供可靠的研究基础。     

X‐ray photoelectron spectroscopy and time‐of‐flight secondary ion mass spectrometry characterization of aging effects on the mineral fibers treated with aminopropylsilane and quaternary ammonium compounds

X‐ray photoelectron spectroscopy (XPS) and time‐of‐flight secondary ion mass spectrometry were used to investigate the aging effects on the aminopropylsilane (APS) and quaternary ammonium surfactant‐treated mineral fibers. APS‐coated mineral fiber samples were treated with cationic surfactant and mineral oil and aged at 70 °C temperature and 95% humidity. From quantitative XPS measurements, an increase in the atomic composition of oxygen, nitrogen, and silicon is observed after aging. An increase in the protonated amino groups in the N1s high‐resolution spectra and C–N group in the C1s high‐resolution spectra is also observed. These results indicate that the concentration of hydrocarbon groups decreases after aging due to the partial removal of the long hydrocarbon chains of the surfactant and mineral oil and/or hydrolysis and segregation of APS to the fiber surface. The principal component analysis (PCA) was applied to the time‐of‐flight secondary ion mass spectrometry spectra, and an increase in the intensities of APS characteristic peaks were observed after aging. The observed increase in the signals of APS originates from underlying silanized fibers after the removal of the surfactant and mineral oil from the surface. Copyright © 2012 John Wiley & Sons, Ltd.     

Chemometrics‐enhanced laser‐induced thermal emission detection of PETN and other explosives on various substrates

Infrared emissions (IREs) of samples of pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination of the important nitrate ester from the emissions of the substrates. Mid‐infrared emissions were generated by heating the samples remotely using laser‐induced thermal emission (LITE). Chemometrics multivariate analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares‐discriminant analysis (PLS‐DA), support vector machines (SVMs), and neural network (NN) were employed to generate the models for the classification and discrimination of PETN IREs from substrate thermal emissions. PCA exhibited less variability for the LITE spectra of PETN/substrates. SIMCA was able to predict only 44.7% of all samples, while SVM proved to be the most effective statistical analysis routine, with a discrimination performance of 95%. PLS‐DA and NN achieved prediction accuracies of 94% and 88%, respectively. High sensitivity and specificity values were achieved for five of the seven substrates investigated. Copyright © 2015 John Wiley & Sons, Ltd.     

Characterization of heteroscedastic measurement noise in the absence of replicates

A method is described for the characterization of measurement errors with non-uniform variance (heteroscedastic noise) in contiguous signal vectors (e.g., spectra, chromatograms) that does not require the use of replicated measurements. High-pass digital filters based on inverted Blackman windowed sinc smoothing coefficients are employed to provide point estimates of noise from measurement vectors. Filter parameters (number of points, cutoff frequency) are selected based on the amplitude spectrum of the signal in the Fourier domain. Following this, noise estimates from multiple signals are partitioned into bins based on a variable that correlates with the noise amplitude, such as measurement channel or signal intensity. The noise estimates in each bin are combined to estimate the standard deviation and, where appropriate, a functional model of the noise can be obtained to characterize instrumental errors (e.g., shot noise, proportional noise). The proposed method is demonstrated and evaluated with both simulated and experimental data sets, and results are compared with replicated measurements. Experimental data includes fluorescence spectra, ion chromatograms from liquid chromatography/mass spectrometry, and UV–vis absorbance spectra. The limitations and advantages of the new method compared to replicate analysis are presented.     

NMR spectroscopy evaluation of direct relationship between soils and molecular composition of red wines from Aglianico grapes

¹H NMR spectroscopy was employed to investigate the molecular quality of Aglianico red wines from the Campania region of Italy. The wines were obtained from three different Aglianico vineyards characterized by different microclimatic and pedological properties. In order to reach an objective evaluation of “terroir” influence on wine quality, grapes were subjected to the same winemaking procedures. The careful subtraction of water and ethanol signals from NMR spectra allowed to statistically recognize the metabolites to be employed in multivariate statistical methods: Principal Component Analysis (PCA), Discriminant Analysis (DA) and Hierarchical Clustering Analysis (HCA). The three wines were differentiated from each other by six metabolites: α-hydroxyisobutyrate, lactic acid, succinic acid, glycerol, α-fructose and β-d-glucuronic acid. All multivariate analyses confirmed that the differentiation among the wines were related to micro-climate, and carbonate, clay, and organic matter content of soils. Additionally, the wine discrimination ability of NMR spectroscopy combined with chemometric methods, was proved when commercial Aglianico wines, deriving from different soils, were shown to be statistically different from the studied wines. Our findings indicate that multivariate statistical elaboration of NMR spectra of wines is a fast and accurate method to evaluate the molecular quality of wines, underlining the objective relation with terroir.     

A Tin(II) Chloride Based Reduction Method for Multiple-Metal Trace Analysis of Natural Waters

Based on tin(II) chloride reduction in basic medium, a method of general use is proposed for the estimation of the whole gamut of multiple trace metals in natural waters by the atomic absorption method. The quantitative aspects of the method related to variables such as pH of the medium, amount of reductant and operational conditions are studied as a function of absorption sensitivity. The proposed method utilizes only a 50.0 ml portion of the water sample and yields above 90 per cent recoveries for various metals from a single aliquot of the sample. The reported data pertain to the estimation of silver, chromium, copper, iron, lead, mercury, manganese, magnesium, nickel and strontium, reported as averaged over replicate measurements at +-2S confidence level. The precision in individual cases is found to be better than +1.8%.     

A pilot study on colonic mucosal tissues by fluorescence spectroscopy technique: Discrimination by principal component analysis (PCA) and artificial neural network (ANN) analysis

Pulsed laser‐induced autofluorescence spectra of pathologically certified normal and malignant colonic mucosal tissues were recorded at 325 nm excitation. The spectra were analysed using three different methods for discrimination purposes. First, all the spectra were subjected to the principal component analysis (PCA) and the discrimination between normal and malignant cases were achieved using parameters like, spectral residuals, Mahalanobis distance and scores of factors. Second, to understand the changes in tissue composition between the two classes (normal, and malignant), difference spectrum was constructed by subtracting mean spectrum of calibration set samples from simulated mean of all spectra of any one class (normal/malignant) and in third, artificial neural network (ANN) analysis was carried out on the same set of spectral data by training the network with spectral features like, mean, median, spectral residual, energy, standard deviation, number of peaks for different thresholds (100, 250 and 500) after carrying out 1st‐order differentiation of the training set samples and discrimination between normal and malignant conditions were achieved. The specificity and sensitivity were determined in PCA and ANN analyses and they were found to be 100 and 91.3% in PCA, and 100 and 93.47% in ANN, respectively. Copyright © 2008 John Wiley & Sons, Ltd.     

Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer

The application of a new method to the multivariate analysis of incomplete data sets is described. The new method, called maximum likelihood principal component analysis (MLPCA), is analogous to conventional principal component analysis (PCA), but incorporates measurement error variance information in the decomposition of multivariate data. Missing measurements can be handled in a reliable and simple manner by assigning large measurement uncertainties to them. The problem of missing data is pervasive in chemistry, and MLPCA is applied to three sets of experimental data to illustrate its utility. For exploratory data analysis, a data set from the analysis of archeological artifacts is used to show that the principal components extracted by MLPCA retain much of the original information even when a significant number of measurements are missing. Maximum likelihood projections of censored data can often preserve original clusters among the samples and can, through the propagation of error, indicate which samples are likely to be projected erroneously. To demonstrate its utility in modeling applications, MLPCA is also applied in the development of a model for chromatographic retention based on a data set which is only 80% complete. MLPCA can predict missing values and assign error estimates to these points. Finally, the problem of calibration transfer between instruments can be regarded as a missing data problem in which entire spectra are missing on the ‘slave’ instrument. Using NIR spectra obtained from two instruments, it is shown that spectra on the slave instrument can be predicted from a small subset of calibration transfer samples even if a different wavelength range is employed. Concentration prediction errors obtained by this approach were comparable to cross-validation errors obtained for the slave instrument when all spectra were available.