Application of factor analysis for resolving thermogravimetric-mass spectrometric analysis spectra

Factor Analysis was used for extracting information out of the mixture mass spectra recorded in a thermogravimetric-mass spectrometric analysis. Principal component analysis (PCA) and a special diagram, the contour variance diagram (ContVarDia), were used for performing the factor analysis. The method was applied for studying the thermal decomposition of Kraton 1107 copolymer. Pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) was used for identification of the pyrolysis products of Kraton 1107. The application of factor analysis resulted in the determination of the main thermal decomposition steps and the prediction of the mass spectrum corresponding to each step. Those mass spectra were either pure spectra corresponding to main evolved gases or average spectra corresponding to multiple gases evolved in one decomposition step. The advantages and the limitations of the chemometric approach were discussed.     

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.     

Methods for extracting biochemical information from bacterial Raman spectra: focus on a group of structurally similar biomolecules--fatty acids

In this paper we explore the possibilities of Raman spectroscopy in order to deduce information on the fatty acid composition of bacterial cells. Therefore, representative strains of two bacterial taxa were each cultured in different conditions and in parallel analyzed by Raman spectroscopy and gaschromatographic FAME analysis. Raman spectra of pure fatty acids were recorded and used as reference spectra. The culturing conditions for each strain could be easily distinguished by the fatty acid information retrieved from bacterial Raman spectra. Chemometric techniques such as EMSC and PCA allowed to extract information about groups of fatty acids, that was consistent with the results from FAME analysis. Although the information retrieved from Raman spectroscopy is not as refined as that from FAME analysis, the presented methods could be useful to obtain basic information on the fatty acid present in bacteria when performing Raman spectroscopic analysis for fast whole cell profiling, which provides information for different types of cell components (fatty acids, amino acids, primary metabolites, etc.).     

Chemometric methods for extracting information from temperature-dependent near-infrared spectra

Temperature-dependent near-infrared(NIR) spectroscopy is a new technique for measuring the NIR spectra of a sample at different temperatures. Taking the advantage of the temperature effect, the technique has shown its potential in both quantitative and qualitative analysis. The technique has been proved to be powerful in determination of the analytes in complex samples,particularly in studying the functions of water in aqueous systems due to the significant effect of temperature on the NIR spectra of water. Because of the complicated interactions in the samples and the overlapping of the broad peaks in NIR spectra, it is difficult to extract the temperature-dependent information from the spectra. Chemometric methods, therefore, have been developed for improving the spectral resolution and extracting the temperature-induced spectral information. In this review, recent advances in the studies of chemometric methods and the applications in resolution, quantitative and structural analysis of temperature-dependent NIR spectra were summarized.     

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.     

基于近红外光谱技术与化学计量学的绿茶无损鉴别方法研究

该文利用近红外光谱技术结合化学计量学方法开发了不同品种绿茶的无损鉴别方法。通过近红外光谱技术得到了8个品种绿茶样品的近红外光谱,比较了单一以及优化组合光谱预处理方法对光谱的影响,利用无监督的主成分分析(PCA)与有监督的线性判别分析方法(LDA)分别构建了茶叶品种鉴别模型。结果表明：对比单一预处理方法,优化组合预处理具有更优的鉴别准确性。标准正态变量变换预处理消除了茶叶样品大小不均造成的光谱散射影响,一阶导数预处理实现了变动背景的消除,减少了基线漂移的影响,突出了图谱中的有效信息,采用二者相结合的预处理方式并结合无监督的主成分分析法可实现较为准确的绿茶样品种类鉴别分析,准确率达75.0%。此外,采用有监督的线性判别分析方法处理原始光谱数据,可达到100%的鉴别准确率,但该方法需提供类别的先验知识。因此,采用近红外光谱技术和化学计量学相结合的手段可实现不同品种绿茶的快速无损鉴别。     

茶叶化学成分指纹识别中样品制备方法的优化

根据指纹识别实验对样品的要求, 以¹H NMR和主成分分析(PCA)为测试与评估手段, 对茶叶中化学成分提取条件的优化方法进行了研究. 针对生物指纹识别实验中样本体系复杂及化学组分含量多等特点, 利用主成分分析对检测样品质控的多因素进行综合分析, 最终确立了茶叶中化学成分提取制备的实验条件. 利用所建立的样品制备程序对4种茶叶进行预处理和测试, 结果表明, 所建立的方法稳定、可靠, 可以满足茶叶代谢物¹H NMR指纹识别研究的要求. 本文所提出的优化方法简单可靠, 可用于复杂样本体系标准样品预处理方法的建立.     

Characterization of the measurement error structure in 1D H NMR data for metabolomics studies

NMR-based metabolomics is characterized by high throughput measurements of the signal intensities of complex mixtures of metabolites in biological samples by assaying, typically, bio-fluids or tissue homogenates. The ultimate goal is to obtain relevant biological information regarding the dissimilarity in patho-physiological conditions that the samples experience. For a long time now, this information has been obtained through the analysis of measured NMR signals via multivariate statistics.NMR data are quite complex and the use of such multivariate statistical methods as principal components analysis (PCA) for their analysis assumes that the data are multivariate normal with errors that are identical, independent and normally distributed (i.e. iid normal). There is a consensus that these assumptions are not always true for these data and, thus, several methods have been devised to transform the data or weight them prior to analysis by PCA. The structure of NMR measurement noise, or the extent to which violations of error homoscedasticity affect PCA results have neither been characterized nor investigated.A comprehensive characterization of measurement uncertainties in NMR based metabolomics was achieved in this work using an experiment designed to capture contributions of several sources of error to the total variance in the measurements. The noise structure was found to be heteroscedastic and highly correlated with spectral characteristics that are similar to the mean of the spectra and their standard deviation. A model was subsequently developed that potentially allows errors in NMR measurements to be accurately estimated without the need for extensive replication.     

Autofluorescence of oral tissue for optical pathology in oral malignancy

Pulsed laser-induced-fluorescence studies of pathologically certified oral tissues are carried out at different excitations and time delays. Among the several excitations used, 325 nm produced noticeably different spectral profile for normal and malignant tissues. Extensive curve analysis was carried out in order to understand changes in biochemical composition of tissue based on spectral profiles. Curve resolution and principal component analysis (PCA) show that the fluorescence intensity changes from normal to malignant tissue samples are not completely explained in terms of simple collagen and NAD(P)H intensity changes. The spectra require at least five components to be fully accounted for. Several discrimination methodologies based on PCA and intensity differences between different emission peaks (resultant peaks of curve analysis) were also evaluated. The results obtained indicate PCA using Mahalanobis distance and spectral residual as discrimination parameters provides best discrimination and can be used for matching unknown samples to standard calibration sets. Intensity ratio of bound NAD(P)H to collagen seems to be more suitable for discrimination between normal and malignant oral tissue, compared to ratio of collagen to total intensity of all the other components together.     

Application of near infrared spectroscopy (NIR), X-ray fluorescence (XRF) and chemometrics to the differentiation of marmora samples from the Mediterranean basin

Abstract

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models. Moreover, a mid-level data fusion protocol allowed integrating the information from the different techniques and, in particular, to correctly identify (based on the distance in the score space) three test samples of known type. Moreover, it should be stressed that positive results on the differentiation and identification of marmora were obtained using two completely non-invasive, non-destructive and relatively inexpensive techniques, which can also be used in situ.     

基于显微共聚焦拉曼光谱技术快速检测3种梭菌中肉毒梭菌

提出了一种基于显微共聚焦拉曼光谱技术的肉毒梭菌快速鉴别方法.利用共聚焦显微拉曼光谱技术(CRM)采集了肉毒梭菌、艰难梭菌和产气荚膜梭菌的拉曼光谱,比较了3种梭菌的平均拉曼光谱,采用基线校正、标准正态变换、Savitzky-Golay 5点平滑和最大最小值归一化预处理后,借助主成分分析(PCA)降维并提取特征变量,对样本...     

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.     

Application of probabilistic neural networks in qualitative analysis of near infrared spectra: determination of producing area and variety of loquats

Near infrared (NIR) spectra of a sample can be treated as a signature, allowing samples to be grouped on basis of their spectral similarities. Near infrared spectroscopy (NIRS) combined with probabilistic neural networks (PNN) have been used to discriminate producing area and variety of loquats. Two varieties of loquats (‘Dahongpao’ and ‘Jiajiaozhong’) picked from two producing areas of ‘Tangxi’ and ‘Cunan’ in Zhejiang province were analyzed in this study. Principal component analysis (PCA) was applied before PNN modeling and the results indicated that the dimension of the vast spectral data can be effectively reduced. For each model, half samples were used to train the network and the remaining half were used to test the network. The results of the PCA-PNN models for discriminating the variety of samples from the same producing area or for discriminating the producing area of the same variety samples were much better than those of the PCA-PNN models for discriminating variety or producing area of all loquat samples. The results of this study show that NIRS combined with PCA-PNN is a feasible way for qualitative analysis of discriminating fruit producing areas and varieties.     

Multivariate analysis of laser-induced breakdown spectroscopy chemical signatures for geomaterial classification

A large suite of natural carbonate, fluorite and silicate geological materials was studied using laser-induced breakdown spectroscopy (LIBS). Both single- and double-pulse LIBS spectra were acquired using close-contact benchtop and standoff (25 m) LIBS systems. Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to identify the distinguishing characteristics of the geological samples and to classify the materials. Excellent discrimination was achieved with all sample types using PLS-DA and several techniques for improving sample classification were identified. The laboratory double-pulse LIBS system did not provide any advantage for sample classification over the single-pulse LIBS system, except in the case of the soil samples. The standoff LIBS system provided comparable results to the laboratory systems. This work also demonstrates how PCA can be used to identify spectral differences between similar sample types based on minor impurities.     

Efficient and sample-specific interpretation of ToF-SIMS data by additional postprocessing of principal component analysis results

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool for surface analysis, but fragmentation of molecular species during the SIMS process may lead to complex mass spectra. While the fragmentation pattern is typically characteristic for each compound, industrial samples are engineered materials, and, thus, may contain a mixture of many compounds, which may result in a variety of overlapping peak patterns in ToF-SIMS spectra. Consequently, the process of data evaluation is challenging and time-consuming. Principal component analysis (PCA) can be used to simplify data analysis for complex sample systems. Especially, correlation loadings were observed as an ideal tool to identify relevant signals in PCA results, which induce the separation of different sample groups. This is because correlation loadings show the relevance of signals independent from their intensity in the raw data. In correlation loadings, however, fragmentation patterns are no longer observed and the identification of peaks' sum formulas is challenging. In this study, a new approach is presented, which simplifies peak identification and assignment in ToF-SIMS spectra after PCA is performed. The approach uses a mathematical transformation that projects PCA results, in particular loadings and correlation loadings, in the direction of specific sample groups. The approach does not change PCA results but rather presents them in a new way. This method allows to visualize characteristic spectra for specific sample groups that contain only relevant signals and, additionally, visualize fragmentation patterns. Data analysis is simplified and helps the user to focus on data interpretation rather than processing.     

Classification of the Biopolymer Sodium Pentosan Polysulfate by Infrared Spectroscopy

Summary Sodium pentosan polysulfate (NaPPS) is a glycosaminoglycan that is of increasing interest due to its medical properties. It has been investigated for the treatment of osteoarthritis, HIV and Prion based diseases. This work describes an investigation into the application of infrared spectroscopy (IR) for the differentiation between sources of NaPPS. Multivariate techniques such as principle components analysis were applied to detect differences between the IR and near IR (NIR) spectra and to classify the biopolymers based on their manufacturer. This study compared two samples of NaPPS from different manufacturers. Principle components analysis (PCA) together with soft independent modeling of class analogies (SIMCA) was used to successfully classify the different samples. Clear differentiation between all batches was achieved using PCA and class distances using first derivative spectra (500–1800 cm^–1).Presented at: International Symposium on Separation and Characterization of Natural and Synthetic Macromolecules, Amsterdam, The Netherlands, February 5–7, 2003     

Forensic discrimination of photocopy and printer toners II. Discriminant analysis applied to infrared reflection-absorption spectroscopy

Copy toner samples were analyzed using reflection-absorption infrared microscopy (R-A IR). The grouping of copy toners into distinguishable classes achieved by visual comparison and computer-assisted spectral matching was compared to that achieved by multivariate discriminant analysis. For a data set containing spectra of 430 copy toners, 90% (388/430) of the spectra were initially correctly grouped into the classifications previously established by spectral matching. Three groups of samples that did not classify well contained too few samples to allow reliable classification. Samples from two other pairs of groups were similar and often misclassified. Closer examination of spectra from these groups revealed discriminating features that could be used in separate discriminant analyses to improve classification. For one pair of groups, the classification accuracy improved to 91% (81/89) and 97% (28/29), for the two groups, respectively. The other pair of groups were completely distinguishable from one another. With these additional tests, multivariate discriminant analysis correctly classified 96% of the 430 R-A IR toner spectra into the toner groups found previously by spectral matching.This is publication number 03–03 of the Laboratory Division of the Federal Bureau of Investigation. Names of commercial manufacturers are provided for identification only, and inclusion does not imply endorsement by the Federal Bureau of Investigation.     

Multivariate analysis of reflectance spectra from propolis: Geographical variation in Romanian samples

The present study described reflectance spectroscopy as a suitable analytical tool to discriminate the floral origin of 39 Romanian propolis samples. Relevant differences between the UV-vis reflectance spectra of the investigated propolis samples within the 220-850 nm spectral range were found. The results obtained applying cluster analysis, principal component analysis and linear discriminant analysis to the digitized data of zero order, zero order normalized and first order derivative spectra support the reliability of this technique. In addition, the application of the linear discriminant analysis to the score matrices corresponding to the first principal components appeared to be an illuminating solution. Generally, the samples have been assigned to two large groups in a good agreement with their vegetal sampling location, samples originating from predominant forest area and samples originating from meadows. Within the first group, two subgroups were identified according to the dominant type of the forest, deciduous or resinous, while within the last group three subgroups were found according to the extend and variety of the meadow.     

Preliminary study of UV ageing process of proteinaceous paint binder by FT-IR and principal component analysis

This work presents a preliminary study on the ageing process of proteinaceous binder materials used in painting under UV light. With this aim, two sets of model samples were prepared: samples prepared using a single protein material and complex samples prepared in a similar way to the sequence of layers in a real painting from lowest to highest complexity (protein, drying oils, pigment and varnish). The study focuses on acquiring information about the possible degradation process of proteinaceous binders due to ageing and how this process be affected by the presence of characteristic non-proteinaceous painting materials, such as lipids from linseed oil, terpenic compounds from varnish and inorganic pigments. Samples simulated the accelerated ageing process, as did the UV light exposition. The FT-IR spectra were recorded after 100, 500, 1000 and 1500 h of exposition. The study of the accelerated ageing process was performed by means of principal component analysis (PCA) using the FT-IR spectra obtained. Loadings from the significant principal components were analysed to find the FT-IR frequency (cm⁻¹) involved in the degradation process. The study showed the lack of any relevant modification on the proteins in the single model samples. On the contrary, the complex model samples showed the ageing process. The accelerated ageing process can be explained by a principal component from PCA. The most affected IR region was 2900-3600 cm⁻¹, where the amide band was included.