Principal component analysis of TOF-SIMS images of organic monolayers

Principal component analysis (PCA) is a statistical method used to find combinations of variables or factors that describe the most important trends in the data. PCA has been combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) data to extract new information and find relations between species contained in complex systems. Monolayers of dipalmitoylphosphatidylcholine alone and mixed with palmitoyloleoylphosphatidylglycerol prepared using the Langmuir-Blodgett technique are discussed. PCA software provides image scores and corresponding loadings for each significant principal component. Image plots of the scores show the spatial distribution and intensity of the species defined by the loading plots (mass spectral features). The intensity and resolution of the image scores can result in substantial improvement over that of the regular TOF-SIMS images especially when static conditions are used for small analysis areas. Also, some of the effects of topography and matrix in the images can be removed, allowing for a better presentation of chemical variations.     

Spectral simulation study on the influence of the principal component analysis step on principal component regression

Principal component regression (PCR) is unique in that the principal component analysis (PCA) step is explicitly involved in the central part of the method. In the present paper, the PCA part is examined in order to study the influence of noise in spectra on PCR by spectral simulation. It has been suggested, as a result, that PCR calibration would have a large inaccuracy when the estimated number of basis factors analyzed by the eigenvalue method is less than that by cross-validation, which was studied by use of synthesized spectra. This instability is because the minute noise is largely enhanced by the PCA calculation via the normalization of loadings. At the same time, the noise enhancement by PCA has also been characterized to influence the estimation of basis factors.     

Simultaneous optimization of the microextraction of coffee volatiles using response surface methodology and principal component analysis

It is well known that no single experimental condition can be found under which the extraction of all the volatile compounds in a gas chromatographic analysis of roasted coffee beans by headspace-solid phase microextraction (HS-SPME) is maximized. This is due to the large number of peaks recorded. In this work, the scores vector of the first principal component obtained from PCA on chromatographic peak areas was used as the response to find the optimal conditions for simultaneous optimization of coffee volatiles extraction via response surface methodology (RSM). This strategy consists in compressing several highly correlated peak areas into a single response variable for a central composite design (CCD). RSM was used to identify an optimal factor combination that reflects a compromise between the partially conflicting behavior of the volatiles groups. This simultaneous optimization approach was compared with the desirability function method. The versatility of the PCA-RSM methodology allows it to be used in other chromatographic applications, resulting in an interpretable procedure to solve new analytical problems.     

Dimensionality reduction and visualization in principal component analysis

Many modern applications of analytical chemistry involve the collection of large megavariate data sets and subsequent processing with multivariate analysis techniques (MVA), two of the more common goals being data analysis (also known as data mining and exploratory data analysis) and classification. Classification attempts to determine variables that can distinguish known classes allowing unknown samples to be correctly assigned, whereas data analysis seeks to uncover and understand or confirm relationships between the samples and the variables. An important part of analysis is visualization which allows analysts to apply their expertise and knowledge and is often easier for the samples than the variables since there are frequently far more of the latter. Here we describe principal component variable grouping (PCVG), an unsupervised, intuitive method that assigns a large number of variables to a smaller number of groups that can be more readily visualized and understood. Knowledge of the source or nature of the variables in a group allows them all to be appropriately treated, for example, removed if they result from uninteresting effects or replaced by a single representative for further processing.     

Connections between multiple co-inertia analysis and consensus principal component analysis

Consensus Principal Component Analysis is a multiblock method which is designed to reveal covariant patterns between and within several multivariate data sets. The computation of the parameters of this method namely, block scores, block loadings, global loadings and global scores are based on an iterative procedure. However, very few properties are known regarding the convergence of this iterative procedure. The paper discloses a monotony property of CPCA and exhibits an optimisation criterion for which CPCA algorithm provides a monotonic convergent solution. This makes it possible to highlight new properties of this method of analysis and pinpoint its connection to existing methods such as Generalized Canonical Correlation Analysis and Multiple Co-inertia Analysis.     

Clustering and feature selection using sparse principal component analysis

In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief introduction and motivation on sparse PCA and detail our implementation of the algorithm in d’Aspremont et al. (SIAM Rev. 49(3):434–448, 2007). We then apply these results to some classic clustering and feature selection problems arising in biology.     

Reconstructing spectral reflectance by dividing spectral space and extending the principal components in principal component analysis

Principal component analysis (PCA) is widely used to reconstruct the spectral reflectance of surface colors. However, the estimated spectral accuracy is low when using only one set of three principal components for three-channel color-acquisition devices. In this study, the spectral space was first divided into 11 subgroups, and the principal components were calculated for individual subgroups. Then the principal components were further extended from three to nine through the residual spectral error of the reflectance in each subgroup. For each target sample, the extended principal components of the corresponding subgroup samples were used in the common PCA method to reconstruct the spectral reflectance. The results show that this proposed method is quite accurate and outperforms other related methods.     

A plug-in approach to sparse and robust principal component analysis

We propose a method for sparse and robust principal component analysis. The methodology is structured in two steps: first, a robust estimate of the covariance matrix is obtained, then this estimate is plugged-in into an elastic-net regression which enforces sparseness. Our approach provides an intuitive, general and flexible extension of sparse principal component analysis to the robust setting. We also show how to implement the algorithm when the dimensionality exceeds the number of observations by adapting the approach to the use of robust loadings from ROBPCA. The proposed technique is seen to compare well for simulated and real datasets.     

一种新的鲁棒主成分分析方法及其应用

背景建模在视频运动分析中具有重要作用.视频序列背景图像通常具有低秩性,为了更好地刻画该特性,精确提取视频背景,提出了一种基于截断核范数的鲁棒主成分分析模型.同时设计了一种两步迭代算法来求解该模型,最后将该算法应用于视频背景建模.不同视频数据库实验表明,该算法对于求解背景建模问题是有效的.     

飞行时间二次离子质谱--强有力的表面、界面和薄膜分析手段

二次离子质谱（Secondary ion mass spectrometry，简称SIMS）是一种对表面灵敏的质谱技术，建立在表面各种类型带正、负电荷原子或分子发射的基础上。用飞行时间（Time of flight，简称TOF）仪器对这些二次离子进行质量分析，能确保并行质量登录、高质量范围、高流通率下的高分辨和精确质量测定这些优异性能。配合细聚焦扫描一次离子束，可在优于1nm的高深度分辨和优于50nm的横向分辨本领下，实现对表面优于单层ppm(百万分之一）量级的极高检测灵敏度。当今TOF－SIMS已发展为一种成熟且完善的表面分析技术。极高的灵敏度，再加上即使对大分子及不易挥发性分子都独具的敏感性，使它成为很多高技术领域不可缺少的分析手段，这些领域包括微电子学、化学和材料科学以至纳米技术和生命科学等。本文简述了TOF－SIMS的原理、仪器及其多方面的应用和展望。     

基于主成分分析的护听器优选方法研究

为了探究不同护听器对抽水蓄能电站内不同工作场所的降噪效果及适用情况,以便于工作人员根据不同需要选择合适的护听器,根据112种护听器的插入损失测试结果,应用主成分分析(Principal Component Analysis, PCA)对数据进行分析。结合某抽水蓄能电站10个工作场所的现场测试结果,得出文中所测试的112种护听器大部分适用于该蓄水电站中1#发电机隔声罩内、1#水车室外、1#水车室内、2#尾水锥管室外、2#尾水锥管检修门、3#尾水锥管室外和3#尾水锥管检修门7个工作场所,其他场所需要有针对性地选择适合的护听器。该文同时可以为其他不同工作场所情况下护听器的选择提供借鉴。     

TOF-SIMS characterization and imaging of controlled-release drug delivery systems

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) was used for the analysis of multilayer drug beads that serve as controlled-release drug delivery systems. TOF-SIMS analysis of a cross section of each bead system allowed molecular chemical information to be gained from all of the layers simultaneously, in situ. The integrity of each of the layers was evaluated through imaging of specific ion species for the drug, excipient, and coating materials. The three beads in this study each showed a unique distribution of ingredients. Images of the parent molecular ion for each drug (theophylline, paracetamol, prednisolone) showed their distribution ranged from micrometer-sized particles in one bead cross section to almost homogeneous in another bead cross section. The chemical composition of each of the layers in the beads was evaluated through mass spectrometry; the ingredients did not always match the manufacturer's specification. In addition, many common drug bead ingredients were analyzed as pure substances, providing TOF-SIMS reference spectra of these materials for the first time.     

结合主分量分析与DOA估计的语音盲分离

在欠定语音盲分离中,W-分离正交性假设通常使问题简化,但这种简化是以降低分离性能为代价。在语音信号满足近似W-分离正交性的假设下,提出利用主分量分析（PCA）检测只有一个源信号存在的时频点,检测出的时频点均满足W-分离正交性,因此提高了混合矩阵的估计精度。通过从混合矩阵中估计源信号的波达方向,可以较好地解决置换模糊问题。仿真结果表明,提出的方法与经典的DUET方法相比具有更优的性能,平均信干比提高了2.77dB。     

基于非负矩阵分解与主元分析的时频图像识别方法研究

本文应用非负矩阵分解与主元分析对时频图像处理,在此基础上进行设备状态识别。论述了对振动信号应用Hilbert谱构建二维时频图像,并用非负矩阵分解对时频图像构造特征向量,应用主元分析对提取的特征向量进行了降维处理,并在三维坐标系中进行表示和状态识别。以滚动轴承不同状态的识别为例,验证方法的有效性。研究表明此方法能够提高设备状态识别的准确性,有利于设备故障诊断的发展。     

Bacillus spore classification via surface-enhanced Raman spectroscopy and principal component analysis

Surface-enhanced Raman spectroscopy (SERS) can provide rapid fingerprinting of biomaterial in a nondestructive manner. The adsorption of colloidal silver to biological material suppresses native biofluorescence while providing electromagnetic surface enhancement of the normal Raman signal. This work validates the applicability of qualitative SER spectroscopy for analysis of bacterial species by utilizing principal component analysis (PCA) to show discrimination of biological threat simulants, based upon multivariate statistical confidence limits bounding known data clusters. Gram-positive Bacillus spores (Bacillus atrophaeus, Bacillus anthracis, and Bacillus thuringiensis) are investigated along with the Gram-negative bacterium Pantoea agglomerans.     

Maximum likelihood principal component analysis with correlated measurement errors: theoretical and practical considerations

Procedures to compensate for correlated measurement errors in multivariate data analysis are described. These procedures are based on the method of maximum likelihood principal component analysis (MLPCA), previously described in the literature. MLPCA is a decomposition method similar to conventional PCA, but it takes into account measurement uncertainty in the decomposition process, placing less emphasis on measurements with large variance. Although the original MLPCA algorithm can accommodate correlated measurement errors, two drawbacks have limited its practical utility in these cases: (1) an inability to handle rank deficient error covariance matrices, and (2) demanding memory and computational requirements. This paper describes two simplifications to the original algorithm that apply when errors are correlated only within the rows of a data matrix and when all of these row covariance matrices are equal. Simulated and experimental data for three-component mixtures are used to test the new methods. It was found that inclusion of error covariance information via MLPCA always gave results which were at least as good and normally better than PCA when the true error covariance matrix was available. However, when the error covariance matrix is estimated from replicates, the relative performance depends on the quality of the estimate and the degree of correlation. For experimental data consisting of mixtures of cobalt, chromium and nickel ions, maximum likelihood principal components regression showed an improvement of up to 50% in the cross-validation error when error covariance information was included.     

Recycling of a plastic car component having a multilayer structure: Morphological and mechanical analysis

The recycling of two types of dashboards having a multilayer structure presently available on the market and hence on the scrap cars has been extensively investigated. In both the cases the recycled material showed a strong worsening in the mechanical and impact properties. This effect was attributed to the incompatibility of the different materials constituting the item and to the occurrence of degradation phenomena taking place during processing. To overcome these limitations suitable polymeric additives were added during the recycling. By this procedure materials with improved mechanical properties able to be reused for the same or for similar applications within the car have been obtained.     

New method for spectral data classification: two-way moving window principal component analysis

Two-way moving window principal component analysis (TMWPCA), which considers all possible variable regions by using variable and sample moving windows, is proposed as a new spectral data classification method. In TMWPCA, the similarity between model function and the index obtained by variable and sample moving windows is defined as "fitness". For each variable region selected by a variable moving window, the fitness is obtained through the use of a model function. By maximizing the fitness, an optimal variable region can be searched. A remarkable advantage of TMWPCA is that it offers an optimal variable region for the classification. To demonstrate the potential of TMWPCA, it has been applied to the classification of visible-near-infrared (Vis-NIR) spectra of mastitic and healthy udder quarters of cows measured in a nondestructive manner. The misclassification rate of TMWPCA has been compared with those of other chemometric methods, such as principal component analysis (PCA), soft independent modeling of class analogies (SIMCA), and principal discriminant variate (PDV). TMWPCA has yielded the lowest misclassification rate. The result indicates that TMWPCA is a powerful tool for the classification of spectral data.     

Surface spectroscopy of adsorbed proteins: input of data treatment by principal component analysis

X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectroscopy (ToF–SIMS), two surface-sensitive spectroscopic methods, are commonly used to characterize adsorbed protein layers. Principal component analysis (PCA) is a statistical method which aims at reducing the number of variables in complex sets of data while retaining most of the original information. The aim of this paper is to review work carried out in our group regarding the use of PCA with a view to facilitate and deepen the interpretation of ToF–SIMS or XPS spectra acquired on adsorbed protein layers. ToF–SIMS data acquired on polycarbonate membranes after albumin and, or insulin adsorption were treated with PCA. The results reveal the preferential exposure of particular amino acids at the outermost surface depending on the adsorption conditions (nature of the substrate and of the proteins involved, concentration in solution), giving insight into the adsorption mechanisms. PCA was applied on XPS data collected on three different substrates after albumin or fibrinogen adsorption, followed in some cases by a cleaning procedure with oxidizing agents. The results allow samples to be classified according to the nature of the substrate and to the adsorbed amount and, or the level of surface coverage by the protein. Chemical shifts of particular interest are also identified, which may facilitate further peak decomposition. It is useful to recall that the outcome of PCA strongly depends on data selection and normalisation.     

X-ray multilayer optics: growth and characterization

Growth of X-ray multilayer optics is done using an ultra high-vacuum electron beam deposition. High-vacuum reflectometer station on Indus-1 synchrotron source is described. Representative studies on Mo/C X-ray multilayers are presented.