Multivariate analysis of TOF-SIMS spectra of monolayers on scribed silicon

Static time-of-flight secondary ion mass spectrometry (TOF-SIMS) was performed on monolayers on scribed silicon (Si(scr)) derived from 1-alkenes, 1-alkynes, 1-holoalkanes, aldehydes, and acid chlorides. To rapidly determine the variation in the data without introducing user bias, a multivariate analysis was performed. First, principal components analysis (PCA) was done on data obtained from silicon scribed with homologous series of aldehydes and acid chlorides. For this study, the positive ion spectra, the negative ion spectra, and the concatentated (linked) positive and negative ion spectra were preprocessed by normalization, mean centering, and autoscaling. The mean centered data consistently showed the best correlations between the scores on PC1 and the number of carbon atoms in the adsorbate. These correlations were not as strong for the normalized and autoscaled data. After reviewing these methods, it was concluded that mean centering is the best preprocessing method for TOF-SIMS spectra of monolayers on Si(scr). A PCA analysis of all of the positive ion spectra revealed a good correlation between the number of carbon atoms in all of the adsorbates and the scores on PC1. PCA of all of the negative ion spectra and the concatenated positive and negative ion spectra showed a correlation based on the number of carbon atoms in the adsorbate and the class of the adsorbate. These results imply that the positive ion spectra are most sensitive to monolayer thickness, while the negative ion spectra are sensitive to the nature of the substrate-monolayer interface and the monolayer thickness. Loadings show an inverse relationship between (inorganic) fragments that are expected from the substrate and (organic) fragments expected from the monolayer. Multivariate peak intensity ratios were derived. It is also suggested that PCA can be used to detect outlier surfaces. Partial least squares showed a strong correlation between the number of carbon atoms in the adsorbate and the number it predicted.     

Principal component analysis of dynamical features in the peroxidase-oxidase reaction

Inherent variance due to oscillations in the peroxidase-oxidase (PO) reaction was studied using principal component analysis (PCA). The substrates were oxygen and reduced nicotinamide adenine dinucleotide (NADH). Horseradish peroxidase (HRP) catalyzed the reaction. The concentration of a cofactor, methylene blue (MB), was varied, and 2,4-dichlorophenol was kept constant. Increase in the NADH influx was used to change the reaction dynamics from periodic to chaotic. The reaction space was abstracted to the most significant, mutually independent, pairs of absorption and kinetic basis vectors (principal components). Typically, two significant principal components were extracted from the periodic time series and three from the chaotic data. The PCA models accounted for 70-97% of experimental variance. The greatest fraction of the total variance was accounted for in experiments exhibiting periodic dynamics and less than 25 nM MB. More MB induced an increased contribution of NADH to the PO oscillator variance, as did increased NADH influx. A simulated absorption time series, computed from a mass-action model of the chemistry, was analyzed by PCA as well. The comparison of simulation with experiment indicates that the chemical model renders the time series for HRP oxidation forms with fidelity, but incompletely represents NADH chemistry and other salient processes underlying the observed dynamics.     

从飞行时间二次离子质谱像解析航天器污染的信息

飞行时间二次离子表面质谱能对航天材料上肉眼可见微量污染物实现包括元素、同位素和各种化合物在内的指纹鉴别,特别是样品量有限航天器污染成份分析的理想手段.本文重点讨论如何从带有金一次离子源的飞行时间二次离子像中提取有关航天器污染的信息.由于飞行时间二次离子质谱独具的并行质量登录能力,二次离子表面像上每一像素都储存着完整的质谱,任意质量的二次离子像都可重构.与四极和磁二次离子质谱相比,飞行时间二次离子质谱更适于复杂航天器污染物的成像分析.     

Principal component analysis applied to filtered signals for maintenance management

This paper presents an approach for detecting and identifying faults in railway infrastructure components. The method is based on pattern recognition and data analysis algorithms. Principal component analysis (PCA) is employed to reduce the complexity of the data to two and three dimensions. PCA involves a mathematical procedure that transforms a number of variables, which may be correlated, into a smaller set of uncorrelated variables called ‘principal components’. In order to improve the results obtained, the signal was filtered. The filtering was carried out employing a state–space system model, estimated by maximum likelihood with the help of the well‐known recursive algorithms such as Kalman filter and fixed interval smoothing. The models explored in this paper to analyse system data lie within the so‐called unobserved components class of models. Copyright © 2009 John Wiley & Sons, Ltd.     

TOF-SIMS and principal component analysis characterization of the multilayer surface grafting of small molecules: antibacterial furanones

Furanone compounds (fimbrolides) have attracted interest as antibacterial compounds for use in human health care, for instance, as an antibacterial coating for medical devices to combat device-centered infections. To ensure effectiveness for extended periods of time, they must be immobilized covalently onto a device surface; in this study, this was done via azide/nitrene chemistry and photochemical coupling. However, the detection and quantification of surface-immobilized small molecules such as furanones presents a considerable analytical challenge, yet is necessary for optimization of coatings and reliable interpretation of biological responses. We have utilized the surface sensitivity and chemical specificity of time-of-flight secondary ion mass spectrometry (TOF-SIMS) to characterize each step of the grafting sequence. On account of the complexity of the data, principal component analysis (PCA) was used to interpret and compare spectra. The results demonstrate the utility of TOF-SIMS with PCA for the detection of the surface-grafted small molecules azidoaniline and a brominated furanone; imaging of the bromine ion peaks also enabled assessment of grafting uniformity. Thus, successful multilayer coating and furanone grafting was observed, and substantial and uniform coverage of furanone molecules on the surface. Even multiple grafting steps involving, in the present case, two low molecular weight compounds can readily be disentangled by PCA. The utility of TOF-SIMS analysis with PCA is particularly well illustrated in the present case by the grafting of the furanone molecules, which did not yield a singular unique peak in the positive ion mass spectra, whereas the collective spectral changes elucidated by PCA provided unambiguous verification of successful grafting of this low molecular weight compound.     

Principal alarms in multivariate statistical process control using independent component analysis

This article proposes a methodology that helps to predict the main mean shifts, denoted as principal alarms, in a non-normal multivariate process using the available in-control data. The analysis is based on the transformation of the observed correlated variables into independent factors using independent component analysis. These independent components allow us to simulate shifts preserving the covariance structure. The graphical representations of those simulated shifts are helpful in improving the design and control of the process. Two real manufacturing processes are presented showing the advantage of the proposed methodology.     

Using independent component analysis for material estimation in hyperspectral images

We develop a method for automated material estimation in hyperspectral images. The method models a hyperspectral pixel as a linear mixture of unknown materials. The method is particularly useful for applications in which material regions in a scene are smaller than one pixel. In contrast to many material estimation methods, the new method uses the statistics of large numbers of pixels rather than attempting to identify a small number of the purest pixels. The method is based on maximizing the independence of material abundances at each pixel. We show how independent component analysis algorithms can be adapted for use with this problem. We demonstrate properties of the method by application to airborne hyperspectral data.     

Reactivity of organic molecules in monolayers

Monolayers are considered not only as an oriented but also as a reactive medium whose reactivity is strongly dependent on the choice of the molecules used. The main types of chemical reactions which take place at the interfaces of multilayer-substrate systems are described. This is only a survey of a large field of experiments but it is intended to point out the versatility of monolayers.     

Principal component analysis applied to multiwavelength lidar aerosol backscatter and extinction measurements

The use of powerful Raman backscatter lidars enables one to measure the stratospheric aerosol extinction profile independently of the backscatter, thereby obtaining additional information to aid in retrieving the physical characteristics of the sampled aerosol. We used principal component analysis to construct a self-consistent method for the retrieval of aerosol bulk physical and optical properties from multiwavelength elastic and/or inelastic Raman backscatter lidar signals. The procedure is applied to synthetic and actual lidar signals. We found that aerosol surface area and volume can be usefully estimated and that the use of Raman-derived extinction data leads to a notable improvement in the accuracy of the estimations.     

Separating reflections from images by use of independent component analysis.

The image of an object can vary dramatically, depending on lighting, specularities, reflections, and shadows. It is often advantageous to separate these incidental variations from the intrinsic aspects of an image. We describe a method for photographing objects behind glass and digitally removing the reflections from the surface of the glass, leaving the image of the objects behind the glass intact. We describe the details of this method, which employs simple optical techniques and independent component analysis and show its efficacy with several examples.     

Submolecular potential profiling across organic monolayers

Potential profiles across molecular layers are constructed by means of noncontact electrically stimulated photoelectron spectroscopy, probing for the first time the molecule-substrate interface potential and resolving local screening effects across inner phenyl groups.     

Modeling organic surfaces with self-assembled monolayers

The interfacial properties of organic materials are of critical importance in many applications, especially the control of wettability, adhesion, tribology, and corrosion. The relationships between the microscopic structure of an organic surface and its macroscopic physical properties are, however, only poorly understood. This short review presents a model system that has the ease of preparation and the structural definition required to provide a firm understanding of interfacial phenomena. Long-chain thiols, HS(CH₂)_nX, adsorb from solution onto gold and form densely packed, oriented monolayers. By varying the terminal functional group, X, of the thiol, organic surfaces can be created having a wide range of structures and properties. More, complex systems can be constructed by coadsorbing two or more thiols with different terminal functional groups or with different chain lengths onto a common gold substrate. By these techniques, controlled degrees of disorder can be introduced into model surfaces. We have used these systems to explore the relationships between the microscopic structure of the monolayers on a molecular and supramolecular scale and their macroscopic properties. Wettability is a macroscopic interfacial property that has proven of particular interest.     

基于独立分量分析的荧光造影图像降噪算法

荧光造影图像分析是一种重要的糖尿病眼底病变测试方法,针对荧光造影图像易受噪声污染以及白内障病变影响导致眼底视网膜图像辨识困难等问题,基于盲信号处理理论,运用维纳滤波与独立分量分析进行降噪,并运用FASTICA算法进行源信号提取.测试图像处理结果表明:该方法能够有效地抑制噪声,提高分离图像的信噪比,且较好地恢复原始图像.     

SPM and TOF-SIMS investigation of the physical and chemical modification induced by tip writing of self-assembled monolayers

The nanoelectrochemical modification of alkyl self-assembled monolayers (SAMs) obtained on hydrogenated silicon surfaces via radical-initiated reactions of 1-octadecene has been investigated. Scanning Probe Microscopy (SPM) showed that the modification of the organic layer occurs by applying either positive or negative biases to the tip at a threshold of about ±5 V. When the bias absolute value was ≤6 V, the height of the monolayer was only faintly modified, whereas a consistent increase in tip/sample friction force was observed, in agreement with the formation of hydrophilic moieties at the organic surface. In addition to the increase of friction, bias absolute values larger than 6 V led to a significant raise of the surface height, the application of negative biases resulting in stronger effects. This suggests the occurrence of a concomitant growth of silicon oxide underneath the organic layer. Time of Flight Secondary Ion Mass Spectrometry (TOF-SIMS) experiments, including chemical imaging and analysis of the retrospective spectra, were performed by writing patterns of some microns in size on the SAM. These experiments allowed to investigate the features of the chemical modification as a function of the applied bias. Positive spectra from the modified regions display the presence of C_xH_yO and C_xH_yN type peaks that increase with the tip bias, whereas an intensity reduction of the SiC_xH_y signals with respect to the unmodified regions was observed.     

Principal component analysis of X-ray diffraction patterns to yield morphological classification of brucite particles

Principal component analysis was applied to XRD data from a series of Mg(OH)2 samples prepared under different hydrothermal conditions from bischofite (MgCl2.6H2O) and carnallite (KCl.MgCl2.6H2O), owing to differences in full width at half-maximum (fwhm) as well as in the intensity ratio I001/I101 of the respective diffraction peaks. According to the PCA results, the four principal components are able to explain 93% of the total variance and the samples can be classified into four main groups. For instance, the principal component PC1 can be interpreted as the crystallite size along the 101 direction since it is related to the fwhm of this peak. On the other hand, PC3 is related to orientation effects along 001 and 101 directions as it is dominated by the relative intensities of the two peaks. Finally, a comparison of the scanning electron microscopy images of the samples classified in each group revealed that in most of the cases a distinct morphology predominates within each group, which can be explained on the basis of the brucite growth mechanism.     

Principal component analysis of fluorescence changes upon growth conditions and washing of Pseudomonas aeruginosa

We have measured the autofluorescence from suspensions of Pseudomonas aeruginosa in the growth medium and after one, two, and three washes. The bacterium was grown in two different media, nutrient broth and King's B broth. The bacterium was harvested after 12, 24, and 48 h of growth. The fluorescence was measured with excitation every 10 nm from 200 nm to 600 nm. The fluorescence profiles were analyzed using principal component analysis. We found that most of the information is in the first three principal components. Stark differences in the value of the first principal component were noted between the samples in broth and those with one, two, or three washings. The second and third principal components noted differences between the samples washed once and those washed two or three times. There was no significant difference between samples washed two and three times. There are small differences noted between the samples grown in the two different broths, and no differences were noted among the samples harvested at different times.     

Principal component transform — Outer product analysis in the PCA context

Outer product analysis is a method that permits the combination of two spectral domains with the aim of emphasizing co-evolutions of spectral regions. This data fusion technique consists in the product of all combinations of the variables that define each spectral domain. The main issue concerning the application of this technique is the very wide data matrix obtained which can be very hard to handle with multivariate techniques such as PCA or PLS, due to computer resources constraints. The present work presents an alternative way to perform outer product analysis in the PCA context without incurring into high demands on computational resources. This works shows that by decomposing each spectral domain with PCA and performing the outer product on the recovered scores, one can obtain the same results as if one calculated the outer product in the original variable space, but using much less computational resources. The results show that this approach will make possible to apply outer product analysis to very wide domains.     

Principal component analysis: a versatile method for processing and investigation of XPS spectra

Given the relevance of principal component analysis (PCA) to the treatment of spectrometric data, we have evaluated potentialities and limitations of such useful statistical approach for the harvesting of information in large sets of X-ray photoelectron spectroscopy (XPS) spectra. Examples allowed highlighting the contribution of PCA to data treatment by comparing the results of this data analysis with those obtained by the usual XPS quantification methods. PCA was shown to improve the identification of chemical shifts of interest and to reveal correlations between peak components. First attempts to use the method led to poor results, which showed mainly the distance between series of samples analyzed at different moments. To weaken the effect of variations of minor interest, a data normalization strategy was developed and tested. A second issue was encountered with spectra suffering of an even slightly inaccurate binding energy scale correction. Indeed, minor shifts of energy channels lead to the PCA being performed on incorrect variables and consequently to misleading information. In order to improve the energy scale correction and to speed up this step of data pretreatment, a data processing method based on PCA was used. Finally, the overlap of different sources of variation was studied. Since the intensity of a given energy channel consists of electrons from several origins, having suffered inelastic collisions (background) or not (peaks), the PCA approach cannot compare them separately, which may lead to confusion or loss of information. By extracting the peaks from the background and considering them as new variables, the effect of the elemental composition could be taken into account in the case of spectra with very different backgrounds. In conclusion, PCA is a very useful diagnostic tool for the interpretation of XPS spectra, but it requires a careful and appropriate data pretreatment.     

Multivariate statistical analysis of three-spatial-dimension TOF-SIMS raw data sets

Three-spatial-dimension (3D) time-of-flight-secondary ion mass spectrometry (TOF-SIMS) analysis can be performed if an X-Y image is saved at each depth of a depth profile. In this paper, we will show how images reconstructed from specified depths, depth profiles generated from specific X-Y coordinates, as well as three-spatial-dimensional rendering provide for a better understanding of the sample than traditional depth profiling where only a single spectrum is collected at each depth. We will also demonstrate, for the first time, that multivariate statistical analysis (MVSA) tools can be used to perform a rapid, unbiased analysis of the entire 3D data set. In the example shown here, retrospective analysis and MVSA revealed a more complete picture of the 3D chemical distribution of the sample than did the as-measured depth profiling alone. Color overlays of the MVSA components as well as animated movies allowing for visualization (in 3D) from various angles will be provided.