Quantitative analysis of adsorbed proteins by X-ray photoelectron spectroscopy

Protein adsorption at solid surfaces is central to many phenomena of medical and technological interest. The determination of the amount of protein attached to the surface is a critical measurement performed by using a wide range of methods. X-ray photoelectron spectroscopy (XPS) is able to provide a straightforward quantitative analysis of the amount of protein adsorbed as an overlayer on a material surface. While XPS is commonly employed to assess qualitatively the amount of adsorbed protein, this is usually expressed in terms of the elemental fraction (or at. %) of nitrogen calculated using an assumption of depth homogeneity despite the fact that this does not linearly scale with the amount of protein. In this paper, we have shown that thicknesses derived from XPS data linearly correlated with spectroscopic ellipsometry data on the same samples with a scatter of 10%. A straightforward equation to convert the concentration of nitrogen from XPS into an equivalent thickness of a protein film is presented. We highlight some discrepancies in the absolute thicknesses determined by XPS and ellipsometry on dried films and quartz crystal microbalance on wet films, which appear likely to result from the inclusion of a contribution from water in the latter two techniques.     

Robust principal component analysis for functional data

A method for exploring the structure of populations of complex objects, such as images, is considered. The objects are summarized by feature vectors. The statistical backbone is Principal Component Analysis in the space of feature vectors. Visual insights come from representing the results in the original data space. In an ophthalmological example, endemic outliers motivate the development of a bounded influence approach to PCA.     

Three-way principal component analysis applied to noodles sensory data analysis

The results presented in this paper are issued from the study and the interpretation of a 3-way data matrix constituted from the sensory analysis of wheat noodles. The aim of this work was to provide a complementary understanding of internal relationships between the chemical composition of the noodles and sensory attributes such as color, surface smoothness, elasticity or chewiness. The application of the Tucker3 algorithm involving the noodles composition, the sensory attributes and the assessors as the three modes, facilitates the interpretation of the differences among the types of noodles and also the estimation of the effect of different sources of variability on the sensory evaluation. A joint interpretation of the first and of the second mode (noodles and sensory attributes) allows to link appearance and texture attributes with the composition of the noodles.     

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.     

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.     

A study on water adsorption onto microcrystalline cellulose by near-infrared spectroscopy with two-dimensional correlation spectroscopy and principal component analysis

Water adsorption onto microcrystalline cellulose (MCC) in the moisture content (M(c)) range of 0.2-13.4 wt % was investigated by near-infrared (NIR) spectroscopy. In order to distinguish heavily overlapping O-H stretching bands in the NIR region due to MCC and water, principal component analysis (PCA) and generalized two-dimensional correlation spectroscopy (2DCOS) were applied to the obtained spectra. The NIR spectra in four adsorption stages separated by PCA were analyzed by 2DCOS. For the low M(c) range of 0.2-3.1 wt %, a decrease in the free or weakly hydrogen-bonded (H-bonded) MCC OH band, increases in the H-bonded MCC OH bands, and increases in the adsorbed water OH bands are observed. These results suggest that the inter- and intrachain H-bonds of MCC are formed by monomeric water molecule adsorption. In the M(c) range of 3.8-7.1 wt %, spectral changes in the NIR spectra reveal that the aggregation of water molecules starts at the surface of MCC. For the high M(c) range of 8.1-13.4 wt %, the NIR results suggest that the formation of bulk water occurs. It is revealed from the present study that approximately 3-7 wt % of adsorbed water is responsible for the stabilization of the H-bond network in MCC at the cellulose-water surface.     

Discrimination between virgin and recycled polystyrene containers by Fourier transform infrared spectroscopy and principal component analysis

A rapid and reliable method for discriminating virgin and recycled expanded polystyrene (EPS) containers was developed using Fourier transform infrared spectroscopy combined with principal component analysis. Standard normal variate, first and second‐order derivative spectra were compared for the discrimination results. The results show that carbonyl region (1780‐1620 cm^?1) spectra using first derivative transformation give the optimum classification results. In addition, the carbonyl compounds in EPS containers were detected to clarify the chemical difference between virgin and recycled containers, with a higher concentration of carbonyl compounds observed in recycled EPS containers. The combination of carbonyl region of Fourier transform infrared spectroscopy with chemometrics proved to be a promising method to discriminate virgin and recycled EPS containers, which could function as an additional tool for quality control of plastics.     

A least squares approach to principal component analysis for interval valued data

Principal Component Analysis (PCA) is a well-known technique, the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components. In this paper, an extension of PCA to deal with interval valued data is proposed. The method, called Midpoint Radius Principal Component Analysis (MR-PCA), recovers the underlying structure of interval valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to analyze how MR-PCA works, the results of a simulation study and two applications on chemical data are proposed.     

Improving automotive dimensional quality by using principal component analysis

Dimensional quality is a measure of conformance of the actual geometry of products with the designed geometry. In the automotive body assembly process, maintaining good dimensional quality is very difficult and critical to the product. In this paper, a dimensional quality analysis and diagnostic tool is developed based on principal component analysis (PCA). In quality analysis, the quality loss due to dimensional variation can be partitioned into a mean deviation and piece-to-piece variation. By using PCA, the piece-to-piece variation can be further decomposed into a set of independent geometrical variation modes. The features of these major variation modes help in identifying the underlying causes of dimensional variation in order to reduce the variation. The variation mode chart developed in this paper provides the explicit and exact geometrical interpretation of variation modes, making PCA easily understood. A case study using an automotive body assembly dimensional quality analysis will illustrate the value and power of this methodology in solving actual engineering problems in a practical manner.     

Forecasting with unequally spaced data by a functional principal component approach

The Principal Component Regression model of multiple responses is extended to forccast a continuous-time stochastic process. Orthogonal projection on a subspace of trigonometric functions is applied in order to estimate the principal components using discrete-time observations from a sample of regular curves. The forecasts provided by this approach are compared with classical principal component regression on simulated data.     

Studies on the structure of water using two-dimensional near-infrared correlation spectroscopy and principal component analysis

The structure of water molecules in the pure liquid state has been subjected to extensive research for several decades. Questions still remain unanswered, however, and no single model has been found capable of explaining all the anomalies of water. In the present study, near-infrared spectra of water in the temperature region 6-80 degrees C have been analyzed by use of principal component analysis and two-dimensional correlation spectroscopy in order to study the dynamic behavior of a band centered around 1,450 nm at room temperature, which is due to the combination of symmetric and antisymmetric O-H stretching modes (first overtone) of water. It has been found that the wavelengths 1,412 and 1,491 nm account for more than 99% of the spectral variation, representing two major water species with weaker and stronger hydrogen bonds, respectively. A third species located at 1438 nm, whose concentration was relatively constant as a function of temperature, is also indicated. A somewhat distorted two-state structural model for water is suggested.     

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.     

Robust principal component analysis using facial reduction

Optimization and Engineering - We introduce a novel approach for robust principal component analysis (RPCA) for a partially observed data matrix. The aim is to recover the data matrix as a sum of a...     

主成分分析的入侵检测方法研究

直接将入侵检测算法应用在粗糙数据上,其入侵检测分析的效率非常低.为解决该问题,提出了一种基于主成分分析的入侵检测方法.该方法通过提取网络连接中的相关信息,对它进行解码,并将解码的网络连接记录与已知的网络连接记录数据进行比较,发现记录中的变化和连接记录分布的主成分,最后将机器学习方法和主成分分析方法结合实现入侵检测.实验结果表明该方法应用到各种不同KDD99入侵检测数据集中可以有效减少学习时间、降低各种数据集的表示空间,提高入侵检测效率.     

Multivariate process capability using principal component analysis

Wang and Chen (Qual. Eng. 1998; 11:21–27) have defined process capability indices (PCIs) for multivariate normal processes data using principal component analysis (PCA). Veevers (Statistical Process Monitoring and Optimization. Marcel Dekker: New York, NY, 1999; 241–256) has suggested a multivariate capability index based on the first principal component (PC). In this paper we demonstrate the problem in the definition of PCIs given by Wang and Chen (Qual. Eng. 1998; 11:21–27) and the non‐suitability of PCI given by Veevers (Statistical Process Monitoring and Optimization. Marcel Dekker: New York, NY, 1999; 241–256) through some examples. We also suggest an alternative method for assessing multivariate process capability based on the empirical probability distribution of PCs. This method has been performed on industrial and simulated data. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

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。     

Identifying product shape relationships using principal component analysis

Product forms with multiple features, like automobiles, have traditionally accepted feature definitions and relationships between those features. These relationships drive how the product is created by focusing on expected, and accepted, feature development to push the form outside the traditional bounds. This paper uses principal component analysis to determine the fundamental characteristics within vehicle classes. The results of this analysis can then be considered by product designers to create new designs based upon the derived shape relationships. These new designs will be novel due to the non-traditional grouping of characteristics.     

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.