An advanced multivariate approach for processing X-ray fluorescence spectral and hyperspectral data from non-invasive in situ analyses on painted surfaces

In the last decades, in situ non-invasive analytical techniques have been widely used for the analysis of paintings. These techniques are useful to extensively map the surface in a non-invasive way, in order to identify the most representative areas to be sampled. When spectroscopic investigations, such as X ray fluorescence (XRF), are conducted, they usually imply the acquisition of a huge amount of measurements. Subsequently, all these data should be processed in situ, in order to immediately support the sampling strategies. To this aim, an appropriate and fast strategy for multivariate treatment of XRF spectral and hyperspectral data sets is presented, able to account for inter-correlation among variables, which is an issue of high importance for elemental analyses. The main advantage of the approach is that XRF spectral profiles are analysed directly, without computation of derived parameters, by means of principal component analysis (PCA). This procedure allows a fast interpretation of results that can be accomplished in situ. Particular attention was paid to the selection of proper spectral pre-treatments to be applied on data together with the use of several chemometric tools (peak alignment, spectra normalisation and exploratory analysis) aimed at improving the interpretation of XRF results. In addition, the application of multivariate exploratory analysis on XRF hyperspectral maps was studied by using an interactive brushing procedure. The multivariate approach was validated on data obtained from the analysis of the famous Renaissance panel painting “The Ideal City”, exhibited in Palazzo Ducale of Urbino, Italy.     

Characterization of Automobile Plastics by Principal Component Analysis and Near-Infrared Spectroscopy

Based on near-infrared diffuse reflection spectroscopy, multivariate calibration models for discarded automobile plastic were constructed using principal component analysis and clustering analysis to rapidly characterize four widely employed materials: polypropylene, polyethylene, acrylonitrile butadiene styrene, and polymethylmethacrylate with an accuracy rate of 97%. The method was shown to rapidly discriminate waste automobile plastic.     

Using combinations of principal component scores from different spectral ranges in near-infrared region to improve discrimination for samples of complex composition

Principal component analysis (PCA) is widely used as an exploratory data analysis tool in the field of vibrational spectroscopy, particularly near-infrared (NIR) spectroscopy. PCA represents original spectral data containing large variables into a few feature-containing variables, or scores. Although multiple spectral ranges can be simultaneously used for PCA, only one series of scores generated by merging the selected spectral ranges is generally used for qualitative analysis. Alternatively, the combined use of an independent series of scores generated from separate spectral ranges has not been exploited.The aim of this study is to evaluate the use of PCA to discriminate between two geographical origins of sesame samples, when scores independently generated from separate spectral ranges are optimally combined. An accurate and rapid analytical method to determine the origin is essentially required for the correct value estimation and proper production distribution. Sesame is chosen in this study because it is difficult to visually discriminate the geographical origins and its composition is highly complex. For this purpose, we collected diffuse reflectance near-infrared (NIR) spectroscopic data from geographically diverse sesame samples over a period of eight years. The discrimination error obtained by applying linear discriminant analysis (LDA) was improved when separate scores from two spectral ranges were optimally combined, compared to the discrimination errors obtained when scores from singly merged two spectral ranges were used.     

A new regression method based on independent component analysis

Based on independent component analysis (ICA), a new regression method, independent component regression (ICR), was developed to build the model of NIR spectra and the routine components of plant samples. It is found that ICR and principal component regression (PCR) are completely equivalent when they are applied in quantitative prediction. However, independent components (ICs) can give more chemical explanation than principal components (PCs) because independence is a high-order statistic that is a much stronger condition than orthogonality. Three ICs are obtained by ICA from the NIR spectra of plant samples; it is found that they are strongly correlated to the NIR spectra of water, hydrocarbons and organonitrogen compounds, respectively. Therefore, ICA may be a promising tool to retrieve both quantitative and qualitative information from complex chemical data sets.     

主成分分光光度法中主成分的选择

主成分分析是全光谱分析度分析中常用的校正方法。本文提出第一主成分并不是与因最线性相关的主成分。为此，我们利用扫描算法众多主成分中选择与因变量（浓度）最相关的主成分，从而使计算结果更准确可信。本文还对单因变量和多因变量两种情况下主成分选择的统计量进行了讨论。     

Extended data analysis strategies for high resolution imaging MS: New methods to deal with extremely large image hyperspectral datasets

The large size of the hyperspectral datasets that are produced with modern mass spectrometric imaging techniques makes it difficult to analyze the results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable overview. Multivariate statistics are commonly used for this purpose. Computational power and computer memory limit the resolution at which the datasets can be analyzed with these techniques. We introduce the use of a data format capable of efficiently storing sparse datasets for multivariate analysis. This format is more memory-efficient and therefore it increases the possible resolution together with a decrease of computation time. Three multivariate techniques are compared for both sparse-type data and non-sparse data acquired in two different imaging ToF-SIMS experiments and one LDI-ToF imaging experiment. There is no significant qualitative difference in the use of different data formats for the same multivariate algorithms. All evaluated multivariate techniques could be applied on both SIMS and the LDI imaging datasets. Principal component analysis is shown to be the fastest choice; however a small increase of computation time using a VARIMAX optimization increases the decomposition quality significantly. PARAFAC analysis is shown to be very effective in separating different chemical components but the calculations take a significant amount of time, limiting its use as a routine technique. An effective visualization of the results of the multivariate analysis is as important for the analyst as the computational issues. For this reason, a new technique for visualization is presented, combining both spectral loadings and spatial scores into one three-dimensional view on the complete datacube.     

Multivariate Observations of the Distribution of Polychlorinated Biphenyls in Environmental Compartments of Two Harbours

Abstract

The objectives of this study were to identify, classify and categorize polychlorinated biphenyl (PCB) residues in samples collected from the Hamilton and Wheatley Harbour environmental compartments. The study is built around the use of a principal components analysis method, namely the soft independent modelling of class analogy (SIMCA) technique. This multivariate method is widely used for evaluating differences and observing similarities among multiple objects. The results obtained from this work confirm that the gas chromatographic data sets obtained with the samples provide a good approximation of the pattern derived from a determination of the composition of commercial Aroclors in water, sediment and biota samples. The data analysis technique provides insight into the origin of PCB contamination in environmental samples and indicates pathways for the environmental degradation or bioaccumulation of PCBs. This investigation contributes some evidence that multivariate reduction techniques are suitable for the investigation of complex data sets in environmental studies.     

Variable alignment of high resolution data by cluster analysis

High resolution time-of-flight secondary ion mass spectrometry (HR TOF-SIMS) is a powerful surface analytical method. For complex samples, this technique may yield intricate spectra that are difficult to interpret visually. Chemometric methods are useful for data analysis. However, these methods require that spectra are represented in a matrix format. Variances in mass measurements caused by calibration or instrumental effects may present difficulties in properly aligning mass spectral peaks into the correct columns of the data matrix. Cluster analysis of resolution elements is proposed as an alternative approach to construct the data matrix. An automated method for optimizing the data alignment is presented and evaluated for standard steel samples.     

Chemometric quality control of chromatographic purity

It is common practice in chromatographic purity analysis of pharmaceutical manufacturing processes to assess the quality of peak integration combined by visual investigation of the chromatogram. This traditional method of visual chromatographic comparison is simple, but is very subjective, laborious and seldom very quantitative. For high-purity drugs it would be particularly difficult to detect the occurrence of an unknown impurity co-eluting with the target compound, which is present in excess compared to any impurity. We hypothesize that this can be achieved through Multivariate Statistical Process Control (MSPC) based on principal component analysis (PCA) modeling. In order to obtain the lowest detection limit, different chromatographic data preprocessing methods such as time alignment, baseline correction and scaling are applied. Historical high performance liquid chromatography (HPLC) chromatograms from a biopharmaceutical in-process analysis are used to build a normal operation condition (NOC) PCA model. Chromatograms added simulated 0.1% impurities with varied resolutions are exposed to the NOC model and monitored with MSPC charts. This study demonstrates that MSPC based on PCA applied on chromatographic purity analysis is a powerful tool for monitoring subtle changes in the chromatographic pattern, providing clear diagnostics of subtly deviating chromatograms. The procedure described in this study can be implemented and operated as the HPLC analysis runs according to the process analytical technology (PAT) concept aiming for real-time release.     

Real-Time Distinguishing of the Xylene Isomers Using Photoionization and Dissociation Mass Spectra Obtained by Femtosecond Laser Mass Spectrometry (FLMS)

Abstract

Distinguishing chemicals and improvement on analytical methods has a direct impact on modern chemical analysis. In this work, the dissociative ionization of xylene isomers was investigated using a femtosecond laser mass spectrometry (FLMS) method with a custom-built linear time-of-flight (TOF) instrument. Laser beams at 800?nm and 400?nm were used and intensity-dependent analysis of the obtained mass spectra was performed using principal component analysis (PCA) to distinguish the xylene isomers, which give identical mass spectra in appearance that cannot be distinguished using normal mass spectrometry methods. The results show that there is a statistically highly significant difference between the xylene isomers for two principal components (1 ? α?>?99.99%) and minimal information loss (<5%) took place during the PCA procedure. Also, the use of the k-medoid clustering method showed that the isomers may be distinguished in real-time for a wide range of ionization laser pulse powers with approximately 99% accuracy. The results suggest that real-time isomer analysis by the FLMS method is suitable for mass spectral identification applications. The FLMS method has been shown to be an important alternative to other mass spectrometric methods that use different ionization mechanisms.     

Methods of multivariate data analysis applied to the investigation of fen soils

Fen soils from two sites of the Rhin-Havel-Luch, a peatland in the north-east of Germany, have been investigated. The samples have been collected in two horizons, representing different degrees of degradation and mineralisation of peat. Gravimetric measurements, energy dispersive X-ray fluorescence (EDXRF), elemental analysis, and ¹H low resolution nuclear magnetic resonance (LR-NMR) of the fen soil samples have been performed. By multivariate analysis of all the experimental data, especially by the principal component analysis (PCA) and by the cluster analysis, respectively, it was possible to classify the fen soils, to identify their characteristic properties, to detect temporal and local variations, and to prove representative field sampling. Furthermore, the correlation between variables of the applied analytical methods could be interpreted in context to the composition of fen soils and mutual influences of their properties.     

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.     

Deciphering key proteins of oil palm (Elaeis guineensis Jacq.) fruit mesocarp development by proteomics and chemometrics

Palm oil is an edible vegetable oil derived from lipid‐rich fleshy mesocarp tissue of oil palm (Elaeis guineensis Jacq.) fruit and is of global economic and nutritional relevance. While the understanding of oil biosynthesis in plants is improving, the fundamentals of oil biosynthesis in oil palm still require further investigations. To gain insight into the systemic mechanisms that govern oil synthesis during oil palm fruit ripening, the proteomics approach combining gel‐based electrophoresis and mass spectrometry was used to profile protein changes and classify the patterns of protein accumulation during these complex physiological processes. Protein profiles from different stages of fruit ripening at 10, 12, 14, 15, 16, 18 and 20 weeks after anthesis (WAA) were analysed by two‐dimensional gel electrophoresis (2DE). The proteome data were then visualised using a multivariate statistical analysis of principal component analysis (PCA) to get an overview of the proteome changes during the development of oil palm mesocarp. A total of 68 differentially expressed protein spots were successfully identified by matrix‐assisted laser desorption/ionisation‐time of flight (MALDI‐TOF/TOF) and functionally classified using ontology analysis. Proteins related to lipid production, energy, secondary metabolites and amino acid metabolism are the most significantly changed proteins during fruit development representing potential candidates for oil yield improvement endeavors. Data are available via ProteomeXchange with identifier PXD009579. This study provides important proteome information for protein regulation during oil palm fruit ripening and oil synthesis.     

Principal component analysis versus fuzzy principal component analysis A case study: the quality of danube water (1985-1996)

Principal component analysis (PCA) is a favorite tool in environmetrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approach to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of the outliers. In this paper we discuss and apply a robust fuzzy PCA algorithm (FPCA). The efficiency of the new algorithm is illustrated on a data set concerning the water quality of the Danube River for a period of 11 consecutive years. Considering, for example, a two component model, FPCA accounts for 91.7% of the total variance and PCA accounts only for 39.8%. Much more, PCA showed only a partial separation of the variables and no separation of scores (samples) onto the plane described by the first two principal components, whereas a much sharper differentiation of the variables and scores is observed when FPCA is applied.     

不相交主成分分析(PCA)和遗传算法(GA)用于差异表达基因的识别

建立了一种基于不相交主成分分析(Disjoint PCA)和遗传算法(GA)的特征变量选择方法, 并用于从基因表达谱(Gene expression profiles)数据中识别差异表达的基因. 在该方法中, 用不相交主成分分析评估基因组在区分两类不同样品时的区分能力; 用GA寻找区分能力最强的基因组; 所识别基因的偶然相关性用统计方法评估. 由于该方法考虑了基因间的协同作用更接近于基因的生物过程, 从而使所识别的基因具有更好的差异表达能力. 将该方法应用于肝细胞癌(HCC)样品的基因芯片数据分析, 结果表明, 所识别的基因具有较强的区分能力, 优于常用的基因芯片显著性分析(Significance analysis of microarrays, SAM)方法.     

Enhanced monitoring of biopharmaceutical product purity using liquid chromatography-mass spectrometry

LC-MS is a widely used technique for impurity detection and identification. It is very informative and generates huge amounts of data. However, the relevant chemical information may not be directly accessible from the raw data map, particularly in reference to applications where unknown impurities are to be detected. This study demonstrates that multivariate statistical process control (MSPC) based on principal component analysis (PCA) in conjunction with multiple testing is very powerful for comprehensive monitoring and detection of an unknown and co-eluting impurity measured with liquid chromatography-mass spectrometry (LC-MS). It is demonstrated how a spiked impurity present at low concentrations (0.05% (w/w)) is detected and further how the contribution plot provides clear diagnostics of the unknown impurity. This tool makes a fully automatic monitoring of LC-MS data possible, where only relevant areas in the LC-MS data are highlighted for further interpretation.     

Multivariate analysis of 2-DE protein patterns--practical approaches

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.     

Combining synchronous fluorescence spectroscopy with multivariate methods for the analysis of petrol-kerosene mixtures

Synchronous fluorescence spectroscopy (SFS) is a rapid, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. The present study demonstrates the use of SFS and multivariate methods for the analysis of petroleum products which is a complex mixture of multiple fluorophores. Two multivariate techniques principal component regression (PCR) and partial least square regression (PLSR) have been successfully applied for the classification of petrol-kerosene mixtures. Calibration models were constructed using 35 samples and their validation was carried out with varying composition of petrol and kerosene in the calibration range. The results showed that the method could be used for the estimation of kerosene in kerosene-mixed petrol. The model was found to be sensitive, detecting even 1% contamination of kerosene in petrol.     

Discrimination of Scrophularia spp. according to geographic origin with HPLC-DAD combined with multivariate analysis

Discrimination of Scrophularia spp. according to the geographic origin was tried in the present study using HPLC-DAD combined with multivariate analysis techniques. Five active constituents, angoroside C, harpagoside, 8-O-(E-p-methoxycinnamoyl)harpagide, E-cinnamic acid and E-p-methoxycinnamic acid, in forty four Scrophularia samples were simultaneously determined using HPLC-DAD. A principal component analysis of the content measurements clustered the samples according to their geographic origins, Andong, Uisung and China. A partial least squares-discrimination analysis was subsequently developed for the effective classification of the samples. This model showed comparatively good prediction ability for samples from Andong or China. The proposed method shows an efficient strategy for quality control of Scrophularia spp.