EDXRF and multivariate statistical analysis of fragments from Marajoara ceramics

In this work, 102 fragments of Marajoara ceramics, belonging to the National Museum collection (Rio de Janeiro, Brazil), were analyzed using energy dispersive X‐ray fluorescence (EDXRF) and principal component analysis (PCA) in order to identify possible groups of samples that present similar behaviors or different characteristics. This information will give an important aid to a more accurate classification of these artifacts. The EDXRF measurements were carried out with a portable system developed in the Nuclear Instrumentation Laboratory consisting of an Oxford TF3005 X‐ray tube, with W anode, and an Si‐PIN XR‐100CR detector from Amptek, working at 25 kV and 100 µA, acquisition time of 600 s and a beam collimation of 2 mm. PCA applied to the X‐ray fluorescence results revealed a clear cluster separation to the samples. Copyright © 2009 John Wiley & Sons, Ltd.     

Quantitative characterisation of historic paper using THz spectroscopy and multivariate data analysis

THz spectroscopy in the time domain was explored in combination with multivariate data analysis, for quantitative determination of chemical and mechanical properties of historic paper, such as lignin content, tensile strength, and ash content. Using partial least squares (PLS) regression, it was shown that quantitative prediction of the material properties is possible, which indicates the potential of THz spectroscopy for chemical characterisation of complex organic materials of natural origin. In addition, the results demonstrate that THz spectra and PLS loading weights for lignin content differ significantly, which leads to the conclusion that THz spectra of composite macromolecular materials do not represent sums of spectra of the individual components. This supports the premise that THz spectra reflect intermolecular interactions. The study was carried out using 250 historical paper samples from the sixteenth century to present. Although the measurements were performed in vacuum to improve the quality of spectra, THz spectroscopy is in principle non-destructive. This research therefore reinforces the role of THz spectroscopy in characterisation of valuable historic materials, where invasive analysis is often not possible.     

Decomposition of in vivo spatially offset Raman spectroscopy data using multivariate analysis techniques

The decomposition of spatially offset Raman spectra for complex multilayer systems, such as biological tissues, requires advanced techniques such as multivariate analyses. Often, in such situations, the decomposition methods can reach their limits of accuracy well before the limits imposed by signal‐to‐noise ratios. Consequently, more effective reconstruction methods could yield more accurate results with the same data set. In this study we process spatially offset Raman spectroscopy (SORS) data with three different multivariate techniques (band‐target entropy minimization (BTEM), multivariate curve resolution and parallel factor analysis (PARAFAC)) and compare their performance when analysing a spectrally challenging plastic model system and an even more challenging problem, the analysis of human bone transcutaneously in vivo. For the in vivo measurements, PARAFAC's requirement of multidimensional orthogonal data is addressed by recording SORS spectra both at different spatial offsets and at different anatomical points, the latter providing added dimensionality through the variation of skin/soft tissue thickness. The BTEM and PARAFAC methods performed the best on the plastic system with the BTEM more faithfully reconstructing the major Raman bands and PARAFAC the smaller more heavily overlapped features. All three methods succeeded in reconstructing the bone spectrum from the transcutaneous data and gave good figures for the phosphate‐to‐carbonate ratio (within 2% of excised human tibia bone); the PARAFAC gave the most accurate figure for the mineral‐to‐collagen ratio (20% less than excised human tibia bone). Previous studies of excised bones have shown that certain bone diseases (such as osteoarthritis, osteoporosis and osteogenesis imperfecta) are accompanied by compositional abnormalities that can be detected with Raman spectroscopy, the utility of a technique which could reconstruct bone spectra accurately is manifest. The results have relevance on the use of SORS in general. © 2014 Crown copyright. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.     

Classification of bioaccumulative and non-bioaccumulative chemicals using statistical learning approaches

The present work aimed at developing in silico models allowing for a reliable prediction of bioaccumulative compounds and non-bioaccumulative compounds based on the definition of Bioconcentration Factor (BCF) using a diverse data set of 238 organic molecules. The partial least squares analysis (PLS), C4.5, support vector machine (SVM), and random forest (RF) algorithms were applied, and their performance classifying these compounds in terms of their quantitative structure-activity relationships (QSAR) was evaluated and verified with 5-fold cross-validation and an independent evaluation data set. The obtained results show that the overall prediction accuracies (Q) of the optimal PLS, C4.5, SVM and RF models are 84.5-87.7% for the internal cross-validation, with prediction accuracy (CO) of 86.3-91.1% in the external test sets, and C4.5 is slightly better than the three other methods which presents a Q of 87.7%, and a CO of 91.1% for the test sets. All these results prove the reliabilities of the in silico models, which should be valuable for the environmental risk assessment of the substances.     

Classification of iron‐based inks by means of micro‐Raman spectroscopy and multivariate data analysis

In this work, multivariate data analysis methods were applied to the analysis and interpretation of micro‐Raman spectra, collected from a broad set of historical iron‐based ink samples, previously characterised for the content of organic acids (gallic acid, ellagic acid and protocatechuic acid). The proposed method relies on principal component analysis of the noisy spectra typically obtained on original, degraded, organic samples, where fluorescence could affect the Raman signal. The signal components could be distinguished from the noise components and then used to build a linear discriminant analysis (LDA) model, achieving separation of the spectra into three classes. Selection of pure signal factors also improved effectiveness and performances of partial least square regression (PLS) algorithms, allowing quantification of condensed tannic acid residuals. Application of multivariate methods to discriminate signal from noise removes the need for spectral data manipulation (filtering, smoothing and differentiating). The obtained classification method for discrimination of historic inks and the regression method for determination of condensed tannic acid residuals supports the use of Raman analysis of fluorescing organic materials, and may provide information to scholars on ink composition and potentially on its provenance. Copyright © 2013 John Wiley & Sons, Ltd.     

Parametric and non-parametric statistical analysis of DT-MRI data

In this work parametric and non-parametric statistical methods are proposed to analyze Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. A Multivariate Normal Distribution is proposed as a parametric statistical model of diffusion tensor data when magnitude MR images contain no artifacts other than Johnson noise. We test this model using Monte Carlo (MC) simulations of DT-MRI experiments. The non-parametric approach proposed here is an implementation of bootstrap methodology that we call the DT-MRI bootstrap. It is used to estimate an empirical probability distribution of experimental DT-MRI data, and to perform hypothesis tests on them. The DT-MRI bootstrap is also used to obtain various statistics of DT-MRI parameters within a single voxel, and within a region of interest (ROI); we also use the bootstrap to study the intrinsic variability of these parameters in the ROI, independent of background noise. We evaluate the DT-MRI bootstrap using MC simulations and apply it to DT-MRI data acquired on human brain in vivo, and on a phantom with uniform diffusion properties.     

Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that prediction methods are applied to multiple time series and several seizures. Their performance is illustrated utilizing a bivariate seizure prediction method based on synchronization theory.     

Classification of thyroid hormone receptor agonists and antagonists using statistical learning approaches

Molecular Diversity - In silico models are presented for modeling and predicting thyroid hormone receptor (TR) agonists and antagonists. A data set consisting of 258 compounds is used in the...     

Classification and discrimination of some cosmetic face powders using XRF spectrometry with chemometric data analysis

Cosmetic foundation powders are one of the most commonly used cosmetic products in modern society. In recent years, the so‐called mineral‐based foundation products have become increasing popular in the cosmetic consumer market and can demand a premium price from the consumer. Manufacturers and suppliers often refer to mineral foundations as being ‘chemical free’, implying that these materials contain no potential skin irritants. However, there are no laws determining the use of the term ‘mineral cosmetic’ and interpretation of the use of the term is at the discretion of the manufacturer. In this study, wavelength‐dispersive X‐ray fluorescence spectrometry has been employed for multi‐elemental analysis of 39 mineral and traditional ingredient cosmetic powders. Quantitative measurements were obtained for Al, Bi, Ca, Fe, K, Mg, Si, Ti and Zn. Using cluster analysis and principal components analysis, we could clearly identify the samples as being of traditional or mineral formulation, and in the case of the mineral samples, specific manufacturers could be distinguished. Of the samples examined, samples from two suppliers marketed as mineral‐based were determined as being indistinguishable from traditional formulations. Copyright © 2012 John Wiley & Sons, Ltd.     

Clustering gene expression data analysis using an improved EM algorithm based on multivariate elliptical contoured mixture models

Clustering gene expression data is an important research topic in bioinformatics because knowing which genes act similarly can lead to the discovery of important biological information. Many clustering algorithms have been used in the field of gene clustering. The multivariate Gaussian mixture distribution function was frequently used as the component of the finite mixture model for clustering, however the clustering cannot be restricted to the normal distribution in the real dataset. In order to make the cluster algorithm strong adaptability, this paper proposes a new scheme for clustering gene expression data based on the multivariate elliptical contoured mixture models (MECMMs). To solve the problem of over-reliance on the initialization, we propose an improved expectation maximization (EM) algorithm by adding and deleting initial value for the classical EM algorithm, and the number of clusters can be treated as a known parameter and inferred with the QAIC criterion. The improved EM algorithm based on the MECMMs is tested and compared with some other clustering algorithms, the performance of our clustering algorithm has been extensively compared over several simulated and real gene expression datasets. Our results indicated that improved EM clustering algorithm is superior to the classical EM algorithm and the support vector machines (SVMs) algorithm, and can be widely used for gene clustering.     

Differences in chewing sounds of dry-crisp snacks by multivariate data analysis

Chewing sounds of different types of dry-crisp snacks (two types of potato chips, prawn crackers, cornflakes and low calorie snacks from extruded starch) were analysed to assess differences in sound emission patterns. The emitted sounds were recorded by a microphone placed over the ear canal. The first bite and the first subsequent chew were selected from the time signal and a fast Fourier transformation provided the power spectra. Different multivariate analysis techniques were used for classification of the snack groups. This included principal component analysis (PCA) and unfold partial least-squares (PLS) algorithms, as well as multi-way techniques such as three-way PLS, three-way PCA (Tucker3), and parallel factor analysis (PARAFAC) on the first bite and subsequent chew. The models were evaluated by calculating the classification errors and the root mean square error of prediction (RMSEP) for independent validation sets.It appeared that the logarithm of the power spectra obtained from the chewing sounds could be used successfully to distinguish the different snack groups. When different chewers were used, recalibration of the models was necessary. Multi-way models distinguished better between chewing sounds of different snack groups than PCA on bite or chew separately and than unfold PLS. From all three-way models applied, N-PLS with three components showed the best classification capabilities, resulting in classification errors of 14-18%. The major amount of incorrect classifications was due to one type of potato chips that had a very irregular shape, resulting in a wide variation of the emitted sounds.     

XRF analysis without sampling of Etruscan depurata pottery for provenance classification

In order to settle the provenance of a set of ancient ceramic shards, the elemental composition data acquired are usually treated by multivariate analysis techniques. The quantitative X‐ray fluorescence (XRF) analysis is an appropriate tool if it is possible to grind ceramics and analyze a sample that is representative of the object. If we deal particularly with well‐preserved objects, we are often not allowed to sample them. Moreover, moving these objects from museum could be unfeasible as well. The aim of this work is to evaluate if spot XRF analysis on integral objects is adequate to classify row clay provenance even if ceramics is not an intrinsically homogeneous material. So, we performed measurements on a set of Etruscan fine ware already classified according to the archaeological, chemical and mineralogical examination. For each sample, several measurement points in polished areas were considered for XRF analyses, allowing a correct provenance classification. Copyright © 2010 John Wiley & Sons, Ltd.     

Quality control of fragrances using Raman spectroscopy and multivariate analysis

An analytical methodology using Raman spectroscopy and chemometrics was developed for direct, fast and non‐destructive discrimination and prediction of the properties of fragrances according to their composition. The soft independent modeling of class analogies was used as a supervised classification method for fragrances classification, and partial least squares regression as a multivariate calibration method for the prediction of physicochemical properties of fragrances, such as density and refractive index. From 155 fragrance samples, the model exhibited a high success rate for all of the studied fragrance classes, with 100% correct classification. In the multivariate calibration model, adequate correlation was observed between the measured and partial least squares regression‐predicted data for refractive index and density, with a relative standard error of prediction between 0.02% and 0.07%, respectively. This study demonstrates the wide applicability of the methodology for the discrimination, classification, and prediction of complex olfactory mixtures in quality control of fragrances. Copyright © 2015 John Wiley & Sons, Ltd.     

New multivariate noise model and data detection using the expectation maximization algorithm

A signal sequence detector in a high areal density recording channel is required to provide robust compensation against unexpected error events. Primarily, a number of error events are caused by media noise and nonlinear distortion. The same problem of signal sequence detection remains to be solved in a future magnetic recording system that comes in predisposed to trend for recording by large-sector size instead of existing single-sector one that consists of 512 information 8-bits bytes. For the above problem, this paper shows the signal estimation method based on statistical inference for such a finite mixture model with known number of degraded noise components. Our signal detection scheme with multivariate autoregressive models for total noise and the expectation maximization algorithm is applied to maximum a posteriori estimation for multivariate mixtures of noise. Furthermore, a non-binary low-density parity-check (LDPC) code is used for an error-correcting code that satisfies the specific run-length limited condition in the proposed system. It shows that the proposed error-correcting and signal detection methods are effective in estimating signal sequences degraded by media noise and in improving the error rate performances with respect to the conventional system using the binary LDPC code and univariate autoregressive model.     

Marked point processes for statistical and morphological analysis of astronomical data

This chapter presents fundamental notions concerning stochastic modelling, simulation and statistical inference based on marked point processes. The application aimed by this presentation is pattern detection in spatial astronomical data.     

TOF-SIMS analysis of polystyrene/polybutadiene blend using chemical derivatization and multivariate analysis

Chemical imaging with high spatial resolution is one of the features of TOF-SIMS. However, degradation of the sample due to primary ion bombardment becomes problematic when the analysis area is small. Although polystyrene (PS) and polybutadiene (PB) separately show relatively distinct spectra, observation of their phase separation in PS/PB blends is difficult when the analysis area is small because degradation of both polymers and especially PS leads to disappearance of their characteristic peaks, resulting in low chemical image contrast. We therefore investigated the application of various forms of multivariate analysis (MVA) to the TOF-SIMS image data to improve the chemical image contrast. PCA, MCR, and the other forms of MVA provided improvement in contrast, but the images were still obscure and observation of phase separation remained difficult. Chemical derivatization using osmium tetroxide was also investigated, and found to give clear images of phase separation in the PS/PB blend. In quantitative determinations with MVA and chemical derivatization, PLS demonstrated the best predictive capability and chemical derivatization resulted in large deviations from both the bulk chemical composition and the determinations with MVA, particularly in regions of low PB content.     

Application of a multivariate statistical technique to interpreting data from multichannel equipment for the example of the KLEM spectrometer

A multivariate statistical procedure for solving problems of estimating physical parameters on the basis of data from measurements with multichannel equipment is described. Within the multivariate procedure, an algorithm is constructed for estimating the energy of primary cosmic rays and the exponent in their power-law spectrum. They are investigated by using the KLEM spectrometer (NUCLEON project) as a specific example of measuring equipment. The results of computer experiments simulating the operation of the multivariate procedure for this equipment are given, the proposed approach being compared in these experiments with the one-parameter approach presently used in data processing.     

Qualitative and quantitative analysis of complex temperature-programmed desorption data by multivariate curve resolution

The substantial amount of information carried in temperature-programmed desorption (TPD) experiments is often difficult to mine due to the occurrence of competing reaction pathways that produce compounds with similar mass spectrometric features. Multivariate curve resolution (MCR) is introduced as a tool capable of overcoming this problem by mathematically detecting spectral variations and correlations between several m/z traces, which is later translated into the extraction of the cracking pattern and the desorption profile for each desorbate. Different from the elegant (though complex) methods currently available to analyze TPD data, MCR analysis is applicable even when no information regarding the specific surface reaction/desorption process or the nature of the desorbing species is available. However, when available, any information can be used as constraints that guide the outcome, increasing the accuracy of the resolution. This approach is especially valuable when the compounds desorbing are different from what would be expected based on a chemical intuition, when the cracking pattern of the model test compound is difficult or impossible to obtain (because it could be unstable or very rare), and when knowing major components desorbing from the surface could in more traditional methods actually bias the quantification of minor components. The enhanced level of understanding of thermal processes achieved through MCR analysis is demonstrated by analyzing three phenomena: i) the cryogenic desorption of vinyltrimethylsilane from silicon, an introductory system where the known multilayer and monolayer components are resolved; ii) acrolein hydrogenation on a bimetallic Pt–Ni–Pt catalyst, where a rapid identification of hydrogenated products as well as other desorbing species is achieved, and iii) the thermal reaction of Ti[N(CH₃)₂]₄ on Si(100), where the products of surface decomposition are identified and an estimation of the surface composition after the thermal reaction is afforded. Since this work constitutes, to the best of our knowledge, the first effort to introduce multivariate analysis to TPD data, the procedures, algorithms and strategies employed are described in full detail.     

The effect of statistical noise on structural parameters in EXAFS data analysis

Summary We analyse the effect of the statistical noise on the physical parameters derived from an EXAFS spectrum. Two separate classes of experimental cases are discussed, namely heavy and light backscatterers. The weight of the noise in the two cases is, in fact, quite different, depending on thek range useful for the analysis of the data. We find that for heavy backscatterers the statistical noise can usually be neglected, while for light backscatterers it can be one of the main limitations in obtaining reliable results from EXAFS. Paper presented at the Workshop on ?EXAFS Data Analysis in Disordered Systems?, held in Parma, October 5–7, 1981.     

HPM效应实验中区间删失数据的处理与统计分析

在HPM效应实验中经常可以获得区间删失数据,为了能够合理利用这类数据对HPM效应进行有效分析,需要对它进行处理。根据电子器件的微波失效机理和实验现象,基于插值方法的思想,在充分利用删失数据信息情况下建立了不同阶插值精度的数据处理方法。理论分析可知,高阶精度处理方法要优于低阶精度方法。此外,根据构建的区间删失数据,通过统计分析可知,处理后数据与原始数据在统计意义上没有显著差异,可用于HPM效应研究,为数据的可靠分析提供了有利支撑。