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.     

Chemometric Analysis of Compositional Variation in Bison and Cow Patties: A Biogeochemistry–Environmental Chemistry Experiment

Manure has been used as a fertilizer throughout recorded history; however, the high levels of nutrients in farm and feedlot runoff present many environmental problems. Analyses of nutrient cycling in manure and soil litter are important to issues such as global warming and land use. In this laboratory, chemical differences in aged manure patties from buffalo (Bison bison) and cow (Bos taurus) raised on ranches versus nature preserves are examined. These measurements offer a chance to introduce the basics of chemometrics such as factorial experimental design and multivariate analysis in an environmental context that captures student interest. These important methods are appropriate to instrumental analysis or environmental chemistry, but are rarely integrated into these courses.     

Quantification and methodology issues in multivariate analysis of ToF‐SIMS data for mixed organic systems

Multivariate methods, such as principal component analysis (PCA) and multivariate curve resolution (MCR), are often employed to aid the analysis of large complex data sets such as time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) images. There is, however, much confusion over the most appropriate choice of method for any given application and the effects of data preprocessing, which is exacerbated by the confusing terminologies and the use of jargon in this field. In the present study, a simple model system consisting of a ToF‐SIMS image of an immiscible polymer blend is used to evaluate PCA and MCR in the accurate identification, localisation and quantification of the phase‐separated polymer domains, using four data preprocessing methods (no scaling, normalisation, variance scaling and Poisson scaling). This highlights significant issues and challenges in the quantitative multivariate analysis of mixed organic systems, including the discrimination of chemically significant features from experimental noise, the resolution of weak chemical contributions and potential bias introduced by data preprocessing. Multivariate analysis using Poisson scaling, identified as the most suitable data preprocessing method for both PCA and MCR, demonstrates a marked improvement upon traditional (manual) analysis and provides valuable additional information that is difficult to detect using traditional analysis. Using these results, we present recommendations for the optimum use of multivariate analysis by analysts and provide guidance on selecting the most appropriate methods. Confusing terminology is also clarified. © Crown copyright 2008. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.     

Photochemistry and chemometrics—An overview

Photochemistry has made significant contributions to our understanding of many important natural processes as well as the scientific discoveries of the man-made world. The measurements from such studies are often complex and may require advanced data interpretation with the use of multivariate or chemometrics methods. In general, such methods have been applied successfully for data display, classification, multivariate curve resolution and prediction in analytical chemistry, environmental chemistry, engineering, medical research and industry. However, in photochemistry, by comparison, applications of such multivariate approaches were found to be less frequent although a variety of methods have been used, especially with spectroscopic photochemical applications. The methods include Principal Component Analysis (PCA; data display), Partial Least Squares (PLS; prediction), Artificial Neural Networks (ANN; prediction) and several models for multivariate curve resolution related to Parallel Factor Analysis (PARAFAC; decomposition of complex responses). Applications of such methods are discussed in this overview and typical examples include photodegradation of herbicides, prediction of antibiotics in human fluids (fluorescence spectroscopy), non-destructive in- and on-line monitoring (near infrared spectroscopy) and fast-time resolution of spectroscopic signals from photochemical reactions. It is also quite clear from the literature that the scope of spectroscopic photochemistry was enhanced by the application of chemometrics.To highlight and encourage further applications of chemometrics in photochemistry, several additional chemometrics approaches are discussed using data collected by the authors. The use of a PCA biplot is illustrated with an analysis of a matrix containing data on the performance of photocatalysts developed for water splitting and hydrogen production. In addition, the applications of the Multi-Criteria Decision Making (MCDM) ranking methods and Fuzzy Clustering are demonstrated with an analysis of water quality data matrix. Other examples of topics include the application of simultaneous kinetic spectroscopic methods for prediction of pesticides, and the use of response fingerprinting approach for classification of medicinal preparations. In general, the overview endeavours to emphasise the advantages of chemometrics’ interpretation of multivariate photochemical data, and an Appendix of references and summaries of common and less usual chemometrics methods noted in this work, is provided.     

Multiple factor analysis in environmental chemistry

In environmental chemistry studies, it may be necessary to analyze data sets constituted by different blocks of variables, possibly of different types, measured on the same samples. Multiple factor analysis (MFA) is presented as a tool for exploring such data. The most important features of MFA are shown on a real environmental data set, consisting of two blocks of data, namely heavy metals and polycyclic aromatic hydrocarbons, measured for sediment samples. They are discussed and compared to principal component analysis (PCA). The usefulness of the weighting scheme used in MFA as a preprocessing step for other chemometric methods, such as clustering, is also highlighted.     

Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

Spectroscopic imaging techniques provide spatial and spectral information about a sample simultaneously and are finding ever-increasing application in the pharmaceutical industry. Effective extraction of chemical information from imaging data sets is a crucial step during the application of imaging techniques. Multivariate imaging data analysis methods have been reported but few applications of these methods for pharmaceutical samples have been demonstrated. In this study, a bilayer model tablet consisting of avicel, lactose, sodium benzoate, magnesium stearate and red dye was prepared using custom press tooling, and Raman mapping data were collected from a 400 μm × 400 μm area of the tablet surface. Several representative multivariate methods were selected and used in the analysis of the data. Multivariate data analysis methods investigated include principal component analysis (PCA), cluster analysis, direct classical least squares (DCLS) and multivariate curve resolution (MCR). The relative merits and drawbacks of each technique for this application were evaluated. In addition, some practical issues associated with the use of these methods were addressed including data preprocessing, determination of the optimal number of clusters in cluster analysis and the optimization of window size in second derivative calculation.     

High resolution applications of laser-induced breakdown spectroscopy for environmental and forensic applications

Laser-induced breakdown spectroscopy (LIBS) has been used in the elemental analysis for a variety of environmental samples and as a proof of concept for a host of forensic applications. In the first application, LIBS was used for the rapid detection of carbon from a number of different soil types. In this application, a major breakthrough was achieved by using a multivariate analytical approach that has brought us closer towards a “universal calibration curve”. In a second application, it has been demonstrated that LIBS in combination with multivariate analysis can be employed to analyze the chemical composition of annual tree growth rings and correlate them to external parameters such as changes in climate, forest fires, and disturbances involving human activity. The objectives of using this technology in fire scar determinations are: 1) To determine the characteristic spectra of wood exposed to forest fires and 2) To examine the viability of this technique for detecting fire occurrences in stems that did not develop fire scars. These examples demonstrate that LIBS-based techniques are inherently well suited for diverse environmental applications. LIBS was also applied to a variety of proof of concept forensic applications such as the analysis of cremains (human cremation remains) and elemental composition analysis of prosthetic implants.     

Resolving NMR signals of short‐chain fatty acid mixtures using unsupervised component analysis

Nuclear magnetic resonance (NMR) is a very powerful instrumental technique suited to identify and characterize organic compounds. NMR has been successfully used in the analysis of complex biological and environmental samples; however, these applications are still rather limited. In this work, we describe unsupervised component analysis as a multivariate unsupervised method suited to identify the number of relevant NMR signal contributions and to deconvolve mixed signals into signal individual sources and respective contributions. Using this approach, we were able to advance further in the field of quantification of NMR spectra, and this methodology will help in the characterization of complex biological samples. Copyright © 2017 John Wiley & Sons, Ltd.     

Classification for high-throughput data with an optimal subset of principal components

High-throughput data have been widely used in biological and medical studies to discover gene and protein functions. Due to the high dimensionality, principal component analysis (PCA) is often involved for data dimension reduction. However, when a few principal components (PCs) are selected for dimension reduction or considered for dimension determination, they are typically ranked by their variances, eigenvalues. However, this approach is not always effective in subsequent multivariate analysis, particularly classification. To maximize information from data with a subset of the components, we apply a different ranking criterion, canonical variate criterion, which considers within- and between-group variance rather than total variance in the classical criterion. Four prevalent classification methods are considered and compared using leave-one-out cross-validation. These methods are illustrated with three real high-throughput data sets, two microarray data sets and a nuclear magnetic resonance spectra data set.     

Chemometrics in the works of Russian analysts

A review of studies made in 1991–2010 using chemometric methods and aimed at solving basic chemical analytical problems is presented. Russian experts actively using chemometric methods are not numerous, but the attention of the analytical community to these methods rapidly grows. The review covers the scientific achievements in the development of identification (classification) algorithms, their application to qualitative analysis, and the use of multivariate calibrations in quantitative analysis.     

A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.     

Environmental analysis of fluorinated alkyl substances by liquid chromatography–(tandem) mass spectrometry: a review

Fluorinated alkyl substances (FASs) are widely distributed contaminants that have been found in many environmental, human and biological samples throughout the world. Perfluorochemicals are used in many industry and consumer products, such as polymers and surfactants, because they have unique and useful properties (they are stable, chemically inert and generally unreactive). However, these compounds have also been found to be toxic, persistent and bioaccumulative. In recent years various analytical methods have been developed for the analysis of FASs in environmental samples. Most of these methods are based on liquid chromatography coupled to mass spectrometry (LC–MS) or tandem mass spectrometry (LC–MS/MS), since this is considered to be the technique of choice. This article reviews the various LC–(tandem)MS methods described so far for the analysis of FASs in water, sediment, sludge and biota samples. It discusses the main experimental conditions used for sample pretreatment and for analysis as well as the most relevant problems encountered and the limits of detection achieved.     

Development of diagnostic models for canine osteoarthritis based on serum and joint fluid mid‐infrared spectral data using five different discrimination and classification methods

Osteoarthritis (OA) is an insidious joint disease that gradually leads to cartilage loss and the morphological impairment of other joint tissues. Therefore, early diagnosis and timely therapeutic intervention are of importance. Although there are a few diagnostic techniques used in clinics, these methods have various drawbacks. Infrared spectroscopy has emerged as an important analytical technique with wide applications in a variety of areas including clinical diagnosis. Research has shown that the presence of OA is associated with biochemical changes that are presumed to be reflected in serum or joint fluid. Hence, OA may be detected provided that serum or joint fluid is measured by infrared spectroscopy and appropriate data analysis methods are used to extract the diagnostic information from the infrared spectra. In this work, 5 discrimination and classification methods ([1] principal component analysis coupled with linear discriminant analysis, [2] principal component analysis coupled with multiple logistic regression, [3] partial least squares discriminant analysis, [4] regularized linear discriminant analysis, and [5] support vector machine) were used to build OA diagnostic models based on mid‐infrared spectra of serum and joint fluid. Useful diagnostic models were developed, indicating that infrared spectroscopy coupled with multivariate data analysis methods is very promising as a simple and accurate approach for OA diagnosis. The results also showed that models built from the 5 methods were different, as were the models' predictive performances. Therefore, choice of appropriate data analysis methods in model development should be taken into account.     

Application of Compound-Specific Isotope Analysis in Environmental Forensic and Strategic Management Avenue for Pesticide Residues

Unintended pesticide pollution in soil, crops, and adjacent environments has caused several issues for both pesticide users and consumers. For users, pesticides utilized should provide higher yield and lower persistence while considering both the environment and agricultural products. Most people are concerned that agricultural products expose humans to pesticides accumulating in vegetation. Thus, many countries have guidelines for assessing and managing pesticide pollution, for farming in diverse environments, as all life forms in soil are untargeted to these pesticides. The stable isotope approach has been a useful technique to find the source of organic matter in studies relating to aquatic ecology and environmental sciences since the 1980s. In this study, we discuss commonly used analytical methods using liquid and gas chromatography coupled with isotopic ratio mass spectrometry, as well as the advanced compound-specific isotope analysis (CSIA). CSIA applications are discussed for tracing organic pollutants and understanding chemical reactions (mechanisms) in natural environments. It shows great applicability for the issues on unintended pesticide pollution in several environments with the progress history of isotope application in agricultural and environmental studies. We also suggest future study directions based on the forensic applications of stable isotope analysis to trace pesticides in the environment and crops.     

Characterization of the measurement error structure in 1D H NMR data for metabolomics studies

NMR-based metabolomics is characterized by high throughput measurements of the signal intensities of complex mixtures of metabolites in biological samples by assaying, typically, bio-fluids or tissue homogenates. The ultimate goal is to obtain relevant biological information regarding the dissimilarity in patho-physiological conditions that the samples experience. For a long time now, this information has been obtained through the analysis of measured NMR signals via multivariate statistics.NMR data are quite complex and the use of such multivariate statistical methods as principal components analysis (PCA) for their analysis assumes that the data are multivariate normal with errors that are identical, independent and normally distributed (i.e. iid normal). There is a consensus that these assumptions are not always true for these data and, thus, several methods have been devised to transform the data or weight them prior to analysis by PCA. The structure of NMR measurement noise, or the extent to which violations of error homoscedasticity affect PCA results have neither been characterized nor investigated.A comprehensive characterization of measurement uncertainties in NMR based metabolomics was achieved in this work using an experiment designed to capture contributions of several sources of error to the total variance in the measurements. The noise structure was found to be heteroscedastic and highly correlated with spectral characteristics that are similar to the mean of the spectra and their standard deviation. A model was subsequently developed that potentially allows errors in NMR measurements to be accurately estimated without the need for extensive replication.     

An Assessment of Dust Analyses: With Particular Reference to Lead and Certain Other Metals

Abstract

The last few years have shown an increase in the number of publications describing studies on dust as indicators of environmental pollution, especially by trace metals. The techniques of sampling and analysis used in these studies are reviewed, with particular attention to inconsistencies and differences which make data incomparable. The need for work on the development of standard methods is demonstrated. Only by the application of good quality control, reference materials and reliable techniques will an unsatisfactory situation be improved.     

Miniaturization in sample treatment for environmental analysis

The increasing demand for faster, more cost-effective and environmentally friendly analytical methods is a major incentive to improve the classical procedures used for sample treatment in environmental analysis. In most classical procedures, the use of rapid and powerful instrumental techniques for the final separation and detection of the analytes contrasts with the time-consuming and usually manual methods used for sample preparation, which slows down the total analytical process. The efforts made in this field in the past ten years have led to the adaptation of existing methods and the development of new techniques to save time and chemicals, and improve overall performance. One route has been to develop at-line or on-line and, frequently, automated systems. In these approaches, miniaturization has been a key factor in designing integrated analytical systems to provide higher sample throughput and/or unattended operation. Selected examples of novel developments in the field of miniaturized sample preparation for environmental analysis are used to evaluate the merits of the various techniques on the basis of published data on real-life analyses of trace-level organic pollutants. Perspectives and trends are briefly discussed.     

Molecular speciation of microparticles: application of pattern recognition techniques to laser microprobe mass spectrometric data

Attempts were made to enhance the ability of laser microprobe mass spectrometry (LAMMS) to identify molecular species in individual microparticles by applying pattern recognition methods. Principal component analysis (PCA) and canonical discriminant analysis were applied to LAMMS data for nickel-containing environmental particles. Detailed comparison of the two statistical methods demonstrated the utility of PCA. The successful application was highly dependent on the use of appropriate spectral normalization and feature extraction techniques prior to PCA. Although the test system involved only a small number of standard compounds, the LAMMS data were complicated by the effects of intra-particle heterogeneity common to environmental samples and by instrumental limitations. Pattern recognition techniques provided more accurate quantitative assignments of molecular species than were available by qualitative inspection of characteristic cluster ions or by simple spectral subtraction to compare particle data with a library of standard compounds. Results were substantiated by comparison with bulk analysis studies using wet chemical techniques.     

环境中锑的形态分析研究进展

评述了环境样品中痕量锑的形态分析概况及近年来的发展趋势，主要包括分光光度法、电化学方法、原子光谱法和色谱法等。     

Application of maximum likelihood multivariate curve resolution to noisy data sets

In this work, two different maximum likelihood approaches for multivariate curve resolution based on maximum likelihood principal component analysis (MLPCA) and on weighted alternating least squares (WALS) are compared with the standard multivariate curve resolution alternating least squares (MCR‐ALS) method. To illustrate this comparison, three different experimental data sets are used: the first one is an environmental aerosol source apportionment; the second is a time‐course DNA microarray, and the third one is an ultrafast absorption spectroscopy. Error structures of the first two data sets were heteroscedastic and uncorrelated, and the difference between them was in the existence of missing values in the second case. In the third data set about ultrafast spectroscopy, error correlation between the values at different wavelengths is present. The obtained results confirmed that the resolved component profiles obtained by MLPCA‐MCR‐ALS are practically identical to those obtained by MCR‐WALS and that they can differ from those resolved by ordinary MCR‐ALS, especially in the case of high noise. It is shown that methods that incorporate uncertainty estimations (such as MLPCA‐ALS and MCR‐WALS) can provide more reliable results and better estimated parameters than unweighted approaches (such as MCR‐ALS) in the case of the presence of high amounts of noise. The possible advantage of using MLPCA‐MCR‐ALS over MCR‐WALS is then that the former does not require changing the traditional MCR‐ALS algorithm because MLPCA is only used as a preliminary data pretreatment before MCR analysis. Copyright © 2013 John Wiley & Sons, Ltd.