Exploring groundwater hydrochemistry of alluvial aquifers using multi-way modeling

A three-way data set pertaining to hydrochemistry of the groundwater of north Indo-Gangetic alluvial plains was analyzed using three-way component analysis method with the purpose of extracting the information on spatial and temporal variation trends in groundwater composition. Three-way data modeling was performed using PARAFAC and Tucker3 models. The models were tested for their stability and goodness of optimal fit using core consistency diagnostic and split-half analysis. Although, a two-component PARAFAC model, explaining 50.47% of data variance, yielded 100% core consistency, it failed to qualify the validation test. Tucker3 model (3, 3, 1) captured 55.18% of the data variance and yielded simple diagonal core with three significant elements, explaining 100% of the core variability. Interpretation of the information obtained through Tucker3 model revealed that the groundwater quality in Khar watershed is mainly dominated by water hardness and related variables, whereas, water composition of the dug wells is dominated by alkalinity and carbonate/bicarbonates. Moreover, shallow groundwater sources in the region are contaminated with nitrate derived from fertilizers application in the region. The shallow aquifers are relatively more contaminated during the post-monsoon season.     

Health risk assessment of heavy metals and their source apportionment in drinking water of Kohistan region, northern Pakistan

The present study was conducted to investigate heavy metal (Cu, Co, Cr, Mn, Ni, Pb, Zn and Cd) concentrations of drinking water (surface water and groundwater) samples in Kohistan region, northern Pakistan. Furthermore, the study aimed to ascertain potential health risk of heavy metal (HM) concentrations to local population. HM concentrations were analyzed by using graphite furnace atomic absorption spectrometer (Perkin Elmer, AAS-PEA-700) and were compared with permissible limits set by Pakistan Environmental Protection Agency (Pak EPA) and World Health Organization (WHO). Based on HM concentrations the health risk assessment like chronic daily intake (CDI) and hazard quotient (HQ) was calculated. The values for CDI were found in the order of Zn > Cu > Mn > Pb > Cr > Ni > Cd > Co and the values of HQ were < 1 for all HM in drinking water samples indicating no health risk. Furthermore, multivariate statistical analysis like one-way ANOVA, inter-metal correlation, cluster analysis (CA) and principal component analysis (PCA) results revealed that geogenic and anthropogenic activities were major sources of water contamination in Kohistan region.     

Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz ¹H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Chemometric data analysis of pollutants in wastewater—a case study

In this study, chemometric techniques such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and partial least squares (PLS) were used to analyse the wastewater dataset to identify the factors which affect the composition of sewage of domestic origin, spatial and temporal variations, similarity/dissimilarity among the wastewater characteristics of cis- and trans-drains and discriminating variables. Samples collected from 24 wastewater drains in Lucknow city and from three sites on Gomti river in the month of January/February, May, August and November during the period of 5 years (1994-1999) were characterized for 32 parameters. The multivariate techniques successfully described the similarities/dissimilarities among the sewage drains on the basis of their wastewater characteristics and sources signifying the effect of routine domestic/commercial activities in respective drainage areas. Spatial and seasonal variations in wastewater composition were also determined successfully. CA generated six groups of drains on the basis of similar wastewater characteristic. PCA provided information on seasonal influence and compositional differences in sewage generated by domestic and industrial waste dominated drains and showed that drains influenced by mixed industrial effluents have high organic pollution load. DA rendered six variables (TDS, alkalinity, F, TKN, Cd and Cr) discriminating between cis- and trans-drains. PLS-DA showed dominance of Cd, Cr, NO₃, PO₄ and F in cis-drains wastewater. The results suggest that biological-process based STPs could treat wastewater both from the cis- as well as trans-drains, however, prior removal of toxic metals will be required from the cis-drains sewage. Further, seasonal variations in wastewater composition and pollution load could be the guiding factor for determining the STPs design parameters. The information generated would be useful in selection of process type and in designing of the proposed sewage treatment plants (STPs) for safe disposal of wastewater.     

Danube River Water Data Modelling by Multivariate Data Analysis

 A data set (48×19) consisting of Danube river water analytical data collected at Galati site, Romania, during a four-year period has been treated by principal components analysis (PCA). The PCA indicated that seven latent factors (“hardness”, “biochemical”, “waste inlets”, “turbidity”, “acidity”, “soil extracts” and “organic wastes”) are responsible for the data structure and explain over 80 % of the total variance of the system. Its complexity is further proved by the application of multiple linear regression analysis on the absolute principal components scores (APCS) where the contribution of each natural or anthropogenic sources in the factor formation is shown. The apportioning makes clear that each variable participates to a different extent to each source and, in this way, no pure natural or pure anthropogenic influence could be determined. No specific seasonality for the variables in consideration is found. Received January 24, 2001. Revision July 6, 2001.     

Discrimination of Polygoni Multiflori radix and Cynanchi Auriculati radix using ultra‐high performance liquid chromatography fingerprints and chemical pattern recognition

In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulterant (Cynanchi Auriculati Radix, CAR). Ultra‐high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS‐DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA) and counter‐propagation artificial neural network (CP‐ANN). A plot of PCA score showed that PMR and CAR samples belonged to separate clusters (PMR class and CAR class), and samples of mixtures were located near PMR or CAR classes. Analysis by PLS‐DA, SVMDA and CP‐ANN performed well for recognition and prediction in terms of PMR and CAR samples. Moreover, the PLS‐DA method performed best in the detection of adulterated samples, even if the adulterant was about 25%.     

Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)

This article describes the applicability of multivariate projection techniques, such as principal-component analysis (PCA) and partial least-squares (PLS) projections to latent structures, to the large-volume high-density data structures obtained within genomics, proteomics, and metabonomics. PCA and PLS, and their extensions, derive their usefulness from their ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. Three examples are used as illustrations: the first example is a genomics data set and involves modeling of microarray data of cell cycle-regulated genes in the microorganism Saccharomyces cerevisiae. The second example contains NMR-metabonomics data, measured on urine samples of male rats treated with either of the drugs chloroquine or amiodarone. The third and last data set describes sequence-function classification studies in a set of G-protein-coupled receptors using hierarchical PCA.     

Assessment of Organic Source Contributions in Coastal Waters by Principal Component and Factor Analysis of the Dissolved and Particulate Hydrocarbon and Fatty Acid Contents

Abstract

Principal component (PCA) and factor analysis (FA) are evaluated for the interpretation of the information contained in large datasets resulting from the study of environmental samples by gas chromatography (GC) and GC coupled to mass spectrometry (GC-MS). A case involving the identification and quantitation of 64 variables (hydrocarbons and fatty acids) in 87 water samples (dissolved and particulate fractions) of a coastal system (Ebre Delta) has been selected for examination.

PCA has evidenced important differences between the dissolved and particulate materials, as well as between the particulates collected in the bays and those obtained in the river and channels. PCA has also allowed the identification of outlier samples in the dissolved fraction. Independent application of FA to each of these groups has provided a useful method for the characterization of diverse algal, terrestrial, microbial and anthropogenic inputs. Direct correspondences between these source inputs and factor loadings have provided a selection of representative components of each contribution in the coastal system.     

Voltammetric Electronic Tongue for the Qualitative Analysis of Beers

This paper deals with the application of a voltammetric electronic tongue (ET) towards beers classification. For this purpose, samples were analyzed using cyclic voltammetry without performing any sample pretreatment, albeit its dilution with distilled water. The voltammetric signals were first preprocessed employing Fast Fourier Transform (FFT). Then, using the obtained coefficients, responses were evaluated using three different clustering techniques: Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS‐DA) and Linear Discriminant Analysis (LDA). In this case, the ET has demonstrated a good capability to correctly discriminate and classify the different beer samples according to its type (Lager, Stout and IPA) and manufacture process (commercial and craft).     

Chemometrics‐enhanced laser‐induced thermal emission detection of PETN and other explosives on various substrates

Infrared emissions (IREs) of samples of pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination of the important nitrate ester from the emissions of the substrates. Mid‐infrared emissions were generated by heating the samples remotely using laser‐induced thermal emission (LITE). Chemometrics multivariate analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares‐discriminant analysis (PLS‐DA), support vector machines (SVMs), and neural network (NN) were employed to generate the models for the classification and discrimination of PETN IREs from substrate thermal emissions. PCA exhibited less variability for the LITE spectra of PETN/substrates. SIMCA was able to predict only 44.7% of all samples, while SVM proved to be the most effective statistical analysis routine, with a discrimination performance of 95%. PLS‐DA and NN achieved prediction accuracies of 94% and 88%, respectively. High sensitivity and specificity values were achieved for five of the seven substrates investigated. Copyright © 2015 John Wiley & Sons, Ltd.     

An application of cluster analysis and multivariate classification methods to spring water monitoring data

An optimized model of multivariate classification for the monitoring of eighteen spring waters in the land of Serra St. Bruno, Calabria, Italy, has been developed. Thirty analytical parameters for each water source were investigated and reduced to eight by means of Principal Component Analysis (PCA). Water springs were grouped in five distinct classes by cluster techniques (CA) and a model for their classification was built by a Partial Least Squares–Discriminant Analysis (PLS–DA) procedure. The model was optimized and validated and then applied to new data matrices, containing the analytical parameters carried out on the same sources during the successive years. This model proved to be able to notice deviations of the global analytical characteristics, by pointing out in the course of time a different distribution of the samples within the classes. The variation of nitrate concentration was demonstrated to be the major responsible for the observed class shifts. The shifting sources were localized in areas used as sowable lands and high variability of nitrate content was ascribed to the practice of crop rotation, involving a varying use of the nitrogenous chemical fertilizers.     

Characterization of Gasoline by Raman Spectroscopy with Chemometric Analysis

A rapid Raman spectroscopy protocol is reported to classify gasoline according to its distributor and to identify and quantify common adulterants. Gasoline from three distributors was collected from 19 stations in São Paulo, Brazil. Principal component analysis (PCA) showed specific clusters for each distributor, and partial least squares discriminant analysis (PLS-DA) correctly identified the origin of the samples. To evaluate the technique for the identification and quantification of the adulterants, authentic samples from each distributor were fortified at levels from 2.5 up to 25.0% (v/v) using ethanol, methanol, toluene, and turpentine to obtain 120 altered samples. PCA showed clear separation among the samples with the adulterants and PLS-DA precisely identified the adulterants (478 in 480 predictions by cross-validation), irrespective of the distributor and the concentration. One classification model was used to characterize all distributors. To quantify the adulterants, 36 multivariate calibration models were constructed using partial least squares (PLS), interval PLS, and PLS genetic algorithm for each distributor and for each adulterant. Cross-validation errors of less than 5.0% were obtained for all adulterants regardless of the distributor. Raman spectroscopy and multivariate analysis were shown to be powerful for rapid and inexpensive for the characterization of gasoline origin and the identification and quantification of common adulterants.     

Characterization of soil samples according to their metal content

Soil samples were collected from two small agricultural fields located in Médanos and Hilario Ascasubi, Bahía Blanca, Argentina and analyzed for Ce, Co, Cr, Cs, Eu, Fe, Hf, K, La, Na, Rb, Sc, Th, U, Yb, and Zn by instrumental neutron activation analysis (INAA). In order to evaluate the contribution of anthropogenic sources and the similarity/dissimilarity between the samples, the database was studied by means of enrichment factors (EF) and discriminant analysis (DA), respectively. In addition to identifying redundant variables without losing essential information, the data set was studied using forward stepwise discriminant analysis.     

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.     

Partial least‐squares discriminant analysis optimized by particle swarm optimization: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

The complexity of metabolic profiles makes chemometric tools indispensable for extracting the most significant information. Partial least‐squares discriminant analysis (PLS‐DA) acts as one of the most effective strategies for data analysis in metabonomics. However, its actual efficacy in metabonomics is often weakened by the high similarity of metabolic profiles, which contain excessive variables. To rectify this situation, particle swarm optimization (PSO) was introduced to improve PLS‐DA by simultaneously selecting the optimal sample and variable subsets, the appropriate variable weights, and the best number of latent variables (SVWL) in PLS‐DA, forming a new algorithm named PSO‐SVWL‐PLSDA. Combined with ¹H nuclear magnetic resonance‐based metabonomics, PSO‐SVWL‐PLSDA was applied to recognize the patients with lung cancer from the healthy controls. PLS‐DA was also investigated as a comparison. Relatively to the recognition rates of 86% and 65%, which were yielded by PLS‐DA, respectively, for the training and test sets, those of 98.3% and 90% were offered by PSO‐SVWL‐PLSDA. Moreover, several most discriminative metabolites were identified by PSO‐SVWL‐PLSDA to aid the diagnosis of lung cancer, including lactate, glucose (α‐glucose and β‐glucose), threonine, valine, taurine, trimethylamine, glutamine, glycoprotein, proline, and lipid. Copyright © 2015 John Wiley & Sons, Ltd.     

Determination of uranium isotopes in environmental samples

The determination of isotopes of uranium by alpha spectrometry in different environmental components (sediments, soil, water, plants and phosphogypsum) is presented and discussed in this paper. The alpha spectrometry is a very convenient and good technique for activity concentration of natural uranium isotopes (²³⁴U, ²³⁵U, ²³⁸U) in environmental samples and provides the most accurate determination of isotopic activity ratios between ²³⁴U and ²³⁸U. The analysis were provided information about possible sources of high concentrations of uranium in the examined sites determined by anthropogenic sources. The calculation of values ²³⁴U/²³⁸U in all analyzed samples was applied to identifying natural or anthropogenic uranium origin. Activity concentration of uranium isotopes in analyzed environmental samples shows that measurement of uranium levels is of great importance for environmental and safety assessment especially in contaminated areas (phosphogypsum waste heap).     

Feasibility of near-infrared spectroscopy to detect and to quantify adulterants in cow milk.

Cow milk adulteration involves the dilution of milk with a less-expensive component, such as water or whey. Near-infrared spectroscopy (NIRS) was employed to detect the adulterations of milk, non-destructively. Two adulteration types of cow milk with water and whey were prepared, respectively. NIR spectra of milk adulterations and natural milk samples in the region of 1100 - 2500 nm were collected. The classification of milk adulterations and natural milk were conducted by using discriminant partial least squares (DPLS) and soft independent modelling of class analogy (SIMCA) methods. PLS calibration models for the determination of water and whey contents in milk adulteration were also developed, individually. Comparisons of the classification methods, wavelength regions and data pretreatments were investigated, and are reported in this study. This study showed that NIR spectroscopy can be used to detect water or whey adulterants and their contents in milk samples.     

Study of interactions between Gallic Acid and Skin Surface using Infrared Spectroscopy

The transdermal transmission of model substance on the pigskin samples was investigated using the attenuated total reflection (ATR) technique of infrared (IR) spectroscopy. The collected vibrational spectroscopic data were evaluated by multidimensional statistical methods as principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares (PLS) regression which enable detection of individual substances in the skin, their identification and mutual differentiation. Gallic acid (GA), a natural phenolic anti-oxidant with many potential healing properties suitable e.g. for atopic dermatitis treatment, was used as an analyte. Effect of GA on the skin surface was examined for four different solvents namely ethanol (EtOH), methanol (MeOH), dimethyl sulfoxide (DMSO) and ultrahigh purity water (H₂O). Moreover, the effects of temperature related to GA solubility in H₂O were investigated. During the series of experiments, nonsystematic changes of untreated skin samples were observed; while systematic changes are evident after the skin treatment. The systematic effects correspond to structural changes of the skin constituents during substance penetration.     

Powered partial least squares discriminant analysis

From the fundamental parts of PLS‐DA, Fisher's canonical discriminant analysis (FCDA) and Powered PLS (PPLS), we develop the concept of powered PLS for classification problems (PPLS‐DA). By taking advantage of a sequence of data reducing linear transformations (consistent with the computation of ordinary PLS‐DA components), PPLS‐DA computes each component from the transformed data by maximization of a parameterized Rayleigh quotient associated with FCDA. Models found by the powered PLS methodology can contribute to reveal the relevance of particular predictors and often requires fewer and simpler components than their ordinary PLS counterparts. From the possibility of imposing restrictions on the powers available for optimization we obtain an explorative approach to predictive modeling not available to the traditional PLS methods. Copyright © 2008 John Wiley & Sons, Ltd.