Multivariate Data Analysis Tools Research Articles

A Front Line on Klebsiella pneumoniae Capsular Polysaccharide Knowledge: Fourier Transform Infrared Spectroscopy as an Accurate and Fast Typing Tool.

Genomics-based population analysis of multidrug-resistant (MDR) Klebsiella pneumoniae motivated a renewed interest on the capsule as an evolutionary and virulence marker of clinically relevant strains. Whole-genome sequencing (WGS)-based approaches have provided great insights into the genetic variability of the capsular locus, but genotypic-biochemical capsular (K)-type correlations are lacking, hindering the establishment of a reliable framework for K-type characterization and typing. To fill this gap, we combined molecular, comparative genomics, and multivariate data analysis tools with biochemical data on the capsular locus to support the usefulness of Fourier transform infrared (FT-IR) spectroscopy as a reliable K typing tool. To validate our approach, we used a representative collection of well-defined MDR K. pneumoniae lineages involved in local or nationwide epidemics in multiple countries. With this, we demonstrate a high accuracy and resolution of our FT-IR-based spectroscopy approach for K-type discrimination that is even higher than that provided by WGS. Moreover, the specific associations established between certain K types and specific K. pneumoniae lineages with high clinical relevance, together with the accuracy, simplicity, short time to result, and inexpensive features of the method, support the value of the developed FT-IR-based approach for an easy, fast, and cost-effective strain typing. This fulfills a still unmet need for tools to support real-time monitoring and control of K. pneumoniae infections. In addition, the genotypic-biochemical correlations established provide insights on sugar composition/structure of newly defined K. pneumoniae capsular types.IMPORTANCE Klebsiella pneumoniae is nowadays recognized as one of the most defiant human pathogens, whose infections are increasingly more challenging to treat and control. Whole-genome sequencing (WGS) has been key for clarifying the population structure of K. pneumoniae, and it is still instrumental to provide insights into potential pathogenicity and evolutionary markers, such as the capsular locus. However, this information and WGS are still far from being accessible and translated into routine clinical microbiology laboratories as quick and cost-efficient strain diagnostic tools. Here, we propose a biochemical fingerprinting approach based on Fourier transform infrared spectroscopy (FT-IR) and multivariate data analysis tools for K. pneumoniae capsular typing that, because of its high resolution, speed, and low cost, can be an asset to provide enough information to support real-time epidemiology and infection control decisions. Besides, it provides a simple framework for phenotypic/biochemical validation of K. pneumoniae capsular diversity.

Big Data Classification Efficiency Based on Linear Discriminant Analysis

The proliferation of online platforms recently has led to unprecedented increase in data generation; this has given rise to the concept of big data which characterizes data in terms of volume, velocity, variety, and veracity. One of the common multivariate statistical data analysis tools is linear discriminant analysis (LDA) which relies on the concept of obtaining the separation among groups through LDA. The prediction of the class of a given class of data points can be achieved through classification, a supervised learning technique but prior to a classification process, a classification model must first be built using classification algorithms. Several classification algorithms are available for prediction tasks. LDA is commonly used for the reduction of the dimensionality of datasets. In this article, the use of LDA to improve the classification performance of different classification model was presented.

Application of multivariate analysis on naphthalene adsorption in aqueous solutions.

Naphthalene (NAP) is found as a pollutant in water, soil, and air, and adsorption is the most prominent removal process of this compound, among the methods studied. A study concerning the types of adsorbents and the parameters with the greatest influence on the adsorption process is interesting to direct future works on new adsorbents. The use of multivariate data analysis tools becomes an appealing way to compile data obtained from bibliographic reviews and to establish a behavior in NAP adsorption. This work aims to evaluate the parameters with greater influence on NAP adsorption process regarding adsorption capacity (qeexp) with the principal component analysis (PCA), and to group common NAP adsorbents by chemical characteristics through hierarchical cluster analysis (HCA). The variables qeexp, S, [NAP]0, T, CT, and [Ads] were used to perform PCA with correlation matrix. For the HCA, the variables S, [NAP]0, T, CT, and [Ads] with average linkage method (UPGMA) and Euclidean distance were used. Through PCA, it is possible to infer that S and [NAP]0 are the factors with greater influence in qeexp of NAP, while T, CT, and [Ads] have little correlation. PCA also shows that activated charcoal is the adsorbent with higher qeexp. HCA grouped the adsorbents into four groups by their chemical classes, except group A. Both PCA and HCA methods show themselves as potential tools to evaluate a data set of NAP adsorption processes.

Characterization of Fatty Acids in Cereals and Oilseeds from the Republic of Serbia by Gas Chromatography – Mass Spectrometry (GC/MS) with Chemometrics

This study investigates the potential of fatty acid profiles of various industrial crops to discriminate them based on their botanical origin. All of the analyzed plants were grown on experimental fields at Rimski Šančevi, Republic of Serbia. Cereal samples (corn, wheat, barley, and oat) were milled into flour and lipid fractions were extracted using n-hexane. Lipid fractions of oilseed samples (rape, flax, safflower, and pumpkin) were obtained by mechanical pressing. The fatty acids present in lipid fractions, were derivatized into corresponding volatile methyl esters (FAMEs) and determined on a gas chromatography/mass spectrometry instrument (GC-MS). Peaks of eluting compounds were simultaneously identified using mass spectra libraries, giving a match quality of over 90%. The peaks of molecular ions of methyl esters of 9 dominant fatty acids were extracted, and their abundances used to create numerical matrices for further data processing. Chemometric multivariate data analysis tools—principal component analysis (PCA), cluster analysis (CA), principal coordinate analysis (PCoA) and linear discriminant analysis (LDA), were used to extract significant variables, visualize discriminations, and classify between analyzed plant samples. The applied tools demonstrated separations of analyzed cereals in three groups: corn, oat and the samples of small grains—wheat and barley. In the case of oilseeds a clear separation between all four plant species was obtained. This work represents a new concept of the application of fatty acid analysis in developing authentication methods for industrial crops and their staple food products.

Identification of pyrene in complex sample matrix using time-resolved fluorescence measurement coupled with PARAFAC analysis

This article reports an approach to use pyrene time-resolved fluorescence decay coupled with parallel factor analysis (PARAFAC) as an indicator of the presence of pyrene in complex and interfering sample matrix. Pyrene is selectively excited using 340 nm LED and the fluorescence decay is collected at 390 nm with a cut-off filter at 350 nm; pyrene reports through its long excited-state fluorescence lifetime. Humic acid (HA), a component of fluorescent dissolved organic matter, and diesel samples, consisting of PAHs at unknown concentration levels are used as interfering analytes in this study. Compared to previous reported methods (which used time-delayed technique with the help of delay generators or low temperature fluorescence decay measurement), this method uses a proven multivariate data analysis tool (PARAFAC) to segregate fluorescence decay profile of background intense and short-lived chromophores; it is verified that PARAFAC model effectively resolves the interfering decay components. Data are analysed by two approaches: in the first method, decay data are directly subjected to PARAFAC to extract pyrene contribution. In the second method, the decay curves are first globally analysed using separate curve fitting software and then the resultant relative amplitudes or fractional intensities are subjected to PARAFAC analysis. The second method provides better decomposition in terms of relative fluorescence decay contributions of pyrene.

Characterization of acoustic emission analysis in applications for inhalation device performance assessment

Acoustic Emission (AE) measurement technology has gained wide appreciation in material sciences and process monitoring. In inhalation research, AE has been used for adherence indicating applications in clinical studies. Promising results from feasibility studies using AE combined with multivariate data analysis (AE-MVDA) in the analysis of devices for inhalation have prompted a broader study reported in this paper. This work presents the novel application of AE-MVDA for assessment of the combined inhalation device and formulation performance. The purpose is to evaluate the benefits that this technology can provide to inhalation product development programs. The work was carried out using two different dry powder inhaler device model systems while investigating different performance features. The devices were filled with dry powder formulations with both placebo and with active pharmaceutical ingredient (API). The acquired AE data was analyzed using multivariate data analysis tools such as Principal component analysis (PCA) and orthogonal projections to latent structures (OPLS). The AE profiles were indicative for device and formulation performance. Normal and deviating performances were readily picked up in the AE data. Moreover, performance trends between doses withdrawn from the inhalers were also observable. Lastly, differences in the AE profile between the formulations could be detected. The overall conclusion from the AE-MVDA measurement approach evaluation is that it has the potential to add value as a cost-effective, non-invasive quality and performance monitoring technology both in development and in production of inhaled medicines.

A novel LED-based 2D-fluorescence spectroscopy system for in-line bioprocess monitoring of Chinese hamster ovary cell cultivations-Part II.

This study was performed in order to evaluate a new LED-based 2D-fluorescence spectrometer for in-line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455nm to collect spectral data from six 10-L fed-batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D-fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed-batch process was used for model validation and prediction. An almost continuous in-line bioprocess monitoring was realized because 2D-fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D-fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D-fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.

Direct Determination of Ni2+-Capacity of IMAC Materials Using Near-Infrared Spectroscopy.

The present paper reports a new method for the quantification of the Ni2+-capacity of an immobilized metal affinity chromatography (IMAC) material using near-infrared spectroscopy (NIRS). Conventional analyses using UV absorption spectroscopy or atomic absorption spectrometry (AAS) need to dissolve the silica-based metal chelate sorbent as sample pretreatment. In the first step, those methods were validated on the basis of an ideal homogenous NiSO4-solution and unveiled that UV with an intermediate precision of 2.6% relative standard deviation (RSD) had an advantage over AAS with an intermediate precision of 6.5% RSD. Therefore, UV analysis was chosen as reference method for the newly established NIRS model which has the advantage of being able to measure the material directly in diffuse reflection mode. Partial least squares regression (PLSR) analysis was used as multivariate data analysis tool for quantification. The best PLSR result obtained was: coefficient of determination (R2) = 0.88, factor = 2, root mean square error of prediction (RMSEP) = 22 µmol/g (test-set validation) or 7.5% RSDPLSR. Validation of the Ni2+-capacity using UV absorption spectroscopy resulted in an intermediate precision of ±18 µmol/g or 5.0% RSD. Therefore, NIRS provides a fast alternative analysis method without the need of sample preparation.

Untargeted lipidomic evaluation of hydric and heat stresses on rice growth

Environmental stresses are the major factors that limit the geographical distribution of plants. As a consequence, plants have developed different strategies to adapt to these environmental changes among which can be outlined the maintenance of membranes' integrity and fluidity. Lipids are key molecules for this environmental adaptation and a comprehensive understand of the molecular mechanisms underlying is still required. Here, lipidome changes in Japanese rice (Oryza sativa var. Japonica) upon heat and hydric stresses are assessed using an untargeted approach based on liquid chromatography coupled with mass spectrometry (LC-MS). The obtained data were analyzed using different multivariate data analysis tools. A total number of 298 lipids responded to these abiotic stresses, and 128 of them were tentatively identified. Diacylglycerols (DG), triacylglycerols (TG), phosphatidylcholines (PC) and phosphatidylethanolamines (PE) were the most altered lipid families heat and hydric stress. Interpretation of the obtained results showed relevant changes related to the unsaturation degree in the identified lipids. In the case of heat stress, a decrease in the unsaturation degree of lipids can be linked to an increase in the cell membranes' rigidity. In contrast, the hydric stress produced an increase in the lipids unsaturation degree causing an increase in the cell membranes' fluidity, in an attempt to adapt to these non-optimal conditions.

In-Depth Evaluation of Data Collected During a Continuous Pharmaceutical Manufacturing Process: A Multivariate Statistical Process Monitoring Approach

The present work presents an in-depth evaluation of continuously collected data during a twin-screw granulation and drying process performed on a continuous manufacturing line. During operation, the continuous line logs 49 univariate process variables, hence generating a large amount of data. Three identical 5-h continuous manufacturing runs were performed. Multivariate data analysis tools, more specifically latent variable modeling tools such as principal component analysis, were used to extract information from the generated data sets unveiling process trends and drifts. Furthermore, a statistical process monitoring strategy is presented. The approach is based on the application of multivariate statistical process monitoring to model the variables that remain around a steady state.

Assessment of spinach seedling health status and chlorophyll content by multivariate data analysis and multiple linear regression of leaf image features

Plant health and physiological status significantly influence chlorophyll content and photosynthetic capacity. Analysis of leaf reflectance information from digitized leaf images allows high-throughput, non-invasive and real-time estimation of chlorophyll content in a cost-effective manner. In the present study the application of multivariate data analysis tools, viz. principal component analysis (PCA) and agglomerative hierarchical clustering analysis (AHCA), has been discussed for distinguishing between spinach seedlings having high and low chlorophyll contents by simultaneously using the information provided by various image features. Further, leaf color information contained within different color spaces, viz. RGB (red, green and blue), rgb (normalized red, green and blue), HSI (hue, saturation and intensity), CIE (Commission Internationale de l’Eclairage) L∗a∗b∗, CIE-XYZ, and CIE-xyY color spaces, has been used to predict chlorophyll content in terms of SPAD (Soil Plant Analysis Development) chlorophyll meter values by multiple linear regression. It was observed that the color indices R, G, R + G, R−B, G−B, R + G−B, Y (luminance) and DGCI (dark-green color index) exhibited high correlation (R2 > 0.8) with the SPAD values. Further, subjecting the leaf reflectance information provided by these color indices to PCA and AHCA enabled a clear segregation of seedlings with high and low chlorophyll contents. SPAD values predicted by the L∗a∗b∗ color space information yielded the lowest RMSE (root mean square error) and the highest R2 (coefficient of determination) amongst the six color space features assessed. The findings of the present study indicate that concatenation of leaf reflectance information provided by different color indices may be more useful than individual color indices for assessing plant health status and predicting chlorophyll content using machine vision.

When remote sensing meets topological data analysis

Hyperspectral remote sensing plays an increasingly important role in many scientific domains and everyday life problems. Indeed, this imaging concept ends up in applications as varied as catching tax-evaders red-handed by locating new construction and building alterations, searching for aircraft and saving lives after fatal crashes, detecting oil spills for marine life and environmental preservation, spying on enemies with reconnaissance satellites, watching algae grow as an indicator of environmental health, forecasting weather to warn about natural disasters and much more. From an instrumental point of view, we can say that the actual spectrometers have rather good characteristics, even if we can always increase spatial resolution and spectral range. In order to extract ever more information from such experiments and develop new applications, we must, therefore, propose multivariate data analysis tools able to capture the shape of data sets and their specific features. Nevertheless, actual methods often impose a data model which implicitly defines the geometry of the data set. The aim of the paper is thus to introduce the concept of topological data analysis in the framework of remote sensing, making no assumptions about the global shape of the data set, but also allowing the capture of its local features.

A procedural approach for realizing prescriptive maintenance planning in manufacturing industries

Prescriptive maintenance planning is an essential enabler of smart and highly flexible production processes. Due to increasing complexity, traditional maintenance strategies lack in fulfilling present-day production requirements. This paper proposes a novel procedural approach for prescriptive maintenance planning in manufacturing companies. Multivariate data analysis and simulation tools are utilized to analyse historical data (product quality data, machine failure data and production program data). Based on identified data correlations and incoming real-time machine data, system failures are predicted and prescriptive maintenance measures are proposed. Results from real implementations in the automotive manufacturing industry are presented to demonstrate the effectiveness of the proposed approach.

Mixture experimental design applied to gallium-rich GaO3/2-GeO2-NaO1/2 glasses

A mixture-design method was used as a multivariate data analysis tool to investigate properties of gallate glasses in the GaO3/2–GeO2–NaO1/2 system. The objective was to design compositions having gallium oxide as the main component of the glass expected for further applications like wave guided transmission in the near- and mid-infrared optical domain. Accordingly to this tool a set of glass compositions were defined in a constrained domain of the ternary glass system rich in gallium oxide and prepared by the traditional melt-quenching technique. Sample density, refractive index, multiphonon cut-off wavelength as well as thermal characteristics were measured as representative multiple responses and a linear model fitting those physical properties vs glass composition was established. The obtained model allows to predict and optimize the different responses for any composition in the studied domain and a better understanding of the influence of each glass component on its physical properties. This study demonstrates that gallium-richest compositions lead to an increase in the polarizability and contribute to a slight enlargement of the transmission window which are the primary conditions requested for intended applications. Complementary structural investigation allows to confirm the influence of the component content in the glass composition revealed by the model.

The Application of Vibrational Spectroscopy Techniques in the Qualitative Assessment of Material Traded as Ginseng.

The name “ginseng” is collectively used to describe several plant species, including Panax ginseng (Asian/Oriental ginseng), P. quinquefolius (American ginseng), P. pseudoginseng (Pseudoginseng) and Eleutherococcus senticosus (Siberian ginseng), each with different applications in traditional medicine practices. The use of a generic name may lead to the interchangeable use or substitution of raw materials which poses quality control challenges. Quality control methods such as vibrational spectroscopy-based techniques are here proposed as fast, non-destructive methods for the distinction of four ginseng species and the identification of raw materials in commercial ginseng products. Certified ginseng reference material and commercial products were analysed using hyperspectral imaging (HSI), mid-infrared (MIR) and near-infrared (NIR) spectroscopy. Principal component analysis (PCA) and (orthogonal) partial least squares discriminant analysis models (OPLS-DA) were developed using multivariate analysis software. UHPLC-MS was used to analyse methanol extracts of the reference raw materials and commercial products. The holistic analysis of ginseng raw materials revealed distinct chemical differences using HSI, MIR and NIR. For all methods, Eleutherococcus senticosus displayed the greatest variation from the three Panax species that displayed closer chemical similarity. Good discrimination models with high R2X and Q2 cum vales were developed. These models predicted that the majority of products contained either /P. ginseng or P. quinquefolius. Vibrational spectroscopy and HSI techniques in tandem with multivariate data analysis tools provide useful alternative methods in the authentication of ginseng raw materials and commercial products in a fast, easy, cost-effective and non-destructive manner.

Metabolic profiling of Daphnia magna exposed to environmental stressors by GC–MS and chemometric tools

Climate change is a major concern for the scientific community, demanding novel information about the effects of environmental stressors on living organisms. Metabolic profiling is required for achieving the most extensive possible range of compounds and their concentration changes on stressed conditions. Individuals of the crustacean species Daphnia magna were exposed to three different abiotic factors linked to global climate change: high salinity, high temperature levels and hypoxia. Advanced chemometric tools were used to characterize the metabolites affected by the exposure. An exploratory analysis of gas chromatography-mass spectrometry (GC–MS) data was performed to discriminate between control and exposed daphnid samples. Due to the complexity of these GC–MS data sets, a comprehensive untargeted analysis of the full scan data was performed using multivariate curve resolution-alternating least squares (MCR-ALS) method. This approach enabled to resolve most of the metabolite signals from interference peaks caused by derivatization reactions. Metabolites with significant changes in their peak areas were tentatively identified and the involved metabolic pathways explored. D. magna metabolic biomarkers are proposed for the considered physical factors. Metabolites related with energy metabolic pathways including some amino acids, carbohydrates, organic acids and nucleosides were identified as potential biomarkers of the investigated treatments. The proposed untargeted GC–MS metabolomics strategy and multivariate data analysis tools were useful to investigate D. magna metabolome under environmental stressed conditions.

Antimicrobial activity and chemometric modelling of South African propolis.

This study reports on the inhibitory and bactericidal properties of 39 South African (SA) propolis samples and three propolis samples from Brazil. Ethanolic extracts of propolis (EEP) were prepared and their antimicrobial activities tested using the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays. Some samples displayed substantial antimicrobial activity with MIC and MBC values as low as 6μgml(-1) against Staphylococcus aureus. The correlation between liquid chromatography-mass spectrometry (LC-MS) chemical data and the antimicrobial activity of propolis extracts was investigated using multivariate data analysis tools. Orthogonal projections to latent structures (OPLS) models were created for the two Gram-positive bacteria (Enterococcus faecalis and S.aureus) and Candida albicans. Using the S-plot function, it was possible to identify the bioactive constituents in propolis as chrysin, pinocembrin, galangin and pinobanksin-3-O-acetate. The SA propolis samples tested displayed noteworthy antimicrobial activity, favourably comparable to that of the Brazilian comparator and 'gold standard'. The observed antimicrobial activity of SA propolis can possibly be attributed to its flavonoid content. Based on the good antimicrobial activity observed for SA propolis, this natural resource shows promise and should be considered for development which may contribute to growing the bio-economy in the region.

A Methodology for Applying Social Network Analysis Metrics on Biodiversity

This paper proposes a methodology for applying Social Network Analysis (SNA) metrics to biological interaction network studies in the Biodiversity Informatics domain, which may serve as a guide for this activity to other researchers. The methodology is formalized by means of Business Process Modeling Notation (BPMN) and structured in four steps: (i) mapping the data types and the interactions available; (ii) defining the key-questions to be answered and the analysis variables; (iii) choosing the SNA metrics appropriate to the context of the research; and (iv) performing the biological analysis with the support of SNA. As material resources, the SNA metrics were used, as well as a set of computational (such as R packages, Dieta, Pajek and Ucinet software) and statistical analysis (Exploratory Data Analysis and Multivariate Data Analysis) tools. The results show the benefits that providing a systematic method to guide the steps of one research can bring to a researcher ‑ be it due to the support of the resources recommended, be it by the organization of the research activities. In addition, there is the possibility of applying this methodology to underexplored knowledge fields, such as Agrobiodiversity.

Prediction of wastewater quality using amperometric bioelectronic tongues.

Wastewater samples from a Swedish chemi-thermo-mechanical pulp (CTMP) mill collected at different purification stages in a wastewater treatment plant (WWTP) were analyzed with an amperometric enzyme-based biosensor array in a flow-injection system. In order to resolve the complex composition of the wastewater, the array consists of several sensing elements which yield a multidimensional response. We used principal component analysis (PCA) to decompose the array's responses, and found that wastewater with different degrees of pollution can be differentiated. With the help of partial least squares regression (PLS-R), we could link the sensor responses to the Microtox® toxicity parameter, as well as to global organic pollution parameters (COD, BOD, and TOC). From investigating the influences of individual sensors in the array, it was found that the best models were in most cases obtained when all sensors in the array were included in the PLS-R model. We find that fast simultaneous determination of several global environmental parameters characterizing wastewaters is possible with this kind of biosensor array, in particular because of the link between the sensor responses and the biological effect onto the ecosystem into which the wastewater would be released. In conjunction with multivariate data analysis tools, there is strong potential to reduce the total time until a result is yielded from days to a few minutes.

Dynamic Proteomic Profiling of Extra-Embryonic Endoderm Differentiation in Mouse Embryonic Stem Cells

During mammalian preimplantation development, the cells of the blastocyst's inner cell mass differentiate into the epiblast and primitive endoderm lineages, which give rise to the fetus and extra-embryonic tissues, respectively. Extra-embryonic endoderm (XEN) differentiation can be modeled in vitro by induced expression of GATA transcription factors in mouse embryonic stem cells. Here, we use this GATA-inducible system to quantitatively monitor the dynamics of global proteomic changes during the early stages of this differentiation event and also investigate the fully differentiated phenotype, as represented by embryo-derived XEN cells. Using mass spectrometry-based quantitative proteomic profiling with multivariate data analysis tools, we reproducibly quantified 2,336 proteins across three biological replicates and have identified clusters of proteins characterized by distinct, dynamic temporal abundance profiles. We first used this approach to highlight novel marker candidates of the pluripotent state and XEN differentiation. Through functional annotation enrichment analysis, we have shown that the downregulation of chromatin-modifying enzymes, the reorganization of membrane trafficking machinery, and the breakdown of cell-cell adhesion are successive steps of the extra-embryonic differentiation process. Thus, applying a range of sophisticated clustering approaches to a time-resolved proteomic dataset has allowed the elucidation of complex biological processes which characterize stem cell differentiation and could establish a general paradigm for the investigation of these processes.