Raw Experimental Data Research Articles

Modeling of the Instantaneous Transvalvular Pressure Gradient in Aortic Stenosis.

The simplified and modified Bernoulli equations break down in estimating the true pressure gradient across the stenotic aortic valve due to their over simplifying assumptions of steady and inviscid conditions as well as the fundamental nature in which aortic valves are different than idealized orifices. Nevertheless, despite having newer models based on time-dependent momentum balance across an orifice, the simplified and modified Bernoulli continue to be the clinical standard because to date, they remain the only models clinically implementable. The objective of this study is to (1) illustrate the fundamental considerations necessary to accurately model the time-dependent instantaneous pressure gradient across a fixed orifice and (2) propose empirical corrections when applying orifice based models to severely stenotic aortic valves. We introduce a general model to predict the time-dependent instantaneous pressure gradient across an orifice that explicitly model the Reynolds number dependence of both the steady and unsteady terms. The accuracy of this general model is assessed with respect to previous models through pulse duplicator experiments on a round orifice model as well as an explanted stenotic surgical aortic valve both with geometric areas of 0.6cm2 (less than 1cm2 which is the threshold for stenosis determination) over cardiac outputs of 3 and 5L/min and heart rates of 60, 90 and 120bpm. The model and the raw experimental data corresponding to the orifice showed good agreement over a wide range of cardiac outputs and heart rates (R2 exceeding 0.91). The derived average and peak transvalvular pressure gradients also demonstrated good agreement with no significant differences between the respective peaks (p > 0.09). The new model proposed holds promise with its physical and closed form representation of pressure drop, however accurate modeling of the time-variability of the valve area is necessary for the model to be applied on stenotic valves.

Optimal Multivariate Gaussian Fitting with Applications to PSF Modeling in Two-Photon Microscopy Imaging

Fitting Gaussian functions to empirical data is a crucial task in a variety of scientific applications, especially in image processing. However, most of the existing approaches for performing such fitting are restricted to two dimensions and they cannot be easily extended to higher dimensions. Moreover, they are usually based on alternating minimization schemes which benefit from few theoretical guarantees in the underlying nonconvex setting. In this paper, we provide a novel variational formulation of the multivariate Gaussian fitting problem, which is applicable to any dimension and accounts for possible nonzero background and noise in the input data. The block multiconvexity of our objective function leads us to propose a proximal alternating method to minimize it in order to estimate the Gaussian shape parameters. The resulting FIGARO algorithm is shown to converge to a critical point under mild assumptions. The algorithm shows a good robustness when tested on synthetic datasets. To demonstrate the versatility of FIGARO, we also illustrate its excellent performance in the fitting of the point spread functions of experimental raw data from a two-photon fluorescence microscope.

Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies

Scientific research is increasingly characterised by the volume of documents and data that it produces, from experimental plans and raw data to reports and papers. Researchers frequently struggle to manage and curate these materials, both individually and collectively. Previous studies of Electronic Lab Notebooks (ELNs) in academia and industry have identified semantic web technologies as a means for organising scientific documents to improve current workflows and knowledge management practices. In this paper, we present a qualitative, user-centred study of researcher requirements and practices, based on a series of discipline-specific focus groups. We developed a prototype semantic ELN to serve as a discussion aid for these focus groups, and to help us explore the technical readiness of a range of semantic web technologies. While these technologies showed potential, existing tools for semantic annotation were not well-received by our focus groups, and need to be refined before they can be used to enhance current researcher practices. In addition, the seemingly simple notion of “tagging and searching” documents appears anything but; the researchers in our focus groups had extremely personal requirements for how they organise their work, so the successful incorporation of semantic web technologies into their practices must permit a significant degree of customisation and personalisation.

A Quantitative Intelligent Diagnosis Method for Early Weak Faults of Aviation High-speed Bearings

An intelligent diagnosis method based on support vector machine (SVM) is proposed to quantitatively diagnose early weak faults of high-speed aero-engine’s bearings. In order to achieve a better performance in contrast with conventional kernel function, a mixed kernel function is constructed and particle swarm optimization (PSO) is used to optimize kernel coefficients and other parameters. Experimental raw data is preprocessed by sparse decomposition and reconstruction method to remove noise in original signals, which can provide effective and reliable samples for SVM. In order to verify the validity of the proposed method, experiments on different fault types with different defect sizes of high-speed bearings working at 30000rpm are carried out. The results show that the accuracy of the proposed method is greatly improved compared with traditional SVM. The proposed method can not only distinguish different types of failure but also distinguish different degrees of the same fault pattern, which achieves a quantitative intelligent diagnosis of early weak faults in aviation’s high-speed bearings.

Heat transport and storage processes in differential scanning calorimeter: Computational analysis and model validation

Experimental results provided by differential scanning calorimetry (DSC) can be affected by systematic errors, which are difficult to identify and quantify correctly by the end-users, as a DSC device is commonly used as a gray box. The signal delay due to thermal inertia and the effects of sample size or heating rate present the most common sources of uncertainties. In this paper, a 3-D computational model of a differential scanning calorimeter is constructed, calibrated and validated. Five reference standards are used for both experimental and computational calibration, resulting in a very good agreement (R2 > 0.999794) of the computational model with experimental outputs. Model is validated using two different materials and processes. The analysis of melting of aluminum, as one of the standards not used at the calibration, shows a maximum difference of 0.279 mW·mg−1 at the peak top, which is well within the accuracy limits. The application of the model for the determination of effective specific heat capacity of quartz, as a representative of commonly studied materials which are though not standardized for DSC, reveal a good agreement with both the measured data and the results of independent experiments reported by several other investigators. The main advantage of the model consists in the detailed analysis and separation or quantification of particular heat evolving/consuming mechanisms in both the DSC device and the studied materials. Therefore, contrary to the empirical calibration, it can identify exactly the physical sources of measurement uncertainties. The raw experimental data provided by the DSC device can then be corrected in a straightforward way and the systematic errors can be eliminated.

Deposition of UF5 on the surface of steel material having oxide coating in contact with gaseous UF6

ABSTRACT When UF6 is handled in leak tight system, the amount of uranium compound formed on the surface of containers, valves, and others in UF6 handling facilities is not so significant for a short term that special attention has not been given to this problem. The present work was done to throw some more light on this problem based on the recent experiment. We discuss the possibility that the intermolecular transfer of a fluorine atom from UF6 to UF5 may participate in the formation of uranium compound. The discussion includes also the unique features contained in the experimental result, the reaction processes assumed in this problem, and the derivation of a rate equation for expressing the deposition of uranium compound. Furthermore, we propose a new method for determining nonlinear parameters included in a governing differential equation having two variables for expressing the deposition of uranium compound from experimental raw data.

Electrochemical Data Science Infrastructure for Open Battery Innovation

Despite significant investments in battery research and development, progress remains modest, with more advances made at the level of engineering rather than basic science. Important interdisciplinary work is being done, but many projects and initiatives unfortunately lack the coordination or incentivization to have an impact beyond academic publications, and many learnings, tools and datasets remain siloed or forgotten. The electrochemical and battery communities have data generated by experimentalists, algorithms developed by theoreticians, and the electrochemical domain expertise to solve thorny problems. However, what is missing is the common infrastructure. Leveraging modern software and data science tools allows for many new opportunities to better equip scientists and engineers to have greater impact, but requires greater communication between the relevant stakeholders. Our goal is to grow an ecosystem of battery innovators that tightens the feedback loop between basic science at academic institutions and applied engineering that takes place in industry to advance battery technology. Components of this infrastructure include: 1) Open-source modeling and data analysis tools 2) Access to open datasets 3) Emergent best practices for managing data and contributing to the open-source community We want to shorten the journey from intuition to experimental raw data to insights and intelligent decision making to solve important scientific and technological challenges. We have already started this process by developing workflow tools for routine battery data management and analysis. Our platform parses electrochemical datasets from leading hardware vendors, and provides a user interface for recording project and testing metadata. Automated plotting and reporting are among the other available features. To date, our software handles basic cycling statistics and voltage profiles as well as more advanced tools such as differential capacity analysis. Future work will allow for custom analysis with an environment for scripting with Python. We aim to arm aspiring electrochemical data scientists with tools that save time and energy. Beyond this, we help users increase the visibility of their work by curating a platform where new battery models and analyses can be hosted, rather than be delegated to “shelfware” after PhD students graduate and leave their former labs. Further, by hosting data sets and analysis tools, researchers benefit from greater reproducibility and transparency among labs, which helps to engender greater trust between the battery community and the broader public. In doing so, we hope this platform will serve to accelerate battery innovation.

Load and harmonic distortion characterization of modern low-energy lighting under applied voltage variation

This paper investigates the voltage level dependencies of different modern lighting types. Experiments are undertaken and the results used to formulate polynomial load models, presented in a ZIP format. These characterize active power, reactive power and harmonic current behaviors; are compatible with established power systems simulation practice; and are generally demonstrated to muster a good fit to the raw experimental data. Despite exceedingly high levels of current distortion being noted for several lamp instances, the attenuating effects of lowering voltage and harmonic diversity, as captured within a pair of diversity factor terms, are shown to help significantly alleviate the associated harmonic pollution impact on low voltage networks.

Obtaining High Quality SCAL Data: Combining Different Measurement Techniques, Saturation Monitoring, Numerical Interpretation and Continuous Monitoring of Experimental Data

SCAL parameters (i.e., Relative Permeability and Capillary Pressure curves) are key inputs to understand and predict reservoir behavior in all phases of development. Techniques to measure relative permeability and capillary pressure have been well established and applied to a wide variety of core samples both from sandstone and carbonate reservoirs. On the other hand, we frequently encounter quality compromised data due to challenges in experimental procedures, lack of understanding of measurement techniques, and poor quality of raw data. As a result, relative permeability is often viewed as a parameter with large uncertainties and a fitting parameter in history matching. A special core analysis program was recently carried out on selected core samples from a deep-water sandstone reservoir in the Gulf of Mexico. In this frontier, relative permeability has been ranked among the top subsurface uncertainties. It greatly impacts the production forecast and field development plan. However, due to the high temperature, high salinity and fluid compatibility issues, the core measurements faced very specific challenges and a good relative permeability dataset has not been obtained in the past for this area. In this work, we demonstrate that a quality set of relative permeability data can be obtained through close collaboration across disciplines, a properly designed protocol, adequate engagement with the laboratory, timely QA/QC of experimental raw data, and appropriate interpretation incorporating numerical simulations. Well-defined and constrained relative permeability curve shave been derived with the combination of steady state and centrifuge techniques. The average trend can be described by a residual oil saturation of 22%, end-point relative permeabilities of 0.6 and 0.2 to oil and water, respectively and Corey exponents between 2 and 3.

Evaluated Site-Specific Rate Constants for Reaction of Isobutane with H and CH3: Shock Tube Experiments Combined with Bayesian Model Optimization.

Evaluated site-specific rate constants for the reactions of isobutane with CH3 and H were determined in a combined analysis of new shock tube experiments and existing literature data. In our shock tube experiments, CH3 radicals, produced from the pyrolysis of di- tert-butylperoxide, and H atoms, produced from the pyrolysis of C2H5I, were reacted with dilute mixtures of isobutane in argon at 870-1130 K and 140-360 kPa, usually with a radical chain inhibitor. Propene and isobutene, measured with GC/FID and MS, were quantified as characteristic of H-abstraction from the primary and tertiary carbons, respectively. Using the method of uncertainty minimization using polynomial chaos expansions (MUM-PCE), a comprehensive Cantera kinetics model based on JetSurF 2.0 was optimized to our experiments and available literature data spanning ambient temperatures to 1327 K. Based on Bayes' theorem, MUM-PCE constrains the kinetics model to the experimental data. The isobutane literature data used for optimization included both raw experimental data and reported branching and total rate measurements. Data for ethane were also included to better define the absolute rate constant for abstraction of H from primary carbons. For both H and CH3, the optimization increased the relative rate of tertiary to primary H-abstraction compared with existing estimates, especially at higher temperatures. We combine the present data for primary and tertiary sites with previous results from our group on 1-butane to derive site-specific rate constants for the reaction of H and CH3 with generic primary, secondary, and tertiary carbons suitable for a wide range of temperatures.

PiGx: reproducible genomics analysis pipelines with GNU Guix.

In bioinformatics, as well as other computationally intensive research fields, there is a need for workflows that can reliably produce consistent output, from known sources, independent of the software environment or configuration settings of the machine on which they are executed. Indeed, this is essential for controlled comparison between different observations and for the wider dissemination of workflows. However, providing this type of reproducibility and traceability is often complicated by the need to accommodate the myriad dependencies included in a larger body of software, each of which generally comes in various versions. Moreover, in many fields (bioinformatics being a prime example), these versions are subject to continual change due to rapidly evolving technologies, further complicating problems related to reproducibility. Here, we propose a principled approach for building analysis pipelines and managing their dependencies with GNU Guix. As a case study to demonstrate the utility of our approach, we present a set of highly reproducible pipelines called PiGx for the analysis of RNA sequencing, chromatin immunoprecipitation sequencing, bisulfite-treated DNA sequencing, and single-cell resolution RNA sequencing. All pipelines process raw experimental data and generate reports containing publication-ready plots and figures, with interactive report elements and standard observables. Users may install these highly reproducible packages and apply them to their own datasets without any special computational expertise beyond the use of the command line. We hope such a toolkit will provide immediate benefit to laboratory workers wishing to process their own datasets or bioinformaticians seeking to automate all, or parts of, their analyses. In the long term, we hope our approach to reproducibility will serve as a blueprint for reproducible workflows in other areas. Our pipelines, along with their corresponding documentation and sample reports, are available at http://bioinformatics.mdc-berlin.de/pigx

On Metric Correction and Conditionality of Raw Featureless Data in Machine Learning

Recently, raw experimental data in machine learning often appear as direct comparisons between objects (featureless data). Different ways to evaluate difference or similarity of a pair of objects in image and data mining, image analysis, bioinformatics, etc., are usually used in practice. Nevertheless, such comparisons often are not distances or correlations (scalar products) like a correct function defined on a limited set of elements in machine learning. This problem is denoted as metric violations in ill-posed matrices. Therefore, it needs to recover violated metrics and provide optimal conditionality of corresponding matrices of pairwise comparisons for distances and similarities. This is the correct basis for using of modern machine learning algorithms.

A new approach to modeling the behavior of frozen soils

In this paper a new approach is presented for modeling the behavior of frozen soils. A data-mining technique, Evolutionary Polynomial Regression (EPR), is used for modeling the thermo-mechanical behavior of frozen soils including the effects of confining pressure, strain rate and temperature. EPR enables to create explicit and well-structured equations representing the mechanical and thermal behavior of frozen soil using experimental data.A comprehensive set of triaxial tests were carried out on samples of a frozen soil and the data were used for training and verification of the EPR model. The developed EPR model was also used to simulate the entire stress-strain curve of triaxial tests, the data for which were not used during the training of the EPR model. The results of the EPR model predictions were compared with the actual data and it was shown that the proposed methodology can extract and reproduce the behavior of the frozen soil with a very high accuracy. It was also shown that the EPR model is able to accurately generalize the predictions to unseen cases. A sensitivity analysis revealed that the model developed from raw experimental data is able to extract and effectively represent the underlying mechanics of the behavior of frozen soils. The proposed methodology presents a unified approach to modeling of materials that can also help the user gain a deeper insight into the behavior of the materials. The main advantages of the proposed technique in modeling the complex behavior of frozen soil have been highlighted.

Automatically discovering clusters of algorithm and problem instance behaviors as well as their causes from experimental data, algorithm setups, and instance features

In the fields of heuristic optimization and machine learning, experimentation is the way to assess the performance of an algorithm setup and the hardness of problems. Most algorithms in the domain are anytime algorithms, meaning that they can improve their approximation quality over time. This means that one algorithm may initially perform better than another one, but converge to worse solutions in the end. Instead of single final results, the whole runtime behavior of algorithms needs to be compared. Moreover, a researcher does not just want to know which algorithm performs best and which problem is the hardest – she/he wants to know why. In this paper, we introduce a process which can 1) automatically model the progress of algorithm setups on different problem instances based on data collected in experiments, 2) use these models to discover clusters of algorithm (or problem instance) behaviors, and 3) propose causes why a certain algorithm setup (or problem instance) belongs to a certain algorithm (or problem instance) behavior cluster. These high-level conclusions are presented in form of decision trees relating algorithm parameters (or instance features) to cluster ids. We emphasize the duality of analyzing algorithm setups and problem instances. Our process is implemented as open source software and tested in two case studies, on the Maximum Satisfiability Problem and the Traveling Salesman Problem. Besides its basic application to raw experimental data, yielding clusters and explanations of “quantitative” algorithm behavior, our process also allows for “qualitative” conclusions by feeding it with data which is normalized based on problem features or algorithm parameters. It can also be applied recursively, e.g., to further investigate the behavior of the algorithms in the cluster with the best-performing setups on the problem instances belonging to the cluster of hardest instances. Both use cases are investigated in the case studies. We conclude our article by a comprehensive analysis of the drawbacks of our method and with suggestions on how it can be improved.

Quantitative Estimates of Nonlinear Flow Characteristics of Deformable Rough-Walled Rock Fractures with Various Lithologies

The existence of surface roughness, various contact conditions and the occurrence of flow nonlinearity make the flow process in natural rock fractures more complicated. To evaluate the fluid flow regimes in deformable rough-walled rock fractures, a great many hydromechanical tests were conducted on nine real fractures artificially produced from a wide range of lithological diversity. For fractures with a certain JRC (fracture roughness coefficient) value, the confining pressure varied from 5 to 20 MPa, and the hydraulic pressure was increased from 0.4 to 6.0 MPa. The experimental results display that (i) regression analyses of the raw experimental data indicate that the Forchheimer’s law provides a perfect description for flow process through the fractures. The coefficients of viscous and inertial pressure drops undergo a growth of 2–3 orders of magnitude with an increase in the confining pressure; (ii) the hydraulic aperture decreases by approximately 87.41–92.81% as the confining pressure increases, and experiences a decrease of 1.52–2.96 times with the JRC values. A power-law function is used to evaluate the hydraulic aperture as a function of the nonlinear coefficient. The nonlinear coefficient decreases with increasing hydraulic aperture; (iii) using Forchheimer equation, the critical Reynolds number Rec was successfully assessed by choosing E percentage (generally 10%) of the nonlinear effect as the critical value between the linear and nonlinear flow regimes. The obtained Rec steadily increases with increasing confining pressure, while it diminishes with the JRC values; and (v) the transmissivity decreases as the pressure gradient increases. Additionally, transmissivity also exhibits a decreasing trend with both the confining pressures and JRC values due to fracture closure and tortuous and channeling flow paths in rougher fractures, and the rate of its decrease for a smaller confining pressure (5, 10 MPa) is more significant.

A close look onto structural models and primary ligands of metallo-β-lactamases.

β-Lactamases are hydrolytic enzymes capable of opening the β-lactam ring of antibiotics such as penicillin, thus endowing the bacteria that produce them with antibiotic resistance. Of particular medical concern are metallo-β-lactamases (MBLs), with an active site built around coordinated Zn cations. MBLs are pan-reactive enzymes that can break down almost all classes of β-lactams, including such last-resort antibiotics as carbapenems. They are not only broad-spectrum-reactive but are often plasmid-borne (e.g., the New Delhi enzyme, NDM), and can spread horizontally even among unrelated bacteria. Acquired MBLs are encoded by mobile genetic elements, which often include other resistance genes, making the microbiological situation particularly alarming. There is an urgent need to develop MBL inhibitors in order to rescue our antibiotic armory. A number of such efforts have been undertaken, most notably using the 3D structures of various MBLs as drug-design targets. Structure-guided drug discovery depends on the quality of the structures that are collected in the Protein Data Bank (PDB) and on the consistency of the information in dedicated β-lactamase databases. We conducted a careful review of the crystal structures of class B β-lactamases, concluding that the quality of these structures varies widely, especially in the regions where small molecules interact with the macromolecules. In a number of examples the interpretation of the bound ligands (e.g., inhibitors, substrate/product analogs) is doubtful or even incorrect, and it appears that in some cases the modeling of ligands was not supported by electron density. For ten MBL structures, alternative interpretations of the original diffraction data could be proposed and the new models have been deposited in the PDB. In four cases, these models, prepared jointly with the authors of the original depositions, superseded the previous deposits. This review emphasizes the importance of critical assessment of structural models describing key drug design targets at the level of the raw experimental data. Since the structures reviewed here are the basis for ongoing design of new MBL inhibitors, it is important to identify and correct the problems with ambiguous crystallographic interpretations, thus enhancing reproducibility in this highly medically relevant area.

BrainWave: A Matlab Toolbox for Beamformer Source Analysis of MEG Data.

BrainWave is an easy-to-use Matlab toolbox for the analysis of magnetoencephalography data. It provides a graphical user interface for performing minimum-variance beamforming analysis with rapid and interactive visualization of evoked and induced brain activity. This article provides an overview of the main features of BrainWave with a step-by-step demonstration of how to proceed from raw experimental data to group source images and time series analyses. This includes data selection and pre-processing, magnetic resonance image co-registration and normalization procedures, and the generation of volumetric (whole-brain) or cortical surface based source images, and corresponding source time series as virtual sensor waveforms and their time-frequency representations. We illustrate these steps using example data from a recently published study on response inhibition (Isabella et al., 2015) using the sustained attention to response task paradigm in 12 healthy adult participants. In this task participants were required to press a button with their right index finger to a rapidly presented series of numerical digits and withhold their response to an infrequently presented target digit. This paradigm elicited movement-locked brain responses, as well as task-related modulation of brain rhythmic activity in different frequency bands (e.g., theta, beta, and gamma), and is used to illustrate two different types of source reconstruction implemented in the BrainWave toolbox: (1) event-related beamforming of averaged brain responses and (2) beamformer analysis of modulation of rhythmic brain activity using the synthetic aperture magnetometry algorithm. We also demonstrate the ability to generate group contrast images between different response types, using the example of frontal theta activation patterns during error responses (failure to withhold on target trials). BrainWave is free academic software available for download at http://cheynelab.utoronto.ca/brainwave along with supporting software and documentation. The development of the BrainWave toolbox was supported by grants from the Canadian Institutes of Health Research, the National Research and Engineering Research Council of Canada, and the Ontario Brain Institute.

MODELING EXPERIMENTAL DATA WITH POLYNOMIALS CHAOS

Given a raw data sample, the purpose of this paper is to design a numerical procedure to model this sample under the form of polynomial chaos expansion. The coefficients of the polynomial are computed as the solution to a constrained optimization problem. The procedure is first validated on samples coming from a known distribution and it is then applied to raw experimental data of unknown distribution. Numerical experiments show that only five coefficients of the Chaos expansions are required to get an accurate representation of a sample.

Procedure for the determination of effective temperatures employing VESUVIO spectrometer

We describe the procedure to determine effective temperatures employing the data collected by the detectors of the VESUVIO spectrometer (ISIS pulsed neutron source), employing the Deep Inelastic Neutron Scattering technique. A scheme to group the detectors is proposed, based on their positions and angular ranges, in order to add their spectra and improve the statistics. We show the details of all the corrections that must be made on the raw experimental data to reach the spectra on which the desired effective temperatures can be fitted. Most of the correction stages require ad-hoc auxiliary simulations by the Monte Carlo method, that were deviced and described in previous papers. The corrections, that involve background subtraction, empty cell subtraction, and multiple scattering corrections, are described in detail. The efficiencies of the defined detector groups are determined, and employed in the expressions used in data fitting. Data treatment steps performed in hydrogenated and deuterated alcohols, are shown as examples of application of this procedure.

Qualitative and quantitative analysis of Teucrium polium essential oil components by GC-MS coupled with MCR and PARAFAC methods.

Teucrium polium is a herbal plant which has curative potential and pharmacological characteristics. Therefore, establishment of accurate analytical methods for characterising its active components is of prime importance. Because of the complex nature of essential oil samples, some results of direct gas chromatography-mass spectrometry (GC-MS) analysis maybe unreliable. Therefore, GC-MS is coupled with chemometric methods to resolve experimental information taken from chromatographic peaks, and assign compounds to them more correctly. Essential oil of T. Polium of Koohbanan suburb (Kerman, Iran) was extracted by hydro-distillation. Then the T. polium essential oil was qualitatively analysed using GC-MS coupled with multivariate curve resolution (MCR), to resolve experimental information taken from chromatographic peaks, and assign compounds to them more correctly. After that, the method of parallel factor analysis (PARAFAC) was implemented to quantify the concentrations of five major components of the T. polium essential oil. The number of identified components increases from 74 using the direct method of analysis to 106 using the MCR method. It is shown that α-pinene (8.93μg/L), cis-verbenol (3.98μg/L) and myrtenal (1.02μg/L) are the most abundant components in the T. polium essential oil. By coupling GC-MS and chemometric resolution methods such as MCR-alternating least squares (ALS) and PARAFAC more information with higher precision and accuracy can be obtained from raw experimental data. Also, it is shown that by implementing the PARAFAC method on the GC-MS data, it is possible to measure the concentration of the constituents of co-eluted, overlapped or embedded chromatographic peaks.