16 results match your criteria Chemometrics And Intelligent Laboratory Systems[Journal]

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An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based corona detection method using lung X-ray image.

Chemometr Intell Lab Syst 2020 May 18:104054. Epub 2020 May 18.

Department of Software Engineering, College of Engineering, Firat University, Elazig, Turkey.

Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. Read More

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http://dx.doi.org/10.1016/j.chemolab.2020.104054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233238PMC

Using Differential Evolution to Design Optimal Experiments.

Chemometr Intell Lab Syst 2020 Apr 28;199. Epub 2020 Jan 28.

Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095.

Differential Evolution (DE) has become one of the leading metaheuristics in the class of Evolutionary Algorithms, which consists of methods that operate off of survival-of-the-fittest principles. This general purpose optimization algorithm is viewed as an improvement over Genetic Algorithms, which are widely used to find solutions to chemometric problems. Using straightforward vector operations and random draws, DE can provide fast, efficient optimization of any real, vector-valued function. Read More

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http://dx.doi.org/10.1016/j.chemolab.2020.103955DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088454PMC

BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes.

Chemometr Intell Lab Syst 2019 Feb 11;185:122-134. Epub 2019 Jan 11.

Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC.

Clustered binary outcomes and datasets with many predictor variables are frequently encountered in clinical research (e.g. longitudinal studies). Read More

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http://dx.doi.org/10.1016/j.chemolab.2019.01.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813794PMC
February 2019
1 Read

Standardized maximim -optimal designs for enzyme kinetic inhibition models.

Chemometr Intell Lab Syst 2017 Oct 6;169:79-86. Epub 2017 Sep 6.

Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA 90095-1772, USA.

Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters. An alternative is the maximin approach that allows the user to specify a range of values for each parameter of interest. However, the maximin approach is difficult because we first have to determine the locally optimal design for each set of nominal values before maximin types of optimal designs can be found via a nested optimization process. Read More

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http://dx.doi.org/10.1016/j.chemolab.2017.08.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761082PMC
October 2017
4 Reads

A scoring metric for multivariate data for reproducibility analysis using chemometric methods.

Chemometr Intell Lab Syst 2017 Mar 23;162:10-20. Epub 2016 Dec 23.

Chemical Sciences Division, National Institute of Standards and Technology, Hollings Marine Laboratory, 331 Fort Johnson Road, Charleston, SC 29412, USA.

Process quality control and reproducibility in emerging measurement fields such as metabolomics is normally assured by interlaboratory comparison testing. As a part of this testing process, spectral features from a spectroscopic method such as nuclear magnetic resonance (NMR) spectroscopy are attributed to particular analytes within a mixture, and it is the metabolite concentrations that are returned for comparison between laboratories. However, data quality may also be assessed directly by using binned spectral data before the time-consuming identification and quantification. Read More

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http://dx.doi.org/10.1016/j.chemolab.2016.12.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5500873PMC
March 2017
7 Reads

Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

Chemometr Intell Lab Syst 2016 Apr;153:92-105

School of Mathematics, University of Nairobi, P.O Box 30196-00100 GPO, Nairobi, Kenya.

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. Read More

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http://dx.doi.org/10.1016/j.chemolab.2016.02.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4834557PMC
April 2016
5 Reads

Determination of total organic carbon and soluble solids contents in injection intermediates with NIR spectroscopy and chemometrics.

Chemometr Intell Lab Syst 2016 Mar 2;152:140-145. Epub 2016 Jan 2.

Pharmaceutical Informatics Institute, Zhejiang University, Hangzhou 310058, China.

Near infrared spectroscopy combined with chemometrics was investigated for the fast determination of total organic carbon (TOC) and soluble solids contents (SSC) of injection intermediates. The NIR spectra were collected in transflective mode, and the TOC and SSC reference values were determined with Multi N/C UV HS analyzer and loss on drying method. The samples were divided into calibration sets and validation sets using the Kennard-Stone (KS) algorithm. Read More

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http://dx.doi.org/10.1016/j.chemolab.2015.12.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7114577PMC

Model-based optimal design of experiments - semidefinite and nonlinear programming formulations.

Chemometr Intell Lab Syst 2016 Feb;151:153-163

Centro de Investigação em Processos Químicos e Produtos da Floresta, Department of Chemical Engineering, University of Coimbra, Pólo II, R. Sílvio Lima, 3030-790 Coimbra, Portugal. Tel. +351 239 798700.

We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Read More

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http://dx.doi.org/10.1016/j.chemolab.2015.12.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4772777PMC
February 2016
4 Reads

A Sequential Algorithm for Multiblock Orthogonal Projections to Latent Structures.

Chemometr Intell Lab Syst 2015 Dec;149(Pt B):33-39

Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304.

Methods of multiblock bilinear factorizations have increased in popularity in chemistry and biology as recent increases in the availability of information-rich spectroscopic platforms has made collecting multiple spectroscopic observations per sample a practicable possibility. Of the existing multiblock methods, consensus PCA (CPCA-W) and multiblock PLS (MB-PLS) have been shown to bear desirable qualities for multivariate modeling, most notably their computability from single-block PCA and PLS factorizations. While MB-PLS is a powerful extension to the nonlinear iterative partial least squares (NIPALS) framework, it still spreads predictive information across multiple components when response-uncorrelated variation exists in the data. Read More

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http://dx.doi.org/10.1016/j.chemolab.2015.10.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4668594PMC
December 2015
16 Reads

Generalized adaptive intelligent binning of multiway data.

Chemometr Intell Lab Syst 2015 Aug;146:42-46

Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304.

NMR metabolic fingerprinting methods almost exclusively rely upon the use of one-dimensional (1D) H NMR data to gain insights into chemical differences between two or more experimental classes. While 1D H NMR spectroscopy is a powerful, highly informative technique that can rapidly and nondestructively report details of complex metabolite mixtures, it suffers from significant signal overlap that hinders interpretation and quantification of individual analytes. Two-dimensional (2D) NMR methods that report heteronuclear connectivities can reduce spectral overlap, but their use in metabolic fingerprinting studies is limited. Read More

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http://dx.doi.org/10.1016/j.chemolab.2015.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4456038PMC
August 2015
29 Reads

A Bayesian approach to an interlaboratory comparison.

Chemometr Intell Lab Syst 2015 Feb 27;141:94-99. Epub 2014 Dec 27.

Department of Earth and Environmental Sciences, Tulane University 70118.

Interlaboratory comparisons are an important check of the quality of a measurement technique. In this paper we examine the accelerator mass spectrometry (AMS) measurement of Ca, an unstable isotope of calcium that has emerged as a valuable tracer for a variety of studies. We use a Bayesian framework to explore the quality and consistency of the AMS measurements made by Lawrence Livermore National Laboratory (LLNL) and the Purdue Rare Isotope Measurement Laboratory (PRIME Lab). Read More

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http://dx.doi.org/10.1016/j.chemolab.2014.12.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5993439PMC
February 2015
1 Read

Constructing Metabolic Association Networks Using High-dimensional Mass Spectrometry Data.

Chemometr Intell Lab Syst 2014 Nov;138:193-202

Department of Chemistry, University of Louisville, Louisville, KY 40292, USA.

The goal of metabolic association networks is to identify topology of a metabolic network for a better understanding of molecular mechanisms. An accurate metabolic association network enables investigation of the functional behavior of metabolites in a cell or tissue. Gaussian Graphical model (GGM)-based methods have been widely used in genomics to infer biological networks. Read More

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http://dx.doi.org/10.1016/j.chemolab.2014.07.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4233464PMC
November 2014
7 Reads
2.321 Impact Factor

Coefficient of Variation, Signal-to-Noise Ratio, and Effects of Normalization in Validation of Biomarkers from NMR-based Metabonomics Studies.

Chemometr Intell Lab Syst 2013 Oct;128:9-16

Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.

A primary goal of metabonomics research is biomarker discovery for human diseases based on differences in metabolic profiles between and patient populations. One of the most significant challenges in biomarker discovery is validation, which implicitly depends on the coefficient of variation (CV) associated with the measurement technique. This paper investigates how the CV of metabolite resonances measured by nuclear magnetic resonance spectroscopy (NMR) depends on signal-to-noise ratio (SNR) and normalization method. Read More

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http://dx.doi.org/10.1016/j.chemolab.2013.07.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3963315PMC
October 2013
27 Reads

Simultaneous Phase and Scatter Correction for NMR Datasets.

Chemometr Intell Lab Syst 2014 Feb;131:1-6

Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304.

Nuclear magnetic resonance (NMR) spectroscopy has proven invaluable in the diverse field of chemometrics due to its ability to deliver information-rich spectral datasets of complex mixtures for analysis by techniques such as principal component analysis (PCA). However, NMR datasets present a unique challenge during preprocessing due to differences in phase offsets between individual spectra, thus complicating the correction of random dilution factors that may also occur. We show that simultaneously correcting phase and dilution errors in NMR datasets representative of metabolomics data yields improved cluster quality in PCA scores space, even with significant initial phase errors in the data. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01697439130021
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http://dx.doi.org/10.1016/j.chemolab.2013.11.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907089PMC
February 2014
7 Reads

Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies.

Chemometr Intell Lab Syst 2011 Dec;109(2):162-170

Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.

Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures makes it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported by a standard and rigorous assessment of whether or not the cluster separation is statistically significant. Read More

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http://dx.doi.org/10.1016/j.chemolab.2011.08.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4523310PMC
December 2011
6 Reads

Chemometric Resolution and Quantification of Four-Way Data Arising from Comprehensive 2D-LC-DAD Analysis of Human Urine.

Chemometr Intell Lab Syst 2011 Mar;106(1):131-141

Department of Chemistry, PO Box 842006, Virginia Commonwealth University, Richmond, VA 23284-2006, USA.

Two-dimensional liquid chromatography (LC×LC) is quickly becoming an important technique for the analysis of complex samples, owing largely to the relatively high peak capacities attainable by this analytical technique. With the increase in the complexity of the sample comes a corresponding increase in the complexity of the collected data. Thus the need for chemometric methods capable of resolving and quantifying such data is ever more urgent in order to obtain the maximum information available from the data. Read More

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http://dx.doi.org/10.1016/j.chemolab.2010.07.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3762254PMC
March 2011
4 Reads
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