Multivariate data fitting with error control
Sign Up to like & getrecommendations! Published in 2019 at "BIT Numerical Mathematics"
DOI: 10.1007/s10543-018-0721-1
Abstract: We show how a recently developed multivariate data fitting technique enables to solve a variety of scientific computing problems in filtering, queueing, networks, metamodelling, computational finance, graphics, and more. We can capture linear as well… read more here.
Keywords: data fitting; error; multivariate data; error control ... See more keywords
Effect of dataset size on modeling and monitoring of chemical processes
Sign Up to like & getrecommendations! Published in 2020 at "Chemical Engineering Science"
DOI: 10.1016/j.ces.2020.115928
Abstract: Abstract Multivariate data analysis is a powerful tool for process monitoring and data analysis. The theoretical methodology of real-time multivariate data analysis has been studied in the last decade. However, the effect of dataset size… read more here.
Keywords: dataset size; data analysis; multivariate data;
Analysis of volatile compound changes in fried shallot (Allium cepa L. var. aggregatum) oil at different frying temperatures by GC-MS, OAV, and multivariate analysis.
Sign Up to like & getrecommendations! Published in 2020 at "Food chemistry"
DOI: 10.1016/j.foodchem.2020.128748
Abstract: Flavor is a key attribute of fried oil that shows a critical correlation with temperature. Therefore, selecting the appropriate temperature is important in preparing fried shallot oil (FSO). Volatile compounds from five different FSOs were… read more here.
Keywords: fsos; analysis; multivariate data; fried shallot ... See more keywords
Qualitative and quantitative analysis of peanut adulteration in almond powder samples using multi-elemental fingerprinting combined with multivariate data analysis methods
Sign Up to like & getrecommendations! Published in 2017 at "Food Control"
DOI: 10.1016/j.foodcont.2017.06.014
Abstract: Abstract In this study, adulteration of almond powder samples with peanut was analyzed using multi-elemental fingerprinting based on inductively coupled plasma optical emission measurements (ICP-OES) combined with chemometric methods. The ability of multivariate data analysis… read more here.
Keywords: multi elemental; almond; analysis; adulteration ... See more keywords
A multivariate data analysis approach for investigating daily statistics of countries affected with COVID-19 pandemic
Sign Up to like & getrecommendations! Published in 2020 at "Heliyon"
DOI: 10.1016/j.heliyon.2020.e05575
Abstract: Background To understand the impact and volume of coronavirus (COVID-19) crisis, univariate analysis is tedious for describing the datasets reported daily. However, to capture the full picture and be able to compare situations and consequences… read more here.
Keywords: data analysis; analysis; analysis approach; multivariate data ... See more keywords
Solid‐phase microextraction coupled to gas chromatography–mass spectrometry followed by multivariate data analysis for the identification of volatile organic compounds as possible biomarkers in lung cancer tissues
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Pharmaceutical and Biomedical Analysis"
DOI: 10.1016/j.jpba.2017.08.049
Abstract: HIGHLIGHTSSPME–GC–MS as valuable tool for the identification of lung cancer biomarkers.Use of multivariate data analysis for discriminating healthy and carcinogenic tissues.Seven potential lung cancer biomarkers were identified.Correct classification of more than 98% of the samples.… read more here.
Keywords: data analysis; analysis; lung cancer; solid phase ... See more keywords
Production of Amphidinols and Other Bioproducts of Interest by the Marine Microalga Amphidinium carterae Unraveled by Nuclear Magnetic Resonance Metabolomics Approach Coupled to Multivariate Data Analysis.
Sign Up to like & getrecommendations! Published in 2019 at "Journal of agricultural and food chemistry"
DOI: 10.1021/acs.jafc.9b02821
Abstract: This study assessed the feasibility of an NMR metabolomics approach coupled to multivariate data analysis to monitor the naturally present or stresses-elicited metabolites from a long-term (>170 days) culture of the dinoflagellate marine microalgae Amphidinium… read more here.
Keywords: approach coupled; production; carterae; coupled multivariate ... See more keywords
Metabolome-Based Analysis of Herbal Cough Preparations Via Headspace Solid-Phase Microextraction GC/MS and Multivariate Data Analyses: A Prospect for Its Essential Oil Equivalency
Sign Up to like & getrecommendations! Published in 2020 at "ACS Omega"
DOI: 10.1021/acsomega.0c04923
Abstract: Liquid cough preparations containing essential oils pose a challenge for isolating and quantifying their volatile components from such a complex matrix enriched with nonvolatile constituents and excipients. This study aims to develop a strategy integrating… read more here.
Keywords: headspace solid; cough preparations; data analyses; cough ... See more keywords
MVApp Flies Its Flag to the Challenging Frontier of Multivariate Data Analysis
Sign Up to like & getrecommendations! Published in 2019 at "Plant Physiology"
DOI: 10.1104/pp.19.00454
Abstract: We live in an era where omic approaches are essential to decode scientific hypotheses. Indeed, technological advances have accelerated science, resulting in a plethora of insights. During the past decade, the plant science community has… read more here.
Keywords: mvapp flies; challenging frontier; frontier multivariate; multivariate data ... See more keywords
Multivariate Data Analytics in PV Manufacturing—Four Case Studies Using Manufacturing Datasets
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Journal of Photovoltaics"
DOI: 10.1109/jphotov.2017.2778571
Abstract: Many industries are being revolutionized through the use of advanced analytical tools that generate insights from large sets of data. These tools are used as a part of a diversely described but analogous set of… read more here.
Keywords: data analytics; case studies; manufacturing; multivariate data ... See more keywords
Learning Sparse Graphs for Prediction of Multivariate Data Processes
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2019.2896435
Abstract: We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-free and computationally efficient manner. Specifically, the… read more here.
Keywords: multivariate data; graphs; prediction multivariate; learning sparse ... See more keywords