Articles producció científica> Química Analítica i Química Orgànica

Navigating the complexity: Managing multivariate error and uncertainties in spectroscopic data modelling

  • Identification data

    Identifier: imarina:9391606
    Handle: https://hdl.handle.net/20.500.11797/imarina9391606
    Authors:
    Ezenarro, JokinRiu, JordiBoqué, Ricard
    Abstract:
    Spectroscopy and chemometrics, supported by computer science, have yielded promising outcomes, as evidenced by trends observed in literature searches. However, while researchers meticulously construct chemometric models for exploratory, quantitation and classification purposes, the investigation of data quality, particularly error analysis, remains less frequent. Understanding and quantifying measurement errors is crucial for robust spectroscopic modeling and uncertainty estimation. By unraveling complexities related to multivariate errors and uncertainties in spectroscopic data, the scientific community is empowered to extract reliable information from spectroscopic analyses, paving the way for enhanced analytical practices. This review underscores the necessity for the scientific community to integrate error analysis and uncertainty estimation into multivariate analysis methods, offering tailored solutions for diverse data types and analysis objectives.

  • Others:

    Author, as appears in the article.: Ezenarro, Jokin; Riu, Jordi; Boqué, Ricard
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S016599362400534X?via%3Dihub
    Department: Química Analítica i Química Orgànica
    URV's Author/s: Giussani, Barbara; Gorla, Giulia; Ezenarro, Jokin; Riu, Jordi; Boqué, Ricard
    Article's DOI: 10.1016/j.trac.2024.118051
    Abstract: Spectroscopy and chemometrics, supported by computer science, have yielded promising outcomes, as evidenced by trends observed in literature searches. However, while researchers meticulously construct chemometric models for exploratory, quantitation and classification purposes, the investigation of data quality, particularly error analysis, remains less frequent. Understanding and quantifying measurement errors is crucial for robust spectroscopic modeling and uncertainty estimation. By unraveling complexities related to multivariate errors and uncertainties in spectroscopic data, the scientific community is empowered to extract reliable information from spectroscopic analyses, paving the way for enhanced analytical practices. This review underscores the necessity for the scientific community to integrate error analysis and uncertainty estimation into multivariate analysis methods, offering tailored solutions for diverse data types and analysis objectives.
    Journal publication year: 2024
    licence for use: https://creativecommons.org/licenses/by/3.0/es/