Articles producció científica> Química Analítica i Química Orgànica

J-Score: A new joint parameter for PLSR model performance evaluation of spectroscopic data

  • Identification data

    Identifier: imarina:9322006
  • Authors:

    Ezenarro, J
    Schorn-García, D
    Aceña, L
    Mestres, M
    Busto, O
    Boqué, R
    Chemometrics And Intelligent Laboratory Systems
    10.1016/j.chemolab.2023.104883
    Chemometrics And Intelligent Laboratory Systems. 240
  • Others:

    Project code: PID2019-104269RR-C33 / AEI / 10.13039/501100011033
    Keywords: Vibrational spectroscopy Validation Root mean square error Preprocessing Partial least squares regression Latent variables Cross-validation vibrational spectroscopy validation root mean square error preprocessing latent variables calibration
    Record's date: 2024-07-20
    Papper version: info:eu-repo/semantics/publishedVersion
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Chemometrics And Intelligent Laboratory Systems. 240
    APA: Ezenarro, J; Schorn-García, D; Aceña, L; Mestres, M; Busto, O; Boqué, R (2023). J-Score: A new joint parameter for PLSR model performance evaluation of spectroscopic data. Chemometrics And Intelligent Laboratory Systems, 240(), -. DOI: 10.1016/j.chemolab.2023.104883
    Acronym: ALLFRUIT4ALL
    Publication Type: Journal Publications
    Project code 3: 2021PMF-BS-12
    Author, as appears in the article.: Ezenarro, J; Schorn-García, D; Aceña, L; Mestres, M; Busto, O; Boqué, R
    Department: Química Analítica i Química Orgànica
    URV's Author/s: Aceña Muñoz, Laura / Boqué Martí, Ricard / Busto Busto, Olga / EZENARRO GARATE, JOKIN / Mestres Solé, Maria Montserrat / Schorn García, Daniel
    Abstract: Since its beginnings, many parameters have been proposed to evaluate the goodness of Partial Least Squares Regression (PLSR) models and thus help chemometricians to choose the most appropriate one. This article proposes a new performance evaluation parameter for regression models based on spectroscopic data, the J-Score, which combines some of the most commonly used model evaluation parameters (Ratio of Performance to Deviation, Calibration and Validation Root Mean Square Errors and Regression Vector) into a single indicator. The J-Score can help non-experienced analysts select both the adequate number of Latent Variables (LVs) and the best preprocessing technique for their dataset in an automated way. The performance of the J-Score has been compared to other evaluation methods with different datasets, demonstrating that it can be used for different types of samples and spectroscopic data; that it is stable and objective, and offers an easy way to select the optimal number of LVs.
    Program founding action 2: Agencia de Gestión de Ayudas Universitarias y de Investigación (AGAUR)
    Thematic Areas: Statistics & probability Spectroscopy Software Robotics & automatic control Química Process chemistry and technology Mathematics, interdisciplinary applications Matemática / probabilidade e estatística Interdisciplinar Instruments & instrumentation Farmacia Engenharias iv Engenharias iii Engenharias ii Computer science, artificial intelligence Computer science applications Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, analytical Biotecnología Automation & control systems Analytical chemistry
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: jokin.ezenarro@urv.cat jokin.ezenarro@urv.cat daniel.schorn@urv.cat daniel.schorn@urv.cat montserrat.mestres@urv.cat ricard.boque@urv.cat olga.busto@urv.cat laura.acena@urv.cat
    Author identifier: 0000-0001-9234-7877 0000-0001-9234-7877 0000-0003-0997-2191 0000-0003-0997-2191 0000-0001-9805-3482 0000-0001-7311-4824 0000-0002-2318-6800 0000-0001-5942-9424
    Founding program action 3: Universitat Rovira i Virgili - Banco Santander
    Project code 2: 2020 FISDU 00221
    Founding program 2: Ayudas de apoyo a departamentos y unidades de investigación universitarios para la contratación de personal investigador predoctoral en formación (FI SDUR 2020)
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S0169743923001338
    Funding program: Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i y de I+D+i Orientada a los Retos de la Sociedad. Proyectos de I+D+i Retos Investigación 2017-2020
    Founding program 3: Contratos de personal investigador predoctoral en formación
    Article's DOI: 10.1016/j.chemolab.2023.104883
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2023
    Funding program action: Ciencias y tecnologías de alimentos
  • Keywords:

    Analytical Chemistry,Automation & Control Systems,Chemistry, Analytical,Computer Science Applications,Computer Science, Artificial Intelligence,Instruments & Instrumentation,Mathematics, Interdisciplinary Applications,Process Chemistry and Technology,Robotics & Automatic Control,Software,Spectroscopy,Statistics & Probability
    Vibrational spectroscopy
    Validation
    Root mean square error
    Preprocessing
    Partial least squares regression
    Latent variables
    Cross-validation
    vibrational spectroscopy
    validation
    root mean square error
    preprocessing
    latent variables
    calibration
    Statistics & probability
    Spectroscopy
    Software
    Robotics & automatic control
    Química
    Process chemistry and technology
    Mathematics, interdisciplinary applications
    Matemática / probabilidade e estatística
    Interdisciplinar
    Instruments & instrumentation
    Farmacia
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Computer science, artificial intelligence
    Computer science applications
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Ciência da computação
    Chemistry, analytical
    Biotecnología
    Automation & control systems
    Analytical chemistry
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