Articles producció científica> Química Analítica i Química Orgànica

Selection of reference samples for updating multivariate calibration models used in the analysis of pig faeces

  • Identification data

    Identifier: imarina:9290098
    Authors:
    Cruz-Conesa, AndresFerre, JoanRuisanchez, ItziarPerez-Vendrell, Anna M
    Abstract:
    Monitoring and updating calibration models are common tasks when analytical methods are based on near-infrared spectroscopy. This work describes a situation in which a PLS calibration model that is used routinely for the determination of phosphorus content in pig faeces in digestibility studies had to be updated in order to be used with the faeces collected in a new trial with phytases. An approach based on D-optimality is presented that selects a reduced number of the new samples to be analyzed with the reference analytical method so that the small set is used to confirm the need to update the model and validate it. The rest of the new samples that had not been selected by the algorithm were accurately predicted with the updated model. The updated model maintained its previous performance for the samples in the validation set (an RMSEP of 1.58 g kg−1 compared with an RMSEP of 1.54 g kg−1 before the update) and the prediction error for the new samples was RMSECV = 1.95 g kg−1, much lower than the RMSEP = 11.38 g kg−1 obtained before the model update. In addition, the predictive ability of the updated PLS model was significantly better than updated models selecting the reduced dataset using other sample selection methods such as Kennard-Stone, a leverage-based selection method and random selection.
  • Others:

    Author, as appears in the article.: Cruz-Conesa, Andres; Ferre, Joan; Ruisanchez, Itziar; Perez-Vendrell, Anna M
    Department: Química Analítica i Química Orgànica
    URV's Author/s: CRUZ CONESA, ANDRES / Ferré Baldrich, Joan / Ruisánchez Capelastegui, María Iciar
    Keywords: Sample selection Pls Phosphorus Near-infrared spectroscopy Model updating D-optimal
    Abstract: Monitoring and updating calibration models are common tasks when analytical methods are based on near-infrared spectroscopy. This work describes a situation in which a PLS calibration model that is used routinely for the determination of phosphorus content in pig faeces in digestibility studies had to be updated in order to be used with the faeces collected in a new trial with phytases. An approach based on D-optimality is presented that selects a reduced number of the new samples to be analyzed with the reference analytical method so that the small set is used to confirm the need to update the model and validate it. The rest of the new samples that had not been selected by the algorithm were accurately predicted with the updated model. The updated model maintained its previous performance for the samples in the validation set (an RMSEP of 1.58 g kg−1 compared with an RMSEP of 1.54 g kg−1 before the update) and the prediction error for the new samples was RMSECV = 1.95 g kg−1, much lower than the RMSEP = 11.38 g kg−1 obtained before the model update. In addition, the predictive ability of the updated PLS model was significantly better than updated models selecting the reduced dataset using other sample selection methods such as Kennard-Stone, a leverage-based selection method and random selection.
    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: andres.cruz@estudiants.urv.cat andres.cruz@estudiants.urv.cat joan.ferre@urv.cat itziar.ruisanchez@urv.cat
    Author identifier: 0000-0002-1565-0992 0000-0002-1565-0992 0000-0001-6240-413X 0000-0002-7097-3583
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S016974392200260X
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Chemometrics And Intelligent Laboratory Systems. 234 104749-
    APA: Cruz-Conesa, Andres; Ferre, Joan; Ruisanchez, Itziar; Perez-Vendrell, Anna M (2023). Selection of reference samples for updating multivariate calibration models used in the analysis of pig faeces. Chemometrics And Intelligent Laboratory Systems, 234(), 104749-. DOI: 10.1016/j.chemolab.2022.104749
    Article's DOI: 10.1016/j.chemolab.2022.104749
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2023
    Publication Type: Journal Publications
  • 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
    Sample selection
    Pls
    Phosphorus
    Near-infrared spectroscopy
    Model updating
    D-optimal
    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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