Publikationen, an denen er mitarbeitet MARIA SAGRARIO SANCHEZ PASTOR (72)

2024

  1. Latent variable model inversion for intervals. Application to tolerance intervals in class-modelling situations, and specification limits in process control

    Chemometrics and Intelligent Laboratory Systems, Vol. 251

  2. Model inversion and three-way decompositions in the analytical quality by design strategy for the determination of phthalates BY HS-SPME-GC-MS

    Talanta, Vol. 267

  3. Suitability of multiway methods to ensure food safety

    Data Handling in Science and Technology (Elsevier Ltd), pp. 381-407

2023

  1. The inversion of multiresponse partial least squares models, a useful tool to improve analytical methods in the framework of analytical quality by design

    Analytica Chimica Acta, Vol. 1276

2022

  1. Metodología de diseño de experimentos en Trabajos Fin de Grado, Trabajos Fin de Máster y Prácticum

    Actualidad Analítica, Vol. 77, pp. 31-37

  2. Simultaneous class-modelling in chemometrics: A generalization of Partial Least Squares class modelling for more than two classes by using error correcting output code matrices

    Chemometrics and Intelligent Laboratory Systems, Vol. 227

2021

  1. A modified entropy-based performance criterion for class-modelling with multiple classes

    Chemometrics and Intelligent Laboratory Systems, Vol. 217

  2. A new approach based on inversion of a partial least squares model searching for a preset analytical target profile. Application to the determination of five bisphenols by liquid chromatography with diode array detector

    Analytica Chimica Acta, Vol. 1149, Núm. 338217, pp. 1-12

  3. Handling Variables, via Inversion of Partial Least Squares Models for Class-Modelling, to Bring Defective Items to Non-Defective Ones

    Frontiers in Chemistry, Vol. 9

  4. Method operable design region obtained with a partial least squares model inversion in the determination of ten polycyclic aromatic hydrocarbons by liquid chromatography with fluorescence detection

    Journal of Chromatography A, Vol. 1657

  5. Partial Least Squares model inversion in the chromatographic determination of triazines in water

    Microchemical Journal, Vol. 164, Núm. 105971, pp. 1-9

2020

  1. 1.01 - Quality of Analytical Measurements: Statistical Methods for Internal Validation

    Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Second Edition: Four Volume Set (Elsevier), pp. 1-52

  2. 1.03 - Quality of Analytical Measurements: Univariate Regression

    Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Second Edition: Four Volume Set (Elsevier), pp. 71-105

  3. Quality of analytical measurements: statistical methods for internal validation

    Comprehensive Chemometrics [Recurso electrónico]: chemical and biochemical data analysis. Volume 1 (Elsevier), pp. 1-52

  4. Quality of analytical measurements: univariate regression

    Comprehensive Chemometrics [Recurso electrónico]: chemical and biochemical data analysis. Volume 1 (Elsevier), pp. 71-105

  5. Residual spaces in latent variables model inversion and their impact in the design space for given quality characteristics

    Chemometrics and Intelligent Laboratory Systems, Vol. 203

  6. Response surface methodology

    Comprehensive Chemometrics [Recurso electrónico]: chemical and biochemical data analysis. Volume 1 (Elsevier), pp. 287-326

  7. Three-way PARAFAC decomposition of chromatographic data for the unequivocal identification and quantification of compounds in a regulatory framework

    Chemometrics and Intelligent Laboratory Systems, Vol. 200, Núm. 104003, pp. 1-14

2019

  1. Multivariate analysis for overall assessment of student learning based on several individual scores obtained in different activities

    Edulearn19: 11th international conference on education and new learning technologies

2018

  1. A computational approach to partial least squares model inversion in the framework of the process analytical technology and quality by design initiatives

    Chemometrics and Intelligent Laboratory Systems, Vol. 182, pp. 70-78