Project code: PID2019-104269RR-C33 / MCIN / AEI / 10.13039/501100011033
Keywords: Uv–visible Raman Partial least squares regression (plsr) Near-infrared (nir) Mid-infrared (mir) Automatic
Record's date: 2024-11-16
Papper version: info:eu-repo/semantics/publishedVersion
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Chemometrics And Intelligent Laboratory Systems. 247 105096-
APA: Ezenarro, J.; Schorn-García, D.; Busto, O.; Boqué, R. (2024). ProSpecTool: A MATLAB toolbox for spectral preprocessing selection. Chemometrics And Intelligent Laboratory Systems, 247(), 105096-. DOI: 10.1016/j.chemolab.2024.105096
Acronym: ALLFRUIT4ALL
Publication Type: Journal Publications
Project code 3: 2021PMF-BS-12
Author, as appears in the article.: Ezenarro, J.; Schorn-García, D.; Busto, O.; Boqué, R.
Department: Química Analítica i Química Orgànica
URV's Author/s: Boqué Martí, Ricard / Busto Busto, Olga / EZENARRO GARATE, JOKIN / Schorn García, Daniel
Abstract: This paper introduces the ProSpecTool, a MATLAB toolbox for automated selection of preprocessing methods for obtaining optimal PLS regression models in vibrational spectroscopy. Trial-and-error approaches for preprocessing can be time-consuming, and the success of the process relies on the experience of analysts. The ProSpecTool addresses this challenge by using objective criteria analogous to expert judgment to filter and iterate preprocessing methods based on raw data properties. The toolbox quantifies noise, identifies multiplicative and additive scatter-effects, and selects preprocessing algorithms to correct them. Results demonstrate that the ProSpecTool can produce models that resemble those proposed by experienced analysts based on trial-and-error in terms of performance and robustness, making it a valuable exploratory tool for vibrational spectroscopy practitioners.
Program founding action 2: Departament de Recerca i Universitats, Generalitat de Catalunya
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@alumni.urv.cat daniel.schorn@alumni.urv.cat ricard.boque@urv.cat olga.busto@urv.cat
Author identifier: 0000-0001-9234-7877 0000-0001-9234-7877 0000-0003-0997-2191 0000-0003-0997-2191 0000-0001-7311-4824 0000-0002-2318-6800
Founding program action 3: Universitat Rovira i Virgili - Banco Santander
Project code 2: ref.2021 SGR 00705
Founding program 2: Chemometrics and Sensorics for Analytical Solutions (CHEMOSENS)
Link to the original source: https://www.sciencedirect.com/science/article/pii/S0169743924000364
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.2024.105096
Entity: Universitat Rovira i Virgili
Journal publication year: 2024
Funding program action: Action of the financing program Ciencias y tecnologías de alimentos
Repositori URV