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Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

  • Dades identificatives

    Identificador: imarina:9290335
    Autors:
    Schorn-García, DGiussani, BBusto, OAceña, LMestres, MBoqué, R
    Resum:
    In food manufacturing and processing, food matrix complexity usually makes it difficult to detect unwanted subprocesses, which can impact the quality of the final product. In the case of wine alcoholic fermentation, the main process is the conversion of sugars into ethanol and carbon dioxide, but the presence of some unwanted microorganisms could lead to wine contamination by production of undesired minor compounds. In the study we present, an intentional contamination of the vinification process by the addition of acetic acid bacteria was studied using a portable Fourier transform infrared (FT-IR) spectrometer. ANOVA simultaneous component analysis (ASCA) was used to unravel these minor variability sources. However, as the subprocess is two orders of magnitude lower in concentration than the main process, different methodologies were used to enhance the ASCA results, such as to select a specific spectral region related to acetic acid bacteria metabolism, to divide the process in time intervals related to the different phases, or to unfold the data matrix in different ways. In addition, spectral preprocessing was optimized to scale up small peaks related to the subprocess. Our results show that several methodologies to build ASCA models can be applied to emphasize and better characterize bacteria contamination subprocesses.
  • Altres:

    Codi de projecte: PID2019-104269RR-C33 / AEI / 10.13039/501100011033
    Paraules clau: Spectroscopy Process deviation Portable ft-ir Anova simultaneous component analysis Acetic acid bacteria process deviation portable ft-ir nir anova simultaneous component analysis
    Data d'alta del registre: 2024-08-03
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Journal Of Chemometrics. 37 (7):
    Referència de l'ítem segons les normes APA: Schorn-García, D; Giussani, B; Busto, O; Aceña, L; Mestres, M; Boqué, R (2023). Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study. Journal Of Chemometrics, 37(7), -. DOI: 10.1002/cem.3465
    Acrònim: ALLFRUIT4ALL
    Tipus de publicació: Journal Publications
    Autor segons l'article: Schorn-García, D; Giussani, B; Busto, O; Aceña, L; Mestres, M; Boqué, R
    Departament: Química Analítica i Química Orgànica
    Acrònim 2: 2020 FISDU 00221
    Autor/s de la URV: Aceña Muñoz, Laura / Boqué Martí, Ricard / Busto Busto, Olga / Giussani, Barbara / Mestres Solé, Maria Montserrat / Schorn García, Daniel
    Resum: In food manufacturing and processing, food matrix complexity usually makes it difficult to detect unwanted subprocesses, which can impact the quality of the final product. In the case of wine alcoholic fermentation, the main process is the conversion of sugars into ethanol and carbon dioxide, but the presence of some unwanted microorganisms could lead to wine contamination by production of undesired minor compounds. In the study we present, an intentional contamination of the vinification process by the addition of acetic acid bacteria was studied using a portable Fourier transform infrared (FT-IR) spectrometer. ANOVA simultaneous component analysis (ASCA) was used to unravel these minor variability sources. However, as the subprocess is two orders of magnitude lower in concentration than the main process, different methodologies were used to enhance the ASCA results, such as to select a specific spectral region related to acetic acid bacteria metabolism, to divide the process in time intervals related to the different phases, or to unfold the data matrix in different ways. In addition, spectral preprocessing was optimized to scale up small peaks related to the subprocess. Our results show that several methodologies to build ASCA models can be applied to emphasize and better characterize bacteria contamination subprocesses.
    Acció del programa de finançament 2: Ayudas de apoyo a departamentos y unidades de investigación universitarios para la contratación de personal investigador predoctoral en formación (FI SDUR 2020)
    Àrees temàtiques: Statistics & probability Química Mathematics, interdisciplinary applications Matemática / probabilidade e estatística Interdisciplinar Instruments & instrumentation Engenharias iv Engenharias iii Engenharias ii Computer science, artificial intelligence Ciências agrárias i Ciência da computação Chemistry, analytical Biotecnología Biodiversidade Automation & control systems Astronomia / física Applied mathematics Analytical chemistry
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: barbara.giussani@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
    Identificador de l'autor: 0000-0003-0997-2191 0000-0003-0997-2191 0000-0001-9805-3482 0000-0001-7311-4824 0000-0002-2318-6800 0000-0001-5942-9424
    Programa de finançament 2: Agencia de Gestión de Ayudas Universitarias y de Investigación (AGAUR)
    Enllaç font original: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/full/10.1002/cem.3465
    Programa de finançament: 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
    DOI de l'article: 10.1002/cem.3465
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2023
    Acció del programa de finançament: Ciencias y tecnologías de alimentos
  • Paraules clau:

    Analytical Chemistry,Applied Mathematics,Automation & Control Systems,Chemistry, Analytical,Computer Science, Artificial Intelligence,Instruments & Instrumentation,Mathematics, Interdisciplinary Applications,Statistics & Probability
    Spectroscopy
    Process deviation
    Portable ft-ir
    Anova simultaneous component analysis
    Acetic acid bacteria
    process deviation
    portable ft-ir
    nir
    anova simultaneous component analysis
    Statistics & probability
    Química
    Mathematics, interdisciplinary applications
    Matemática / probabilidade e estatística
    Interdisciplinar
    Instruments & instrumentation
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Computer science, artificial intelligence
    Ciências agrárias i
    Ciência da computação
    Chemistry, analytical
    Biotecnología
    Biodiversidade
    Automation & control systems
    Astronomia / física
    Applied mathematics
    Analytical chemistry
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