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Updated: Mar 2, 2026

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
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PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

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Evaluación crítica de enfoques de aprendizaje automático para clasificación, predicción dinámica y modelado sustituto

Núria Campo-Manzanares1, Artai R Moimenta1, Eva Balsa-Canto2

  • 1Biosystems and Bioprocess Engineering, Bio2Eng, IIM-CSIC, Vigo, Spain; Applied Mathematics Dept. II, University of Vigo, Vigo, Spain.

Food research international (Ottawa, Ont.)
|February 28, 2026
PubMed
Resumen

Los modelos de aprendizaje automático (ML) muestran promesa en la ciencia de los alimentos pero luchan con datos limitados en comparación con los modelos basados en conocimiento (KB). Un enfoque híbrido que combina modelos de ML y KB ofrece una precisión de predicción mejorada en sistemas de fermentación complejos.

Palabras clave:
Fermentación de alimentosModelado híbridoModelado cinéticoAprendizaje automáticoModelos sustitutosCepas de levadura

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