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Endpoints metabólicos específicos de cepa y clasificación predictiva de fases en la fermentación de kimchi
Yujin Kim1, Se Hee Lee2, Mi-Ja Jung2
1Kimchi Functionality Research Group, World Institute of Kimchi, Gwangju 61755, Republic of Korea; Division of Food and Nutrition, Chonnam National University, Youngbong-ro, Buk-gu, Gwangju 61186, Republic of Korea.
Food chemistry
|January 11, 2026
Resumen
Desarrollamos un modelo de fermentación de kimchi controlado utilizando consorcios definidos de bacterias del ácido láctico. El aprendizaje automático identificó metabolitos clave para predecir las fases de fermentación, lo que permitió el control predictivo de la producción de kimchi impulsada por bacterias del ácido láctico.
Área de la Ciencia:
- Microbiología de Alimentos
- Ciencia de la Fermentación
- Biología de Sistemas
Sus antecedentes:
- La fermentación depende de interacciones microbianas complejas, lo que dificulta el control predictivo.
- Los consorcios microbianos naturales en alimentos como el kimchi son difíciles de gestionar de forma predecible.
Objetivo del estudio:
- Crear un modelo de kimchi gnotobiótico para el control predecible de la fermentación.
- Identificar la dinámica microbiana y de metabolitos clave durante la fermentación del kimchi.
- Establecer un marco predictivo para la producción de kimchi impulsada por bacterias del ácido láctico (BAL).
Principales métodos:
- Inoculación de kimchi con consorcios definidos de BAL y cepas individuales a temperaturas controladas.
- Monitorización de la estructura de la comunidad microbiana y los perfiles de metabolitos durante la fermentación.
- Aplicación de normalización de fases, aprendizaje automático y análisis de redes.
Principales resultados:
- Las fermentaciones con consorcios mostraron un crecimiento y acidificación dependientes de la temperatura, con cambios en la comunidad a 15°C.
- Una firma de nueve metabolitos clasificó con precisión las fases de fermentación en todos los conjuntos de datos.
- El análisis de redes identificó a Leuconostoc mesenteroides y Lactococcus lactis como especies clave.
Conclusiones:
- Se estableció un marco predictivo para la fermentación de kimchi impulsada por BAL.
- Los consorcios definidos de BAL permiten una fermentación controlada con resultados predecibles.
- La metabolómica y el aprendizaje automático son herramientas potentes para comprender la dinámica de la fermentación.
Palabras clave:
kimchi gnotobióticobacterias del ácido lácticoaprendizaje automáticometabolómicametataxonómicaMás Videos Relacionados
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