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Evaluación de la frescura de la carne basada en aprendizaje automático: principio, proceso y aplicación

Yahong Han1, Xiaoxue Jia2, Lei Yu3

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PubMed
Resumen
Este resumen es generado por máquina.

El aprendizaje automático (ML) ofrece una forma rápida y no destructiva de verificar la frescura de la carne, mejorando la seguridad alimentaria. Esta revisión explora algoritmos y procesos de ML para la evaluación de la calidad de la carne en tiempo real, superando las limitaciones tradicionales.

Palabras clave:
CNNSVMaprendizaje automáticoevaluación de la frescura de la carne

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Área de la Ciencia:

  • Ciencia de los Alimentos
  • Ciencias de la Computación
  • Inteligencia Artificial

Sus antecedentes:

  • Los métodos tradicionales de evaluación de la frescura de la carne son lentos, costosos y destructivos.
  • El aprendizaje automático (ML) presenta una alternativa viable para la evaluación de la calidad de la carne en tiempo real y no destructiva.
  • El monitoreo preciso de la frescura de la carne es vital para la seguridad alimentaria y la confianza del consumidor.

Objetivo del estudio:

  • Revisar los principios y aplicaciones del ML en la evaluación de la frescura de la carne.
  • Detallar el proceso de detección basado en ML, desde la adquisición de datos hasta el ajuste fino del modelo.
  • Destacar los avances y desafíos en ML para la evaluación de la calidad de la carne.

Principales métodos:

  • Revisión de algoritmos clave de ML: Regresión de Componentes Principales (PCR), Mínimos Cuadrados Parciales (PLS), Máquinas de Vectores de Soporte (SVM), K-Vecinos Más Cercanos (KNN) y redes neuronales.
  • Examen de las etapas del proceso de ML: adquisición de datos, preprocesamiento, selección de modelos y ajuste fino.
  • Enfoque en técnicas avanzadas de ML como Redes Neuronales Convolucionales (CNN) y aprendizaje de conjunto.

Principales resultados:

  • Los algoritmos de ML demuestran efectividad en la detección de frescura de carne en tiempo real y no destructiva.
  • Los modelos avanzados como las CNN y los métodos de conjunto muestran promesa para abordar el deterioro y la composición compleja de la carne.
  • La revisión describe un marco integral de detección basado en ML.

Conclusiones:

  • El ML proporciona una solución potente y eficiente para monitorear la frescura de la carne, mejorando la seguridad alimentaria.
  • Se necesita más investigación para abordar desafíos como la calidad de los conjuntos de datos y la interpretabilidad del modelo para una adopción más amplia.
  • La implementación exitosa de ML conducirá a productos cárnicos más seguros y de mayor calidad para los consumidores.