Marco integrado de estadística y aprendizaje automático para la optimización termo-mecánica de masas compuestas de amaranto y chocho
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La optimización de formulaciones farináceas compuestas requiere enfoques analíticos capaces de integrar múltiples atributos reológicos interdependientes. El objetivo de este estudio fue evaluar el comportamiento termo-mecánico de masas formuladas mediante la sustitución progresiva de harina de amaranto por harina de chocho (0–40%). Los parámetros reológicos se determinaron mediante Mixolab, considerando consistencia, debilitamiento proteico, gelatinización, estabilidad térmica y retrogradación. El análisis se desarrolló mediante un enfoque integrado que combinó estadística univariada, análisis multivariado y aprendizaje automático. Los resultados evidenciaron diferencias significativas entre tratamientos, con efectos de gran magnitud en la consistencia inicial y la retrogradación, lo que indica una fuerte influencia de la formulación sobre el comportamiento estructural de la masa. El análisis multivariado permitió identificar patrones diferenciados y explicar una alta proporción de la variabilidad total del sistema, mientras que el aprendizaje automático confirmó la discriminación entre formulaciones y la estabilidad del ranking. En conjunto, la integración metodológica mejora la confiabilidad en la toma de decisiones y constituye una estrategia robusta para la optimización de masas compuestas en sistemas alimentarios complejos.
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