AIMC Topic: Plants, Edible

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Comparative metabolomics profiling reveals the aroma and nutritional diversity of eight wild edible plants.

Food chemistry
Wild edible plants are important yet undervalued vegetables, due to limited knowledge of their bioactive components and nutritional/functional potential. In this study, we used full-spectrum metabolomics and machine learning to analyze the aroma-rela...

Integration of Digital Twin, Machine-Learning and Industry 4.0 Tools for Anomaly Detection: An Application to a Food Plant.

Sensors (Basel, Switzerland)
This work describes a structured solution that integrates digital twin models, machine-learning algorithms, and Industry 4.0 technologies (Internet of Things in particular) with the ultimate aim of detecting the presence of anomalies in the functioni...