Articles
A data-driven approach for fresh produce preservation inside cold room
Article number
1458_60
Pages
455 – 462
Language
English
Abstract
Food refrigeration processes are primarily governed by heat and mass transfer.
Both crucial factors directly impact food quality.
Cold room storage presents spatiotemporal non-uniform temperature and humidity conditions, along with non-uniform airflow.
These dynamic conditions can affect the integrity and safety of fresh produce during cold storage.
In the evolving landscape of apple cold storage practices, integrating advanced monitoring and control systems with data science and analytics offers a revolutionary approach to precision energy management.
The objective of the current research was to obtain real-time apple cooling data that can later be used to control the dynamic cooling system.
Parameters such as temperature, airspeed profile, and heat release from the fruit surface are critical data for analysis and decision-making.
In addition, when coupled with the refrigeration system control, it can ensure that the set conditions are precisely adjusted to the needs of the fresh produce.
These spatially distributed parameters were collected inside an industrial cold room for apple storage.
The findings enhance the understanding of the cooling process within apple bins and improve the prediction of current fruit status at the industrial level by leveraging real-time data from a streamlined set of sensors, facilitating adaptive adjustments to cooling parameters.
Incorporating real-time data into the control of dynamic cold storage facilities shows great promise for data-driven enhancements, transforming postharvest management and providing a more sustainable and efficient food supply chain.
Both crucial factors directly impact food quality.
Cold room storage presents spatiotemporal non-uniform temperature and humidity conditions, along with non-uniform airflow.
These dynamic conditions can affect the integrity and safety of fresh produce during cold storage.
In the evolving landscape of apple cold storage practices, integrating advanced monitoring and control systems with data science and analytics offers a revolutionary approach to precision energy management.
The objective of the current research was to obtain real-time apple cooling data that can later be used to control the dynamic cooling system.
Parameters such as temperature, airspeed profile, and heat release from the fruit surface are critical data for analysis and decision-making.
In addition, when coupled with the refrigeration system control, it can ensure that the set conditions are precisely adjusted to the needs of the fresh produce.
These spatially distributed parameters were collected inside an industrial cold room for apple storage.
The findings enhance the understanding of the cooling process within apple bins and improve the prediction of current fruit status at the industrial level by leveraging real-time data from a streamlined set of sensors, facilitating adaptive adjustments to cooling parameters.
Incorporating real-time data into the control of dynamic cold storage facilities shows great promise for data-driven enhancements, transforming postharvest management and providing a more sustainable and efficient food supply chain.
Authors
T.G. Hoffmann, R. Jedermann, U. Praeger, F. Büchele, A.D. Sonawane, D.A. Neuwald, B. Sturm, P.V. Mahajan
Keywords
apple, cold chain, cold storage, fresh produce, heat transfer, postharvest, refrigeration
Online Articles (67)