3 years ago

Artificial Neural Network Modeling of Water Activity: a Low Energy Approach to Freeze Drying

Navin Chandra Shahi, Ranjna Sirohi, Ayon Tarafdar, Anupama Singh

Abstract

A method for reducing the energy consumption during freeze drying has been proposed. Water activity variation with time has been explored for button mushroom (Agaricus bisporus L.). The effect of primary and secondary drying temperatures on water activity was found significant (p < 0.05) as compared to sample thickness and pressure. The economics of the process showed that an energy reduction up to 34.9% could be achieved if the final water activity was constrained at 0.6. Artificial neural network tool has been used to develop a model for predicting the water activity precisely for a given combination of time, initial moisture content, vacuum pressure, sample thickness, and primary and secondary drying temperatures. The model-predicted values were found to be in good agreement (R = 0.97) with the experimental data. The model developed is expected to extend its aid in energy reduction for freeze drying of other food products.

Publisher URL: https://link.springer.com/article/10.1007/s11947-017-2002-4

DOI: 10.1007/s11947-017-2002-4

You might also like
Never Miss Important Research

Researcher is an app designed by academics, for academics. Create a personalised feed in two minutes.
Choose from over 15,000 academics journals covering ten research areas then let Researcher deliver you papers tailored to your interests each day.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.