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pH prediction for a semi-batch cream cheese fermentation using a grey-box model

  • Shiying Guo , Wei Yu EMAIL logo , David I. Wilson and Brent R. Young
Published/Copyright: January 16, 2023
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 18 Issue 4

Abstract

Cream cheese, a popular condiment, is widely used in people’s daily diet and in dessert making. To ensure high-quality cream cheese production, the pH value is generally used as the indicator to determine the end point of cream cheese fermentation. The inoculation time and time-dependent concentrations of biomass, lactose, lactic acid are all crucial for pH prediction. However, the inoculation time could vary for industrial applications with multiple fermenters. Moreover, the inoculation time impact on fermentation has not been investigated. This paper aims to build a cream cheese fermentation model predicting pH. The model includes a semi-batch kinetic model and an artificial neural network (ANN) model. The outcome of the model will help the cream cheese industries understand the inoculation time impact on fermentation time and organise better fermenter scheduling.

Keywords: artificial neural network; cream cheese fermentation; kinetic model; pH prediction
Corresponding author: Wei Yu, Department of Chemical & Materials Engineering, The University of Auckland, Auckland, New Zealand; and Industrial Information and Control Centre, University of Auckland, Auckland 1023, New Zealand, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2021-08-01
Accepted: 2022-12-04
Published Online: 2023-01-16

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cppm-2021-0048
Keywords for this article
artificial neural network; cream cheese fermentation; kinetic model; pH prediction

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. MSP designing with optimal fractional PI–PD controller for IPTD processes
  4. A novel nonlinear sliding mode observer to estimate biomass for lactic acid production
  5. pH prediction for a semi-batch cream cheese fermentation using a grey-box model
  6. Modeling of carbon dioxide and hydrogen sulfide pollutants absorption in wetted-wire columns with alkanolamines
  7. Pharmaceutical wastewater treatment using TiO2 nanosheets deposited by cobalt co-catalyst as hybrid photocatalysts: combined experimental study and artificial intelligence modeling
  8. Numerical simulation of fluid flow mixing in flow-focusing microfluidic devices
  9. A nonlinear autoregressive exogenous neural network (NARX-NN) model for the prediction of solvent-based oil extraction from Hura crepitans seeds
  10. Intensification of thorium biosorption onto protonated orange peel using the response surface methodology
  11. Investigating the energy, environmental, and economic challenges and opportunities associated with steam sterilisation autoclaves
  12. Short Communication
  13. Molecular dynamics simulations of water-ethanol droplet on silicon surface
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