Home pH prediction for a semi-batch cream cheese fermentation using a grey-box model
Article
Licensed
Unlicensed Requires Authentication

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
Become an author with De Gruyter Brill
Submit Manuscript Author Information
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.

References

1. Market Research Future. Global cream cheese market research report: information by source (milk, soy, almond, coconut and others), category (plain, flavored), distribution channel (store-based, non-store based) by region – forecast till 2026; 2020. Available from: https://www.marketresearchfuture.com/reports/cream-cheese-market-10019.Search in Google Scholar

2. IMARC. Cheese market: global industry trends, share, size, growth, opportunity and forecast 2021–2026; 2020. Available from: https://www.imarcgroup.com/cheese-manufacturing-plant.10.1016/j.focat.2021.07.011Search in Google Scholar

3. Masulli, D. Determining pH during cheesemaking. Food Quality & Safety; 2015. Available from: https://www.foodqualityandsafety.com/article/determining-ph-during-cheesemaking/2/.Search in Google Scholar

4. Phadungath, C. Cream cheese products: a review. Songklanakarin J Sci Technol 2005;27:191–9.Search in Google Scholar

5. Akin, H, Brandam, C, Meyer, XM, Strehaiano, P. A model for pH determination during alcoholic fermentation of a grape must by Saccharomyces cerevisiae. Chem Eng Process: Process Intensif 2008;47:1986–93. https://doi.org/10.1016/j.cep.2007.11.014.Search in Google Scholar

6. Cho, B, Irudayaraj, J, Bhardwaj, MC. Rapid measurement of physical properties of Cheddar Cheese using a non–contact ultrasound technique. Trans ASAE 2001;44:1759–62.10.13031/2013.6984Search in Google Scholar

7. Du, C, Sun, D. Learning techniques used in computer vision for food quality evaluation: a review. J Food Eng 2006;72:39–55. https://doi.org/10.1016/j.jfoodeng.2004.11.017.Search in Google Scholar

8. Jimenez-Marquez, SA, Thibault, J, Lacroix, C. Prediction of moisture in cheese of commercial production using neural networks. Int Dairy J 2005;15:1156–74. https://doi.org/10.1016/j.idairyj.2004.12.005.Search in Google Scholar

9. Zheng, H, Fang, S, Lou, H, Chen, Y, Jiang, L, Lu, H. Neural network prediction of ascorbic acid degradation in green asparagus during thermal treatments. Expert Syst Appl 2011;38:5591–602. https://doi.org/10.1016/j.eswa.2010.10.076.Search in Google Scholar

10. Latrille, E, Corrieu, G, Thibault, J. pH prediction and final fermentation time determination in lactic acid batch fermentations. Comput Chem Eng 1993;17:S423–428. https://doi.org/10.1016/0098-1354(93)80261-K.Search in Google Scholar

11. Ebrahimpour, M, Yu, W, Young, B. Artificial neural network modelling for cream cheese fermentation pH prediction at lab and industrial scales. Food Bioprod Process 2021;126:81–9. https://doi.org/10.1016/J.FBP.2020.12.006.Search in Google Scholar

12. Li, B, Lin, Y, Yu, W, Wilson, DI, Young, BR. Application of mechanistic modelling and machine learning for cream cheese fermentation pH prediction. J Chem Technol Biotechnol 2021;96:125–33. https://doi.org/10.1002/jctb.6517.Search in Google Scholar

13. Lin, Y. Cream cheese fermentation pH prediction [Master thesis]. Auckland, New Zealand: The University of Auckland; 2018.Search in Google Scholar

14. Hochreiter, S, Schmidhuber, J. Long short-term memory. Neural Comput 1997;9:1735–80. https://doi.org/10.1162/NECO.1997.9.8.1735.Search in Google Scholar PubMed

15. Cook, GM, Russell, JB. The effect of extracellular pH and lactic acid on pH homeostasis in Lactococcus lactis and Streptococcus bovis. Curr Microbiol 1994;28:165–8. https://doi.org/10.1007/BF01571059.Search in Google Scholar

16. Sanders, ER. Aseptic laboratory techniques: plating methods. JoVE 2012;63:1–18. https://doi.org/10.3791/3064.Search in Google Scholar PubMed PubMed Central

17. Megazyme. D-Lactic acid (D-Lactate) (rapid) and L-Lactic acid (L-Lactate) assay procedures; 2018. Available from: https://www.megazyme.com/documents/Assay_Protocol/K-DLATE_DATA.pdf.Search in Google Scholar

18. Megazyme. Lactose & D-GALactose (rapid) assay procedure; 2020. Available from: https://www.megazyme.com/documents/Assay_Protocol/K-LACGAR_DATA.pdf.Search in Google Scholar

19. Boonmee, M, Leksawasdi, N, Bridge, W, Rogers, PL. Batch and continuous culture of Lactococcus lactis NZ133: experimental data and model development. Biochem Eng J 2003;14:127–35. https://doi.org/10.1016/S1369-703X(02)00171-7.Search in Google Scholar

20. Guo, S. Effect of inoculation time on fermentation ending time in cream cheese fermentation [Master thesis]. Auckland, New Zealand: The University of Auckland; 2021.Search in Google Scholar

21. Saxena, S. Introduction to long short term memory (LSTM) [Internet]; 2021. Available from: https://www.analyticsvidhya.com/blog/2021/03/introduction-to-long-short-term-memory-lstm/.Search in Google Scholar

22. Fu, W, Mathews, AP. Lactic acid production from lactose by Lactobacillus plantarum: kinetic model and effects of pH, substrate, and oxygen. Biochem Eng J 1999;3:163–70. https://doi.org/10.1016/S1369-703X(99)00014-5.Search in Google Scholar
Received: 2021-08-01
Accepted: 2022-12-04
Published Online: 2023-01-16

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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
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
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 30.11.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cppm-2021-0048/pdf?lang=en
Scroll to top button