Dimensional Analysis Model Predicting the Number of Food Microorganisms

Cuiqin Li; Laping He; Yuedan Hu; Hanyu Liu; Xiao Wang; Li Chen; Xuefeng Zeng

doi:10.3389/fmicb.2022.820539

Dimensional Analysis Model Predicting the Number of Food Microorganisms

Front Microbiol. 2022 Feb 8:13:820539. doi: 10.3389/fmicb.2022.820539. eCollection 2022.

Authors

Cuiqin Li^{1

2

3}, Laping He^{1

2}, Yuedan Hu^{1

2}, Hanyu Liu^{1

2}, Xiao Wang^{1

2}, Li Chen^{1

2}, Xuefeng Zeng^{1

2}

Affiliations

¹ Key Laboratory of Agricultural and Animal Products Storage and Processing of Guizhou Province, Guizhou University, Guiyang, China.
² College of Liquor and Food Engineering, Guizhou University, Guiyang, China.
³ School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, China.

Abstract

Predicting the number of microorganisms has excellent application in the food industry. It helps in predicting and managing the storage time and food safety. This study aimed to establish a new, simple, and effective model for predicting the number of microorganisms. The dimensional analysis model (DAM) was established based on dimensionless analysis and the Pi theorem. It was then applied to predict the number of Pseudomonas in Niuganba (NGB), a traditional Chinese fermented dry-cured beef, which was prepared and stored at 278 K, 283 K, and 288 K. Finally, the internal and external validation of the DAM was performed using six parameters including R ², R ² _adj , root mean square error (RMSE), standard error of prediction (%SEP), A _f , and B _f . High R ² and R ² _adj and low RMSE and %SEP values indicated that the DAM had high accuracy in predicting the number of microorganisms and the storage time of NGB samples. Both A _f and B _f values were close to 1. The correlation between the observed and predicted numbers of Pseudomonas was high. The study showed that the DAM was a simple, unified and effective model to predict the number of microorganisms and storage time.

Keywords: Pi theorem; dimensional analysis; predicting microorganism model; storage time; validation.