Jul 20, 2019   5:40 p.m. Iľja
Academic information system

Persons at STU


This page displays all publicly accessible information about the desired person. Some information about the person's occupation and offices may be hidden.

doc. Ing. Pavel Ačai, PhD.
Identification number: 4436
University e-mail: pavel.acai [at] stuba.sk
 
Odborný asistent CSc.,PhD. - Department of Chemical and Biochemical Engineering (ICEE FCFT)

Contacts     Lesson     Final thesis     Publications     Supervised theses     

Basic information

Basic information about a final thesis

Type of thesis: Diploma thesis
Thesis title:Growth prediction of microorganisms in unripened ewes' lump cheese
Written by (author): Ing. Richard Pisko
Department: Institute of Chemical and Environmental Engineering (FCFT)
Thesis supervisor: doc. Ing. Pavel Ačai, PhD.
Opponent:prof. Ing. Vladimír Báleš, DrSc.
Final thesis progress:Final thesis was successfully defended.


Additional information

Additional information about the final thesis follows. Click on the language link to display the information in the desired language.

Language of final thesis:Slovak

Slovak        English

Title of the thesis:Growth prediction of microorganisms in unripened ewes' lump cheese
Summary:The subject of this diploma thesis is prediction of growth of Staphyloccocus aureus (S. aureus) and Lactobacillus Fresco (LAB Fresco) in co-culture during traditional artisanal production of fresh ewes´ lump cheese from milking at the chalet to the consumer consumption. For the prediction of simultaneous growth of S. aureus and LAB (Fresco) in milk, two mathematical models were applied, based on Jameson effect. Firstly, Huang model (2013) (HM) combined with the model of Gimenez and Dalgaard (2004) (GD) (H-GD model) was used. Its ability to predict the coculture growth of S. aureus and Fresco in milk was tested only by means of parameters determined by regression analysis of individual growth curves of pure cultures (“approximate” prediction) at different temperatures. Afterwards, the predictive mathematical model of mixed culture growth is extended (“extended”; E-H-GD model) by empirical competitive coefficients, ILS and ISL, determined by non-linear regression from growth curves of coculture, with the aim of improving it. Prediction ability of both models (E-H-GD model with average values of competitive coefficients, ILS and ISL is used) compared to experimental data of coculture growth curves is evaluated based on statistical indicators. To compare both interaction models, Akaike information criterion (AIC) was used, which confirmed, that extended E-H-GD model is more suitable. E-H-GD model is therefore used for validation of the model and simulation calculations (predictions) of simultaneous growth of S. aureus and LAB (Fresco) in ewes´ lump cheese and deterministic exposure assessment.
Key words:LAB Fresco, Primary and secondary predictive models, Deterministic exposure assessment, Staphylococcus aureus, Ewes´ lump cheese

Display and download files

To display the final thesis assignment form click on the Display the final thesis assignment form icon. The following icons - Final thesis, Thesis appendices, Supervisor's review, Opponent's review - relate to the final thesis and can be downloaded. They could be displayed on condition they have been inserted and are available publicly.

Display the assignment form

Parts of thesis with postponed release:

Final thesis (final thesis appendices) unlimited
Reviews for final thesis unlimited