Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration

Probabilistic microbial modeling using logistic regression was used to predict the boundary between growth and no growth of Saccharomyces cerevisiae at selected incubation periods (50 and 350 h) in the presence of growth-controlling factors such as water activity (a(w); 0.97, 0.95, and 0.93), pH (6....

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Autores principales: López-Malo Vigil, Aurelio, Guerrero, Sandra, Alzamora, Stella Maris
Publicado: 2000
Materias:
fungal growth
growth inhibition
growth period
mathematical model
pH
regression analysis
sorbate potassium
Saccharomyces cerevisiae
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0362028X_v63_n1_p91_LopezMalo
http://hdl.handle.net/20.500.12110/paper_0362028X_v63_n1_p91_LopezMalo
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_0362028X_v63_n1_p91_LopezMalo
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spelling paper:paper_0362028X_v63_n1_p91_LopezMalo2023-06-08T15:34:58Z Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration López-Malo Vigil, Aurelio Guerrero, Sandra Alzamora, Stella Maris fungal growth growth inhibition growth period mathematical model pH regression analysis sorbate potassium Saccharomyces cerevisiae Saccharomyces cerevisiae Probabilistic microbial modeling using logistic regression was used to predict the boundary between growth and no growth of Saccharomyces cerevisiae at selected incubation periods (50 and 350 h) in the presence of growth-controlling factors such as water activity (a(w); 0.97, 0.95, and 0.93), pH (6.0, 5.0, 4.0, and 3.0), and potassium sorbate (0, 50, 100, 200, 500, and 1,000 ppm). The proposed model predicts the probability of growth under a set of conditions and calculates critical values of a(w), pH, and potassium sorbate concentration needed to inhibit yeast growth for different probabilities. The reduction of pH increased the number of combinations of a(w) and potassium sorbate concentration with probabilities to inhibit yeast growth higher than 0.95. With a probability of growth of 0.05 and using the logistic models, the critical pH values were higher for 50 h of incubation than those required for 350 h. With lower a(w) values and increasing potassium sorbate concentration the critical pH values increased. Logistic regression is a useful tool to evaluate the effects of the combined factors on microbial growth. Fil:López-Malo, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Guerrero, S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Alzamora, S.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2000 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0362028X_v63_n1_p91_LopezMalo http://hdl.handle.net/20.500.12110/paper_0362028X_v63_n1_p91_LopezMalo
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic fungal growth
growth inhibition
growth period
mathematical model
pH
regression analysis
sorbate potassium
Saccharomyces cerevisiae
Saccharomyces cerevisiae
spellingShingle fungal growth
growth inhibition
growth period
mathematical model
pH
regression analysis
sorbate potassium
Saccharomyces cerevisiae
Saccharomyces cerevisiae
López-Malo Vigil, Aurelio
Guerrero, Sandra
Alzamora, Stella Maris
Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration
topic_facet fungal growth
growth inhibition
growth period
mathematical model
pH
regression analysis
sorbate potassium
Saccharomyces cerevisiae
Saccharomyces cerevisiae
description Probabilistic microbial modeling using logistic regression was used to predict the boundary between growth and no growth of Saccharomyces cerevisiae at selected incubation periods (50 and 350 h) in the presence of growth-controlling factors such as water activity (a(w); 0.97, 0.95, and 0.93), pH (6.0, 5.0, 4.0, and 3.0), and potassium sorbate (0, 50, 100, 200, 500, and 1,000 ppm). The proposed model predicts the probability of growth under a set of conditions and calculates critical values of a(w), pH, and potassium sorbate concentration needed to inhibit yeast growth for different probabilities. The reduction of pH increased the number of combinations of a(w) and potassium sorbate concentration with probabilities to inhibit yeast growth higher than 0.95. With a probability of growth of 0.05 and using the logistic models, the critical pH values were higher for 50 h of incubation than those required for 350 h. With lower a(w) values and increasing potassium sorbate concentration the critical pH values increased. Logistic regression is a useful tool to evaluate the effects of the combined factors on microbial growth.
author López-Malo Vigil, Aurelio
Guerrero, Sandra
Alzamora, Stella Maris
author_facet López-Malo Vigil, Aurelio
Guerrero, Sandra
Alzamora, Stella Maris
author_sort López-Malo Vigil, Aurelio
title Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration
title_short Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration
title_full Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration
title_fullStr Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration
title_full_unstemmed Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration
title_sort probabilistic modeling of saccharomyces cerevisiae inhibition under the effects of water activity, ph, and potassium sorbate concentration
publishDate 2000
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0362028X_v63_n1_p91_LopezMalo
http://hdl.handle.net/20.500.12110/paper_0362028X_v63_n1_p91_LopezMalo
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AT guerrerosandra probabilisticmodelingofsaccharomycescerevisiaeinhibitionundertheeffectsofwateractivityphandpotassiumsorbateconcentration
AT alzamorastellamaris probabilisticmodelingofsaccharomycescerevisiaeinhibitionundertheeffectsofwateractivityphandpotassiumsorbateconcentration
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