Modeling the Growth of Byssochlamys fulva on Solidified Apple Juice at Different Temperatures

Longhi, Daniel Angelo; Tremarin, Andréia; Carciofi, Bruno Augusto Mattar; Laurindo, João Borges; Aragão, Gláucia Maria Falcão de

doi:10.1590/s1516-8913201402619

Cited by 12 publications

(3 citation statements)

References 23 publications

(41 reference statements)

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“…Although model of Baranyi and Roberts was primarily constructed for bacterial growth prediction, it has been frequently used also to describe fungal growth (Gougouli and Koutsoumanis, 2013;Hudecova´et al, 2009b;Ko nuchova´and Valı´k, 2017;Valı´k and Pieckova´, 2001). On the other hand, there has been only one study (Longhi et al, 2014), where Huang's model was used to describe growth of microscopic fungi. In our study, both models reliably described the growth of LAB as well as growth of microscopic fungus.…”

Section: Comparison Of Two Primary Predictive Models Used For Growth Prediction Of Lab and Microscopic Fungusmentioning

confidence: 99%

Growth dynamics of lactic acid bacteria and dairy microscopic fungus Geotrichum candidum during their co-cultivation in milk

Šipošová

Koňuchová

Valı́k

et al. 2020

Food sci. technol. int.

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Production of high-quality and microbiologically safe fermented dairy products requires controlled growth and microbial interactions between lactic acid bacteria and microscopic fungi. For this purpose, detailed knowledge of their growth characteristics is needed. Therefore, the objective of this study was to analyse the growth dynamics of lactic acid bacteria of commercial DVS® FRESCO® 1000NG culture and dairy isolate of microscopic fungus Geotrichum candidum during their co-cultivation in milk. The growth dynamics of microorganisms was studied in dependence on their initial counts at 12, 15, 18, 21 and 30 °C. Growth parameters were calculated by two primary predictive models, model of Baranyi and Roberts and Huang’s model. Both models showed good ability to describe the growth dynamics of studied microorganisms, as it was confirmed by low values of RMSE index. Both microbial cultures, Fresco culture and Geotrichum candidum, showed good growth ability in milk since they reached the average maximum density of 9.50 ± 0.13 log CFU/mL and 5.85 ± 0.69 log CFU/mL ( n = 45), in order. Maximum density of studied microorganisms was not affected by their initial counts or incubation temperature. On the other hand, effect of mutual ratio of microbial initial counts and increasing temperature had a significant impact on growth dynamics.

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Section: Comparison Of Two Primary Predictive Models Used For Growth Prediction Of Lab and Microscopic Fungusmentioning

confidence: 99%

Growth dynamics of lactic acid bacteria and dairy microscopic fungus Geotrichum candidum during their co-cultivation in milk

Šipošová

Koňuchová

Valı́k

et al. 2020

Food sci. technol. int.

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“…O modelo de Baranyi e Roberts é utilizado na microbiologia preditiva, como observado nas obras de Hunang (2013) e Longhi et al (2014). Muitos modelos propostos anteriormente a este modelo descreveram o crescimento de microrganismos, estes não foram deduzidos a partir de considerações mecanísticas, isto é, os seus termos não foram escritos a partir de mecanismos biológicos.…”

Section: O Modelo De Baranyi E Robertsunclassified

Microbiologia Preditiva: Aspectos Gerais E Tendências

Schlei¹,

Reiter²,

Bertoli³

et al. 2018

REPECT

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Os primeiros trabalhos que procuraram predizer o crescimento de microrganismos em função do tempo foram realizados por pesquisadores como Bigelow, Esty e Meyer na década de 1920. Nos últimos anos, órgãos de saúde pública de diversos países incentivaram estudos referentes à cinética de microrganismos em alimentos a fim de entender processos de crescimento de deteriorantes e patógenos. A partir destes trabalhos obtiveram-se modelos primários determinísticos e não determinísticos do crescimento de microrganismos. Novas abordagens permitem modelar o comportamento de microrganismos em função de variáveis do meio. Nas últimas décadas a criação de modelos estocásticos permitiu o desenvolvimento de novos modelos determinísticos, porém agora baseados em redes de fluxos metabólicos que permitiram o aumento da efetividade da predição do crescimento em função do tempo e de diversas variáveis e substratos. A modelagem em redes neurais artificiais pode ser aplicada em conjunto com outros modelos matemáticos e esta nova abordagem permite incluir qualquer variável na modelagem a partir de treinamento da rede neural. O desenvolvimento de softwares que utilizam modelos matemáticos de crescimento é de grande importância para a indústria de alimentos, pois permite maior compreensão dos processos microbiológicos, estimativa de vida de prateleira e as inovações na área permitem simulação da influência de diversos fatores.

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“…Secondary models deal with the response of parameters that appear in primary modeling approaches such as temperature, pH, and so forth ( Giannuzzi et al, 1998 ; Molina and Giannuzzi, 1999 ; Xiong et al, 1999 ; Davey, 2010 ; Zimmermann et al, 2013 ). The tertiary level combines the first two types of models with user-friendly applications ( Dyer, 1996 ; McKellar and Lu, 2003 ; Longhi et al, 2014 ). The growth of microorganisms requires nutrients, but none of the existing models consider the impact of nutrients on the growth of microorganisms.…”

Section: Introductionmentioning

confidence: 99%

Dimensional Analysis Model Predicting the Number of Food Microorganisms

et al. 2022

Front. Microbiol.

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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 R2, R2adj, root mean square error (RMSE), standard error of prediction (%SEP), Af, and Bf. High R2 and R2adj 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 Af and Bf 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.

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Modeling the Growth of Byssochlamys fulva on Solidified Apple Juice at Different Temperatures

Cited by 12 publications

References 23 publications

Growth dynamics of lactic acid bacteria and dairy microscopic fungus Geotrichum candidum during their co-cultivation in milk

Growth dynamics of lactic acid bacteria and dairy microscopic fungus Geotrichum candidum during their co-cultivation in milk

Microbiologia Preditiva: Aspectos Gerais E Tendências

Dimensional Analysis Model Predicting the Number of Food Microorganisms

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