Modelling and predicting growth of psychrotolerant pseudomonads in milk and cottage cheese

Martinez-Rios, Veronica; Østergaard, Nina Bjerre; Gkogka, Elissavet; Rosshaug, Per Sand; Dalgaard, Paw

doi:10.1016/j.ijfoodmicro.2015.09.020

Cited by 15 publications

(10 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The findings are comparable with those of other studies; for example, on average, about 83% of Listeria monocytogenes and 75% of LAB observed in cottage cheese were within the ASZ [35]; and 78% of measured psychrotolerant pseudomonads in milk and cottage cheese lied in the ASZ [33].…”

Section: Validation Of the Kinetics Models In Dynamic Temperaturesupporting

confidence: 90%

“…The above results indicate that the developed kinetics models were acceptable for further application in prediction of TVC and Pseudomonas growth in Pangasius fillets at low temperatures, since more than 70% of the observed counts were within the ASZ [33][34][35][36].…”

Section: Validation Of the Kinetics Models In Dynamic Temperaturementioning

confidence: 74%

“…The primary and secondary models were combined to predict the bacterial growth at dynamic temperature regimes within time-temperature history intervals of constant temperatures [20]. The acceptable simulation zone (ASZ), defined as ±0.5 log CFU g −1 from the predicted counts, was used for comparison with bacterial observed growth; and the models would be considered acceptable if at least 70% of the observed counts was within the ASZ of prediction [33][34][35][36].…”

Section: Validation Of the Kinetics Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Kinetics of Quality Changes ofPangasiusFillets at Stable and Dynamic Temperatures, Simulating Downstream Cold Chain Conditions

Huynh

2017

Journal of Food Quality

View full text Add to dashboard Cite

This study was about the quality changes of Pangasius fillets during storage under simulated temperature conditions of downstream cold chain. Sensory, chemical, and microbiological analyses were conducted over storage time and bacterial growth was modelled. Sensory quality index (QI), at five stable (1, 4, 9, 15, and 19 ± 1 ∘ C) and three dynamic temperatures, progressed faster at higher temperatures, especially with sooner temperature abuses. Total volatile basic nitrogen remained under the acceptable limit throughout all the storage conditions. Total viable psychrotrophic counts (TVC) were around 5.68 ± 0.24 log CFU g −1 at the beginning and exceeded the limit of 6 log CFU g −1 after 216, 96, 36, 16, and 7 h at 1, 4, 9, 15, and 19 ± 1 ∘ C, respectively. Meanwhile, Pseudomonas counts started at 3.81 ± 0.53 log CFU g −1 and reached 4.60-6.36 log CFU g −1 by the time of TVC rejection. Since lower shelf lives were given by TVC rather than QI, it should be appropriate to base the product shelf life on the TVC acceptable limit. Kinetics models based on the Baranyi and Roberts and square root models, developed for TVC and Pseudomonas spp., gave acceptable bacterial estimations at dynamic temperatures, with over 80% of observed counts within the acceptable simulation zone, revealing promising model applicability as a supporting tool for cold chain management. However, further improvement and validation of the models are needed.

show abstract

Section: Validation Of the Kinetics Models In Dynamic Temperaturesupporting

confidence: 90%

Section: Validation Of the Kinetics Models In Dynamic Temperaturementioning

confidence: 74%

Section: Validation Of the Kinetics Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Kinetics of Quality Changes ofPangasiusFillets at Stable and Dynamic Temperatures, Simulating Downstream Cold Chain Conditions

Huynh

2017

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…The former is the well-known Baranyi and Roberts model (1994) [Eq. 1] and the latter is the equation of Martinez-Rios et al (2016) [Eq. 2] for calculating the growth rate as function of environmental parameters (Temperature, pH and AW) for Pseudomonads.…”

Section: Methodsmentioning

confidence: 99%

Characterization of the temperature fluctuation effect on shelf life of an octopus semi-preserved product

et al. 2020

View full text Add to dashboard Cite

The aim of this work is to study the effect of temperature fluctuations on spoilage microbial flora behaviour of a semi-preserved seafood product in modified atmosphere packaging (MAP) as well as to find correct interpretation criteria for simulating temperature fluctuations during storage tests. The study concerned 54 packages of “Octopus carpaccio” that were grouped in three batches and stored at 3 different temperature profiles: the first (16 packages - Group 4°C) was stored at 4±0.5°C; the second (16 packages - Group 8°C) was stored at 8±0.5°C; the third (16 packages - Group F) was stored under a fluctuating temperature regime between 2°C and 14°C. Spoilage microflora, pH and AW has been monitored, at regular intervals, along the storage period (44 days). A predictive model was constructed according to the accredited scientific literature and validated against the observed growth curves of the above three groups. Afterwards, the predictive model has been used setting the temperature at the mean value of fluctuations (6.72°C), at the kinetic mean value of fluctuations (7.80°C) and at the 75th percentile value of fluctuations (11.14°C). The best fitting to the observed data was obtained with the kinetic mean temperature value and this result shows that this parameter can be proposed to reproduce the temperature fluctuation along the distribution and the domestic storage when a storage test has to be carried out.

show abstract

“…The concentrations of lactic, acetic and citric acid were determined by HPLC using external standards for identification and quantification. In order to improve extraction, a centrifugation step was applied (Dalgaard and Jørgensen, 2000;Martinez-Rios et al, 2016). Phosphate and citrate salt concentrations were determined by Eurofins, New Orleans, USA (test method QA02S).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Growth and growth boundary model with terms for melting salts to predict growth responses of Listeria monocytogenes in spreadable processed cheese

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

Modelling and predicting growth of psychrotolerant pseudomonads in milk and cottage cheese

Abstract: Document Version

Cited by 15 publications

References 55 publications

Kinetics of Quality Changes ofPangasiusFillets at Stable and Dynamic Temperatures, Simulating Downstream Cold Chain Conditions

Kinetics of Quality Changes ofPangasiusFillets at Stable and Dynamic Temperatures, Simulating Downstream Cold Chain Conditions

Characterization of the temperature fluctuation effect on shelf life of an octopus semi-preserved product

Growth and growth boundary model with terms for melting salts to predict growth responses of Listeria monocytogenes in spreadable processed cheese

Contact Info

Product

Resources

About