Research Issues in Inactivation of<i>Listeria monocytogenes</i>Associated with New Zealand Greenshell Mussel Meat (<i>Perna canaliculus</i>) Using High-Pressure Processing

Fletcher, Graham C.; Youssef, Joseph F.; Gupta, Sravani

doi:10.1080/10498850801937208

Cited by 16 publications

(9 citation statements)

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“…Table 3 (transesterification reaction) shows the differences between the calculated arithmetic average temperature and the effective temperature for reaction time of 20 and 100 s. It is obvious that only when reaction time excessively exceeds the transient heating time, the two temperatures become close to each other. (Fletcher et al 2008) temperature (UHT, 140°C) of liquid food such as milk (Ramaswamy and Marcotte 2006). The pre-heating and cooling periods cannot be ignored as they are significant compared to the holding period.…”

Section: Results Based On Work Published In the Literature On Differementioning

confidence: 99%

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Determination of an Effective Treatment Temperature of Chemical and Biological Reactions

Farid

Alkhafaji

2009

Food Bioprocess Technol

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Thermal microbial inactivation kinetics can be influenced significantly by the non-isothermal condition of the sample tested. In many instances, it is practically difficult to maintain the temperature constant during chemical and biological reactions. To minimise error caused by such non-isothermal condition, an effective treatment temperature was derived based on fundamental principles of reaction kinetics. This effective temperature was significantly different from the arithmetic mean temperature due to the exponential effect of temperature on reaction rate. In order to validate the developed concept, Escherichia coli ATCC 25922 suspended in simulated milk ultra filtrate was thermally inactivated using test tubes of different diameters. As expected, the D value was accurately related to the water bath temperature only when small capillary tube was used for the measurements. As the tube diameter increased, the D value increased and was found not to be related to the water bath temperature. On the other hand, the measured D values using different tube diameters correlated very well with the calculated effective treatment temperature. Our calculations show that the effective temperature can be calculated with reasonable degree of accuracy even if there is some uncertainty in the value of the activation energy of the reaction. A number of important applications are addressed in this paper to show the importance of using such a concept. The use of effective temperature will allow accurate comparison of reaction kinetics reported in the literatures on chemical and biological reactions.

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Section: Results Based On Work Published In the Literature On Differementioning

confidence: 99%

“…A typical temperature profile during HPP treatment is shown in Fig. 8 (Fletcher et al 2008). The rapid increase in the sample temperature during pressurisation is due to compression.…”

Section: High Pressure Processingmentioning

confidence: 99%

Determination of an Effective Treatment Temperature of Chemical and Biological Reactions

Farid

Alkhafaji

2009

Food Bioprocess Technol

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“…4.8 , Cruz et al 2008c ). Fletcher et al ( 2008 ) indicated that shucking of New Zealand Greenshell mussels ( Perna canaliculus ) by HPT (400 MPa) has potential benefi ts in product quality, increased yield, and inactivation of Listeria monocytogenes .…”

Section: Oystersmentioning

confidence: 99%

Recent Developments in High Pressure Processing of Foods

Rastogi¹

2013

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“…Other work has investigated the thermal inactivation of histamine-producing bacteria [78,83], but because Listeria is more heat resistant to thermal inactivation and may also be present in products that cause histamine poisoning, heat processes predicted to eliminate L. monocytogenes are recommended for heat processing seafood [76,84]. The Bigelow model has also been applied to determine the effectiveness of marinades [77] and high pressure processing (HPP) at 400 MPa [85] to inactivate L. monocytogenes (Fig 20.2). Although microbial inactivation is often not log linear, all the available models for thermal inactivation of bacteria in seafood use log linear models of inactivation.…”

Section: Predicting Inactivationmentioning

confidence: 99%

Using Predictive Models for the Shelf‐Life and Safety of Seafood

Fletcher

2010

Handbook of Seafood Quality, Safety and Health Applications

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In terms of quality, the seafood industry needs to know what their products will look, smell, and taste like when it is consumed next week in the case of a chilled product, or next year in the case of a frozen product. With regard to microbial safety, the seafood industry needs to not only know that its products are safe now but will be safe next week when their customers eat them. However, because tests take time to complete, microbiologists can only confirm that a product was safe a few hours, or more often, a few days ago. For the seafood industry, predicting the future is not an optional feature. It is an essential for doing business. It is this need that predictive modelling of seafood shelf-life and safety is designed to address.Although great progress has been made in the science and mathematics of predictive food modelling over the last four decades, it is beyond the scope of this chapter to review all of this. Books have been written on this subject [1-3] and international conferences [4] provide effective reviews of advances in the area. This chapter highlights predictive models that have been developed specifically for seafood.Two main types of models are usually used in predictive modelling.1) Primary models indicate the effect of time on a particular attribute (e.g. bacterial number) under particular constant processing or storage conditions (e.g. 0 • C storage). 2) Secondary models indicate how parameters derived from the primary model (e.g. the bacterial growth rate) change in response to the product being held under different constant processing or storage conditions (e.g. temperatures between -5 and 20 • C).A number of predictive models for food have been incorporated into software packages (socalled tertiary models). The user can input their product and environmental parameters and obtain predictions of microbial numbers or product quality at selected times. Dalgaard [5,6] has produced and made freely available an invaluable piece of software for predicting seafood safety and spoilage under various conditions (Fig. 20.1). The software contains 15 models to predict the shelf-life and safety of seafood and can be accessed in 15 languages. The Fish Shelf-life Prediction Program [7], an add-in for Excel, is also freely available. It models Handbook of Seafood Q uality, Safety and Health Applications Edited by Cesarettin Alasalvar, Fereidoon Shahidi, Kazuo Miyashita and Udaya Wanasundara

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Research Issues in Inactivation ofListeria monocytogenesAssociated with New Zealand Greenshell Mussel Meat (Perna canaliculus) Using High-Pressure Processing

Cited by 16 publications

References 35 publications

Determination of an Effective Treatment Temperature of Chemical and Biological Reactions

Determination of an Effective Treatment Temperature of Chemical and Biological Reactions

Recent Developments in High Pressure Processing of Foods

Using Predictive Models for the Shelf‐Life and Safety of Seafood

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