Title: The impact of temperature on the inactivation of enteric viruses in food and water: a review
Abstract: Journal of Applied MicrobiologyVolume 112, Issue 6 p. 1059-1074 REVIEW ARTICLEFree Access The impact of temperature on the inactivation of enteric viruses in food and water: a review I. Bertrand, I. Bertrand Laboratoire de Chimie Physique et Microbiologie pour l’Environnement (LCPME), Université de Lorraine, CNRS, Nancy, FranceSearch for more papers by this authorJ.F. Schijven, J.F. Schijven National Institute of Public Health and the Environment, Expert Centre for Methodology and Information Services, Bilthoven, the NetherlandsSearch for more papers by this authorG. Sánchez, G. Sánchez Institute of Agrochemistry and Food Technology (IATA-CSIC), Valencia, SpainSearch for more papers by this authorP. Wyn-Jones, P. Wyn-Jones IGES, University of Aberystwyth, Ceredigion, UKSearch for more papers by this authorJ. Ottoson, J. Ottoson Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, SwedenSearch for more papers by this authorT. Morin, T. Morin ADRIA Normandie, Villers Bocage, France Present address: Unité Pathologie Virale des Poissons/Laboratoire de Ploufragan – PLouzané, ANSES, Plouzané, France.Search for more papers by this authorM. Muscillo, M. Muscillo Istituto Superiore di Sanità, Rome, ItalySearch for more papers by this authorM. Verani, M. Verani Laboratory of Hygiene and Environmental Virology, Department of Biology, University of Pisa, Pisa, ItalySearch for more papers by this authorA. Nasser, A. Nasser Institute of Natural Sciences, Beit-Berl College, Beit Berl, IsraelSearch for more papers by this authorA.M. de Roda Husman, A.M. de Roda Husman Laboratory for Zoonoses and Environmental Microbiology, Centre for Infectious Disease Control – National Institute for Public Health and the Environment (RIVM), Bilthoven, the NetherlandsSearch for more papers by this authorM. Myrmel, M. Myrmel Department of Food Safety and Infection Biology, Norwegian School of Veterinary Science, Oslo, NorwaySearch for more papers by this authorJ. Sellwood, J. Sellwood Health Protection Agency, Environmental Virology Unit, Microbiology Laboratory, Royal Berkshire Hospital, Reading, UKSearch for more papers by this authorN. Cook, N. Cook Food and Environment Research Agency, York, UKSearch for more papers by this authorC. Gantzer, C. Gantzer Laboratoire de Chimie Physique et Microbiologie pour l’Environnement (LCPME), Université de Lorraine, CNRS, Nancy, FranceSearch for more papers by this author I. Bertrand, I. Bertrand Laboratoire de Chimie Physique et Microbiologie pour l’Environnement (LCPME), Université de Lorraine, CNRS, Nancy, FranceSearch for more papers by this authorJ.F. Schijven, J.F. Schijven National Institute of Public Health and the Environment, Expert Centre for Methodology and Information Services, Bilthoven, the NetherlandsSearch for more papers by this authorG. Sánchez, G. Sánchez Institute of Agrochemistry and Food Technology (IATA-CSIC), Valencia, SpainSearch for more papers by this authorP. Wyn-Jones, P. Wyn-Jones IGES, University of Aberystwyth, Ceredigion, UKSearch for more papers by this authorJ. Ottoson, J. Ottoson Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, SwedenSearch for more papers by this authorT. Morin, T. Morin ADRIA Normandie, Villers Bocage, France Present address: Unité Pathologie Virale des Poissons/Laboratoire de Ploufragan – PLouzané, ANSES, Plouzané, France.Search for more papers by this authorM. Muscillo, M. Muscillo Istituto Superiore di Sanità, Rome, ItalySearch for more papers by this authorM. Verani, M. Verani Laboratory of Hygiene and Environmental Virology, Department of Biology, University of Pisa, Pisa, ItalySearch for more papers by this authorA. Nasser, A. Nasser Institute of Natural Sciences, Beit-Berl College, Beit Berl, IsraelSearch for more papers by this authorA.M. de Roda Husman, A.M. de Roda Husman Laboratory for Zoonoses and Environmental Microbiology, Centre for Infectious Disease Control – National Institute for Public Health and the Environment (RIVM), Bilthoven, the NetherlandsSearch for more papers by this authorM. Myrmel, M. Myrmel Department of Food Safety and Infection Biology, Norwegian School of Veterinary Science, Oslo, NorwaySearch for more papers by this authorJ. Sellwood, J. Sellwood Health Protection Agency, Environmental Virology Unit, Microbiology Laboratory, Royal Berkshire Hospital, Reading, UKSearch for more papers by this authorN. Cook, N. Cook Food and Environment Research Agency, York, UKSearch for more papers by this authorC. Gantzer, C. Gantzer Laboratoire de Chimie Physique et Microbiologie pour l’Environnement (LCPME), Université de Lorraine, CNRS, Nancy, FranceSearch for more papers by this author First published: 01 March 2012 https://doi.org/10.1111/j.1365-2672.2012.05267.xCitations: 162 Christophe Gantzer, Laboratoire de Chimie Physique et Microbiologie pour l’Environnement (LCPME), Faculté de Pharmacie, Université de Lorraine, CNRS, 5 rue Albert Lebrun, BP 80403, F-54001 Nancy Cedex, France. E-mail: [email protected] AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary Temperature is considered as the major factor determining virus inactivation in the environment. Food industries, therefore, widely apply temperature as virus inactivating parameter. This review encompasses an overview of viral inactivation and virus genome degradation data from published literature as well as a statistical analysis and the development of empirical formulae to predict virus inactivation. A total of 658 data (time to obtain a first log10 reduction) were collected from 76 published studies with 563 data on virus infectivity and 95 data on genome degradation. Linear model fitting was applied to analyse the effects of temperature, virus species, detection method (cell culture or molecular methods), matrix (simple or complex) and temperature category (<50 and ≥50°C). As expected, virus inactivation was found to be faster at temperatures ≥50°C than at temperatures <50°C, but there was also a significant temperature–matrix effect. Virus inactivation appeared to occur faster in complex than in simple matrices. In general, bacteriophages PRD1 and PhiX174 appeared to be highly persistent whatever the matrix or the temperature, which makes them useful indicators for virus inactivation studies. The virus genome was shown to be more resistant than infectious virus. Simple empirical formulas were developed that can be used to predict virus inactivation and genome degradation for untested temperatures, time points or even virus strains. Introduction There is growing concern over human exposure to enteric viruses through contaminated water or food products (Koopmans and Duizer 2004). Data on viral waterborne and foodborne diseases are still fragmented (WHO/FAO 2008), focusing either on particular countries or on particular pathogens. Thus, ‘the foodborne viruses in Europe’ (FBVE) network was initiated in 1999 to promote the exchange of data on outbreaks of gastroenteritis because of noroviruses (NoV) (Kroneman et al. 2007). Epidemiological evidence indicates that NoV is the major cause of foodborne outbreaks of gastroenteritis worldwide. To a lesser extent, rotavirus is also implicated in foodborne illness and constitutes an important cause of death in young children in low-income countries (WHO/FAO 2008; EFSA 2011). Hepatitis A virus (HAV) is less common in countries with a high standard of hygiene where it may cause infection later in life, with the risk of leading to a more severe disease outcome (WHO/FAO 2008). While consumption of ready-to-eat foods contaminated by infected food handlers remains an important risk factor for enteric virus outbreaks (Barrabeig et al. 2010), shellfish, particularly oysters, are commonly considered as the most frequently associated food vehicles in NoV outbreaks (EFSA 2011). Other foods, especially raw materials like soft fruits and vegetables, are also being recognized as relevant food vehicles of enteric viruses (Maunula et al. 2009; Ethelberg et al. 2010; EFSA 2011). RT-PCR shows relatively high positivity rates of NoV and/or HAV genome in bivalve molluscs, with positivity prevalence ranging from 4·8 to 53·2% (Croci et al. 2007; David et al. 2007; Phan et al. 2007; Le Guyader et al. 2008; Nakagawa-Okamoto et al. 2009; Mesquita et al. 2011). Contamination rates of NoV genome ranging from 6·6 to 34·5% on soft red fruits and from 28·2 to 50% on leafy greens have been observed in Belgium, Canada and France (Baert et al. 2011). Water can also be a source of disease outbreaks either directly via recreational and drinking water (Maunula et al. 2005; Sinclair et al. 2009) or indirectly via irrigation or contamination of shellfish growing areas (Koopmans and Duizer 2004; Westrell et al. 2010). Viral outbreaks linked to recreational or drinking water are often underestimated, because of low virus concentrations in water together with poor detection efficiency (Maunula et al. 2005; Sinclair et al. 2009). Human pathogenic viruses, such as enterovirus, adenovirus, NoV, reovirus, rotavirus and HAV, have been detected in groundwater with molecular and/or cell culture techniques with prevalence rates varying from 8 to 23% (Borchardt et al. 2003, 2007; Fout et al. 2003). Consumption of drinking water sourced from groundwater contaminated with human pathogenic viruses may lead to epidemics that cause severe illness and even death (Parshionikar et al. 2003; Kim et al. 2005; Gallay et al. 2006; Jean et al. 2006). When groundwater was involved, it often concerned vulnerable geologic settings as fractured rock aquifers, cross-connecting well bores, or leaking well-cases in sandstone and shallow aquifers (Powell et al. 2003; Borchardt et al. 2007) in combination with the presence of significant sources of contamination, such as wastewater treatment facilities, septic tanks and animal manure (Parshionikar et al. 2003; Gallay et al. 2006; Jean et al. 2006; Fong et al. 2007). When a viral inactivation process is available, even for some food products (Deboosere et al. 2004; Butot et al. 2009), the assessment of treatment efficiency remains difficult. In the absence of cell culture systems for some enteric viruses, different virus types have been evaluated as surrogates (Bae and Schwab 2008; Baert et al. 2008b). Even when a culture system is available, laboratory strains might not reflect the resistance of naturally occurring strains. Viral inactivation is also highly variable between virus types, type of treatment or type of matrix. In addition, viral inactivation is usually studied for a limited number of viruses, matrices and inactivating parameters. Therefore, it is not easy to determine the most resistant virus for a particular treatment in a particular matrix, and so there is no single treatment regimen which is applicable for every virus in every matrix. Other concerns include the methodology of viral detection used to estimate treatment efficiency and the data analysis (Teunis et al. 2009). The detection of infectious virus is based on cell culture but no susceptible cell lines has yet been identified for noroviruses (Vashist et al. 2009), and cell culture options are limited for strains of HAV or rotaviruses. Molecular tools may detect all viral types but it is now generally accepted that they may largely underestimate treatment efficiency (Gassilloud et al. 2003; Hewitt and Greening 2006). Thus, a recommended practice is to use a cultivable virus indicator to represent uncultivable pathogenic viruses (Gassilloud et al. 2003; Baert et al. 2008b; Butot et al. 2009). The temperature is largely known as the major factor determining virus inactivation in the environment and also is widely applied in food industries. The objective of this study was to perform a statistical analysis of literature data on virus inactivation to develop empirical formulae predicting inactivation of specific viruses in specific matrices as a function of temperature. The data were collected by the Working Group 4 ‘Viral Inactivation’ of COST Action 929 ‘A European Network for Food and Environmental Virology’ in a spreadsheet (available at the following website http://www.cost929.pcrlab.net/login.php). Data Collection and Statistical Analyses Selection of the reviewed literature Peer-reviewed papers on viral inactivation and genome degradation mainly in food and water at different temperatures were collected. Data obtained with biological samples such as blood were not included in the present work. The major information of interest coming from these published studies was the time needed (in days) to observe the first log10 reduction [time to first log (TFL) value] either for infectious virus by using cell culture (CC) or for viral genome by using molecular methods (PCR). This work is not an exhaustive review; this is notably because of the criteria of inclusion in the spreadsheet described below. In this review, a decrease (linear or nonlinear) in viral infectivity or genome of more than 0·5 log10 below the initial viral titre during the time of experiment was considered the threshold for inclusion in the analyses. If a linear decrease was shown over the whole period of analysis by regression analysis (e.g. squared correlation given) or suggested by the authors, the TFL value could be estimated in the following ways: (i) For a decrease higher than 1 log10, the slope of the regression analysis (decay rate) or a graphical estimation was used. (ii) For a decrease between 0·5 and 1 log10, an extrapolation was allowed if the decrease was observed over at least 10 days by using at least five experimental points. (iii) The TFL value could also be estimated from the T90 or T99 value, if available. In case of a nonlinear decrease over the whole period of analysis, the TFL value could be estimated graphically or by using the slope value (decay rate) if either (i) a linear line could be drawn for the first log reduction over at least three experimental points or (ii) two experimental points were given with the second point at least one log higher than the detection limit. Data classification A spreadsheet was created for the collection and the analysis of the selected studies. For each study, the spreadsheet (available at: http://www.cost929.pcrlab.net/login.php) was filled with 13 different parameters organized in columns. Among them, the taxonomy of the viruses (family, genus and species), the detection method (either cell culture [CC] for inactivation of infectious virus or molecular methods [PCR] for virus genome degradation), the type of matrix and the experimental temperature were used for the classification of the data. Ten types of matrices were identified from the published studies and were considered as ‘simple’ or ‘complex’ matrices. Three types of matrices were considered as simple: (i) synthetic media (synthetic and sterile media without suspended matter, e.g., phosphate-buffered saline, cell culture medium, artificial seawater, artificial groundwater), (ii) drinking water (dechlorinated tap water, bottled water, filtered water, sterilized water, distilled or deionized water), and (iii) groundwater (filtered or non-filtered groundwater or well water). The seven other types of matrices did not correspond to the above criteria and were considered as complex: (i) freshwater (water from river, stream, lake, bog, pond and well water if the authors reported high level of suspended matter), (ii) natural seawater (seawater and estuarine water), (iii) sewage (primary and secondary sewage), (iv) soil, (v) dairy products (e.g. milk, skim milk, cream, reconstituted dry skim milk…), (vi) food (e.g. vegetables, fruits, mussels, meat…) and (vii) urine (reuse in agricultural purpose). A distinction was made between matrices as an influence of the matrix composition has been previously suggested, especially for food (Bidawid et al. 2000; Deboosere et al. 2004). Other parameters in the spreadsheet were temperature, TFL and log10TFL values. The other data reported in the spreadsheet were R2, the use of graphical estimation, log decrease, number of days corresponding to the log decrease, study period, initial viral titre, literature reference and remarks if needed (such as ‘linear regression analysis’, ‘TFL value estimated from the data’…). The studies included in the spreadsheet and in the figures of the present work are labelled in the References with a (*) and/or a (‡) according to their use for the statistical analysis of viral inactivation and/or genome degradation, respectively. Statistical analyses A linear relationship between log10TFL and temperature was apparent from plotting the data, as shown in 1, 2; therefore, the following relationship was analysed: (1) Figure 1Open in figure viewerPowerPoint Values of log10 time to first log (n = 563) as a function of temperature, categorized according to detection by cell culture (CC), in simple (S) or complex (C) matrices and temperatures <50°C or ≥50°C. The values shown in this figure were obtained from studies marked by the * after the year of publication in the References. Figure 2Open in figure viewerPowerPoint Values of log10 time to first log (n = 95) as a function of temperature, categorized according to detection by molecular methods (PCR), in simple (S) or complex (C) matrices and temperatures <50°C or ≥50°C. The values shown in this figure were obtained from studies marked by the ‡ after the year of publication in the References. with Y-intercept α0, slope α1 [°C−1], and temperature T [°C]. If α1 = 0, then α0 = log 10TFL, which means that, in this case, virus inactivation is not affected by temperature. A higher value of α0 implies a longer time to the first log10 reduction in virus concentration, hence a more stable virus. It should be noted that α1 ≤ 0. A stronger negative value of the slope (α1) implies a greater sensitivity to temperature leading to a faster viral inactivation at higher temperature. The governing equation for first-order rate virus inactivation is as follows: (2) where Ct is the virus concentration [number of viral units per volume or mass] at time t, C0 is the initial virus concentration and μ is the virus inactivation rate coefficient [per time unit]. Equation (2) can be rearranged as follows: (3) From Eqn (3), it is possible to determine the virus inactivation rate coefficient: (4) Equation (3), 1, 4 can be combined in the following way: (5) Figure 3Open in figure viewerPowerPoint Distribution of the data on viral inactivation (n = 563) in function of virus species. Figure 4Open in figure viewerPowerPoint Distribution of the data on viral inactivation (n = 563) in function of the type of matrix. 1, 2 show that the values of log10TFL, as a function of temperature, can be categorized according to detection method (CC and PCR), matrix (S and C) and temperature < or ≥50°C (low and high temperature). Each category of log 10TFL values may be characterized by their distinct values of α0 and α1. To that aim, the effects of virus species, detection, matrix and temperature categories were analysed by means of linear model fitting using the statistical package R (ver. 2.12.2; Bell Laboratories, Lucent Technologies, http://www.r-project.org). Effects of virus species, detection, matrix, temperature categories and their interactions may be significant or not. By means of stepwise model selection by Akaike’s Information Criterion (AIC) with k, the multiple of the number of degrees of freedom used for the penalty in AIC set to 3·84, the best model describing the data was selected. A k value of 3·84 corresponds to the Chi-square value with 95% confidence and one degree of freedom. The best model selected in R was transferred to Mathematica 8 (Wolfram Inc, Champaign, Illinois), where it can be easily implemented into a function for abstracting the linear equations for combinations of values of the categorical variables. In addition, Eqn (1) was extended to calculate so-called mean prediction bands that encompass 95% of the mean predicted log10TFL values. The governing equation for calculating the 95% confidence interval of the mean log 10TFL is: (6) The same way, single prediction bands can be derived that encompass 95% of single log 10TFL values. The latter equation should be used for predicting the 95% confidence interval for a single log 10TFL value. The governing equation for calculating the 95% confidence intervals of a single log 10TFL value is: (7) In Eqns (6) and (7), βm, βs, β1 and β2 are linear combinations of the standard errors of the estimated model parameters, including covariances. It should be noted that for a particular virus–matrix combination, Eqns (6) and (7) should only be applied in the relevant low (0–50°C) or high (50–100°C) temperature range. In other words, the equation for a particular virus–matrix combination that was found for the low temperature range cannot be used to calculate virus inactivation in the high temperature range and vice versa. Results A total of 563 data were collected on viral inactivation (CC) from 73 published studies. The data represent twenty viral species belonging to nine virus families (Fig. 3). Among these families, Picornaviridae (Hepatitis A virus, Poliovirus, Coxsackievirus, Echovirus) and Leviviridae (F-specific RNA phages) were the most represented and covered 34·3 and 22·2% of the data, respectively. In contrast, only 0·7% of the data consisted of Astroviridae (astrovirus). The decreasing classification of the six other viral families is: Tectiviridae (14·6%), Siphoviridae (10·3%), Caliciviridae (7·3%), Adenoviridae (4·3%), Microviridae (3·9%) and Reoviridae (2·5%). Figure 4 shows that among the viral inactivation data, 315 data (56%) resulted from experiments performed with complex matrices. Ninety-five PCR data on viral genome degradation were collected from 13 publications. These data came from 11 viral species belonging to the Picornaviridae, the Caliciviridae and the Leviviridae families. The higher percentage of data (67·5%) was represented by the Caliciviridae, whereas this family represented only 7% of the data on inactivation. This is a result of the numerous studies performed on the norovirus genome. The Picornaviridae and Leviviridae represented 24 and 8·5% of the data, respectively. As for CC, 56% of the PCR data came from studies using complex matrices. As shown in 1, 2, two sets of data were observed in function of the temperature applied to the samples. Data of the first set were obtained from experiments performed at temperatures ranging from 5 and 42°C and the second one corresponded to data obtained at temperatures ranging between 51 and 95°C. Virus inactivation data were analysed for low and high temperatures (< and ≥50°C), respectively. The range of temperatures <50°C concerns a wide range of environmental samples included in our work, especially water (e.g. bottled mineral water, surface water, groundwater, wastewater), whereas higher temperatures (≥50°C) were deliberately applied for virus inactivation in industrial processes (e.g. in food or dairy products). Thus, all the experiments with food or dairy products were conducted at temperatures ≥50°C. Irrespective of the type of matrix and the type of method, 69% (452 data) of the experiments on virus inactivation were performed at temperatures <50°C. According to the best model selection in R, the variables and their interactions that best describe log10TFL values as a function of temperature are listed in Table 1. Note that the variable virus species did not significantly interact with any of the other variables except for low/high temperature. This implies that virus species are inactivated at different rates in the two temperature categories. Table 1. Best model description: significant variable and interactions Dependent variable Log10 TFL Numerical variable Temperature Categorical variables Species Detection Matrix Low/high temperature* Significant variables and interactions Species Detection Matrix Temperature Low/high temperature Detection × Matrix Detection × Temperature Matrix × Temperature Species × Low/high temperature Detection × Low/high temperature Matrix × Low/high temperature Temperature × Low/high temperature Detection × Matrix × Temperature Detection × Matrix × Low/high temperature Matrix × Temperature × Low/high temperature TFL, time to first log. *<50°C/≥50°C, respectively. By consecutively substituting values of the categorical variables in the general model, linear equations in the form of Eqn (1) were derived for each of the virus species. Tables 2–9 summarize all the values of α0 and α1 corresponding to these equations, and also the values of βs, βm, β1 and β2 for calculation of 95% confidence intervals. These tables also include predicted single log10TFL values, including 95% confidence intervals for 0 and 50°C for the low temperature category and for 50 and 100°C for the high temperature category. As examples, Fig. 5(a)–(d) show the data and the model predictions with mean and single 95% prediction bands for poliovirus, HAV, PRD1 phage and F-specific RNA phage genogroup I, respectively. Table 2. Parameters for calculation of virus inactivation and log10 TFL values including 95% prediction intervals (95% CI) calculated by using Eqn (7) for 0 and 50°C. Detection by cell culture (CC), inactivation at temperature <50°C (lo), in simple matrices (S) CC-lo-S α 1 = −0·036, β2 = 0·000019 log10 TFL (0°C) log10 TFL (50°C) Virus species α 0 β s β m β 1 N Mean 95% CI Mean 95% CI 0·87 0·38 0·093 −0·00099 2 0·87 −0·34 2·1 −0·91 −2·1 0·3 CaCV 1·4 0·31 0·021 −0·00075 12 1·4 0·31 2·5 −0·37 −1·5 0·73 FCV 1·4 0·31 0·019 −0·00060 14 1·4 0·35 2·5 −0·34 −1·5 0·78 Echovirus 1·4 0·60 0·310 −0·00140 1 1·4 −0·081 3·0 −0·34 −1·8 1·2 Human rotavirus 1·6 0·32 0·034 −0·00072 4 1·6 0·48 2·7 −0·18 −1·3 0·95 Simian rotavirus 1·7 0·34 0·051 −0·00083 4 1·7 0·54 2·8 −0·092 −1·2 1·1 FRNAPH genogroup II 1·7 0·34 0·051 −0·00083 4 1·7 0·56 2·8 −0·076 −1·2 1·1 FRNAPH genogroup III 1·7 0·33 0·046 −0·00082 5 1·7 0·58 2·8 −0·065 −1·2 1·1 FRNAPH genogroup IV 1·8 0·31 0·020 −0·00062 10 1·8 0·70 2·9 0·0096 −1·1 1·1 Coxsackievirus 1·8 0·30 0·010 −0·00068 45 1·8 0·73 2·9 0·027 −1·1 1·1 FRNAPH GGI 1·9 0·36 0·077 −0·00064 3 1·9 0·7 3·1 0·11 −1·1 1·3 Human astrovirus 1·9 0·30 0·011 −0·00073 57 1·9 0·82 3·0 0·12 −0·97 1·2 Poliovirus 2·1 0·31 0·020 −0·00064 6 2·1 0·99 3·2 0·3 −0·81 1·4 Human adenovirus 2·3 0·37 0·083 −0·00084 2 2·3 1·1 3·5 0·52 −0·68 1·7 MNV 2·5 0·31 0·024 −0·00071 11 2·5 1·4 3·6 0·71 −0·4 1·8 HAV 2·5 0·30 0·013 −0·00067 6 2·5 1·4 3·6 0·72 −0·38 1·8 PRD1 phage 2·6 0·32 0·035 −0·00066 1 2·6 1·5 3·7 0·82 −0·32 2·0 PhiX174 phage CaCV, canine calicivirus; FCV, feline calicivirus, FRNAPH, F-specific RNA phages; HAV, hepatitis A virus; MNV, murine norovirus; TFL, time to first log reduction. Table 3. Parameters for calculation of virus inactivation and log10TFL values including 95% prediction intervals (95% CI) calculated by using Eqn (7) for 0 and 50°C. Detection by cell culture (CC), inactivation at temperature <50°C (lo), in complex matrices (C) CC-lo-C α 1 = −0·030, β2 = 0·000014 log10 TFL (0°C) log10 TFL (50°C) Virus species α 0 β s β m β 1 N Mean 95% CI Mean 95% CI 0·78 0·58 0·290 −0·00057 1 0·78 −0·71 2·3 −0·73 −2·2 0·77 Bacteroides fragilis phage 0·91 0·31 0·020 −0·00055 5 0·91 −0·18 2·0 −0·61 −1·7 0·49 FCV 0·94 0·31 0·022 −0·00057 6 0·94 −0·15 2·0 −0·57 −1·7 0·53 Echovirus 1·1 0·32 0·032 −0·00057 7 1·1 −0·0089 2·2 −0·42 −1·5 0·71 Simian rotavirus 1·2 0·34 0·050 −0·00061 2 1·2 0·05 2·3 −0·33 −1·5 0·82 FRNAPH genogroup II 1·2 0·34 0·050 −0·00061 2 1·2 0·065 2·3 −0·31 −1·5 0·84 FRNAPH genogroup III 1·2 0·33 0·045 −0·00060 2 1·2 0·086 2·3 −0·3 −1·4 0·84 FRNAPH genogroup IV 1·3 0·31 0·021 −0·00058 8 1·3 0·2 2·4 −0·23 −1·3 0·88 Coxsackievirus 1·3 0·30 0·009 −0·00054 44 1·3 0·24 2·4 −0·21 −1·3 0·88 FRNAPH genogroup I 1·3 0·32 0·033 −0·00050 10 1·3 0·23 2·5 −0·18 −1·3 0·95 FRNAPH all genogroups 1·4 0·37 0·079 −0·00056 1 1·4 0·2 2·6 −0·13 −1·3 1·10 Human astrovirus 1·4 0·30 0·010 −0·00055 31 1·4 0·33 2·5 −0·12 −1·2 0·97 Poliovirus 1·6 0·30 0·015 −0·00041 18 1·6 0·5 2·7 0·065 −1·0 1·20 Human adenovirus 1·8 0·37 0·083 −0·00064 1 1·8 0·61 3·0 0·29 −0·9 1·50 MNV 2·0 0·31 0·025 −0·00061 6 2·0 0·9 3·1 0·48 −0·63 1·60 HAV 2·0 0·30 0·011 −0·00066 76 2·0 0·93 3·1 0·48 −0·59 1·60 PRD1 phage 2·1 0·32 0·030 −0·00046 10 2·1 1·0 3·2 0·59 −0·54 1·70 PhiX174 phage FCV, feline calicivirus; FRNAPH, F-specific RNA phages; HAV, hepatitis A virus; MNV, murine norovirus; TFL, time to first log reduction. Table 4. Parameters for calculation of virus inactivation and log10TFL values including 95% prediction intervals (95% C
Publication Year: 2012
Publication Date: 2012-03-02
Language: en
Type: review
Indexed In: ['crossref', 'pubmed']
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Cited By Count: 223
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