% Encoding: UTF-8
@article{Geeraerd2005,
title = {GInaFiT, a freeware tool to assess non-log-linear microbial survivor curves},
journal = {International Journal of Food Microbiology},
volume = {102},
number = {1},
pages = {95-105},
year = {2005},
issn = {0168-1605},
doi = {10.1016/j.ijfoodmicro.2004.11.038},
author = {A.H. Geeraerd and V.P. Valdramidis and J.F. {Van Impe}},
keywords = {Predictive microbiology, Microbial inactivation, Microbial survival, Freeware tool, Excel add-in},
abstract = {This contribution focuses on the presentation of GInaFiT (Geeraerd and Van Impe Inactivation Model Fitting Tool), a freeware Add-inn for Microsoft® Excel aiming at bridging the gap between people developing predictive modelling approaches and end-users in the food industry not familiar with or not disposing over advanced non-linear regression analysis tools. More precisely, the tool is useful for testing nine different types of microbial survival models on user-specific experimental data relating the evolution of the microbial population with time. As such, the authors believe to cover all known survivor curve shapes for vegetative bacterial cells. The nine model types are: (i) classical log-linear curves, (ii) curves displaying a so-called shoulder before a log-linear decrease is apparent, (iii) curves displaying a so-called tail after a log-linear decrease, (iv) survival curves displaying both shoulder and tailing behaviour, (v) concave curves, (vi) convex curves, (vii) convex/concave curves followed by tailing, (viii) biphasic inactivation kinetics, and (ix) biphasic inactivation kinetics preceded by a shoulder. Next to the obtained parameter values, the following statistical measures are automatically reported: standard errors of the parameter values, the Sum of Squared Errors, the Mean Sum of Squared Errors and its Root, the R2 and the adjusted R2. The tool can help the end-user to communicate the performance of food preservation processes in terms of the number of log cycles of reduction rather than the classical D-value and is downloadable via the KULeuven/BioTeC-homepage http://cit.kuleuven.be/biotec/ at the topic “Downloads” (Version 1.4, Release date April 2005).}
}


@Article{Dolan2013,
  author   = {Dolan, Kirk D. and Mishra, Dharmendra K.},
  journal  = {Annual Review of Food Science and Technology},
  title    = {Parameter Estimation in Food Science},
  year     = {2013},
  note     = {PMID: 23297775},
  number   = {1},
  pages    = {401-422},
  volume   = {4},
  abstract = {Modeling includes two distinct parts, the forward problem and the inverse problem. The forward problem—computing y(t) given known parameters—has received much attention, especially with the explosion of commercial simulation software. What is rarely made clear is that the forward results can be no better than the accuracy of the parameters. Therefore, the inverse problem—estimation of parameters given measured y(t)—is at least as important as the forward problem. However, in the food science literature there has been little attention paid to the accuracy of parameters. The purpose of this article is to summarize the state of the art of parameter estimation in food science, to review some of the common food science models used for parameter estimation (for microbial inactivation and growth, thermal properties, and kinetics), and to suggest a generic method to standardize parameter estimation, thereby making research results more useful. Scaled sensitivity coefficients are introduced and shown to be important in parameter identifiability. Sequential estimation and optimal experimental design are also reviewed as powerful parameter estimation methods that are beginning to be used in the food science literature.},
  doi      = {10.1146/annurev-food-022811-101247},
}


@Article{Boekel2002,
  Title                    = {On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells},
  Author                   = {Martinus A.J.S {van Boekel}},
  Journal                  = {International Journal of Food Microbiology},
  Year                     = {2002},
  Number                   = {1},
  Pages                    = {139-159},
  Volume                   = {74},

  Doi                      = {10.1016/S0168-1605(01)00742-5},
  ISSN                     = {0168-1605},
  Keywords                 = {Weibull model, Thermal inactivation, Kinetic modelling, Vegetative cells},
}

@Article{Buchanan1997,
  Title                    = {When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves},
  Author                   = {R.L Buchanan and R.C Whiting and W.C Damert},
  Journal                  = {Food Microbiology},
  Year                     = {1997},
  Number                   = {4},
  Pages                    = {313-326},
  Volume                   = {14},

  Abstract                 = {The use of primary mathematical models with curve fitting software is dramatically changing quantitative food microbiology. The two most widely used primary growth models are the Baranyi and Gompertz models. A three-phase linear model was developed to determine how well growth curves could be described using a simpler model. The model divides bacterial growth curves into three phases: the lag and stationary phases where the specific growth rate is zero (gm=0), and the exponential phase where the logarithm of the bacterial population increases linearly with time (gm=constant). The model has four parameters: No(Log8of initial population density), NMAX(Log8of final population density), tLAG(time when lag phase ends), and tMAX(time when exponential phase ends). A comparison of the linear model was made against the Baranyi and Gompertz models, using established growth data forEscherichia coli0157:H7. The growth curves predicted by the three models showed good agreement. The linear model was more ‘robust' than the others, especially when experimental data were minimal. The physiological assumptions underlying the linear model are discussed, with particular emphasis on assuring that the model is consistent with bacterial behavior both as individual cells and as populations. It is proposed that the transitional behavior of bacteria at the end of the lag phase can be explained on the basis of biological variability.},
  Doi                      = {10.1006/fmic.1997.0125},
  ISSN                     = {0740-0020},
  Owner                    = {ubarron},
  Timestamp                = {2020.04.21},
}

@Article{Fang2012,
  Title                    = {Growth Kinetics and Model Comparison of Cronobacter sakazakii in Reconstituted Powdered Infant Formula},
  Author                   = {Fang, Ting and Gurtler, Joshua B. and Huang, Lihan},
  Journal                  = {Journal of Food Science},
  Year                     = {2012},
  Number                   = {9},
  Pages                    = {E247-E255},
  Volume                   = {77},

  Abstract                 = {Abstract: Cronobacter sakazakii is a life-threatening bacterium, infrequently implicated in illnesses associated with the consumption of powdered infant formula (PIF). It can cause rare but invasive infections in neonatal infants who consume contaminated PIF. The objective of this research was to investigate the growth kinetics and develop mathematical models to predict the growth of heat-injured C. sakazakii in reconstituted PIF (RPIF). RPIF, inoculated with a 6-strain cocktail of non-heat-treated (uninjured) or heat-injured C. sakazakii, was incubated at different temperatures to develop growth models. Except for storage at 6 °C, C. sakazakii grew well at all test temperatures (10 to 48 °C). Uninjured C. sakazakii exhibited no observable lag phase, while a lag phase was apparent in heat-treated cells. A simple 3-parameter logistic equation was used to fit growth curves for non-heat-treated cells, while both Baranyi and Huang models were suitable for heat-treated C. sakazakii. Calculated minimum and maximum growth temperatures were 6.5 and 51.4 °C for non-heat-treated cells, and 6.9 and 50.1 ºC for heat-treated cells of C. sakazakii in RPIF, respectively. There was no significant difference between growth rates of non-heat-treated and heat-injured cells in RPIF. For heat-treated cells of C. sakazakii, the lag phase was temperature-dependent and very short (between 25 ºC and 48 ºC). These results suggest that both non-heat-treated and heat-injured C. sakazakii cells may present a risk to infants if the pathogens are not completely destroyed by heat in RPIF and then exposed to subsequent temperature abuse. Practical Application: C. sakazakii is a life-threatening bacterium found in powdered infant formula (PIF). This study shows that the uninjured bacterium exhibits very short or no lag phase if not refrigerated and can grow well in reconstituted PIF (RPIF), while the heat-injured cells can multiply at an equivalent rate following metabolic recovery. Temperature abuse may allow C. sakazakii to grow and endanger infants fed with RPIF. Predictive models developed in this study can be used to estimate the growth and conduct risk assessments of this pathogen.},
  Doi                      = {10.1111/j.1750-3841.2012.02873.x},
  Keywords                 = {C. sakazakii, growth kinetics, modeling, powdered infant formula, predictive microbiology},
  Owner                    = {ubarron},
  Timestamp                = {2020.04.21},
}

@Article{Fang2013,
  Title                    = {Growth kinetics of Listeria monocytogenes and spoilage microorganisms in fresh-cut cantaloupe},
  Author                   = {Ting Fang and Yanhong Liu and Lihan Huang},
  Journal                  = {Food Microbiology},
  Year                     = {2013},
  Number                   = {1},
  Pages                    = {174-181},
  Volume                   = {34},

  Abstract                 = {The main objective of this study was to investigate the growth kinetics of Listeria monocytogenes and background microorganisms in fresh-cut cantaloupe. Fresh-cut cantaloupe samples, inoculated with three main serotypes (1/2a, 1/2b, and 4b) of L. monocytogenes, were incubated at different temperatures, ranging from 4 to 43 ºC, to develop kinetic growth models. During storage studies, the population of both background microorganisms and L. monocytogenes began to increase almost immediately, with little or no lag phase for most growth curves. All growth curves, except for two growth curves of L. monocytogenes 1/2a at 4 ºC, developed to full curves (containing exponential and stationary phases), and can be described by a 3-parameter logistic model. There was no significant difference (P = 0.28) in the growth behaviors and the specific growth rates of three different serotypes of L. monocytogenes inoculated to fresh-cut cantaloupe. The effect of temperature on the growth of L. monocytogenes and spoilage microorganisms was evaluated using three secondary models. For L. monocytogenes, the minimum and maximum growth temperatures were estimated by both the Ratkowsky square-root and Cardinal parameter models, and the optimum temperature and the optimum specific growth rate by the Cardinal parameter model. An Arrhenius-type model provided more accurate estimation of the specific growth rate of L. monocytogenes at temperatures <4 ºC. The kinetic models developed in this study can be used by regulatory agencies and food processors for conducting risk assessment of L. monocytogenes in fresh-cut cantaloupe, and for estimating the shelf-life of fresh-cut products.},
  Doi                      = {10.1016/j.fm.2012.12.005},
  ISSN                     = {0740-0020},
  Keywords                 = {Growth kinetics, , Spoilage microorganism, Cantaloupe},
  Owner                    = {ubarron},
  Timestamp                = {2020.04.21}
}

@Article{Huang2008,
  Title                    = {Growth Kinetics of Listeria monocytogenes in Broth and Beef Frankfurters-Determination of Lag Phase Duration and Exponential Growth Rate under Isothermal Conditions},
  Author                   = {Huang, L.},
  Journal                  = {Journal of Food Science},
  Year                     = {2008},
  Number                   = {5},
  Pages                    = {E235-E242},
  Volume                   = {73},

  Doi                      = {10.1111/j.1750-3841.2008.00785.x},
  Keywords                 = {growth kinetics, modeling, predictive microbiology}
}

@Article{Zwietering1990,
  Title                    = {Modeling of the Bacterial Growth Curve},
  Author                   = {Zwietering, M. H. and Jongenburger, I. and Rombouts, F. M. and {van't Riet}, K.},
  Journal                  = {Applied and Environmental Microbiology},
  Year                     = {1990},
  Number                   = {6},
  Pages                    = {1875-1881},
  Volume                   = {56},
  Abstract                 = {Several sigmoidal functions (logistic, Gompertz, Richards, Schnute, and Stannard) were compared to describe a bacterial growth curve. They were compared statistically by using the model of Schnute, which is a comprehensive model, encompassing all other models. The t test and the F test were used. With the t test, confidence intervals for parameters can be calculated and can be used to distinguish between models. In the F test, the lack of fit of the models is compared with the measuring error. Moreover, the models were compared with respect to their ease of use. All sigmoidal functions were modified so that they contained biologically relevant parameters. The models of Richards, Schnute, and Stannard appeared to be basically the same equation. In the cases tested, the modified Gompertz equation was statistically sufficient to describe the growth data of Lactobacillus plantarum and was easy to use.},
  ISSN                     = {0099-2240},
  Owner                    = {ubarron},
  Publisher                = {American Society for Microbiology Journals},
  Timestamp                = {2020.04.21},
}



@article{Baranyi1993Modelling,
	author = {J Baranyi and PJ McClure and JP Sutherland and TA Roberts},
	journal = {Journal of Industrial Microbiology},
	number = {3-5},
	owner = {ubarron},
	pages = {190–194},
	publisher = {Springer},
	timestamp = {2015.02.19},
	title = {Modeling bacterial growth responses},
	volume = {12},
	year = {1993}
}

@article{Baranyi-1999Validating,
	author = {József Baranyi and Carmen Pin and Thomas Ross},
	journal = {Internat al Journal of Food Microbiology},
	owner = {vcadavez},
	pages = {159–166},
	timestamp = {2015.02.22},
	title = {Validating and comparing predictive models},
	volume = {48},
	year = {1999}
}

@article{Baranyi1993Some,
	author = {J. Baranyi and T. A. Roberts and P. J. McClure},
	journal = {Mathematical Medicine and Biology},
	owner = {vcadavez},
	pages = {293–299},
	timestamp = {2015.02.24},
	title = {Some properties of a non-autonomous deterministic growth model describing the adjustment of the bacterial population to a new environment},
	volume = {10},
	year = {1993}
}

@article{Cadavez2012Seemingly,
	abstract = {The aim of this study was to develop and evaluate models for predicting the carcass composition of lambs. Forty male lambs were slaughtered and their carcasses were cooled for 24 hours. The subcutaneous fat thickness was measured between the 12th and 13th rib and breast bone tissue thickness was taken in the middle of the second sternebrae. Left side of carcasses was dissected and the proportions of lean meat (LMP), subcutaneous fat (SFP), intermuscular fat (IFP), kidney and knob channel fat (KCFP), and bone plus remainder (BP) were obtained. Models were fitted using the seemingly unrelated regression (SUR) estimator which is novel in this area, and compared to ordinary least squares (OLS) estimates. Models were validated using the \{PRESS\} statistic. Our results showed that \{SUR\} estimator performed better in predicting \{LMP\} and \{IFP\} than the \{OLS\} estimator. Although objective carcass classification systems could be improved by using the \{SUR\} estimator, it has never been used before for predicting carcass composition.},
	author = {V.A.P. Cadavez and A. Henningsen},
	doi = {10.1016/j.meatsci.2012.05.025},
	issn = {0309-1740},
	journal = {Meat Science},
	keywords = {Carcass; Quality; Ordinary least squares; Seemingly unrelated regression},
	number = {4},
	pages = {548–553},
	title = {The use of seemingly unrelated regression to predict the carcass composition of lambs},
	volume = {92},
	year = {2012}
}

@incollection{Fao2004Assessment,
	abstract = {http://www.fao.org/docrep/006/y4743e/y4743e0o.htm\#TopOfPage ISBN 92-5-104954-8},
	address = {Rome},
	author = {Paw Dalgaard},
	booktitle = {Assessment and management of seafood safety and quality},
	editor = {H. H. Huss and L. Ababouch and L. Gram},
	number = {444},
	organization = {Food and Agriculture Organization of the United Nations},
	owner = {vcadavez},
	pages = {204–209},
	publisher = {FAO},
	series = {FAO Fisheries Techincal Paper},
	timestamp = {2015.03.20},
	title = {Predictive microbiology},
	year = {2004}
}

@article{Dalgaard1995Modelling,
	author = {Paw Dalgaard},
	journal = {International Journal of Food Microbiology},
	owner = {vcadavez},
	pages = {305–317},
	timestamp = {2015.02.22},
	title = {Modelling of microbial activity and prediction of shelf life for packed fresh fish},
	volume = {26},
	year = {1995}
}

@article{Fakruddin2011Predictive,
	author = {Md. Fakruddin and Reaz Mohammad Mazumder and Khanjada Shahnewaj Bin Mannan},
	journal = {Ceylon Journal of Science (Bio. Sci.)},
	number = {2},
	owner = {vcadavez},
	pages = {121–131},
	timestamp = {2014.12.24},
	title = {Predictive microbiology: Modeling microbial responses in food},
	volume = {40},
	year = {2011}
}

@article{Gibson1987Theeffect,
	author = {A. M. Gibson and N. Bratchell and T. A. Roberts},
	journal = {The Journal of applied bacteriology},
	number = {6},
	owner = {vcadavez},
	pages = {479–490},
	timestamp = {2015.02.23},
	title = {The effect of sodium chloride and temperature on the rate and extent of growth of Clostridium botulinum type A in pasteurized pork slurry},
	volume = {62},
	year = {1987}
}

@inbook{Gonzales-Barron2012Thermal,
	author = {Ursula Andrea Gonzales-Barron},
	chapter = {Modelling thermal microbial inactivation kinetics},
	editor = {Da-Wen Sun},
	owner = {vcadavez},
	pages = {151–194},
	publisher = {Taylor \& Francis},
	timestamp = {2015.02.13},
	title = {Thermal Food Processing: New Technologies and Quality Issues},
	year = {2102}
}

@article{GonzalesBarron2014Conducting,
	abstract = {Abstract Mixed Poisson distributions have been shown to be able to represent low microbial counts more efficiently than the lognormal distribution because of its greater flexibility to model microbial clustering even when data consist of a large proportion of zero counts. The objective of this study was to develop an alternative modelling framework for low microbial counts based on heterogeneous Poisson regressions. As an illustration, Poisson-gamma regression models were used to assess the effect of chilling on the concentration of total coliforms from beef carcasses (n = 600) sampled at eight large Irish abattoirs. Three Poisson-gamma and three zero-modified (hurdle and zero-inflated) models were appraised with a series of random-effects variants in order to extract any variability in microbial mean concentration, dispersion and/or proportion of zero counts. Models were compared and validated in their ability to predict the coliforms counts on carcasses after chilling. In all five test batches, the hurdle Poisson-gamma distributions predicted the observed post-chill counts closer than the Poisson-gamma distributions. This is justified by the better capacity of the hurdle model to represent a higher proportion of zero counts, which were in fact observed in the post-chill batches. Thus, with a coded variable (pre-chill/post-chill) as treatment, and extracting the significant variability of batches nested in abattoirs for the coliforms mean concentration (σ2u = 2.68), the dispersion measure (σ2v = 2.39) and the probability of zero counts (σ2w = 0.89), the validated hurdle Poisson-gamma model confirmed that chilling has a decreasing effect on the viability of coliforms from beef carcasses, and that the concentration is reduced by an average (pre-chill to post-chill) factor of 2.2 (95\% CI: 2.15–2.24) at batch level. The model also indicated that chilling increases the odds of producing a zero count from a carcass swab in about 13.5 times, and that the higher the coliforms concentration in a batch, the weaker the effect that chilling has to reduce such contamination on the beef carcasses.},
	author = {Ursula Gonzales-Barron and Vasco Cadavez and Francis Butler},
	doi = {10.1016/j.foodcont.2013.09.032},
	issn = {0956-7135},
	journal = {Food Control},
	keywords = {Carcass; Chilling; Coliforms; Negative binomial; Zero-inflated; Plate counts},
	pages = {385–394},
	title = {Conducting inferential statistics for low microbial counts in foods using the Poisson-gamma regression},
	volume = {37},
	year = {2014}
}

@article{GonzalesBarron2013Modelling,
	abstract = {Abstract The effect of chilling on the occurrence of Salmonella on pig carcasses was investigated at study, abattoir and batch level by meta-analysis. Both the fixed-effects and random-effects model confirmed (p\&\#xa0;\&lt;\&\#xa0;0.05) the significant effect of chilling in decreasing Salmonella occurrence on pig carcasses; although the random-effects solution was preferred to account for the significant variability in effect size (p\&\#xa0;\&lt;\&\#xa0;0.001) estimated from the 13 primary studies considered, the 32 abattoirs surveyed, and the 51 sampled batches. Conservatively, it can be said that chilling reduces the Salmonella incidence on pig carcasses by a mean ratio of ~\&\#xa0;1.6 (95\% CI: 1.0–2.6). Multilevel meta-analysis models investigating study characteristics that could explain the heterogeneity (τ2) in the true effect size among primary studies (τ2\&\#xa0;=\&\#xa0;0.578), among surveyed abattoirs (τ2\&\#xa0;=\&\#xa0;0.431), and among sampled batches (τ2\&\#xa0;=\&\#xa0;0.373), revealed that study size (represented by the moderating variables of ‘total sample size’ and ‘number of batches sampled in an abattoir’) and ‘carcass swabbed area’ have a significant impact (p\&\#xa0;\&lt;\&\#xa0;0.05) on the measured effect size of chilling. The fact that swabbed area explained between 56 and 62\% and total sample size between 23 and 38\% of the total heterogeneity in the chilling true effect size, indicates that differences in experimental design greatly affect our substantive conclusion about the effect of chilling on Salmonella occurrence. This inconsistency to elucidate the effect of chilling arises because of the many factors influencing both the performance of the chilling operation and the measurement itself. Meta-analysis was not only instrumental to show that small-size studies (i.e., only one batch sampled per abattoir, total number of sampled carcasses per batch\&\#xa0;\&lt;\&\#xa0;50) and small swabbed areas (\&lt;\&\#xa0;100\&\#xa0;cm2) lead to imprecise and even conflicting conclusions, but most importantly, enabled definition of the characteristics of a well-designed study having a minimum statistical power to produce precise results. A sound experimental design derived by multilevel meta-analysis consists of swabbing carcass areas of at least 500\&\#xa0;cm2 from 25 pre-chill and 25 post-chill carcasses from a single production batch, with a minimum of two batches sampled per surveyed abattoir. If the survey were to be conducted in more than one abattoir, the total sample size should not be less than 400. Two methods to test for publication bias, a common problem in meta-analysis, suggested that whilst the presence of unpublished small-size studies is probable, it is not likely that this would significantly bias the overall chilling effect estimated in this study.},
	author = {Ursula Gonzales-Barron and Vasco Cadavez and James J. Sheridan and Francis Butler},
	doi = {10.1016/j.ijfoodmicro.2013.03.006},
	issn = {0168-1605},
	journal = {International Journal of Food Microbiology},
	keywords = {Meta-analysis; Multilevel model; Chilling; Risk; Pig; Slaughterhouse},
	number = {2-3},
	pages = {101–113},
	title = {Modelling the effect of chilling on the occurrence of Salmonella on pig carcasses at study, abattoir and batch levels by meta-analysis},
	volume = {163},
	year = {2013}
}

@Article{Gram1996Microbiological,
  author   = {Lone Gram and Hans Henrik Huss},
  journal  = {International Journal of Food Microbiology},
  title    = {Microbiological spoilage of fish and fish products},
  year     = {1996},
  issn     = {0168-1605},
  note     = {Specific Spoilage Organisms},
  number   = {1},
  pages    = {121–137},
  volume   = {33},
  doi      = {10.1016/0168-1605(96)01134-8},
  keywords = {Spoilage association}
}

@article{Huang2015-Direct,
	author = {Lihan Huang},
	journal = {Food Control},
	pages = {76–81},
	title = {Direct construction of predictive models for describing growth of Salmonella Enteritidis in liquid eggs - a one-step approach},
	volume = {57},
	year = {2015}
}

@article{Huang2017-Dynamic,
	author = {Lihan Huang and C. A. Hwang},
	journal = {Food Control},
	pages = {125–130},
	title = {Dynamic analysis of growth of Salmonella Enteritidis in liquid egg whites},
	volume = {80},
	year = {2017}
}

@book{Iannelli-2014-Introduction,
	author = {Mimmo Iannelli and Andrea Pugliese},
	doi = {10.1007/978-3-319-03026-5},
	owner = {vc},
	publisher = {Springer},
	timestamp = {2017.08.25},
	title = {An introduction to mathematical population dynamics},
	year = {2014}
}

@Book{McMeekin1993book,
  author    = {T. A. McMeekin and J. Olley and T. Ross and D. A. Ratkowsky},
  publisher = {Research Studies Press},
  title     = {Predictive microbiology: theory and application},
  year      = {1993},
  address   = {Taunton, UK},
  owner     = {vcadavez},
  timestamp = {2014.12.25},
}

@article{Monod1949growth,
	author = {J. Monod},
	journal = {Annual Review of Microbiology},
	number = {371–394},
	owner = {vcadavez},
	timestamp = {2015.02.23},
	title = {The growth of bacterial cultures},
	volume = {3},
	year = {1949}
}

@article{Peleg1997Modelling,
	author = {M Peleg},
	journal = {Critical Reviews in Food Science and Nutrition},
	owner = {vcadavez},
	pages = {471–490},
	timestamp = {2014.11.26},
	title = {Modelling microbial populations with the original and modified versions of the continuous and discrete logistic equations},
	volume = {37},
	year = {1997}
}

@Article{Planckett1946,
  author    = {R. L. Plackett and J. P. Burman},
  journal   = {Biometrika},
  title     = {The Design of Optimum Multifactorial Experiments},
  year      = {1946},
  number    = {4},
  pages     = {305–25},
  volume    = {33},
  doi       = {10.1093/biomet/33.4.305},
  owner     = {vc},
  timestamp = {2017.08.15}
}

@article{Roos2000Predictive,
	author = {Tom Ross and Paw Dalgaard and Suwunna Tienungoon},
	journal = {International Journal of Food Microbiology},
	owner = {vcadavez},
	pages = {231–245},
	timestamp = {2015.03.29},
	title = {Predictive modelling of the growth and survival of Listeria in fishry products},
	volume = {62},
	year = {2000}
}

@article{Schaffner2000,
	author = {DW Schaffner and TP Labuza},
	journal = {Food Technology},
	owner = {vcadavez},
	pages = {95–99},
	timestamp = {2014.11.26},
	title = {Predictive microbiology: where are we and where are we going?},
	volume = {51},
	year = {2000}
}

@article{Silva2014Modelling,
	abstract = {Abstract In this work, all publicly-accessible published findings on Alicyclobacillus acidoterrestris heat resistance in fruit beverages as affected by temperature and pH were compiled. Then, study characteristics (protocols, fruit and variety, °Brix, pH, temperature, heating medium, culture medium, inactivation method, strains, etc.) were extracted from the primary studies, and some of them incorporated to a meta-analysis mixed-effects linear model based on the basic Bigelow equation describing the heat resistance parameters of this bacterium. The model estimated mean D* values (time needed for one log reduction at a temperature of 95°C and a pH of 3.5) of Alicyclobacillus in beverages of different fruits, two different concentration types, with and without bacteriocins, and with and without clarification. The zT (temperature changes needed to cause one log reduction in D-values) estimated by the meta-analysis model were compared to those (‘observed’ zT values) reported in the primary studies, and in all cases they were within the confidence interval of the model. The model was capable of predicting the heat resistance parameters of Alicyclobacillus in fruit beverages beyond the types available in the meta-analytical data. It is expected that the compilation of the thermal resistance of Alicyclobacillus in fruit beverages, carried out in this study, will be of utility to food quality managers in the determination or validation of the lethality of their current heat treatment processes.},
	author = {Leonardo P. Silva and Ursula Gonzales-Barron and Vasco Cadavez and Anderson S. Sant{\'}Ana},
	doi = {10.1016/j.fm.2014.09.019},
	issn = {0740-0020},
	journal = {Food Microbiology},
	keywords = {Bigelow; secondary model; mixed linear model; D-value; z-value; juice; pasteurization},
	pages = {541–52},
	title = {Modeling the effects of temperature and pH on the resistance of Alicyclobacillus acidoterrestris in conventional heat-treated fruit beverages through a meta-analysis approach},
	volume = {46},
	year = {2014}
}

@inbook{Silva2012ComputerVision,
	abstract = {Abstract: In this chapter the use of real-time ultrasonography to predict in vivo carcass composition and meat traits will be reviewed. The chapter begins by discussing background and principles of ultrasound. Then aspects affecting the suitability of realtime ultrasonography and image analysis for predicting carcass composition and meat traits of meat producing species and fish will be presented. This chapter also provides an overview of the present and future trends in the application of real-time ultrasonography in the meat industry.},
	author = {S. R. Silva and V. P. Cadavez},
	booktitle = {Computer Vision Technology in the Food and Beverage Industries},
	chapter = {11 - Real-time ultrasound (RTU) imaging methods for quality control of meats},
	doi = {10.1533/9780857095770.3.277},
	editor = {Da-Wen Sun},
	isbn = {978-0-85709-036-2},
	keywords = {meat quality; ultrasound; carcass; image analysis},
	pages = {277–329},
	publisher = {Woodhead Publishing},
	series = {Woodhead Publishing Series in Food Science, Technology and Nutrition},
	title = {Computer Vision Technology in the Food and Beverage Industries},
	year = {2012}
}

@article{WhitingBuchanan1993Classification,
	author = {R. C. Whiting and R. L. Buchanan},
	journal = {Food Microbiology},
	owner = {vcadavez},
	pages = {175–177},
	timestamp = {2015.01.07},
	title = {A classification of models for predictive microbiology},
	volume = {10},
	year = {1993}
}

@Article{Moller2013,
  author   = {C.O.A. Møller and Y. Ilg and S. Aabo and B.B. Christensen and P. Dalgaard and T.B. Hansen},
  journal  = {Food Microbiology},
  title    = {Effect of natural microbiota on growth of Salmonella spp. in fresh pork – A predictive microbiology approach},
  year     = {2013},
  issn     = {0740-0020},
  number   = {2},
  pages    = {284–295},
  volume   = {34},
  doi      = {10.1016/j.fm.2012.10.010},
  keywords = {Pork; Growth; Microbial interaction; Predictive microbiology; Modeling},
}

@Article{Lemarc2009,
  author   = {Y. Le Marc and L. Valík and A. Medveďová},
  journal  = {International Journal of Food Microbiology},
  title    = {Modelling the effect of the starter culture on the growth of {Staphylococcus} aureus in milk},
  year     = {2009},
  issn     = {0168-1605},
  number   = {3},
  pages    = {306–311},
  volume   = {129},
  doi      = {10.1016/j.ijfoodmicro.2008.12.015},
  keywords = {Predictive microbiology; Lactic acid bacteria; Milk; Microbial interactions},
}

@Article{Ross1996-Indices,
  author  = {T. Ross},
  journal = {J Appl Bacteriol},
  title   = {Indices for performance evaluation of predictive models in food microbiology},
  year    = {1996},
  number  = {3},
  pages   = {501–608},
  volume  = {81},
}

@Article{Baranyi1994,
  Title                    = {A dynamic approach to predicting bacterial growth in food},
  Author                   = {Baranyi, J. and Roberts, T. A.},
  Journal                  = {International Journal of Food Microbiology},
  Year                     = {1994},
  Pages                    = {277-294},
  Volume                   = {23}
}

@Article{vanBoekel2002,
  author   = {Martinus A.J.S {van Boekel}},
  title    = {On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells},
  journal  = {International Journal of Food Microbiology},
  year     = {2002},
  volume   = {74},
  number   = {1},
  pages    = {139-159},
  issn     = {0168-1605},
  doi      = {10.1016/S0168-1605(01)00742-5},
  keywords = {Weibull model, Thermal inactivation, Kinetic modelling, Vegetative cells},
}


@Article{Huang2013,
  Title                    = {Optimization of a new mathematical model for bacterial growth},
  Author                   = {Lihan Huang},
  Journal                  = {Food Control},
  Year                     = {2013},
  Number                   = {1},
  Pages                    = {283 - 288},
  Volume                   = {32},

  Abstract                 = {The objective of this work was to optimize a mathematical equation for use as a primary kinetic model that employed a new approach to describe the three-phase growth of bacteria under constant temperature conditions. This research adopted an optimization algorithm in combination with the Runge–Kutta method to solve the differential form of the new growth model in search of an optimized lag phase transition coefficient (LPTC), which is used to define the adaption and duration of lag phases of bacteria prior to exponential growth. Growth curves of Listeria monocytogenes, Escherichia coli O157:H7, and Clostridium perfringens, selected from previously published data, were analyzed to obtain an optimized LPTC for each growth curve and a global LPTC for all growth curves. With the new optimized LPTC, the new growth model could be used to accurately describe the bacterial growth curves with three distinctive phases (lag, exponential, and stationary). The new optimized LPTC significantly improved the performance and applicability of the new model. The results of statistical analysis (ANOVA) suggested that the new growth model performed equally well with the Baranyi model. It can be used as an alternative primary model for bacterial growth if the bacterial adaption is more significant in controlling the lag phase development.},
  Doi                      = {10.1016/j.foodcont.2012.11.019},
  ISSN                     = {0956-7135},
  Keywords                 = {Primary models, Numerical analysis, Modeling},
  Owner                    = {ubarron},
  Timestamp                = {2020.04.21},
}

@Article{Rosso1996,
  Title                    = {Differential growth of Listeria monocytogenes at 4 and 8 ºC: Consequences for the Shelf Life of Chilled Products},
  Author                   = {Rosso, L. and Bajard, S. and Flandrois, J. P. and Lahellec, C. and Fournaud, J. and Veit, P.},
  Journal                  = {Journal of Food Protection},
  Year                     = {1996},
  Number                   = {9},
  Pages                    = {944-949},
  Volume                   = {59},

  Doi                      = {10.4315/0362-028X-59.9.944},
  ISSN                     = {0362-028X},
}

@article{Baranyi1995,
	author = {Baranyi, J. and Roberts, T.A.},
	journal = {International Journal of Food Microbiology},
	pages = {199-218},
	title = {Mathematics of predictive microbiology},
	volume = {26},
    number = {},
	year = {1995}
}

@article{Richards1959,
	author = {J. F. Richards},
	journal = {J Exp Bot},
	pages = {290-310},
	title = {A flexible growth function for empirical use},
	volume = {1},
        number = {10},
	year = {1959}
}


@article{Peleg1999,
	author = {M. Peleg},
	journal = {Food Research International},
	pages = {271-278},
	title = {On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model},
	volume = {32},
	year = {1999}
}

@article{Mafart2002,
	author = {Mafart, P. and Couvert, O. and Gaillard, S. and Leguerinel},
	journal = {International Journal of Food Microbiology},
	pages = {107-113},
	title = {On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model},
	volume = {72},
	year = {2002}
}

@article{Mafart2005,
	author = {I. Albert and P. Mafart},
	journal = {International Journal of Food Microbiology},
	pages = {197-211},
	title = {A modified Weibull model for bacterial inactivation},
	volume = {100},
	year = {2005}
}

@article{Ratkowsky1983,
	abstract = {The "square-root" relationship proposed by Ratkowsky et al. (J. Bacteriol. 149:1-5, 1982) for modeling the growth rate of bacteria below the optimum growth temperature was extended to cover the full biokinetic temperature range. Two of the four parameters of this new nonlinear regression model represent minimum and maximum temperature bounds, respectively, for the predicted growth of the culture. The new model is easy to fit and has other desirable statistical properties. For example, the least-squares estimators of the parameters of the model were almost unbiased and normally distributed. The model applied without exception to all bacterial cultures for which we were able to obtain data. Results for 30 strains are reported.},
	author = {Ratkowsky, D A and Lowry, R K and McMeekin, T A and Stokes, A N and Chandler, R E},
	issn = {0021-9193},
	journal = {Journal of Bacteriology},
	number = {3},
	pages = {1222–1226},
	publisher = {American Society for Microbiology Journals},
	title = {Model for bacterial culture growth rate throughout the entire biokinetic temperature range.},
	volume = {154},
	year = {1983}
}

@article{Rosso1995,
	author = {Rosso, L and Lobry, Jean and {Charles (Bajard)}, Sandrine and Flandrois, Jean-Pierre},
	doi = {10.1128/AEM.61.2.610-616.1995},
	journal = {Applied and environmental microbiology},
	month = {03},
	pages = {610–6},
	title = {Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth},
	volume = {61},
	year = {1995}
}

@Article{Zwietering1991,
  journal = {Appl. Environ. Microbiol},
  title   = {Modeling of bacterial growth as a function of temperature},
  year    = {1991},
  pages   = {1094–1101},
  volume  = {57},
  author = {Zwietering, M. H. and J. T. DEKoos and B. E. Hasenack and J. C. DeWit and K.Van’tRiet},
}

@book{Seber1989cap7,
  author    = {G. A. F. Seber and C. J. Wild},
  publisher = {John Wiley & Sons, Ltd},
  isbn = {9780471725312},
  title = {Nonlinear Regression},
  year = {1989},
  keywords = {classes of models, nonlinear regression, exponential growth curves, monomolecular growth curves, sigmoidal growth models},
abstract = {Summary This chapter includes the following topics: Introduction Exponential and Monomolecular Growth Curves Sigmoidal Growth Models Fitting Growth Models: Deterministic Approach Stochastic Growth-Curve Analysis Yield-Density Curves}
}


@Article{Huang2009Inac,
  author   = {Lihan Huang},
  journal  = {Journal of Food Engineering},
  title    = {Thermal inactivation of Listeria monocytogenes in ground beef under isothermal and dynamic temperature conditions},
  year     = {2009},
  issn     = {0260-8774},
  number   = {3},
  pages    = {380-387},
  volume   = {90},
  doi      = {10.1016/j.jfoodeng.2008.07.011},
  keywords = {Thermal inactivation, Modeling, Kinetic analysis},
}

@article{Oscar2002,
title = {Development and validation of a tertiary simulation model for predicting the potential growth of Salmonella typhimurium on cooked chicken},
journal = {International Journal of Food Microbiology},
volume = {76},
number = {3},
pages = {177-190},
year = {2002},
issn = {0168-1605},
doi = {10.1016/S0168-1605(02)00025-9},
author = {Thomas P Oscar},
keywords = {Cooked chicken, , Predictive model, Growth, Simulation, Validation}
}

@Comment{jabref-meta: databaseType:bibtex;}
