Silva, Cristina L. M.

English

Repositório Institucional da Universidade Católica Portuguesa

OBTENTION	  AND	  DATA	  MANAGEMENT	  IN	  PREDICTIVE	  MICROBIOLOGY	  MODELS	  	  Cris6na	  L.M.	  Silva	  	  	  5th	  September	  2012	  CBQF	  –	  Centro	  de	  Biotecnologia	  e	  Química	  Fina,	  Escola	  Superior	  de	  Biotecnologia,	  Centro	  Regional	  do	  Porto	  da	  Universidade	  Católica	  Portuguesa	  OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  ObjecFves	  of	  food	  industry	   PredicFon	  of	  shelf	  life	   PreservaFon	  of	  foods	  	   Control/monitor	  the	  growth	  of	  microorganisms	   Predict	  microbial	  death	  6	  Product  Design Primary  Production Industrial Production Product  Transport Logistics Trade  Home  Storage Consumption  Disposal   DistribuFon	  chain	  7	  Product  Design Primary  Production Industrial Production Product  Transport Logistics Trade  Home  Storage Consumption  Disposal   DistribuFon	  chain	  OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  The	  challenge	   Microorganisms	  response	  depends	  on:	  	    - Intrinsic	  factors	  	  	  -­‐  Extrinsic	  factors	  -­‐  System	  dynamics	  pH	  aw	  others	  T	  pH	  humidity	  	  salt	  gas	  concentraFon	  others	  •  	  microbial	  interac6on	  • 	  	  natural	  strains	  diversity	  	  • 	  	  history	  of	  ini6al	  popula6on	  • 	  	  complexity	  of	  food	  structure	  • 	  	  interac6on	  food/microorganism	  	  • 	  	  predic6ons	  in	  real	  and	  varying	  environmental	  condi6ons	  OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  PredicFve	  microbiology	  The use of mathematical models in the description of microbial responses to environmental stressing factors   Thermal	  lethality	  	  	  	  	  	  -­‐	  D	  and	  z	  values	  –	  Bigelow	  model	  	  	  	  F dtcT T ztp c refm m= −∫ 100( )/The	  idea	  is	  not	  even	  recent!	  mathematics microbiology statistics predictive microbiology  Model    à    mathematical expression  	  	  	         i=1,2,...,n (number of experimental runs/observations)   j=1,2,...,v   k=1,2,...,p 	  yi = f(xij, θk) + εi 00.10.20.30.40.50.60.70.80 1000 2000 3000 4000 5000xyymodel yexp θ*	Precise ? Accurate ? Minimize differences  precise and accurate description of observations model adequacy quality of model parameters objective  "    knowledge of the process "    process effects on product "    control of process variables advantages Processes Chemical Physical Food Processes Transport Phenomena •  heat •  mass •  momentum Reaction kinetics  Properties Modelling mathematical function	  variables parameters data  regression schemes	  experimental design design Criterium	  validation control optimization Objectives	  quality safety  " prediction / simulation       " development of efficient processes              contribution to safety  aplication sigmoidal behaviour presence of aggregated microorganisms or sub populations  more heat (or other stress factor) resistent inactivation 01234567890 500 1000 1500 2000time(s)log N0123456780 20 40 60 80 100 120 140Miller (2004) 52.5 ºC Listeria innocua liquid medium  55 ºC 57.5 ºC 60 ºC 62.5 ºC 65 ºC log N time (min) 0123456780 20 40 60 80 100 120 14052.5 ºC 55 ºC 57.5 ºC 60 ºC 62.5 ºC 65 ºC log N time (min) Miller (2004) Listeria innocua liquid medium  v  primary         0123456780 0.5 1 1.5 2 2.5Time  (min)Log CFU/gkinetics parameters Types	  of	  models	  v  primary         v  secondary     0123456780 0.5 1 1.5 2 2.5Time  (min)Log CFU/gparameters pH aw temperature v  primary         v  secondary v  terciary -  integration of the previous models - software    0123456780 0,5 1 1,5 2 2,5Time (min)Log CFU/gparameters pH aw temperature v  primary  01234567890 500 1000 1500 2000logN k logN0 logNres L Time (s) empirical fundamental N0      number of initial viable spore cells Nres    number of residual spore cells k       maximum inactivation rate  L        lag or shoulder  InacFvaFon	  models	  v  primary  First order  ( )ktexpNN 0 −= DtNloglogN 0 −=( ) ( ) ( )tkexpF1tkexpFNN21110−−+−= Cerf  (1977) Kamau et al.  (1990) ( )( )( )⎟⎟⎠⎞⎜⎜⎝⎛+−++=tkexp1F12tkexp1F2logNNlog21110D – decimal reduction time F1 – fraction of inactivated microorganisms k1 e k2– kinetic constants  biphasic 0123456780 2 4 6 8Time (min)LogCFU/g0123456780 1 2 3 4 5 6Time (min)Log CFU/mlv  primary  Cole et al.  (1993) ( )⎟⎠⎞⎜⎝⎛−−+−+=σwtlogλσ4exp1αwαNlog0123456780 0.5 1 1.5 2 2.5Time (min)Log CFU/g( )( )( )( )( ) ( )( )( )( ) ⎟⎟⎠⎞⎜⎜⎝⎛−+−+−+−+−+=Ltkexp1Lkexp1F1Ltkexp1Lkexp1FlogNNlog2211110Whiting &  Buchanan (1992) distribution of heat sensibility of microbial populations L – lag or shoulder v  primary  Baranyi et al. (1993) ( ) ( )( ) 0N0tNNttkdtdN==−= βα‘lag’ function ‘tail’ function 01234567890 500 1000 1500 2000tempo (s)log Nv  primary  Baranyi et al. (1993) ( ) ( )( ) 0N0tNNttkdtdN==−= βα‘lag’ function ‘tail’ function 01234567890 500 1000 1500 2000tempo (s)log N( )( ) ( )( ) ( )tkexp0Q10Q1tkexplogNNlogmaxmax0 −++−=⎟⎟⎠⎞⎜⎜⎝⎛Geeraerd et al. (2000) ( )QkdtdQNQkkdtdNmaxQmax−=−=Q – variable related to the physiological state of the cells v  primary  Gompertz  01234567890 500 1000 1500 2000tempo (s)log NBhaduri et al (1991) Linton et al. (1995, 1996) Xiong et al. (1999) ( )⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛+−⎟⎟⎠⎞⎜⎜⎝⎛−⎟⎟⎠⎞⎜⎜⎝⎛−= 1tLNNlogek expexpNNloglogNlogNres0res00Logistic  Listeria monocytogenes ( )( )L-tkexp1clogN+= c – constant  reparameterized for inactivation based in Zwitering (1990) 	  v  secondary        	  Arrhenius  Davey / Arrhenius modified  “Square-root type models”       Ratkowsky et al. (1982)       McMeekin et al. (1987)       Adams et al. (1991)       McMeekin et al. (1992)  )Tb(Tk min−=)a(a)Tb(Tk minwwmin −−=)pH(pH)Tb(Tk minmin −−=⎟⎠⎞⎜⎝⎛=RTE-expkk a0 RTElnklnk a0 −=2W4W32210 aCaCTCTCClnk ++++=)pH(pH)a(a)Tb(Tk minminwwmin −−−=min – minimal value for growth ⎟⎟⎠⎞⎜⎜⎝⎛⎟⎟⎠⎞⎜⎜⎝⎛−=refaref T1T1RE-expkkv  tertiary  softwares Microbial growth  Shelf life prediction	  	  Microbial inactivation  Difficul6es	  in	  food	  processes	  modelling:	  	  "  	  	  Dynamic	  processes	  "  	  	  Complexity	  and	  heterogeneity	  of	  products	  "  	  	  Structural	  and	  physicochemical	  changes	  Gompertz	  ( ))time(dNlogd( ) ( ) 'dt1'tLNNlog)1exp(kexpexp1'tLNNlog)1exp(kexp)1exp(kNlogNlogt0res0res00 ∫⎥⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎢⎣⎡⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛+−⎟⎟⎠⎞⎜⎜⎝⎛−⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛+−⎟⎟⎠⎞⎜⎜⎝⎛−=dynamic situation of temperature ⎟⎟⎠⎞⎜⎜⎝⎛⎟⎟⎠⎞⎜⎜⎝⎛−=refaref T1T1RE-expkk⎟⎟⎠⎞⎜⎜⎝⎛⎟⎟⎠⎞⎜⎜⎝⎛−=refT1T1bexpaLGill (2011) Linear  ( ))time(dNlogddynamic situation of temperature ( )refTTz1ref 10DD−−=⎟⎟⎠⎞⎜⎜⎝⎛ −∫=−PT0ZrefTTrefTdt10D1010NNapproach by Vieira et al. (2002) Cupuaçu nectar different temperature histories Case	  studies:	  ( )⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛+−⎟⎟⎠⎞⎜⎜⎝⎛−⎟⎟⎠⎞⎜⎜⎝⎛−= 1tLNNlogekexpexpNNloglogNlogNres0maxres00Square-root!!"( )dTckmax −=Arrhenius!!"""⎟⎟⎠⎞⎜⎜⎝⎛⎟⎟⎠⎞⎜⎜⎝⎛−=refarefmax T1T1RE-expkkWilliams-Landel-Ferry!!""( )( )⎟⎟⎠⎞⎜⎜⎝⎛+= minminT-TbT-Ta10LArrhenius!""""⎟⎟⎠⎞⎜⎜⎝⎛⎟⎟⎠⎞⎜⎜⎝⎛−=refT1T1bexpaLc, d !constants!Kref !reaction rate at Tref!Ea !activation energy!a, b !constants!1!2!4!3!Gill (2011) log N!time (s)!01234567890 1000 2000 3000 4000 5000 6000 7000T=52ºC T=56ºC logN time (s) 01234567890 100 200T=60ºC T=64ºC T=68ºC 050010001500200025003000320 325 330 335 340 345T (K)L (s)00,050,10,150,20,250,3320 325 330 335 340 345T (K)k max (s-1)01234567890 1000 2000 3000 4000 5000 6000 7000time (s)log NT=52ºC T=56ºC 01234567890 100 200time (s)log NT=60ºC T=64ºC T=68ºC Gompertz"Two-step"L= f(T)!kmax= f(T)!One-step"Equations 1 and 4 selected"L.	  Monocytogenes	  in	  half-­‐cream	  at	  52,	  56,	  60,	  64	  e	  68ºC	  (Casadei	  et	  al.	  1998)	  Tank with water  +  L. innocua 15 minutes   Weibull model  Inumeration OzonaFon	  OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  How	  to	  obtain	  the	  data	   Experiments	  in	  broth	  at	  various	  condiFons	  (pH,	  T,	  aw,	  [growth	  inhibitors],	  etc.)	    InoculaFon	  studies	  in	  foods	  under	  various	  condiFons	   Derive	  model	  parameters	   "    Heuris6c	  sampling	  "  	  	  Experimental	  design	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	    Minimize	  variance	  of:	  	  "  	  	  predicted	  response	  "  	  	  parameter	  es.mates	  Sampling:	      "    Regression schemes    "   Analysis of residuals ( )[ ]2n1ikijin1i2i ,xfyeSSR ∑∑==θ−== Least-squares  method Data	  analysis:	  Mathematical  complexity Adequate description model parameters quality OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  Available	  soMware	  à predicts	  shelf-­‐life	  as	  well	  as	  growth	  of	  spoilage	  and	  pathogenic	  bacteria	  in	  seafood	  	  	  	  à evaluates	  the	  effect	  of	  constant	  or	  fluctuaFng	  temperature	  storage	  condiFons	  (Dalgaard	  et	  al.	  2002,	  2003,	  2008)	  	  	  à more	  than	  40	  models	  for	  different	  bacterial	  pathogens	  à  the	  soMware	  allows	  growth	  or	  inacFvaFon	  of	  pathogens	  to	  be	  predicted	  for	  different	  combinaFons	  of	  constant	  temperature,	  pH,	  NaCl/aw	  and,	  in	  some	  cases,	  other	  condiFons	  such	  as	  organic	  acid	  type	  and	  concentraFon,	  atmosphere,	  or	  nitrate	  	  à  includes	  more	  than	  40.000	  curves/data	  on	  growth,	  survival	  or	  inacFvaFon	  of	  microorganism	  in	  foods.	  	  à data	  has	  been	  obtained	  from	  the	  literature	  or	  provided	  by	  supporFng	  insFtuFons	  à  the	  modelling	  toolbox	  within	  ComBase	  includes	  the	  Combase	  Predictor	  (previously	  Growth	  Predictor	  and	  Food	  MicroMoodel).	  	  à French	  decision	  support	  system	  that	  includes	  (i)	  a	  database	  with	  growth	  and	  inacFvaFon	  responses	  of	  microorganisms	  in	  foods	  and	  (ii)	  predicFve	  models	  for	  growth	  and	  inacFvaFon	  of	  pathogenic	  bacteria	  and	  some	  spoilage	  microorganisms	  	  	  à predicts	  the	  effect	  of	  organic	  acids,	  temperature,	  pH	  and	  moisture	  on	  growth	  of	  Listeria	  monocytogenes	  in	  products	  	  	  predict	  the	  growth	  of	  Listeria	  monocytogenes	  and	  Staphylococcus	  aureus	  on	  Ready-­‐To-­‐Eat	  meat	  products	  as	  a	  funcFon	  of	  pH	  and	  water	  acFvity	  	  OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  ValidaFon	  studies	   Before	  market	  introducFon,	  valida6on	  has	  to	  be	  carried	  out	  for	  new	  or	  altered	  products	   AMer	  the	  concept/prototype	  	  à	  acceptance	  tests	  à	  refinement	  of	  models	  à	  final	  formulaFon	  à	  validaFons	  in	  challenge	  test	  	  	  	  	  	  	  have	  to	  be	  performed	  	  	  	  	  	  Milkowski (2012)  This	  is	  the	  main	  difference	  between	  free	  and	  paied	  soMware	   A	  complete	  challenge	  test	  takes	  aproximately	  3	  months	  +	  evaluaFon	  of	  process	  variaFons	  +	  idenFficaFon	  of	  acceptable	  limits	  for	  formulaFon	  limits	  	  à	  establish	  a	  theoreFcal	  shelf	  life	    Industry	  saves	  costs	  and	  Fme	  when	  using	  reliable	  	  predicFve	  micro	  modelling	  	  	  	  	  	  Milkowski (2012)   Industry	  has	  to	  perform	  validaFon	  studies,	  for	  final	  verificaFon	   The	  analyFcal	  values	  for	  pH,	  water	  acFvity,	  moisture,	  etc	  	  are	  crucial	  	   However,	  predicFve	  microbiology	  does	  not	  replace	  	  hygiene	  measure	  or	  Good	  Manufacturing	  PracFces	  	  	  	  	  	  à	  models	  can	  not	  be	  the	  only	  hurdle	  to	  pathogens	  	  	  	  	  	  Milkowski (2012) OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  Acknowledgement	  by	  regulaFon	  	  	  	  	  	  	  Milkowski (2012)  United	  States:	  	  	  	  	  	  	  	  	  	  -­‐	  U.S.	  9CFR	  RegulaFons	  	  	  	  	  	  -­‐	  2008	  USDA	  Supplementary	  Guidance	   European	  Union:	  	  	  	  	  	  -­‐	  2005	  &	  2010	  EU	  regulaFon	  	  	  	  	  	  OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  The	  complexity	  of	  dynamic	  condiFons	  	  	    The	   greatest	   modeller’s	   effort	   has	   been	   given	   to	   data	   obtained	  under	  constant	  (or	  staFc)	  environmental	  condiFons	    From	  a	  realisFc	  point	  of	  view	  this	  is	  somehow	  restricFve,	  since	  the	  majority	   of	   thermal	   processes	   occur	   under	   Fme-­‐varying	  environmental	  condiFons,	  and	  kineFc	  parameters	  obtained	  under	  such	   circumstances	  may	   differ	   from	   the	   ones	   esFmated	   at	   staFc	  condiFons,	  which	  compromises	  safety	  control	  	  Case	  study	  OUTLINE	   ObjecFves	  of	  food	  industry	   The	  challenge	   PredicFve	  microbiology	   How	  to	  obtain	  the	  data	   Available	  soMware	   ValidaFon	  studies	   Acknowledgement	  by	  regulaFon	   The	  complexity	  of	  dynamic	  condiFons	   Conclusions	  	  Conclusions	   Great	  progresses	  in	  the	  past	  20	  years	   There	  are	  many	  models	  available,	  each	  with	  its	  benefits	  and	  limitaFons	   Yet	  much	  work	  has	  sFll	  to	  be	  developed,	  parFcularly	  kineFc	  studies	  under	  dynamic	  condiFons	  	   ValidaFon	  studies	  have	  to	  be	  carried	  out	  for	  new	  or	  altered	  products,	  due	  to	  the	  complexity	  of	  the	  systems	  	   PredicFve	  microbiology	  is	  a	  powerful	  tool,	  but	  does	  not	  replace	  	  hygiene	  measures	  or	  Good	  Manufacturing	  PracFces	  	  	  Thank	  you!	  	  	  Teresa	  Brandão	  Mª	  Manuel	  Gil	  Mª	  FáFma	  Miller	  Elisabeth	  Alexandre	  

Obtention and data management in predictive microbiology models

https://repositorio.ucp.pt/bitstream/10400.14/10062/1/OI17.pdf

Obtention and data management in predictive microbiology models

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