Tiempo de esterilización (Valor F o )

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Francisco Martín Cruz
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1 iemp de esterilización (Valr F ) Impact f experimental parameter variability n thermal fd prcessing decisins based n micrbial safety bjectives # 153 Definitins Prcessr must estimate prcess time ( ) inactivating bacterial spres f the pathgen f interest - Clstridium btulinum - Heat-resistant bacterial spre f mst severe safety risk Predictin Mdel Decimal reductin time - = SV D Number f decimal reductins #

inactivating bacterial spres f the pathgen f interest - Clstridium btulinum - Heat-resistant bacterial

2 Histry f Sterilizatin Objectives Safety bjective: 12D = Same prcess Safety bjective: N = 10-9 (1 in 1 billin cntainers) = Same final lad High N Lw N High N Lw N 12D 12D N = 10-9 SV # 155 iemp de esterilización (Valr F ) N distribución F SV D Variabilidad D distribución Objetiv: N = 10-9 (cn 95% cnfianza) SV N Lg N Valr fij Objectiv del prces 10-9 espra/unidad #

# 155 iemp de esterilización (Valr F ) N distribución F SV D Variabilidad D distribución

3 iemp de esterilización (Valr F ) Natural prevalence f C. btulinum in fresh mushrms Sample Mean N lg CFU/g Fresh mushrms Decimal reductin time (D 110C ),C. btulinum in cmmercially canned mushrms Sample emp Mean D (ºC) (min) Cmmercial canned mushrm # 157 Data generatin: Sample size f each generated dataset must be equal t sample size reprted in the literature (1) - N refers t N frm simulatin (12 values) - D refers t D frm simulatin (9 values) Determinatin f data acceptability: Metric helps t determine if the distributin f the generated data is apprximately equivalent t the distributin f the data reprted in the literature (1) Chu, 2009 #

17 # 157 Data generatin: Sample size f each generated dataset must be equal t sample size reprted in the literature (1) - N refers t N frm simulatin (12 values) - D

4 Acceptable errr in the minimum metric f1 a a a f 2 z z z f 3 Acceptable errr in the mean Acceptable errr in the maximum f i weight factr, a judgment decisin acceptable metric errr f errr f errr mean 1 minimum 2 maximum f3 Statistical cnsulting grup. Summer Dept f Statistics, OSU # 159 Determinatin f acceptable errr An acceptable errr was defined fr N and D values - hermal prcess time errr 0.1 min reprted N = N generated N = N reprted D = D generated D = D Errr reprted F generated 0.1 Statistical cnsulting grup. Summer Dept f Statistics, OSU #

Dept f Statistics, OSU # 159 Determinatin f acceptable errr An acceptable errr was defined fr N and D values - hermal prcess time errr 0.

5 Evaluate metric fr N and D values: metric ( ) f 1 ( a a a ) f 2 ( z z z ) f 3 Simulatin set accepted if the metric value was less than metric 1 f f f metric N 393 metric D C If rejected, a new set was generated. his test was a necessary but nt a sufficient test as the riginal experimental dataset was nt available t determine its true distributin Statistical cnsulting grup. Summer Dept f Statistics, OSU # 161 Micrbial Inactivatin Objective = 10-9 F SV ( D ) (lg N lg N) D Experimental values N, D F lg N lg N D lg N j lg N, j F D, j Calculated prcess based n average values, N, D What is the real prcess safety fr data set j? #

his test was a necessary but nt a sufficient test as the riginal experimental dataset was nt available t determine its true distributin Statistical cnsulting grup.

6 An example f the prbability distributin (n = 108) fr the estimatin C. btulinum spre survival (lg N, CFU/cntainer) based n ne accepted N and D 110C dataset 25% 20% Safe prcesses Unsafe prcesses Frequency (%) 15% 10% % under prcessing = 55% 5% 0% lg N (CFU/cntainer) # 163 Determinatin f the recmmended number f datasets fr the Mnte Carl simulatins: Estimate the standard deviatin (SD) and cefficient f variatin (CV) fr the % under prcessing fr 2, 3, 4, s = 500 datasets he recmmended sample size S is the ne with an acceptable CV value Efrn & ibshirani,1986; Almnacid-Merin & rres, 2009 #

prcessing = 55% 5% 0% 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 lg N (CFU/cntainer) # 163 Determinatin f the recmmended number f datasets fr the Mnte Carl

7 Determinatin f the recmmended number f Mnte Carl simulatins fr the estimatin f thermal prcessing time ( ) Standard deviatin 10 Unsafe prducts (%) Cefficient f variatin Number f datasets samples a sample size f 100 was cnsidered sufficient # 165 Based n the acceptable 100 datasets: Calculate Lg N (= 12 x 9 = 108 values) fr each dataset based n the prcess time calculated using average values (N & D ) Use the predictive mdel t estimate an increased prcessing time N D increased fr each unique & cmbinatin (n = 100 sets) t achieve N = 10-9 spres/cntainer with 95% cnfidence recmmended increased Recmmend a prcess time ( ) larger than 95% f the increased prcessing time values estimated (, n = 100) #

fr each dataset based n the prcess time calculated using average values (N & D ) Use the predictive mdel t estimate an increased prcessing time N D increased fr each unique & cmbinatin (n =

8 Definitin f a recmmended prcess time Mnte Carl generated distributin ( increased, n = 100 values) Frequency Mnte Carl generated distributin Prcess time based n average values ( ) High under prcessing risk Recmmended prcess time Lw under prcessing risk 5% Increased prcess increased time ( ) # 167 Prcessr decisins hermal Sterilizatin Mdel arget Organism N & Mdel parameters Mean, SD MS Excel Generated Data Randm number Mean, SD Initial Lad, N Lgnrmal distributin 12 values D 110C Nrmal ds distributin 9 values Safety Objective Max acceptable N Estimate F 110C Average N, D 110C Metric Maximum acceptable errr Accept dataset Determine % underprcessing 108 values, S = 2, 500 datasets Estimate SD, CV Reject dataset Acceptable CV S = 100 datasets Acceptable Risk % cnfidence Increased F 110C Distributin 100 values Recmmended hermal Prcess 95% cnfidence #

Generated Data Randm number Mean, SD Initial Lad, N Lgnrmal distributin 12 values D 110C Nrmal ds distributin 9 values Safety Objective Max acceptable N Estimate F 110C Average N, D 110C Metric

9 Micrbial inactivatin Effect f increasing the prcessing time t reduce the risk f under prcessing (an example) F 110 = 5.96 min F 110 = 8.89 min F (a) (b) Frequency (%) Safe prcesses Safe prcesses Frequency (%) lg N (CFU/cntainer) lg N (CFU/cntainer) 55% under prcessing 5% under prcessing # (a) 50 Original SD Frequency (%) Recmmended prcess time (95% cnfidence level) Increased prcess time (, min) #

lg N (CFU/cntainer) 0-22 -21-20 -19-18 -17-16 -15-14 -13-12 -11-10 -9-8 -7-6 -5-4 -3 lg N (CFU/cntainer) 55% under prcessing 5% under prcessing # 169 60 (a) 50 Original SD

10 60 10% reduced variability 50% reduced variability N (b) 50 90% reduced variability Prcess time shifts twards the left Frequency (%) Increased prcess time (, min) # Freque ency (%) % reduced variability 50% reduced variability 90% reduced variability (c) D Prcess time shifts even mre t the left Increased prcess time (, min) #

4 Increased prcess time (, min) # 171 60 Freque ency (%) 50 40 30 10% reduced variability 50% reduced variability 90% reduced variability

11 60 10% reduced variability (d) 50% reduced variability N & D 90% reduced variability Very large prcess time shift t the left Freque ency (%) Increased prcess time (, min) # 173 Results: Effect f the reductin in parameter variability Average value based estimatin (min) = 5.96 (55% under prcessing) Mnte Carl based estimatin (min) = 9.60 (5% under prcessing) Recmmended prcess time (min), 95% cnfidence SD reductin 10% 50% 90% N D N & D A 3.2 min reductin! #

4 Increased prcess time (, min) # 173 Results: Effect f the reductin in parameter variability Average value based estimatin (min) = 5.

12 Cnclusins Mnte Carl-type simulatins allwed estimatin f thermal prcessing times achieving a desirable surviving spre lad prbability with a specified cnfidence level. Mnte Carl-type simulatins allwed estimatins f the reductin in prcess time gained by the reductin in the variability f prcess design parameters. # 175 iemp de esterilización (Valr F ) 25% 20% Prduct segur Prduct peligrs Frecuencia (%) 15% 10% Usand Valres Prmedi F = 5.96 min 5% 0% lg N (CFU/cntainer) #

Mnte Carl-type simulatins allwed estimatins f the reductin in prcess time gained by the reductin in the variability f prcess

13 Overall cnclusins All prcedures here presented were implemented n MS Excel spreadsheets and used cncepts suitable fr inclusin i in an FS undergraduate prgram Prcedures develped emphasized the imprtance f minimizing variability, but hw can a prcessr achieve this gal? What are the surces f this variability? If the variability surce is the measurement itself, yu can reduce it by training, imprvement f methds and multiple sampling If the surce f the variability is the true hetergeneity f the ppulatin (i.e., it is irreducible by measurement imprvements), ne must find the variability surce # 177 Reducing true hetergeneity: an example hermal prcessing, initial micrbial lad, lg N Larger prcessrs need a larger number f suppliers and ne can expect micrbial lad differences amng them uency Freq specif ficatin lg N Slutin: segregate yur suppliers which will reduce variability and reduce prcessing intensity #

14 Preguntas? # 179 Buen Camin! Have a gd Jurney! #

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