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Final Conference BASELINE . Bologna 11-12 November 2013

BASELINE software tool for calculation of microbiological criteria and risk management metrics for selected foods and hazards WP6 Model Development. Final Conference BASELINE . Bologna 11-12 November 2013. Baseline Software tool: Data and Figures. 58 users registered in the application.

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Final Conference BASELINE . Bologna 11-12 November 2013

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  1. BASELINE software tool for calculation of microbiological criteria and risk management metrics for selected foods and hazards WP6 Model Development Final ConferenceBASELINE . Bologna 11-12 November 2013

  2. Baseline Software tool: Data and Figures 58 users registered in the application Final ConferenceBASELINE . Bologna 11-12 November 2013

  3. Baseline Software tool: Data and Figures Laboratories, R&D institutions, Universities and Official authorities Final ConferenceBASELINE . Bologna 11-12 November 2013

  4. Baseline Software tool: Data and Figures Current position of Baseline software users Final ConferenceBASELINE . Bologna 11-12 November 2013

  5. Baseline Software tool: Data and Figures Main intended use: dissemination, training, teaching, research and training activities and official control. It was presented at the International Conference on Predictive Modelling in Foods (ICPMF 8), Paris (France) being selected at the top five software tools Webinar and training sessions were perfomed over 2013 (Oslo, Northern Spain, Bergamo) EFSA workshop (September 26th, Parma) Improvements and upgrades were carried out related to terminology, units and equations. Final ConferenceBASELINE . Bologna 11-12 November 2013

  6. Example I DERIVING AN MC FROM A PO THAT IS SET AS CONCENTRATION LIMIT OF THE PATHOGEN L. monocytogenes in cold-smoked salmon Raw material (fresh fish) Treatment Consumption Manufacturing Distribution Storage PC PO PO PO FSO=100 cfu/g It is assumed that a Competent Authority has established a PO for the concentration of a specific pathogen in a certain commodity. It is established a maximum concentration level of 100 cfu/g before consumption For simplification, PO is set after product elaboration / storage Final ConferenceBASELINE . Bologna 11-12 November 2013

  7. Example I L. monocytogenes in cold-smoked salmon: Input data Initial concentration: just after packaging ~ 10-20 cfu/g Storage in the industry at 4ºC during 4 days (96h) Product formulation: 2ppm phenol + 3 mg/100g NaCl Final ConferenceBASELINE . Bologna 11-12 November 2013

  8. Example II Final ConferenceBASELINE . Bologna 11-12 November 2013

  9. Example II Final ConferenceBASELINE . Bologna 11-12 November 2013

  10. Example II Final ConferenceBASELINE . Bologna 11-12 November 2013

  11. Final concentration : 1.74 log cfu/g Final ConferenceBASELINE . Bologna 11-12 November 2013

  12. Example I L. monocytogenes in cold-smoked salmon: Input data Estimate the standard deviation of the product in such a way the following PO will be complied One can use the ‘Solver” function by changing values for ‘standard deviation’, when starting with an unknown value for ‘standard deviation’ for a known probability (target cell value equal to 0.95)] P (log cfu/g >3 ) < 5 % of the samples conforming the lot Using the NORMDIST function of MS Excel we obtain: =NORMDIST(3; 1.74;σ; 1) σ ~ 0.76 log cfu/g Therefore, the distribution for the concentration of Lm satisfying the PO would be log normal (1.74; 0.76) Final ConferenceBASELINE . Bologna 11-12 November 2013

  13. Example I L. monocytogenes in cold-smoked salmon: Input data Establish a microbiological limit so that a practical and feasible sampling plan can be applied In this scenario, the value for m is chosen to be 2 log cfu/g (i.e., 100 cfu/g), considering that lower or higher values would be either not practical because of constraints regarding microbiological analysis Calculate what the probability is for ‘n’ samples to be negative for a just compliant batch/lot Decide on the probability with which a non compliant lot should be rejected (95%) How many samples should be taken from the lot so that the probability of rejection is achieved? Final ConferenceBASELINE . Bologna 11-12 November 2013

  14. Final ConferenceBASELINE . Bologna 11-12 November 2013

  15. n = 5; c= 0 Pacpp = 0.1023 Final ConferenceBASELINE . Bologna 11-12 November 2013

  16. n = 7; c= 0 Pacpp = 0.0411 OK Final ConferenceBASELINE . Bologna 11-12 November 2013

  17. Input received by WP1-5 Final ConferenceBASELINE . Bologna 11-12 November 2013

  18. Input received by WP1-5 Final ConferenceBASELINE . Bologna 11-12 November 2013

  19. Input received by WP1-5 Final ConferenceBASELINE . Bologna 11-12 November 2013

  20. Example I Final ConferenceBASELINE . Bologna 11-12 November 2013

  21. Example II - Influence of processing time / temperatureonthegrowth of SalmonellaEnteritidis in eggyolk - Establishment of samplingprocedures in powderedeggs Final ConferenceBASELINE . Bologna 11-12 November 2013

  22. Example II Processing temperature: assume constant temperature of 20°C Latimer et al. 2002 Processing time: scenarioanalysis (8h; 15h)

  23. Example II Estimation of thegrowth of S. Enteritidis at bothstorage times: 1.23 log cfu/ml = 17 cfu/ml a) 8h: 2.89 log cfu/ml = 776 cfu/ml b) 15h: Final ConferenceBASELINE . Bologna 11-12 November 2013

  24. Example II Samplesize Lot weight Contaminatedpart of thelot Concentration in contaminatedsamples Number of samplescollected Final ConferenceBASELINE . Bologna 11-12 November 2013

  25. Example II • SCENARIO ANALYSIS • Low contamination vs high contamination (17 – 776 cfu/g) • Increasing proportion of the contaminated part of the lot (from 0.01 to 0.1) • Lot size effect (1000, 10000, 100000 g) • Combining number of samples and sample size (n and w) Final ConferenceBASELINE . Bologna 11-12 November 2013

  26. Example II Final ConferenceBASELINE . Bologna 11-12 November 2013

  27. Example II • Low contamination vs high contamination (17 – 776 cfu/g) • Sampling is more effective as p increases, since Pacc decreases. No significant impact of the lot size and the combination n/w Impact of highcontaminationwhen p > 0.05. At lowvalues of p, samplingismainlyaffectedbytheinitialprevalence • Increasingproportion of thecontaminatedpart of thelot (from 0.01 to 0.1) • Samplingis more effective as p increases, especiallyfrom 0.01 to 0.05 • Lot sizeeffect (1000, 10000, 100000 g)No significant • Combining number of samples and sample size (n and w) • Increasing n and decreasing w is more effective to detect positives, regardless of the microbial contamination Final ConferenceBASELINE . Bologna 11-12 November 2013

  28. Example II Dependence of probability of acceptance on variability in lot sizes (N, kg). The sampling plan is characterized by sample size (w) = 100 g; microbial concentration (c) = 1 CFU/g ; proportion of contamination (p) = 0.05. The red vertical line corresponds to number of samples = 30. Final ConferenceBASELINE . Bologna 11-12 November 2013

  29. Example II Dependence of probability of acceptance on variability in sample sizes (w, g). The sampling plan is characterized by lot size (N) = 3000 kg; microbial concentration (c) = 1 CFU/g ; proportion of contamination (p) = 0.05. The red vertical line corresponds to number of samples=30. Final ConferenceBASELINE . Bologna 11-12 November 2013

  30. Example II Dependence of probability of acceptance on variability in proportions of the contaminated lot (p). The sampling plan is characterized by lot size (N) = 3000 kg; sample size (w) = 100 g ; microbial concentration (c) = 1 CFU/g. The red vertical line corresponds to number of samples=30. Final ConferenceBASELINE . Bologna 11-12 November 2013

  31. The software tool is currently free available • www.baselineapp.com • Further possibilities: on-demand training sessions, consulting, assistance to SMEs to develop MC and sampling plans according to the real production systems. • Inclusion of new predictive models and models validation etc. Final ConferenceBASELINE . Bologna 11-12 November 2013

  32. THANK YOU FOR YOUR ATTENTION !!!

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