1. 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

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

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

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

5. Baseline Software tool: Data and Figures Final Conference BASELINE. Bologna 11-12 November 2013 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.

6. 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) Manufacturing Treatment Storage Distribution Consumption PO FSO=100 cfu/g PC It is established a maximum concentration level of 100 cfu/g before consumption For simplification, PO is set after product elaboration / storage It is assumed that a Competent Authority has established a PO for the concentration of a specific pathogen in a certain commodity. Final Conference BASELINE. Bologna 11-12 November 2013 Example I

7. 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 Conference BASELINE. Bologna 11-12 November 2013

8. 8 Example II Final Conference BASELINE. Bologna 11-12 November 2013

9. 9 Example II Final Conference BASELINE. Bologna 11-12 November 2013

10. 10 Example II Final Conference BASELINE. Bologna 11-12 November 2013

11. Final concentration : 1.74 log cfu/g Final Conference BASELINE. 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 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) 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)] Final Conference BASELINE. 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 Conference BASELINE. Bologna 11-12 November 2013

14. 14 Final Conference BASELINE. Bologna 11-12 November 2013

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

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

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

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

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

20. Example I CodeAnalysisStandard/guidelineAssessmentSampling plan calculations ncmMSatisfactoryUnsatisfactory 1L. monocytogenes702NA<m/g >m/g in any of the subsamples tested Two class sampling plan based on concentration data Final Conference BASELINE. Bologna 11-12 November 2013

21. 21 - Influence of processing time / temperature on the growth of Salmonella Enteritidis in egg yolk - Establishment of sampling procedures in powdered eggs Example II Final Conference BASELINE. Bologna 11-12 November 2013

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

23. 23 Estimation of the growth of S. Enteritidis at both storage times: a) 8h: b) 15h: 1.23 log cfu/ml = 17 cfu/ml 2.89 log cfu/ml = 776 cfu/ml Example II Final Conference BASELINE. Bologna 11-12 November 2013

24. 24 Number of samples collected Sample size Contaminated part of the lot Lot weight Concentration in contaminated samples Example II Final Conference BASELINE. Bologna 11-12 November 2013

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

26. 26 nw (g)pN (g)c (cfu/g)PaccPrej 20500,0110000170,81790,1821 20500,0510000170,43410,5659 20500,110000170,30750,6925 20500,011000170,81790,1821 20500,01100000170,89150,1085 50200,0110000170,61530,3847 20500,01100007760,81790,1821 20500,05100007760,35850,6415 20500,1100007760,12160,8784 20500,0110007760,81790,1821 20500,011000007760,81790,1821 50200,01100007760,6050,395 Example II Final Conference BASELINE. Bologna 11-12 November 2013

27. 27 a)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 b)Increasing proportion of the contaminated part of the lot (from 0.01 to 0.1) Sampling is more effective as p increases, especially from 0.01 to 0.05 c)Lot size effect (1000, 10000, 100000 g) No significant d)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 Impact of high contamination when p > 0.05. At low values of p, sampling is mainly affected by the initial prevalence Example II Final Conference BASELINE. Bologna 11-12 November 2013

28. 28 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. Example II Final Conference BASELINE. Bologna 11-12 November 2013

29. 29 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. Example II Final Conference BASELINE. Bologna 11-12 November 2013

30. 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 Conference BASELINE. Bologna 11-12 November 2013

31. 31 Final Conference BASELINE. Bologna 11-12 November 2013 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.

32. THANK YOU FOR YOUR ATTENTION !!!