Weak points of HPP difficult to inactivate spores Margosch, et al. - PowerPoint PPT Presentation

5th ICPMF 16-19 Sep. 2007 in Greece Predictive modelling for the recovery of Listeria monocytogenes on sliced cooked ham after high pressure processing (HPP) S. Koseki and K. Yamamoto National Food Research Institute, Japan

Weak points of HPP  difficult to inactivate spores Margosch, et al. (2006): C. botulinum spores in pH 5.5 buffer were not completely inactivated by pressure 600 - 1400 MPa at 120 - 140°C.  recovery after HPP during storage Bull et al . (2005): Recovery of L. monocytogenes following HPP of milk.

Bacterial recovery after HPP Stored@25ºC 8 E. coli (log CFU/ml) 6 Immediately after 550 MPa treatment 4 2 0 120 0 20 40 60 80 100 Time (h)

Objectives Food processors need to know how long is safe of product? not only the number of specific microorganisms, but also the risk or safety level (probability) of the product to develop a predictive model for bacterial recovery on sliced cooked ham after HPP by probabilistic approach

Materials and methods ◆ Bacteria ： Listeria monocytogenes ATCC19117 ◆ Food ： sliced cooked ham (pasteurized, initial flora <10 CFU/g) ◆ Inoculum level ： 3 and 5 log CFU/g ◆ HPP: 400, 450, 500, 550, and 600 MPa (5 conditions), 1, 5, 10, 20, 30, 45, and 60 min (7 conditions) (25°C) ◆ Storage ： at 10°C for up to 70 days ◆ Sampling ： each 14 days (0, 14, 28, 42, 56, 70 days)

Model development Definition recovery / no recovery At the day of sampling during storage, the number of L. monocytogenes is 8 ≥ 10 2 CFU/g ⇒ recovery (1) L. monocytogenes (log 10 CFU/g) 7 <10 2 CFU/g ⇒ no recovery (0) 6 Logistic regression 5 ( ) + a 3 Storage + a 4 IC Logit ( P ) = a 0 + a 1 Press + a 2 log 10 Time 4 immediately after HPP a i : parameters 3 Press : pressure applied recovery Time : pressure-holding time 2 no recovery Storage : duration of storage at 10°C ND ND ND 1 IC : inoculum level (log CFU/g) 0 10 20 30 40 50 60 70 80 time@10°C (days) Total 1260 data points (70 conditions × 6 sampling days × 3 replicates )

No treatment Recovery of L. monocytogenes on sliced cooked ham after HPP 8 L. monocytogenes (log 10 CFU/g) 7 6 5 4 immediately after HPP 3 2 500MPa-10 min 600MPa-10 min ND ND ND 1 0 10 20 30 40 50 60 70 80 time@10°C (days)

Recovery / No recovery interface of L. monocytogenes on sliced cooked ham after HPP during storage ( ) + 0.1508 Storage + 4.9037 IC Logit ( P ) = 16.8423 � 0.0722 Press � 7.8899log 10 Time Inoculum level 3 log CFU/g Inoculum level 5 log CFU/g for 28 days for 28 days 600 600 Pressure (MPa) Pressure (MPa) 500 500 400 400 ~ ~ ~ ~ 0.1 0.1 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 Pressure-holding time (min) Pressure-holding time (min) different responses among Recovery No recovery the three triplicate trials All the three triplicate trials are same

Prediction of recovery time taken for the recovery ( P = 0.5; 50% probability of recovery) Inoculum level 3 log CFU/g Inoculum level 5 log CFU/g 70 70 (a) (b) Inoculum level Inoculum level 400 MPa 3 log CFU/g 5 log CFU/g 450 MPa 500 MPa 60 60 pressure holding time (min) pressure holding time (min) 50 50 40 40 550 MPa 30 30 450 MPa 20 20 600 MPa 500 MPa 10 10 550 MPa 0 0 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 storage time@10ºC (days) storage time@10ºC (days)

Prediction of recovery probability probability of recovery during storage after HPP at 500 MPa for 1 - 60 min . Inoculum level 3 log CFU/g Inoculum level 5 log CFU/g treated by 500 MPa treated by 500 MPa 1 1 (a) (b) Inoculum level Inoculum level 3 log CFU/g 5 log CFU/g treated@500MPa treated@500MPa 0.8 0.8 probability of recovery probability of recovery 1 min 0.6 0.6 10 min 20 min 30 min 45 min 60 min 5 min 0.4 0.4 10 min 0.2 0.2 20 min 0 0 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 storage time@10ºC (days) storage time@10ºC (days)

Summary 1. A probabilistic predictive model was developed, which can estimate the recovery of L. monocytogenes on sliced cooked ham after HPP. 2. The model calculate appropriate shelf lives and processing conditions, taking into account for bacterial recovery on the products. 3. The model can estimate the risk of recovery of bacteria during storage.

Future work To obtain more broad estimation of the model parameters Hieratical Bayesian statistical modelling

5th ICPMF 16-19 Sep. 2007 in Greece Thank you for your attention! S. Koseki and K. Yamamoto National Food Research Institute, Japan