Evaluating the applicability of Time Temperature Integrators as process exploration and validation tools.

Transcription

1 Evaluating the applicability of Time Temperature Integrators as process exploration and validation tools. S. Bakalis, P.W. Cox, K. Mehauden and P. J. Fryer Centre for Formulation Engineering Dept. of Chemical Engineering University of Birmingham

2 Why do we process food? Increase self life / Safety Flavor generation Structure development

3 Thermal Processing a long tradition Nicolas Appert in 1809 identified four critical steps : In inclosing in bottles the substances to be preserved In corking the bottles with the utmost care for it is chiefly on the corking that the success of the process depends In submitting these inclosed substances to the action of boiling water in a water bath for greater or less time according to their nature and in the manner pointed out with respect to each several kinds of substance In withdrawing the bottles from the water bath at the period prescribed Tin cans were used by 1820, although can openers were not invented for another 30 years

4 Microbial Inactivation Different models of Inactivation and growth have been developed over the years (Ball & Olson, 1957) N N 0 t/d = 10 T T(t) z D = D ref 10 T ref More complex models are available

5 Current state of the Food Industry Initial Pathogen load Uncertain: Prevalence - Concentration Processing Prevalence of Salmonella spp in UK chicken is ~ 5.8% (Food Standards Agency). Concentration? Initial Pathogen load Legal requirements Salmonella spp should not be detected in 25 g

6 How does the industry treats uncertainty The principle of 12D Establish the critical microorganism (select D value) Select processing time = 12*D (at which temperature?) In principle overprocess to guarantee safety Equivalent processing time P: The time of processing at temperature Tref required to achieve the same result N N 0 = 10 P/D Tref

7 Changing environment for the food industry Processing as making food safer- reduces nutritional quality decreases sensory quality Consumers demand for fresher more nutritional products Drive for the food industry to reduce processing time while ensuring safe products

8 A representation of food processing A man is going home after a pub session. How close to the cliff could he walk? Uncertainty

9 Need for tools to validate processing conditions under realistic situations Traditionally thermocouples were used to evaluate thermal treatment of the product

10 Time Temperature Integrators (TTIs)- Background Enzymatic solutions that can quantify the overall effect of processing Developed originally in the 80s to evaluate the effect of storage Temperature Processing time time

11 Recent developments in Time Temperature Integrators Time Temperature Integrators (TTIs) have been suggested as tools to measure Time Temperature profiles where thermocouples can not be used P can be evaluated from the residual enzymatic activity In the TTIs α-amylase (isolated from Bacillus amyloliquefaciens

12 Temperature effect on TTI inactivation P = D(T) log A A initial final P t = 10 0 T ( t) Tref z. dt = D Tref. log A A initial final z value of TTI should be reasonably close to microbial z value to get accurate results Temperature history of the TTI should be representative of the flow TTIs follow pathlines TTIs do not affect the temperature and flow field

13 Some errors involved in using TTIs Is the TTI representative of the flow in the system? A method to follow the path of a radioactive tracer Typical diameter of the tracer: µm

14 Examine flow patterns during canning using PEPT Effect of 10% air NO TTIs Fully filled can 10% air

15 Data just acquired - Occupancy 1 % CMC 1 % CMC Tracer on a TTI Differences appear to exist : Disturbance to the flow less than thermocouples though!!

16 TTI studies: Estimate the inactivation parameters of the TTIs Establish variability of measurement system - TTIs Examine an ideal system using TTIs Investigate an industrial system

17 Accuracy of TTIs Determination of the kinetic parameters of TTIs Log(A) -2.4 Log D T Time (min) Temperature ( C) D Tref z = 12 = 6.1min o C

18 A Peltier stage was used to deliver various time temperature profiles The Peltier is a semiconductor based system which functions as a small heat pump P values TTIs (min) The variability of the TTIs was evaluated by comparing P values with those obtained using thermocouples

19 How does the variability of the TTIs affect the information that can be obtained in industrial applications? Conduct a Monte-Carlo simulation in a hypothetical process Methodology could be used. Temperature and flow field (CFD or Experiment) Temperature history P - values TTI variability Variability of P values

20 Hypothetical process similar to a tubular heat exchanger: Umax =0.1 m/s L=2.5 m R = 15 mm Linear Temperature profile across the radious 15,000 TTIs are used Probability Distribution of the radial location of TTIs radius (m) x 10-3

21 Some results: Probability Distribution of P values if no variability TTI was assumed residence time Distribution of TTI residence time Probability P ideal

22 For each TTI an error was added to the ideal P value according to the uncertainty obtained experimentally The resulting P value: Probability P TTI

23 Comparison between the two: P real P ideal Probability P Difference is due to the error of the TTIs Given that 1) How many TTIs do we need to make a statement about the real P value of the process 2) What about the variability of the process

24 What information could be derived from TTIs? Mean P values 18 P Volumetric P average of the process = 14.7 min Mean P value of the process = 14 min number TTI

25 What information could be derived from TTIs? Minimum P value Minimum value from process (8.34 min) 1.3 standard deviations of TTI min P number TTI

26 What information could be derived from TTIs? Standard deviation values std(p value) Process standard deviation n TTI Standard deviation

27 An industrial example: mixing tanks Evaluate processing in Gusti s vessel Materials: starch suspensions steam at 121 o C

28 Representative results 41 TTIs were used One temperature logger # TTIs P (min) P vessel (thermocouple) = 21 min P thermocouple = 22 min

29 Effect of Speed High speed Low speed 8 Number of TTIs P (min)

30 Preliminary conclusions TTI s are a relatively novel method that can be used to obtain information where thermocouples can not be used More information could be extracted. Careful when small numbers of TTIs are used There is an inherited uncertainty Process and measurement tools Could we consider this when a process is designed?