Thermal inactivation of alicyclobacillus spores in fruit product processing

Teresa Brandão Cristina Silva

Brighton 4th _{– 7}th _{July 2005}

Summer Meeting 2005 Spore forming bacteria

emerging and re-emerging issues

UNIVERSIDADE CATÓLICA PORTUGUESA Escola Superior de Biotecnologia

Thermal inactivation of

Thermal inactivation of

Alicyclobacillus

Alicyclobacillus spores

spores

in fruit product processing

in fruit product processing



non-pathogenic



spore-forming bacterium



thermoacidophilic

Alicyclobacillus acidoterrestris

found in commercial pasteurized fruit juices spoilage

Historical background

Alicyclobacillus acidoterrestris

in the 1980s …

fruit juice industry faced a serious problem

!!

consumers complained about spoilage juices

before shelf life had expired

only a spore former could survive a thermal treatment in the pasteurization range

it had to be acidophilic to grow in acid juices

off flavor

loss of color no gas production

Fruit products juices, nectars, concentrates of purées

acidic pH < 4.6

Pasteurization _{90 – 95 ºC}

adequate for stabilisation at ambient temperature

Clostridium botulinum yeasts molds nonspore-forming bacteria cooling cooling 82 – 84 ºC T time 15 – 20 s 2 s

Design of the pasteurization processes Design of the pasteurization processes

Fruit products juices, nectars, concentrates of purées

acidic pH < 4.6

Pasteurization _{90 – 95 ºC}

adequate for stabilisation at ambient temperature

Clostridium botulinum yeasts molds nonspore-forming bacteria cooling cooling 82 – 84 ºC T time 15 – 20 s 2 s

Historical background

Alicyclobacillus acidoterrestris

1984 The microbial growth was isolated and identified as a new

type of spoilage bacterium

First studies in aseptically packaged apple juice (Cerny et al.)

1987 The species was first named as Bacillus acidoterrestris

(Deinhard et al.)

1992 Reclassified as a new genus Alicyclobacillus, becoming …

Alicyclobacillus acidoterrestris

Pasteurization

… should inactivate microorganisms’ vegetative cells

and enzymes

bacteria yeasts molds

prevent the degradation of the original organoleptic and

nutritive fruit characteristics

quality quality

safety safety

Design and optimization of pasteurization processes Design and optimization of pasteurization processes

however …

thermal processes affect negatively quality factors

process design

quality quality

safety safety

acidic foods

inactivation of non-pathogenic microorganisms and enzymes

non-acidic foods

inactivation of pathogenic microorganisms

Design and optimization of pasteurization processes Design and optimization of pasteurization processes

Pasteurization methods

… applied to several fruit products

hot filling

aseptic process

traditional canning

hot filling

mixing thermometer puree heating filling seaming can reversing

Design and optimization of pasteurization processes Design and optimization of pasteurization processes

aseptic process

food product temperature control holding food product heat exchanger aseptic packaging section

Design and optimization of pasteurization processes Design and optimization of pasteurization processes

not very clear !

Target microorganisms/enzymes and its inactivation requirements are not defined or vary with product

time

/

temperature

?

Target

… for shelf-stable high acidic fruit products

in 2000

establishment of a new pasteurization criterion for shelf-stable high-acidic fruit products

Alicyclobacillus acidoterrestris

Alicyclobacillus acidoterrestris sporesspores

Filipa Silva (2000) Ph.D. thesis developed in Escola Superior de Biotecnologia Universidade Católica Portuguesa, Portugal.

Research project:

Multidisciplinary Study of the Transformation of Amazonian Fruits for their Commercial Valorization Aiming at the Development of Local Rural Communities

EU (DGXII) - program STD3

Portugal, Belgium, France and Brazil

Alicyclobacillus acidoterrestris

Cupuaçu

Design and optimization of pasteurization processes Design and optimization of pasteurization processes

Design and optimization of pasteurization processes Design and optimization of pasteurization processes

pasteurization conditions for cupuaçu pulp

hot-filling 8.0 8.2 10.3 11.3 13.8 15 10 5 2 0 70 75 80 85 87

treatment heating time (min) holding time (min) holding temperature (ºC) isothermal pasteurization conditions 3.7 3.7 1.3 1.3 70 90

at the coldest point

evaluation of the process

pasteurized cupuaçu pulp

estimation of

Design and optimization of pasteurization processes Design and optimization of pasteurization processes

How was it achieved ????















 





 PT 0 Z ref T T ref T

dt

10

D

1

0

10

N

N

estimated parameters

N number of viable spore cells

N₀ number of initial viable spore cells

T temperature

T_ref reference temperature PT total process time

D decimal reduction time

z- value

ambience storage for 6 months samples total counts moulds yeasts colour soluble solids pH reducing sugars

evaluation of the process

pasteurized cupuaçu pulp

microbial inactivation

mathematics

microbiology

statistics

predictive predictive microbiology microbiology

Predictive microbiology

is gaining considerable importance in the food processing domain, particularly in the design of

efficient and safe inactivation treatments

The use of mathematical models in the description of

The role of mathematical modelling

precise and accurate description of observations

model adequacy

quality of model parameters

knowledge of the process

process effect on product

control of process variables

advantages

Processes Chemical Physical Food Processes Transport Phenomena • heat • mass • momentum Reaction kinetics Properties Modelling mathematical function variables parameters data regression schemes experimental design

Processes Chemical Physical Food Processes Transport Phenomena • heat • mass • momentum Reaction kinetics Properties mathematical function variables parameters regression schemes experimental design design Criterium

Processes Chemical Physical Food Processes Transport Phenomena • heat • mass • momentum Reaction kinetics Properties Modelling mathematical function variables parameters data regression schemes experimental design

The role of mathematical modelling

design Criterium

Processes Chemical Physical Food Processes Transport Phenomena • heat • mass • momentum Reaction kinetics Properties mathematical function variables parameters regression schemes experimental design design Criterium validation control

Processes Chemical Physical Food Processes Transport Phenomena • heat • mass • momentum Reaction kinetics Properties Modelling mathematical function variables parameters data regression schemes experimental design

The role of mathematical modelling

design Criterium

validation

control

Processes Chemical Physical Food Processes Transport Phenomena • heat • mass • momentum Reaction kinetics Properties mathematical function variables parameters regression schemes experimental design design Criterium validation control optimization Objectives quality quality safety safety

prediction / simulation

development of efficient

inactivation processes

contribution to

safety

aplication

sigmoidal behaviour

presence of aggregated microorganisms or sub populations more heat (or other stress factor) resistent

inactivation

0 1 2 3 4 5 6 7 8 9 0 500 1000 1500 2000 tim e(s) lo g N

0 1 2 3 4 5 6 7 8 0 20 40 60 80 100 120 140 0 1 2 3 4 5 6 7 8 0 20 40 60 80 100 120 140 Examples Miller (2004) 52.5 ºC Listeria innocua liquid medium 55 ºC 57.5 ºC 60 ºC 62.5 ºC 65 ºC log N time (min)

0 1 2 3 4 5 6 7 8 0 20 40 60 80 100 120 140 52.5 ºC 55 ºC 57.5 ºC 60 ºC 62.5 ºC 65 ºC log N time (min) Miller (2004) Listeria innocua liquid medium



primary

Mathematical models 0 1 2 3 4 5 6 7 8 0 0.5 1 1.5 2 2.5 Tim e (m in) L o g C F U /g kinetics parameters



primary



secondary

0 1 2 3 4 5 6 7 0 0.5 1 1.5 2 2.5 Tim e (m in) L o g C F U /g parameters pH _aw temperature



primary



secondary



terciary -

integration of the previous models

-

software

0 1 2 3 4 5 6 7 8 0 0,5 1 1,5 2 2,5 Tim e (min) L o g C F U /g parameters pH _aw temperature Mathematical models



primary

0 1 2 3 4 5 6 7 8 0 500 1000 1500 2000 logN _k logN0 logNres L Time (s)

empirical

empirical

_fundamental

_fundamental

N₀ number of initial viable spore cells

N_res number of residual spore cells k maximum inactivation rate L lag or shoulder



primary

First order



kt



exp N N  ₀  D t N log logN  ₀ 

D – decimal reduction time

0 1 2 3 4 5 6 7 8 0 2 4 6 8 Tim e (m in) L o g C F U /g Inactivation models



primary

First order



kt



exp N N  ₀  D t N log logN  ₀ 



k t

 

1 F



exp



k t



exp F N N 2 1 1 1 0      Cerf (1977) Kamau et al. (1990)













_          t k exp 1 F 1 2 t k exp 1 F 2 log N N log 2 1 1 1 0

D – decimal reduction time

F₁ – fraction of inactivated microorganisms k₁ e k₂– kinetic constants biphasic 0 1 2 3 4 5 6 7 8 0 2 4 6 8 Tim e (m in) L o g C F U /g 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 Time (min) L o g C F U /m l



primary

0 1 2 3 4 5 6 7 8 0 0.5 1 1.5 2 2.5 Tim e (m in) L o g C F U /g





































_                L t k exp 1 L k exp 1 F 1 L t k exp 1 L k exp 1 F log N N log 2 2 1 1 1 1 0 Whiting & Buchanan

(1992)

L – lag or shoulder Inactivation models



primary

Cole et al. (1993)





_            σ w t log λ σ 4 exp 1 α w α N log 0 1 2 3 4 5 0 0.5 1 1.5 2 2.5 Tim e (m in) L o g C F U /g





































_                L t k exp 1 L k exp 1 F 1 L t k exp 1 L k exp 1 F log N N log 2 2 1 1 1 1 0 Whiting & Buchanan

(1992)

distribution of heat sensibility of microbial populations



primary

Baranyi et al. (1993)    



t 0



N₀ N N t t k dt dN       ‘lag’ function ‘tail’ function 0 1 2 3 4 5 6 7 8 9 0 500 1000 1500 2000 tempo (s) lo g N Inactivation models



primary

Baranyi et al. (1993)    



t 0



N₀ N N t t k dt dN       ‘lag’ function ‘tail’ function 0 1 2 3 4 5 6 7 0 500 1000 1500 2000 tempo (s) lo g N









 

 

0 exp



k t



Q 1 0 Q 1 t k exp log N N log max max 0              Geeraerd et al. (2000)   Q k dt dQ N Q k k dt dN max Q max    



primary

Gompertz 0 1 2 3 4 5 6 7 8 9 0 500 1000 1500 2000 tempo (s) lo g N Bhaduri et al (1991) Linton et al. (1995, 1996) Xiong et al. (1999)





                                           L t 1 N N log e k exp exp N N log logN logN res 0 res 0 0

reparameterized for inactivation based in Zwitering (1990)

Listeria monocytogenes



primary

Gompertz 0 1 2 3 4 5 6 7 0 500 1000 1500 2000 tempo (s) lo g N Bhaduri et al (1991) Linton et al. (1995, 1996) Xiong et al. (1999)





                                           L t 1 N N log e k exp exp N N log logN logN res 0 res 0 0 Logistic Listeria monocytogenes







k t - L



exp 1 c logN   c – constant

Examples

Gompertz

Gompertz

Data of

Data of L.monocytogenesL.monocytogenes Scott A at 52,56,60,64,68ºCScott A at 52,56,60,64,68ºC

(24 hours incubation at 5ºC in half cream) (24 hours incubation at 5ºC in half cream)

0 1 2 3 4 5 6 7 8 9 0 1000 2000 3000 4000 5000 6000 7000 time (s) lo g N T=52 ºC T=56 ºC T=60 ºC Gil (2002) Casadei et al. (1998) Statistica 6.0

Silva (2000) _{Statistica 6.0} 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 5 10 15 20 25 time (min) N /N 0 85 ºC 91 ºC 95 ºC D t 0 0 1 N N  

First order

First order

D ) 10 ln( k 

Bigelow

Bigelow

97 ºC



primary



secondary

Mathematical models 0 1 2 3 4 5 6 7 8 0 0.5 1 1.5 2 2.5 Tim e (m in) L o g C F U /g parameters pH _aw temperature



secondary

Arrhenius

Davey / Arrhenius modified “Square-root type models”

Ratkowsky et al. (1982) McMeekin et al. (1987) Adams et al. (1991) McMeekin et al. (1992) ) T b(T k   _min ) a (a ) T b(T k   _{min w}  _wmin ) pH (pH ) T b(T k   _min  _min        RT E -exp k k ₀ a lnk  lnk₀  _RTEa 2 W 4 W 3 2 2 1 0 C a C a T C T C C lnk      ) pH (pH ) a (a ) T b(T

k   _{min w}  _wmin  _min

min – minimal value for growth

                  ref a ref exp - E_{R T}1 _T1 k k

Examples

Gompertz k=0.0216 exp(-203.3/R*(1/T-1/333.15))

Logística k=0.0337 exp(-206.6/R*(1/T-1/333.15)) Data of

Data of L.monocytogenesL.monocytogenes Scott AScott A (24 hours incubation at 5ºC in half cream) (24 hours incubation at 5ºC in half cream)

0,00 0,05 0,10 0,15 0,20 0,25 320 325 330 335 340 345 Temperature (K) k ( 1 /s ) DEa=28.85 kJ/mol DEa=27.56 kJ/mol DKref=4.58x10-3s-1 DKref=7.31x10-3s-1 Arrhenius Gil (2002) _{Statistica 6.0}

Temperature

effect on k

0 500 1000 1500 2000 2500 3000 320 325 330 335 340 345 Temperature (K) L ( s ) L=116.3exp(344.1/R*(1/T-1/333.15)) R2_=0.9998 DEa=7.485 kJ/mol DLref=7.595 s-1 Gil (2002) _{Statistica 6.0}

Examples

Silva (2000)

Temperature

effect on D-value of A.acidoterrestris

spores

Data of

Data of Alicyclobacillus acidoterrestris Alicyclobacillus acidoterrestris spores at 85,91,95,97ºCspores at 85,91,95,97ºC Cupuaçu extract pH=3.6 11.3 ºBrix

Cupuaçu extract pH=3.6 11.3 ºBrix

0 5 10 15 20 25 84 86 88 90 92 94 96 98 Tem perature (ºC) D ( m in )   z T T ref ref 0 1 D D     9 . 8 T 91 0 1 x 57 . 4 D   Dz=1.45 ºC DD_ref=1.12 min T_ref=91 ºC

Stata 3.0

Temperature

,

Soluble Solids

and

pH

effect on D-value

of A.acidoterrestris spores

malt extract broth

Response surface methodology ranges 85 < T (ºC) < 97 5 < SS (ºBrix) < 60 2.5 < pH < 6.0 ) pH T 59083 . 0 ( ) pH 147 . 57 ( ) SS T 04699 . 0 ( ) SS 6096 . 4 ( ) T 56042 . 0 ( ) T 96 . 102 ( 1 . 4715 D   2    

Examples

Silva et al. (1999) IJFM

Temperature

,

Soluble Solids

and

pH

effect on D-value

Complexity !! … more realistic conditions!!

Variable conditions of

pH _aw





)

time

(

d

N

log

d

Mathematical models

Gompertz

Gompertz





) time ( d N log d







L t'



1 dt' N N log ) 1 exp( k exp exp 1 ' t L N N log ) 1 exp( k exp ) 1 exp( k N log N log t 0 res 0 res 0 0



                                                                          

dynamic situation of temperature

                  ref a ref exp - E_{R T}1 _T1 k k                   ref T 1 T 1 b exp a L

Linear

Linear





) time ( d N log d

dynamic situation of temperature



T T_ref



z 1 ref 10 D D   















 





 PT 0 Z ref T T ref T

dt

10

D

1

0

10

N

N

approach by Vieira et al. (2002)

Cupuaçu nectar

Design and optimization of pasteurization processes Design and optimization of pasteurization processes

ambience storage for 6 months

samples total counts moulds yeasts colour soluble solids pH reducing sugars

evaluation of the process

reminding ….

microbial inactivation

Criterion

Alicyclobacillus acidoterrestris spores

1

.

0

N

N

0





tertiary

Mathematical models

softwares

Microbial growth

Shelf life prediction

Microbial inactivation



‘Bugdeath’ - funded by the European Commission under the EC Framework 5; Quality of Life and Management of Living Resources Programme

Software Program

‘Bugdeath’ - funded by the European Commission under the EC Framework 5; Quality of Life and Management of Living Resources Programme

‘Bugdeath’ - funded by the European Commission under the EC Framework 5; Quality of Life and Management of Living Resources Programme

‘Bugdeath’ - funded by the European Commission under the EC Framework 5; Quality of Life and Management of Living Resources Programme

‘Bugdeath’ - funded by the European Commission under the EC Framework 5; Quality of Life and Management of Living Resources Programme

‘Bugdeath’ - funded by the European Commission under the EC Framework 5; Quality of Life and Management of Living Resources Programme

Drawbacks

• microbial interaction

• natural strains diversity

• complexity of food structure

• food/microorganism

• modelling of the ‘lag’ phase

• modelling of the ‘tail’ phase

• predictions in real and varying environmental conditions Predictive microbiology

non-thermal treatments

quality

quality

safety

safety

efficient microbial inactivation

maximum quality retention

avoiding the negative effects of heat at tissues level

Future challenges

non-thermal treatments

ozone

aquous solution

atmosphere

EMERCON

non-thermal treatments

ultrasounds

heat treatments

+

thermosonication

EMERCON

Future challenges

thermosonication

Cruz, Vieira & Silva (2005) JFOE

x blanching

thermosonication

non-thermal treatments

UV radiation

EMERCON

Future challenges

non-thermal treatments

UV radiation

0.E+00 1.E+05 2.E+05 3.E+05 4.E+05 5.E+05 0 2 4 6 8 10 12 14 Tim e (m in) U F C /g 8.4 W UV-C courgettes EMERCON

New Processing Technologies for Frozen Fruits and Vegetables

Teresa Brandão Cristina Silva

Brighton 4th _{– 7}th _{July 2005}

UNIVERSIDADE CATÓLICA PORTUGUESA Escola Superior de Biotecnologia

Thank you

Thank you

Teresa Brandão Cristina Silva