Modelling Food Quality Changes Kinetics During Thermal Processing

(1)

Thermal processing conference, Campden BRI, Chipping Campden, Gloucestershire – UK, 12-13 June, 2014

Modelling Food Quality Changes Kinetics

During Thermal Processing

Cristina L.M. Silva

Escola Superior de Biotecnologia

Universidade Católica Portuguesa

(2)

• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(3)

• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(4)

… Originally designed to inactivate

spoiling and pathogenic microorganisms

and

enzymes

bacteria

yeasts

molds

safety

(5)

… consumers request

 Fresh like, appealing, convenient, healthy

 Environmental care

(6)

Thermal Processes

prevent the degradation of the original organoleptic and nutritive food

characteristics

quality

Objectives of thermal processing

(7)

thermal processes affect negatively

quality factors

_{process design}

quality

safety

Texture Microstructure Colour Vitamin C … Pathogenic bacteria Chemical contaminants …

Objectives of thermal processing

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• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(9)

(10)

Importance of thermal processes

Thermal Processing

Continues to play a crucial role

(11)

Importance of thermal processes

(12)

Importance of thermal processes

(13)

population growth

(14)

population growth

(15)

Product preparation

Product processing

(steaming, blanching, cooking)

Minimally

processed

Packaging and Dispatch

Semi-

processed

Highly

processed

Further processing

(freezing, canning, preserving)

(16)

• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(17)

Quality attributes response depends on:

- Intrinsic factors

- Extrinsic factors

- System dynamics

pH

a

_w

others

time

T

pH

Pressure

Other hurdles

(18)

Grauwet et al, 2014

(19)

• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(20)

Model 

mathematical expression

i=1,2,...,n (number of experimental runs/observations)

j=1,2,...,v

k=1,2,...,p

y

_i

= f(x

_ij

,



_k

) +



_i

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 1000 2000 3000 4000 5000 x y

y

_model

y

_exp



*

Precise ?

Accurate ?

Minimize differences

Modelling approaches

(21)

precise

and

accurate

description of observations

model adequacy

quality of model parameters

objective

(22)

Heuristic sampling

Experimental design

Minimize variance of:

predicted response

parameter estimates

Sampling:

(23)

Regression schemes

Analysis of residuals









2 n

1 i

k

ij

i

n

1 i

2 i

y

f

x

,

e

SSR











Least-squares

_method

Data analysis:

Modelling approaches

(24)

Mathematical

complexity

Adequate description

model

parameters

quality

(25)

knowledge of the process

process effects on product

control of process variables

advantages

(26)

• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(27)

Quality changes kinetics

Historically:  single-response studies

 static conditions

(28)

t

k

0 e

(T)

C





kt

eq

0 eq

e

C

-C

C

-C

_



t

k

C

(T )

0 



Time

Initial value

Atribute at time t

Zero order

1st order

Fractionary

 





























ref ref T

_R

_T

1 _T

1 Ea

exp

k

T effect on k

Arrhenius law

Activation

energy

Reaction rate

Kinetic Models

Quality changes kinetics

Single-response  empirical

(29)

Quintas et al, 2007

Multiresponse Modelling  mechanistic

(30)

(31)

(32)

The complexity of dynamic conditions

(33)

Quality changes kinetics

 Need to have temperature

(34)

Quality changes kinetics

 Need to have temperature

histories and distributions

(35)

Isothermal conditions

_{Non-Isothermal conditions}

Integration

(36)

Quality changes kinetics

Reverse Engineering

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• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(38)

(39)

Predictive microbiology

The use of mathematical models in the description of

microbial responses to environmental stressing factors

Predictive quality

(40)

Processes

Chemical

Physical

Food Processes

Transport Phenomena

• heat

• mass

• momentum

Reaction kinetics

Properties

Modelling

mathematical function variables parameters data

regression schemes experimental design

design

Criterium

validation

control

quality

safety

Predictive quality

(41)



primary model

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

Predictive quality

(42)



primary model



secondary model

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

a

_w

temperature

Predictive quality

(43)



primary



secondary



terciary -

integration of the previous models

-

software

parameters

pH

_a

_w

temperature

(44)

• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(45)

Ozone

Ultrasonication / Thermosonication

UV-C radiation

UV-C chamber (University of Algarve), 4 germicidal UV lamps (TUV G30T8, 16 W, Philips, peak emission at 254 nm), average intensity 12.36 W/m2

ultrasound equipment (Bandelin Sonorex RK 100H) operating at 32 kHz

ozone generator, concentration

measured by potential difference, 0.3 ppm

Combining with other technologies

(46)

watercress

Nasturtium officinale

Types of combined treatments with ultrasound

Heat + Ultrasound

+

T

hermosonication

(47)

watercress

Nasturtium officinale

Quality

Colour changes

Vitamin C

Peroxidase

(48)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.5 1 1.5 2 Time (min) C /C o 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.5 1 1.5 2 Time (min) C /C o 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.5 1 1.5 2 C /C o 0.4 0.6 0.8 1 1.2 1.4 C /C o 82.5 ºC Blanching US 87.5 ºC

Peroxidase

Blanching Blanching Blanching 85.0 ºC 90.0 ºC US

(49)

The application of thermosonication

- temperatures above 85 ºC and for the same blanching times

led to higher enzyme inactivation when compared to heat

blanching

processes

These results allow the application of shorter blanching times at this range of

temperatures, leading to a product with a higher quality, or minimized

processing

Peroxidase

(50)

Colour

0 2 4 6 8 10 12 0 20 40 60 80 100 120 Time (s) T C D 0 2 4 6 8 10 12 0 20 40 60 80 100 120 Time (s) T C D 0 2 4 6 8 10 12 0 20 40 60 80 100 120 Time (s) T C D 0 2 4 6 8 10 12 0 20 40 60 80 100 120 Time (s) T C D 4 6 8 10 12 T C D Blanching Blanching 85 ºC 82.5 ºC 85.0 ºC 87.5 ºC 90.0 ºC 92.5 ºC Blanching Blanching Blanching US US US _US

(51)

The application of thermosonication

Reaction rates of watercress colour changes due to heat and

thermosonication blanchings were not significantly different

Colour

(52)

Vitamin C

0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.5 1 1.5 2 Time (min) C /C o 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.5 1 1.5 2 Time (min) C /C o 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.5 1 1.5 2 Time (min) C /C o 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.5 1 1.5 2 Time (min) C /C o 0.8 1 1.2 1.4 /C o 82.5 ºC 85 ºC 87.5 ºC 90 ºC 92.5 ºC Blanching Blanching Blanching Blanching US US US US US

Vitamin C

(53)

The application of thermosonication

Vitamin C

Results showed no significant differences between heat and thermosonication

treatments

The treatment will allow good vitamin C retention

(54)

The thermosonication treatments can be a good alternative

to the traditional heat blanching processes,

since higher quality products are attained

The application of thermosonication

Quality

(55)

• Objectives of thermal processing

• Importance of thermal processes

• Factors affecting quality changes

• Modelling approaches

• Quality changes kinetics

• Predictive quality

• Combining with other technologies

• Post-harvest treatment

• Challenges

(56)

(57)

Pinheiro et al, 2014

(58)

Postharvest treatment

Firmness

HIPO WHT HIPO_Predicted WHT_Predicted Storage period (days)

Turning 0 7 14 6,5 7,0 7,5 8,0 8,5 9,0 9,5 10,0 10,5 11,0 11,5 12,0 12,5 F ir m n e s s (N )

Storage period (days) Pink

(59)

Pinheiro et al, 2014

Postharvest treatment

Colour

HIPO WHT HIPO_Predicted WHT_Predicted Storage period (days)

Turning 0 7 14 -0,5 -0,4 -0,3 -0,2 -0,1 0,0 0,1 0,2 0,3 0,4 0,5 0,6 a */b *

Storage period (days) Pink

(60)

t

k

0 e

(T)

C





Postharvest treatment

 Results provide strong evidence that postharvest water heat

treatment (40 ºC - 30 min) for tomato fruits (cv. ‘Zinac’) at turning

maturity stage guarantees the overall quality at 10 ºC, twice as long

of fruits washed with chlorinated water.

Maturity stages Treatment a*/b* Firmness (N)

Turning HIPO C0 = - 0.32 ± 0.12 k10ºC (day -1) = 0.25 ± 0.32 C0 = 11.16 ± 0.52 k10ºC (day -1) = 0.02 ± 0.006 WHT C0 = - 0.28 ± 0.11 k10ºC (day -1 ) = 0.11 ± 0.10 C0 = 10.77 ± 0.69 k10ºC (day -1 ) = 0.01 ± 0.01 Pink HIPO C0 = 0.13 ± 0.07 k10ºC (day -1) = - 0.07 ± 0.05 C0 = 8.90 ± 0.43 k10ºC (day -1) = 0.01 ± 0.01 WHT C0 = 0.13 ± 0.07 k10ºC (day -1 ) = - 0.08 ± 0.04 C0 = 8.60 ± 0.72 k10ºC (day -1 ) = 0.01 ± 0.01