Dynamic approach for assessing food quality and safety: the case of processed foods

Teresa R.S. Brandão

, Cristina L.M. Silva

CBQF – Centro de Biotecnologia

e Química Fina

School of Biotechnology

Catholic University of Portugal

Dynamic approach for

assessing food quality

and safety

Why are foods processed ?

to be safe

to be appelative

to be preserved

to be in more

convenient forms

Food processing

Food processing

Laboratory research

Industrial scale



Studies are often

carried out at constant

temperatures

isothermal



Time-varying

temperature conditions

are common

non-isothermal

Modelling

The

use

of

mathematical

models

that

describe/predict changes of processed foods

characteristics with accuracy and precision in such

realistic dynamic conditions is an important tool

in developing new products

Modelling



The greatest modeller’s effort has been given to data

obtained under constant (or static) environmental

conditions



From a realistic point of view this is somehow

restrictive, since the majority of thermal processes

occur under time-varying environmental conditions,

and kinetic parameters obtained under such

circumstances may differ from the ones estimated at

static conditions, which compromises safety control

and quality prediction

This presentation will focus on …



Study of food products in terms of quality and

safety characteristics, when they are submitted to

processes with time-varying temperature conditions



Assessment

of

mathematical

models

that

adequately describe the observed responses

Several combinations of food/characteristics

were used as case studies

3 cases will be presented

Case 1

Case 2

Case 3

Thermal inactivation of Listeria in

culture media and foods

Frozen storage of vegetables

Solar drying of grapes

Thermal inactivation of Listeria in culture media and foods

Case 1

Listeria monocytogenes

Listeria innocua

Listeria innocua as non-pathogenic surrogate of Listeria monocytogenes

• pathogenicity

• similar characteristics

• found in the same food products

• found in the same environments

• use validated in numerous studies

Isothermal conditions

Experimental design

52.5 C

55.0 C

57.5 C

60.0 C

62.5 C

65.0 C

L. innocua NCTC 10528

L. innocua 2030c

Exponential phase

Stationary phase

30 ºC/20h

30 ºC/9h

culture media

TSBYE

52.5 °C

65.0 °C

1

t

L

0

N

res

N

log

e

max

k

exp

exp

0

N

res

N

log

0

N

N

log

-6

-5

-4

-3

-2

-1

0

0

25

50

75

100

125

150

175

200

log

(N

/N

0

)

time (min)

Significant temperature effects

L. innocua 2030c – exponential phase L. innocua 2030c – stationary phase

L. innocua 10528 – exponential phase L. innocua 10528 – stationary phase

N

₀

10

7

_cfu/mL

Some results …

-6

-5

-4

-3

-2

-1

0

0,0

0,5

1,0

1,5

2,0

2,5

3,0

log

(N

/N

0

)

time (min)

Maximum inactivation rate

Shoulder parameter

1

t

L

0

N

res

N

log

e

max

k

exp

exp

0

N

res

N

log

0

N

N

log

Significant temperature effects

Some results …

2

d

T

c

L

ref

ref

max

T

1

T

1

R

Ea

exp

k

k

c and d are model parameters

k

_ref

is inactivation rate at temperature T

_ref

Ea is the inactivation energy

(P1) 1.5 C / min

(P2) 1.8 C / min

(P3) 2.6 C / min

T

(°C)

time

20

65 °C

Experimental design

Non-isothermal conditions

culture media

TSBYE

(P1) 1.5 C / min

(P2) 1.8 C / min

(P3) 2.6 C / min

Petroselinum crispum

parsley

Experimental design

Non-isothermal conditions

T

(°C)

time

20

65 °C

Experimental procedure

parsley

artificially inoculated

TSBYE bacterial suspension

~ 10

7

_{cfu/mL of L. innocua}

samples vacuum sealed

thermal treatment

sampling

stomaker

Palcam agar

The model

'

dt

dt

N

N

log

d

N

N

log

t

0

isothermal

0

isothermal

non

0

'

dt

1

'

t

'

t

L

N

N

log

e

'

t

k

exp

exp

1

'

t

'

t

L

N

N

log

e

'

t

k

exp

e

'

t

k

N

N

log

t

0

0

res

max

0

res

max

max

isothermal

non

0

Gompertz model encompassing the time-temperature effect

2

d

T

c

L

ref

ref

max

T

1

T

1

R

Ea

exp

k

k

T=f(t)

Results

culture media

TSBYE

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

-6,0

-5,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

0

10

20

30

40

50

T

e

m

p

e

ra

tu

re

(

ºC

)

L

o

g

(

N

/N

0

)

Time (min)

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

-6,0

-5,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

0

10

20

30

40

50

T

e

m

p

e

ra

tu

re

(

ºC

)

L

o

g

(

N

/N

0

)

Time (min)

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

-6,0

-5,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

0

10

20

30

40

50

T

em

p

er

at

u

re

(

ºC

)

L

o

g

(

N

/N

0

)

Time (min)

P1

P2

P3

N

₀

10

7

_cfu/mL

P1=1.5 °C/min P2=1.8 °C/min P3=2.6 °C/min

Constant temperature of 65 °C

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

-6,0

-5,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

0

10

20

30

40

50

T

e

m

p

e

ra

tu

re

(

ºC

)

L

o

g

(

N

/N

0

)

Time (min)

P3

P2

P1

TSAYE

Results

culture media

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

-7,0

-6,0

-5,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

0

5

10

15

20

25

30

35

40

45

T

e

m

p

e

ra

tu

re

(º

C

)

L

o

g

(

N

/N

0

)

Time (min)

P2

media

parsley

Palcam

Agar

Results

N

0

10

7

cfu/mL

Can results obtained in broth be applied in predicting

microbial responses in solid foods ?

Results corroborate that microbial kinetic

behaviour in “real” food surfaces differs to

the one observed in broth. Consequently,

caution should be taken when using the

latter ones in food processing predictions

Attention !

Conclusions



Broth-based experiments highlights the importance of studying the

influence of dynamic conditions on the thermal resistance of

microorganisms, since the heating up phases can contribute to an increase

in cells thermotolerance.



Results obtained in parsley demonstrated that the product greatly affects

bacteria thermal resistance; although the heat resistance of Listeria

increased (when compared to liquid medium), the inactivation began

earlier.

Conclusions



Overall it can be said that the model assumed has the ability of

dealing with time-varying temperature conditions, which is a key

value to predict microbial loads of foods that suffer a thermal

process.

Frozen storage of vegetables

Case 2

good alternative

to fresh products

Quality

of frozen

vegetables

Quality

Quality

Storage conditions in the cold chain

Freezing conditions

Blanching conditions

Preparation conditions

Raw material

Integrated

optimization

Maximizing

content

Maximizing

content

Inactivation

(90%

POD) is required

to prevent

degradation

Nutritional

• Vitamins

• Minerals

• Pigments

Physical and organoleptic

• Texture

• Colour

however…

adversely affects the

quality factors …

Inactivate degradative enzymes

Reference: Peroxidase (POD)

Eliminate bacteria

and chemical contaminants

…

Objective

and

Temperature

time

Vegetal

Vegetable

Temperature

time

[rate=constant]

[Attribute]

time

T<<

T<

T

Studies of Time-Temperature-Tolerance (TTT)

Studies with accelerated life tests

Equipment

Ti

m

e

Temperature

Storage conditions

• Fitotherm chambers

• Regime I:

Isothermal conditions

Temperature (ºC): -7, -15 , -25 time (days):140

• Regime II:

Non-isothermal conditions

“step-stress”

Temperature

(ºC)/

time(

days):

-30

/

21

;

-20

/

16

;

-10

/

11

total: 57 days and

-5

/

9

Storage

Texture

Colour

Vitamin C

Peroxidase

Freezing

Blanching

Pumpkin (Cucurbita maxima L.)

• Parameters: a*/a

0

*; hue (tan

-1

b*/a*)

Material & métodos de análise

Broccoli (Brassica oleracea L. ssp.)

Storage

Colour

Vitamin C

Peroxidase

Freezing

Drip loss

Blanching

Experimental procedures

• Fitotherm chambers

• Regime I:

Isothermal conditions

Temperature (ºC): -7, -15 , -25 time (days):140

• Regime II:

Non-isothermal conditions

“step-stress”

Temperature

(ºC)/

time(

days):

-30

/

21

;

-20

/

16

;

-10

/

11

• Parameters:

L*; hue (h*)

Carrot (Daucus carota L.)

Storage

Texture

Colour

Vitamin C

Peroxidase

_Freezing

Blanching

Drip loss

Experimental procedures

• Fitotherm chambers

• Regime I:

Isothermal conditions

Temperature (ºC): -7, -15 , -25 time (days):140

• Regime II:

Non-isothermal conditions

“step-stress”

Temperature

(ºC)/

time(

days):

-30

/

21

;

-20

/

16

;

-10

/

11

Watercress (Nasturtium officinale R. Br.)

Storage

Colour

Vitamin C

Peroxidase

Freezing

Blanching

Clorofilas

Experimental procedures

• Fitotherm chambers

• Regime I:

Isothermal conditions

Temperature (ºC): -7, -15 , -25

time (days): 390

33

Assessment of the time-temperature efects on degradation rates of quality attributes

Kinetic models

Isothermal conditions

_{Non-isothermal conditions}

Integration of

time effects

The models

zero-order

first order

fractional conversion model

C

₀

= 0

C

_eq

= 17.5

k

_-15ºC

= 19 days

-1

Ea = 24.8 kJ mol

-1

R

2

_{= 0.85}

fractional conversion model

C

₀

= 10.6 mg/100g

C

_eq

= 1.2 x10

-6

k

_-15ºC

= 1.5 x10

-2

_days

-1

Ea = 3.2 kJ mol

-1

R

2

_{= 0.85}

fractional conversion model

C

₀

= 0

C

_eq

= 24.2

k

_-15ºC

= 7.8 x 10

-2

_days

-1

Ea = 2.0 x 10

-6

_{kJ mol}

-1

R

2

_{= 0.60}

fractional conversion model

C

₀

= 9.7 mg/100g

C

_eq

= 2.9

k

_-15ºC

= 25.5 days

-1

Ea = 41.4 kJ mol

-1

R

2

_{= 0.93}

Vitamin C

C

ol

ou

r

_T

C

D

Results

first order

C

₀

= 12.1 %

k

_-15ºC

= 4.2 x 10

3

_days

-1

Ea = 0.0047 kJ mol

-1

R

2

_{= 0.75}

first order

C

₀

= 11.6 %

k

_-15ºC

= 42.6 days

-1

Ea = 42.3 kJ mol

-1

R

2

_{= 0.94}

first order

C

₀

= 34.7 mg/100g

k

_-15ºC

= 7.5 days

-1

Ea = 0.0039 kJ mol

-1

R

2

_{= 0.67}

Vitamin C

Drip loss

first order

C

₀

= 33 mg/100g

k

_-15ºC

= 6.8 days

-1

Ea = 60.2 kJ mol

-1

R

2

_{= 0.96}

Results

fractional conversion model

C

₀

= 22.5

C

_eq

= 7.5

k

_-15ºC

= 31.1days

-1

Ea =1.3 x 10

-6

_{kJ mol}

-1

R

2

_{= 0.86}

fractinal conversion model

C

₀

= 21.9

C

_eq

= 4.2

k

_-15ºC

= 6.7 days

-1

Ea = 95.0 kJ mol

-1

R

2

_{= 0.95}

fractional conversion model

C

₀

= 22.8

C

_eq

= 15.8

k

_-15ºC

= 8.8 x 10

-2

_days

-1

Ea = 1.93 kJ mol

-1

R

2

_{= 0.78}

fractional conversion model

C

₀

= 22.6

C

_eq

= 4.8

k

_-15ºC

= 11.9 days

-1

Ea = 43.0 kJ mol

-1

R

2

_{= 0.90}

carrot

Results

pumpkin

texture

texture

first order

C

₀

= 1.92 x 10

5

_U/gmin

k

_-15ºC

= 2.6x 10

3

_days

-1

Ea = 17.3 kJ mol

-1

R

2

_{= 0.72}

first order

C

₀

= 32.6 mg/100g

k

_-15ºC

= 4.3 x 10

3

_days

-1

Ea = 24.7 kJ mol

-1

R

2

_{= 0.79}

POD

Vitamin C

AA+DHAA

Time (days) V it a m in C (m g /10 0 g ) Time (days) V it a m in C (m g /10 0 g ) Time (days) V it a m in C (m g /10 0 g )

-7ºC

-15ºC

-25ºC

Results

Tempo de vida útil

Prediction

Results

61

days

125

days

118

days

6

months

Conclusions



Accelerated life tests applied to all vegetables were

a satisfactory methodology for studying kinetics of

quality changes during frozen storage



Activation energies are considerable lower when

compared to the ones estimated under isothermal

conditions

Solar drying of grapes

Case 3

Solar Dryer in Mirandela – Portugal

Solar drying of grapes

The objective was to simulate solar drying of grapes by

integrating heat and mass transfer

0

10

20

30

40

50

60

70

80

90

0

0,5

1

1,5

2

2,5

3

3,5

tim e(days)

Temperature(ºC)

Relative Humidity(%)

Air conditions inside solar dryer

Energy increment

Radiant energy

absorbed

heat loss

by convection

heat loss

by evaporation

heat loss

by radiation

F

)

T

(T

ζ

ε

A

dt

)

m

(λ

d

-T)

-(T

A

h

I(t)

A

dt

)

T

Cp

(m

d

4

₄

p

s

w

p

s

p

p

Qr

Qe

Qc

I(t)

A

dt

)

T

Cp

(m

d

p

p

Heat and mass transfer model

Global energy balance

Radiation flux

N

d

N

N

d

_g

2

0.5

t

cos

1

g

J

)

I(t

N

d

N

₂

g

1

0.5

t

g

2

1

0.5

2

)

δ

tan

(-tan

arccos

2

g

_N

Meteorogical model

Heat transfer

Sorption-desorption

isotherms

Macroscopic shrinkage

Microstructure

Water difusivity

Xe

Meteorological data

Thermal and radiation

properties

• Finite differences resolution

Mass transfer

Tp

0.6288

Xexp

X

0.3654

R

R

0

0

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.2

0.4

0.6

0.8

1

X / X

0

R

/

R

0

macroscopic

microscopic

Grape radius

Macroscopic shrinkage

48

1x10

-16

_{< D <1x10}

-10

_m

2

_/s

Estimate

Parameter

Experiment 1

Experiment 2

D

0

x10

12

(m

2

s

-1

)

1.75

2.93

a’ (g dm / g H

2

O)

18.3

14.5

b’ (g dm / g H

2

O)

2

32.7

27.1

c’ (K)

687x10

629x10

R

2

_0.9833

_0.9921

s

0.1054

0.0869

av

2

0

0

0

eff

_T

1

-T

1

c'

-X

X

b'

X

X

a'

exp

D

D

Ea = 57.1 and 52.3

kJ/mol

Diffusivity

Simulation of temperature histories

INPUT: , , h, parameters of Cp = f (X) parameters of As, Ap= f (X) initial conditions: m0, As0, Ap0 START INPUT: nnodes, ntimes, t coefficients of D = f (X, T) coefficients of R = f (X) GAB coefficients Xe = f (RH, Ta) data of Ta, RH, I, = f (tD) initial conditions: X0, R0 t = t1 CALCULATE: position ri D (eq. 3.11) Xit CALCULATE: = f (t) (eq. 5.4) R= f (X) OUTPUT: = f (t) i=1 CALCULATE: Qc, Qr = f (T, Ta) Qe = f (X) T = f (t) (eq. 6.5) NO YES t+1

STOP

i = nnodes-1 i-1 OUTPUT: T = f (t) t=n times NO YES CALCULATE: Xe (eqs. 1.21 - 1.23) X X INPUT: , , h, parameters of Cp = f (X) parameters of As, Ap= f (X) initial conditions: m0, As0, Ap0 START INPUT: nnodes, ntimes, t coefficients of D = f (X, T) coefficients of R = f (X) GAB coefficients Xe = f (RH, Ta) data of Ta, RH, I, = f (tD) initial conditions: X0, R0 t = t1 CALCULATE: position ri D (eq. 3.11) Xit CALCULATE: = f (t) (eq. 5.4) R= f (X) OUTPUT: = f (t) i=1 CALCULATE: Qc, Qr = f (T, Ta) Qe = f (X) T = f (t) (eq. 6.5) NO YES t+1

STOP

i = nnodes-1 i-1 OUTPUT: T = f (t) t=n times NO YES CALCULATE: Xe (eqs. 1.21 - 1.23) X X

Algorithm of the developed program

Tp

0

10

20

30

40

50

60

70

80

0

1

2

3

4

5

6

7

time (days)

T

e

m

p

e

ra

tu

re

(

ºC

)

air temperature

product pemperature

Simulation of solar drying of grapes

0

1

2

3

4

5

6

0

2

4

6

8

10

12

14

time (days)

w

a

te

r

c

o

n

te

n

t

(k

g

H

2

O

/

k

g

d

m

)

0

1

2

3

4

5

6

0

2

4

6

8

10

12

14

time (days)

w

a

te

r

c

o

n

te

n

t

(k

g

H

2

O

/

k

g

d

m

)

0

1

2

3

4

5

6

0

2

4

6

8

10

12

14

time (days)

w

a

te

r

c

o

n

te

n

t

(k

g

H

2

O

/

k

g

d

m

)

0

1

2

3

4

5

6

0

2

4

6

8

10

12

14

time (days)

w

a

te

r

c

o

n

te

n

t

(k

g

H

2

O

/

k

g

d

m

)

simualted values

experimental points

Conclusions



Simulation of solar drying achieved by the integrated

model can help in:



prediction of drying times, and consequently the design of

dried fruit production



optimization of the initial amount of product



This integrated model can be easily applied to simulate the

Conclusions



Modeling and simulation of drying in dynamic

conditions, using gradients of water content and

shrinkage of the product simultaneously, were

innovative and represent a contribution to Food

Engineering

Teresa R.S. Brandão

, Cristina L.M. Silva

CBQF – Centro de Biotecnologia

e Química Fina

School of Biotechnology

Catholic University of Portugal

The cases presented clearly illustrate the

application of …

Dynamic approach for assessing food

quality and safety …

successfully!