Heating foods: integrating quality and safety in thermal processes

(1)

Heating Foods

Integrating Quality and Safety in Thermal

Processes

Mafalda Quintas, Teresa Brandão(*) & Cristina L.M. Silva

CBQF – Centro de Biotecnologia e Química Fina School of Biotechnology

Catholic University of Portugal

Harmonization of Standards in Thermal Processing

October 27-29, 2009 Porto, Portugal

(2)

… Originally designed to inactivate

spoiling and pathogenic microorganisms

and

enzymes

bacteria

yeasts

molds

prevent the degradation of the original organoleptic and

nutritive food characteristics

quality quality

safety safety

(3)

thermal processes affect negatively quality factors

process design

quality quality safety safety Texture Microstructure Colour Vitamin C … Pathogenic bacteria Chemical contaminants …

(4)

response

= mathematical function (

variables

_y

_{= f (}

_x

_,

+

_q

_{) + e}

parameters

) + experimental error

Safety

Quality

(5)

y

= f (

x

,

q

) + e

Safety

Quality

process time kinetic parameters

The extension and rate of production /degradation of any safety and quality characteristic can be

quantified

… as well as the effect of process conditions on the kinetic parameters and consequently on final responses

(6)

0 1 2 3 4 5 6 7 8 0 500 1000 1500 2000 logN K maximum inactivation rate logN0 logNres L lag Time (s)

microbial thermal inactivation in which the kinetic parameters

of the model assumed are directly related to specific

features …





                                           L t 1 N N log e k exp exp N N log logN logN res 0 res 0 0 Gompertz-inspired model Safety N – microbial counts kinetic parameters tail

(7)

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 Safety

(8)

Casadei et al. (1998) 0 1 2 3 4 5 6 7 8 9 0 50 100 150 200 250 time (s) lo g N T=64 ºC T=68 ºC T=60 ºC Safety

(9)

0 500 1000 1500 2000 2500 3000 320 325 330 335 340 345 L ( s ) Temperature (K) 0,00 0,05 0,10 0,15 0,20 0,25 320 325 330 335 340 345 k ( 1 /s ) Temperature (K)

k

= 0.0216 exp(-203.3/R*(1/

T

-1/333.15))

**L = 116.3exp(344.1/R*(1/**

T

-1/333.15))

an Arrhenius expression

(10)





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

k

= 0.0216 exp(-203.3/R*(1/

T

-1/333.15))

**L = 116.3exp(344.1/R*(1/**

T

-1/333.15))

The microbial load is just a function of the process temperature, which is a valuable tool to design efficient and controlled processes from a safety point of view.

(11)

Processes

Chemical Physical Food Processes Transport Phenomena • heat • mass • momentum Reaction kinetics Properties

(12)

Processes

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

(13)

Processes

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

(14)

Processes

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

(15)

Processes

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

(16)

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

(17)

Processes

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

(18)

1. 1. Heat

Heat Processing

Processing –

– effect

effect on

on food

food quality

quality and

and safety

safety

2. 2. Combining

Combining Heat

Heat and

and other

other Non

Non--Thermal

Thermal Technologies

Technologies to

to

preserve

preserve foods

foods

3 3..New

New Approaches

Approaches on

on Understanding

Understanding Heat

Heat Degradation

Degradation in

in

Foods

(19)

(20)

1. 1. Heat

Heat Processing

Processing –

– effect

effect on

on food

food quality

quality and

and safety

safety

2. 2. Combining

Combining Heat

Heat and

and other

other Non

Non--Thermal

Thermal Technologies

Technologies to

to

preserve

preserve foods

foods

3 3..New

New Approaches

Approaches on

on Understanding

Understanding Heat

Heat Degradation

Degradation in

in

Foods

(21)

In spite of the benefits of blanching such as prolonging storage

life by the inactivation of enzymes responsible for quality

degradation and reducing the number of bacteria and other

contaminants, it also leads to excessive loss of weight,

alterations in colour, softening of the tissue and loss of nutrients

through their diffusion into the water

Blanching

… particularly important in

VEGETABLES

(22)

pumpkin

broccoli

carrots

Blanching

Cucurbita maxima L. Brassica oleracea L.

Quality

(23)

Quality

pumpkin

Cucurbita maxima L. Colour changes

Firmness

Peroxidase

(24)

0 5 10 15 20 25 30 35 40 0 10 20 30 40 50 Time (min) T C D *

Quality

pumpkin

Firmness

Peroxidase

T=95 ºC T=75 ºC

(25)

0 5 10 15 20 25 30 35 40 0 10 20 30 40 50 Time (min) T C D *

Quality

pumpkin

Firmness

Peroxidase

T=95 ºC T=75 ºC_kt eq 0 eq e TCD -TCD TCD -TCD _ 

Fractional conversion model

(26)

0 5 10 15 20 25 30 35 40 0 10 20 30 40 50 Time (min) T C D * 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 Time (min) F ir m n e ss ( N )

Quality

pumpkin

Firmness

Peroxidase

T=95 ºC T=75 ºC T=95 ºC T=75 ºC

Texture Analyser (Stable Micro-System Ltd, Godalming, UK)

(27)

Quality

pumpkin

Peroxidase

T=95 ºC T=75 ºC T=95 ºC T=75 ºC kt eq 0 eq e Firmness -Firmness Firmness -Firmness _ 

Firmness

(28)

1. Heat Processing

1. Heat Processing –– effect on food quality and safety effect on food quality and safety

Quality

pumpkin

Cucurbita maxima L. 0 20 40 60 80 100 0 10 20 30 40 50 Time (min) P/ P 0 x 1 0 0 Colour changes

Firmness

Peroxidase

T=95 ºC T=95 ºC T=75 ºC T=95 ºC T=75 ºC T=80 ºC

(29)

Quality

pumpkin

Cucurbita maxima L. 0 20 40 60 80 100 0 10 20 30 40 50 Time (min) P/ P 0 x 1 0 0 Colour changes

Firmness

T=95 ºC T=95 ºC T=75 ºC T=95 ºC T=75 ºC T=80 ºC kt e P P _  0

First order kinetics

Peroxidase

(30)

pumpkin

Cucurbita maxima L.

… are recommended to decrease 90% of peroxidase activity, ensuring a good retention of colour. Unavoidably, texture is greatly affected (~ 14% was retained).

Modelling

the kinetics of

peroxidase

inactivation and

colour

and

texture

changes of pumpkin during blanching, will

allow convenient design of thermal processes

Stabilisation of enzymatic deterioration Minimisation of quality losses

Blanching conditions

5.8 min at 90 ºC and

(31)

broccoli

Brassica oleracea L.

Quality

Peroxidase

(32)

0 0,2 0,4 0,6 0,8 1 0 2 4 6 8 10 12 14 16 Time (min) P O D a c ti . ( P /P 0 ) 70 ºC 75 ºC 80 ºC

85 ºC 90 ºC Global mod. fit

broccoli

Peroxidase

Phenolic content

Texture

Quality

(33)

0 0,2 0,4 0,6 0,8 0 2 4 6 8 10 12 14 16 Time (min) P O D a ct i. (P /P 0 ) 70 ºC 75 ºC 80 ºC 85 ºC 90 ºC Global mod. fit

broccoli

Brassica oleracea L. Phenolic content

Quality

kt e P P _  0

Peroxidase

(34)

broccoli

Peroxidase

0 500 1000 1500 2000 2500 3000 3500 0 5 10 15 20 25 30 35 40 45 Time (min) M a x . sh e a r fo rc e ( N )

70 75 80 85 90 Global mod. fit

Texture

Quality

Texture Analyser (Stable Micro-System Ltd, Godalming, UK)

(35)

0 0,2 0,4 0,6 0,8 0 2 4 6 8 10 12 14 16 Time (min) P O D a c ti . ( P /P 0 ) 70 ºC 75 ºC 80 ºC

broccoli

Peroxidase

0 500 1000 1500 2000 2500 3000 0 5 10 15 20 25 30 35 40 45 Time (min) M a x . sh e a r fo rc e ( N )

Quality



Maxshearforce



kt force

shear

Max  ₀

Zero order kinetics

(36)

broccoli

Peroxidase

15 20 25 30 35 40 45 50 55 60 0 5 10 15 20 25 30 35 40 45 Time (min) T o ta l P h e n o lic ( m G A E / 1 0 0 g ) 70 75 80 85 90 Global mod.fit 0 500 1000 1500 2000 2500 3000 3500 0 5 10 15 20 25 30 35 40 45 Time (min) M a x . sh e a r fo rc e ( N )

Texture

Quality

Spectrophotometry (Unicom Ltd, Cambridge, UK)

(37)

0 0,2 0,4 0,6 0,8 0 2 4 6 8 10 12 14 16 Time (min) P O D a c ti . ( P /P 0 ) 70 ºC 75 ºC 80 ºC

broccoli

Peroxidase

15 20 25 30 35 40 45 50 55 60 0 5 10 15 20 25 30 35 40 45 Time (min) T o ta l P h e n o lic ( m G A E / 1 0 0 g ) 70 75 80 85 90 Global mod.fit 0 500 1000 1500 2000 2500 3000 0 5 10 15 20 25 30 35 40 45 Time (min) M a x . sh e a r fo rc e ( N )

Quality

kt

e P

P ₀ 

First order kinetics Phenolic content

(38)

broccoli

Modelling

the kinetics of

peroxidase

inactivation and

phenolic content

and

texture

changes of broccoli during

blanching, will allow convenient design of thermal processes

… are recommended to decrease 90% of peroxidase activity.

Texture was the most temperature sensitive parameter. Thus, attention should be given to texture against other quality parameters for optimizing thermal processes of broccoli.

Blanching conditions

6.5 min at 70 ºC and

(39)

Daucus carota L.

Peroxidase

Firmness

Quality Colour

(40)

carrots

Daucus carota L. 0 5 10 15 20 25 30 35 40 45 Time (min) 0 20 40 60 80 100 T o ta l p h en o li c co n te n t (% ) 70 ºC 75 ºC 80 ºC 85 ºC 90 ºC ___ Global mod. fit

7 0 º C 7 5 º C 8 0 º C 8 5 º C 9 0 º C __ _ mo d . fit 0 5 10 15 20 25 30 35 40 45 Time (min) 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 F ir m n es s (N ) 0 5 10 15 20 25 30 35 40 45 Time (min) 0,0 0,2 0,4 0,6 0,8 1,0 P er o x id as e ac ti v it y ( A/ A0 ) 70 ºC 75 ºC 80 ºC 85 ºC 90 ºC ___ Global mod. fit

7 0 ºC 7 5 ºC 8 0 ºC 8 5 ºC 9 0 ºC _ __ Glo bal mo d . fit

0 5 1 0 15 20 2 5 30 35 4 0 45 Time (min) 6 8 1 0 1 2 1 4 1 6 1 8 2 0 2 2 2 4 a* v al u es

Peroxidase

Firmness

Quality Colour

(41)

Daucus carota L. 0 5 10 15 20 25 30 35 40 45 Time (min) 0 20 40 60 80 100 T o ta l p h en o li c co n te n t (% ) 70 ºC 75 ºC 80 ºC 85 ºC 90 ºC ___ Global mod. fit

7 0 º C 7 5 º C 8 0 º C 8 5 º C 9 0 º C __ _ mo d . fit 0 5 10 15 20 25 30 35 40 45 Time (min) 0 2 4 6 8 10 12 14 16 18 20 22 24 F ir m n es s (N ) 0 5 10 15 20 25 30 35 40 45 Time (min) 0,0 0,2 0,4 0,6 0,8 P er o x id as e ac ti v it y ( A/ A0 ) 75 ºC 80 ºC 85 ºC 90 ºC ___ Global mod. fit

7 0 ºC 7 5 ºC 8 0 ºC 8 5 ºC 9 0 ºC _ __ Glo bal mo d . fit

0 5 1 0 15 20 2 5 30 35 4 0 45 Time (min) 6 8 1 0 1 2 1 4 1 6 1 8 2 0 2 2 2 4 a* v al u es kt eq 0 eq e C -C C -C _ 

Fractional conversion model Colour kt e P P _  0

Peroxidase

kt eq 0 eq e Firmness -Firmness Firmness -Firmness _ 

Firmness

First order kinetics Phenolic content Quality kt e P P _  0 k(T) Arrhenius k(T) Arrhenius k(T) Arrhenius k(T) Arrhenius

(42)

carrots

Daucus carota L.

Modelling

the kinetics of

peroxidase

inactivation and

phenolic content

,

colour

and

texture

changes of carrots

during blanching, will allow convenient design of thermal

processes

… is recommended to decrease 90% of peroxidase activity, ensuring a good retention of phenolic content (70%). Colour was the most temperature sensitive parameter. Thus, attention should be given to colour against other quality parameters for optimizing thermal processes of carrots.

(43)

red bell pepper

Blanching

Capsicum annuum, L.

Petroselinum crispum

(44)

-2,5 -2,0 -1,5 -1,0 -0,5 0,0 0 1 2 3 4 5 6 7

red bell pepper

total mesophiles

time (min)

Initial counts: N0~ 107 CFU/mg

log(N/N₀)

T = 70 ºC

autoctone flora

(45)

-2,5 -2,0 -1,5 -1,0 -0,5 0,0 0 1 2 3 4 5 6 7

red bell pepper

total mesophiles

time (min)

log(N/N₀) T = 70 ºC autoctone flora                                                    1 t L N N log e k exp exp N N log N N log 0 res 0 res 0 Gompertz-inspired model PCA

(46)

parsley

Listeria innocua

time (min)

artificially innoculated

(47)

parsley

Listeria innocua

time (min)

artificially innoculated   L(T) t' dt' N N log e ) T ( k exp exp ' t ) T ( L N N log e ) T ( k exp e ) T ( k N N log t res res isot non   _                                                                            0 0 0 0 1 1 Gompertz-inspired model Palcam Agar

(48)

results obtained in broth



can be applied

in predicting microbial responses in

solid foods ?

However …

Modelling

the

kinetics

of

microbial

inactivation

including the effect of relevant variables (temperature,

pH

and

water activity

) will allow convenient design of thermal

processes

(49)

-6 -5 -4 -3 -2 -1 0 0 20 40 60 80 100 120 lo g (N /N 0 ) tempo (min) -6 -5 -4 -3 -2 -1 0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 lo g (N /N 0 ) tempo (min) -6 -5 -4 -3 -2 -1 0 0 100 200 300 400 500 600 700 800 lo g (N /N 0 ) tempo (min) -6 -5 -4 -3 -2 -1 0 0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 5,0 lo g (N /N 0 ) tempo (min)

aw=0,99 aw=0,95 NaCl aw=0,95 glicerol pH=4.5

pH=7.5

T=52.5 °C T=65.0 °C

(50)

- 7 - 6 - 5 - 4 - 3 - 2 - 1 0 0 1 2 3 4 - 7 - 6 - 5 - 4 - 3 - 2 - 1 0 0 4 8 12 1 6 -7 -6 -5 -4 -3 -2 -1 0 0 50 10 0 1 50 200 log(N/N0) Time (mi n) log(N/N0) log(N/N0) Time (min) Time (min) 52.5°C _60.0°C 65.0°C - 7 - 6 - 5 - 4 - 3 - 2 - 1 0 0 1 2 3 4 - 7 - 6 - 5 - 4 - 3 - 2 - 1 0 0 4 8 12 1 6 -7 -6 -5 -4 -3 -2 -1 0 0 50 10 0 1 50 200 log(N/N0) Time (mi n) log(N/N0) log(N/N0) Time (min) Time (min) - 7 - 6 - 5 - 4 - 3 - 2 - 1 0 0 1 2 3 4 - 7 - 6 - 5 - 4 - 3 - 2 - 1 0 0 4 8 12 1 6 -7 -6 -5 -4 -3 -2 -1 0 0 50 10 0 1 50 200 log(N/N0) Time (mi n) log(N/N0) log(N/N0) Time (min) Time (min) - 7 - 6 - 5 - 4 - 3 - 2 - 1 0 0 1 2 3 4 - 7 - 6 - 5 - 4 - 3 - 2 - 1 0 0 4 8 12 1 6 -7 -6 -5 -4 -3 -2 -1 0 0 50 10 0 1 50 200 log(N/N0) Time (mi n) log(N/N0) log(N/N0) Time (min) Time (min) 52.5°C _60.0°C 65.0°C

Microbial content

= f (time

,

temperature,

pH

,

water activity

)

pH=6.2 aw=0.998

 

           n 0 i n 0 j j k w Tail 0 res _G _a _pH N N log ij













































































1

0

0 t

L

N

log

e

k

exp

N

log

N

log

res

max

res

          



   n 0 i n 0 j n 0 k i j k w k max exp G a pH T k ijk max           



   n 0 i n 0 j n 0 k i j k w L a pH T G exp L ijk pH = 6.2 a_w= 0.998 T = 52.5°C to 65.0 °C

(51)

pH=6.2

(52)

Microbial content

= f (time

,

temperature,

pH

,

water activity

)

pH=6.2

aw=0.998

(53)

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.

(54)

1. 1. Heat

Heat Processing

Processing –

– effect

effect on

on food

food quality

quality and

and safety

safety

2. 2. Combining

Combining Heat

Heat and

and other

other Non

Non--Thermal

Thermal Technologies

Technologies to

to

preserve

preserve foods

foods

3 3..New

New Approaches

Approaches on

on Understanding

Understanding Heat

Heat Degradation

Degradation in

in

Foods

(55)

Ozone

Ultrasonication / Thermosonication

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

ozone generator, concentration

measured by potential difference, 0.3 ppm

(56)

watercress

Nasturtium officinale

Types of combined treatments with ultrasound

Heat + Ultrasound

(57)

watercress

Nasturtium officinale

Quality

Colour changes

Vitamin C

Peroxidase

(58)

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 82.5 ºC Blanching US 87.5 ºC Peroxidase Blanching Blanching Blanching 85.0 ºC 90.0 ºC US US US

(59)

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

(60)

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

(61)

The application of thermosonication

Reaction rates of watercress colour changes due to heat and

thermosonication blanchings were not significantly different

(62)

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 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 82.5 ºC 85 ºC 87.5 ºC 90 ºC 92.5 ºC Blanching Blanching Blanching Blanching Blanching US US US US US Vitamin C

(63)

The application of thermosonication

Vitamin C

Results showed no significant differences between heat and

thermosonication treatments

(64)

The thermosonication treatments can be a good alternative

to the traditional heat blanching processes,

since higher quality products are attained

The application of thermosonication

(65)

red bell pepper

Capsicum annuum, L. Nasturtium officinale

Safety

watercress

strawberries Fragaria ananassa

(66)

red bell pepper

Listeria

Listeria innocua

innocua

(artificially inoculated)

watercress

Total coliforms

(autoctone flora)

strawberries

Total mesophiles

(autoctone flora) Palcam Agar PCA VRBA

(67)

0 1 2 3 4 5 6 7 8 9 US US 50º US 60º US 65º O3 UV L og (N 0 /N ) listeria US H₂O US H₂O US/H₂O US/H₂O O₃ UV 15ºC 50ºC 60ºC 65ºC Treatment

Treatment timetime = 2 = 2 minmin

5

5 replicatesreplicates Listeria innocua

(68)

coliformes 0 1 2 3 4 5 6 7 8 9 US 15º US 50º US 55º US 65º O3 UV L o g ( N /N 0 ) US H₂O US H₂O US/H₂O US/H₂O O₃ UV 15ºC 50ºC 60ºC 65ºC Treatment

5

5 replicatesreplicates Total coliforms

(69)

-1 0 1 2 3 4 5 6 7 8 9

US 2min US 50º 2min US 65º 2min O3 2 min UV

L o g ( N /N 0 ) mesofilos Treatment

5

5 replicatesreplicates Total mesophiles

Initial counts: ~ 107 _CFU/mg

US/H₂O US/H₂O US/H₂O O₃

(70)

Thermosonication

Ozone in aqueous solution

UV-C radiation

• Thermosonication is a promising technique

• Ozonated water-washings are equivalent to simple water-washings

• UV-C radiation is not efficient

(71)

1. 1. Heat

Heat Processing

Processing –

– effect

effect on

on food

food quality

quality and

and safety

safety

2. 2. Combining

Combining Heat

Heat and

and other

other Non

Non--Thermal

Thermal Technologies

Technologies to

to

preserve

preserve foods

foods

3 3..New

New Approaches

Approaches on

on Understanding

Understanding Heat

Heat Degradation

Degradation in

in

Foods

(72)

Food reactions often do not occur isolated but rather within a chain of complex

(73)

reactions, all dependent on several different environmental conditions

Limited Information Pure mathematical

(74)

Limited Information Multiresponse modeling Better Predictions Understanding Reaction Mechanism

Accounts for information of both

reactants and products involved

Measuring several responses of a food system at the same time

Insighful parameter estimation

Pure mathematical Semi-empirical

(75)

• enzymatic reactions (Torres, Lessard & Hill, 2003; Barros & Malcata, 2004)

• chlorophyll degradation in foods (van Boekel, 1999)

• lactic fermentation during pickled carrot manufacture (Nabais & Malcata, 1997)

• Maillard reaction (van Boekel, 1998)

• acrylamide formation (Knol, Van Loon, Linssen, Ruck, Van Boekel & Voragen, 2005)

(76)

Case Study:

Multiresponse Modelling of the Caramelisation Reaction

Glucose Fructose+ H+ Sucrose H+ + H₂O Fructose Oligosaccharides + + Other degradation products Enediol Intermediate HMF Furfural Acidic degradation products _Other degradation products Other degradation products Acidic degradation products Other degradation products Other degradation products 5-methylfurfural Sucrose hydrolysis sucrose thermal sucrose thermal degradation in degradation in the absence the absence of amino groups of amino groups

(77)

Experimental

Glucose Fructose+ H+ Sucrose H+ + H₂O Fructose Oligosaccharides + + Other degradation products Enediol Intermediate HMF Furfural Acidic degradation products _Other degradation products Other degradation products Acidic degradation products Other degradation products Other degradation products 5-methylfurfural Temperature range: 100 to 180ºC Water Content: 3 a 30% (w/w)

HPLC

pH measurements 5-hydroxymethyl-furfural

(78)

Experimental Results Sucrose 0.0E+00 1.0E-03 2.0E-03 3.0E-03 0 20 40 60 80 100 120 140 160 time (min) C ( m o l/ g )

Fructose and Glucose

0.0E+00 1.0E-03 2.0E-03 3.0E-03 0 20 40 60 80 100 120 140 160 time (min) C ( m o l/ g ) HMF and Furfural 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04 0 20 40 60 80 100 120 140 160 time (min) C ( m o l/ g ) 0.0E+00 5.0E-06 1.0E-05

Supercooled sucrose solution (25.30 (w/w)% water content)

T= 140ºC pH 0 2 4 6 8 10 0 50 100 150 200 time (min) p H m e te r re a d in g HMF Furfural Glucose Fructose

(79)

Observed in 12.20 to 30.03 (w/w)% water content solutions at all tested temperatures Glucose Fructose+ H+ Sucrose H+ + H₂O Fructose Oligosaccharides + + degradation products Enediol Intermediate HMF Furfural + H+ Acidic degradation products Other degradation products 2 Other degradation products (Acidic degradation products) Other degradation products Other degradation Products 1 5-methylfurfural + H+ k₁ k₂ k₆ k₄ k₅ k₃ one example of one example of the mechanisms the mechanisms tested tested

(80)

Glucose H+ Sucrose H+ Fructose + + HMF Furfural + H+ OP₂ H+₊ OP₁

k

₁

k

₃

k

₂

k

₅

k

₄

k

₆





 





k

₁

Sucrose

H



Sucrose



 

H k



Glucose



k₁   ₆



Glucose



k₆



Furfural



k

₅



Sucrose



 

H

k



Fructose



k



Fructose



k

₁ 



₃



₂



Fructose



k



HMF



k₃  ₄



HMF



k



Furfural



k

₄



₅



Fructose



k



HMF



k₂  ₄   dt products / ts tan reac d

(81)

Temperature and Water Content effect on reaction kinetics





























_

ref

s

ref

s

T

R

Ea

exp

k

1

Glucose H+ Sucrose H+ Fructose + + HMF Furfural + H+ OP₂ H+₊ OP₁

k

₁

k

₃

k

₂

k

₅

k

₄

k

₆

)

bC

exp(

a

k

, , , ,

ref

₁₃ ₄₅ ₆



_k

_ref

_

_constant

2

constant

(82)

Global Model

Sucrose 0.0E+00 1.0E-03 2.0E-03 3.0E-03 0 50 100 150 200 time (min) C ( m o l/ g )

Fructose and Glucose

0.0E+00 1.0E-03 2.0E-03 3.0E-03 0 50 100 150 200 time (min) C ( m o l/ g ) Furfural 0.0E+00 1.0E-06 2.0E-06 3.0E-06 4.0E-06 0 50 100 150 200 time (min) C ( m o l/ g ) HM F 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04 0 50 100 150 200 time (min) C ( m o l/ g ) Estimates precision: 2 – 45% (95%CI /2p)

T= 140ºC Higher water

content solutions

Athena Visual Studio Software

Glucose

(83)

Model Inadequacy for Reactions Occurring at Extremely Low Water Content HMF 0.0E+00 5.0E-05 1.0E-04 1.5E-04 0 1000 2000 3000 4000 time (min) C ( m o l/ g ) Glucose 0.0E+00 1.0E-03 2.0E-03 3.0E-03 4.0E-03 0 1000 2000 3000 4000 time (min) C ( m o l/ g ) Sucrose 0.0E+00 1.0E-03 2.0E-03 3.0E-03 4.0E-03 0 1000 2000 3000 4000 time (min) C ( m o l/ g ) Fructose 0.0E+00 1.0E-03 2.0E-03 3.0E-03 0 1000 2000 3000 4000 time (min) C ( m o l/ g )

T= 120ºC Low water Low water content content solutions solutions

(84)

Glucose Fructose+ H+ Sucrose H+ + H₂O Fructose Oligosaccharides + + Other degradation products Enediol Intermediate HMF Furfural + H+ Acidic degradation products Other degradation products 2 Other degradation products (Acidic degradation products) Other degradation products Other degradation Products 1 5-methylfurfural + H+

For low water For low water content values a content values a different mechanism different mechanism should be assumed should be assumed