Integrating quality and safety in thermal and non-thermal food processes

(1)

INTEGRATING QUALITY AND SAFETY IN THERMAL

AND NON

AND NON--THERMAL FOOD PROCESSES

THERMAL FOOD PROCESSES

Teresa R.S. Brandão

1

_{, Mafalda Quintas}

1,2

_{, Cristina L.M. Silva}

1 1_{CBQF-Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal}

2_{IBB–Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, Universidade do}

Minho, Braga, Portugal.

August 22-26, 2010 Cape Town, South Africa

(2)

INTEGRATING QUALITY AND SAFETY IN THERMAL

AND NON

AND NON--THERMAL FOOD PROCESSES

THERMAL FOOD PROCESSES

Teresa R.S. Brandão

1

_{, Mafalda Quintas}

1,2

_,

Cristina L.M. Silva

1

CBQF-Escola Superior de Biotecnologia, Universidade Católica

Portuguesa,

Porto, Portugal

August 22-26, 2010 Cape Town, South Africa

(3)

Thermal Processes

… 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

safety

(4)

however …

thermal processes affect negatively quality factors

process design

quality

safety

Texture Microstructure Colour Vitamin C … Pathogenic bacteria Chemical contaminants …

(5)

recently …

Non-thermal processes have been proposed as technologies able to

• inactivate

spoiling and pathogenic microorganisms & enzymes

• while retaining

nutritional and sensorial properties

quality

safety

(6)

Ozone

Ultrasonication /

Thermosonication

UV-C radiation

(7)

Adequate

and

efficient process design

must take into account…

- type of product

- desired shelf-life

- main sensorial properties

- to the consumer

- more “process sensitive”

- main nutritional aspects

- more significant microbial contaminants

- principal spoiling pathways

Mathematical modeling

is a main

allied

in

collecting

data and

systematization

of

information

(8)

Today’s Presentation

1. 1. The

The

role

of

mathematical

modeling

on

understanding

process

process induced

induced changes

changes in

in foods

foods

2. 2. Heat

Heat Processing

Processing –

– effect

effect on

on food

food quality

quality and

and safety

safety

3. 3. Combining

Combining Heat

Heat and

and other

other Non

Non--Thermal

Thermal Technologies

Technologies to

to

preserve

(9)

(10)

(11)

Today’s Presentation

1. 1. The

The

role

of

mathematical

modeling

on

understanding

process

process induced

induced changes

changes in

in foods

foods

2. 2. Heat

Heat Processing

Processing –

– effect

effect on

on food

food quality

quality and

and safety

safety

1. 1. Combining

Combining Heat

Heat and

and other

other Non

Non--Thermal

Thermal Technologies

Technologies to

to

preserve

(12)

Mathematical models

y = f (

x

,

q

) + e

Safety

_Quality

process

time

kinetic

parameters

1. The role of mathematical modeling on understanding process induced changes in foods 1. The role of mathematical modeling on understanding process induced changes in foods

(13)

0 1 2 3 4 5 6 7 8 9 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

N

log

logN

res 0 res 0 0

Gompertz-inspired

model

Safety N – microbial counts

One example of modeling …

kinetic parameters

tail

N0– initial microbial counts

(14)

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

(24 hours incubation at 5ºC in half cream) 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

One example of modeling …

(15)

Processes

Chemical Physical

Food Processes

Transport Phenomena • heat • mass • momentum Reaction kinetics Properties

(16)

Processes

Chemical Physical

Food Processes

1. The role of mathematical modeling on understanding…

Modelling

mathematical function variables parameters data regression schemes experimental design

(17)

Processes

Chemical Physical

Food Processes

Transport Phenomena • heat • mass • momentum Reaction kinetics Properties design Criterium 1. The role of mathematical modeling on understanding…

Modelling

(18)

Processes

Chemical Physical

Food Processes

Transport Phenomena • heat • mass • momentum Reaction kinetics Properties design Criterium validation

Modelling

(19)

Processes

Chemical Physical

Food Processes

Transport Phenomena • heat • mass • momentum Reaction kinetics Properties design Criterium validation control

Modelling

(20)

Processes

Chemical Physical

Food Processes

Modelling

mathematical function variables parameters data regression schemes experimental design design Criterium validation control optimization Objectives 1. The role of mathematical modeling on understanding…

(21)

Processes

Chemical Physical

Food Processes

Transport Phenomena • heat • mass • momentum Reaction kinetics Properties design Criterium validation control optimization Objectives

quality

safety

Modelling

(22)

Today’s Presentation

1. 1. The

The

role

of

mathematical

modeling

on

understanding

process

process induced

induced changes

changes in

in foods

foods

2. 2. Heat

Heat Processing

Processing –

– effect

effect on

on food

food quality

quality and

and safety

safety

3. 3. Combining

Combining Heat

Heat and

and other

other Non

Non--Thermal

Thermal Technologies

Technologies to

to

preserve

(23)

2. Heat Processing

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

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

(24)

pumpkin

broccoli

Blanching

Cucurbita maxima L. Brassica oleracea L.

Quality

(25)

Quality

pumpkin

Cucurbita maxima L.

Colour changes

Firmness

Peroxidase

(26)

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

Quality

pumpkin

Colour changes

Firmness

Peroxidase

T=95 ºC T=75 ºC

(27)

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

Quality

pumpkin

Colour changes

Firmness

Peroxidase

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

Fractional conversion model

k(T) Arrhenius

(28)

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

Colour changes

Firmness

Peroxidase

T=95 ºC T=75 ºC T=95 ºC T=75 ºC

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

single puncture measurement,10 mm depth of penetration, velocity of 1.0 mm s-1

(29)

Quality

pumpkin

Colour changes

Peroxidase

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

Fractional conversion model

Firmness

(30)

Quality

pumpkin

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

Spectrophotometry (Unicom Ltd, Cambridge, UK)

(31)

Quality

pumpkin

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

(32)

pumpkin

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

Modelling

the kinetics of

peroxidase

inactivation and

colour

and

texture

changes of pumpkin during blanching,

allow convenient design of thermal processes

Stabilisation of enzymatic deterioration

Minimisation of quality losses

Blanching conditions

5.8 min at 90 ºC

and

3.9 min at 95 ºC

(33)

pumpkin

broccoli

Blanching

Brassica oleracea L.

(34)

broccoli

Quality

Peroxidase

Phenolic content

Texture

(35)

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

Spectrophotometry (Unicom Ltd, Cambridge, UK)

(36)

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 ct i. (P /P 0 ) 70 ºC 75 ºC 80 ºC 85 ºC 90 ºC Global mod. fit

broccoli

Phenolic content

Texture

Quality

kt

e

P

_



0

First order kinetics

Peroxidase

(37)

broccoli

Peroxidase

0 500 1000 1500 2000 2500 3000 3500 4000 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

Phenolic content

Texture

Quality

Texture Analyser (Stable Micro-System Ltd, Godalming, UK) maximum shear force , test speed 8 mm s-1_{, full-scale load 500 N}

(38)

broccoli

Peroxidase

0 500 1000 1500 2000 2500 3000 3500 4000 0 5 10 15 20 25 30 35 40 45 Time (min) M a x . sh e a r fo rc e ( N )

Phenolic content

Quality



Maxshearforce



kt force

shear

Max  ₀

Zero order kinetics

Texture

(39)

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 4000 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

Phenolic content

Spectrophotometry

(Unicom Ltd, Cambridge, UK)

(40)

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 4000 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

kt

e

P



₀ 

First order kinetics

Phenolic content

(41)

broccoli

Modelling

the kinetics of

peroxidase

inactivation and

phenolic content

and

texture

changes of broccoli during

blanching, 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.

Stabilisation of enzymatic deterioration

Minimisation of quality losses

Blanching conditions

6.5 min at 70 ºC

and

(42)

red bell pepper

Blanching

Capsicum annuum, L.

parsley

Petroselinum crispum

(43)

-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

PCA

(44)

Safety

-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                                                    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

(45)

parsley

Listeria innocua

time (min)

Initial counts: N0~ 107 CFU/mg

artificially innoculated

Palcam Agar

(46)

parsley

Listeria innocua

time (min)

Initial counts: N0~ 107 CFU/mg

artificially innoculated Palcam Agar

Safety

  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

(47)

Modelling

the

kinetics

of

microbial

inactivation

including the effect of relevant variables (

temperature

,

pH

and

water activity

) allow convenient design of thermal

processes

(48)

Today’s Presentation

1. 1. The

The

role

of

mathematical

modeling

on

understanding

process

process induced

induced changes

changes in

in foods

foods

2. 2. Heat

Heat Processing

Processing –

– effect

effect on

on food

food quality

quality and

and safety

safety

3. 3. Combining

Combining Heat

Heat and

and other

other Non

Non--Thermal

Thermal Technologies

Technologies to

to

preserve

(49)

3. Combining Heat and other Non

3. Combining Heat and other Non--Thermal Technologies to preserve foodsThermal Technologies to preserve foods

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

(50)

watercress

Nasturtium officinale

Quality

Colour changes

Vitamin C

Peroxidase

Thermosonication

(51)

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

Quality

(52)

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

Quality

(53)

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

Quality

(54)

Temperatures above 85 ºC and for the same blanching times led to

higher enzyme inactivation when compared to heat blanching

processes

Reaction rates of watercress colour changes due to heat and

thermosonication blanchings were not significantly different

Vitamin C

Results showed no significant differences between heat and

thermosonication treatments. The treatment will allow good vitamin C

retention

Thermosonication

Peroxidase

(55)

Thermosonication

Quality

The thermosonication treatments can be a good alternative

to the traditional heat blanching processes,

(56)

red bell pepper

Capsicum annuum, L. Nasturtium officinale

Safety

watercress

strawberries

Fragaria ananassa

(57)

red bell pepper

Listeria

Listeria innocua

innocua

(artificially inoculated)

watercress

Total coliforms

(autoctone flora)

strawberries

Total mesophiles

(autoctone flora) Palcam Agar PCA VRBA Safety

(58)

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

(59)

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

Total coliforms

(60)

-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

Total mesophiles

Initial counts: ~ 107 _CFU/mg

US/H₂O US/H₂O US/H₂O O₃

(61)

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

(62)