Modeling Prospectives in Food Industry

Modeling and Simulation of Food

and Bio Processes

Capri, 6-10 June 2016

B. Watzke, Nestlé Research Centre

Switzerland

brigitte.watzke@rdls.nestle.com

Modeling and Simulation… and me

• At begining, my research interests were on colloidal medium and solar

energy conversion

• After maternity leaves, return to academic research

• ETHZ, modeling group of Prof. U. Suter

Prediction of polymeric material properties from molecular modeling

• starch

• Kevlar (liquid crystaline polymers)

• EPFL Process modeling

• Static mixer

• Freelance consultant for process modeling (Physchem consulting)

• Joining Nestlé Research as modeling expert

History

Food Industry origins

Napoléon’s wars (1800-1815)

Industrial revolution (1850 onwards)

History

Modeling origins

• 1822 C. Babbage, first mechanical computer («the analytical engine»).

• 1842 Lady Ada Lovelace, 1st program for the analyticalengine

• II world war, Manhattan project

(… Women are made for programming…)

(….. Unfortunately wars seem to be

source of innovations….)

Status - Publications statistics

• 1% of the «Modeling» publications concern «Food»

• 2% of the «Food» publications concern «Modeling»

Progression of modeling publications from 1970 on

Sample of industrial wide employed modelling experts in companies

Food Industries

– Estimated numbers mirror the status

seen in the publication analysis.

– Procter & Gamble is a real outlier

Modeling dense industries

– Pharma

– Oil production and refinery

industries

– Mechanical and Electro-technical

industries

( There are many other modelling-

dense industries)

Expert Network in Nestlé R&D

1. Share modeling expertise across all Nestlé businesses

2. Develop & exploit physics based mathematical models

3. Promote the use & formation of modeling expertise within

R&D

4. Setup & share executables for users

5. Setup and promote collaborations with Academia

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Typical topics of (process) modeling

• Test new ideas / prototypes

• Optimize processes

• Scale-up processes

• Reduce Trial costs

• Identifying the source of problem

• Create a knowledge base

• Train operators

Outline

Food Industry context

Modeling(s)

Some case studies

Prospective – future of modeling

Stepping stones for a future modeling

Technological

Feasibility

Consumer

Needs

Business

Constraints

Sustainable

Development

Product

&

Process

Product attributes

&

Process parameter

Product Design

&

Process Optimization

Modelin

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Ind

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Modeling Experiment

Demand Consumer

Empirical

Statistical

Mechanistic

Material properties

Experimental Analysis

Physical models (machines)

Consumer-oriented

Innovation-driven

Speed-to-market

Cost reduction

Sustainability

Convenience

Health

Emotions

Functionality

Safety

General modeling principles

Apply algorithms based on physical, chemical, biological

laws, empirical equations, mathematical or statistical

relations, or machine learning algorithms.

Define the space and dynamic; determine the boundary

conditions of the problem.

Find «good-enough» solutions which minimize the

difference between calculated and experimental data

values.

Consequences from connecting modeling and industrial contexts

1) Find Relations between what you know and what you

want to solve

2) Confine the problem to an expected solution domain

(i.e. new process in old factory lines)

3) Minimize the distance between your predictions and the

direct application

Consumer driven modeling opportunities

Convenience• time saving / easy to handle / easy opening

Nutritional impact • targeted needs / reduced salt, sugar and fat / health claims

Personalization of Food • health needs / regional preference and constraints

Delight• appearance / mouth feel (texture) / flavor aroma persistence / «like home»

Safety• assurance of no pathogens / spoilage / shelf-life time /safe packaging

Processability• naturalness / freshness / clean Labels / ingredients variability

Sustainability• low energy / reduced water consumption / reduced waste

Integral food manufactoring process targets

Post-harvest treatments / Raw ingredients

transformations

Process - Heat processes combined to mechanical

shear process

Packaging

Warehousing / Storage

Transport / Distribution

In-store preparation

Home preparation

Lasagne

Process parameter

heating in the oven

Product Design

&

Process Optimization

Modelin

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Ind

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Modeling Experiment

Mechanistic Material properties

Experimental Analysis

Demand Consumer

faster oven reheating

than competition

same formulation

Achieving safety requirement

Heterogeneity of product – Lasagne

Demand / Brief

• Can frozen Lasagne be reheated in conventional oven in a shorter time

(than competition)?

18

Problem description

• Party size (3kg) and Family size 2kg (US market!) Lasagne dish for home

preparation

• Conventional heating in oven >1h

• Core temperature reached at this time 65°C

Physical product description / assumptions

• Multicomponents, multi-layers

• Frozen state: Ice crystal, Phase transition

Model

Finite Element Method: Heat transfer model

• Conduction, convection, radiation

• Fourier heat conduction equation.

r Density: sum of weighted contributions of component densities

dH/dT Apparent specific heat: extracted from DSC

l Thermal conductivity: composed from weighted contributions of each

components conductivity

Each layer has its own

proportion of components:

water, ice, protein, lipid,

glucid, fiber, ashPasta

Bechamel

Bolognaise

Model

FEM Heat transfer model - Fourier heat conduction equation

Sz

T

zr

Tr

rS

t

T

dT

dHr

l

l

r

r Density,

dH/dT Apparent specific heat,

l Thermal conductivity

Model

FEM Heat transfer model – Phase Changes

• DSC performed on layer samples of pasta, bolognaise, bechamel

• Ice content empirical law* extracted from DSC measurements

* Riedel

dH/dT determination Ice content determination

Lasagne – Model results

• Prediction of model fits

experimental data from thermal

probe.

• Local profile in the different layers /

different material

thawing

Testing of different approaches - 1

• Airflow:

Passive versus air-pulsed

convections in conventional ovens

mimicked by Surface Heat

Transfer Coefficients

10min

• Variation in tray material:

Core temperature curves of

product with aluminiun and black

anodized aluminium.

Testing of different approaches - 2

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• Restructuring of Product:

Effect of pre-portionning of the

dish (8 blocks)10 min

Chocolate

Product attribute

Shape

Product Design

&

Process Optimization

Modelin

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Ind

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Modeling Experiment

Demand Consumer

Mechanistic

Statistical

Material properties

Sensory Analysis

In-mouth flavor release

Improve delight

same formulation

Sensory improvement

higher flavor and texture

impact

The different strength of models

input

ingredients

operating

conditions

Process

variables

product attributes

sensory evaluation

Statistical

model

Empirical models

are best:

• when

relationships are

too complex.

• when lots of

easily measurable

data are available

.

• are not suitable

for extrapolation.

Empirical

model

Mechanistic

Process model

Mechanistical

models are best:

• when process

relationships are

well understood.

• when material

properties are

known before

hand.

• when results are

experimentally

verifiable.

• are suitable for

extrapolation and

scale-up.

?

Consumer

input

ingredients

operating

conditions

Process

variables

product attributes

sensory evaluation

Statistical

model

Mechanistic

Process model

Consumer

Even more delight ?!

Demand / Brief

• It is known that ingredients, process

and particle size distribution largely

impact the chocolate sensory

perceptions.

• Is shape of chocolate also

impacting?

Problem description• Chocolate composition and weight kept constant.

• Aroma can partition between melt and oral cavity.

Building and destructing of food structures

Freezing/Cooking

Drying

Texturizing

Industrial structuration

In mouth destructuration

Re-warming

Defrosting

Cooking

Biting

Chewing

Mixing with saliva

Sensory perception

Nutritional impact In gut destructuration

Bioavailability

Breaking Mixing

Diluting Shearing

Sensory evaluationswith shapes of same recipe and same weight

Panel of 12 persons

Subjects instructed to let the sample melt in closed mouth.

• 3 flavor attributes (cocoa, caramel, aftertaste) and

• 4 texture attributes (deformation, melting, smooth, powdery)

could be significatively distinguished.

In-vivo measurements with shapes of samerecipe and same weight

Off-line time-resolved nosespace study

Exhaled air, prior and during consumption was analyzed

(GC & PTR-MS).

• Chocolate shape have an impact on in vivo aroma

release kinetics.

Model objectives

• Melting prequisite for release of taste and aroma

compounds.

Conductive heat transfer simulation

Phase transition from crystalline to liquid forms of the fats.

Consumes heat.

Fourier equations with fat phase transition and latent heat

balance included.

No change in topology

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Model creation

Upper palate of human mouth

Ferrario et al 2001

Model use

1) Choose / design shape

2) Dock to palate

Melting boundary

conditions

free gas space

volume

3) Assess parameters

Phase transition (solid <-> melt)

DSC NMR

SFC curve of chocolate

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50Temperature {°C}

Solid F

at

Conte

nt

{%

} SFC exp

SFC fit

Chocolate

0.E+00

2.E+04

4.E+04

6.E+04

8.E+04

1.E+05

0 10 20 30 40 50T [°C]

En

thalp

y [

J /

kg

]

0

500

1000

1500

2000

2500

3000

3500

dH

/d

T [

J /

(kg

°C

)]

0.

Tk

t

TC pr

0.

Tk

t

T

dT

dHr

TCTSFCLHFFTH pC 1

H, enthalpy

Fc, Fat content

LHF, Latent Heat of Fusion

SFC, Solid Fat content

Melting snapshots in oven and in mouth at 37°C

in-oven in-mouth

Modeling results

Melting profiles obtained in the mouth model correlate with

• the melting perception vs shapes.

• AC/V contact to volume ratio, descriptor of rapid heat

transfer and propagation in the chocolate as driver of

melting.

In-mouth

Modeling results

OVV/AC descriptor obtained from the mouth

model correlate with

• the Aroma release time-intensity perception

vs shapes.

• Negative correlation between the melting

attribute and the kinetics of cumulative

aroma (cocoa) release.

Results

• Chocolate shape has a significant impact on in vivo aroma

release.

• Melting profiles of shaped chocolates are different in the

mouth compared to isothermal boundary conditions.

• Sensory melting attributes show a linear relation between

heat exchange surface area over volume (AC/V) of the

shaped chocolate.

• Improved flavour release can be expected from the optimal

combination of the melting profile to the open volume that the

shape permits in the mouth cavity.

Through the appropriate shape we can also control the

New England Journal, 2012 !!!

Product

&

Process

Product attributes

&

Process parameter

Product Design

&

Process Optimization

Modelin

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Ind

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Modeling Experiment

Demand Consumer

increased robustness

Stand-alone executables

backward compatible software

Expert systems

Integration in company IT-IS

Licenses for infrequent use

Nutrition

Health

Wellness

Personalization

Sustainability

Safety perception

Pleasure

Prevention

Health claims

TOOLBOX Open access to experimental data

Data mining tools

Machine learning algorithms

Link with medical profession

Integration of OMICS, system biology

FUTURE SCOPE

Nutrition, Health and Wellness

42

Fo

od

Sa

fety

Raw

MaterialIngredients

PreparationProcessing PRODUCT

Packaging

Storage

Distribution

Home

Preparation

Eating

Body

Effects

NutritionSensoryFunctionality Optimization Shelf-Life

life history of a product

Quality & Safety

Bio-inpired Processing Individualization**p.o.s. ProductionSoft Refining

Health Care

Product

Dysphagia

Product attributes:

safe swallowing

Product Design

&

Process Optimization

Modelin

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Ind

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Modeling Experiment

Demand Consumer

Mechanistic

Statistics

Material properties

In-vivo studies

In-vitro experiments

Artificial mouth

Sensory

Safety

Comfort

Peace of mind

Health Care products

which can be

swallowed safely by

dysphagia patients

Dysphagia

Demand

• Inner understanding in product requirement for safe and comfortable

swallowing, recognized in the medical community.

• Has to be personalized

Problem description

• Bolus ejection problem.

• Build physical model replicating the essential features of the peristaltic

movement of tongue to test real fluids

Model the bolus passage through the larynx

• In-vivo observation(ultrasound)

• Build understanding

of key factors influencing

swallowing outcomes

• Food bolus ejection from

the oral cavity

to initiate swallowing

N. Kabakcioglu, B. Le Révérend

Model the bolus passage through the larynx

• In-vitro experiments

• Defined space oral cavity

• Imitates palate and

tongue mouvements

• Empting space through

peristaltic movement

• Measure force applied

• Torque

• Speed

P. Hayoun, J. Engmann, S. Mowlavi, B. Le Reverend, A. Burbidge, M. Ramaioli, 2015,

J. Biomech.

DOI: http://dx.doi.org/10.1016/j.jbiomech.2015.09.022

Comparing in-vitro vs. in-vivo transport

In vivo

In vitro

In vivo

In vitro

• The dynamic model is consistent with experiments

• identifies two flow regimes

initial transient regime at constant acceleration

Followed by steady viscous regime at constant

velocity

A dynamic model of the “Model Swallowing” experiment

inertiaviscous

dissipation

driving

force

• The emptying of the oral cavity through the larynx is

governed by the constant velocity regime.

A dynamic model of the “Model Swallowing” experiment

Varying driving force (η = 0.053 Pa.s) Varying bolus viscosity (F = 2N)

Simulating bolus flow in mouth to infer sensory properties, based on In Vivo data

•The in Vivo, moving geometry of a tongue was extracted fromUltrasound data.

Simulating bolus flow in mouth to infer sensory properties, based on In Vivo data

Simulating bolus flow in mouth to infer sensory properties, based on In Vivo data

0.65 0.7 0.75 0.8 0.85 0.9 0.95 10

10

20

30

40

50

60

Time [s]

To

ng

ue

Sh

ea

r S

tre

ss [

Pa

]

=0.58

=0.038

=0.007

=0.002

=0.001

Bolus flow in mouth issimulated to predictstresses and link to sensory properties.

Models for bolus ejection from oral cavity:using in-vivo data for oral geometry

Built after the vivo data, an artificial mouth approximating the in-vivo events.

Mechanistics, mathematical model to understand the flow regimes

2-D CFD model of bolus ejection incorporating tongue surface

captured using in-vivo ultrasound data.

CFD model allows us to compare in-vivo tongue’s strength and modeling the

resulting emptying of the oral cavity.

Outcome of the model study so far is used as imput in a human clinical study

with new formulation of dysphagia adapted products.

Conclusions - Prospectives

• In Food Industry, Modeling is (or should be) part of Team work: The results

should be actionable by others, for Renovation (optimization) purposes as

well as for Innovation (Exploration).

• Process Modeling is key for manufactoring. (partially or fully)

For the future, specific accent on

• Health / Nutrition,

• Sustainainable Nutrition

• Reduction of waste

For modeling, the Future will be to

focus on «What Food does on us»

Acknowledgments

The whole Nestlé Expert Network team, especially R. Parker

P. Hayoun, M. Ramaioli, J. Engmann

N. Kabakcioglu, B. Le Révérend

H. Watzke

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