Open Archive Toulouse Archive Ouverte (OATAO) · 2012. 11. 14. · sn3 caprylic caprylic caprylic...

To cite this document: Gerbaud, Vincent and Teles dos Santos, Moises and Carrillo Le Roux, Galo Vegetable oil modeling applied for the formulation of structured lipids. (2012) In: Journées chevreul 2012 : Chimie du végétal et lipochimie, 5-6 Jun 2012, Maison Alfort, France.

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Vegetable oil modeling applied for

the formulation of structured lipids

Vincent Gerbaud

Laboratoire de Génie Chimique, Toulouse

Moises Teles Dos Santos, Galo Carillo Le Roux

Universidad de Sao Paulo (Brasil)

Journées Chevreul 2012 : chimie du végétal et lipochimie

Outline

- Motivations

- SFC : Solid fat content : the core property

- Vegetable Oil modeling• Analysis• Development

- Results• Binary TAGs mixture• Four TAGs mixture• Vegetable oil: 17 TAGs and more..• Oil blends, interesterified or not• Structured lipids application

- Conclusions

Journées Chevreul 2012 : chimie du végétal et lipochi mie2

Motivations

- Growing need for TAG based products = more systematic tools for their design

Journées Chevreul 2012 : chimie du végétal et lipoch imie3

ROSILLO-CALLE, F. et al. A global overview of vegetable oils with reference to biodiesel. Report for the IEA Bioenergy Task 40 (2009)

Our model concerns

food & industrial

uses

Foods

Cosmetics

Lubricants

Cutting fluids

Waxes

Solvents

Vegetable Oil

Solid Fat Content: the core property

- Many evidences that end-user attributes are correlatedwith Solid Fat Content

Journées Chevreul 2012 : chimie du végétal et lipoch imie

Flöter. The role of physical properties data in product development. Eur. J. Lipid Sci. Technol. 2009, 111, 219–226

4

Solid Fat Content: the core property


Physical Chemical

Attributes (end-user perception)

Macroscopic properties

Microstructure

ProcessingStorage

SFC Polymorphism

CompositionFA TAGs Veg.Oil / Fat

stabilityspreadability texture shine Flavor

Natural sources

End-user

The molecular scale

5

Vegetable Oil modeling

- Datas are not uniform and consistent across the scales.

- Predictive models did not exist so far.


Physical Chemical

Attributes (end-user perception)

Macroscopic properties

Microstructure

ProcessingStorage

SFC Polymorphism


stabilityspreadability texture shine Flavor

Natural sources

Data (SP, Tm)

Data (FAME) Data Data

Data

Data (NMR) Data

QSPRcorrelations

6

(IV)

- A predictive model for the missing links: • SFC vs Veg. Oil composition

• By using FA distribution or TAGs composition



SFC Polymorphism


? SFC vs Veg. Oilcomposition



- Model analysis• SFC calculation

• solid – liquid equilibrium (SLE)– Minimizing the gibbs free energy G

• A very complex mixture pb• Veg. Oils blends

m’ Veg. Oils> 95% of n’ TAGs

combination of 3 FA– Each scale give rise to phase

diagrams, SFC, DSC

• Polymorphism affects the SLE

- The final model must bepredictive to help oil blenddesign

SFC Polymorphism


8

Liq.

SolidsTAG SLE

The SLE model

- Model INPUT:• Veg. Oil considered as a mixture of n’ TAGs

• Getting TAGs composition?• (rare) from TAGs analysis• (frequent) from FA distribution

– from which we generate a probable TAGs distribution…

- Model OUTPUT:• The TAGs distribution among the phases liquid and solids vs

temperature• Enable to compute SFC and DSC curves

Journées Chevreul 2012 : chimie du végétal et lipochi mie

α β’’

β

α

LIQUID

α β’’

β

α

LIQUID

+ …….

+

+

α β’’

β

α

LIQUID

+

9

The SLE model

- Polymorphic solid(s) – liquid equilibrium


)ln( liquidi

nc

i

liquidi

liquid xxRTg ∑=

=1

( )

+

−

∆= ∑

=

)()()(

,

)(,)()( ln jsolid

ijsolid

ijsolidim

jsolidim

nc

i

jsolidi

jsolid xTTR

HxRTg γ11

1

Predicted for each phase Function of TAGs composition),,(& positionFAtypeFApolymorhicfTH mm =∆

∑∑= = ∂

∂∆+

∂∂+=

np

j

nc

i

ijj

i

E

papp

p T

nHm

T

GCC

1 1

∑∑= =

=nc

i

np

j

ji

ji ngnnGwithnG

1 1

)()()(min

- Results:• Solid Fat Content obtained

directly from n TAGphase j

• (equilibrium) DSC curves from:

SLE Results: PPP/OOO vs PPP/CCC

- Molecular size greatly affects crystal lattice and the SLE!

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

10

20

30

40

50

60

70

Molar fraction (OOO)

Te

mpe

ratu

re (

ºC)

0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Temperature (ºC)

solid

mo

lar

fra

ctio

n

0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Temperature (ºC)

solid

mol

ar fr

actio

n

PPP (C16:0)

OOO (C18:1)

PPP (C16:0)

CCC (C10:0)

SFC

SFC

Phase diagram

-40 -20 0 20 40 60 800

10

20

30

40

50

60

Temperature (ºC)

Cp

[kJ/

mol

.K]

PPP

OOO

PPP + CCCDSC Curve

11


P: palmitic acid (C16:0) / C: capric acid (C10:0) / O: oleic acid (C18:1)

25% OOO 25% CCC 25% PPP 25% SSS mixture

- Capturing the complexity of TAG behavior in mixture

• OOO and CCC melt together over the same T range • PPP and SSS melt as pure compounds

P: palmitic acid (C16:0) / S: estearic acid (C18:0 ) / C: capric acid (C10:0) / O: oleic acid (C18:1)

12


-20 0 20 40 60 800

0.2

0.4

0.6

0.8

1

Temperature (ºC)

SF

C (

mas

s)

-20 0 20 40 60 800

500

1000

1500

Temperature (ºC)

Cp

(kJ/

K)

-20 0 20 40 60 800

0.05

0.1

0.15

0.2

0.25

Temperature (ºC)mol

s on

sol

id s

tate

/tota

l of m

ols

-20 0 20 40 60 800

0.05

0.1

0.15

0.2

0.25


s on

liqu

id s

tate

/tota

l of m

ols

PPPSSSCCCOOO

PPPSSSCCCOOO

OOO + CCC PPP SSSSFC DSC Curve

Veg. Oil: Palm oil

- Palm oil: 17 TAGs.

• Melting Point correctly calculated• Good SFC agreement, considering that we used two incoherent

data sources• Exp. Data: TAG composition from Sambanthamurthi R et al. (2000)• Exp. Data: mean SFC from Lin, S.W. (2002)


-20 -10 0 10 20 30 40 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Temperature (ºC)

SF

C (

mas

s fr

actio

n)

SFC Vs Temperature

-20 -10 0 10 20 30 40 500

1

2

3

4

5

6

Temperature (ºC)

Cp

[kJ/

mol

.K]

Simulated DSC

Veg. Oil: Palm oil


- DSC: temperature profile and main phase transitions • We solve S- L Equilibrium = we get equilibrium DSC

• = DSC with infinitely slow heating rate

-20 -10 0 10 20 30 40 500

0.01

0.02

0.03

0.04

0.05

0.06

Temperature ºC

Simulated

Experimental(10 ºC/min)

Experimental(5 ºC/min)

PamPamPam

1 °C/minT7 = 41,51 °C

Oil blends and interesterified blends

- Canola Oil (30%) – Fully Hydrogenated Palm Oil Stearin (70%)

• No reaction: • 96 TAGs

• After interesterification: • 162 TAGs

- Analysis:

• Both physical & interesterifiedblends predictions agree with experimental data

• Predicts the trend at lower temperature

• Reaction makes peaks different and broader

-30 -20 -10 0 10 20 30 40 50 600

2

4

6

8

10

12

14

Temperature (ºC)

Cp

[kJ/

mol

.K]

-30 -20 -10 0 10 20 30 40 50 600

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Temperature (ºC)

SF

C (

mas

s fr

actio

n)

Before reactionAfter reaction

15


Ternary Veg. Oil blend

- Influence of interesterification• SFC is higher when SFO fraction is low• Interesterification greatly reduces the SFC


Not interesterified Interesterified

Solid Fat Content Solid Fat Content

T = 10 ºCPREDICTED in 2 hrs

Palm Oil (PO) Sunflower Oil (SFO)

Palm Kernel Oil (PKO)

(SFO)

(PO) (PKO)

(SFO)

(PO) (PKO)

Structured Lipids Application

- Designing functional foods to get nutraceutical benefits• ex. DHA enhanced products


TAG1 TAG2 TAG3 TAG4

sn1 caprylic caprylic caprylic caprylic

sn2 EPA DHA γ-LINOLEIC AA

sn3 caprylic caprylic caprylic caprylic

EPA C20:5

Arachidonic Acid C20:4

γγγγ-linoleic C18:3

Omega-3 Omega-6 Omega-6Omega-3

Medium FA

Medium FA

PUFA

sn2

DHA C22:6


- High melting SL affects palm oil initial melting T

-20 -10 0 10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Temperature (ºC)

SF

C (

mas

s fr

actio

n)

pure palm oil

palm oil +SL01 (30%): C8-EPA-C8

palm oil +SL02 (30%): C8-DHA-C8

palm oil +SL03 (30%): C8-g-linolênico-C8

palm oil +SL04 (30%): C8-AA-C8

Teles dos Santos, Gerbaud, Le Roux. Phase Equilibrium and Optimization Tools: Application forEnhanced Structured Lipids for Foods. J Am Oil Chem Soc (2011) 88:223–233.

18



- The effect on SFC depends on the T range• Palm oil + (Caprylic – EPA – Caprylic)

-20 -10 0 10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Temperature (ºC)

SF

C (

mas

s fr

actio

n)pure palm oil

palm oil + SL01 5%

palm oil + SL01 10%

palm oil + SL01 20%

palm oil + SL01 30%

palm oil + SL01 40%

palm oil + SL01 50%

Teles dos Santos, Gerbaud, Le Roux. Phase Equilibrium and Optimization Tools: Application forEnhanced Structured Lipids for Foods. J Am Oil Chem Soc (2011) 88:223–233.

19


Conclusions

- A knowledge-based predictive model• Solid (multi) – liquid equilibrium based model• Uses FA distribution or TAGs composition data

• FULLY PREDICTIVE model for SFC & equilibrium DSC curves

• Accuracy is good– We need coherent TAG & SFC data to prove that it could be even

better…

• Valid for any TAG mixture: • binary, ternary, vegetable oil, oil blend, interesterified oils

blend

• Not valid for binary FA mixture


Teles dos Santos, Gerbaud, Le Roux. Phase Equilibrium and Optimization Tools:Application for Enhanced Structured Lipids for Foods. J Am Oil Chem Soc (2011)88:223–233.

- Computer aided design of oil blends • Finding the blend that matches targeted SFC and DSC • Objective: to anticipate the most promising lab experiments

We are open to

SLE Results: 4 TAGs mixture

- Composition in each phase• Solid phase is gradually enriched with SSS; • OOO fraction gradually decreases in liquid phase

-20 0 20 40 60 800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Temperature (ºC)

mols

on sol

id/to

tal o

f mols

on s

olid

-20 0 20 40 60 800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Temperature (ºC)

mols

on liqu

id/to

tal o

f mols on

liqu

id

PPPSSSCCCOOO

PPPSSSCCCOOO

-20 0 20 40 60 80Temperature (ºC)

-20 0 20 40 60 80Temperature (ºC)

-20 0 20 40 60 800

0.05

0.1

0.15

0.2

0.25


s on

sol

id s

tate

/tota

l of m

ols

-20 0 20 40 60 800

0.05

0.1

0.15

0.2

0.25


s on

liqu

id s

tate

/tota

l of m

ols

PPPSSSCCCOOO

PPPSSSCCCOOO

21


eq - DSC theoretical prediction Vs Experimental Data

1 °C/minT7 = 41,51 °C

C.P. Tan, Y.B. Che Man.Differential scanning calorimetric analysis of palm oil, palm oilbased products and coconut oil: effects of scanning rate variation .Food Chemistry 76 (2002) 89–102

� Palm Oil

22


A FA-based model could not capture that

Transition Temperatures (ºC)1 2 3 4 5

Exp. Calc. Exp. Calc. Exp. Calc. Exp. Calc. Exp. Calc.

Peanut Oil -51,7 nc* -29,7 -28 -14,57 -11 0,83 2 8,26 7

Grapeseed Oil -39,32 -40 -31,62 -30 -22,82 -21 -15,12 -15 - -

eq - DSC theoretical prediction Vs Experimental Data

Exp Data .: C.P. Tan and Y.B. Che Man . Differential Scanning CalorimetricAnalysis of Edible Oils: Comparison of Thermal Properties and ChemicalComposition. JAOCS, Vol. 77, no. 2 (2000)

Calc .: calculated by VODesign

*nc : no convergence

23


Single VO: Cocoa Butter

- Cocoa butter: 12 TAGs.• High accuracy for lower temperatures• Shorter melting range corrected detected

• time elapsed: 26s

10 20 30 40 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Temperature (ºC)

SF

C (

mas

s fr

actio

n)

SFC Vs Temperature

10 20 30 40 500

1

2

3

4

5

6

7

8

9

10

Temperature (ºC)

Cp

[kJ/

mol

.K]

Simulated DSC

Exp. Data: WON, K. W. THERMODYNAMIC MODEL OF LIQUID-SOLID EQUILIBRIA FOR NATURAL FATS AND OILS. Fluid Phase Equilibria [S.I.], v. 82, p. 261-273, 1993.

PREDICTEDPREDICTED

24


Unconventional oils

- Milkfat – corn oil (80-20) interesterified : 145 TAGs• Simulated and experimental results in agreement (T > 20 ºC)• Time: 15 min 37 s

0 10 20 30 400

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

(a) Temperature (ºC)

SF

C (

mas

s fr

actio

n)

0 10 20 30 400

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

(b) Temperature (ºC)

Cp

[kJ/

mol

.K]

Exp. Data: RODRIGUES, J. N.; GIOIELLI, L. A. Food Research International, v. 36, n. 2, p. 149-159, 2003.MilkCorn_8020.txt 25


Unconventional oils

- Theoretical prediction for value-added vegetable oil• Grapeseed Oil: 10 TAGs

-40 -30 -20 -10 00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

(a) Temperature (ºC)

SF

C (

mas

s fr

actio

n)

-40 -30 -20 -10 00

1

2

3

4

5

6

(b) Temperature (ºC)

Cp

[kJ/

mol

.K]

26


Influence of Temperature: ternary blend not interesterified


PO–SFO–PKO

0 °C 10 °C

15 °C 25 °C

30 °C 35 °C

0.80 0.45

0.35 0.20

0.14 0.09

PREDICTED

Influence of Temperature: ternary blend interesterified


PO–SFO–PKO 0 °C 10 °C

15 °C25 °C

30 °C 35 °C

0.90

0.35

0.12

0.50

0.18

0.06

PREDICTED

Open Archive Toulouse Archive Ouverte (OATAO) · 2012. 11. 14. · sn3 caprylic caprylic caprylic...

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