Vegetable oil modeling applied for the formulation of structured lipids

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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)

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

Motivations

-

Growing need for TAG based products = more

systematic tools for their design

3

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 correlated

with Solid Fat Content

Flöter. The role of physical properties

data in product development. Eur. J.

Lipid Sci. Technol. 2009, 111, 219–226

Solid Fat Content: the core property

Physical

Chemical

Attributes (end-user perception)

Macroscopic properties

Microstructure

Processing

Storage

SFC

Polymorphism

Composition

FA

TAGs

Veg.Oil / Fat

stability

spreadability

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

Processing

Storage

SFC

Polymorphism

Composition

FA

TAGs

Veg.Oil / Fat

stability

spreadability

texture

shine

Flavor

Natural

sources

Data (SP, T

_m

)

Data (FAME)

Data Data

Data

Data (NMR)

Data QSPR correlations 6

(IV)

-

A predictive model for the missing links:

•

SFC vs Veg. Oil composition

• By using FA distribution or TAGs composition

Vegetable Oil modeling

7

SFC

Polymorphism

Composition

FA

TAGs

Veg.Oil / Fat

Vegetable Oil modeling

-

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 be

predictive to help oil blend

design

SFC Polymorphism

Composition

FA TAGs Veg.Oil / Fat

8

Liq.

Solids

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

α β’ ’ β α LIQUID α β’ ’ β α LIQUID

+ …….

+

+

α β’ ’ β α LIQUID

+

9

The SLE model

-

Polymorphic solid(s) – liquid equilibrium

10 ) ln ( _iliquid nc i liquid i liquid _{RT x x} g

∑

= = 1

(

)

       +         − ∆ =

∑

= ) ( ) ( ) ( , ) ( , ) ( ) ( _ln solid j i j solid i j solid i m j solid i m nc i j solid i j solid _x T T R H x RT g 1 1 γ 1

Predicted for each phase

Function of TAGs composition

) ,

, (

&T f polymorhicFAtypeFAposition

H_{m m}= ∆

∑∑

= =

∂

∂

∆

+

∂

∂

+

=

np j nc i i j j i E p app p

T

n

Hm

T

G

C

C

1 1

∑∑

= = = nc i np j j i j i g n n n G with n G 1 1 ) ( ) ( ) ( min

-

Results:

•

Solid Fat Content obtained

directly from n

_TAG

phase j

•

(equilibrium) DSC curves

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 1 0 10 20 30 40 50 60 70

Molar fraction (OOO)

T e m p e ra tu re ( ºC ) 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Temperature (ºC) s o lid m o la r fr a c ti o n 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Temperature (ºC) s o lid m o la r fr a c ti o n

PPP (C16:0)

OOO (C18:1)

PPP (C16:0)

CCC (C10:0)

SFC

SFC

Phase diagram

-400 -20 0 20 40 60 80 10 20 30 40 50 60 Temperature (ºC) C p [ k J /m o l. K ]

PPP

OOO

PPP + CCC

DSC 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: ₁₂

-200 0 20 40 60 80 0.2 0.4 0.6 0.8 1 Temperature (ºC) S F C ( m a s s ) -200 0 20 40 60 80 500 1000 1500 Temperature (ºC) C p ( k J /K ) -200 0 20 40 60 80 0.05 0.1 0.15 0.2 0.25 Temperature (ºC) m o ls o n s o lid s ta te /t o ta l o f m o ls -200 0 20 40 60 80 0.05 0.1 0.15 0.2 0.25 Temperature (ºC) m o ls o n l iq u id s ta te /t o ta l o f m o ls PPP SSS CCC OOO PPP SSS CCC OOO

OOO + CCC

PPP

SSS

SFC 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)

13 -200 -10 0 10 20 30 40 50 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Temperature (ºC) S F C ( m a s s f ra c ti o n ) SFC Vs Temperature -200 -10 0 10 20 30 40 50 1 2 3 4 5 6 Temperature (ºC) C p [ k J /m o l. K ] Simulated DSC

Veg. Oil: Palm oil

14

-

DSC: temperature profile and main phase transitions

•

We solve S- L Equilibrium = we get equilibrium DSC

• = DSC with infinitely slow heating rate

-200 -10 0 10 20 30 40 50 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/min

T7 = 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 & interesterified

blends predictions agree with

experimental data

•

Predicts the trend at lower

temperature

•

Reaction makes peaks

different and broader

-300 -20 -10 0 10 20 30 40 50 60 2 4 6 8 10 12 14 Temperature (ºC) C p [ k J /m o l. K ] -300 -20 -10 0 10 20 30 40 50 60 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Temperature (ºC) S F C ( m a s s f ra c ti o n )

Before reaction

After reaction

15

Ternary Veg. Oil blend

-

Influence of interesterification

•

SFC is higher when SFO fraction is low

•

Interesterification greatly reduces the SFC

16

Not interesterified

_{Interesterified}

Solid Fat Content

_{Solid Fat Content}

T = 10 ºC

PREDICTED 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

17

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-6

Omega-3

Medium FA

Medium FA

PUFA

sn2

DHA C22:6

Structured Lipids Application

-

High melting SL affects palm oil initial melting T

-20

0

-10

0

10

20

30

40

50

60

0.2

0.4

0.6

0.8

1

Temperature (ºC)

S

F

C

(

m

a

s

s

f

ra

c

ti

o

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 for

Enhanced Structured Lipids for Foods. J Am Oil Chem Soc (2011) 88:223–233.

Structured Lipids Application

-

The effect on SFC depends on the T range

•

Palm oil + (Caprylic – EPA – Caprylic)

-200 -10 0 10 20 30 40 50 60 0.2 0.4 0.6 0.8 1

Temperature (ºC)

S

F

C

(

m

a

s

s

f

ra

c

ti

o

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 for

Enhanced Structured Lipids for Foods. J Am Oil Chem Soc (2011) 88:223–233.

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

20

Teles dos Santos, Gerbaud, Le Roux. Phase Equilibrium and Optimization Tools:

Application for Enhanced Structured Lipids for Foods. J Am Oil Chem Soc (2011)

-

Computer aided design of oil blends

•

Finding the blend that matches targeted SFC and DSC

•

Objective: to anticipate the most promising lab experiments

SLE Results: 4 TAGs mixture

-

Composition in each phase

•

Solid phase is gradually enriched with SSS;

•

OOO fraction gradually decreases in liquid phase

-200 0 20 40 60 80 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Temperature (ºC) m o ls o n s o lid /t o ta l o f m o ls o n s o lid -200 0 20 40 60 80 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Temperature (ºC) m o ls o n l iq u id /t o ta l o f m o ls o n l iq u id PPP SSS CCC OOO PPP SSS CCC OOO -20 0 20 40 60 80 Temperature (ºC) -20 0 20 40 60 80 Temperature (ºC) -200 0 20 40 60 80 0.05 0.1 0.15 0.2 0.25 Temperature (ºC) m o ls o n s o lid s ta te /t o ta l o f m o ls -200 0 20 40 60 80 0.05 0.1 0.15 0.2 0.25 Temperature (ºC) m o ls o n l iq u id s ta te /t o ta l o f m o ls PPP SSS CCC OOO PPP SSS CCC OOO 21

eq - DSC theoretical prediction Vs Experimental

Data

1

°

C/min

T7 = 41,51

°

C

C.P. Tan, Y.B. Che Man.

Differential scanning calorimetric analysis of palm oil, palm oil based 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 Calorimetric

Analysis of Edible Oils: Comparison of Thermal Properties and Chemical

Composition. JAOCS, Vol. 77, no. 2 (2000)

Calc.: calculated by VODesign

*nc: no convergence

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 50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Temperature (ºC) S F C ( m a s s f ra c ti o n )

SFC Vs Temperature

10 20 30 40 50 0 1 2 3 4 5 6 7 8 9 10 Temperature (ºC) C p [ k J /m o l. K ]

Simulated DSC

Exp. Data: WON, K. W. THERMODYNAMIC MODEL OF LIQUID-SOLID EQUILIBRIA FOR NATURAL FATS AND OILS. Fluid

PREDICTED PREDICTED

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 40 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 (a) Temperature (ºC) S F C ( m a s s f ra c ti o n ) 0 10 20 30 40 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 (b) Temperature (ºC) C p [ k J /m o l. K ]

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

Unconventional oils

-

Theoretical prediction for value-added vegetable oil

•

Grapeseed Oil: 10 TAGs

-40 -30 -20 -10 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

(a) Temperature (ºC)

S

F

C

(

m

a

s

s

f

ra

c

ti

o

n

)

-40 -30 -20 -10 0 0 1 2 3 4 5 6

(b) Temperature (ºC)

C

p

[

k

J

/m

o

l.

K

]

26

Influence of Temperature:

ternary blend not interesterified

27 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

28 PO–SFO–PKO

₀

_°

_C

10

°

C

15

°

C

25

°

C

30

°

C

35

°

C

0.90 0.35 0.12 0.50 0.18 0.06 PREDICTED