Small-scale Models as a Tool to Identify Key Factors for Starch Functionality in Food Applications

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-from soup sensory to raw materials and vice versa

-Small-scale Models as a Tool to

Identify Key Factors for Starch

Functionality in Food Applications

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TNO Quality of Life, 26 april 2007 2

• AGROBIOKON (TNO, AVEBE, HPA and SNN) is a research project to

strengthen the starch chain from end product to raw materials and vice

versa.

• Model systems are developed as key factors for improvement of the

whole starch chain.

• This will be demonstrated by a straightforward food application: instant

soup.

Introduction

Potato variety

Sensory Functionality in model system

Functionality in end product Potato starch database

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Main question(s)

Is it possible to influence

instant soup

sensory

using

different

potato starches

obtained by

harvest management?

1) Is it possible to determine viscosities differences in an instant

soup using different potato starches?

2) Is it possible to use a small-scale model system ?

3) Is it possible to observe sensorical differences?

4) Is it possible to influence the sensory attributes by harvest

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Small-scale model system

• Instant soup

4% starch

1% salt or stock cube

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Starch database

Criteria: broad range of physico-chemical properties

• Phosphate 0.7 – 1.1 mg/g

• Amylose 19 – 23 %

• Branching degree 3.9 – 4.1 %

• Number Particle Size 19 – 23 μm

• Volume Particle Size 40 – 46 μm

• Peak-viscosity (RVA) 4900 – 6100 cP

• End-viscosity (RVA) 1430 – 1730 cP

• Gelatinization temperature 62 – 65 o_C

Selection of 14 starches from different potato starch varieties based on physico-chemical and harvest management properties.

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TNO Quality of Life, 26 april 2007 6 X B S-04a X B S-04b X B F-04 B N-02 B Sm-02 X B Kt-04 X B M-04 X B Kr-04 X B Av-04 B At-02 B S-02 A S-03b A S-03a A S-02 Benchmark Bad Benchmark Good Harvest (2) Harvest (1) Variety

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Harvest management

• Harvest year

• Harvest time: early or late

• Water management

• Fertilizer management

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Starch functionality = viscosity

• Criteria: after soup making, viscosity (and stability) must be

retained in a salty environment during temperature change

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RVA-cooling profile

0 200 400 600 800 1000 1200 0 500 1000 1500 2000 Time (s) V iscosi ty ( c P ) 0 25 50 75 100 T ( o C) Benchmark Good Benchmark Bad S165 S217 S274 S311 S363 S417 S467 S523 S593 S667 S798 S815 S922 S946 Food grade (1) Food grade (2) Profile

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Sensory

• 12 different potato starch varieties, 1 benchmark good, 1

benchmark bad and 1 test sample

• 15 attributes

• 4 different days, 4 different soups

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Attributes

• Appearance (fat droplets, clarity)

• Thickness at stirring

• Stickiness to the cup

• Change thickness in mouth

• Salty taste • Spicy taste • Taste creaminess • Mouthfeel thickness • Mouthfeel graininess • Mouthfeel sliminess

• After taste paste

• After taste short

• After taste long

• Change thickness in cup

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First impression

Effect panel

Effect order taste session Effect starch variety

Effect order soup in taste session

Thickness at stirring

Thickness at stirring Thickness at stirring

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First results

• Large difference in good and bad benchmark

• No significant differences between same samples

• Significant effect harvest management

Means and 95,0 Percent LSD Intervals

zetm_ras

attr_3

Ast-02-rijp

Ave-04-rijp-bew Fes-04-rijp-bew Kar-04-rijp-bew Kat-04-rijp-bew Mer-04-rijp-bew

Nom-02-rijp Ser-02-onrijp Ser-02-rijp Ser-1a-03-onrijp Ser-1b-03-onrijp S er-2a-04-rijp-bew S er-2b-04-rijp-bew Sma-02-rijp St-Farinex St-perfect 19 29 39 49 59 69

dikte soep bij roerenThickness at stirring

S-02-A S-03a-A S-03b-A S-04a-B -X S-04b-B -X

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Results attributes

• Appearance (fat droplets, clarity) • Thickness at stirring

• Stickiness to the cup

• Change thickness in mouth

• Salty taste • Spicy taste • Taste creaminess • Mouthfeel thickness • Mouthfeel graininess • Mouthfeel sliminess • After taste paste

• After taste short

• After taste long

• Change thickness in cup

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Results attributes

10,0 20,0 30,0 40,0 50,0 60,0 70,0 Thickness at stirring

Stickness to the cup

Change thickness in mouth

Taste creaminess

Mouthfeel thickness

Mouthfeel graininess Mouthfeel sliminess

After taste paste After taste short/long

Change thickness in cup _{Benchmark bad}

S-03a-A S-02-B Kt-04-B-X At-02-B F-04-B-X Kr-04-B-X S-03b-A S-04-B-X Av-04-B-X Sm-02-B N-02-B M-04-B-X Benchmark Good S-04-B-X S-02-A

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Statistical Analysis by Principal Component Analysis

(PCA)

• PC2 versus PC1 PC1(51.2%) PC2(24.0%) As-02-B Ave-04-rijp-bewF-04-B-X Kr-04-_Kt-04-B-X _B_-X M-04-B-X Nom-02-rijp S-02-A Ser-02-rijp S-03a-A S-03b-A S-04a-B-X Ser-2b-04-rijp-bew Sm-02-B Benchmark Good Fat droplets Clarity Thickness at stirring

Stickiness to the cup

Change thickness in mouth

Salty taste Spicey taste Taste creaminess Mouthfeel thickness Mouthfeel graininess Mouthfeel sliminess

After taste paste

After taste short/long Change thickness

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Statistical Analysis by Principal Component Analysis

(PCA)

• PC3 versus PC2 PC2(24.0%) As-02-B Ave-04-rijp-bew F-04-B-X Kr-04-B-X Kt-04-B-X M-04-B-X N-02-B S-02-A Ser-02-rijpS-03a-A S-03b-A S-04a-B-X S-04a-A-X Sm-02-B Benchmark good Fat droplets Clarity Thickness at stirring

Stickiness to the cup

Change thickness in mouth Salty taste Spicey taste Taste creaminess Mouthfeel thickness Mouthfeel graininess Mouthfeel sliminess

After taste paste After taste long/short

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TNO Quality of Life, 26 april 2007 18 Bad Good S -02-A S -03a-A S -03b-A A t- 02-B N-0 2 -B A v -04-B -X S-0 2 -B S m -02-B K t- 04-B -X M-0 4 -B -X K r- 04-B -X F -04-B -X S -04a -B -X S -04 b-B -X 0 10 20 30 40 50 Good S -02-A S -03a-A S -03b-A A t- 02-B N -02-B A v -04-B -X S -02-B S m -02-B Kt -0 4 -B -X M -04-B -X K r- 04-B -X F -04-B -X S -04 a-B -X S -04 b-B -X 4 6 8 10 12 14 16 1 2 : A and B 3 : X Clustering

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Conclusions

• The benchmark ‘bad’ dominates the results.

• 1:

large difference between benchmark ‘good’ and the potato starches

• 2: A and B

Thickness at stirring

• 3: X

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Correlation between

RVA-cooling profile

and

sensory

data

by Partial Least Squares (PLS).

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Rva-SENSORY

25 30 35 40 45 25 30 35 40 45 Vetogen, R2 = 0.45 30 35 40 45 50 30 35 40 45 50 Helderheid, R2 = 0.44 20 30 40 50 60 70 80 20 40 60 80 Roerdikte, R2 = 0.94 0 20 40 60 80 0 50 100 Plakkenaanrand, R2 = 0.82 35 40 45 50 35 40 45 50 Afdunneninmond, R2 = 0.58 40 45 50 55 40 45 50 55 Zoutsmaak, R2 = 0.01 40 42 44 46 48 40 45 50 Kruidensmaak, R2 = 0.08 40 45 50 55 40 45 50 55 Mondvolheid, R2 = 0.41 20 30 40 50 60 20 40 60 Monddikte, R2 = 0.8 14 16 18 20 22 10 15 20 25 Korreligheid, R2 = 0.43 20 30 40 50 60 20 40 60 Slijmachtiginmond, R2 = 0.83 20 25 30 35 40 45 50 20 30 40 50 Stijfselnasmaak, R2 = 0.75 45 50 Nasmaakduur, R2 = 0.48

PLS2-model KOELSOEP met 2LV

50 55

Nadikking, R2 = 0.09

PLS2 RVA-cooling profile and sensory data

Mouthfeel sliminess

Change thickness in cup

After taste long/short

After taste paste Mouthfeel graininess Mouthfeel thickness Taste creaminess Spicy taste Salty taste Change thickness in mouth Stickiness to the cup Thickness at stirring Clarity Fat droplets

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Correlation between

Starch database characteristics

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34 36 38 40 42 34 36 38 40 42 Vetogen, R2 = 0.015 32 34 36 38 40 32 34 36 38 40 Helderheid, R2 = 0.6 50 55 60 65 70 50 55 60 65 70 Roerdikte, R2 = 0.69 40 45 50 55 60 65 70 40 50 60 70 Plakkenaanrand, R2 = 0.45 36 37 38 39 40 41 42 36 38 40 42 Afdunneninmond, R2 = 0.00042 40 45 50 55 40 45 50 55 Zoutsmaak, R2 = 0.057 40 42 44 46 48 40 45 50 Kruidensmaak, R2 = 0.0026 40 45 50 55 40 45 50 55 Mondvolheid, R2 = 0.13 40 45 50 55 40 45 50 55 Monddikte, R2 = 0.51 14 16 18 20 22 10 15 20 25 Korreligheid, R2 = 0.4 35 40 45 50 35 40 45 50 Slijmachtiginmond, R2 = 0.54 35 40 45 35 40 45 Stijfselnasmaak, R2 = 0.4 44 46 Nasmaakduur, R2 = 0.57

PLS2-model SAMENSTELLING met 1LV

48 50

Nadikking, R2 = 0.1

PLS1 Starch database and sensory data

Mouthfeel sliminess

Change thickness in cup

After taste long/short

After taste paste Mouthfeel graininess Mouthfeel thickness Taste creaminess Spicy taste Salty taste Change thickness in mouth Stickiness to the cup Thickness at stirring Clarity Fat droplets

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General conclusions

• Harvest management has influence on the sensory attributes:

- Thickness at stirring - Taste creaminess - Mouth feel thickness

• Small scale instant soup model can be used to mimic end

product instant soup.

• RVA-cooling profile can be used to investigate the effect of

different starches on the attributes: - Thickness at stirring

- Mouth feel thickness - Mouth feel sliminess - Stickiness to the cup

• The effect and influence of database characteristics on sensory

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Acknowledgements

• TNO Quality of Life, Groningen/Zeist

Herman Bos Kommer Brunt Doede Binnema Eduard Derks

• AVEBE, Veendam

Ria van der Laar Rob van Haren

• Hanze University, Groningen

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Small-scale Models as a Tool to Identify Key Factors for Starch Functionality in Food Applications