Sudut Pandang Rekayasa Proses Pangan dalam...

Sudut Pandang Rekayasa Proses Pangan dalam Menjelaskan Fenomena Dasar Ilmu Pangan”

ITP703 – Ilmu Pangan LanjutPurwiyatno [email protected]

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ITP703 - Ilmu Pangan LanjutPurwiyatno [email protected]

Sudut Pandang Rekayasa Proses Pangan dalam Mempelajari Fenomena Dasar pada Ilmu Pangan

Kuantifikasi perubahan selama pengolahan/penyimpanan untuk penarikan kesimpulan.

Contoh Kasus : Penentuan Umur Simpan Produk

PURWIYATNO HARIYADIphariyadi.staff.ipb.ac.id


REKAYASA PROSES PANGAN ?

Food process engineering

• is a broad field that is concerned with the application of engineering principles and concepts to the handling, manufacturing, processing and distribution of foods.

(R P Singh, R.P. And Heldman, D.R. 2008. Introduction to Food Engineering. Academic Press)



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REKAYASA PROSES PANGAN ?

• is concerned with feasibility and practicality, that is, will something work and how much will it cost?

• Food engineers are educated to analyze, synthesize, design, and operate complex

systems that manipulate mass, energy, and information to transform material and

energy into more useful form

Valentas, K.J., Levine, L and Clark, J.P. 1991. Food Processing Operation & Scale-up. Marcel

Dekker Incorporated).

Food process engineering


“MAKE”

REKAYASA PROSES PANGAN ? –Perkembangan 25 tahun terakhir .............

Efisiensi. - Batch continuous processing- Improving process reliability- Reducing waste products, and - Effective energy utilization

“bet

ter

valu

e o

f fo

od

s”

1970 ~1980 ~1990 ~2000 ~2013

FOOD SAFETY



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“SERVICE”

“MAKE” “MAKE”

+ Melayani kebutuhan masyarakat;

Convinience, food service


1970 ~1980 ~1990 ~2000 ~2013

“bet

ter

valu

e o

f fo

od

s”

FOOD SAFETY


“CARE”

“SERVICE”

“MAKE” “MAKE”

“SERVICE”

“MAKE”

+ ‘Care’ food business :

Perawatan kesehatan, kebugaranPerawatan lingkungan

Perawatan thd “sosial” fair trade


1970 ~1980 ~1990 ~2000 ~2013

“bet

ter

valu

e o

f fo

od

s”

FOOD SAFETY



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Lebih personal:Perscribed

food and Nutrition

“CARE”

“SERVICE”

“MAKE”

“CARE”

“SERVICE”

“MAKE”

REKAYASA PROSES PANGAN ? – …KEDEPAN ?

“MAKE”

“SERVICE”

“MAKE”

1970 ~1980 ~1990 ~2000 ~2013

“bet

ter

valu

e o

f fo

od

s”

FOOD SAFETY


- You have to produce (even though with imperfect information)

ITP703 Anda sebagai Ahli Pangan



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- Thinking like an engineer- You don't have to be an engineer to think like one.- Many cases where it is a definite advantage to think

like an engineer.




- Thinking like an engineer- You don't have to be an engineer to think like one.- Many cases where it is a definite advantage to think

like an engineer.

- You have to examine the process(es) involved in the problem to be solved.

- You don’t only look at the symptoms of a problem but looks at the processes involved to get to the root cause of the problem.




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- Thinking like an engineer- ……………………- …………………..

- …………………- ……………………..- Use proven analysis methods. Find and use a set of methods

that have been proven to work in analyzing problems.




- Thinking like an engineer- ……………………- …………………..

- …………………- ……………………..- Use proven analysis methods. Find and use a set of methods

that have been proven to work in analyzing problems. You have to know how to apply those methods BUT, more importantly, you have to know which methods to

apply to a given situation. YOU have to know about and uses appropriate tools

to carry those methods out.




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- You have to produce (even though with imperfect information):

- ……………- Learns why (not just how) something is done.

(If you only know how something is done, you will eventually do it wrong. You need to understand why something is done, to make sure that you are doing it correctly).

- Must come up with an answer to the problem.



- You have to produce (even though with imperfect information):

- ……………- ……………

- Keep your skills updated. (Applying an out-of-date solution is almost as bad as not providing any solution. You have to constantly looks to see if new, better solutions to a problem exist).

You must fully understand the“Physical, chemical, biological + engineering phenomena”

behind the problem you are facing (“object or problem of your study”)



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Back to basics: • Understand the “physical, chemical, biological + engineering

phenomena” behind the problem you are facing (“object or problem of your study”)





"To measure is to know."

"If you can not measure it, you can not improve it."

"In physical science the first essential step in the direction of learning any subject is to find principles of numerical reckoning and practicable methods for measuring some quality connected with it. I often say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the state of Science, whatever the matter may be." [PLA, vol. 1, "Electrical Units of Measurement", 1883-05-03]

Quotations ofLORD KELVIN

(Sir William Thomson)



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BEWARE: Syndrome “the latest version“ software

garbage in garbage out I have one good news and one bad news. The good news is that statistical analysis in now easy to perform. The bad news is that statistical analysis in now easy to perform(Sastroasmoro, 2000)


A

C

B

D

[S]

Vo

Vo

[S]

Rule of thumbs:0,1 Km<[S]<10 Km


Perhatikan: "minimal experimental design" yang diperlukan, dll





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100

80

60

40

20

00 2 4 6 8 10 t

Storage Time (months)

Obs

reve

d Va

lue

T2T3

T1 < T2 < T3

T1





Perhatikan: "Number & frequency of sampling” yang diperlukan


T25 T30 T35 T40t

t1 t1 t1 t1 t1t2 t2 t2 t2 t2t3 t3 t3 t3 t3t4 t4 t4 t4 t4Perhatikan:

"Number & frequency of sampling” yang diperlukan





Menghasilkan 1 data: k, T25C



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Perhatikan: "Number & frequency of sampling” yang diperlukan





Menghasilkan 1 data: k, T25C


Minimal Experimental Design?

Hati-hati dengan well established model :kinetika, reologi,dll






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A B

Linier?










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Check!• Theoretical background

• Assumptions

• Your data?– Unit and dimensions

– Mass balance

– Energy balance

– Momentum balance

– ETC





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ITP703 - Ilmu Pangan LanjutPurwiyatno [email protected] [email protected]

Case 1.Kinetic of food quality changes during processing and storage- Understanding Shelf Life of Foods

Purwiyatno HariyadiDepartment Food Science & Technology, Faculty of Agricultural TechnologyandSoutheast Asian Food & Agricultural Science & Technology (SEAFAST) Center,

Bogor Agricultural University


Main References :

Labuza,T. 2007. Lecture Note (http://www.ardilla.umn.edu/tplabuza.html)

Kilcast, D and Subramaniam, P. (Eds). 2000. The stability and shelf-life of food. Woodhead Publishing Limited, Abington Hall, Abington, Cambridge CB1 6AH, England

Hariyadi, P. 2006. Handouts and Modules of Shelf Life Determination (in Bahasa Indonesia). Seafast Center/Department of Food Science and Technology, Faculty of Agriculture Technology, Bogor Agricultural University.



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What is Shelf life ?

Everything has a shelf life


The period between manufacture and retailpurchase of a food product during which theproduct is of satisfactory quality(IFT; 1974).




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The time during which the food product will :1. remain safe;2. be certain to retain desired sensory, chemical, physical

and microbiological characteristics;3. comply with any label declaration of nutritional data,

when stored under the recommended conditions. (IFST Guidelines, 1993)



Shelf-life as the period during which a food productmaintains its microbiological safety and suitability at a specified storage temperature and, where appropriate, specified storage and handling conditions (Codex Alimentarius, CAC/RCP-46; 1999) .




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Open Dating?Yahoo/Google ?

Open shelf-life dating


Open dating is the use of dates(means the use of legible terms such as a day, month, and year; NOT a code, or a symbol) on a can or package of food that gives the consumer (including distributors, wholeseller or retailers) an indication of when the food was packaged or by when it should be sold or used.




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Why?

1. Encourages better handling practices by wholesalers, retailers, andconsumers by expediting the sale or use of food near the end of shelf life.

2. Increase consumer confidence in the freshness of food purchased.

3. Better handling practices attributed toopen dating could minimize nutrient loss.


Can be expressed as :

1. date of pack or manufacture,2. pull date or sell-by date,3. best-if-used-by date,4. expiration date or use-by date, or5. combination of two of the above.




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Expressed as Expiration date

• Date that indicates that before that date food products is still in “satisfactory quality” or “desired” condition” (provided that product stored under the recommended conditions)

• END of shelf life







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What is “satisfactory quality“ ?What is “desired” ?

Except in situations in which microbiological safety is an issue, the definition of shelf-life is related to the positioning of the product in themarket in terms of quality and customer perceptions of that quality.

END of SHELF LIFE?



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END of SHELF LIFE?

• What is specific quality properties ?• What is “desired” quality? What is “satisfactory quality”? • Who determined “desired”/”satisfactory quality”? • At what condition food product is not in ”satisfactory

quality” condition?

• How “satisfactory quality” changes during distribution, storage, display ???

• Kinetic of quality changes

What is “critical factor” for customer perceptions of quality ?



Kilcast, D and Subramaniam, P.

(Eds). 2000.



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(Eds). 2000.




(Eds). 2000.



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(Eds). 2000.



“critical Factor”

Whichone?

a. (Micro)biological factors : bacteria, mold, yeast

b. Chemical factors : vit C., thiamin, riboflavin, etc

c. Nutritional factors : protein, fats, digestion quality, etc

d. Fucntionality factors : enzyme activity, probiotic acitivity,antioxidant activity, etc

e. Physical factors: Viscosity, texture, color, emulsion stability, etc

f. Organoleptic factor : Appearance quality, odor, taste, etc



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“critical Factor”

Whichone?


FACTORS INFLUENCING SHELF-LIFE OF FOODSA. intrinsic (properties of the final product) :

• Water activity (aw) (available water).• pH value and total acidity; type of acid.• Available oxygen.• Nutrients.• Natural microflora and surviving microbiological

counts.• Natural biochemistry of the product formulation

(enzymes, chemical reactants).• Use of preservatives in product formulation (e.g.

salt).




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FACTORS INFLUENCING SHELF-LIFE OF FOODSB. Extrinsic factors (factors the final product encounters as it moves through the food chain) :

• Time–temperature profile during processing.• Temperature.• Relative humidity (RH).• Exposure to light (UV and IR).• Environmental microbial counts.• Composition of atmosphere within packaging.• Subsequent heat treatment (e.g.

Reheating/cooking before consumption).• Consumer handling.


• Experiments & Analysis

• Emperical Analysis

SHELF LIFE

Informations needed :• Quality critical factor(s)?• Value of critical factor(s)

• Maximum value (quality at the factory gate)

• Minimum value of critical factor(s) : minimum quality at the end of shelf-life

• Kinetic parameters for quality changes (Ea, Q10)

PRINCIPLES OF SHELF LIFE DETERMINATION



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Measure either loss of A, desirable quality marker, or production of B, undesirable quality marker

A = qualityt = time

k = rate constantn = reaction order


KINETIC OF QUALITY CHANGES

A B-dA/dt = kAn


Zero order (n=0) quality changes -dA/dt = kAn





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Zero order (n=0) quality changes-dA/dt = kz or A = A0 - kz t




If Qs is quality level at the end of shelf life, thenAs= A0 - kz ts

orts = (A0-As)/ kz

at whichts is shelf life of the product



Zero order (n=0) quality changes-dA/dt = kz or A = A0 - kz t



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Just Illustration:

Ao = 100 units at time zeroA = 60 units at 20 weeksWhat is kz if quality change follow zero order ?

Remember, kz is a slope ∆Y/∆X= (100-60)/(20-0)= 40/20 = 2 units per day

Thus 2 units per day x 20 days is loss of 40 units (with 60 units of quality left)



Zero order (n=0) quality changes-dA/dt = kz or At = A0 - kz t


100

80

60

40

20

00 2 4 6 8 10


Qua

lity

Val

ue

Firts order (n=1) kinetic of quality changes-dA/dt = kA1 -dA/dt = kfA





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Firts order (n=1) kinetic of quality changes-dA/dt = kA1 -dA/dt = kfA



or ln (A/A0) = - kt


Firts order (n=1) kinetic of quality changes-dA/dt = k A1



or ln (A/A0) = - kft



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Firts order (n=1) kinetic of quality changes-dA/dt = k A1



or log (A/A0) = - (kf/2.3)t


If As is A at the end of shelf life, thenln (As/A0) = - kfts

Or ts = [ln(A0/As)]/kf

Or t1/2 = 0,693/ kf

At which ts is shelf life of product


Firts order (n=1) kinetic of quality changes-dA/dt = kA or ln (A/A0) = - kft




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Just Illustration :

Ao = 100 units at time zeroA = 60 units at 20 weeksWhat is kf if quality changes follows first order?Remember slope is ∆Y/∆X but on Ln scale so

= (Ln( 60/100))/(20-0) = 0.51/20= 0.02554 day-1

A= Aoe-kft A= 100 x e-0.02554x20 = 100 x e-0.511 = 60


Firts order (n=1) kinetic of quality changes-dA/dt = kA or ln (A/A0) = - kft



Examples of kinetic of quality change of slecetd product during storage/processing

Zero Order• Overall quality of frozen food

• Non-enzimatic browning

First Order• Vitamin loss/degradation

• Mikcorbial inactivation

• Oxidatif deterioration of color

• Thermal degradation of texture




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Quality degradation during storage : Difference between zero and first order kinetics

Zero or First Order Kinetics?






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34






xx

Zone of data thatmay distinguishbetween zero andfirst orfer kinetics




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To be able to distinguish easily wwther quality changes is following zero or first order; perform experiment until the extent of quality loss is more than 50%

Published values

(27.7oC)


Recommendation (Labuza, T. 2007)

• If few data points with time and• End Quality level is less than 50%

change from initial

Then KISS : Use zero order




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Case 2: Predicting Shelf life of Food by Accelerated Shelf-life Testing (ASLT) method Using Arrhenius model

Purwiyatno HariyadiDepartment Food Science & Technology, Faculty of Agricultural TechnologyandSoutheast Asian Food & Agricultural Science & Technology (SEAFAST) Center,

Bogor Agricultural University


Main References :

Labuza,T. 2007. Lecture Note (http://www.ardilla.umn.edu/tplabuza.html)

Kilcast, D and Subramaniam, P. (Eds). 2000. The stability and shelf-life of food. Woodhead Publishing Limited, Abington Hall, Abington, Cambridge CB1 6AH, England

Hariyadi, P. 2006. Handouts and Modules of Shelf Life Determination (in Bahasa Indonesia). Seafast Center/Department of Food Science and Technology, Faculty of Agriculture Technology, Bogor Agricultural University.



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Accelerated Shelf Life TestArrhenius Method

Principles : accelerate rate of quality loss by increasing temperature of storage

Quality loss -dA/dt = kAn

Rate constant (k) depends on Temperature :

k = k0.exp-Ea/RT

k = rate constantk0 = frequency factor (NOT as function of T)Ea = Activation EnergyT = Absolute Temperature (K)R = Gas constant; 1,986 kal/mol (8.314 J/mol.K)


Valeu of k as affected by temperature (T) Arrhenius Plot

k

(1/T)

TR

aE

eokk-

=




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Slope- Ea/R

Ln k = Ln ko - (Ea/R)(1/T)

Ln k

(1/T)

Arrhenius Plot

Ln ko



Example : Carbohydrate at amorphous condition may undergoes crystalization at low temperature.

Know your product WELL!

Retrogradation• Texture hardening• Release of water• Bread staling




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• Rate of “bread staling” incease as temperature of storage decrease (4°C - 40°C)

• Maximum “staling” rate : 4°C.

Example : Carbohydrate at amorphous condition may undergoes crystalization at low temperature.




• Arrhenius anomali. Effect of temperature on “bread staling”

• Negative Ea Ea" = - 9 kcal/mol..

3.2 3.3 3.4 3.5 3.6

1/T x 103

Ln (

stal

ing

rate

)





40


100

80

60

40

20

0

0 2 4 6 8 10


Val

ue o

f Qua

lity

Mar

ker

T1

T2T3

T1 < T2 < T3

Number & frequency of sampling !

Accelerated Shelf Life TestArrhenius Method -- in Practices


Do experiment and measurement of quality loss –at least- at 3 different temperatures of storage Statistical problems

• Can’t do predictions with one temperature

Accelerated Shelf Life TestArrhenius Method -- in Practices (1a)

ln k

1/T

?



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• Can’t do predictions with one temperature• Two points always give a straight line


ln k

1/T



• Can’t do predictions with one temperature• Two points always give a straight line• Three points give poor statistics


ln k

1/T



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• Choose temperature of storage higher that normal T of storage

• Temperature od storage will not cause changes in mechanism of quality loss

• Again; know your product WELL hub Arrhenius:

A

B

ln k

1/T

RangeOf

StorageTemp

Range of ASLT

Temperature

Maximum ASLT

Temperature

Accelerated Shelf Life TestArrhenius Method -- in Practices (1b)


Type of Product Temperature of ASLT (°C)

Control Temperature

(°C)Canned foods 30, 35, 40, 45 4

Dried foods 30, 35, 40, 45 -18

Chilled Products 5, 10, 15, 20 0

Frozen products -5, -10, -15 < -40

So, which three (3) different temperatures of storage? Know your product WELL

Accelerated Shelf Life TestArrhenius Method -- in Practices (2)



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• Frequency of sampling Know your product WELL


Shelf life at 5oC= 12 months

If Q10=2 ASLT (at 25oC) : 3 months

If Q10=3 ASLT (at 25oC) : 1.3 months

If Q10=4 ASLT (at 25oC) : 23 days

Number of samples? Statistical problems• Can’t do predictions with one temperature• Two points always give a straight line• Three points give poor statistics


• Frequency of sampling Know your product WELL



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Value of k at normal storage condition May be predicted/calculated using Arrhenius Equation :

k = ko.exp-Ea/RT

ln k = ln ko – Ea/R (1/T)

Ta(normal T of storage)

Ln k

1/TZone of experiment -

accelerated

T1 T2 T3

And, extrapolatevalue of k at normal T of storage:


k1k2

k3


Determinn As (quality value at end of shelf life).• Standard of identity, SNI, etc• Sensory properties

Shelf life may be predicted using :

ts = (A0-As)/kz; if quality changes follow zero order kineticsts = [ln(A0/As)]/kf; if quality changes follow first order kinetics




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Accelerated Shelf Life TestArrheniusMethod --in Practices (8)





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Degree of losses of selected vitamin predicted using Arrhenius model as compared to its actual losses

Losses (%) after 6 months of storage , 20oC, RH 75%

Vitamin Predicted by Result ofArrhenius Model Analysis

Vitamin C(ascorbic acid) 24,0 23,0

Vitamin A 15,0 10,0

Folic Acid 8,1 7,4

Vitamin B12 9,2 7,7




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Thank youterimakasih

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