Application of Response Surface Methodology (RSM) in the Food Sector. Challenges in the application of RSM in the Food Sector. Problem-based on RSM is also mentioned in this slide.
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Response Surface Methodology: In the Food Sector
1. “Response Surface Methodology”
(In the food Sector)
Presented by : Prakash Kumar
(Ph.D. Research Scholar, AGFE,
IIT-Kharagpur, India)
prakashfoodtech@gmail.com
2. Response Surface Methodology (RSM)
Why? RSM in food Sector
RSM application
Challenges of applying RSM in food sector
RSM is the collection of Mathematical and
Statistical techniques useful the modeling
and analysis of problems in which a
response of interest is influenced by
several variables and the objective is to
optimize this responses.
- Douglas C. Montgomery (8th edition, 2015, p. 478 )
3. Why? RSM in the Food Sector
To improve system performance
For maximizing yield
To enhance process efficiency without increasing cost and time
For optimization of the food processes
For designing, developing, and improving processes where a
response or responses are affected by several variables
Application : extraction of oil, protein, phenolic compounds,
pigments, polysaccharides, hydrocolloids etc., manufacturing of
gluten free bread, biscuits, soy-coffee beverages, low caloric mixed
fruit jam, extruded food snacks, cream, walnut oil-in-water beverage
emulsion, homogenized infant foods, sweet potato based pasta etc.
4. RSM application:
Problem statement :To find the effect of ultrasonic extraction
time and temperature on the total phenolic content (TPC) of Papaya
leaf extract
• Phenolic compounds are susceptible to high temperature
• Extraction of phenolic is accelerated at high temperature
• Selection of time and temperature levels for experimentation:
• Time: 30 min, 45 min and 60 min (3 levels)
• Temperature: 40°C, 50°C and 60°C (3 levels)
• Selection of experimental design: Full Factorial
• Response variable
Number of experiments: 32 =9*2 (2 replications) =18 runs
11. Challenges of applying RSM in the food sectors
Correct choice of the range of independent variables : Enough
preliminary work or experience is needed to select the appropriate
range of each factor, which directly affect the success of RSM
optimization.
Correct selection of the polynomial model: A second order equation
is used commonly. If the trend of the responses in the studied range
of factors is not suitable to depict with second-order equation, the
range of independent variables or the form of the dependent should
be transformed to suitable form, in which the trend of the responses
could be depicted with this equation. The RSM approach needs to
be regressed with a polynomial equation.
12. The number of terms in polynomial equation is limited to number of
design points, as well as choice of the suitable polynomial equation
can be very laborious because each response needs its own distinct
polynomial equations. Thus, the accuracy of the RSM modeling can
be increased through combining with other modeling techniques
such
as Artificial Neural Network (i.e., ANN).
ANN present an option to the polynomial regression method
as a modeling approach.
ANN are presented usually as systems of inter-connected
neurons which can compute values from inputs, and are
capable of machine learning as well as pattern recognition
giving to their adaptive nature. This approach presents an
interesting chance to preparing non-linear modeling for
response surface and optimization of food industry processes.
13. References:
YANG, W. X., & GAO, Y. X. (2005). Response surface methodology
& its application in food industry [J]. China Food Additives, 2(2), 68-71.
Erbay, Z., & Icier, F. (2009). Optimization of hot air drying of olive
leaves using response surface methodology. Journal of food
engineering, 91(4), 533-541.
Montgomery, D. C. (2015). Design and analysis of experiments.
John wiley & sons.