The document discusses various methods for optimizing fermentation media to maximize product yield, including: 1. Classical methods that vary one variable at a time require many experiments as more variables are added. 2. Statistical methods like Plackett-Burman design, response surface methodology, and central composite design require fewer experiments while analyzing interactions between multiple variables. They are better for industrial optimization. 3. The optimization process identifies the most important media components and conditions, then determines their optimal concentrations/levels to maximize biomass or desired product concentration through experimental design and statistical analysis.

1. Medium Optimization

2. Introduction
Fermentation industry require particular product from given
organisms.
Only particular product is not important but it should be
produce in large quantity.
For the production of huge amount of particular product,
either medium formulation is proper or there should be
improvement in organism.

3. Introduction
• Process of optimization of media is
done before the media preparation to get
maximum yield at industrial level
• Process of optimization of media should
be target oriented means either for
biomass production or for desire production
• On small scaleit is easyto devisea medium containing
pure compounds
• But in caseof large scaleprocessfor satisfactorygrowth of
microorganisms it can be unsuitable.
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4.  Medium optimization is a process where components
of medium or different conditions either varied in
concentration or changed so that we can get better
growth of the organisms for high productivity.
 Different combinations and sequences of process
conditions need to investigate to determine the
growth conditions, which produce the biomass with
the physiological state best, constituted for product
formation.
 There may be a sequence of phases each with a
specific set of optimal conditions.
Introduction

5. The optimization of a medium should meet the following seven
criteria:
1. Produce maximum yield of product or biomass per gram of substrate
used
2. Produce the maximum concentration of product or biomass
3. Permit the maximum rate of product formation
4. Give the minimum yield of undesired products
5. Has consistent quality
6. Be readily available throughout the year
7. It will cause minimal problems during media making and sterilization
8. It will cause minimal problems in other aspects of the production
process particularly in aeration and agitation, extraction, purification
and waste treatment.
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6. Methods of optimization of media
1. Classical
Method
2.Statistical methods
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7. OPTIMIZATION
1. The term Optimize is defined as “to make perfect”.
2. It is used in UPSTREAM for the formulation of MEDIA.
3. It is the process of finding the best way of using the existing
resources.
4. The factors that influence the YIELD is considered.
5. Optimization by means of an experimental design helps in
shortening the experimenting time.
6. The design of experiments (DOE) is a structured, organized method
used to determine the relationship between the factors affecting a
process and the output of that process.
7. Statistical DOE refers to the process of planning the experiment
in such a way that appropriate data can be collected and
analyzed statistically.

8. MEDIA OPTIMIZATION
Classical Method:
• The process of media optimization can be performed by classical method
of changing one independent variable (Nutrient, antifoam, pH, temperature
etc.).
• Each possible combination of independent variable at appropriate
levels should require a large number of experiments – xn
• where, x – number of levels, n – number of variables.
• This may be quite appropriate for 3 variables at 2 concentrations [23].
• But not suitable for 6 nutrients at 3 concentrations [36].
• By the above method, totally 729 trials will be required.
• Industrially, the aim is to perform minimum number of experiments to
determine optimum conditions.

9. OPTIMIZATION – STATISTICAL
METHODS
• PLACKETT-BURMAN DESIGN
• ANALYSIS OF VARIANCE (ANOVA)
• CENTRAL COMPOSITE DESIGN (CCD)
• RESPONSE SURFACE METHODOLOGY
(RSM)

10. 7
The Plackett-Burman Design
• When more than five independent variables are to be
investigated, the Plackett-Burman design may be used to find
the most important variables in a system, which are then
optimized in further studies
• This technique allows for the evaluation of X-I variables by X
experiments
• X must be a multiple of 4, e.g. 8, 12, 16, 20, 24, etc.
• Factors not assigned to a variable or factors which do not have
any effect can be designated as a dummy variable
• Dummy variable can be used to know the variance of an effect
(experimental error).

11. Table 1: Plackett-Burman design for seven variables (A -G) at high
and low levels in which two factors, E and G, are designated as
'dummy' variables. (From Principles of Fermentation Technology,-
Peter F. Stanbury, Allen Whitaker, Stephen J. Hall, Second Edition)
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12. • Horizontal row represents a trial and each vertical column
represents the H (high) and L (low) values of one variable in all
the trials
• This design (Table 4.16) requires that the frequency of each
level of a variable in a given column should be equal and
that in each test (horizontal row) the number of high and
low variables should be equal.
• Consider the variable A; for the trials in which A is high, B
is high in two of the trials and low in the other two.
Similarly, C will be high in two trials and low in two, as will
all the remaining variables. For those trials in which A is
low, B will be high two times and low two times. This will
also apply to all the other variables.
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13. • The effects of the dummy variables are calculated in
the same way as the effects of the experimental
variables.
• If there are no interactions and no errors in
measuring the response, the effect shown by a
dummy variable should be O.
• This procedure will identify the important variables and allow
them to be ranked in order of importance to decide which to
investigate in a more detailed study to determine the
optimum values to use

14. 10
Table 2: Analysis of the yields shown in Table 1

15. The stages in analysing the data (Tables 4.16 and
4.17) using Nelson's (1982) example are as follows:
1. Determining the difference between the average of the H
(high) and L (low) responses for each independent and
dummy variable.
Difference = ΣA (H) – ΣA(L)
The effect of an independent variable on the response is the
difference between the average response for the four
experiments at the high level and the average value for four
experiments at the low level.
Thus the effect of
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16. 12
2. To estimate the mean square of each variable (the variance of effect).
For A the mean square will be =
3. The experimental error can be calculated by averaging the mean
squares of the dummy effects of E and G.
Thus, the mean square for error =

17. 4.The final stage is to identify the factors which are showing large
effects. In the example this was done using an F-test for
Factor mean square.
Error mean square.
• When Probability Tables are examined
it is found that Factors A, B and F
show large effects which are very
significant.
• Whereas C shows a very low effect
which is not significant and D shows
no effect.
• A, B and F have been identified as the
most important factors.

18.  The next stage would then be the optimization
of the concentration of each factor. This may
be done using response optimization
techniques which were introduced by and
Wilson (1951).
 Hendrix (1980) has given a very readable
account of this technique and the way which
it may be applied.
 Response surfaces are similar to contour plots
or topographical maps. Whilst topographical
maps show lines of constant elevation,
contour plots show lines of constant value.
 Thus, the contours of a response surface
optimization plot show lines of identical
response.
 In this context, response means the result of
an experiment carried out at particular values
of the variables being investigated.
Response surface methodology

19. • To statistically analyze a fermentation process, the
response surface methodology (RSM) can be used to
explore the interactions between one or more
variables (process parameters).
• The concept of using RSM is to perform a
limited number of designed experiments to
obtain an optimized response (maximum
yield).
• RSM can be employed to maximize the biomass
production by optimizing the process parameters.
• A second-degree polynomial equation can be used for
evaluation.
• This method is versatile to implement even when
little is known about the fermentation process.
• In contrast to conventional methods, the interaction
between the process parameters can be
determined by statistical techniques.

20. ANOVA
• ANalysis Of VAriance (ANOVA) is used to determine if there is any
significant difference between the means of groups of data.
• In statistical analysis, ANOVA is based on the design of experiment.
• ANOVA is also applied to evaluate the response data using a statistical
model.
• Ex.: Effect of antibiotics on various bacterial species.
• Typically, a one-way ANOVA is used to test the differences among at least
three groups.

21. DOE
• DOE (design of experiments) helps to investigate the effects of input
variables (factors) on an output variable (response) at the same time.
• These experiments consist of a series of runs (tests), in which,
purposeful changes are made to the input variables.
• Data are collected at each run.
• The process conditions and product components that affect the
quality is identified.
• The factors which yield optimized results were determined.

22. CENTRAL COMPOSITE DESIGN
• A central composite design is an experimental design used in RSM.
• A second order (quadratic) model for the response variable can
be built without using a 3-level factorial experiment.
The CCD method has 3 sets of experimental runs:
1. A factorial design with factors having two levels;
2. A set of center points, experimental runs whose values of each factor
are the medians of the values used in the factorial portion. This point is
often replicated in order to improve the precision of the experiment;
3. A set of axial points, experimental runs identical to the centre points
except for one factor, which will take on values both below and above
the median of the two factorial levels, and typically both outside their
range.

23. • Coded variables (-1, +1) are often used for
constructing the design.
• After the designed experiment is
performed, a linear regression equation is
used to obtain results.
• For EX., in a study, a central composite
design was employed to investigate the
effect of critical parameters of pretreatment
of rice straw including temperature, time,
and ethanol concentration. The residual
solid, lignin recovery, and hydrogen yield
were selected as the response variables or
yield.

24. • Stanbury, Peter F., Allan Whitaker, and Stephen J.
Hall. Principles of fermentation technology. Elsevier, 2013.
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5. Reference