Here are the key points about the differences in antioxidant activity and compounds between vinegars and their source fruits:
- Fruits contain numerous antioxidant compounds like phenolics, flavonoids, anthocyanins etc. that contribute to their high antioxidant activity.
- During fermentation and production of vinegar, some of these compounds degrade or change form. For example, anthocyanins are not stable in acidic conditions.
- New compounds are also formed during fermentation. For example, acetobacter oxidation of ethanol produces acetic acid which contributes to vinegar's antioxidant activity.
- The end profile of antioxidant compounds in a vinegar depends on the source fruit. For example, berries will yield a vinegar higher in phenolics and anthocyan
1. i
STUDY ON KINETICS OF VINEGAR PRODUCTION
AND
MATHEMATICAL MODELLING ON ANTIOXIDANT ACTIVITY
OF FRUIT JUICE
THESIS SUBMITTED FOR PARTIAL FULFILMENT OF THE REQUIREMENT FOR
THE DEGREE OF
MASTER OF TECHNOLOGY
IN
FOOD TECHNOLOGY AND BIOCHEMICAL ENGINEERING
2013-2015
BY
SUMAN KUMAR SAHA
Examination Roll no.-M4FTB1502
REGISTRATION No. 112125 of 2010-11
Under the Guidance of
PROF. RUNU CHAKRABORTY
Professor and Head
Department of Food Technology and Biochemical Engineering
FACULTY OF ENGINEERING AND TECHNOLOGY
Jadavpur University
Kolkata-700032
2. ii
This Project is dedicated to My Beloved Parents
&
My senior Kaustav Chakraborty
3. iii
FACULTY OF ENGINEERING AND TECHNOLOGY
DEPARTMENT OF FOOD TECHNOLOGY AND BIOCHEMICAL ENGINEERING
JADAVPUR UNIVERSITY
KOLKATA-700032
Declaration of originality and compliance of
academic ethics
I hereby declare that this thesis contains literature survey and original research work
by the undersigned candidate, as part of my Master of Technology in Food Technology and
Biochemical Engineering studies.
All information in this document have been obtained and presented in accordance
with academic rules and ethical conduct.
I, also declare that, as required by these rules and conduct, I have fully cited and
referenced all materials and results that are not original to this work.
Name: Suman Kumar Saha
Examination Roll Number: M4FTB1502
Thesis Title: “Study on kinetics of vinegar production and mathematical modelling on
antioxidant activity of fruit juice”
Signature with date:
_____________________________
( SUMAN KUMAR SAHA )
4. iv
FACULTY OF ENGINEERING AND TECHNOLOGY
DEPARTMENT OF FOOD TECHNOLOGY AND BIOCHEMICAL ENGINEERING
JADAVPUR UNIVERSITY
KOLKATA-700032
Certificate of Recommendation
I hereby recommend the thesis entitled “Study on kinetics of vinegar production and
mathematical modelling on antioxidant activity of fruit juice” prepared under my
supervision by Suman Kumar Saha, student of M.Tech, 2nd year (Examination Roll no-
M4FTB1502, Class Roll no.-001310902002, Registration no.-112125 of 2010-11). The thesis
has been evaluated by me and found satisfactory. It is therefore, being accepted in partial
fulfilment of the requirement for awarding the degree of Master of Technology in Food
Technology and Biochemical Engineering.
----------------------------------------------- ------------------------------------------------
Prof. Runu Chakraborty Dean
Professor & Head Faculty Council of Engineering
Department of F.T.B.E & Technology
Jadavpur University Jadavpur University
5. v
FACULTY OF ENGINEERING AND TECHNOLOGY
DEPARTMENT OF FOOD TECHNOLOGY AND BIOCHEMICAL ENGINEERING
JADAVPUR UNIVERSITY
KOLKATA-700032
Certificate of Approval
This is to certify that Mr. Suman Kumar Saha has carried out the research work
entitled “Study on kinetics of vinegar production and mathematical modelling on
antioxidant activity of fruit juice” under the supervision of Prof. Runu Chakraborty,
at the Department of Food Technology and Biochemical Engineering, Jadavpur University.
I am satisfied that he has carried out this work independently with proper care and
confidence. I hereby recommend that this dissertation be accepted in partial fulfilment of the
requirement for awarding the degree of Master of Technology in Food Technology and
Biochemical Engineering.
I am very much pleased to forward this thesis for evaluation.
…………………………..
Prof. Runu Chakraborty
Professor & Head
Dept. of F.T.B.E
Jadavpur University
6. vi
ACKNOWLEDGEMENT
This thesis entitled “study on kinetics of vinegar production and mathematical
modelling on antioxidant activity of fruit juice” is by far the most significant scientific
accomplishment in my life and it would be impossible without people who supported me and
believed in me.
To begin with, I express my deepest regards, unbound gratitude with sincerest thanks
to my guide respected Prof. Runu Chakraborty (Professor & Head, Department of Food
Technology and Biochemical Engineering, Jadavpur University) without who’s efficient and
untiring guidance, my work on this practical would have remained incomplete. She has been
very kind and affectionate and allowed me to exercise thoughtful and intelligent freedom to
proceed with this project work and finally produce this thesis. Her words of encouragement
have left an indelible mark in my mind which I am sure would also guide me in future.
I take this opportunity to express my heartfelt gratitude to the respected Prof. Utpal
Raychaudhuri for his valuable advice, suggestions and encouragement during the course of
my work.
I am also thankful to other respected faculty members Prof. Lalita Gauri Ray, Prof.
Uma Ghosh, Dr. Paramita Bhattacharya and Dr. Dipankar Halder along with library,
laboratory staffs and my friends who have been always the source of motivation and
inspiration for me.
I would like to thank research scholar Mr. Kaustav Chakraborty for his valuable
guidance and tremendous assistance throughout the project work. I am deeply indebted to
him for his help throughout the work by providing fruitful suggestions and cooperations.
Last of all, I would like to express my heartfelt gratitude to my parents, who inspired
me in making this endeavour a success.
May, 2015 Suman Kumar Saha
7. vii
Contents
1. Antioxidant activity of Vinegar as compared to Source-an overview.........................................- 2 -
1.1 Introduction ........................................................................................................................- 2 -
1.2 Vinegar from different sources...........................................................................................- 4 -
1.3 Conclusion...........................................................................................................................- 8 -
1.4 References ..........................................................................................................................- 9 -
2 Process optimization and kinetics study of vinegar production from Manilkara zapota.........- 17 -
2.1 Introduction ......................................................................................................................- 17 -
2.2 Materials and methods.....................................................................................................- 18 -
2.2.1 Chemicals ..................................................................................................................- 18 -
2.2.2 Yeast culture Preparation .........................................................................................- 19 -
2.2.3 Acetobacteraceti culture preparation.......................................................................- 19 -
2.2.4 Preparation of Fermentation medium for Ethanol Production................................- 19 -
2.2.5 Preparation of Fermentation medium......................................................................- 19 -
2.3 Analytical methods ...........................................................................................................- 20 -
2.3.1 Determination of Ethanol concentration..................................................................- 20 -
2.3.2 Determination of acid...............................................................................................- 20 -
2.3.3 Estimation of Biomass Concentration.......................................................................- 20 -
2.3.4 Response Surface Methodology ...............................................................................- 20 -
2.3.5 FTIR study..................................................................................................................- 21 -
2.3.6 Kinetic models...........................................................................................................- 21 -
2.4 Results and Discussion......................................................................................................- 23 -
2.4.1 Response surface analysis of data ............................................................................- 23 -
2.4.2 Microbial and product growth..................................................................................- 25 -
2.5 Conclusion.........................................................................................................................- 26 -
2.6 References ........................................................................................................................- 27 -
3 Mathematical Modelling of growth of Acetobaceter aceti in Vinegar Fermentation reaction- 40 -
3.1 Introduction ......................................................................................................................- 40 -
3.2 Microbial kinetics methods...............................................................................................- 40 -
3.3 Material.............................................................................................................................- 43 -
3.3.1 Chemicals ..................................................................................................................- 43 -
3.3.2 Yeast culture Preparation .........................................................................................- 43 -
3.3.3 Acetobacter aceti culture preparation......................................................................- 44 -
3.3.4 Preparation of Fermentation medium for Ethanol Production................................- 44 -
8. viii
3.3.5 Preparation of Fermentation medium......................................................................- 44 -
3.4 Analytical methods ...........................................................................................................- 45 -
3.4.1 Determination of Ethanol concentration..................................................................- 45 -
3.4.2 Determination of acid...............................................................................................- 45 -
3.4.3 Estimation of Biomass Concentration.......................................................................- 45 -
3.4.4 Statistical Analysis.....................................................................................................- 45 -
3.5 Result and Disscussions ....................................................................................................- 46 -
3.6 Conclusion.........................................................................................................................- 48 -
3.7 References ........................................................................................................................- 49 -
4 Partial Least square modelling for Prediction of Antioxidant activity of Phenolic compounds- 58 -
4.1 Introduction ......................................................................................................................- 58 -
4.2 Method .............................................................................................................................- 60 -
4.3 Statistical Analysis.............................................................................................................- 61 -
4.4 Results and Discussion......................................................................................................- 62 -
4.5 Conclusion.........................................................................................................................- 65 -
4.6 References ........................................................................................................................- 66 -
9. ix
List of Figures:
Figure 2-1: Response surface plot showing the effect of Temp, time and pH on vinegar
production ………………………………………………………………………………….- 31
-
Figure 2-2Variation of acetic acid vs biomass and substrate vs biomass for vinegar
production. .......................................................................................................................... - 32 -
Figure 2-3: Comparison of calculated values and the experimental data from our experiment -
33 -
Figure 3-1: Comparison of Monod, Moser and Haldene equation. ....................................- 52 -
Figure 3-2:Comparison of logistic and gompertz equation ................................................- 53 -
Figure 3-3 Residual plot for logistic and gompertz equation .............................................- 54 -
Figure 4-1 Difference between electron donating and withdrawing effect ........................ - 69 -
Figure 4-2 Rsquared, factor 1 vs factor 2 and VIP plot for compounds containg electron
withdrawing group..............................................................................................................- 70 -
Figure 4-3 Rsquared, factor1 vs factor 2 and vip plot for compounds with electron donating
group ...................................................................................................................................- 71 -
10. x
Nomenclature:
S substrate concentration (gl-1
)
P product concentration (gl-1
)
t time (h)
X cell concentration (g dry weight (l)-1
)
X0 initial biomass concentration (gl-1
)
Xm maximum biomass concentration (gl-1
)
α growth associated product formation coefficient (gg-1
)
β non-growth-associated product formation coefficient (gg-1
h-1
)
γ,η parameters in Luedeking-Piret like equation for substrate uptake ( g S (g cells)-1
, g
S (g cells)-1
h-1
respectively)
μm maximum specific growth rate (h-1
)
Yx/s biomass yield
Yp/s product yield based on the substrate utilized
ms maintenance coefficient (g substrate (g cells-h)-1
)
st. stationary phase
qs rate of substrate utilization
qp rate of product utilization
k proportionality constant indicating growth rate
ε constant indicating toxicity and inhibitory characteristics
11. xi
Abstract
The thesis entitiled “Study on kinetics of vinegar production and mathematical
modelling on antioxidant activity of fruit juice” investigates potentiality of sapodilla fruit as
an ingredient for vinegar with rich phytochemical profile and mathematical modelling of
antioxidant activity of key antioxidant compounds present in fruit juice. Sapodilla is a prime
tropical fruit. It is normally eaten fresh, but sometimes it is served as candy, dehydrated
slices, jelly and juices. It is a rich source of phenolic antioxidants, which is responsible for
key health benefits such as- coronary heart disease, inflammation, ageing, cancer, free radical
production protecting properties. Although, being used for a chief source of gum, sapodilla is
still a un-utilized source for various popular fruit by-products like wine and vinegar. No
previous attempts were made to produce wine or vinegar by using sapodilla as an ingredient.
In the present study, we aimed at producing sapodilla vinegar. The ability of sapaodilla to act
as ingredient and micro-organism to sustain in sapodilla were monitored by measuring pH,
time, temperature, product formation, substrate formation and microbial growth. Also,
antioxidant activity of phenolic compounds were measured by studying various key
molecular descriptors which would allow the prediction of antioxidant activity of other
compounds that is similar to tested compounds.
Chapter 1 deals with the review about the difference in antioxidant activity and
antioxidant compound profile of vinegar as compared with their sources. Fruit contains
numerous compounds as antioxidants which degrades and changes during fermentation.
Several new different compounds are also produced. This results in change in antioxidant
activity and profile of vinegar. In this chapter, changes in profile of different key classes of
antioxidant compounds in vinegar vs fruit is discussed.
12. xii
Chapter 2 deals with the potency of sapodilla as an ingredient for vinegar. Cultivated
worldwide, Sapodilla is a key fruit with several key antioxidant compounds and high
antioxidant activity. However, it is still unutilized as a potential source for fruit by-products.
Vinegar is a widely popular food condiment and is mainly produced form fruit. As a fruit,
sapodilla can be used to produce fruit vinegar with unique flavour and rich antioxidant
activity. But, production of vinegar should be optimized for successful exploitation of
sapodilla. Response surface methodology (RSM) is a statistical tool for optimization of
multivariate system. In this study, RSM was utilized to optimize vinegar production using
sapodilla with pH, temperature and time as process conditions.
Chapter 3 deals with ability of microorganism Acetobacter aceti to survive in
fermentation medium containing sapodilla. Besides C and N sources, microorganism requires
several key ingredients for proper growth; any compounds should not act as inhibitor of
growth of microorganism. In this study, microbial population growth was studied and
modelled with several different equation. Key conclusion on survival ability in a specific
media can be drawn using these equations.
Chapter 4 deals with analysing antioxidant activity of several antioxidant compounds
related to each other on the basis of chemical structures. Antioxidant property is influenced
by underlying molecular mechanisms which also effects other properties. Thus, identifying
these properties will allow proper analysis of antioxidant activity and prediction of
antioxidant activity of similar unknown compound. Partial Least square (PLS) was used to
statistically analysed the variation of antioxidant property and other molecular descriptors to
find reliable models for forecasting.
15. - 2 -
Antioxidant activity of Vinegar as compared to Source-an overview
1.1 Introduction
Vinegar, a popular acidic food condiment is produced mainly from various fruits and
cereals, by the biochemical action of Acetobacter and gluconobacter groups of bacteria. The
mild acidic flavour of vinegar is due to presence of acetic acid, the chief chemical produced
during acetous fermentation, at 4-10 percent level. Apart from its use as condiment, vinegar
has prominent usage in food preservation, pharmaceutical, therapeutic field[1], [2].
As defined by Joint FAO/WHO food standards programme, vinegar production is a
double fermentation process. In the first step, saccharomyces species converts fermentable
sugars to ethanol that is oxidized by acetobacter species bacteria in the next step to yield
acetic acid. An initial high sugar concentration, typically 10% (w/v) or more, and an acidic
pH favour ethanol production by yeast during anaerobic periods of ethanolic fermentation. In
acetous fermentation, alcohol dehydrogenase (ALD) catalyzes oxidation ethanol to
aetaldehyde, which in the subsequent step is oxidized to acetic acid by aldehyde
dehydrogenase (ALDH).
C2H5OH + NAD CH3CHO + NADH + H+
(catalyzed by ALD)
RCHO + NAD+
+ H2OR COOH +NADH+ H+
(catalyzed by ALDH)
Acetic acid bacteria (AAB) are aerobic, gram-variable, nonspore forming cells that
have an optimum pH of 5-6.5 for growth. Twelve genera of bacteria, including Acetobacter,
Gluconobacter, Acidomonus, Asaia, Kozakia, Saccharibacter species, are included into AAB
that are capable to oxidize sugars and alcohols into organic acids as final products. Fruits and
flowers are the natural habitat of AAB[3]. Each kind of vinegar involves unique combination
of organism, resulting in a different yield of acetic acid of variable quality. In traditional
production system of “surface culture method”, organism grows on the media surface. Long
16. - 3 -
time is needed for complete fermentation, but resultant vinegar is of high quality. The longer
fermentation period allows accumulation of “mother of vinegar”, a nontoxic slime composed
of yaest and acetic acid bacteria, possessing numerous unsubstantiated health benefits. In
contrast, modern submerged system has shorter production duration of 24-48 hrs. First, the
liquid is oxygenated by agitation and, subsequently, the bacteria culture is submerged
permitting rapid fermentation[4].
Phenolic compounds may act as antioxidants in different ways, such as direct reaction
with free radicals, scavenging of free radicals, increasing transfomation of free radicals to the
compounds with much lower reactivity, chelation of pro-oxidant metals (mainly iron),
delaying or strengthening activities of many enzymes. Fresh fruit extracts are an excellent
source of polyphenolic compounds. Epidemiological studies suggested that consumption of
red fruit juices such as grape, different berry juices and pomegranate correlate with reduced
risk of coronary heart disease, stroke, certain types of cancers and ageing. For this reason, it
is believed that the consumption of fruit and vegetables, rich in bioactive compounds, is
linked with the increase in resistance against such diseases. The beneficial effects of fruit and
vegetables are becoming increasingly appreciated [5], [6].
The fermented fruit grape products – wine (alcoholic) and vinegar (alcoholic and
acetic fermentations) – are also rich in polyphenols. Evidence of a negative association
between coronary heart disease (CHD) mortality and vinegar consumption has suggested
possible protective effects of vinegar [7] .Brewed vinegar, a commonly used condiment of
food, also has medicinal uses by virtue of its physiological effects, such as promoting
recovery from exhaustion, regulating blood glucose, blood pressure, stimulating the appetite,
and promoting calcium absorption. As a fermented product of fruit juices rich in antioxidant
and phenolic compounds, vinegar is being investigated for potential health benefits to human
health [8].
17. - 4 -
The aim of the study is to examine how antioxidant activity of vinegar differs from
that of their source-corresponding fruit, in terms of phenolic, flavonoid and antioxidant
compound profile and antioxidant activity.
1.2 Vinegar from different sources
1.2.1 Grape Vinegar
Grape, one of the most popular and widely available fruit, is a fruiting berry of
deciduous woody vines of the genus Vitis. It can be eaten as raw or in other forms like jam,
jelly, seed extract, raisins, wine, vinegar. Color of grape can be white, purple, black, dark
blue, yellow, green, orange and it is a major determiner of nutritional profile of fruit. Grape
seeds and skins are a good source of polyphenolic tannins which imparts astringency[9].
Grape juice is also a good source of flavonoids that is responsible for improvement of the
endothelial function, increase of the serum antioxidant capacity, protection of LDLs against
oxidation, decrease of native plasma protein oxidation, and reduction of platelet aggregation
[7]. In addition to epicatechin present as main polyphenolic antioxidants, catechin, gallic acid
and procyanidins are the other major antioxidants. These compounds possess hydroxyl,
peroxyl, superoxide and DPPH adical scavenging activity. Not only color of grapes, but also
the species of grapes, location, prevailing climatic condition and postharvest handling so
influence the phenolic content and antioxidant activity. Thus, Catechin and epicatechin
contents of V. Vinifera grapes were higher than in V. rotundifolia grapes, but the latter
contained more gallic acid. In general, grape seeds had much higher monomeric flavonol
contents than skins. Catechin and epicatechin concentrations in Chardonnay grape skins were
3 times higher than in Merlot grape skins [9]. Also red grape juice, with higher tannins,
possesses higher oxygen radical absorbance capacity (ORAC-FL) value than white grape
juice.
18. - 5 -
During acetic fermentation, phenolic compounds with high antioxidant activity may
be degraded to new phenolic compounds with lower antioxidant activity. This leads to
reduction in radical scavenging activity of vinegar. The ORAC-FL value was decreased from
14.6-25.0 μ mol of trolox equivalent/ml to 4.5-11.5 μ mol of trolox equivalent/ml[7].
Type of Wood of the barrel and ageing time also influence the phenolic compound
amount and profile of vinegar, and thus, antioxidant activity is influenced [10]. For balsamic
vinegar, significant increase was observed for samples aged in cherry, chestnut and oak wood
barrel and for chestnut the increase is most significant [11]. Chestnut releases a higher
concentration of gallic acid and, therefore, the formation of gallic ethyl ester is more likely in
chestnut barrels. But, the concentration of catechin and resveratrol were decresed [12].
Increase in ageing time allows the release of important phenolic compounds, especially
aldehydes. Four compounds, namely 5-hydroxymethylfurfuraldehyde (HMF), 2-
furfuraldehyde, proto- catechualdehyde and vanillin, were affected by ageing time [11]. The
study by Natera et al have confirmed the influence of ageing in wood on phenolic and volatile
compound profile of vinegar [13].
Produced as a result of malliard reaction, melanoidins could be responsible for high
antioxidant activity of vinegars [14]. Melanoidins are materials formed by interactions
between reducing sugars and compounds possessing a free amino group, such as free amino
acids and the free amino groups of peptides. High molecular weight melanoidins synthesizes
and accumulated during ageing of vinegar, especially balsamic vinegar [15], [16]. They can
account for upto 50% of antioxidant activity of aged vinegar.
1.2.2 Jujube Vinegar.
Native to south asia, Jujube is a deciduous shrub of Rahmnaceae family. In China and
adjacent area, for treatment related to respiratory, gastrointestinal, anti-inflammatory and
19. - 6 -
urinary diseases, jujube and it’s seed is prescribed [17]. “Fruit of life” contains several
important classes of phytochemical such as polysaccharides, phenolics, flavonoids and
saponins responsible for several biological activities [18]. Kamiloglu et al, [19] found total
phenolic content of jujube genotypes selected from turkey has phenolic content ranged from
25 to 42 mg GAE g-1
DW. For jujube genotypes from India, Koley et al 2011 found total
phenolic content varied twofold from 172 to 328.61 mg GAE/100 gm. After fermentation,
phenolic content decreased from 56.21 mg % to 45.75 mg %. This 18.6% decrease is
consistent with decrease occurred in other vinegars during acetous fermentation. However,
increase in flavonoid content from that of juice indicates synthesis during acetous
fermentation or liberation from cell wall [20].
1.2.3 Persimmon Vinegar
Persimmon has long been medically used for bronchial, paralysis and other blood
related chronic diseases due to presence of important phenolic compounds specially tannins
[21], [22]. DPPH radical scavenging and Radical scavenging activity of persimmon seed
extract are comparable with that of grapes, due to higher tannin concentration of 577.37 mg/
100 g as compared to 535mg/ 100 g in grapes [22]. The total phenolic content can reach upto
67mg GAE/g extract, depending upon the genotypes and various other environmental factors
[23]. The antioxidant activity and total phenol index for persimmon vinegar are 1601 μ mol
TE/kg and 324 mg gallic acid/kg which higher than white and redwine vinegar [21].
Persimmon vinegar has shown significantly higher DPPH radical scavenging activity of 52%-
higher than apple vinegar (11%), rice vinegar (40%) and pomegranate vinegar (35%) [24],
[25].
1.2.4 Pomegranate Vinegar
20. - 7 -
Pomegranate or Punica granatum, is a deciduous, red-rounded fruit bearing shrub of
Lythraceae, native to South asia stretching from Iran to India. Presence of several key
bioactive compounds such as hydrolysable tannins, monomeric anthocyanins, 3glucosides,
3,5 diglucosides and hydroxyl-cinnamic acids is responsible for several key biofunctions
(Lansky et al,1998, Du et al,1975, Nawwar et al, 1994a, Gil et al, 2000). [26]found the total
phenolic content of pomegranate juice was 1387 mg GAE/l, same as berry fruits. After
acetous fermentation, the phenolic content slightly reduced to 1254 mg GAE/l which is
higher than vinegar produced form other constituents [25]. A 9% decrease is lowest of all
vinegar but white wine which has almost similar change in polyphenolic content. The
phenolic content higher than rabbit-eye blueberry vinegar [26].
1.2.5 Strawberry Vinegar:-
Widely recognized for its characteristics aroma and unique flavor, strawberry is an
evergreen shrub of genus Fragaria [27]. It have shown presence of key minerals, vitamins,
antioxi- dants and secondary metabolites [28]. Presence of 224 mg GAE/g fresh tissue weight
of phenolic content results in an EC50 of 9.7 mg/ml [27]. During acetous fermentation,
phenolic content decreases from 2000 mg GAE/kg to 1000-2377 mg GAE/kg, depending on
treatment levels, which has resulted in a DPPh capacity of 6000-14000 μ mol TE/kg.
Anthocyanin loss can be attributed to polymerization and condensation reaction with other
phenols. Vinegar stored in glass barrel has lowest nutritional quality while those in cherry has
highest nutritional parameters[29].
1.2.6 Tartary Buckwheat vinegar
Native to east asia, tartary buckwheat is a food plant in the genus fagopyrum and
mainly consumed as tea, sprouts or milled products [30]–[32]. Due to presence of pheneolic
and flavonoid compounds such as rutin, quercetin, phenyl propanoid glycosides and catchins
21. - 8 -
and also important phytosterols, fagopyrins, it is used for aging, hypocholesterolemic and
antidiabetic activities[30], [32], [33]. Highest flavonoid and phenolic content of 22.6 and
12.99 mg/g dry weight is recorded in raw seeds. Higher free phenolic phenolic acid content
may indicates suitability of bran for therapeutic usage [34]. Rich in phenolics and flavonoids
especially rutin, tartary buckwheat vinegar shows good DPPH radical scavenging activity,
having IC50 value of 17 mg/ml. Flavonoid and phenolic content have decreased during
acetous fermentation. However, numerous different volatile compounds, including
antioxidant compounds like furfural and 5-methyl furfural, have appeared after fermentation
that improves radical scavenging potential of vinegar. Buckwheat Vinegar is rich in
tetramethyl pyrazine, or Ligustrazine, which is being investigated for potential inhibitor of
platelet aggregation [35].
1.3 Conclusion
Fruit is the main source of phenolic compounds in vinegar. During acetic
fermentation, antioxidant activity of vinegar is usually reduced from fresh fruit due to change
in phenolic and other antioxidant compound profile. Compounds with higher antioxidant
activity are converted to compounds with lower antioxidant activity by microoganisms.
Severel new compounds produced during processing and ageing in barrels can compensate
for lost activity of vinegar.
22. - 9 -
1.4 References
[1] S. Ji-yong, Z. Xiao-bo, H. Xiao-wei, Z. Jie-wen, L. Yanxiao, H. Limin, and Z.
Jianchun, “Rapid detecting total acid content and classifying different types of vinegar
based on near infrared spectroscopy and least-squares support vector machine,” Food
Chem., vol. 138, no. 1, pp. 192–199, 2013.
[2] P. Saha and S. Banerjee, “OPTIMIZATION OF PROCESS PARAMETERS FOR
VINEGAR,” Internatinal J. Res. Eng. Technol., vol. 02, no. 09, pp. 501–514, 2013.
[3] N. Saichana, K. Matsushita, O. Adachi, I. Frébort, and J. Frébortová, “Acetic acid
bacteria : A group of bacteria with versatile biotechnological applications,” Biotechnol.
Adv., 2014.
[4] M. Gullo, C. Caggia, L. De Vero, and P. Giudici, “Characterization of acetic acid
bacteria in ‘traditional balsamic vinegar,’” Int. J. Food Microbiol., vol. 106, no. 2, pp.
209–212, 2006.
[5] D. S. Dimitrijevic, D. A. Kostic, G. S. Stojanovic, A. N. A. S. Mitic, M. N. Miti, and
A. S. Dordevic, “Phenolic composition , antioxidant activity , mineral content and
antimicrobial activity of fresh fruit extracts of Morus alba L .,” J. Food Nutr. Res., vol.
53, no. 1, pp. 22–30, 2014.
[6] K. Gündüz and E. Özdemir, “The effects of genotype and growing conditions on
antioxidant capacity, phenolic compounds, organic acid and individual sugars of
strawberry,” Food Chem., vol. 155, pp. 298–303, 2014.
23. - 10 -
[7] R. M. Callejón, M. J. Torija, A. Mas, M. L. Morales, and A. M. Troncoso, “Changes of
volatile compounds in wine vinegars during their elaboration in barrels made from
different woods,” Food Chem., vol. 120, no. 2, pp. 561–571, 2010.
[8] A. Sugiyama, M. Saitoh, A. Takahara, Y. Satoh, and K. Hashimoto, “Acute
cardiovascular effects of a new beverage made of wine vinegar and grape juice,
assessed using an in vivo rat,” Nutr. Res., vol. 23, no. 9, pp. 1291–1296, 2003.
[9] Y. Yilmaz and R. T. Toledo, “Major Flavonoids in Grape Seeds and Skins:
Antioxidant Capacity of Catechin, Epicatechin, and Gallic Acid,” J. Agric. Food
Chem., vol. 52, no. 2, pp. 255–260, 2004.
[10] M. C. García Parrilla, F. J. Heredia, and A. M. Troncoso, “Sherry wine vinegars:
Phenolic composition changes during aging,” Food Res. Int., vol. 32, no. 6, pp. 433–
440, 1999.
[11] A. B. Cerezo, W. Tesfaye, M. E. Soria-Díaz, M. J. Torija, E. Mateo, M. C. Garcia-
Parrilla, and A. M. Troncoso, “Effect of wood on the phenolic profile and sensory
properties of wine vinegars during ageing,” J. Food Compos. Anal., vol. 23, no. 2, pp.
175–184, 2010.
[12] A. B. Cerezo, W. Tesfaye, M. J. Torija, E. Mateo, M. C. García-Parrilla, and A. M.
Troncoso, “The phenolic composition of red wine vinegar produced in barrels made
from different woods,” Food Chem., vol. 109, no. 3, pp. 606–615, 2008.
[13] R. Natera, R. Castro, M. de Valme García-Moreno, M. J. Hernández, and C. García-
Barroso, “Chemometric studies of vinegars from different raw materials and processes
of production.,” J. Agric. Food Chem., vol. 51, no. 11, pp. 3345–51, May 2003.
24. - 11 -
[14] F. Masino, F. Chinnici, A. Bendini, G. Montevecchi, and A. Antonelli, “A study on
relationships among chemical, physical, and qualitative assessment in traditional
balsamic vinegar,” Food Chem., vol. 106, no. 1, pp. 90–95, 2008.
[15] E. Verzelloni, D. Tagliazucchi, and A. Conte, “From balsamic to healthy : Traditional
balsamic vinegar melanoidins inhibit lipid peroxidation during simulated gastric
digestion of meat,” Food Chem. Toxicol., vol. 48, no. 8–9, pp. 2097–2102, 2010.
[16] Q. Xu, W. Tao, and Z. Ao, “Antioxidant activity of vinegar melanoidins,” Food
Chem., vol. 102, no. 3, pp. 841–849, 2007.
[17] T. K. Koley, C. Kaur, S. Nagal, S. Walia, S. Jaggi, and Sarika, “Antioxidant activity
and phenolic content in genotypes of Indian jujube (Zizyphus mauritiana Lamk.),”
Arab. J. Chem., 2011.
[18] O. Dahiru, D., Obidoa, “Evaluation of the antioxidant effects of Zizyphus mauritiana
Lamk. Leaf extracts against chronic ethanol- induced hepatotoxicity in rat liver.,” Afr.
J. Trad. Comp. Alt. Med., 2008.
[19] O. Kamiloglu, S. Ercisli, M. Sengul, C. Toplu, and S. Serce, “Total phenolics and
antioxidant activity of jujube (Zizyphus jujube Mill.) genotypes selected from
Turkey,” African J. Biotechnol., vol. 8, no. 2, pp. 303–307, 2009.
[20] V. A. Vithlani and H. V. Patel, “production of Functional Vinegar from Indian Jujube
(Zizyphus mauritiana ) and its Antioxidant properties,” J. Food Technol., vol. 8, no. 3,
pp. 143–149, 2010.
[21] C. Ubeda, C. Hidalgo, M. J. Torija, a. Mas, a. M. Troncoso, and M. L. Morales,
“Evaluation of antioxidant activity and total phenols index in persimmon vinegars
25. - 12 -
produced by different processes,” LWT - Food Sci. Technol., vol. 44, no. 7, pp. 1591–
1596, 2011.
[22] H. S. Ahn, T. Il Jeon, J. Y. Lee, S. G. Hwang, Y. Lim, and D. K. Park, “Antioxidative
activity of persimmon and grape seed extract: In vitro and in vivo,” Nutr. Res., vol. 22,
no. 11, pp. 1265–1273, 2002.
[23] E. Celep, A. Aydin, and E. Yesilada, “A comparative study on the in vitro antioxidant
potentials of three edible fruits: Cornelian cherry, Japanese persimmon and cherry
laurel,” Food Chem. Toxicol., vol. 50, no. 9, pp. 3329–3335, 2012.
[24] S. Sakanaka and Y. Ishihara, “Comparison of antioxidant properties of persimmon
vinegar and some other commercial vinegars in radical-scavenging assays and on lipid
oxidation in tuna homogenates,” Food Chem., vol. 107, no. 2, pp. 739–744, 2008.
[25] S. A. Ordoudi, F. Mantzouridou, E. Daftsiou, C. Malo, E. Hatzidimitriou, N. Nenadis,
and M. Z. Tsimidou, “Short communications Pomegranate juice functional
constituents after alcoholic and acetic acid fermentation,” J. Funct. Foods, vol. 8, pp.
161–168, 2014.
[26] M. S. Su and P. J. Chien, “Antioxidant activity, anthocyanins, and phenolics of
rabbiteye blueberry (Vaccinium ashei) fluid products as affected by fermentation,”
Food Chem., vol. 104, no. 1, pp. 182–187, 2007.
[27] P. C. Mandave, P. K. Pawar, P. K. Ranjekar, N. Mantri, and A. a. Kuvalekar,
“Comprehensive evaluation of in vitro antioxidant activity, total phenols and chemical
profiles of two commercially important strawberry varieties,” Sci. Hortic.
(Amsterdam)., vol. 172, pp. 124–134, 2014.
26. - 13 -
[28] A. Aharoni, L. C. P. Keizer, B. H. C. Van den, R. Blanco-Portales, and J. Munoz-
Blanco, “Novel insight into vascular, stress, and auxin-dependent and independent
gene expression programs in strawberry, a non-climacteric fruit. Plant,” Plant Physio,
vol. 29, pp. 1019–1031, 2002.
[29] C. Ubeda, R. M. Callejón, C. Hidalgo, M. J. Torija, a. M. Troncoso, and M. L.
Morales, “Employment of different processes for the production of strawberry
vinegars: Effects on antioxidant activity, total phenols and monomeric anthocyanins,”
LWT - Food Sci. Technol., vol. 52, no. 2, pp. 139–145, 2013.
[30] L. Wang, X. Yang, P. Qin, F. Shan, and G. Ren, “Flavonoid composition , antibacterial
and antioxidant properties of tartary buckwheat bran extract,” vol. 49, pp. 312–317,
2013.
[31] A. K. Ghimeray, P. Sharma, P. Phoutaxay, T. Salitxay, S. H. Woo, S. U. Park, and C.
H. Park, “Far infrared irradiation alters total polyphenol, total flavonoid, antioxidant
property and quercetin production in tartary buckwheat sprout powder,” J. Cereal Sci.,
vol. 59, no. 2, pp. 167–172, 2014.
[32] P. Qin, L. Wu, Y. Yao, and G. Ren, “Changes in phytochemical compositions ,
antioxidant and α -glucosidase inhibitory activities during the processing of tartary
buckwheat tea,” vol. 50, pp. 562–567, 2013.
[33] Q. Ren, Y. Li, C. Wu, C. Wang, Y. Jin, and J. Zhang, “Metabolism of secondary
metabolites isolated from Tartary buckwheat and its extract,” Food Chem., vol. 154,
pp. 134–144, 2014.
27. - 14 -
[34] X. Guo, C. Wu, Y. Ma, J. Parry, Y. Xu, H. Liu, and M. Wang, “Comparison of milling
fractions of tartary buckwheat for their phenolics and antioxidant properties,” vol. 49,
pp. 53–59, 2012.
[35] A. Wang, J. Zhang, and Z. Li, “Correlation of volatile and nonvolatile components
with the total antioxidant capacity of tartary buckwheat vinegar: Influence of the
thermal processing,” Food Res. Int., vol. 49, no. 1, pp. 65–71, 2012.
30. - 17 -
Process optimization and kinetics study of vinegar production
from Manilkara zapota
2.1 Introduction
Native to Mexico and Central America, Sapodilla (Manilkara zapota) belongs to the
family Sapotaceae and is an evergreen, glabrous tree, 8-15 m in height. It is cultivated in all
tropical countries including Indian subcontinent. The fruit is a fleshy berry, generally
globose, conical or oval with one or more seeds. The fruit generally weighs about 75–200 g,
ranging from 5 to 9 cm in diameter. The fruit has a thin rusty brown scurfy skin and a
yellowish brown or red pulp with a pleasant, mild aroma and an excellent taste[1] The seeds
of M. zapota are aperients, diuretic tonic and febrifuge. Stem bark is astringent and febrifuge.
The leaves and bark are used as medicine to treat cough, cold, dysentery and diarrhoea.
Antimicrobial and antioxidant activities are also reported from the leaves of M. zapota. The
major constituents isolated from fruits of M. zapota are polyphenols (methyl chlorogenate,
dihydromyricetin, quercitrin, myricitrin, (+)-catechin, (-)-epicatechin, (+)-gallocatechin, and
gallic acid[2]. The antioxidant activity of sapodilla fruit has been reported to be very high in
the ABTS assay (3396 mg kg-1
; ~76 μmol TE g-1
DW) [3]. Sapodilla has stronger nitric oxide
scavenging activity and inhibitory effects against tumor cell proliferation than pomegranate,
apple, dragon fruit, and grape [4]. Phenolic compounds are the main source of antioxidant
activity of sapodilla[5]. Although Sapodilla is cultivated mainly for its edible fruit, it is also
the source of chicle, the principle ingredient in chewing gum [1]. The protein content of
sapodilla is very low (0.4–0.7 g per 100 g pulp)[6]. Fruit is becoming popular throughout the
world. It is normally eaten fresh, but sometimes it is served as candy, dehydrated slices, jelly
and juices[7].
Production of vinegar with improved phytochemical attribute has been key interest
area for the research for past decades. Besides traditional benefits from acetic acid, vinegar is
31. - 18 -
now being investigated for other potential benefits arising from ingredients such as fruits,
spices used for seasoning that can be used. Contemporary vinegars such as fruit vinegar,
herbal vinegar, vinegar seasoned with spices and cereal vinegars has exhibited their ability to
prohibit and alleviate several chronic diseases such as free radical induced cell damage,
arthritis, gastrointestinal disorder etc. Fruits such as sapodilla may provide an ideal ingredient
for production of vinegar which may exhibit similar medicinal property as that of sapodilla
and can be easily available and taken by masses.
Response surface methodology (RSM) is an efficient experimental strategy to
determine optimal conditions for a multivariable system rather than by the conventional
method, which involves changing one independent variable while keeping the other factors
constant. These time consuming methods are incapable of detecting the true optimum.
Response surface methodology has been successfully used to model and optimize
biochemical and bio- technological processes related to food systems [8]. To our knowledge,
there have been no studies on the response surface optimization of vinegar production from
sapodilla [9].
The aim of this study is to optimize the physical parameters for improved productions
of herbal vinegar from sapodilla. Also, substrate utilization and product formation kinetics of
vinegar production will be studied.
2.2 Materials and methods
2.2.1 Chemicals
Dextrose, calcium carbonate (GR), KH2PO4, K2HPO4, MgSO4.7H2O, FeSO4.7H2O
and urea were purchased from Merck, India. Yeast extract, malt extract, tryptone, agar and
peptone were obtained from Himedia, India.
32. - 19 -
2.2.2 Yeast culture Preparation
Stock culture of Saccharomyces cerevisiae (NCIM 3315) was obtained from the
National Chemical Laboratory (NCL), Pune, India. The culture medium consisted of 3 malt
extract, 10 glucose, 3 yeast extract and 5 peptone (g/l). The organisms were grown at a
temperature of 300
C and pH 6.5. The incubation period was 45 hours. After incubation, the
culture was stored at 40
C in a refrigerator.
2.2.3 Acetobacter aceti culture preparation
Stock culture of Acetobacter aceti (NCIM 2116) was obtained from the National
Chemical Laboratory (NCL), Pune, India. The composition of the culture medium: 10
tryptone, 10 yeast extract, 10 glucose, 10 calcium carbonate and 20 agars (g/l). The
organisms were grown at a temperature of 300
C and pH 6.0. The incubation period was 24
hours. After incubation, the culture was stored at 40
C in the refrigerator.
2.2.4 Preparation of Fermentation medium for Ethanol Production
Sapodilla (Manilkara zapota) was purchased from market in Kolkata. These were
preserved at -500
C in an ultra-low temperature Freezer (Model C340, New Brunswick
Scientific, England).The fermentation medium consisted glucose 10, urea 3, KH2PO4 0.5,
K2HPO4 0.5, MgSO4.7H2O 0.5, FeSO4.7H2O 0.01 (g/l). The fermentation process was carried
out in a 250 ml flask; 100 ml of fermentation media were inoculated with yeast culture. The
pH and temperature were adjusted to 5.5 and 320
C for each experiment. The incubation time
was 10 days and the flask was made airtight by paraffin paper for maintaining anaerobic
conditions.
2.2.5 Preparation of Fermentation medium
After ethanol fermentation, 120 g/l of sterile sugar was added to the medium and
inoculated with Acetobacter aceti starter culture. The temperature and pH were adjusted as
33. - 20 -
per the experiments. The incubation time was 140 hours and flask was agitated at 150 rpm to
maintain an aerobic condition. Samples were withdrawn with a sterile injection syringe at
predefined interval for analysis.
2.3 Analytical methods
2.3.1 Determination of Ethanol concentration
A 5 ml fermented sample was centrifuged (Remi C-24, Mumbai, India) at 3500 g for
10 minutes. The supernatant solution was used to determine the ethanol concentration by gas
chromatography (Agilent Technologies: GC system-7890A gas chromatography, column-
Agilent JKWDB-624 with column ID- 250μm, length- 60m and film length-1.4μm). The
ethanol content was calculated by the GC peak areas.
2.3.2 Determination of acid
Acetic acid concentration was quantified by a HPLC system (JASCO, MD 2015 Plus,
Multiwave length detector) equipped with absorbance detectors set to 210 nm. The column
(ODS-3) was eluted with 0.01 (N) H2SO4as the mobile phase at a flow rate of 0.5 ml/min and
a sample injection volume of 20 μl. Standard acetic acid (Merck, India) was used as an
external standard.
2.3.3 Estimation of Biomass Concentration
The dry weights of mycelium were obtained after centrifuging the broth samples at
1100 g for 20 minutes. The harvested biomass was then washed with deionized water, dried
for 8 h at 1050
c, cooled in desiccators and weighed [10].
2.3.4 Response Surface Methodology
Natural vinegar production from sample was studied and the process was optimised
with Response surface methodology (RSM). Different types of RSM designs include 3-level
factorial design, central composite design (CCD), Box-Behnken design (BBD) and D-optimal
34. - 21 -
design. Among all designs, CCD is the most widely used response surface designed
experiment and allows us to efficiently estimate first and second order terms. A 3-factor, 3-
level design would require a total of 20 unique runs. Hence, CCD was applied to optimise
vinegar production with time, temperature and pH were the independent variable. Th factors
and their respective coding is given in table 2.1. These parameters have been optimised on
the basis of the highest yield of vinegar from the sample. A 3-factor, 3-level CCD design with
3 centre points was created using Design Expert 7 (2008, USA) and given in table 2.2. The
design was used to explore quadratic response surfaces and constructing second-order
polynomial model. The nonlinear quadratic model is given as:
Y=b0+b1x1+b2x2+b3x3+b12x1x2+b13x1x3+b23x2x3+b11x1
2
+b22x2
2
+b33x3
2
(1)
Where Y is the measured response associated with each factor level combination; b0 is an
intercept; b1 to b33 are the regression coefficients and x1, x2 and x3are the independent
variable.
The polynomial equation for the response was validated by the statistical test called
ANOVA (Analysis of Variance), for determination of significance of each term in equation
and also to estimate the goodness of fit. Response surfaces were drawn for experimental
results obtained from the effect of different variables on the acetic acid concentration in order
to determine the individual and cumulative effects of these variables [11].
2.3.5 FTIR study
A Fourier-transform infrared (FT-IR) spectrum of the fermented vinegar on KBr discs
was recorded in FTIR-8400S (Shimadzu, Japan). The scanning range covered 400-4000 cm-1
with resolution of 4 cm-1
[12].
2.3.6 Kinetic models
Product formation
35. - 22 -
The kinetics of product formation by a microorganism was based on Luedeking and
Piret equations which combine both growth-associated and nongrowth-associated
contributions [13].
𝑑𝑃
𝑑𝑡
= 𝛼
𝑑𝑥
𝑑𝑡
+ 𝛽𝑥 (6)
According to this model, the product formation rate depends on both the instantaneous
biomass concentration, x, and growth rate, dx/dt, in a linear manner and α and β may be
identified with energy used for growth and maintenance, respectively. At stationary phase (dx
/ dt= 0) and (x = xm), Luedeking-Piret kinetics of batch culture imply:
β =
(
𝑑𝑝
𝑑𝑡
) 𝑠𝑡
𝑥 𝑚
(7)
The product formation is growth associated when α ≠ 0 and β = 0. The integrated form of Eq.
(6) using P = 0 (t = 0) expresses P as a function of t [14].
P = 𝛼𝑥0(
𝑒 𝜇 𝑚 𝑡
(1−(
𝑥0
𝑥 𝑚
)(1−𝑒 𝜇 𝑚 𝑡))
− 1) + 𝛽(
𝑥 𝑚
𝜇 𝑚
) ln(1 − (
𝑥0
𝑥 𝑚
)(1 − 𝑒 𝜇 𝑚 𝑡
)) (8)
Thus, Eq. (8) can be written in the form:
P = αX + K (9)
Substrate utilization
The substrate utilization kinetics was based on Luedeking-Piret like equation which
considers substrate conversion to cell mass, to product and substrate consumption for
maintenance.
𝑑𝑠
𝑑𝑡
= - 𝛾
𝑑𝑥
𝑑𝑡
− 𝜂𝑥 (10)
At stationary phase (dx / dt= 0) and (x = xm), η can be obtained using the following equation:
36. - 23 -
η = (-(ds / dt))st./ xm(11)
Integrating the equation (10) using s = so (t =0) yields the following equation [14], [15]:
s = so –
(𝑥0 𝑥 𝑚 𝑒 𝜇 𝑚 𝑡)
𝛾(𝑥 𝑚−𝑥0+𝑥0 𝑒 𝜇 𝑚 𝑡)
+ (
𝑥0
𝛾
) − (𝜂
𝑥 𝑚
𝜇 𝑚
) ln(
(𝑥 𝑚−𝑥0+𝑥0 𝑒 𝜇 𝑚 𝑡)
𝑥 𝑚
) (12)
2.4 Results and Discussion
2.4.1 Response surface analysis of data
The maximum amount of acetic acid was produced in run 19 and the amount was 5.89
at pH 6.0 for 10 days of fermentation at 280
c. The minimum amount of acetic acid was
produced in run 1 and the amount was 3.12 at pH 4.0 for 6 days of fermentation at 240
c. This
is similar to optimized parameters of palm vinegar production [10]. The experimental data are
analysed using R (version 3.10, Austria) and given in Table 2.2. For a model to become
significant, it should have a high model F value and low lack-of fit F value. Lack-of fit
compares the residual error to pure error and it is not desirable [16]. So, a small F value and
high P value for lack-of fit term are desired. The obtained model has F value of 63.78 and
lack-of fit F value of 4.04, both of these values indicate the suitability of model (table 2.3).
The second-order polynomial equation for the measured response is given below:-
Y=5.67+0.24x1+0.064x2-0.093x3-0.055x1*x2-0.012x1*x3+0.068x2*x3-0.056x1
2
-0.57x2
2
-
1.06x3
2
(2)
The R2
value provides a measure of how much variability in the observed response
values can be explained by the experimental values and their interactions. A R2
value of
0.9829 indicates that 98.29% of the variability in the response could be explained by the
model. A positive value for regression coefficients represents an effect that favours the
optimization, while a negative value indicates an antagonistic effect.
37. - 24 -
By studying the regression coefficients for vinegar production (table 2.4), it can be
concluded that only Time (x1), Time2
(x1
2
), temperature2
(x2
2
) and pH2
(x3
2
) are the only
significant variable as they each has a p value<0.005. Values of “Prob>F” less than 0.0500
indicate model terms are significant while values greater than 0.1000 indicate the model
terms are not significant [17]. It can also be concluded that temperature, pH and all of the
interactions are insignificant variable. Among the significant variable, pH2
is the most
important terms followed by temperature2
and time2
, as it has highest t value.
Figure 2.1 (a)-(c) shows the surface response plot for optimization of the conditions
for acetic acid fermentation. Surface plots were based on regression equation, holding three
variables constant at the level of zero while varying the other two within their experimental
range. The effect of temperature and time, pH and temperature and time and pH on the acetic
acid production is shown in fig 2.1 (a)-(c). The graph shows optimum point for acetic acid
production was 5.698813, the optimum pH, temperature and time being 5.888989,
28.1678930
c and 10.888222 days.
The stationary point thus obtained is a maximum as all eigenvalues are negative (-
0.5272164, -0.5930815, -1.0665203). The largest eigenvalue (-1.0665203) corresponds to the
eigenvector (0.115930, -0.061246, 0.991367), the largest component of which (0.99136735)
is associated with pH; similarly, the second-largest eigenvalue (-0.5930815) is associated
with temperature. The third eigenvalue (-0.5272164) associated with time. These reiterate the
fact that acetic acid production is more sensitive to changes in pH than other two variables.
This fact can be rationalised by considering the stability of microorganism in the zone for
independent variables defined in the experiment. The zone of optimum pH stability for this
microorganism falls within the experimental pH range, whereas for temperature and time the
optimum stability zone encompasses the experimental zone.
38. - 25 -
2.4.2 Microbial and product growth
Saccharomyces cerevisiae, the organism used in the study, showed a normal growth
trend. It had a distinct exponential growth phase and stationary phase . As vinegar is a
primary metabolite, it was mainly formed during exponential growth phase. All experimental
data were analysed with R 3.1.0 (2013, Austria).
Product formation
Fitting the experimental data to Luedeking-Piret kinetics equation yielded the value of
parameters as follows: α=8.9625 g/g of biomass, β=0.1291 g/mg of biomass.h-1
. A plot of
acetic acid vs biomass concentration given in fig 2.2 a, will give the value of α and K. The
equation representing the relationship between the rate of product formation and microbial
growth is given as:
P=9.042X-4.597
The fitting of the results was satisfactory. A large α value compared to β indicates that the
synthesis of vinegar is primarily a growth associated type. In this model, α is the growth
associated product formation coefficient and can be associated with the product on biomass
yield (Yp/x).
Substrate Utilization
In vinegar bio-synthesis, glucose is converted to acetic acid by Saccharomyces
cerevisiae during exponential growth phase. A plot of substrate concentration and time given
in fig. 2.2 b will give value of S0, δ, γ. Fitting the experimental data to equation (12) yielded
the value of parameters as follows: S0=20.0837 g/l, Yx/s=0.08779 gg-1
, ms=0.1369 gg-1
day-1
.
The fitting of results was satisfactory.
39. - 26 -
Acetic acid is a powerful bacteriostatic, more in the undissociated acid from than
anion, can exhibit growth uncoupling action on microorganisms[18], [19]. High penetration
capability, owing to non-polar nature, allows it to accumulate into cytosol which could
reduce the cytoplasmic pH . Bacteria maintains cytoplasmic pH by extruding H+
by means of
the membrane H+
-ATPase in a process energized by glycolytically generated ATP [20], [21].
Upon accumulation of acids, to maintain ΔpH microorganism produce more ATP for H+
-
ATPese which reduces growth rate [22]. This, increasing ATP unavailability ceases growth.
Product formation rate increases initially, but, reduction in growth rate slows the product
formation rate.
2.5 Conclusion
Fermentation is a very complex process, and it is often very difficult to obtain a
complete picture of it. The response surface methodology based on a three variable CCD was
used to determine the effect of pH, time and temperature on acetic acid production. The
optimum pH, temperature and time were 5.89, 26.180
C and 10.89 respectively for the highest
yield of acetic acid (5.70%). The model parameters Xm, X0, μm, α, β, S0, Yx/s, ms were
determined. Model has established that acetic acid is a growth-associated product with high α
value. Analysis of data substantiated the inhibitory effect of the vinegar on the growth of
Acetobacter aceti. Growth uncoupling effect of this weak acid is mainly responsible for this
inhibitory action.
40. - 27 -
2.6 References
[1] H. N. Sin, S. Yusof, N. S. Abdul, and R. A. Rahman, “Optimization of hot water
extraction for sapodilla juice using response surface methodology,” J. Food Eng., vol.
74, pp. 352–358, 2006.
[2] M. A. Osman, M. M. Rashid, M. A. Aziz, M. R. Habib, and M. Rezaul, “Inhibition of
Ehrlich ascites carcinoma by Manilkara zapota L . stem bark in Swiss albino mice,”
Asian Pac. J. Trop. Biomed., vol. 1, no. 6, pp. 448–451, 2011.
[3] M. Leontowicz, H. Leontowicz, J. Drzewiecki, Z. Jastrzebski, R. Haruenkit, S.
Poovarodom, Y. Park, S. Jung, S. G. Kang, S. Trakhtenberg, and S. Gorinstein, “Food
Chemistry Two exotic fruits positively affect rat ’ s plasma composition,” Food
Chem., vol. 102, pp. 192–200, 2007.
[4] M. Kanlayavattanakul and N. Lourith, “Sapodilla seed coat as a multifunctional
ingredient for cosmetic applications,” Process Biochem., vol. 46, no. 11, pp. 2215–
2218, 2011.
[5] M. Isabelle, B. Lan, M. Thiam, W. Koh, D. Huang, and C. Nam, “Antioxidant activity
and profiles of common fruits in Singapore,” Food Chem., vol. 123, no. 1, pp. 77–84,
2010.
[6] H. G. A. Kumar and Y. P. Venkatesh, “In silico analyses of structural and allergenicity
features of sapodilla ( Manilkara zapota ) acidic thaumatin-like protein in comparison
with allergenic plant TLPs ଝ,” Mol. Immunol., vol. 57, no. 2, pp. 119–128, 2014.
41. - 28 -
[7] H. N. Sin, S. Yusof, N. S. Abdul, and R. A. Rahman, “Optimization of enzymatic
clarification of sapodilla juice using response surface methodology,” vol. 73, pp. 313–
319, 2006.
[8] C. J. B. De Lima, L. F. Coelho, and J. Contiero, “The Use of Response Surface
Methodology in Optimization of Lactic Acid Production : Focus on Medium
Supplementation , Temperature and pH Control,” Food Technol. Biotechnol., vol. 48,
no. 2, pp. 175–181, 2010.
[9] M. Blibech, M. Neifar, A. Kamoun, B. B. E. N. Mbarek, and R. Ellouze-ghorbel,
“ENHANCING POTATO CHIPS QUALITY BY OPTIMIZING COATING AND
FRYING CONDITIONS USING RESPONSE SURFACE METHODOLOGY,” J.
Food Process. Preserv., vol. 38, pp. 1416–1426, 2014.
[10] S. Ghosh, R. Chakraborty, G. Chatterjee, and U. Raychaudhuri, “Study on
fermentation conditions of palm juice vinegar by response surface methodology and
development of a kinetic model,” Brazilian J. Chem. Eng., vol. 29, no. 3, pp. 461–472,
Sep. 2012.
[11] D. Granato, R. Grevink, A. F. Zielinski, D. S. Nunes, and S. M. Van Ruth, “Analytical
Strategy Coupled with Response Surface Methodology To Maximize the Extraction of
Antioxidants from Ternary Mixtures of Green, Yellow, and Red Teas ( Camellia
sinensis var. sinensis ),” J. Agric. Food Chem., vol. 62, pp. 10283–10296, 2014.
[12] R. Pal, S. Panigrahi, D. Bhattacharyya, and A. S. Chakraborti, “Characterization of
citrate capped gold nanoparticle-quercetin complex: Experimental and quantum
chemical approach,” J. Mol. Struct., vol. 1046, pp. 153–163, Aug. 2013.
42. - 29 -
[13] R. Luedeking and E. L. Piret, “A kinetic study of the lactic acid fermentation. Batch
process at controlled pH. Reprinted from Journal of Biochemical and Microbiological
Technology Engineering Vol. I, No. 4. Pages 393-412 (1959).,” Biotechnol. Bioeng.,
vol. 67, no. 6, pp. 636–44, Mar. 2000.
[14] J.-Z. Liu, L.-P. Weng, Q.-L. Zhang, H. Xu, and L.-N. Ji, “A mathematical model for
gluconic acid fermentation by Aspergillus niger,” Biochem. Eng. J., vol. 14, no. 2, pp.
137–141, May 2003.
[15] M. Elibol and F. Mavituna, “A kinetic model for actinorhodin production by
Streptomyces coelicolor A3(2),” Process Biochem., vol. 34, no. 6–7, pp. 625–631,
Sep. 1999.
[16] M. Masmoudi, S. Besbes, M. Chaabouni, and C. Robert, “Optimization of pectin
extraction from lemon by-product with acidified date juice using response surface
methodology,” Carbohydr. Polym., vol. 74, no. 2, pp. 185–192, 2008.
[17] C. Chen and F. Chen, “Study on the conditions to brew rice vinegar with high content
of γ-amino butyric acid by response surface methodology,” Food Bioprod. Process.,
vol. 87, no. 4, pp. 334–340, Dec. 2009.
[18] G. Wang and D. I. Wang, “Elucidation of Growth Inhibition and Acetic Acid
Production by Clostridium thermoaceticum.,” Appl. Environ. Microbiol., vol. 47, no. 2,
pp. 294–8, Feb. 1984.
[19] R. Bar, J. L. Gainer, and D. J. Kirwan, “An Unusual Pattern of Product Inhibition:
Batch Acetic Acid Fermentation,” vol. XXIX, pp. 796–798, 1987.
43. - 30 -
[20] A. A. Herrero, “End-product inhibition in anaerobic fermentations,” Trends
Biotechnol., vol. 1, no. 2, pp. 49–53, May 1983.
[21] D. J. Clarke, F. M. Fuller, and J. G. Morris, “The proton-translocating adenosine
triphosphatase of the obligately anaerobic bacterium Clostridium pasteurianum. 1.
ATP phosphohydrolase activity.,” Eur. J. Biochem., vol. 98, no. 2, pp. 597–612, Aug.
1979.
[22] J. J. Baronofsky, W. J. Schreurs, and E. R. Kashket, “Uncoupling by Acetic Acid
Limits Growth of and Acetogenesis by Clostridium thermoaceticum.,” Appl. Environ.
Microbiol., vol. 48, no. 6, pp. 1134–9, Dec. 1984.
44. - 31 -
Figure 2-1: Response surface plot showing the effect of a)Temp and time b) pH and time and
c)pH and temp on vinegar production.
45. - 32 -
Figure 2-2 Variation of a) acetic acid vs biomass and b)substrate vs biomass for vinegar
production.
46. - 33 -
Figure 2-3: Comparison of calculated values and the experimental data from our experiment
47. - 34 -
Table 1 Variables in the Central composite Design
Variables Coded levels
-1 0 1
Time 6 10 14
Temperature 24 28 32
pH 4 6 8
49. - 36 -
Table 3 Summary of the analysis of variance result for the response models
Source Sum of Squares df R Square F value Prob>F
Total Model 18.7569 9 0.9829 63.78 <0.0001
Residual Mean square
Lack of Fit 0.2619 5 0.05238 4.04 0.1487
Pure Error 0.064883 5 0.01298
Total Error 0.326785 10 0.03267
Eigenvalues Eigenvectors
Time Temperature pH
-0.5272 0.8002 -0.5856 -0.1298
-0.5931 0.5885 0.8083 -0.018878
-1.0665 0.1159 -0.0613 0.991367
50. - 37 -
Table 4 Statistical significance of the regression coefficients for vinegar production of vinegar
by A. aceti
Estimate Std. Error VIF
Intercept 5.67 0.062 1.00
x1 0.24 0.057 1.00
x2 0.064 0.057 1.00
x3 -0.093 0.057 1.00
x1:x2 -0.055 0.064 1.00
x1:x3 -0.12 0.064 1.00
x2: x3 0.068 0.064 1.00
x1
2
-0.56 0.11 1.82
x2
2
-0.57 0.11 1.82
x3
2
-1.06 0.11 1.82
53. - 40 -
Mathematical Modelling of growth of Acetobaceter aceti in Vinegar
Fermentation reaction
3.1 Introduction
Acetic acid fermentation is one of the oldest biochemical processes, have been known
to ancient civilizations for thousands of years. During acetic acid fermentation, ethanol is
oxidized to acetic acid by Acetobacter:
C2H5OH+O2 CH3COOH+H2O
Vinegar production involves the conversion of ethanol to acetic acid by microbial
cells and strongly influenced by the difficulties to carry on acetic acid bacteria cultures [1].
High acid concentrations decrease the pH down to 2 which strongly affect microbial growth
and can lead to cell death. Besides the acidic environment, the medium contains also ethanol,
known as an inhibitory substrate. These unfavourable conditions make difficult the growth of
the bacteria. Apart from the study of microbial behaviour in a specific media, microbial
kinetics will help to optimize substrate concentration in order to yield high amount of vinegar
that will improve overall productivity. To model acetic acid production, it is therefore
important to begin to describe microbial kinetics [2], [3].
3.2 Microbial kinetics methods
The specific growth rate, µ (h-1
), is defined as the ratio between the biomass
production rate, rX (g/l.h), and the biomass concentration, X (g/l), equation (1).
µ = rX ×
1
𝑋
(1)
When carried out in a batch culture, the biomass balance is expressed as equation (2).
Μ=
1
𝑋
×
𝑑𝑋
𝑑𝑇
(2)
54. - 41 -
The product and substrate balances in batch culture lead to the expression of the acetic
acid production rate, rP, and the ethanol consumption rate, rS.
rP=
𝑑𝑃
𝑑𝑇
(3)
rS=−
𝑑𝑆
𝑑𝑇
(4)
P and S represent the product (acetic acid) and the substrate(ethanol) concentration (g/L).
The specific acetic acid production rate, νp , is defined as the ratio between rP and X and
describes the acetic acid production rate per one unit of biomass. It is expressed in gm. Acetic
acid per g biomass per hour, and can be calculated by expression 5.
νP=
1
𝑋
×
𝑑𝑃
𝑑𝑇
(5)
An analytical or numerical solution of the mass balances is possible once the function µ has
been specified [2].
Out of the several available kinetics models, the structured models consider
intracellular metabolic pathway that is difficult to apprehend without proper knowledge of
invivo reaction rates of implied enzymes. Unstructured growth models, simpler between the
two, describes growth rate as a function of initial microbial population.
𝑑𝑋
𝑑𝑇
=f(x) (6)
Unstructured model proposes:
1. The biomass concentration and the rate of cell mass production are proportional.
2. The cells need substrate and can continuously synthesize metabolic products even
after the growth has finished.
3. The evolution of the biomass throughout the culture time (growth rate) presents an
asymptote as upper limit (saturation level) different for each substrate or level of
substrate used [4].
55. - 42 -
Monod equation describes specific growth rate is proportional to concentration of nutrient
presents at a limiting concentration.
𝜇 =
𝜇 𝑚𝑎𝑥
(𝐾𝑠+𝑆)
× 𝑆 (7)
However, Moser and Haldene proposed two different kinetic models, in order to overcome
the limitations of the monod models, which takes into account the effect of K*
and Ki,
provided that K*
>1, and Ki is the inhibition constant [5]–[7].
𝜇 =
𝜇 𝑚𝑎𝑥
(𝐾𝑠+𝑆2)
× 𝑆2
(8)
𝜇 =
𝜇 𝑚𝑎𝑥×𝑆
(𝐾𝑠+𝑆+
𝑆2
𝐾 𝑖
)
(9)
The more simplified form of 7,8,9 is [5]:-
1
𝜇
=
𝐾𝑠
𝜇 𝑚𝑎𝑥
×
1
𝑆
+
1
𝜇 𝑚𝑎𝑥
(10)
1
𝜇
=
𝐾𝑠
𝜇 𝑚𝑎𝑥
×
1
𝑆2 +
1
𝜇 𝑚𝑎𝑥
(11)
1
𝜇
=
𝐾𝑠
𝜇 𝑚𝑎𝑥
×
1
𝑆2
+
1
𝜇 𝑚𝑎𝑥
+
𝑆
𝐾 𝑖
(12)
Sigmoid function can also be used to conceptualise the growth pattern, taking lag time phase
into the consideration, occurring in different culture media.
Subtrate independent models assume, when nutrients are present in abundant,
population growth is proportional to the population.
𝑑𝑥
𝑑𝑡
= 𝜇𝑥
56. - 43 -
Growth curves contain a final phase in which the rate decreases and finally reaches zero, so
that an asymptote (A) is reached.
However, when population tend to reach the maximum, owing to reduced nutrient
content, the population growth tend to fall which is measured by entity introduced in
substrate independent model and proportional to population[8], [9].
Logistic (14) and Gompertz (15) are the most popular sigmoid equation used to model
microbial growth.
𝜇 = 𝜇 𝑚 × (1 −
𝑥
𝑥 𝑚
) (14)
𝜇 = 𝜇 𝑚 × log (
𝑋 𝑚
𝑋
)(15)
Gompertz and logistic, both being substrate independent model, can be successfully used to
analyse effect of population on the growth[10]–[12].
3.3 Material
3.3.1 Chemicals
Dextrose, calcium carbonate (GR), KH2PO4, K2HPO4, MgSO4.7H2O, FeSO4.7H2O
and urea were purchased from Merck, India. Yeast extract, malt extract, tryptone, agar and
peptone were obtained from Himedia, India.
3.3.2 Yeast culture Preparation
Stock culture of Saccharomyces cerevisiae (NCIM 3315) was obtained from the
National Chemical Laboratory (NCL), Pune, India. The culture medium consisted of 3 malt
extract, 10 glucose, 3 yeast extract and 5 peptone (g/l). The organisms were grown at a
temperature of 300
C and pH 6.5. The incubation period was 45 hours. After incubation, the
culture was stored at 40
C in a refrigerator.
57. - 44 -
3.3.3 Acetobacter aceti culture preparation
Stock culture of Acetobacteraceti(NCIM 2116) was obtained from the National
Chemical Laboratory (NCL), Pune, India. The composition of the culture medium: 10
tryptone, 10 yeast extract, 10 glucose, 10 calcium carbonate and 20 agars (g/l). The
organisms were grown at a temperature of 300
C and pH 6.0. The incubation period was 24
hours. After incubation, the culture was stored at 40
C in the refrigerator.
3.3.4 Preparation of Fermentation medium for Ethanol Production
Sapodilla (Manilkara zapota) was purchased from market in Kolkata. These were
preserved at -500
C in an ultra-low temperature Freezer (Model C340, New Brunswick
Scientific, England).The fermentation medium consisted glucose 10, urea 3, KH2PO40.5,
K2HPO4 0.5, MgSO4.7H2O 0.5, FeSO4.7H2O 0.01 (g/l). The fermentation process was carried
out in a 250 ml flask; 100 ml of fermentation media were inoculated with yeast culture. The
pH and temperature were adjusted to 5.5 and 320
C for each experiment. The incubation time
was 10 days and the flask was made airtight by paraffin paper for maintaining anaerobic
conditions.
3.3.5 Preparation of Fermentation medium
After ethanol fermentation, 120 g/l of sterile sugar was added to the medium and
inoculated with Acetobacter aceti starter culture. The temperature and pH were adjusted as
per the experiments. The incubation time was 140 hours and flask was agitated at 150 rpm to
maintain an aerobic condition. Samples were withdrawn with a sterile injection syringe at
predefined interval for analysis [13].
58. - 45 -
3.4 Analytical methods
3.4.1 Determination of Ethanol concentration
A 5 ml fermented sample was centrifuged (Remi C-24, Mumbai, India) at 3500 g for
10 minutes. The supernatant solution was used to determine the ethanol concentration by gas
chromatography (Agilent Technologies: GC system-7890A gas chromatography, column-
Agilent JKWDB-624 with column ID- 250μm, length- 60m and film length-1.4μm). The
ethanol content was calculated by the GC peak areas[14] .
3.4.2 Determination of acid
Acetic acid concentration was quantified by a HPLC system (JASCO, MD 2015 Plus,
Multiwave length detector) equipped with absorbance detectors set to 210 nm. The column
(ODS-3) was eluted with 0.01 (N) H2SO4as the mobile phase at a flow rate of 0.5 ml/min and
a sample injection volume of 20 μl. Standard acetic acid (Merck, India) was used as an
external standard.
3.4.3 Estimation of Biomass Concentration
The dry weights of mycelium were obtained after centrifuging the broth samples at
1100 g for 20 minutes. The harvested biomass was then washed with deionized water, dried
for 8 h at 1050
c, cooled in desiccators and weighed.
3.4.4 Statistical Analysis
Fitting to the model and parametric estimations calculated from the results were
carried out by minimisation of the sum of quadratic differences between observed and model-
predicted values, using the curve-fitting module provided by scipy.stats module. Module was
used to evaluate the significance of the parameters estimated by the adjustment of the
experimental values to the proposed mathematical models and the consistency of these
equations. The results were visualised with Matplotlib. The models were compared on the
59. - 46 -
basis of standard error between obtained value and predicted value, which reduces as fitting
of model become good.
3.5 Result and Disscussions
Experimental data for glucose and biomass concentration during the growth phase of
A. aceti were used for determination of different kinetic parameters. Monod, Moser,
Andrews. Considering cell dry weight as microbial concentration values (X) and glucose
substrate as limiting substrate concentration (S), values of μ and other inhibiting parameters
were determined from eq (10,11,12). The calculated value of different kinetic parameters is
given in table 1.
All the models predicted that the reaction has negative μmax and Ks value, which
indicates reduction of microbial population during “growth phase” despite the abundance of
nutrients even after growth phase. Although high level of fitting has been achieved, as
evidenced by low sum of square of residuals, the calculated values of μ do not correlated with
experimental parameters at all fig 3-1. Inhibition of growth is resulting from accumulation of
acetic acid, a mild bacteriostatic, within the cell and is most prominent during later phase of
fermentation. Thus high initial microbial population and low final population at the end of
growth phase is resulted which produces an artificial value of kinetic parameters, that do not
corroborate experimental conditions.A negative value of μmax is a result of relative lower
population at the end of growth phase, which also supports the theory of severe inhibition of
microbial growth by product. Hence, substrate dependent models like Monod or moser
cannot provide ideal framework for proper modelling of A. aceti growth during vinegar
production. For application of these equations, none but amount of limiting nutrient must
influence the growth of microorganism [9].
60. - 47 -
In these cases, sigmoid growth equations such as logistics and gompertz can be
utilized to model microbial growth. These equations include growth inhibition factors, often
proportional to population. The variation of Logistic and gompertz equation is shown in fig
3-2. It shows gompertz growth equation, with same μmax and Xm, have higher microbial
population value. The experimental value of μmaxand Xm for logistic and gompertz equation
are given below:
Logistic: μmax:-0.2554 h-1
, Xm:-1.258998 gm/l, residual sum of square:-8.99×10-6
Gompertz: μmax:-0.1496 h-1
, Xm:-1.43887 gm/l, residual sum of square:- 4.56×10-5
Logistic equation has lower residual sum of square which indicates better capacity of Logistic
equation over Gompertz to predict values for microbial population within the experimental
range. The observed result is close to those obtained for palm vinegar fig 3-3 [13]
Gompertz and Logistic function both assumes growth is slowest at initial and final
phase, but differs in the approach of both asymptote by the curve. Thus, Gompertz equation
may be termed as a special case of generalised Logistic function. But, Logistic equation
assumes the symmetrical approach by the curve, whereas logistic equation assumes right
hand asymptote approaches much more gradually than left hand [11]. Carefully examining
the models, we can see that models over-predict the initial and final population, with the
values being higher for gompertz equation, and under-predict during middle phase and the
value is again for gompertz equation. Even during end phase, the limiting nutrient
concentration is present in adequate amount. This signifies the growth inhibition by acetic
acid is as equal powerful as initial lag phase and can be termed as “secondary lag phase”.
During this secondary lag phase, microorganisms, in order to acclimatize with adverse
situations, ends growth phase prematurely and divert additional ATP to maintain proton
pump[15], [16]. Acetic acid can exert growth uncoupling effect by lowering pH which
61. - 48 -
microorganism try to oppose with “proton pump” or H+
-ATPase [17], [18]. Being a
bacteriostatic and nonpolar, acetic acid can easily accumulate in cytosol and reduce pH below
a level at which bacteria has to reduce growth to maintain ΔpH with H+
-ATPase [19], [20].
3.6 Conclusion
Vinegar fermentation study was carried out to study the growth of A. aceti in the
fermentation media. Substrate dependent kinetics models failed to account for the
experimental data and observations. Substrate independent model such as logistic and
gompertz can be used for modelling of microbial growth. Substrate inhibition by acetic acid
sets in a secondary lag phase which ends growth phase prematurely.
62. - 49 -
3.7 References
[1] K. R. Patil, “Microbial Production of Vinegar ( Sour wine ) by using Various Fruits,”
Indian J. Appl. Res., vol. 3, no. 8, pp. 602–604, 2013.
[2] C. Pochat-Bohatier, C. Bohatier, and C. Ghommidh, “Modeling the kinetics of growth
of acetic acid bacteria to increase vinegar production: analogy with mechanical
modeling,” Proc. Fourteenth Int. Symp. Math. Theory Networks Syst. - MTNS 2000,
2000.
[3] D. Cantero and J. M. Gomez, “Kinetics of substrate consumption and product
formation in closed acetic fermentation systems,” Bioprocess Eng., vol. 18, pp. 439–
444, 1998.
[4] J. A. vazquez and M. A. Murado, “Unstructured mathematical model for biomass ,
lactic acid and bacteriocin production by lactic acid bacteria in batch,” Chem.
Technol., vol. 96, no. August 2007, pp. 91–96, 2008.
[5] F. Ardestani, “Investigation of the Nutrient Uptake and Cell Growth Kinetics with
Monod and Moser Models for Penicillium brevicompactum ATCC 16024 in Batch
Bioreactor,” Iran. J. Energy Environ., vol. 2, no. 2, pp. 117–121, 2011.
[6] G. C. Okpokwasili and C. O. Nweke, “Microbial growth and substrate utilization
kinetics,” African J. Biotechnol., vol. 5, no. 4, pp. 305–317, 2005.
[7] N. Debasmita and M. Rajasimman, “Optimization and kinetics studies on
biodegradation of atrazine using mixed microorganisms,” Alexandria Eng. J., vol. 52,
no. 3, pp. 499–505, 2013.
63. - 50 -
[8] J. Liu, L. Weng, Q. Zhang, H. Xu, and L. Ji, “Short communication A mathematical
model for gluconic acid fermentation by Aspergillus niger,” vol. 14, pp. 137–141,
2003.
[9] M. Elibol and F. Mavituna, “A kinetic model for actinorhodin production by
Streptomyces coelicolor A3(2),” Process Biochem., vol. 34, no. 6–7, pp. 625–631,
Sep. 1999.
[10] M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. van ’t Riet, “Modeling of
the bacterial growth curve.,” Appl. Environ. Microbiol., vol. 56, no. 6, pp. 1875–1881,
1990.
[11] C. Winsor, “Gompertz Curve as a Growth curve,” in national acdemy of Sciences,
1984, vol. 173, no. 2, pp. 253–258.
[12] D. A. Mitchell, O. F. Von Meien, N. Krieger, F. Diba, and H. Dalsenter, “A review of
recent developments in modeling of microbial growth kinetics and intraparticle
phenomena in solid-state fermentation,” vol. 17, pp. 15–26, 2004.
[13] S. Ghosh, R. Chakraborty, G. Chatterjee, and U. Raychaudhuri, “Study on
fermentation conditions of palm juice vinegar by response surface methodology and
development of a kinetic model,” Brazilian J. Chem. Eng., vol. 29, no. 3, pp. 461–472,
Sep. 2012.
[14] K. Chakraborty, J. Saha, U. Raychaudhuri, and R. Chakraborty, “Optimization of
bioprocessing parameters using response surface methodology for bael (Aegle
marmelos L.) wine with the analysis of antioxidant potential, colour and heavy metal
concentration,” Nutrafoods, vol. 80, no. 1, pp. 51–64, 2015.
64. - 51 -
[15] A. A. Herrero, “End-product inhibition in anaerobic fermentations,” Trends
Biotechnol., vol. 1, no. 2, pp. 49–53, May 1983.
[16] D. J. Clarke, F. M. Fuller, and J. G. Morris, “The proton-translocating adenosine
triphosphatase of the obligately anaerobic bacterium Clostridium pasteurianum. 1.
ATP phosphohydrolase activity.,” Eur. J. Biochem., vol. 98, no. 2, pp. 597–612, Aug.
1979.
[17] G. Wang and D. I. Wang, “Elucidation of Growth Inhibition and Acetic Acid
Production by Clostridium thermoaceticum.,” Appl. Environ. Microbiol., vol. 47, no. 2,
pp. 294–8, Feb. 1984.
[18] N. V Narendranath, K. C. Thomas, and W. M. Ingledew, “Effects of acetic acid and
lactic acid on the growth of Saccharomyces cerevisiae in a minimal medium.,” J. Ind.
Microbiol. Biotechnol., vol. 26, no. 3, pp. 171–7, Mar. 2001.
[19] J. J. Baronofsky, W. J. Schreurs, and E. R. Kashket, “Uncoupling by Acetic Acid
Limits Growth of and Acetogenesis by Clostridium thermoaceticum.,” Appl. Environ.
Microbiol., vol. 48, no. 6, pp. 1134–9, Dec. 1984.
[20] R. Bar, J. L. Gainer, and D. J. Kirwan, “An Unusual Pattern of Product Inhibition:
Batch Acetic Acid Fermentation,” vol. XXIX, pp. 796–798, 1987.
65. - 52 -
Figure 3-1: Comparison of Monod, Moser and Haldene equation.
66. - 53 -
Figure 3-2:Comparison of logistic and gompertz equation
67. - 54 -
Figure 3-3 Residual plot for logistic and gompertz equation
68. - 55 -
Table 1: Values of parameters for Monod,Moser and Haldene models
Model
Parameter
Monod Moser Haldene
μmax
-0.0428 -0.16295 -0.0056256
Ks
-24.734 -773.57 -10.371
Ki - -
-38.543
71. - 58 -
Partial Least square modelling for Prediction of Antioxidant
activity of Phenolic compounds
4.1 Introduction
Reactive oxygen species (ROS) is responsible for inflammation, aging, fibrosis,
carcinogenesis, neurological, cardiovascular diseases and cancers- a number of chronic
diseases rapidly spreading among world population and leading to increasingly higher work-
power, capability and life loss. Normal body defense system maintains a healthy balance of
ROS in the body, mainly for growth factor stimulation, control of inflammatory responses,
regulation of various cellular processes including differentiation, proliferation, growth,
apoptosis, cytoskeletal regulation, migration; but excessive production may be the result of
imbalanced cellular respiration and enzyme systems[1], [2].
Mitigation of Reactive oxygen species (ROS) stress is partially achieved by
application of antioxidant, any compounds capable of preventing or removing oxidative
damage to other molecules. Vitamins, minerals, enzymes and many other different classes of
compounds can act as antioxidants and thus can be used therapeutically or as medicine in
treatment of various diseases.
Natural fruits and vegetable, an important contributor to daily antioxidant intake by
human, are a rich source of various phytochemical compounds and therapeutically used
throughout the world for centuries [3]. Among various phytochemical compounds, phenolic
acid remains an important one owing to its growth controlling and radical scavenging effect.
The phenolic acids of plant-origin are predominantly of C6-C3 (phenypropanoid) type; but
C6-C1 (phenylmethyl) is predominantly formed by microbes (Sarakanen & Ludwig, 1971). A
vast array of 8000 different phenolic compounds can be broadly classified into two classes-
simple phenol and polyphenols; first class contains single phenol unit whereas latter contains
72. - 59 -
multiple subunits . Simple phenol is further classified into hydroxyl-benzoic structure and
hydroxyl-cinnamic structure[4].
Antioxidants can directly scavenge free radicals, chelate metals, activate antioxidant
enzymes, inhibit oxidases, mitigate nitric acid oxidation stress and improve antioxidant
activity of low MW antioxidants. Direct scavenging of radicals can occur via 3 different,
nonexclusive mechanism of hydrogen abstraction (HAT), proton coupled electron transfer
(PCET) and sequential proton coupled electron transfer (SPLET)[5].
Hydrogen atom transfer (HAT): R + ArOH RH + ARO.
,
One electron transfer (SPLET): ArOH ArO-
+H+
R + ArO-
R-
+ ArO.
R-
+H+
RH
Proton coupled Electron transfer (PCET):R+ArOH R-
+ArOH+.
ArOH+.
ArO.
+H+
R-
+H+
RH
Selection of individual pathway depends on structure of phenolics and specially
characteristics and placement of chemical moieties relative to OH group, only group capable
of donating H+
ion to radicals for rendering them into harmless quantity[6], [7]. Hence,
quantification of antioxidant activity of individual compounds includes study of the
scavenging pathway, placement and characteristics of OH group and other chemical moieties.
Pro-oxidant activity, an area of concern for antioxidants, is observable only if the
respective compound is present at higher level. A way of ensuring successful application of
an compounds as an antioxidants is to determine the antioxidant activity. Several molecular
73. - 60 -
properties are found to influence the antioxidant activity which is quantitatively and
qualitatively studied by using Structure activity Relationship (SAR). SAR allows prediction
about antioxidant activity of compound based on molecular property it share with other
structurally similar compounds[8].
4.2 Method
A wide range of in vitro methods using different artificial species such as 2,2´-
azinobis-3 ethylbenzothiazoline-6-sulfonic acid (ABTS), 1,1´-diphenyl-2-picrylhydrazyl
(DPPH), N, N-dimethyl-p-phenylendiamine (DMPD) has been employed to assess
antioxidant activity . DPPH assay employs DPPH free radical that shows a characteristic UV-
vis spectrum with maximum of absorbance close to 515 nm (methanol) FIG 1. Antioxidant
activity of compound is proportional to decrease of absorbance upon addition. It is easy to
perform, highly reproducible and comparable with other assay methods.There are various
ways to express assay results e.g.- TEAC, EC50, antiradical Power, TEC50, AE [9] .
There are various ways to express assay results.
1. TEAC- Trolox Equivalent Antioxidant Capacity is the antioxidant capacity of a given
substance compared to that of the standard antioxidant Trolox, an analogous
hydrosoluble of Vitamin E
2. EC50- It expresses the amount of antioxidant needed to decrease the radical
concentration by50%.
3. Antiradical Power:- ARP=1 ÷ EC50
4. TEC50- It espresso the time at equilibrium reached with a concentration of antioxidant
equal to EC50
74. - 61 -
5. AE- Antioxidant Efficacy comprises both electron or hydrogen atom-donating ability
and rate of their reaction towards the free radicals
The antioxidant activity data for several simple phenols are taken from Brand-Williams et al.
1995 and Villano et al. 2007[10], [11].
4.3 Statistical Analysis
PLS is a widely used chemometric method for multivariate calibration which was
developed around 1975 by Herman Wold and then introduced into chemometrics by Svante
Wold. A partial least squares regression (PLSR) model was used to evaluate the importance
of molecular properties as determinants of the antioxidant activity of simple phenols. Five
molecular parameters namely- Refractivity, Refraction index, surface tension, density and
polarizability were selected and calculated using ACD labs molecular property plugin for
ACD 3D viewer (ACD Labs, 2012). PLSR is a generalization of multiple linear regression
and it is particularly useful for analysing data with numerous, correlated and independent
variables [12]. It is a method to relate a matrix X to a vector Y or to a matrix Y. In the PLS
analysis, X space was projected to a hyperplane and the PLS factors were extracted to replace
the original X space. In this process, each PLS factor was produced by linear combination of
the selected predictor variables. By choosing a number of factors, the number of dimensions
could be reduced significantly and antioxidant activity was regressed on these extracted PLS
factor. All variables were manipulated with mean centering and scaling to unit variance and
the model was trained with data from samples. Percent variation accounted for by PLS
factors are used to obtain the appropriate number of components of each PLSR model [13].
A PLSR regression model is thought to provide significant and good predictions when
high percentage of predictor and response variation can be accounted for by fewer factors,
reducing the chances of overfitting [14]. The variable importance plots (VIP) can be used to
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explain contribution of each redictors in fitting the PLS model for both
predictors and response. Thus, it is possible to determine which molecular property has most
strongly influence on antioxidant activity. In general, an independent variable with a VIP
value greater than 1 is thought to be most relevant and significant for explaining the
dependent variable, whereas a value less than 0.5 indicates that the variable does not
significantly explain the dependent variable. In the interval between 0.5 and 1, the importance
level depends on the VIP value. If a predictor has a relatively small coefficient (in absolute
value) and a small value of VIP, then it is a prime candidate for deletion[15].
After optimizing for number of variables and components with validation, the PLS
model was applied to predict the antioxidant and Hotelling T2
for each observation was
derived to check the confidence level of predictability. A large value of Hotelling T2
would
indicate that the observation was suspected to be an outlier, possibly leading to a poor
prediction. After the trained model was internally validated, it was applied to an external test
sample with known cytotoxicity and the PRESS between predicted and observed cytotoxicity
was calculated to test the external applicability of the model [16]. All of the analyses were
conducted using the PLSR procedure implemented in SAS University Edition (SAS Institute
USA).
4.4 Results and Discussion
Based on characteristics of chemical moieties, other than OH group, present in a
compound, the simple phenols can be divided into 2 different classes- compound containing
electron withdrawing group and compound containing electron donating group. An electron
donating group releases electron density to a conjugated π system, whereas an electron
withdrawing group withdraws electron density from it. Thus, electron donating groups make
system more nuclephilic. On the other hand, electron withdrawing groups makes system more
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electrophilic which slows electrophilic substitution reaction[5]. Traditionally, electron
withdrawing groups are associated with poor antioxidant activity that is difficult to apprehend
experimentally fig. 4-1.
Development of PLS model
Electron withdrawing group
The performance of the model including 5 molecular properties and ARP value was
satisfactory. 3 PLS factors were able to accounted for 99.33% variation of predictor variable
and 97.62% variation in responses (Fig 2a). The plot in Fig 4-2 of the proportion of variation
explained (or R square) makes it clear that there is a plateau in the response variation after
three factors are included in the model. The correlation loading plot summarizes many
features of this two-factor model:
The X-scores are plotted as numbers for each observation. Vanillin (3), Vanillic acid
(4) and γ resorcylic acid (5) are found to remain closed together, separated from
phenol (1) and coumaric acid (2) present at periphery, which indicates presence of
electron donating group modifies the antioxidant activity.
The loadings show how much variation in each variable is accounted for by the first
two factors, jointly by the distance of the corresponding point from the origin and
individually by the distance for the projections of this point onto the horizontal and
vertical axes. The position of ARP (AR) in an area between 50-75% shows additional
factors are needed for proper explanation of response variation.
Projection interpretation can be to relate variables to each other. Thus, polarizability
(v5) is found to be highly positively correlated with ARP, and Refraction index (v2) is
negatively correlated. Other variables have very little correlation with ARP as
evidenced by their grouping around bottom centre of the circle.
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The variance importance plot in fig 4-2 can be used to find out relative importance
of predictor variables on response variables. Refraction index (v2) and refractivity (v1) have
the higher influence than surface tension and density which have lowest influence[13].
The resultant PLS regression equation is:-
ARP=-3.619 + 0.025×Refractivity + 0.691×Refraction index - 0.00036×Surface tension +
0.416×Density + 0.0641×Polarizibility (R2
=0.9762)
In table 1, value for ARP, predicted ARP, PRESS and T2
are given for each
observation. Model does not hold good for phenol as indicated by higher PRESS and T2
value
[17].
Electron donating group
The performance of the model including 5 molecular properties and EC50 value was
satisfactory. 2 PLS factors were able to accounted for 95.65% variation of predictor variable
and 97.88% variation in responses (Fig 4-3). The plot of the proportion of variation explained
(or R square) makes it clear that there is a plateau in the response variation after two factors
are included in the model. The correlation loading plot summarizes many features of this
two-factor model:
Protocatechuic acid (2), Caffeic acid (3) and ferulic acid (4) are found to remain
closed together, separated from gallic acid (1) and caftaric acid (5).
The position of EC50 (EC) at apposition close to 100% line shows these factors are
sufficient for proper explanation of response variation.
Projection interpretation can be to relate variables to each other. Thus, polarizability
(v5) and refractivity (v1) is found to be highly positively correlated with ARP, while
others are negatively correlated.
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The variance importance plot (4-3) can be used to find out relative importance of
predictor variables on response variables. Refraction index (v2) and refractivity (v1) have the
higher influence than surface tension and density which have lowest influence [18], [19].
The resultant PLS regression equation is:-
EC50=116.1977 + 0.199×Refractivity – 59.934×Refraction index - 0.084×Surface tension -
8.18×Density + 0.491×Polarizibility (R2
=0.9788)
In table 2, value for EC50, predicted EC50, PRESS and T2
are given for each
observation. Model is satisfactory for compounds as indicated by low PRESS and T2
value.
Similarity between influential predictor variables along with their importance in VIP
plot indicates not only structural similarity among the compounds but also the existing
similarity between reaction mechanism. Presence of chemically different group can only
influence the rate of H+
atom donation, but they cannot alter reaction mechanism at least for
phenolic class of compounds[20].
4.5 Conclusion
Partial least square was applied to found regression equation to predict ARP and EC50
values for compounds containing electron withdrawing and electron donating group. Results
indicates same predictors variables namely-refractivity, refraction index and polarizability
influence antioxidant activity of both classes. Surface tension and density have no effect and
so can be neglected. The resultant equations show good predictability of response variables,
and thus can be utilized to predict values for other compounds belong in these classes.
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4.6 References
[1] K. Brieger, S. Schiavone, F. J. Miller, and K.-H. Krause, “Reactive oxygen species:
from health to disease.,” Swiss Med. Wkly., vol. 142, p. w13659, Jan. 2012.
[2] K.-H. Krause, “Aging: a revisited theory based on free radicals generated by NOX
family NADPH oxidases.,” Exp. Gerontol., vol. 42, no. 4, pp. 256–62, Apr. 2007.
[3] M. Leopoldini, N. Russo, and M. Toscano, “The molecular basis of working
mechanism of natural polyphenolic antioxidants,” Food Chem., vol. 125, no. 2, pp.
288–306, 2011.
[4] R. J. Nijveldt, E. Van Nood, D. E. C. Van Hoorn, P. G. Boelens, K. Van Norren, and
P. a M. Van Leeuwen, “Flavonoids: A review of probable mechanisms of action and
potential applications,” Am. J. Clin. Nutr., vol. 74, no. 4, pp. 418–425, 2001.
[5] D. Amić, D. Davidović-Amić, D. Beslo, V. Rastija, B. Lucić, and N. Trinajstić, “SAR
and QSAR of the antioxidant activity of flavonoids.,” Curr. Med. Chem., vol. 14, no.
7, pp. 827–845, 2007.
[6] M. N. J. L. S.A.B.E. Van Acker, M.J. De Groot, D.J. van den Berg and a. B. Tromp,
G.D.O. den Kelder, W.J.F. van der Vijgh, “A quantum chemical explanation of the
antioxidant activity of flavonoid,” Chem Res Toxicol, vol. 6, pp. 1305–1312, 1996.
[7] S. a B. E. Van Acker, D. J. Van Den Berg, M. N. J. L. Tromp, D. H. Griffioen, W. P.
Van Bennekom, W. J. F. Van Der Vijgh, and A. Bast, “Structural aspects of
antioxidant activity of flavonoids,” Free Radic. Biol. Med., vol. 20, no. 3, pp. 331–342,
1996.
80. - 67 -
[8] M. J. T. J. Arts, J. S. Dallinga, H. Voss, G. R. M. M. Haenen, and A. Bast, “A critical
appraisal of the use of the antioxidant capacity ( TEAC ) assay in defining optimal
antioxidant structures,” Food Chem., vol. 80, pp. 409–414, 2003.
[9] V. Bondet, C. Berset, and W. Brand-Williams, “Kinetics and Mechanisms of
Antioxidant Activity using the DPPH • Free Radical Method,” LWT - Food Sci.
Technol., vol. 615, pp. 609–615, 1997.
[10] W. Brand-Williams, M. E. Cuvelier, and C. Berset, “Use of a Free Radical Method to
Evaluate Antioxidant Activity,” LWT - Food Sci. Technol., vol. 30, pp. 25–30, 1995.
[11] D. Villano, M. S. Fernandez-Pachon, M. L. Moya, A. M. Troncoso, and M. C. Garcia-
Parrilla, “Radical scavenging ability of polyphenolic compounds towards DPPH free
radical,” Talanta, vol. 71, pp. 230–235, 2007.
[12] P. L. Pisano, M. F. Silva, and A. C. Olivieri, “Anthocyanins as markers for the
classification of Argentinean wines according to botanical and geographical origin .
Chemometric modelling of liquid chromatography-mass spectrometry data,” FOOD
Chem., 2014.
[13] J. C. Billaut, L. Nadal-desbarats, P. F. Pradat, D. Devos, C. Moreau, C. R. Andres, P.
Emond, P. Corcia, and R. Słowin, “Comparative analysis of targeted metabolomics :
Dominance-based rough set approach versus orthogonal partial least square-
discriminant analysis,” J. Biomed. Inform., vol. 53, pp. 291–299, 2015.
[14] F. Liu, Y. He, and L. Wang, “Determination of effective wavelengths for
discrimination of fruit vinegars using near infrared spectroscopy and multivariate
analysis,” Anal. Chim. Acta, vol. 5, pp. 10–17, 2008.
