Survival and Exopolysaccharide Production of Lactic Acid ...
Transcript of Survival and Exopolysaccharide Production of Lactic Acid ...
Survival and Exopolysaccharide Production of Lactic Acid Bacteria
Grown on Grape Pomace _______________________________________
A Thesis
presented to
the Faculty of the Graduate School
at the University of Missouri
_______________________________________________________
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
_____________________________________________________
by
WARREN E. AULD
Dr. Azlin Mustapha, Thesis Supervisor
DECEMBER 2010
© Copyright by Warren E. Auld 2010
All Rights Reserved
The undersigned, appointed by the dean of the Graduate School, have
examined the thesis entitled
SURVIVAL AND EXOPOLYSACCHARIDE PRODUCTION
OF LACTIC ACID BACTERIA GROWN ON GRAPE POMACE
presented by Warren E. Auld,
a candidate for the degree of master of science,
and hereby certify that, in their opinion, it is worthy of acceptance.
Dr. Azlin Mustapha, Food Science
Dr. Ingolf U. Grün, Food Science
Dr. Mengshi Lin, Food Science
Dr. Mark Ellersieck, Statistics
To Becky
What a long strange trip it's been. The Grateful Dead
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ACKNOWLEDGEMENTS
I would like to thank Les Bourgeois Vineyards and their head
winemaker Cory Bomgaars for supplying the grape pomace used in
these experiments.
I would also like to thank Richard Oberto and Harold Huff who
patiently explained the care and feeding of a freeze drier.
My thanks to the Robert T. Marshall and Marion Fields Fellowships
for their generous assistance in funding my studies.
Many thanks to my lab mates for all your support and letting me
think out loud.
And to my commitee members, especially Dr. Mustapha, thank
you for putting up with me all these years.
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Table of Contents
ACKNOWLEDGEMENTS .................................................................. ii
LIST OF ILLUSTRATIONS .............................................................. ix
LIST OF TABLES .......................................................................... xi
Chapter
1. INTRODUCTION .................................................................... 1
2. LITERATURE REVIEW ............................................................. 4
2.1 Fiber .............................................................................. 4
2.1.1 Early definitions of fiber ........................................... 4
2.1.2 Current definitions of fiber ........................................ 6
2.1.3 Properties of fiber in a food system ............................ 9
2.1.3.1 Solubility ..................................................... 9
2.1.3.2 Viscosity ................................................... 10
2.1.3.3 Fermentability............................................ 11
2.1.4 Health benefits of fiber ........................................... 12
2.1.5 Fiber sources ........................................................ 13
2.2 Lactic acid bacteria (LAB) ................................................ 16
2.2.1 Lactic acid bacteria defined ..................................... 16
2.2.2 Divisions within LAB ............................................... 17
2.2.3 Lactic acid bacteria in food ..................................... 20
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2.2.4 LAB nutritional requirements ................................... 21
2.2.5 Lactobacillus culture media. .................................... 23
2.2.5.1 Nutritional and growth requirements of
Lactobacillus ......................................................... 24
2.3 Grape pomace ............................................................... 25
2.4 Exopolysaccharides (EPS) ............................................... 27
2.5 Composition of EPS from LAB used in this study................. 28
2.6 Fiber isolation and extraction methods .............................. 28
2.7 Structural analysis of fiber .............................................. 35
2.8 Fourier transform infrared (FTIR) spectroscopy .................. 36
2.8.1 Use of IR spectroscopy in the food industry .............. 37
2.8.1.1 Lipids ........................................................ 37
2.8.1.2 Milk .......................................................... 39
2.8.1.3 Protein ...................................................... 40
2.8.1.4 Carbohydrates ........................................... 40
2.8.2 IR spectroscopy and microbiology ............................ 42
2.8.2.1 Identification of bacterial species .................. 42
2.8.2.2 FTIR and EPS ............................................. 45
2.9 Objectives ..................................................................... 47
3. MATERIALS AND METHODS .................................................. 48
3.1 Bacterial strains ............................................................. 48
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3.2 Grape pomace ............................................................... 48
3.3 pH measurement ........................................................... 49
3.4 Preliminary studies ......................................................... 49
3.4.1 Growth on pomace (broth) ..................................... 49
3.4.2 Growth on pomace (agar) ....................................... 50
3.4.3 Further growth and preliminary survival studies ........ 51
3.4.3.1 Growth of lactic acid bacteria in pomace
solution without pH adjustment ............................... 51
3.4.3.2 Growth of lactic acid bacteria in pomace
solution with pH adjustment ................................... 52
3.5 Primary studies .............................................................. 53
3.5.1 pH Adjustment ...................................................... 53
3.5.2 Survival study ....................................................... 54
3.5.3 Exopolysaccharide (EPS) production study ................ 55
3.5.3.1 Sample preparation .................................... 55
3.5.3.2 Soluble fiber extraction ............................... 55
3.5.3.3 Freeze-drying ............................................ 56
3.5.4 Fourier transform infrared spectroscopy ................... 56
3.5.5 Sample preparation ............................................... 57
3.5.6 Sample collection .................................................. 57
3.5.7 Growth and EPS production in tomato juice .............. 59
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3.6 Statistical and spectral analyses ...................................... 60
4. RESULTS ............................................................................ 61
4.1 Preliminary Studies ........................................................ 61
4.1.1 Growth on pomace (broth) ..................................... 61
4.1.2 Growth on pomace (agar) ....................................... 61
4.1.3 Further growth and preliminary survival studies ........ 62
4.1.3.1 Growth of lactic acid bacteria in pomace
solution without pH adjustment ............................... 62
4.1.3.2 Growth of lactic acid bacteria in pomace
solution with pH adjustment ................................... 63
4.2 Primary Studies ............................................................. 69
4.2.1 pH adjustment ...................................................... 69
4.2.2 Survival Study ...................................................... 69
4.2.3 Exopolysaccharide (EPS) production study ................ 79
4.2.3.1 Soluble fiber extraction ............................... 79
4.2.4 Fourier transform infrared spectroscopy ................... 81
4.2.5 Growth and EPS production in tomato juice .............. 82
5. DISCUSSION ...................................................................... 84
5.1 Preliminary studies ......................................................... 84
5.1.1 Growth on pomace (broth) ..................................... 84
5.1.2 Growth on pomace (agar) ....................................... 85
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5.1.3 Further growth and preliminary survival studies ........ 86
5.1.3.1 Growth of lactic acid bacteria in pomace
solution without pH adjustment ............................... 86
5.1.3.2 Growth of lactic acid bacteria in pomace
solution with pH adjustment ................................... 87
5.2 Primary studies .............................................................. 88
5.2.1 Survival study ....................................................... 88
5.2.2 Exopolysaccharide (EPS) production study ................ 93
5.2.2.1 Soluble fiber extraction ............................... 93
5.2.3 Fourier transform infrared spectroscopy ................... 95
5.3 Growth and EPS production in tomato juice ....................... 98
6. CONCLUSION ................................................................... 100
APPENDIX
A. Survivor regression lines .................................................... 102
B. FTIR spectra ..................................................................... 113
C. Programs ......................................................................... 116
C.1 survival-slopes-aov.r .................................................... 116
C.2 extract.r ..................................................................... 117
C.3 peak-compare.r ........................................................... 119
REFERENCES ........................................................................... 124
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LIST OF ILLUSTRATIONS
Figure Page
3-1. AOAC method 991.43 as modified for this study.. ................... 58
3-2. Samples on a slide. ............................................................. 59
4-1. Growth of LAB on pomace (9-day trial) .................................. 64
4-2. Growth of LAB on pomace (83-day trial) ................................. 65
4-3. Growth of LAB on pH adjusted Pomace ................................... 67
4-4. Adjusted pH resulting from addition of 4M NaOH and subsequent autoclaving ............................................................... 68
4-5. Growth of L. paracasei on pH adjusted grape pomace .............. 68
4-6. Adjusted pH resulting from addition of 4 M NaOH to second
batch of pomace and subsequent autoclaving ................................ 69
4-7. Average survival curve (adjusted pH) .................................... 74
4-8. pH (pH adusted).................................................................. 75
4-9. Average survival curves (unadjusted pH) ............................... 76
4-10. pH (unadjusted) ................................................................ 77
4-11. Average slope of survivor count regression lines .................... 78
x
4-12. Average slope of pH regression lines .................................... 79
4-13. Average material recovered after extraction and freeze-drying 82
A-1. Survival and pH curves for L. paracasei ................................ 102
A-2. Survival and pH curves for L. acidophilus LA-2 ...................... 103
A-3. Survival and pH curves for L. fermentum ............................. 104
A-4. Survival and pH curves for Kefir Coccus AM-W1 .................... 105
A-5. Survival and pH curves for L. acidophilus ADH ...................... 106
A-6. Survival and pH curves for L. bulgaricus Lb-12 ...................... 107
A-7. Survival and pH curves for L. rhamnosus AM-L3 .................... 108
A-8. Survival and pH curves for L. paracasei AM-L4 ...................... 109
A-9. Survival and pH curves for L. paracasei AM-L6 ...................... 110
B-1. FTIR spectra of pH-adjusted extract samples ........................ 113
B-2. FTIR spectra of unadjusted extract samples .......................... 114
B-3. FTIR spectra of samples grown in tomato juice. .................... 115
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LIST OF TABLES
Table Page
2-1. Gums and Thickeners of microbial origin ................................ 14
2-2. Families and genera of LAB (adapted from Jay 2005). .............. 18
2-3. LAB used in AOAC vitamin bioassays. ..................................... 23
2-4. Nutritional requirements of species used in this study .............. 25
2-5. AOAC Methods for Determination of Dietary Fiber .................... 34
3-1. Bacterial strains used. .......................................................... 49
3-2. Pomace Agar ...................................................................... 51
4-1. Growth of lactic acid bacteria on pomace (agar) ...................... 62
4-2. Growth ranges (CFU/mL) of LAB in pomace solution for 83 d. ... 66
4-3. Extraction results after growth in diluted tomato juice. ............. 83
5-1. Peak Assignments ............................................................... 96
A-1. Tukey's HSD for the slopes of the survivor regression lines. .... 111
A-2. Tukey's HSD for pH-adjusted and pH-unadjusted subsets of the slopes of the survivor regression lines. .................................. 112
1
CHAPTER 1
INTRODUCTION
The food industry is continuously seeking new ingredients for use
as thickeners, emulsifiers, or dietary fiber. Historically, these
ingredients have largely come from plants. However, microbial
production of such ingredients have been explored. For example,
xanthan gum, which was approved by the Food and Drug
Administration (FDA) in 1969 for use as a thickening agent
(Sutherland 2004) is produced by growing the bacterium,
Xanthamonas campestris, on sucrose or glucose (Sutherland 2001).
Other thickeners produced by microorganisms include gellan and
curdlan which are also approved for use in food (Sutherland 2001).
Dietary fiber is a functional ingredient that has received much
attention recently. In addition to its properties within food, a diet high
in fiber is believed to provide a number of health benefits. These
include improved laxation, and possible reduction of serum cholesterol.
Fermentable fibers can supply nutrients for friendly (probiotic) bacteria
that reside in the colon. The Food and Nutrition Board of Institute of
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Medicine (FNB/IOM) has recommended a daily intake of 14 g/1000
kcal of fiber based on epidemiological and clinical data suggesting that
adequate fiber may reduce the risk of coronary heart disease (Otten
and others 2006). Cardiovascular disease is the cause of 1.4 million
deaths and 865,000 heart attacks annually in the United States
(American Heart Association 2005).
The concept of converting food industry waste by-products into
something valuable is not new. In many cases, the waste is simply
milled into a powder for direct addition to food (Laufenberg and others
2003, Larrauri 1999). This approach has been considered for using
grape pomace as a food additive (Valiente and others 1995; Martin-
Carron and others 1997; Goni and others 2005). Sutherland (1996)
notes that many bacteria which produce EPS, including X. campestris,
do not have strict dietary requirements and are capable of secreting a
number of enzymes which can hydrolyze molecules such as cellulose,
pectin, amylose and protein. Whey from cheese production, at one
time considered an inconvenient waste product, is now exploited as a
source of whey protein, lactose, and as feedstock for biogas and
single-cell protein fermentations, as reviewed by Marwaha and
Kennedy (1988) and Gonzalez Siso (1996). More recently, work has
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been done to utilize whey in alcohol production (Athanasiadis and
others 2002; Kargi and Ozmihci 2006).
There are two reasons for believing that microbial conversion is a
superior method for recycling such waste products back into the food
supply. First, the product is an odorless, tasteless, light colored
powder which does not impart the flavor of the substrate to whatever
it is added to. In addition, this product is consistent. Our input
substrates are agricultural products which vary from season to season
and field to field. Second, the enzymes which catalyze the catabolic
reactions which assemble EPS produce very specific stereochemistry
which can be difficult to replicate. There is evidence which suggests
that the quantity and composition of the product depends not only on
bacterial species (De Vuyst and Degeest 1999, Laws and others 2001,
Sutherland 2001) but the substrate as well (Mozzi and others 2001).
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CHAPTER 2
LITERATURE REVIEW
2.1 Fiber
2.1.1 Early definitions of fiber
One of the earliest mentions of fiber as a necessary dietary
component is found in an account by Dr. William Beaumont (1834), a
US Army surgeon. The book recounts Dr. Beaumont's treatment of his
patient, Alexis St. Martin, who suffered a wound which, after healing,
left a permanent opening leading into his stomach. Regarding fiber,
Dr. Beaumont says,
The quality of nutriment is a matter of considerable importance in dietetic regulations. Bulk is, perhaps, nearly
as necessary to the articles of diet as the nutrient principle. They should be so managed that one should be
in proportion to the other. Too highly nutritive diet is probably as fatal to the prolongation of life and health, as
that which contains an insufficient quantity of nutriment. (Beaumont 1834)
What Beaumont is describing would later become known as the
dietary fiber hypothesis. More modern versions of the hypothesis
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state that there is a link between a diet with adequate fiber intake and
a low incidence of diabetes, heart disease, some forms of cancer and
chronic bowel disorder (Dreher 2001).
Beaumont's idea had little impact on the scientific community for
some time. For instance, Atwater, working in the latter half of the 19th
century, included fiber, ash and moisture content in his food analyses
but discounted them as acaloric and therefore unimportant (Carpenter
2003). A century after Beaumont's book was published, Hipsley
(1953) suggested a link between dietary fiber and the pregnancy
diseases eclampsia and toxemia. In this paper, Hipsley coined the
term "dietary fiber" and defined it to mean lignin, cellulose and
hemicellulose, what we would now term insoluble dietary fiber.
The next major step was taken by Trowell (1972) who defined
dietary fiber as "the skeletal remains of plant cells that are resistant to
hydrolysis by the enzymes of man." At the time, Trowell believed that
dietary fiber components were only located in plant cell walls (Trowell
and Burkitt 1987). Trowell and others (1976) would later expand this
definition to include "plant polysaccharides and lignin which are
resistant to hydrolysis by the digestive enzymes of man".
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2.1.2 Current definitions of fiber
At the time of this writing, there are numerous competing
definitions for dietary fiber. The Institute of Medicine/Food Nutrition
Board (IOM/FNB) included an extensive historical collection of
definitions of dietary fiber in their report on a proposed definition of
dietary fiber (IOM 2001). The variation reflects changes in our
understanding of dietary fiber over time and the particular interests
and weltanschauung (world-view) of the people writing the definitions
(Prosky 2001). Here we present a small sample of some of the
approaches taken.
The American Association of Cereal Chemists (AACC) has
adopted the following definition:
Dietary fiber is the edible parts of plants or analogous carbohydrates that are resistant to digestion and
absorption in the human small intestine with complete or partial fermentation in the large intestine. Dietary fiber
includes polysaccharides, oligosaccharides, lignin, and associated plant substances. Dietary fibers promote
beneficial physiological effects including laxation, and/or blood cholesterol attenuation, and/or blood glucose
attenuation. (AACC 2001)
The definitions given in the Dietary Reference Intakes published
by the Institute of Medicine/Food Nutrition Board in cooperation with
Health Canada are as follows:
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Dietary fiber consists of nondigestible carbohydrates and
lignin that are intrinsic and intact in plants.
Functional fiber consists of isolated nondigestible carbohydrates that have beneficial physiological effects in
humans.
Total fiber is the sum of dietary fiber and functional fiber. (Otten and others 2006)
The AACC responded to this definition with an article titled "All
Dietary Fiber Is Fundamentally Functional" which included the
following summary:
The recent publication of definitions relating to Dietary
Fiber by the Institutes of Medicine are not supported by current science on the subject. These definitions will not
have the desired objective of increasing both consumer knowledge about and consumption of dietary fiber.
Instead, they will result in confusion regarding health messages about dietary fiber. AACC believes a science-
based definition and research directions are needed to
optimize use of resources to promote healthier life styles utilizing accurate and clear consumer information. (AACC
2003)
These definitions, while in many ways similar, illuminate major
differences between these groups originating from their underlying
organizational purposes. Both groups seek to advance their own
agenda through the adoption of their definition1.
1 No value judgement regarding either agenda is intended.
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In the United States, the Food and Drug Administration pursuant
to the Nutrition Labeling and Education Act of 1990 (NLEA) has
given us a de facto definition of fiber:
The nondigestible dietary fiber will be determined by the
method "Total Dietary Fiber in Foods, Enzymatic
Gravimetric Method, First Action." [sic] in the Journal of the Association of Official Analytical Chemists (JAOAC),
68:399, 1985, as amended in JAOAC, 69:370, 1986. Both methods are incorporated by reference. (FDA 1987)
The method referred to in this definition is now called "AOAC
INTERNATIONAL Enzymatic-Gravimetric Method 985.29" (FDA 2007).
According to this definition, the insoluble carbohydrate fraction
combined with that portion of the soluble carbohydrate fraction which
can be precipitated with ethanol is considered dietary fiber (Horwitz
and Latimer 2007). The potential exists for large errors from this
method due to precipitation of non-SDF components as well as
incomplete precipitation of the SDF fraction (Mañas and Saura-Calixto
1993). Southgate (1969) highlighted the underlying problem by
saying "Procedures for the determination of available carbohydrates in
foods are concerned with the measurement of a relatively small
number of well-defined substances. This is not the case as far as the
unavailable carbohydrates are concerned; they form a group of
heterogeneous substances whose chemistry is, in many cases, poorly
9
understood." In addition, inulin and oligofructose which are often
classified as dietary fiber are not recovered by the AOAC method
(Coussement 1999) as well as resistant starch (Asp 1987). AOAC
Method 985.29 was designed as a compromise between speed of
analysis and accuracy of results (Prosky 2001).
2.1.3 Properties of fiber in a food system
Fiber has a number of interesting properties within a food
system. The most important of these are solubility, viscosity and
fermentability.
2.1.3.1 Solubility
Solubility in this context has the usual meaning in chemistry;
how much of one substance can be disolved in a given quantity of
another substance (Daintith 2000). Pectin, gums, and -glucans are
considered soluble fibers while cellulose, lignin, chitin and chitosan are
insoluble. Hemicellulosic fibers can fall into either category depending
on the material (Gropper and others 2009). Solubility plays a role
both in our definition (2.1.2) and analysis of fiber (2.6). From a
product development perspective, solubility can be an important
10
characteristic when selecting an additive. If the end product is
intended to be smooth and creamy or a liquid, insoluble fiber would
not be an appropriate choice. Bonnand-Ducasse and others (2010)
found that the addition of insoluble dietary fiber improved the water
holding capacity of dough and shorten the time for optimum dough
development while addition of soluble dietary fiber altered the
Newtonian viscosity and shear rate. Peressini and Sensidoni (2009),
working with inulin2, found changes in the viscoelastic and
breadmaking characteristics of dough depended on both the quantity
and type of additive used.
2.1.3.2 Viscosity
Viscosity is a measure of a material's resistance to shear stress
(Singh and Heldman 2009). Less formally, viscosity refers to the
thickness of a liquid; water, vegetable oil and honey have increasing
thickness and thus increasing viscosity. The food industry uses gums
and pectins to thicken and stabilize foods (Potter and Hotchkiss 1998).
In some instances viscous fiber serves to both improve the rheological
properties of the food product and fortify the food with additional fiber.
2 A soluble dietary fiber additive derived from chicory and Jerusalem
artichoke (Peressini and Sensidoni 2009).
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For example, Chen and others (2010) added soluble fiber derived from
okara as thickeners in dairy-based products. Viscous fiber can also be
used as a fat replacer. Inglett and others (2004) reported success
replacing coconut cream with Ricetrim in Thai foods. Morin and others
(2002) suggested the use of -glucan gum in the manufacture of
reduced-fat sausages. Nutritionally, viscosity is related to water-
binding and gel formation, both of which tend to slow gastric emptying
and digestion which increase the transit time through the digestive
system. At the same time, the increased viscosity also reduces
enzymatic access to nutrients in the lumen of the small intestine
leading to reduced absorption (Gropper and others 2009)3.
2.1.3.3 Fermentability
As was noted above, fiber is not digested by the enzymes of the
human small intestine. The large intestine, however, contains an
ecosystem made up of trillions of bacteria (Fraune and Bosch 2010)4
and some dietary fiber is fermentable by these bacteria (Dongowski
and others 2000; El Oufir and others 2000; Park and Floch 2007;
3 Insoluble fiber can also limit absorption by acting as a physical barrier (Gropper and others 2009). 4 This is roughly ten times the number of somatic cells in the human body (Fraune and Bosch 2010). As one commentator put it, "we're all just petri
dishes with shoes" (anyletter 2010).
12
Resta 2009; Sahakian and others 2010; Mirande and others 2010).
The products of this fermentation include lactate and short chain fatty
acids (SCFA). Both serve to acidify the intestinal lumen leading to a
reduction in secondary bile synthesis. SCFA increase sodium and
water absorption and encourage mucosal cell growth and possibly
reduce the risk of some cancers (Gropper and others 2009).
Fermentable fiber may also play a role in the control of obesity
(Keenan and others 2006; Venema 2010).
2.1.4 Health benefits of fiber
In addition to their structural and textural roles in food, fiber
may also confer health benefits on the consumer. Possible benefits
include improved laxation, reduced risk of obesity by promoting
satiety, normalization of blood lipids and reduced risk of coronary
heart disease (Otten and others 2006). Soluble fiber may also have a
prebiotic effect which promotes the the growth of probiotic bacteria
(reviewed by Slavin and others 2009).
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2.1.5 Fiber sources
Most edible plant materials, fruits, nuts, vegetables, pulses and
grains, contain dietary fiber. This is true of both unrefined and refined
plant materials. However, the latter will generally have a lower fiber
content (Otten and others 2006). Processing operations such as
milling wheat into flour or juicing apples remove components such as
bran and apple pomace, thus lowering the fiber content of the final
product (Potter and Hotchkiss 1998). The material removed may be
added as an enrichment to the same or a different product; however,
the desired physiological effects will be reduced when compared to
those of the intact plant material (Gropper and others 2009).
As a rule, animal products are not sources of fiber. The
exceptions are chitin, made from the shells of marine crustaceans and
its deacetylated derivative chitosan (Aranaz and others 2009; Gropper
and others 2009)5. Japan and Korea permit the use of chitin and
chitosan as food additives (Korea 2004; JFCRF 2007; Aranaz and
others 2009). They are not permitted for use as food additives in the
United States, the EU or by the Codex Alimentarius (Aranaz and others
2009).
5 The first and second most abundant biopolymers found in nature are
cellulose and chitin (Aranaz and others 2009).
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Many of the thickeners and gums used by the food industry are
also classified as dietary fiber. Among these are plant materials such
as pectins, galactomannans, plant exudates and products derived from
marine algae (Belitz and others 2004; Daniel and others 2007). Also
included in this group are a number of products derived from
microorganisms (Table 2-1).
Table 2-1 Gums and Thickeners of microbial origin
Product Source
Agrobacterium succinoglycan (e) Agrobacterium sp.
Bacillus natto gum (e) Bacillus natto6
Curdlan (a) Alcaligenes faecalis var. myxogenes
Gellan gum (b) Pseudomonas elodea
Levan (e) Bacillus subtilis
Macrophomopsis gum (e) Macrophomopsis sp.
Pullulan (d) Aureobasidium pullulans
Rhamsan gum (e) Alcaligenes sp.
Sclero gum (e) Sclerotium glucannicum
Welan gum (e) Alcaligenes sp.
Xanthan gum (c) Xanthamonas campestris
(a) FDA 2006, (b) FDA 2008a, (c) FDA 2008b, (d) Hayashibara 2002,
(e) JETRO 2009
6 Probably a synonym for Bacillus subtilis (Buchanan and Gibbons 1974).
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Which microbial polysaccharides are permitted for use as food
additives depends on both where the food is made and where it is to
be sold. Xanthan gum, gellan gum, curdlan and pullulan have been
granted GRAS status in the United States (EAFUS 2010; Hayashibara
2002). These four additives are also listed as permissible in the Codex
Alimentarius in the General Standard for Food Additives (GFSA online
2010) and the Korea Food Additives Code (Korea 2004). Current
Canadian, Australian and New Zealand regulations only permit addition
of xanthan and gellan gums (Food Additive Dictionary 2006; Food
Standards Code 2010) while those in the European Union permit
xanthan and gellan gums and pullulan (European Parliament 2006).
In addition to the four microbial polysaccharides permitted in the
United States, Japanese regulations also permit the use of
agrobacterium succinoglycan, levan and bacillus natto,
macrophomopsis, rhamsan, sclero and welan gums (JFCRF 2007)7.
7 A USDA Foreign Agriculture Service report indicates that the Japanese
government is in the process of rescinding approval for levan and sclero gum for use as food additives. The basis for the change is the belief that these
additives are not in current use for foods sold in Japan (USDA 2009).
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2.2 Lactic acid bacteria (LAB)
2.2.1 Lactic acid bacteria defined
The term lactic acid bacteria (LAB) refers to a group of Gram-
positive bacteria, all of which produce lactic acid as the primary end-
product of hexose fermentation (Jay and others 2005). Historically,
LAB were all classified within four genera: Lactobacillus, Leuconostoc,
Pediococcus and Lactococcus (Carr and others 2002). The use of 16S
rDNA to revise the phylogenetic tree (Woese and Fox 1977) has
increased the number from the original 4 to 13 genera (Jay and others
2005)8. These genera (Table 2-2) are all members of the order
Lactobacillales which along with the order Bacillaceae make up the
class Bacilli (De Vos and others 2009)9. The order includes six families.
In addition to the families shown in Table 2-2, Lactobacillales also
contains the family Aerococcaceae which does not include LAB. The
species Lactococcus, Leuconostoc, Streptococcus and Lactobacillus are
used in the dairy industry (Walstra and others 2006). Lactobacillus,
8 Aarnikunnas (2006), citing Axelsson, gives the number of genera as 20. 9 The class name, Bacilli, is derived from the Latin word baculum meaning a
rod or stick (Morris 1969) and refers to the appearance of many of these organisms when viewed through a microscope. Yet a number of the genera
included in this class are coccus, a name derived from the Greek word for berry (Morris 1969) and again referring to the appearance of the organism under the microscope. Originally the class Bacilli was expected to contain
only rod-shaped species. Later changes based on biochemical tests and 16S rDNA rather than morphology have made our system of nomenclature quite
perplexing at times.
17
Leuconostoc and Pediococcus cerevisae are used in the production of
fermented meat products (Pearson and Gillett 1999).
2.2.2 Divisions within LAB
LAB are divided into two groups, as suggested by Orla-Jensen,
based on the nature of their metabolic products when fermenting
glucose. The terms homo- and heterofermentive were later suggested
by Kluyver and Donker (Nelson and Werkman 1935).
Homofermentative LAB produce a single metabolite, lactic acid using
the Embden-Meyerhof (glycolysis) pathway to produce pyruvate (De
Vos and others 2009). Pyruvate is then reduced to lactate by the
oxidation of NADH to NAD+; the latter is then recycled for use in
further glycolysis (Nelson and Cox 2005). For each mole of glucose
consumed, two moles of lactate are formed.
Heterofermentative LAB ferment glucose using the 6-
phosphogluconate/phosphoketolase (6-PG/PK) pathway (Zalán and
others 2010)10. Here, glucose is decarboxylated to ribulose, yielding
one molecule of CO2. Ribulose is then cleaved by phosphoketolase
10 This pathway is also refered to as the Phosphogluconate pathway which is another name for the Pentose Phosphate Pathway. Both share a series of
enzymes which oxidize glucose-6-phosphate to 6-phosogluconate which is then decarboxylated to form ribulose-5-phosphate. In the Pentose
Phosphate Pathway, ribulose-5-phosphate goes through a series of catabolic
18
Table 2-2 Families and genera of LAB (adapted from Jay 2005).
Family Genus
Carnobactriaceae Carnobacterium
Lactosphaera11
Enterococcaceae Enterococcus
Tetragenococcus
Vagococcus
Lactobacillaceae Lactobacillus
Paralactobacillus
Pediococcus
Leuconostocaceae Leuconostoc
Oenococcus
Weissella
Streptococcaceae Streptococcus
Lactococcus
into a glyceraldhyde molecule (3 carbons) which can enter glycolysis
and ultimately become lactate and acetate (2 carbons) which can be
used to reoxidize NADH to NAD+ by converting the acetate to ethanol
reactions which yield sugars ranging in length from 3 to 7 carbons while in
the 6-PG/PK pathway, the 5 carbon molecule is split into a 3-carbon and a 2-carbon molecule. 11 Janssen and others (1995) proposed the transfer of Ruminococcus
pasteurii into a new genus, Lactosphaera. Subsequent work by Liu and others (2002) has led to the transfer of the sole member of this new genus,
Lactosphaera pasteurii, to the genus Trichococcus.
19
(Aarnikunnas 2006). Thus, for each mole of glucose consumed, 1
mole of CO2, 1 mole of lactate and 1 mole of ethanol are formed.
In addition to differing metabolites, these pathways yield
differing amounts of energy. Homofermentative LAB route both 3-
carbon halves of the original glucose through the Embden-Meyerhof
pathway and as a result, extract twice as much energy (Jay and others
2005; De Vos and others 2009).
LAB may be further divided into obligate homofermentative,
facultative heterofermentative and obligate heterofermentative
species. Obligate homofermentative species ferment hexoses to
lactate but cannot ferment pentoses. Facultative heterofermentative
species also ferment hexoses to lactate and they can also be induced
to ferment pentoses. The products of their pentose fermentation do
not include CO2 as there is no decarboxylation from hexose to pentose
required. Obligate heterofermentative species always ferment hexoses
into equimolar quantities of lactate, acetate (or its byproducts) and
CO2 (Jay and others 2005; Corsetti and Settanni 2006; De Vos and
others 2009).
20
2.2.3 Lactic acid bacteria in food
Preserving food from periods of plenty for use in periods of
scarcity has long been crucial in human survival. Among the various
preservation strategies adopted, fermentation is one of the oldest
(Steinkraus 1996a; Prajapati and Nair 2008). Even today,
fermentation by LAB is an important step in production and
preservation of many foods from around the world (reviewed in
Steinkraus 1996b). Many of these fermentations also serve as a
means of vitamin enrichment (Steinkraus 1998)12.
Numerous dairy products are made using LAB which contribute
flavor, help preserve the product, thicken and, in the case of cheese
manufacture, assist in curd formation (Oberman and Libudzisz 1998;
Stanley 1998; Walstra and others 2006; Bertolami and Farnworth
2008; Farnworth and Mainville 2008; Van de Water and Naiyanetr
2008; Miyazuki and Matsuzaki 2008; Vinderola and others 2008; Heller
and others 2008). Vegetables may also be fermented with LAB to
produce products such as sauerkraut and kimchee. Again the primary
goal of this fermentation is to preserve and add flavor (Harris 1998;
Surh and others 2008; Molin 2008; Holzapfel and others 2008; Hamdi
12 Many food fermentations involve mixed cultures or sequential fermentations making difficult to know without further study, which organism
is producing the vitamin.
21
2008; Hsieh and others 2008). LAB are used in the production of
fermented sausages to lower the pH of the product for better
preservation and their contribution of flavor compounds (Lücke 1998;
Pearson and Gillett 1999; Hammes and others 2008). LAB are used in
the production of grain products. In the United States, the most
familiar of these products is sourdough bread whose flavor and
keeping qualities are directly attributable to LAB (Hammes and Gänzle
1998; Vogel and others 1994; Corsetti and others 1998; Corsetti and
Settanni 2006; Kam and others 2007). LAB also play important roles
in the production of cocoa, coffee and tea (Fowler and others 1998),
the production of congeners for whiskey (Varnam and Sutherland
1994) and the malolactic fermentation of wine (Fleet 1998; Uthurry
and others 2006; Bloem and others 2007; García-Ruiz and others
2008).
2.2.4 LAB nutritional requirements
As discussed above, LAB can ferment hexose and
heterofermentative species can also ferment pentose. Some LAB can
make use of organic acids including citric, tartaric, succinic and malic
acids to produce energy for the cell (De Vos and others 2009). The
22
process by LAB of converting malic acid to lactate, referred to as
malolactic fermentation, is an important step in winemaking (Fleet
1998). The acids are usually degraded through oxaloacetic acid to
pyruvate. Lactobacillus fermentum lacks the malolactic enzyme (mle)
and instead converts malate to fumerate to succinate following a
reversal of part of the tri-citric acid cycle (De Vos and others 2009).
In addition to these sources of energy and carbon, LAB require other
growth factors. These are fastidious bacteria which have a long
history of being difficult to grow on well defined, simple media (Kulp
1927). Additional growth factors include amino acids, purine and
pyrimidine base and B vitamins (Jay and others 2005). Researchers
used this requirement to identify and characterize B vitamins such as
folate (Mitchell and others 1941)13 which was at one time refered to as
Lactobacillus casei factor (Johnson 1946). LAB continue to be used in
standard AOAC bioassay methods as shown in Table 2-3 (Horwitz and
Latimer 2007).
13 "Four tons of spinach have been extracted and carried through the first
stages of concentration." Mitchell and others 1941.
23
Table 2-3 LAB used in AOAC vitamin bioassays.
Species Analyte
Lactobacillus leichmannii (ATCC 7830) Cobalamin (B-12)
Streptococcus faecalis (ATCC 8043) Folic Acid
Lactobacillus plantarum (ATCC 8014) Niacin and Pantothenic Acid
Lactobacillus casei subsp. rhamnosus
(ATCC 7469)
Riboflavin (B-2)
2.2.5 Lactobacillus culture media.
A number different culture media have been tried for use with
LAB (reviewed in Schillinger and Holzapfel 2003). Growth is generally
poor on those media composed of only simple carbohydrates and
protein digests (Kulp 1927). It was found that supplementing agar
medium containing peptone with tomato juice produced satisfactory
growth due to an "accessory substance" in the tomato juice (Kulp
1927; Kulp and White 1932). Tomato juice agar using a slightly
revised formula14, along with tomato juice broth are continue to be
used for the maintenance and growth of Lactobacillus. The latter is
made with yeast extract rather than peptonized proteins and includes
a number of inorganic salts (Difco 1998). AOAC bioassays specify the
use of "Lactobacilli broth/agar AOAC" which are further refinements of
tomato juice broth and agar (Difco 1998; Horwitz and Latimer 2007).
14 Peptonized milk is added in addition to peptone.
24
De Man and others (1960) proposed a media which while still not
completely defined, more closely approximates the minimum
nutritional requirements of Lactobacillus. These media are today
refered to as MRS broth and agar (Difco 1998).
2.2.5.1 Nutritional and growth requirements of Lactobacillus
Of the nine species used in this study, eight belong to the genus
Lactobacillus representing five species15. Lactobacilli are generally
mesophillic and aerotolerant. They are also aciduric and some species
are capable of growing at a pH as low as 3.6. Most members of this
genus require pantothenic and nicotinic acid. Heterofermentative
species require thiamine. The specific nutritional requirements of the
species used here (which may vary by strain) as outlined in Bergey's
Manual of Systematic Bacteriology (De Vos and others 2009) are
shown in Table 2-4.
15 The ninth, a wild isolate from keffir, is of unknown genus.
25
2.3 Grape pomace
Pomace is the material leftover from juice or oil extraction
(Daintith 2000)16. In the manufacture of wine, grape pomace
primarily includes the skins and seeds which are separated from the
Table 2-4 Nutritional requirements of species used in this study
Fermentation Species Requirements
Homofermentative L. bulgaricus Cobalamin
Folic Acid Pantothenic Acid
Thymidine
L. acidophilus Folic Acid
Niacin
Pantothenic Acid Riboflavin
Facultative Heterofermentative
L. paracasei Folic Acid Niacin
Pantothenic Acid Riboflavin
L. rhamnosus Folic Acid
Niacin Pantothenic Acid
Riboflavin
Obligate
Heterofermentative
L. fermentum Niacin
Pantothenic Acid Thiamine
juice either before or after primary fermenation as is the case for white
and red wines respectively (Belitz 2004). Sufficient sugars remain in
the pomace for a second fermentation which can then be distilled
16 Pomace is also the name of a grade of olive oil produced by solvent
extraction.
26
producing grappa or marc17 (McGee 2004). Another biproduct of
pomace is grape seed oil (Pickett 1940) which contains a number of
antioxidants (Wie and others 2009; Xu and others 2010). Grape
pomace extract also contains antioxidants (Wang, Tong and others
2010) which may be of use in the treatment or prevention of diabetes
(Hogan and others 2010) and some cancers (Torres and others 2002;
González-Paramás and others 2004; Cai and others 2010). The use of
grape pomace as a dietary fiber additive has also been proposed
(Valiente and others 1995; Martin-Carron and others 1997; González-
Centeno and others 2010).
The composition of grape pomace varies depending on the
variety of grape used. Bravo and Saura-Calixto (1998), reported the
dry weight of red Cencibel grape skins contains an average of 54.16%
dietary fiber, 26.86% condensed tannins, 14.39% protein, 6.87% fat,
9.19% ash, 2.80% soluble sugars and 3.76% soluble polyphenols18.
For white Airén grape skins, the same group reported 59.04% dietry
fiber, 20.47% condensed tannins, 11.60% protein, 7.78% fat, 6.89%
ash, 2.71% soluble sugars and 4.48% soluble polyphenols. Airén
grape seeds contained 56.17% dietary fiber, 15.96% condensed
17 Grape marc is also a synonym for grape pomace (Cortés and others 2010). 18 Some proteins and condensed tannins are also included in the total dietary
fiber percentage.
27
tannins, 12.23% protein, 12.41% fat, 5.70% soluble sugars and
5.22% soluble polyphenols as percentages of dry weight19. Goni and
others (2005) reported the insoluble fraction of Airén grape skins to
contains 77.2% dietary fiber, 13.9% protein and 3.10% extractable
polyphenols. Seeds were found to contain 78.9% dietary fiber, 13.6%
protein and 3.61% extractable polyphenols (dry weight basis).
2.4 Exopolysaccharides (EPS)
Exopolysaccharides are polysaccharides produced by bacteria.
EPS are found outside the cell wall and may remain close to the cell
wall forming a capsule or detach from the cell to form an extracellular
slime (Sutherland 1977). EPS are composed of repeated polymer
subuits assembled within the cell. A membrane-bound lipid carrier is
used as an assembly base and serves to tether the subunit to the
inside of the cell wall until it is completed and transported to the
exterior of the cell. Once outside, the EPS subunit is attached to
another subunit forming an extended polymer. Laws and others
(2001) provide a more detailed description of this process focusing
specifically on LAB. See Guo and others 2008 review the details of the
three primary pathways used for biosynthesis of most microbial
19 Pomace material used in this and the following study were not fermented.
28
polysaccharides. There are a number of functions attributed to EPS.
It can moderate the effects of of dessicating conditions and protect the
cell from toxins and attack. It also provides the mechanism whereby a
cell can attach to surfaces (De Vuyst and Degeest 1999; Wingender
and others 1999).
2.5 Composition of EPS from LAB used in this study
LAB produces one of two types of EPS: homopolysaccharides and
heteropolysaccharides (De Vuyst and Degeest 1999). The former
group includes glucans and fructans which are each composed of a
single type of monosaccharide. The literature includes descriptions of
strains of four of the species used in this study.
2.6 Fiber isolation and extraction methods
AOAC has approved six methods related to determination of
dietary fiber content (Horwitz and Latimer 2007). These methods are
listed in Table 2-5. All of those listed except 993.2120 are enzymatic-
gravimetric. One of the methods is designed to determine the amount
20 This nonenzymatic method is intended for foods containing more than 10% fiber and less than 2% starch (dry basis) such as fruits, vegetables and fiber
isolates (Horwitz and Latimer 2007).
29
of insoluble dietary fiber (IDF) in a sample. Another is designed to
find the soluble dietary fiber (SDF). The remaining four methods are
intended to determine the total dietary fiber (TDF) in a sample. While
the details vary, all of these methods share a common logic.
To begin with, the sample is dried, ground and weighed. Fat
should be removed if it constitutes a significant fraction (>10%
usually) of the dry sample weight. Enzymatic methods digest the
sample with alpha-amylase, amyloglucosidase and protease to
solublize starch and proteins. Filtration of the sample at this point will
capture the IDF in the retentate which can be washed, dried and
weighed. SDF in the filtrate can be recovered by ethanol precipitation,
filtered, dried and weighed. If proximate analysis is required, the
extracts may be ashed and reweighed.
In addition to the AOAC methods, other methods have been
investigated for soluble fiber extraction and analysis. Theander and
Westerlund (1986) proposed a method in which the sample is first
dried and ground, then sequentially sonicated with ethanol and
petroleum ether to remove simple sugars and lipids. The sample is
rehydrated and treated with amylase and amyloglucosidase to
hydrolyze any starches present and then centrifuged. The solids
30
L. paracasei (cited in DeVuyst and Degeest 1999)
3)--D-GalpNAc-(14)--D-Galp-(16)--D-Galp-(16)--D-Galp-(1 3
sn-glycerol-3-phosphate
L. paracasei (cited in Laws and others 2001)
-D-Glcp 1
6
6)--D-Galp-(13)--L-Rhap-(14)--D-Glcp-(14)--D-GlcpNAc-(1 3
1
-L-Rhap
Glc glucose GlcNAc N-acetyl glucosamine Gal galactose GalNAc N-acetyl galactosamine
Rha rhamnose f furanose
p pyranose
31
L. acidophilus LMG9433 (cited in DeVuyst and Degeest 1999)
-D-GlcpNAc 1
3
4)--D-GlcpA-(16)--D-Glcp-(14)--D-Galp-(14)--D-Glcp-(1
L. acidopilus 5e2 (Laws and others 2008)
-D-Galp 1
6
6)--D-Glcp-(13)--D-Glcp-(13)--D-GlcpNAc-(13)--D-Galp-(1 6
1
-D-Galp-(14)--D-Glcp
32
L. delbrueckii subsp. bulgaricus 291 (cited in Laws and others 2001)
-D-Galp-(14)--D-Glcp 1
6
4)- -D-Galp-(14)--D-Glcp-(14)--D-Glcp-(1
L. delbrueckii subsp. bulgaricus rr (cited in DeVuyst and Degeest 1999)
-D-Galp -D-Galp -L-Rhap 1 1 1
3 4 3
2)--D-Galp-(13)--D-Glcp-(13)--D-Galp-(14)--D-Galp-(1
L. delbruekii ssp bulgaricus LBB.B26 (Sánchez-Medina and others 2007) 1
6
3)--D-Galp-(13)--D-Galp-(14)--D-Glcp-(13)--D-Galf-(1
33
L. rhamnosus RW 9595 M and R (cited in Laws and others 2001)
HOOC 6
R -D-Galp
H3C 4 1
2
3)--L-Rhap-(13)--L-Rhap-(12)--D-Glcp-(13)--L-Rhap-(13)--D-Glcp-(13)--L-Rhap-(1
L. rhamnosus C83 (cited in Laws and others 2001)
6)--D-Galp-(16)--D-Glcp-(13)--D-Galf-(13)--D-Glcp-(12)--D-Galf-(1
L. rhamnosus GG (cited in Laws and others 2001)
-D-Galf 1
6
3)--L-Rhap-(13)--D-Galp-(13)--D-Galf-(13)- -D-Galp(14)--D-GlcfNAc-(1
34
Table 2-5 AOAC Methods for Determination of Dietary Fiber
AOAC 991.42 Insoluble Dietary Fiber in Food and Food Products
AOAC 993.19 Soluble Dietary Fiber in Food and Food Products
AOAC 991.43 Total Soluble and Insoluble Dietary Fiber in Food
AOAC 992.16 Total Dietary Fiber
AOAC 985.29 Total Dietary Fiber in Foods
AOAC 993.21 Total Dietary Fiber in Food and Food Products with
<2% starch
consist of the IDF fraction of the sample. To recover the SDF, the
supernatant is dialysed using filters with a molecular cut-off of 12000-
14000 Daltons. This addition of a protein digestion step to the method
was shown to increase the quantity of SDF recovered (Marlett and
others 1989). The Southgate method provides a more detailed
analysis of a sample with results including free sugars, the hexose,
pentose, uronic acid and starch composition of the soluble fiber
fraction and a similar compositional analysis of the insoluble fiber
fraction including also lignin, cellulose and ash (Southgate 1969). As
noted earlier, these more complete analytical methods were passed
over in favor of a more expeditious method (Prosky 2001).
35
2.7 Structural analysis of fiber
Cellulose, starch and EPS are all polysaccharides. What
differentiates them is the proportion of monosaccharides in their
composition and how those molecules are connected (Nelson and Cox
2005). Hydroxyl groups on the monosaccharide units may be
substituted with structures including acetyl, phosphate, sulphate or
amino groups (Cui 2005; Nelson and Cox 2005). To determine the
monosaccharide composition of a polysaccharide, an inorganic acid is
used to hydrolyze the linkages betweeen monosaccharide units (Pazur
1986; Brummer and Cui 2005). The composition may then be
analyzed using paper or thin-layer chromatography, ion-exchange
(Pazur 1986), GLC or HPLC (Pazur 1986; Brummer and Cui 2005).
The composition and sequence of short repeating polysaccharides
(such as those from bacterial) may also be determined using NMR
(Sletmoen and others 2003).
Once the constituents are established, the next tasks are to
determine the sequence of the components, their linkages and
anomeric configurations and the locations of substitutions (Cui 2005).
Unlike properly formed proteins which have fixed regular structures,
those of polysaccharides can vary. Glycogen includes branches which
occur every 8 to 12 units (Nelson and Cox 2005). Branches in
36
amylopectin occur in clusters rather than at regular intervals (Jane
2007). Linkage patterns are typically determined by methylation of
the free hydroxyl groups in the intact polysaccharide which is then
hydrolyzed and the residues analyzed using GC-MS (Pazur 1986;
Sletmoen and others 2003; Cui 2005). Periodate oxidation in
conjuction with TLC or GC (Pazur 1986) or FAB-MS, MALDI-MS or NMR
(Cui 2005) may be used for sequence determination and to gain
further structural and linkage information. Anomeric configuration
may be determined by enzymatic hydrolysis, chromium trioxide
oxidation (Pazur 1986; Cui 2005) or Smith degradation21 (Cui 2005).
Anomeric configuration may also be determined by NMR analysis of
oligosaccharide fragments of the original polysaccharide (Sletmoen
and others 2003).
2.8 Fourier transform infrared (FTIR) spectroscopy
Spectroscopy is the "study of interaction of electromagnetic
waves and matter" (Banwell 1972). Infrared (IR) spectroscopy is
concerned with the study of electromagnetic waves in the infrared
portion of the spectrum interact with matter. The infrared portion of
the spectrum extends from the near-infrared (NIR) which abuts the
21 Periodation, reduction and mild acid hydrolysis (Cui 2005).
37
visible portion of the spectrum, to the far-infrared (FIR) which is just
below the microwave region of the spectrum (Günzler and Gremlich
2002). FTIR is distinguished from other forms of IR spectroscopy by
its instrumentation. FTIR spectrophotometers use a broadband IR
source, modulated by an interferometer, to collect data in the time
domain (mirror displacement vs intensity). A Fourier transform
algorithm is then used to convert the data to a spectrum in the
frequency domain (wavenumber vs absorbance) (Smith 1996).
Multiple spectra are then averaged to improve the signal to noise
ratio22.
2.8.1 Use of IR spectroscopy in the food industry
IR spectroscopy can be used for both quantitative and qualitative
analysis of food (Stuart 2004).
2.8.1.1 Lipids
The earliest application of IR spectroscopy to food appears to be
Gibson (1920) who reported on a number of invariant peaks observed
22 Earlier types of IR spectrophotometers used optics to select a single monochromatic frequency for sampling. Collection of a complete spectrum
required sampling at each frequency (Smith 1996).
38
in the IR spectra of vegetable oils despite their obvious differences in
oils in visible light. IR spectroscopy can be used to monitor lipid
oxidation and hydrogenation and measure the amounts of cis and
trans fatty acids in a food products (Eskamani 1977). Cis double
bonds produce characteristic peaks at 840 and 770 cm-1 while trans
double bonds show a characteristic peak at 996 cm-1. A further peak
at 1163 cm-1, attributable to the ester bonds between the fat acids and
the glyerol backbone, is used as an internal reference for quantitative
analysis (Stuart 2004). The current method of total trans fat
determination is AOCS Cd 14e-09. This ATR-FTIR23 method measures
the characteristic peak at 966 cm-1 to determine the trans fat content
of a food sample. The method is sufficiently sensitive (>1.8% trans
fat as a percentage of total fat24) to meet current regulatory
requirements (Mossoba and others In Press).
23 Attenuated total reflectance. This technique makes use of the property of
an evanescent wave passing through a wave guide. If a sample is pressed against the outer reflecting surface of the wave guide, the sample will alter the evanescent wave providing information about the sample (Smith 1996). 24 Mossoba and others (In Press) arrived at this figure by dividing the statutory lower reporting limit for trans fats by the fat grams in a serving of
high fat food (ranch dressing).
39
2.8.1.2 Milk
The initial prototypes for an infrared milk analyzer (IRMA) were
introduced in 1964. These machines measured absorptions attributable
to ester bonds, peptide bonds and hydroxyl groups allowing a
determination of fat, protein and lactose contents of liquid milk (Biggs
1972). Barbano and Dellavalle (1987) suggested that milk casein
could also be determined using an IRMA by splitting the sample. One
portion of the sample is tested normally while the second is acidified to
precipitate the casein and the filtrate resampled for protein. The
difference between these two measurements represents the casein
fraction. IRMAs continue in use today, governed by AOAC Method
972.16 (Min and Boff 2003). There has been discussion of a need to
improve the standardization of calibration sample sets (Kaylegian and
others 2006a,b). Questions have also been raised about the
susceptibility of this method to allowing adulteration in particular with
melamine (Moore and others 2010). Monitoring moisture, protein and
fat using IR spectroscopy for on-line quality control in feta cheese
production was demonstrated by Adamopoulos and others (2001).
40
2.8.1.3 Protein
In addition to estimating the protein content milk, the protein in
wheat can be determined based on the intensities of peaks in the
amide I to IV bands. Accuracy is sufficient to distingish between
different type of flour (Eskamani 1977). IR spectroscopy can also
provide information about the secondary structures of proteins.
Carbonaro and Nucara (2010) provide an extensive review.
2.8.1.4 Carbohydrates
IR spectroscopy has been well received by the carbohydrate
chemists. The use of IR for carbohydrate analysis began in 1950
(Eskamani 1977). Kuhn (1950) presented the spectra of cellulose and
a group of sugars along with their derivatives25 as an aid in
identification. Differences in the anomeric forms were also noted.
Whistler and House (1953) determined that while the peaks associated
with the anomeric forms generally fell into bands, the positions and
amplitudes were insufficiently to reliably determine from without a
reference. White and others (1958) presented spectra for
disaccharides and their octaacetates. In 1961, Goulden proposed
25 The paper includes spectra for seventy nine substances.
41
using IR to measure the lactose content of milk which led to the
development of IRMA (Biggs 1972; Eskamani 1977).
IR spectroscopy can be used to monitor gel formation and to
measure the methyoxl content of pectins (Eskamani 1977). Monsoor
and others (2001) used DRIFTS to measure the polygalacturonic acid
content of pectin. Results were comparable to those from colorometric
and HPLC methods. Advantages of this method are minimal sample
preparation. Assifaoui and others (2010) compared the FTIR spectra
of calcium and zinc pectinate films. Ca2+ was found to have greater
interaction than Zn2+ with the functional groups of the pectin leading
to differences in gel formation.
Sucrose production from cane juice can be monitored with IR
spectroscopy by following the extinction of reducing sugars (Eskamani
1977). Cadet and Offmann (1997) proposed us the use of FTIR-ATR to
determine the raw sugar content of cane juice. The spectrum of cane
juice samples were were compared to the spectra of reference samples
using principle component analysis and principle componenet
regression. The method was found to be more accurate than
polarimetric techniques and avoids the need for purification which is
required before polarimetric and HPLC methods. Tewari and Irudayaraj
(2003) proposed a partial least squares predictive model. Input for
42
the model consisted of IR spectra from samples of known sugar
content. Results were again more accurate than polorimetric methods
and obviated the need for purification.
The carbohydrate fractions of soy beans and wheat can be
measured using ATR-FTIR (Eskamani 1977). Goodfellow and Wilson
(1990) used FTIR to track the gelation and retrogradation of starch as
the molecules uncoil and recoil. Amylose and amylopectin were both
observed to go through a brief retrogelation period of less than an
hour. Amylopectin continued through a further process which lasted
up to two weeks.
2.8.2 IR spectroscopy and microbiology
2.8.2.1 Identification of bacterial species
Efforts to differentiate bacteria using infrared spectroscopy have
been ongoing since at least the 1950's (reviewed by Arai and others
1963). The majority of their paper is concerned with developing a IR
spectrographic method for differentiating Nocardia, Mycobacterium and
Streptomyces using lyophilized cells mixed with KBr powder.
Combinations of absorption patterns in four regions of the specta were
then used to differentiate of the test strains to the species level.
43
Amiel and others (2000) proposed using FTIR for the
identification of LAB used in the dairy industry. The intial library was
constructed for use with a two stage algorithm. The first stage, using
a spectral pattern match, identifies the genus of the unknown. The
second stage determines the Mahalanobis distance to determine the
species. The method accurately identified the genus of wild-type
bacterial but was only 69% successful at identifying the species. The
same author also proposed the use of FTIR in addition to genomics for
bacterial classification (Amiel and others 2001).
Whittaker and others (2003) proposed a method of
differentiating bacteria based on the lipid content of their cell walls.
Fatty acids were extracted from the bacterial cell walls and converted
to fatty acid methyl esters (FAME). Spectra were collected and
analyzed using principle component analysis. Both Gram positive and
negative genera and species were correctly differentiated.
Sahu and others (2006) demonstarted the use of FTIR to
distinguish Streptococcus pneumoniae serotypes based on the
bacterias' absorbance spectra between 900 and 1185 cm-1. Cluster
analysis was used to automate the identification.
Dziuba and others (2006) developed a spectral library of LAB.
Pearson's correlation coefficients were calculated for each spectrum
44
which in turn were used to calculate a differentiation index which in
turn was used to distinguish between species. The authors found
incubation time and temperature affect the resuts and recommend
standardized culture conditions. In a subsequent paper (Dziuba and
others 2007a), the authors applied their methodology to the
identification of LAB. By selection of suitable regions, they were in
many cases able to correctly differentiate bacterial strains. They
conclude that a more sophisticated method is required which they
provide in their next paper (Dziuba and others 2007b). Artificial
neural networks were constructed using previously collected spectra26.
It was found that a network trained using complete spectra or trained
using certain combinations of specfic regions could correctly identify
unknowns. Savić and others (2008) describe the use of hierarchical
cluster analysis (HCA), principle component analysis (PCA) and multi-
dimensional scaling (MDS) for the differentiation of Lactobacillus
strains. All three could differentiate the strains used however HCA
would be unsuited to a significantly larger sample set. Based on their
results, the authors conclude that FTIR not a reliable tool for
taxonomic studys.
26 The spectra were segragated into nonoverlapping training, validating and
testing sets.
45
Increasing awareness and mounting losses both human and
economic, have made rapid detection of food-borne pathogens an
important on-going research topic. The use of FT-NIR for detection and
identification of bacteria in liquids was evaluated by Rodriguez-Saona
and others (2001). Principle component analysis permited the species
identification in pure cultures. High cell concentrations of 108 or 109
were required to sufficently cover the filter for detection.
Davis and others (2010) proposed two rapid methods for
detecting E. coli O157:H7 in ground beef: a filter method and an
immunomagnetic separation method. Both methods use a partial least
squares regression model to determine the the pathogen load of a
sample. The detection limit for both methods is 105 CFU/g of sample.
Following a 6 h incubation, the detection was reduced to 10 CFU/g.
2.8.2.2 FTIR and EPS
Verhoef and others (2005) tested the use of FTIR spectroscopy
for EPS sugar compositional analysis. Spectra from EPS isolates were
reduced using partial least squares followed by linear discriminant
analysis. The authors concluded this method could simplify EPS
compositional analysis given a sufficient sample set.
46
Marcotte and others (2007) proposed an FTIR method to
quantify purified EPS samples. Their method is an alternative to
existing colormetric methods which are sensitive to the sugar
composition of the sample Total sugar content is estimated using the
area under the region associated with sugars (970-1182 cm-1)
normalized using either the area under the C-H stretch region (an
estimate of total mass) or the amide II region (an estimate of total
protein). Results were comparable to those obtained from
colorometric assays.
Other constituents can interfere with FTIR quantification of EPS.
Michel and others (2009) reported that the presence of iron and
calcium sulphates lead to an overestimation of the EPS and that it is
difficult to compensate for their effects.
In EPS analysis, FTIR is used to identify functional groups that
make up the material. Wang Y and others (2010), working with EPS
isolated from kefir, noted the probable presence of hydroxyl, amide
and methyl groups along with absense of peaks indicative of glucuronic
acid or diacyl ester. Kodali and others (2009) also found evidence of
hydroxyl groups along with that for carbonyl groups. Information
about the linkage pattern was also derived. This information was
subsequently used to support further analysis by other methods.
47
2.9 Objectives
The objectives of this study were to determine the long term
survival and ability of certain lactic acid bacteria to grow and produce
EPS on grape pomace, and to analyze the EPS extract by Fourier
Transform Infrared (FTIR) spectroscopy.
48
CHAPTER 3
MATERIALS AND METHODS
3.1 Bacterial strains
Bacterial strains used in this study are shown in Table 3-1.
Unless otherwise noted, cultures were prepared by subculturing 0.5 ml
of culture into 10 mL of MRS broth (Difco Labs, St. Louis MO) and
incubating anaerobically at 37C for 48 h. Lactobacillus rhamnosus GG
was used only in the pilot studies.
3.2 Grape pomace
Chambourcin grape pomace was obtained from Les Bourgeois
Vineyards (Rocheport MO). Pomace was removed from the primary
fermenter, pressed dry and stored at -16C until use. The preliminary
studies for this work used pomace of an earlier vintage than that used
for the primary studies.
49
3.3 pH measurement
The pH of cultures was measured using a Corning Model 220 pH
meter (Corning, Corning NY).
Table 3-1 Bacterial strains used.
Strain Source
Lactobacillus paracasei 25598 ATCC
Lactobacillus rhamnosus GG ConAgra
Lactobacillus acidophilus LA-2 Chr. Hansen
Lactobacillus acidophilus ADH Chr. Hansen
Lactobacillus bulgaricus Lb-12 Chr. Hansen
Lactobacillus fermentum 14931 ATCC
Lactobacillus rhamnosus AM-L3 Wild type from poultry litter
Lactobacillus paracasei AM-L4 Wild type from poultry litter
Lactobacillus paracasei AM-L6 Wild type from poultry litter
Kefir Coccus AM-W1 Wild type from kefir
3.4 Preliminary studies
3.4.1 Growth on pomace (broth)
In order to determine if grape pomace is a viable substrate for
lactic acid bacteria, a preliminary growth study was first conducted
50
with broth cultures. Two 40 mL screw cap tubes were filled with 10 g
grape pomace and 20 mL of distilled water. These tubes were
autoclaved at 121C and 15 psig for 15 m and cooled to room
temperature. An additional 10 mL of sterile distilled water was added
to each tube to make up for what was absorbed. The tubes were
inoculated with 1 mL of a 48-h culture of Lactobacillus paracasei or L.
rhamnosus GG and incubated anaerobically at 37C for 48 h.
3.4.2 Growth on pomace (agar)
To confirm the findings from the broth culture test in the
previous section, four types of agar plates were streaked: MRS agar
(Difco Labs), MRS-pomace agar, pomace agar and pH-adjusted
pomace agar. MRS-pomace agar was made from individual
ingredients replacing the dextrose with an equal mass of grape
pomace and adding 0.04 g of bromcresol purple. The recipe for
pomace agar is shown in Table 3-2. The addition of NaOH was
intended to promote the development of a harder agar medium and
shift the pH of the medium above 6.8 into the purple range of the
indicator. Pomace was ground in a commercial-grade blender (Waring
Commercial, Torrington CT) and passed through a 1.4 mm screen
before being added to the agar. The agar plates were streaked with a
51
24-h culture of L. rhamnosus GG, L. bulgaricus LB12, L. paracasei, L.
acidophilus LA-2, L. fermentum, L. acidophilus ADH or Kefir Coccus
AM-W1 and incubated at 37C for 24 h.
Table 3-2 Pomace Agar
Ingredient Amount per L
Grape pomace 20 g
Agar 15 g
Bromcresol purple 0.04 g
Distilled water 1 L
0.1 M NaOH
(pH-adjusted)
48 mL
3.4.3 Further growth and preliminary survival studies
3.4.3.1 Growth of lactic acid bacteria in pomace solution
without pH adjustment
Having demonstrated qualitative growth, the next step was to
collect quantitative data. A set of 40 mL screw capped tubes were
filled with 5 g of grape pomace and 20 mL of distilled water,
autoclaved and cooled to room temperature. The average pH of this
solution was 3.5. Each group was then inoculated with 1 mL of a 24-h
52
culture of L. paracasei, L. fermentum or Kefir Coccus AM-W1. Two
uninoculated tubes were prepared in the same fashion as controls. All
of the tubes were aerobically incubated at 23C. Tubes were removed
on days 0, 3, 6 and 9 for plating and enumeration. The controls were
plated on days 0 and 9. After plating, the pH of each suspension was
measured. The experiment was repeated using seven species (L.
paracasei, L. fermentum, Kefir Coccus AM-W1, L. bulgaricus LB12, L.
acidophilus LA-2, L. acidophilus ADH or L. rhamnosus LGG) and
removing tubes for plating on days 0, 11, 33 and 83.
3.4.3.2 Growth of lactic acid bacteria in pomace solution with
pH adjustment
In order to determine if increasing the pH of the grape pomace
solution to near neutral would result in improved bacterial growth, the
experiment was repeated using seven strains. To determine the
quantity of base required, aliquots of 1 M NaOH were added to one of
the tubes until the pH exceeded neutral. This volume, 600 µL 1 M
NaOH, was then added to the remaining tubes before autoclaving.
Plating was done on days 0, 3 and 6. Autoclaving the suspension in a
pH of 4. Therefore, to determine how much base was required to
produce a near neutral pH after autoclaving, 40 mL tubes were filled
53
with 5 g of pomace, 30 mL of distilled water and aliquots of 4 M NaOH
ranging from 350 to 600 µL. The tubes were then autoclaved and
cooled. Using these results, the experiment was again repeated with a
single strain, L. paracasei. An aliquot of 575 µL of 4 M NaOH was
added to each tube before autoclaving. Plating was done on days 0, 3,
6 and 9.
3.5 Primary studies
The results from the preliminary studies demonstrated that
grape pomace can be used as substrate for the growth of lactic acid
bacteria (LAB). They also led us to conclude that the pH of the pomace
suspension has an important effect on the growth of LAB which was
further investigated in the primary study described below. The results
also led to the question of how long the LAB can survive in these
environments.
3.5.1 pH Adjustment
Studies were done both with and without pH adjustment. A later
vintage of grape pomace was used in the primary studies requiring a
recalibration of the quantity of base needed to adjust the pH to
approximately 6, the pH of MRS broth (Difco 1998). The quantity of
54
NaOH used was chosen by preparing six 40 mL tubes with 5 g of
pomace, 30 mL of distilled water and between 275 and 525 µL of 4 M
NaOH (Fisher, Fairlawn NJ). The tubes were autoclaved and their pH
measured after cooling to room temperature. Based on the results of
this test, each container in pH-adjusted replicates was treated with
350 µL (Survival Study) or 3.5 mL (Exopolysaccharide Production
Study) of 4 M NaOH.
3.5.2 Survival study
A group of ten 40 mL test tubes was prepared for each strain.
Each tube contained 30 mL deionized water and 5 g grape pomace. In
addition, pH-adjusted tests received 350 µL of 4 M NaOH. Tubes were
autoclaved, cooled overnight to room temperature, inoculated with
500 µL of a 48-h culture and incubated aerobically at 23C. Tubes
were removed on days 0, 1, 2 and 3 and periodically thereafter for
plating and enumeration. The pH of each tube was also measured
after plating. Samples were plated by serial dilution in 0.1% peptone
water (Difco Labs, Sparks MD) and pour-plated with MRS agar (Difco
Labs). Plates were anaerobically incubated at 37C for 48 h and
colonies enumerated.
55
3.5.3 Exopolysaccharide (EPS) production study
3.5.3.1 Sample preparation
A 375 mL jar containing 300 mL deionized water and 50 g grape
pomace was prepared for each strain. In addition, pH-adjusted tests
received 3.5 mL of 4 M NaOH. Tubes were autoclaved, cooled
overnight to room temperature, inoculated with 5 mL of a 48-h culture
and incubated aerobically at 23C for 4 d. Jars were them immediately
transferred to a 90C water bath and held there for 60 min. The jars
were stored at 4C prior to extraction.
3.5.3.2 Soluble fiber extraction
A modified version of AOAC Method 991.43 (Horwitz and Latimer
2007) was used to extract the soluble fiber fraction from the samples
(Figure 3-1). Solids were removed by filtering through a Büchner
funnel fitted with a 15 cm Whatman #3 filter (Whatman, Maidenstone
Kent UK) and 3 g of Celite 421 (Aldrich, Milwaukee WI) into a 1 L
vacuum flask (a). The retentate was rinsed twice with ~300 mL of
deionized water. The filtrate was then quantitatively transferred to a 1
L beaker and boiled down to approximately 100 mL (b). While still
hot, approximately 300 mL of 95% ethanol was added to the boiled
filtrate (c). This mixture was then filtered through a Büchner funnel
56
fitted with a 7 cm Whatman #2 filter and 1 g of Celite 421 (d). The
retentate was rinsed three times with 70% ethanol. The retentate and
filter aid were then redissolved/resuspended in approximately 250 mL
of deionized water and filtered through a Büchner funnel fitted with a 7
cm Whatman #2 filter (e, f). The retentate was rinsed three times
with deionized water and the filtrate was then quantitatively
transferred to a 500 mL beaker and boiled down to ~50mL (g). This
sample was then quantitatively transferred to a 100 mL beaker,
covered with foil and cooled to -18C.
3.5.3.3 Freeze-drying
Frozen samples from above (3.5.3.2) were packed in groups of
four into 2 L freeze-drying flasks (Labconco, Kansas City MO) and
dried for 5 d using a Labconco Lyophlock 12 Freeze Dry System.
Samples were then weighed and transferred to sterile 50 mL
centrifuge tubes and stored at room temperature.
3.5.4 Fourier transform infrared spectroscopy
Infrared spectra were collected using a Nicolete 380 FTIR
spectrophotometer (Thermo Scientific, Waltham MA) equipped with a
57
VeeMAX II spectral reflectance attachment (Pike Technologies,
Madison WI) fitted with a 5/8 inch mask plate. Following trial and
error testing, the angle of incidence was set to 50. Data collection
was done using OMNIC, a software package supplied by the
spectrometer manufacturer.
3.5.5 Sample preparation
Samples were prepared by making a 1% solution (w/v) of
extracted material in deionized water. Beads of liquid (200 µL each)
were then pipetted onto gold-coated slides (Biogold – Gold Coated
Microarray Substrates, Themo Scientific). Six beads were pipetted for
each sample with three beads per slide (Figure 3-2).
3.5.6 Sample collection
Following an initial warm-up period of at least 15 min, a
background spectrum was collected using a clean gold-coated slide
placed directly on the mask. Slides were fitted in a simple cardstock
frame to prevent the sample from contacting the mask. Spectrum
collection parameters were 64 scans and 2 cm-1.
58
Figure 3-1 AOAC method 991.43 as modified for this study. Details of
each step are provided in section 3.5.3.2.
Boil down to 100 mL
Precipitate with Ethanol
Redissolve/resuspend retentate
Filter
Filter
Filter
Boil down to 50 mL
(a)
(b)
(c)
(d)
(e)
(f)
(g)
59
3.5.7 Growth and EPS production in tomato juice
The extract from the samples prepared in section 3.5.3.1
contained components derived from both the bacteria and the
substrate. In order to minimize the latter, another set of samples was
prepared using tomato juice. Tomato juice was chosen for two
reasons: it has long been recognized as a suitable substrate for LAB
(Kulp 1927) and because it has a low fiber content27. A 20% solution
of tomato juice (v/v) was made by combining 200 mL of canned
tomato juice from concentrate (obtained locally) in a 1 L graduated
cylinder with sufficient distilled water to make up the volume. Glass
27 The "Nutrition Facts" panel on the can indicates that a 240 mL serving
contains 2 g of dietary fiber.
Figure 3-2 Samples on a slide.
60
jars (375 mL capacity) were filled with 30 mL aliquots of this solution
and then handled as described in the preceeding sections. There were
no pH-adjusted replicates using tomato juice.
3.6 Statistical and spectral analyses
Regression and Honest significant difference were calculated
using R (R Development Core Team 2008). Scripts for data
normalization, Savitzky-Golay smoothing and derivation (Savitzky and
Golay 1964, Steinier and others 1972) and other transformations were
written by the author in Perl (Wall and others 1996). Source code for
these scripts is provided in APPENDIX C. Plotting was done using
Microsoft Excel (Redmond WA) and R (R Development Core Team
2008).
61
CHAPTER 4
RESULTS
4.1 Preliminary Studies
4.1.1 Growth on pomace (broth)
The tubes for L. paracasei and L. rhamnosus GG both showed
positive signs of growth. White precipitation / flocculation was
observed in both tubes.
4.1.2 Growth on pomace (agar)
As expected, all species grew well on MRS agar (Table 4-1). All
species also grew well on MRS-Pomace (P) agar, but the indicator
remained purple (pH > 5.2), indicating insufficient fermentation of the
pomace. None of the species grew on Agar+P, and the low pH kept
the indicator in the yellow range and made the agar soft and difficult
to streak. All species grew on pH adjusted Agar+P, but the color
remained purple. Colonies for all species were small, round and
colorless.
62
Table 4-1 Growth of lactic acid bacteria on pomace (agar)
MRS MRS-P Agar+P
Unadjusted pH
Agar+P
Adjusted pH
L. rhamnosus GG + + - +
L. bulgaricus Lb-12 + + - +
L. paracasei + + - +
L. acidophilus LA-2 + + - +
L. fermentum + + - +
L. acidophilus ADH + + - +
Kefir Coccus AM-W1 + + - +
4.1.3 Further growth and preliminary survival studies
4.1.3.1 Growth of lactic acid bacteria in pomace solution without pH adjustment
The LAB were grown in pomace solution without adjusted pH and
the results of the 9 day trial are shown in Figure 4-1. Counts for L.
paracasei, L. fermentum and AM-W1 had an average range28 of 0.39
log(CFU/mL) (SD=0.15). The average pH range over this same period
was 0.25 (SD=0.06). Low and high pH values for L. paracasei were
3.6 and 3.78 while those for L. fermentum and AM-W1 were 3.4 and
3.69. The results of the 83-day trial are shown in Figure 4-2. The
28
Average Range
max speciesi min speciesi i1
n
n
63
average range of counts was 1.92 log (CFU/mL) (SD=1.05).
Individual growth ranges are shown in Figure 4-2. The largest change
was observed with L. bulgaricus LB-12 whose population declined by
nearly four log cycles. L. acidophilus, Kefir coccus AM-W1 and L.
rhamnosus GG each declined by approximately two log cycles. L.
fermentum and L. acidophilus ADH declined by 1.28 and 1.42 log
cycles, respectively. L. paracasei showed the least change at 0.58 log.
The average pH range for this same period was 0.42 (SD=0.08). All
species exhibited a pH increase over the course of the experiment.
Start and ending pH of the controls were 3.68 and 3.62, respectively.
No colonies were observed when either tube was plated.
4.1.3.2 Growth of lactic acid bacteria in pomace solution with
pH adjustment
The results of our initial attempt to adjust the pH of the pomace
solution are shown in Figure 4-3. The pH and colony counts remained
essentially unchanged for the duration of the experiment. The
average colony count range was 0.27 CFU/mL (SD=0.20) and the
average pH range was 0.13 (SD=0.10). The reader will recall that the
pH of all the tubes was adjusted to near neutral before autoclaving,
64
yet the average pH during the course of the experiment was 3.98
(SD=0.11). To compensate for this drop in pH, increasing quantities
Figure 4-1 Growth of LAB on pomace (9-day trial) of NaOH were added, producing the result shown in Figure 4-4.
Addition of each aliquot of base resulted in an incremental increase in
pH except the last between 550 and 600 µL which resulted in a sharp
increase.
65
Figure 4-2 Growth of LAB on pomace (83-day trial)
66
Table 4-2 Growth ranges (CFU/mL) of LAB in pomace solution for 83 d.
Species Range (CFU/mL)
L. paracasei 0.58
L. bulgaricus LB-12 3.94
L. acidophilus LA-2 2.03
L. fermentum 1.28
AM-W1 2.04
L. acidophilus ADH 1.42
L. rhamnosus GG 2.15
The results of the final preliminary pH adjusted experiment are
shown in Figure 4-5. The colony counts had a range of 0.31 CFU/mL.
Counts for L. paracasei rose from 7.81 to 8.11 CFU/mL between days
0 and 6 before falling back down to 7.87 CFU/mL on day 9. No
colonies were observed on the control plates. The pH range was 0.96
beginning at 6.43 and ending at 5.53. The pH of the beginning and
ending control tubes was 6.46 and 7.03, respectively.
67
Figure 4-3 Growth of LAB on pH adjusted Pomace
68
Figure 4-4 Adjusted pH resulting from addition of 4M NaOH and
subsequent autoclaving
Figure 4-5 Growth of L. paracasei on pH adjusted grape pomace
69
4.2 Primary Studies
4.2.1 pH adjustment
Results of pH adjustment calibration for the later vintage of
grape pomace used in the primary studies are shown in Figure 4-6.
Figure 4-6 Adjusted pH resulting from addition of 4 M NaOH to second
batch of pomace and subsequent autoclaving
4.2.2 Survival Study
Average survival and pH curves for adjusted pH replicated are
shown in Figure 4-7 and Figure 4-8. Curves for unadjusted replicates
are shown in Figure 4-9 and Figure 4-10. Missing values were
replaced by linear interpolation between two known points29. At the
outset, the pH-adjusted replicates of L. acidophilus LA-2 began an
29
y b ya yc ya xc xb
xc xa
where a b c
70
immediate steady decline. Other organisms show a slight increase
before declining in numbers. Kefir Coccus AM-W1, L. bulgaricus Lb-12
and L. paracasei AM-L4 rose by approximately 0.25 log CFU before
declining in numbers while L. rhamnosus AM-L3 and L. paracasei AM-
L6 rose by approximately 0.15 log and L. fermentum by 0.1 log CFU.
In most of these cases, the peak of the rise occurred on day 2.
However, the peak for L. AM-L6 came on day 1 while Kefir Coccus AM-
W1 did not peak until day 3. The remaining pH-adjusted replicates
show a pattern of decline, rebound and decline. In the case of L.
paracasei 25598, counts dropped by approximately 0.25 log, regained
most of the loss (~0.2 log) before following the overall pattern of
decline. The counts for L. acidophilus ADH dropped ~0.6 log, held
steady for two days before rising ~0.2 log and then declining. The pH
of these replicates declined by an average of 0.8 over the course of
this experiment. L. bulgaricus Lb-12, Kefir Coccus AM-W1 and L.
rhamnosus AM-L3 had the largest pH declines of 1.2, 1.1 and 1.0
respectively. The pH of L. fermentum declined by only 0.3. The
remaining strains fell by 0.9 (L. acidophilus LA-2), 0.8 (L. paracasei
AM-L4) and 0.7 (L. paracasei 25598, La ADH and L. paracasei AM-L6).
All replicates had an initial pH drop of ~0.2 (L. paracasei AM-L6) to
~0.6 (L. acidophilus LA-2 and Kefir Coccus AM-W1) within this first few
71
days. The pH of some replicates (L. acidophilus LA-2, L. bulgaricus Lb-
12, L. paracasei AM-L4 and L. paracasei AM-L6) continued a more
gradual decline for the remainder of the experiment. Others, (L.
paracasei 25598, L. acidophilus ADH, Kefir Coccus AM-W1 and L.
rhamnosus AM-L3) rose slightly before beginning the final descent.
The remaining replicate, L. fermentum, rebounded after the initial drop
before settling in to a relatively constant pH of ~5 for the duration.
In replicates for which the initial pH was not adjusted, counts
again declined as time progressed. Counts for L. paracasei 25598, L.
acidophilus LA-2, L. acidophilus ADH and Kefir Coccus AM-W1 all
began an immediate decline. The counts for L. bulgaricus Lb-12, L.
rhamnosus AM-L3 and L. paracasei AM-L6 exhibited a slight pattern of
decline, rebound and decline with an amplitude of 0.2 log. L. paracasei
AM-L4 exhibited a more extreme oscillation, rising and falling by
roughly 0.5 log before settling into a stable fixed population. The
counts for L. fermentum follow an initial pattern of 0.5 log decline over
the first three days. Subsequent counts a month later were one log
cycle higher (0.5 log higher than the initial population). No
contamination was observed in the tubes nor was there anything
remarkable about the plated colonies. The average pH of all strains in
this group of replicates rose by 0.1 to 0.3 in the first two days followed
72
by a decline of similar magnitude. The average pH then remained
stable for the balance of the experiment. No unadjusted replicate
experiments were performed for Kefir Coccus AM-W1, L. acidophilus
Lb-12, or L. paracasei AM-L4. Data for the first replicate is presented
here but will not be included in further discussion.
The average slopes of the survivor and pH regression lines are
shown in Figure 4-11 and Figure 4-12 respectively30. In general,
colony counts declined over time with counts declining more slowly
when the pH was initially adjusted upward. The pH for these adjusted
experiments subsequently declined over time. In those experiments
where the pH was not adjusted, an increase in pH over time was
observed similar to that in the preliminary studies. Two-way ANOVA
was used to test for differences in the survivor and pH slopes. For the
population slopes we used the linear model
CFU ~ species adj species* adj31
where:
CFU = slope of the population regression line for a given
replicate
species = the strain used to inoculate the replicate
30 Graphs of the regression lines underlying this section along with the R
scripts used to analyse the data are presented in APPENDIX A. 31 The "*" denotes an interaction term between independent variables.
73
adj = was this replicate pH adjusted.
ANOVA indicated the presence of significant differences in both
independent variables (species: p=2.9e-5, adj: p=4.8e-6) and the
interaction term (p=7.00e-7). We then used Tukey's HSD to locate
these differences, the results of which are available in appendix
APPENDIX A. This same analysis was performed using the linear
model
pH ~ species adj species* adj
where:
pH = slope of the pH regression line for a given replicate
A significant difference was found between the levels of adj
(p=1.6e-6).
ANOVA was also applied to the pH-adjusted and unadjusted
subsets of the data. Significant differences were found between the
species in both subsets of the survior slopes. Tukey's HSD was used
to locate the differences. No differences were found within either
subset of the pH slope data.
74
Figure 4-7 Average survival curve (adjusted pH)
75
Figure 4-8 pH (pH adusted)
76
Figure 4-9 Average survival curves (unadjusted pH)
77
Figure 4-10 pH (unadjusted)
78
Figure 4-11 Average slope of survivor count regression lines
79
Figure 4-12 Average slope of pH regression lines
4.2.3 Exopolysaccharide (EPS) production study 4.2.3.1 Soluble fiber extraction
The quantities of material recovered after extraction and freeze-
drying are presented in Figure 4-13. The values shown are averages
of all pH adjusted or unadjusted replicates for a given species. Within
the unadjusted samples, the largest average was 1.74 g [SD=0.29,
n=2], recovered from Kefir Coccus AM-W1. The minimum recovered
80
was 1.21 g [SD=0.19, n=3] from L. paracasei. The remaining strains
fell between these values (L. acidopilus LA-2=1.50 g [SD=0.20, n=3],
L. fermentum=1.43 g [SD=0.62, n=3], L. acidopilus ADH=1.48 g
[SD=0.01, n=2], L. bulgaricus Lb12=1.34 g [0.63, n=2], L.
rhamnosus AM-L3=1.36 g [0.44, n=3], L. paracasei AM-L4=2.02 g32,
L. paracasei AM-L6=1.52 g [SD=0.56,n=3]). Samples containing only
water and pomace averaged 1.19 g [SD=0.31, n=3].
Taken by species, the average material recovered from adjusted
pH samples was less than that for unadjusted samples. The largest
quantity, 1.40 g [SD=0.54, n=2], was recovered from L. paracasei
AM-L4. The smallest, 0.88 g [SD=0.22, n=2], was recovered from L.
acidophilus ADH. The remaining strains again fall between these
values (L. paracasei=1.03 g [SD=0.38, n=3], L. acidopilus La2=1.16 g
[SD=0.30, n=3], L. fermentum=1.35 g [SD=0.45, n=2], Kefir Coccus
AM-W1=1.21 g [SD=0.51, n=2], L. bulgaricus Lb12=1.07 g [SD=0.12,
n=2], L. rhamnosus AM-L3=0.92 g [SD=0.45, n=3], L. paracasei AM-
L6=0.91 g [SD=0.21,n=3]). Samples containing only water, pomace
and NaOH averaged 1.00 g [SD=0.23, n=5].
32 The replicate was lost during extraction;the value given here is the result of extracting a single sample and is intended for reference only and will not
be used in further calculations.
81
ANOVA analysis of the extraction results using the linear model
extract ~ species adj species* adj
where:
extract = grams of extracted material
species = the strain used to inoculate the replicate
adj = was this replicate pH adjusted.
indicates a significant difference beween samples with and without
initial pH adjustment (p=1.8e-3) which we confirmed using Tukey's
HSD.
4.2.4 Fourier transform infrared spectroscopy
The spectra discussed in this paper are presented in APPENDIX
B. The spectra in Figure B-1 were collected from pH-adjusted extract
samples while those in Figure B-2 were collected from unadjust
samples. Potential of potential interest were identified by Peaks, an R
package which implements the algorithm described by Mariscotti
(1967). Peaks present in both the treatment and control samples
were assumed to arise from the substrate and were ignored. A
parameter, "Q", was used to specify the minimum required distance
between peaks before they would be considered different. In the
figures, Q=3 indicates a minimum peak spacing of 2.8929/cm.
82
Figure 4-13 Average material recovered after extraction and freeze-
drying
4.2.5 Growth and EPS production in tomato juice
Results for the extraction and freeze-drying of samples grown in
tomato juice are shown in Table 4-3. IR spectra for these samples
are presented in APPENDIX B.
83
Table 4-3 Extraction results after growth in diluted tomato juice.
Species grams
L. fermentum 0.21
L. acidophilus La-2 0.16
L. acidophilus ADH 0.20
L. rhamnosus AM-L3 0.11
L. paracasei AM-L6 0.16
control 0.22
84
CHAPTER 5
DISCUSSION
5.1 Preliminary studies
5.1.1 Growth on pomace (broth)
LAB are used to ferment a wide variety of plant materials for
foodstuffs (Steinkraus 1996b). They are also responsible for the
malolactic fermentation of wine (Fleet 1998) and they make important
contributions to the production of silage (Woolford and Pahlow 1998).
And silage can be made from grape pomace (Spanghero and others
2009). With this in mind, it is reasonable to predict that grape
pomace will make a suitable substrate for LAB.
The white precipitate observed in our initial experiment was
assumed to be clumps of bacterial cells settling out of suspension. This
behavior is consistent with our experience growing these species in a
broth culture.
85
5.1.2 Growth on pomace (agar)
To confirm our conclusion from the previous experiment, agar
plates were made up and streaked with bacteria. MRS agar was
developed specifically for the laboratory culture of LAB (De Man and
others 1960). Failure of the LAB to grow on this medium would have
been quite surprising. By substituting pomace for the dextrose in the
formulation, the presence of fermentable sugars available in grape
pomace could be determined. The pH of plates of both the MRS-
Pomace agar and the pH adjusted Agar+P remained above 5.2 after
the formation of colonies leading to the conclusion that the sugars
available in grape pomace for fermentation by LAB are limited and/or
not readily metabolized. In lieu of available sugars to ferment, the
bacteria have likely shifted to utilizing organic acids (Fleet 1998).
Grape pomace may provide other essential growth factors or these
may be supplied by autolyzed yeast cells left over from the primary
fermentation. The use of (pH adjusted) Agar+P was intended to
determine whether pomace alone could supply the required nutrients
for the culturing of LAB.
In the results section, we report the negative growth of LAB on
Agar+P which, given the preceding and following sections, may seem a
bit odd. It is quite possible that there really was no growth. It is
86
presumed, however, that the lack of observed colonies was due to the
difficulties of streaking onto very soft agar. The combination of low pH
and autoclaving probably hydrolyzed the media and prevented it from
setting properly (Khalid and others 1993). The loop dug into the agar
surface. If any colorless colonies such as those observed on the other
plates were present, they would have been hidden under the agar
surface. The formation of visible colonies indicates the likely absence
of growth inhibitors in the pomace.
5.1.3 Further growth and preliminary survival studies
5.1.3.1 Growth of lactic acid bacteria in pomace solution
without pH adjustment
The results from this section are substantially in line with those
of the primary studies, discussed below. At this point in the
discussion, it is sufficient to note that the bacteria in this pair of
experiments appear to be in the stationary (LP) or death phases of the
growth curve. There is no initial rise in number before the decline sets
in. These results also indicate an increase in pH over time which,
given the normal habits of LAB, is counterintuitive. Under normal
circumstances, LAB are acid producers who lower the pH of their
87
environment. Bacteria can and will attempt to adapt their
environment to best suit their needs (Jay and others 2005).
In previous experiments, cultures were incubated anaerobically
at 37C which is generally recognized as optimum conditions for LAB
growth (De Vos and others 2009). The optimum conditions for EPS
production are cooler De Vuyst and Degeest 1999; Laws and others
2001). The lower temperature and aerobic conditions were also
selected for practical reasons; incubators large enough to use the
findings of this work on a commercial scale would be quite expensive.
5.1.3.2 Growth of lactic acid bacteria in pomace solution with
pH adjustment
Raising the pH of the pomace mixture to near neutral before
autoclaving produced an increase in pH of 0.5 after autoclaving. The
observed patterns of growth and pH were sufficiently similar to the
previous attempt that the experiment was abandoned after 6 d. We
theorized at the time that the pH change was due to the difference
between titratable and total acidity with the heat from autoclaving
forcing the reaction to proceed. However, based on work by Nef
88
(1914), the more likely explanation is the dissociation of glycosidic
bonds when heated in the presence of a strong base33.
The pH increase of 3.0 produced a remarkably different pattern
of growth. The bacterial population remained fairly constant but unlike
our previous experiments, the pH of the mixture fell with time. This
decrease is unlikely to be due to unreacted hydroxide ions as a similar
decline was not observed in the controls.
5.2 Primary studies
5.2.1 Survival study
During the first 51 days of the experiment, the average colony
counts for most strains declined. On average, pH adjusted replicates
declined by 0.8 log CFU/mL (SD=0.36) while unadjusted replicates
declined by 1.3 (SD=0.69). A 10% remaining viability after roughly 9
weeks compares favorably with previously published results. Hull and
others (1984) reported >15% viability of L. acidophilus in yogurt after
4 weeks. art ne -Villaluenga and others (2006) report one and two
log reductions of L. acidophilus La-5 after 21 days in fermented milk
stored at 4˚C, respectively with or without the addition of raffinose
family oligosaccharides. Buriti and others (2010) report 1 to 3 log
33 Our discovery of this paper is a classic example of the Gruen Effect
wherein an important reference is found after the experiment is completed.
89
reductions over the course of 28 days of La-5 in guava mousse stored
at 4˚C.
Also of interest are the early reactions of the various strains.
Some L. paracasei, L. acidophilus ADH and Kefir coccus AM-W1 in the
unadjusted replicates and L. acidophilus LA-2 in all replicates, simply
declined in population with no apparent growth. These combinations
of media and environment were apparently unsuitable for the growth
of these bacteria. The counts for other pH-adjusted replicates, Kefir
coccus AM-W1, L. bulgaricus Lb-12, L. fermentum, Lactobacillus
rhamnosus AM-L3, Lactobacillus paracasei AM-L4 and Lactobacillus
paracasei AM-L6, increased before declining indicating that they
followed some approximation of a typical growth curve. The data
collection interval is too coarse to determine whether there is an initial
lag phase. The remaining pH-adjusted replicates, Lactobacillus
paracasei and Lactobacillus acidophilus ADH, and a majority of
remaining unadjusted replicates, Lactobacillus bulgaricus Lb-12,
Lactobacillus rhamnosus AM-L3, Lactobacillus paracasei AM-L4 and
Lactobacillus paracasei AM-L6, show a pattern of initial decline,
rebound and then decline, all over a period of three days. We
speculate this pattern is a lag phase possibly prolonged by the low
growth temperature. The apparent absence of a linear growth region
90
between the log and decline phases is probably due to coarse spacing
of data points relative to the phenomenon. A different effect was
observed with L. fermentum. For the first three days, the average
counts plummeted by ~0.5 log CFU. Yet when the next points were
collected a month later, the counts had not only recovered but
exceeded the initial counts. In addition, the trajectory of the counts
suggests an extended linear growth region which rolled into the
decline phase between 50 and 80 days (data not shown). In short, L.
fermentum liked these conditions.
In the case of average pH results of pH-adjusted replicates, the
pH drop was likely due to the fermentation of sugars by the bacteria.
The comparatively steep initial drop followed by a more gradual tail is
probably a matter of numbers. The largest numbers of bacteria in
each tube were present at the outset of the experiment. As the total
number of organisms declines, their ability to affect large-scale
changes on their environment also declines. The subsequent pH rise
observed in many of the adjusted pH replicates and all of the
unadjusted replicates is possibly due to malolactic fermentation, a
process which converts L-malic acid to L-lactic acid thereby raising the
pH by 0.3 to 0.5 (Fleet 1998). Decarboxylation of malate yields
lactate and H+ which are removed from the cell by a malate/lactate
91
antiport. The charge gradient across the cell membrane is increased,
permitting the assembly ATP (Poolman and others 1991). The rate of
malate transfer is pH dependent: lower pH results in increased malate
transport (Olsen and others 1991). This is a two-edged sword
however. At low pH, acids lose their charge and, as neutral molecules,
can pass through the cell membrane (Guzzo and Desroche 2009).
Once in the cytosol, the acid dissociates resulting in the denaturation
of proteins and inhibition of enzymes (Jay and others 2005).
The notion that two very different growth regimes are at play
here is supported by our finding of a significant difference between the
slopes of pH adjusted replicates (negative slope) and unadjusted
replicates (positive slopes). The sign of the slopes is also consistent
with unadjusted replicates making use of acid fermentation. The
ANOVA results for the slopes of the survivor curves indicate two
significant differences in the data. There is a statistically significant
difference between the adjusted / unadjusted replicates of AML6 and
those for L. fermentum. For, AML6, the difference is one of degree -
the viable populations of the unadjusted replicates declined more
quickly than those for which the pH was initially adjusted. However, in
the case of L. fermentum a completely different scenario is observed.
The population trends of the adjusted/unadjusted replicates are in
92
opposition; the adjusted replicates are in decline while the unadjusted
replicates are growing.
The growth of L. fermentum in unadjusted pomace may be due
to a number of factors. Some strains of this species have very high
tolerance for acidic condition. Bao and others (2010) reported 35 of
90 strains of L. fermentum tested grew over a period of 72 h at pH
3.0. Pereira and Gibson (2002) reported two strains capable of
surviving at pH 2. L. fermentum F53 declined approximately 2.5 log
over the course of 2 h while L. fermentum KC5b declined by less than
0.2 log over the same time period. Another factor may be the ability
by L. fermentum to reduce fructose to mannitol (Buchanan and
Gibbons 1974). Heterofermentative species such as L. fermentum,
use this pathway to oxidize NADH. Fructose is used as the final
electron acceptor (Vrancken and others 2008). None of the other
heterofermentative species used in this study reduce fructose to
mannitol.
The ANOVA results for subsets of the slopes of the survivor
regression lines partition both subsets into three groups. In the pH-
adjusted subset, each group spans at least 6 of the 9 species leading
us to wonder if a third replicate might not erase the difference. It is
interesting to note that all three L. paracasei strains are clustered
93
together. In the unadjusted pH subset, Lactobacillus paracasei AM-L4
and Lactobacillus fermentum. The behavior of Lactobacillus
fermentum has been discussed above. And as there is no replicate
data for Lactobacillus paracasei AM-L4, the observed behavior may be
an anomaly.
5.2.2 Exopolysaccharide (EPS) production study
5.2.2.1 Soluble fiber extraction
Based on the ANOVA results, pH adjustment has a significant
influence on the quantity of material recovered from the samples. Less
material was recovered from those samples in which the initial pH was
adjusted. This is consistent with Nef's (1914) findings of the
dissociation of polysaccharides when heated in the presence of a
strong base.
We now face the question as to how much of the material
extracted is derived from pomace and how much is derived from
bacterial action. To our knowledge there are no reports in the
literature specific to chambourcin grapes. There are reports for other
varietals. The soluble fiber fraction of Airèn grape pomace, a white
varietal grown in Spain, is 9.53% of dry weight (Valiente and others
1995). And 14.5% of the dry weight of Manto Negro grape pomace, a
94
red Spanish varietal, is made up of soluble fiber (Llobera and Cañellas
2007). Bravo and Saura-Calixto (1998), report soluble fiber of 4.43%
for Airèn grape skins, 3.23% for Airèn grape seeds and 2.34% for red
Cencibel grape skins. Using the soluble fiber percentage of the Airèn
grape seeds for both red and white, the total soluble fiber for these
pomaces is 7.66% and 5.57%. Based on this admittedly small
sample, we estimate chambourcin should contain approximately 9.3%
soluble fiber or 4.7 g / jar34. Clearly our results fall short of this bar.
Autoclaving was ruled out as a cause; the quantity of material
recovered from unautoclaved material falls within the range of results
for autoclaved materials (data not shown). The discrepancy is likely
due to differences in sample preparation and purpose. The studies
cited were interested in the composition of grape pomace as an end
and ground their samples before analysis. Pomace samples used in
our study were mostly intact, and as such, a large fraction of the
soluble fiber in the pomace remained unextracted. If we ignore the
fraction of soluble fiber contributed by the seeds, and use only the
data from Bravo and Saura-Calixto (1998), we estimate a soluble fiber
content of 3.4% or 1.7 g / jar.
34 Each jar contains 50 g of grape pomace and 300 mL of water.
95
Published work places the quantity of EPS produced anywhere
between 25 and 9800 mg/L depending on strain, growth conditions
and extraction method (reviewed by Behare and others 2009)35. Using
the median value from this dataset, 540 mg/L, we expect to recover
162 mg / jar or roughly one tenth of the expected soluble fiber
recovered from the substrate. Clearly any EPS recovered will not be a
pure isolate.
Returning to the bar chart, we assume samples whose standard
error36 overlaps the standard error of the corresponding pomace-only
sample will only contain material derived from the grape pomace. This
assumption implies that none of the pH adjusted samples will likely
contain appreciable quantities of EPS. Of the unadjusted samples,
Lactobacillus acidophilus LA-2 and Kefir Coccus AM-W1 are the likely
prospects for finding EPS.
5.2.3 Fourier transform infrared spectroscopy
In some instances it is possible to show the presence of or
quantify a substance in an FTIR spectrum despite of background noise
(Cadet and Offmann 1997). Using the peak assignments given in
35 The average reported yield was 1206 mg/L (SD=2339). 36
SE
N
96
Wang, Li and others (2010), the tentative peak assignments shown in
Table 5-1 can be made.
Both the pH-adjusted and unadjusted spectra of Lactobacillus
paracasei and Lactobacillus paracasei AM-L6 have peaks near 1543
cm-1 which Wang, Li and others (2010) ascribe to N-H bending of
proteins. The pH-adjusted spectra of Lactobacillus acidophilus LA-2
and Lactobacillus rhamnosus AM-L3 and the unadjusted spectra of
Lactobacillus fermentum and Kefir Coccus AM-W1 also have peaks
near 1543 cm-1.
Table 5-1 Peak Assignments
Wavenumber
(cm-1)
Substrate Attributed to
Pomace Tomato
pH-adjusted no pH
adjustment
1543 L. paracasei L. paracasei Bending of N-H
in proteins L. paracasei
AM-L6
L. paracasei
AM-L6
L. acidophilus
La-2
L. fermentum
L. rhamnosus
AM-L3
AM-W1
1448 L. paracasei
AM-L6
L. paracasei
AM-L6
L. paracasei
AM-L6
Asymetric
bending of C-
H2 and C-H3
groups in
proteins
L. bulgaricus
LB-12
Kefir Coccus
AM-W1
L. rhamnosus
AM-L3
L. paracasei
AM-L4
L. acidophilus
ADH
L. fermentum
1404 L. fermentum L. fermentum C-O stretch in
carboxylic acids L. rhamnosus
AM-L3
97
Both the pH-adjusted and unadjusted spectra of Lactobacillus
paracasei AM-L6 have peaks near 1448 cm-1 as do the pH-adjusted
spectra of Lactobacillus bulgaricus Lb-12 and Lactobacillus paracasei
AM-L4 and the unadjusted spectrum of Kefir Coccus AM-W1. This
peak is explained by Wang, Li and others (2010) as the result of
asymetric bending of C-H2 and C-H3 groups in proteins. Neither of
these peaks is present in the spectra of pomance-only samples. Taken
together these peaks suggest the presence of either a protein or
another nitrogenous compound. EPS from strains of L. paracasei, L.
acidophilus are known contain N-acetyl glucosamine and N-acetyl
galactosamine (DeVuyst and Degeest 1999; Laws and others 2001).
The spectra for Lactobacillus fermentum for both pH-adjusted
and unadjusted samples have a peak near 1404 cm-1. This peak is
attributed to C-O stretching of carboxylic acids (Wang, Li and others
2010). This peak is also present in the unadjusted pH spectrum of
Lactobacillus rhamnosus AM-L3. Again, this peak is not present in the
spectra of pomace-only samples. The EPS from some strains of L.
rhamnosus include a carboxylic acid (Laws and others 2001). Whether
these peaks are in fact evidence of the presence of EPS in these
samples will require further investigation.
98
5.3 Growth and EPS production in tomato juice
The object of this portion of the study was to collect additional
spectra against a different and possibly less cluttered background.
The average quantity of material recovered, 0.18 g, is approximately
36% of what one would expect based on the nutrition facts panel on
the juice can37. The value given on the nutrition facts panel is for total
fiber while our values are for soluble fiber. This data strongly suggests
that the fraction of the recovered material attributable to bacterial
action is quite small.
Using the peaks assignments discussed above, we find peaks
near 1448 cm-1 in the spectra of L. rhamnosus AM-L3, L. paracasei
AM-L6, L. acidophilus ADH and L. fermentum. This peak was not
present in the control. Peaks were not found at 1543 cm-1 or 1404
cm-1. This pattern suggests the presence of methylene and methyl
groups but is not suggestive of anything peculiar to EPS structures.
There are a number of possible causes for the differences in
peak patterns between the two datasets. Grape pomace may induce
EPS production while tomato juice does not. It's also possible the
37
2 g fiber
240 mL juice
200 mL juice
1000 mL solution
300 mL solution
jar
0.5 g fiber / jar
99
peaks observed in the grape pomace samples are phantoms. More
data will be required to select between these alternatives.
100
CHAPTER 6
CONCLUSION
Based on our results, we conclude that the majority of the
species used in this study can survive for a period of weeks in a
mixture of grape pomace and water. Most do not thrive however. The
one exception was L. fermentum which appeared to thrive in the low
pH environment. There are a several further avenues of research to
develop grape pomace as a suitable substrate for LAB. How would the
results differ had we sterilize the pomace and hydroxide separately?
Would a starting pH nearer neutral result in better growth and
survival? And finally, can a strong base in the presence of heat be
used to completely hydrolyze grape pomace and would the resulting
mixture be a suitable substrate for LAB?
The results of our EPS study were less clear cut. There are hints
in the FTIR data that could indicate the presence of EPS. Further
study is necessary. Future work in this area should include use of
more prolific EPS-producing LAB strains coupled with a less cluttered
101
background signal. This would permit the identification of the
characteristic peaks for each EPS. This data could then be used to
verify the presence of EPS against a background from grape pomace.
With suitable calibration, this data could also be used to estimate
quantity of EPS produced. An alternate EPS extraction method such as
ultrafiltration should also be considered.
102
APPENDIX A
Survivor regression lines
Figure A-1 Survival and pH curves for L. paracasei. () = Unadjusted
pH, () = Adjusted pH. Solid and filled markers indicate first and
second replicates. Regression lines for unadjusted pH runs are dashed. Those for adjusted pH runs are dot-dot-dashed.
103
Figure A-2 Survival and pH curves for L. acidophilus LA-2. () =
Unadjusted pH, () = Adjusted pH. Solid and filled markers indicate first and second replicates. Regression lines for unadjusted pH runs
are dashed. Those for adjusted pH runs are dot-dot-dashed.
104
Figure A-3 Survival and pH curves for L. fermentum. () = Unadjusted
pH, () = Adjusted pH. Solid and filled markers indicate first and second replicates. Regression lines for unadjusted pH runs are dashed.
Those for adjusted pH runs are dot-dot-dashed.
105
Figure A-4 Survival and pH curves for Kefir Coccus AM-W1. () =
Unadjusted pH, () = Adjusted pH. Solid and filled markers indicate first and second replicates. Regression lines for unadjusted pH runs
are dashed. Those for adjusted pH runs are dot-dot-dashed.
106
Figure A-5 Survival and pH curves for L. acidophilus ADH. () =
Unadjusted pH, () = Adjusted pH. Solid and filled markers indicate first and second replicates. Regression lines for unadjusted pH runs
are dashed. Those for adjusted pH runs are dot-dot-dashed.
107
Figure A-6 Survival and pH curves for L. bulgaricus Lb-12. () =
Unadjusted pH, () = Adjusted pH. Solid and filled markers indicate first and second replicates. Regression lines for unadjusted pH runs
are dashed. Those for adjusted pH runs are dot-dot-dashed.
108
Figure A-7 Survival and pH curves for L. rhamnosus AM-L3. () =
Unadjusted pH, () = Adjusted pH. Solid and filled markers indicate first and second replicates. Regression lines for unadjusted pH runs
are dashed. Those for adjusted pH runs are dot-dot-dashed.
109
Figure A-8 Survival and pH curves for L. paracasei AM-L4. () =
Unadjusted pH, () = Adjusted pH. Solid and filled markers indicate first and second replicates. Regression lines for unadjusted pH runs
are dashed. Those for adjusted pH runs are dot-dot-dashed.
110
Figure A-9 Survival and pH curves for L. paracasei AM-L6. () =
Unadjusted pH, () = Adjusted pH. Solid and filled markers indicate first and second replicates. Regression lines for unadjusted pH runs
are dashed. Those for adjusted pH runs are dot-dot-dashed.
111
Table A-1 Tukey's HSD for the slopes of the survivor regression lines.
species adj species:adj
AML6 a Y a AML6:N a LaADH a N b LB12:N a b
La2 a b LaADH:N a b AML3 a b c La2:N a b
Lb12 a b c d AML3:N b c LP b c d e AMW1:N a b c d
AMW1 c d e LP:N b c d Lferm d e Lferm:Y b c d
AML4 e LaADH:Y b c d AML3:Y b c d e
La2:Y b c d e LB12:Y c d e
AML4:Y c d e f AML6:Y d e f
LP:Y d e f
AMW1:Y d e f AML4:N e f
Lferm:N f
112
Table A-2 Tukey's HSD for pH-adjusted and pH-unadjusted subsets of
the slopes of the survivor regression lines.
pH-adjusted not pH-adjusted
L ferm a AML6 a
LaADH a b Lb12 a
AML3 a b c LaADH a
La2 a b c La2 a
Lb12 a b c AML3 a
AML4 a b c AMW1 a b
AML6 b c LP a b
LP c AML4 b c
AMW1 c Lferm c
113
APPENDIX B
FTIR spectra
Figure B-1 FTIR spectra of pH-adjusted extract samples
114
Figure B-2 FTIR spectra of unadjusted extract samples
115
Figure B-3 FTIR spectra of samples grown in tomato juice.
116
APPENDIX C
Programs
C.1 survival-slopes-aov.r
ANOVA and HSD program used to create the data for Table A-1 and
Table A-2.
setwd("~/research/Thesis Data")
cfu = read.csv("survival-slopes-cfu.csv",header=TRUE)
pH = read.csv("survival-slopes-pH.csv",header=TRUE)
cfu
pH
cfuaov = aov(cfu ~ species+adj+species*adj,data=cfu)
summary(cfuaov)
TukeyHSD(cfuaov,which="species",ordered=TRUE)
TukeyHSD(cfuaov,which="adj",ordered=TRUE)
TukeyHSD(cfuaov,which="species:adj",ordered=TRUE)
phaov = aov(pH ~ species+adj+species*adj,data=pH)
summary(phaov)
#TukeyHSD(phaov,which="species",ordered=TRUE)
TukeyHSD(phaov,which="adj",ordered=TRUE)
#
# Now look for between species differences within
# pH adjusted and unadjusted subsets
#
cfu.adj = read.csv("survival-slopes-cfu-
adj.csv",header=TRUE)
cfu.noadj = read.csv("survival-slopes-cfu-
noadj.csv",header=TRUE)
pH.adj = read.csv("survival-slopes-pH-adj.csv",header=TRUE)
117
pH.noadj =
read.csv("survival-slopes-pH noadj.csv",header=TRUE)
cfuaov.adj = aov(cfu ~ species ,data=cfu.adj)
summary(cfuaov.adj)
TukeyHSD(cfuaov.adj, ordered=TRUE)
cfuaov.noadj = aov(cfu ~ species ,data=cfu.noadj)
summary(cfuaov.noadj)
TukeyHSD(cfuaov.noadj, ordered=TRUE)
summary( aov(pH ~ species ,data=pH.adj) )
summary( aov(pH ~ species ,data=pH.noadj) )
C.2 extract.r
ANOVA and HSD program used to analyze extract quantities.
setwd("~/research/Thesis Data")
extract = read.csv("extract.csv",header=TRUE)
extract
extract.aov = aov(extract ~ species + adj + species*adj,
data = extract)
summary(extract.aov)
#TukeyHSD(extract.aov,which="species",ordered=TRUE)
TukeyHSD(extract.aov,which="adj",ordered=TRUE)
#TukeyHSD(extract.aov,which="species:adj",ordered=TRUE)
118
119
C.3 peak-compare.r
Program to locate possible peaks in IR spectra. Locations of peaks in treatment samples are compared with those identified in the untreated
controls. Matches are assumed to come from the substrate and are rejected. This program also plots the spectra.
library(Peaks)
#####
# UniquePeaks(spectrum, backgnd, tolerance)
# Find peaks unique to spectrum, not found in background.
# Tolerence determines how apart peaks can be and still
# be considered the same.
UniquePeaks <- function(spectrum, backgnd, tolerance=1) {
spec_olen = length(spectrum) # remember original lengths
bg_olen = length(backgnd)
UNIQ = array()
spec = sort(spectrum) # order and use side effect(NA removal)
bg = sort(backgnd)
bgidx = 1
uniqidx = 0
for (specidx in 1:length(spec)) { # index for spectrum
idx = 1 # index for background
repeat {
if ( spec[specidx] >= (backgnd[idx] - tolerance)
& spec[specidx] <= (backgnd[idx] + tolerance) ) {
# peak matches - ignore
break
}
idx = idx + 1
if (idx > length(bg)) {
uniqidx = uniqidx + 1
120
UNIQ[uniqidx] = spec[specidx]
break
}
}
}
length(UNIQ) = spec_olen
return(UNIQ)
}
##########################################
myplot = function(X,Y,jar,species) {
plot(X, Y,
xlab = "Wavenumber (1/cm)",
ylab = "% Reflectance",
xlim = (c(2000,500)),
ylim = (c(0,100)),
xaxs = "i",
yaxs = "i",
type = "l"
)
mtext(paste(jar,"-",species,sep=" "))
}
##########################################
pad_length = 100
X = NULL
Y = NULL
POS = NULL
COLUMNS = NULL
setwd("~/research/Thesis Data")
pj_path = "ftir-cooked/"
jar_list = read.csv("jar-list",header=TRUE,colClasses="character")
121
for (file in jar_list$jar) {
count = 6
realN = 0
for ( N in 1:count) {
filename = paste(pj_path,file,"-",N,".txt",sep="")
if(file.exists(filename)) {
spectrum = read.csv(filename,header=FALSE)
realN = realN + 1
}
else {
next
}
if (realN == 1) {
AVG = spectrum
}
else {
AVG = AVG + spectrum
}
}
AVG = AVG / realN
X = data.frame(cbind(X, AVG$V1))
Y = data.frame(cbind(Y, AVG$V2))
z = SpectrumSearch(100-AVG$V2,sigma=2,threshold=10)
z$pos = sort(z$pos)
length(z$pos) = pad_length
POS = data.frame(cbind(POS, z$pos))
print(file)
print(z$pos)
if (is.null(COLUMNS)) {
COLUMNS = file
}
else {
COLUMNS = cbind(COLUMNS, file)
}
}
colnames(POS) = COLUMNS
colnames(X) = COLUMNS
122
colnames(Y) = COLUMNS
#
# At this point, we have x's, y's and peakindicies in X, Y and POS.
# Also we've got a list of jars, matching pomace and species in
jar_list
#
# 1st, get remove peaks in sample and pomace
#
Q = 3 # minimum # of points before peaks considered different.
# Q = 1, one point to either side, Q = 2, two points....
# For 3631 points in 500-4000, data points are 0.9643
cm^-1 apart
# use length(a$b) to get number of rows - using length(a) gives
number of columns
for (i in 1:length(jar_list$jar)) {
jar = jar_list$jar[i]
pomace = jar_list$pomace[i]
species = jar_list$species[i]
if(jar != pomace) {
print(paste("jar =",jar,"pomace =",pomace,"species
=",species,sep=" "))
zpos = UniquePeaks(POS[[jar]],POS[[pomace]],Q)
length(zpos) = pad_length
POS[[jar]] = zpos
}
else {
print(paste("Skipping jar",jar,sep=" "))
}
}
pdf("spectra.pdf",width=6,height=3,onefile=TRUE)
for (i in 1:length(jar_list$jar)) {
jar = jar_list$jar[i]
pomace = jar_list$pomace[i]
species = jar_list$species[i]
p1543 = jar_list$p1543[i]
p1448 = jar_list$p1448[i]
p1404 = jar_list$p1404[i]
123
################
region = c("CHO [stretch]",
"Amide II (N-H)",
"Amide I (C=O)",
"Ketones&COOR"
)
range_lo = c(950,1500,1630,1700)
range_hi = c(1200,1650,1700,1750)
################
# plain plot
#### myplot(X[[jar]], Y[[jar]], jar, species)
################
# plain plot with peaks
opar = par( mar=c(4,4,2,1) )
myplot(X[[jar]], Y[[jar]], jar,
paste(species,"(Q=",Q,")",sep=" "))
# yes, but it's harder to read without the extra assignments
pos = sort( POS[[jar]] )
x = X[[jar]][pos]
y = Y[[jar]][pos]
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~")
print(cbind(jar," ",species))
print(x)
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~")
#### Uncomment to turn on marking all peaks
# segments(x, y , x , y + 10)
# segments(x, y + 10, x - 50, y + 12)
if (p1543 == "Y") {
xa = 1543; xb = xa
ya = Y[[jar]][1083]; yb = ya + 40
if(yb > 80) { yb = ya - 30 }
segments(xa, ya, xb, yb)
text(xb, yb, "1543",adj=c(0,0.5))
}
if (p1448 == "Y") {
124
xa = 1448; xb = xa
ya = Y[[jar]][984]; yb = ya + 30
if(yb > 80) { yb = ya - 20 }
segments(xa, ya, xb, yb)
text(xb, yb, "1448",adj=c(0,0.5))
}
if (p1404 == "Y") {
xa = 1404; xb = xa
ya = Y[[jar]][939]; yb = ya + 20
if(yb > 80) { yb = ya - 10 }
segments(xa, ya, xb, yb)
text(xb, yb, "1404",adj=c(0,0.5))
}
box(which="figure",lwd=3)
par(opar)
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