RESVERATROL AND PROCYANIDIN CONTENT IN SELECT MISSOURI RED WINES 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 LAURA ORTINAU Dr. Ingolf Gruen, Thesis Supervisor DECEMBER 2009
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RESVERATROL AND PROCYANIDIN CONTENT IN SELECT MISSOURI RED WINES
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 LAURA ORTINAU
Dr. Ingolf Gruen, Thesis Supervisor
DECEMBER 2009
The undersigned, appointed by the dean of the Graduate School, have examined the thesis entitled
RESVERATROL AND PROCYANIDIN CONTENT IN MISSOURI RED WINES
presented by Laura Ortinau, a candidate for the degree of Master of Science, and hereby certify that in their opinion it is worthy of acceptance.
Ingolf Gruen, Ph.D., Department of Food Science
Keith Striegler, Ph.D., Department of Food Science
Mark Ellersieck, Ph.D., Experiment Station Statistics
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ACKNOWLEDGEMENTS
I would like to take this opportunity to thank my advisor Dr. Gruen for helping
me work through the complications of this project (the HPLC breaking twice), and also
my committee members Dr. Ellersieck and Dr. Striegler. Each added their expertise to
my project, which was greatly appreciated. Also I would like to give a shout out to
JoAnn for answering all my questions on editing and formatting. Well and I suppose I
could thank the parentals as well for creating me.
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TABLE OF CONTENTS ACKNOWLEDGEMENTS…………………………………………………………
LIST OF FIGURES…………………………………………………………………
LIST OF TABLES ………………………………………………………………….
ABSTRACT ………………………………………………………………………..
Chapter
1. INTRODUCTION ………………………………………………………….
2. LITERATURE REVIEW …………………………………………………. 2.1 Wine …………………………………………………………………
2.1.1 Wine Production and Consumption ………………………... 2.1.2 Missouri Wine History …………………………………….. 2.1.3 Red Wine Production ………………………………………. 2.1.4 Norton Grape ………………………………………………. 2.1.5 Health Benefits of Alcohol …………………………………
2.1.5.1 Blood Pressure ……………………………………. 2.1.5.2 Platelet Aggregation ……………………………… 2.1.5.3 Quantity of Alcohol Consumed ………………….. 2.1.5.4 Type of Alcohol Consumed ……………………….
2.1.6 Health Benefits of Wine …………………………………... 2.1.6.1 Anti-Cancer ………………………………………. 2.1.6.2 Oxidative Stress …………………………………... 2.1.6.3 Nitric Oxide ……………………………………….
Chemical Structure of trans-resveratrol (left) and cis-resveratrol (right) …………………………………………………………………. Procyanidin A and B Structures ………………………………………… Resveratrol Chromatograph of Augusta Norton 2005 Bottle3 …………. Procyanidin Chromatograph of Mount Pleasant Norton 2005 Bottle 3…………………………………………………………………
United States Top Ten Wine Producing States by Amount and Percentage of Production in 2006 ……………………………………… Reported Levels of Resveratrol After Strevbo and others 2007 ………. Winery Name, Wine, and Vintage of Wine Sample …………………… Gradient System for Procyanidin HPLC Solvents ……………………. Resveratrol and pH Results …………………………………………… Catechin and Epicatechin Results ……………………………………... Procyanidin B1 and B2 Results ……………………………………….. Epicatechin Results by Location ………………………………………. Catechin Results by Location …………………………………………. Procyanidin B1 Results by Location …………………………………. Procyanidin B2 Results by Location …………………………………. Resveratrol Results by Location ………………………………………..
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RESVERATROL AND PROCYANIDIN CONTENT IN SELECT MISSOURI RED
WINES
Laura Ortinau
Dr. Ingolf Gruen, Thesis Supervisor
ABSTRACT
Many health benefits have been attributed to procyanidins and resveratrol,
including increased nitric oxide (NO) production, decreased platelet aggregation, and
chemopreventative actions. These polyphenolic molecules are predominantly found in
the skins of grapes and are present in higher quantities in red wines than white wines.
These polyphenols levels have been reported for wines around the world. However, the
Norton grape is one of the predominant grapes grown in Missouri, and there is limited
information on its polyphenolic makeup. This study analyzed wines produced in
Missouri, predominantly Norton, and determined the resveratrol and procyanidin
contents. Resveratrol ranged from 0.07 mg/L to 1.52 mg/L. Procyanidins were of
higher ranges consisting of 0.17 mg/L to 20.79 mg/L catechin, 0.22 mg/L to 29.48 mg/L
epicatechin, ND to 52.16 mg/L procyanidin B1, and ND to 23.95 mg/L procyanidin B2.
There was a significant correlation between the overall resveratrol content and
procyanidin B1, as well as between catechin and procyanidin B1 and B2 (p<0.0001).
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CHAPTER 1
INTRODUCTION
Heart Disease is the leading cause of death in America with 631,636 deaths in
2006, and cancer was a close second with 559,888 deaths in 2006 (Control, 2009). Since
the 1990’s alcohol, specifically wine, has become of great interest because of the “French
Paradox”. The French Paradox is the relationship seen in France of a high saturated fat
diet and a low incidence in coronary heart disease (CHD) (Renaud and De Lorgeril
1992). Resveratrol was originally thought to be the polyphenolic compound mainly
responsible for the cardioprotective effects of red wine consumption. However,
resveratrol concentrations are so low in wines that in order to obtain a functional level
within the body one would have to consume large quantities of red wine. In 2007 Corder
claimed that the health benefits associated with moderate red wine consumption were
from the procyanidin compounds in wine (Marian 2007). Resveratrol and procyanidin
levels have been reported for European countries, Australia, Canada, and the United
States of America. However, only limited reports have been published on American
wines, mainly for those from California.
The objectives of this study were to analyze the resveratrol and procyanidin
content of red wines in Missouri, specifically those of the Norton variety. Since there are
no reported values for this variety, we compared the quantities of resveratrol and
procyanidin to that of wines from other countries. Also, resveratrol and procyanidin
levels were related to the different geographic locations of the wineries in Missouri.
Lastly, each compound was correlated to each other to see if there was a significant
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relationship between the quantity of resveratrol and the procyanidins as well as a
relationship between the quantities of procyanidins themselves.
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CHAPTER 2
LITERATURE REVIEW
2.1 Wine
2.1.1 Wine Production and Consumption
In 2005, 2.37 billion liters of wine were consumed in the world. The USA wine
industry reached a retail value of $30 billion and produced 2.85 billion liters of wine in
2007. The United States wine market has increased in recent years. Table wines
consisted of 2.46 billion liters, dessert wines consisted of 234.7 million liters, and
sparkling wines consisted of 124.9 million liters. Imported table and sparkling wines
accounted for more than 25% of the United States consumption. The top five countries
that the United States imports wine from are France (31%), Italy (28%), Australia (17%),
Spain (5.8%), and Chile (4.5%). From 2002 to 2007 the United States exports grew 56%
in volume and 66% in value. The countries that imported the most wine from the United
States in 2007 include the United Kingdom, Canada, Japan, Italy, and Germany. Of all
the wines sold in the United States, the Department of Commerce estimates that 61% of
wine sold is from California and 26% is from imports. This leaves only 13% from all the
other US states. California wineries produced over 2.18 billion liters of wine in 2006,
which accounted for 89.25% of the total US production. In comparison, Missouri
produced 3.38 million liters in 2006, ranking it below the top 10 producer states
Table 2.1.1 United States Top Ten Wine Producing States by Amount and
Percentage of Production in 2006 State Amount (L) Percent of U.S. Production California 2.18 billion 89.25 New York 106.8 million 4.37 Washington 75.9 million 3.11 Oregon 15.6 million 0.64 Florida 6.6 million 0.27 New Jersey 6.3 million 0.26 Kentucky 4.7 million 0.19 Ohio 4.2 million 0.18 Virginia 3.7 million 0.15 North Carolina 3.5 million 0.14
Table information from (Hodgen, 2008)
2.1.2 Missouri Wine History
In 1837 German settlers founded the town of Hermann, MO. Hermann was an
ideal place to cultivate grapes because the growing conditions were similar to those in
Germany. Before the Prohibition, Missouri produced over 2 million gallons of wine per
year. This ranked Missouri second in the nation for production of wine. However,
during the prohibition all of the Missouri wineries closed with the exception of the St.
Stanislaus Novitiate in St. Louis, which was allowed to continue limited production of
wine for sacramental purposes. Then in 1965, Stone Hill Winery reopened and started a
trend for other wineries to reopen as well. In 1980 Augusta, Missouri became the first
wine appellation in the United States. As of 2005 Missouri was ranked 11th in grape
production, producing approximately 2.65 million liters (702,000 gallons) wine, of which
the St. James Winery produced approximately a third of the state’s wine sales with Stone
Hill Winery coming in second. Missouri’s production increased in 2007 to 3.38 million
liters (894,391 gallons) of wine and Missouri is still currently growing as a wine
producing state. In 2002 there were 31 wineries in Missouri whereas in 2007 there were
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72. Currently the price per ton of grapes in Missouri is increasing in value, and the
tonnage per acres is decreasing, which normally indicates an increased quality of the
fruit. Some of the premier growing regions of Missouri include Augusta, Hermann,
Ozark Highlands, and the Ozark Mountains. These regions often contain optimal
growing conditions for vines, with south facing slopes, and protection from the cold
winters. Some of the varietals that have been successfully grown within these regions
include Norton (Cynthiana), Chardonel, Concord, Vignoles, Catawaba, Vidal Blanc, and
Chambourcin. The Norton grape accounts for 20% of the grapes grown in Missouri.
Many of the varietals grown in Missouri are native to America, or are hybrids of French
vines, because Missouri’s cold winters and relatively humid climate during the summer
will not support the growth of the Noble Grape varietals. Due to these climate
conditions, many of the vines face multiple challenges such as mold, fungi, mildew,
insects, and wildlife (Management 2000; Norton/Cynthiana 2005; Hodgen 2008; Vitis
2009).
2.1.3 Red Wine Production
Approximately 4,000 varieties of Vitis vinifera are used in wine production. There
are different styles of creating wine that can be specific to the type of grape; this is often
how the wine making process is selected. Steps to general red wine production include:
1. Harvesting – can either be done by hand (slow process, but harvesters can
determine if all the berries are ripe and it doesn’t damage the grapes) or by
machine (this is a much faster process, but the berries can be damaged, and all
grapes are harvested, the good and bad)
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2. Crushing – this is done normally within 24 hours of harvesting. The grapes are
usually destemmed because of unwanted flavor compounds (but stems can add
astringency back to the wine if desired because of their high tannin content). The
grapes are crushed between rollers to the desire of the wine maker.
3. Fermentation – the major choices start here by choosing natural vs. inoculated
yeasts, or open vs. closed tanks,
4. Maceration - punch down cap vs. pump-over cap vs. rotary fermenters
5. Pressing – this is done once the free wine has been removed from the pomace. 2
days to 3 weeks after fermentation.
6. Malo-lacto fermention - changes malic acid into lactic acid lowering the
harshness of the acid, which is done in oak barrels or vats.
7. Maturation – can occur from 3 months to 3 years depending on the wine. Can be
done in vats or barrels. Barrels allow for oxidative reactions and wood
extractives.
8. Clarification – removing any sediment from the wine prior to bottling (last
minute changes)
9. Aging – is done in the bottle, it allows for the formation of different flavors to
develop
These steps depend on the wine, winery, and winemaker’s preferences on how to make
the best product from their grapes (Soleas and others 1997; Hornsey 2007). Red wine
processing is uniquely different from that of white wine production. These differences
lead to more phenolic compounds in red wine due to the increased contact time of skins
with the juice.
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2.1.4 Norton Grape
The Norton grape is of the family and genus Vitis aestivalis Michx (Tarara 1991).
It was found in 1835 near Richmond, Virginia. In 1873, the Monticello Wine Company’s
(Charlottesville) Norton Claret won the gold medal in Vienna, and then in Paris the silver
medal in 1878, showing that the American wines could compete at the same level as the
European varieties (Adams 1973). Norton is the oldest American grape variety that is
commercially grown today. It is commercially produced in Arkansas, Illinois, Indiana,
Kansas, Louisiana, Maryland, Missouri, Oklahoma, New Jersey, Pennsylvania,
Tennessee, Texas, Virginia, and West Virginia. The vines are relatively cold hardy and
disease resistant. Because of Missouri’s warm and humid summers, vines are susceptible
to many diseases, insects and molds. Norton is extremely vigorous and needs a divided
canopy training system. The preferred growing conditions of this plant are well drained
soils in full sun, medium moisture, and preferably a south facing slope. Norton is the
latest ripening grape in Missouri. The vines produce medium clusters with blue-black
berries. These grapes produce a very dark colored dry wine that is medium bodied with
fruity overtones. These grapes should be used to produce young style wines, and not be
aged much longer than a year because of their high pH and high titratable acidity.
Norton’s high pH and titratable acidity is from the presence of weak acids (tartaric and
malic acid) that are present in their undissociated forms. Differences in pH can be
attributed to soil type, rootstock, vine vigor, leaf shading, cultivar crop level, and
formic acid, and ammonium phosphate were obtained from Fisher Scientific (St. Louis,
MO). The standards Catechin, Epicatechin, Procyanidin B1, Procyanidin B2, and
Resveratrol were obtained through Sigma-Aldrich (St. Louis, MO).
3.1.2 Wine Samples
Table 3.1.2 Winery Name, Wine, and Vintage of Wine Samples
Wine Samples Winery Wine 2000 2001 2002 2003 2004 2005 2006 2007 Winery 1 Norton x x x x Winery 2 Chambourcin x x Norton x x Winery 3 Chambourcin x x Norton x x Winery 4 Chambourcin x Norton x Premium Claret x x x Winery 5 Norton x x x x Winery 6 Blackberry x Chambourcin x x x Cherry x Norton x x x x Strawberry x School House Red x x Winery 7 Norton x x x x x
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Winery 1 donated 2001 to 2004 vintages of Norton wines. All of these samples
were 100% Estate grown in the Winery 1 vineyard. The Winery 3 wine samples included
a Chambourcin 2003 and 2004 as well as a Cynthiana (another name for Norton) 2003
and 2004. The 2003 Cynthiana consisted of 90% Cynthiana and 10% Chambourcin. The
2004 Cynthiana, 2003 Chambourcin, and 2004 Chambourcin were all 100% varietal
specific. Winery 7 donated Norton vintages from 2003 to 2007. Each of the vintages
was 100% Norton with no other varieties added to them. Also, the 2006 and 2007 Norton
samples were from the barrel and had not been bottled. Winery 6 donated Norton
samples from 2000, 2002, 2003, and 2004, as well as Friendship School Red 2005 and
2006, Cherry, Strawberry, Blackberry, and Chambourcin 2003, 2004, and 2005.
According to the Winery 6, the 2002 and 2003 Norton vintages are of a 100% Norton
Grape blend, the 2004 vintage is a blend of Norton and Cabernet Sauvignon
(86.41%/13.39%), and the 2000 vintage is of an unknown blend. The Chambourcin
2003, 2004, and 2005 vintages are all made from 100% Chambourcin. The blackberry,
strawberry, and cherry wines were all from 2006, and the School House Red vintages
were 2005 and 2006. Between the 2005 and 2006 vintages, the winery switched from
using a cork to a screw cap. Winery 4’s Norton Premium Claret 2004 was made from
100 % Norton Grapes from Missouri river hills (2 different vineyards) and High Plains in
the Ozarks (2 different vineyards). The contribution of grapes from each vineyard was
21% Winery 4, 40% Native Stone, 30% McMurtry, and 9% Neobo. The 2005 Premium
Claret was made from a variety of grapes. Norton was 42% from Winery 4, a mixed
Norton from McMurtry Vineyards contributed to 38% of the final product, 11% was
Cabernet Sauvignon from Oak Grove Vineyards, 6% was Chambourcin from Drunken
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Monkey Vineyards, and 3% was Syrah from Oak Grove Vineyards. As for the 2005
Norton, it was also 100% Norton and the contribution of grapes was from Winery 4,
McMurtry vineyards (Mountain Grove), and Gordon Vineyard (Neobo Cemetary
Columbia/Rocheport). Lastly, the 2005 Chambourcin was a 100% Chambourcin blend
from the Drunken Monkey and Sugar Branch Vineyards. Wines from Winery 5 and
Winery 2 are of unknown blends.
3.2 Resveratrol Method
3.2.1 Sample Preparation
A 1 mL sample of wine was taken from each bottle and placed in a 5 mL vial that
was covered with aluminum foil to protect the sample from UV light. The Samples were
then dried under nitrogen in a chemical fume hood at room temperature (between 25°C
and 28°C). Once dried, the solids were redissolved in the mobile phase consisting of 25%
acetonitrile in HPLC water + 0.1% H3PO4 + 5 mM NaCl. The samples were then placed
in a sonicator for 10-15 minutes. Samples were filtered using a 3 mL syringe and 0.45
µm filter (Kankakee, IL) and transferred into a 5 mL amber vial. Samples were either
analyzed that day or placed into the refrigerator until they were analyzed (less than a
week).
3.2.2 HPLC Conditions
The HPLC system consisted of a Perkin Elmer series 410 pump, a Perkin Elmer
LC 90 UV Spectrophotometric Detector, and a C18 Column 25cm x 4.6mm, 5µm
(Supelcosil 5-8298, COL 018341AP). The mobile phase was isocratic and consisted of,
as stated before: 25% acetonitrile in HPLC water + 0.1% H3PO4 + 5 mM NaCl. The flow
rate was 1 mL/min for 35 minutes with a 3 minute wash-out phase of 50%
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methanol/water (Kolouchová-Hanzliková and others 2004). Detection was set at 306 nm,
and the data were analyzed using Star Chromatography Workstation Version 4.51 by
Varian Associates Inc.
3.2.3 Validation of Resveratrol Method
Standards to create a standard curve were at concentrations of 0.01, 0.1, 0.25, 0.5,
0.75, 1.0, 2.5, and 5.0 µL/mL. The resveratrol standard curve’s regression coefficient
was 0.999638. The coeffiecent of Variation of the HPLC system was determined by
injecting the same sample 10 times. This gave a coefficient of variation of 11.41% for
the HPLC system. As for sample preparation, ten samples were taken from the same
bottle of wine, and each were injected once into the HPLC system. The coefficient of
variation was 2.5% for the sample preparation.
3.3 Flavan-3-ol Method
3.3.1 Preparation of Sample
A 5mL sample of wine was pipetted into a glass vial. The sample was acidified
by the addition of formic acid 1.5% (v/v). The sample was then filtered into 1.5 mL
sample vials for HPLC analysis using a 3 mL syringe and a 0.45 µm filter (Gao and
others 1997; Gomez-Alonso and others 2007).
3.3.2 HPLC Conditions
The HPLC system consisted of a ProStar model 410 Auto Sampler Perkin Elmer
series 410 pump, an SSI 500 Detector variable UV/Vis, a Waters 474 scanning
fluorescence detector, and a C18 Column 25cm x 4.6mm, 5µm (Supelcosil 5-8298, COL
018341AP). The column was kept at ambient temperature throughout the analysis. The
solvent system was a gradient system consisting of three solvents based on the
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procedures of Gao and others (1997) with slight modifications. Solvent A: 50mM
NH4H2PO4, pH=2.6; Solvent B: 20% solvent A and 80% Acetonitrile; Solvent C: 200mM
H3PO4, pH=1.5. The gradient was as shown in Table 3.3.2.
Table 3.3.2 Gradient System for Procyanidin HPLC Solvents
Flavon-3-ols Mobile Phase Gradient of the HPLC Method Step Time
Winery 7 Norton 2003 0.89Dcde 0.08 3.75 0.13 Norton 2004 0.78DEdefg 0.02 3.68 0.04 Norton 2005 0.57FGefghi 0.33 3.64 0.01 Norton 2006 0.66EFefgh 0.03 3.66 0.02 Norton 2007 0.38GHIhijk 0.03 3.78 0.02 The t-test and Tukey’ studentized test results are compound specific ABCDEFGHIJKLM – t-test, each letter is significantly different from the other abcdefghijk –Tukey’s Studentized Range Test, each letter is significantly different
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4.2 Procyanidin Results
All the levels for catechin, epicatechin, procyanidin B1, and procyanidin B2 were
within the ranges previously reported for other wine varietals. Norton Wines from
Winery 5 2006 (15.91 mg/L) and Winery 7 2007 (11.22 mg/L) had the highest levels of
catechin, whereas Norton wines from Winery 3 2003 (0.19 mg/L) and Winery 7 2005
(0.81 mg/L) had the lowest catechin levels. For individual results refer to Table 4.2.1.1
or Appendix A-10 pages 63-64
As for epicatechin, the reported levels again varied by vintage and varietal. The
highest levels were found in Winery 1 Norton 2003 (14.49 mg/L), Winery 2
School House Red 2005 4.91FGfgh 0.30 0.93MNOijkl 0.02
School House Red 2006 20.79Aa 0.58 5.63Fd 0.25
Winery 7 Norton 2003 2.64IJKhijklm 0.32 0.30OPkl 0.26 Norton 2004 0.50NOmn 0.09 1.13LMNijkl 0.03 Norton 2005 1.14LMNO 0.35 0.40OPkl 0.06 Norton 2006 7.46Ede 0.91 3.49Ge 0.22 Norton 2007 15.71Bb 1.29 3.19GHef 0.29 The t-test and Tukey’s studentized test results are compound specific ABCDEFGHIJKLMNOP – t-test results, each letter is significantly different than the other abecdefghijklmn – Tukey’s Studentized Range (HSD) Test, each letter is significantly different than the other.
Winery 5 Norton 2003 26.69Gefgh 0.46 2.39OPQmnopqrs 0.46 Norton 2004 32.56DEcde 0.13 6.74FGHIefgh 0.97 Norton 2005 32.97DEcde 2.35 5.54IJKfghij 0.35 Norton 2006 52.16Aa 1.83 6.76FGHefgh 0.11 The t-test and Tukey’ studentized test results are compound specific ABCDEFGHIJKLMNOPQRST - t test results, each letter is significantly different abcdefghijklmnopqrs – Tukey’s Studentized Range Test, each letter is significantly different ND – Not Detectable
7.71 15.69Bb 2.01 The t-test and Tukey’ studentized test results are compound specific ABCDEFGHIJKLMNOPQRST - t test results, each letter is significantly different abcdefghijklmnopqrs – Tukey’s Studentized Range Test, each letter is significantly different ND – Not Detectable
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4.3 Variations between Varietals and Vintages
Using a t-test and HSD test, each compound measured was analyzed comparing
the specific compound with that of other varietals and vintages. For epicatechin, Winery
2 Chambourcin 2004 had the highest mean and was significantly different from all of the
other wines sampled. Winery 6 Norton 2004 and Winery 1 Norton 2003, the next highest
means, were not significantly different from each other but were significantly different
from all the others. For catechin, Winery 6 School House Red 2006 had the highest
mean, and was significantly different from all of the other samples. The next highest
values were from Winery 5 Norton 2006 and Winery 7 Norton 2007. These two samples
were not significantly different from one another but were significantly different from the
rest of the samples when using the t-test, but for the HSD test Winery 7 Norton 2007 and
Winery 2 Chambourcin 2003 were also in the second grouping that was significantly
different for the rest of the samples, with the exception of Winery 2 Chambourcin 2003
not being significantly different than Winery 2 Norton 2004. Procyanidin B1 content in
Winery 5 Norton 2006 was significantly higher than that of other samples when using the
t-test and HSD. However many of the other samples had overlapping significant
differences from one another. The t-test of the procyanidin B2 indicated that the five
highest means were significantly different from not only one another but also from the
rest of the samples. These five samples included Winery 3 Chambourcin 2004, Winery 7
Norton 2007, Winery 7 Norton 2006, Winery 6 BlackBerry 2006, and Winery 6 School
House Red 2006. For the HSD test, however, only Winery 3 Chambourcin 2004 and
Winery 7 Norton 2007 are significantly different from each other as well as that of the
rest of the samples. The highest mean values of resveratrol were found in Winery 5
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Norton 2004, Winery 2 Norton 2003, and Winery 1 Norton 2004. As a group these
samples were significantly different from the rest of the samples with the exception of
Winery 1 Norton 2004. Winery 1 2004 was not significantly different from Winery 2
Norton 2004 and Winery 3 Norton 2004 according to the t-test. However, with the HSD
were also grouped with Winery 5 Norton 2003 as not being significantly different from
one another but different from the rest of the samples.
4.4 Relationship between Winery Location and Compound Concentration
The t-test, as well as HSD, was also done to see if there is a relationship between
the quantity of each compound in the wines and the location in which the wineries are
located. Wineries in Winery 6 and Hermann had significantly different quantities of
epicatechin as determined by the t-test. Using HSD, St. James, Herman, and Waverly
had significantly higher quantities of epicatechin. Catechin results for Augusta wines had
the highest quantity by location based on the t-test, and with the HSD test Augusta,
Hermann, and St. James grouped together and had significantly higher quantities of
catechin than the other locations. However, this test also grouped Hermann, St. James,
and Waverly together as well. Procyanidin B1 showed no significant differences
between the locations and the quantity measure for either test. Hermann, Augusta, and
Waverly were grouped together for the t-test. Again however, Waverly and St. James
were also grouped together. Using the HSD test all locations were considered equal.
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Lastly, the relationship between resveratrol and winery locations created quite different
results. Based on the t-test, Augusta and Waverly were grouped together, and Hermann
and St. James were grouped together, indicating that the two groups were different from
one another. With the HSD test, however, three groups were created with all three
overlapping.
Table 4.4.1 Epicatechin Concentration by Location
Table 4.4.2 Catechin Concentration by Location
Epicatechin Catechin Location Mean t-test HSD Location Mean t-test HSD St. James 6.81 ± 7.10 A A August 7.76 ± 5.22 A A Herman 4.05 ± 4.98 AB AB Herman 3.56 ± 5.04 B A Waverly 2.68 ± 0.71 B AB St. James 3.43 ± 3.28 B AB Augusta 1.66 ± 1.15 B B Waverly 0.53 ± 0.49 B B
*AB – each letter is significantly different from one another
*AB – each letter is significantly different from one another
Table 4.4.3 Procyanidin B1 Concentration by Location
Table 4.4.4 Procyanidin B2 Concentration by Location
Procyanidin B1 Procyanidin B2 Location Mean t-test HSD Location Mean t-test HSD St. James 27.45 ± 12.11 A A Herman 5.43 ± 5.29 A A Augusta 27.45 ± 15.95 A A Augusta 5.32 ±1.61 A A Waverly 25.14 ± 3.97 A A Waverly 4.61 ± 1.99 AB A Herman 22.24 ±11.74 A A St. James 1.65 ± 2.61 B A
*AB – each letter is significantly different from one another
*AB – each letter is significantly different from one another
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Table 4.4.5 Resveratrol Concentration by Location
Resveratrol Location Mean t-test HSD Augusta 1.07 ± 0.54 A A Waverly 0.97 ± 0.24 A AB Herman 0.57 ± 0.38 B BC St. James 0.32 ± 0.14 B C
*ABC – each letter is significantly different from one another
4.6 Relationship between Compounds
Using the Pearson’s Correlation Coefficients, a significant correlation between the
quantities of catechin measured and the quantities of procyanidin B1 and B2 were
observed. There was also a significant correlation between the quantity of procyanidin
B1 and the resveratrol content in the samples (p<0.001). For full results refer to
Appendix A-16 page 111.
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CHAPTER 5
DISCUSSION
There are many variables that will affect the quantity of resveratrol and
procyanidin content in wine. Some of them include climate conditions, soil type,
geographical location, cultivar practices, varietal blend, and the wine making processes.
Fining agents were shown to decrease the resveratrol content in red wines (Threlfall and
others 1999). Vine vigor has a significant influence on the polyphenolic content of the
grapes skins, with a lower vine vigor more proanthocyanidins were extracted from the
skins of the grapes into the wine (Cortell and others 2005). Cultivar practices, such as
leaf removal, can influence the wine composition as well (Main 2004). Thus, there are
many confounding variables that could neither be controlled nor are they known, and thus
are not reported for the data analyzed. One assumption we can make about the wine is
that the varietal name of the wine on the bottle must be at least 75% of that varietal.
For the variation between wines and procyanidin and resveratrol content, these
often are a reflection of what varietals were used and the percentage contained in the
wine. Winery 2 Chambourcin 2004 was ≥75% Chambourcin, and had the highest content
of epicatechin. Winery 6 School House Red 2006 had the highest content of catechin,
and was of an unknown blend of varietals. Winery 5 Norton 2006 was at least 75%
Norton and had the highest content of procyanidin B1. As for procyanidin B2, Winery 3
Chambourcin 2004 had the highest concentration and was 100% Chambourcin. Winery 5
Norton 2004 had the highest resveratrol content of the samples and was at least 75%
Norton.
54
Analysis of Norton Wines by location indicates that there may not be a location
that is significantly better than another. Again this is due to the many confounding
variables in the cultivation of the grapes and production of the wines.
It is hard to distinguish if the reported results were a contribution from the
Norton Grape or the varietal blended with it. However, within the top five highest values
for resveratrol, catechin, epicatechin, procyanidin B1, and procyanidin B2 there was
consistently at least one sample that was 100% Norton, and often at least two others that
were at least 75% Norton. More analysis of 100% Norton wines as well as other 100%
varietal specific wines will need to be conducted in the future to compare procyanidins
and resveratrol content, and to determine if there is still a correlation between resveratrol
content and procyanidin B1 as well as a correlation between catechin content and
procyanidin B1 and B2 when using 100% of the same grape to produce a wine.
A-5 Epicatechin Standard Curve and Concentrations used
Epicatechin Concentration
(mg/L) Area
(µV/min)
0.00 0.00 1.49 103.69 3.73 520.86 9.38 24.09
250.00 18345.15 500.00 37793.21
y = 75.128xR² = 0.9994
0
5000
10000
15000
20000
25000
30000
35000
40000
0.00 100.00 200.00 300.00 400.00 500.00 600.00
µV/m
in
mg/L
Epicatechin Standard Curve
60
A-6 Coefficient of Variation for the Resveratrol Method
COEFFICIENT OF VARIANCE RESVERATROL SAMPLE METHOD
Sample Resveratrol (mg/mL
1-1SH N07 0.42 1-2 SH N07 0.41 1-3 SH N07 0.41 1-4 SH N07 0.41 1-5 SH N07 0.38 1-6 SH N07 0.40 1-7 SH N07 0.40 1-8 SH N07 0.40 1-9SH N07 0.40 1-10 SH N07 0.40 AVERAGE 0.40
STANDARD DEVIATION 0.01
COEFFICIENT OF VARIANCE 2.50 A-7 Coefficient of Variation of the HPLC for the Resveratrol Method
COEFFICIENT OF VARIANCE OF HPLC SYSTEM FOR RESVERATROL METHOD
Sample Resveratrol (mg/mL) 1-1 AP N01 0.27 1-2 AP N01 0.19 1-3 AP N01 0.18 1-4 AP N01 0.21 1-5 AP N01 0.21 1-6 AP N01 0.22 1-7 AP N01 0.21 1-8 AP N01 0.21 1-9 AP N01 0.22
1-10 AP N01 0.20
AVERAGE 0.21
STANDARD DEVIATION 0.02
COEFFICIENT OF VARIANCE 11.41
61
A-8 Coefficient of Variation of the HPLC for the Procyanidin Method
COEFFICIENT OF VARIANCE OF THE HPLC SYSTEM (mg/L) FOR THE PROCYANIDIN METHOD
A-15 1 The SAS System 11:38 Monday, October 19, 2009 NOTE: Copyright (c) 2002-2003 by SAS Institute Inc., Cary, NC, USA. NOTE: SAS (r) 9.1 (TS1M3) Licensed to THE CURATORS OF THE UNIV OF MISSOURI - T&R, Site 0001242001. NOTE: This session is executing on the XP_PRO platform. NOTE: SAS initialization used: real time 0.53 seconds cpu time 2.34 seconds NOTE: AUTOEXEC processing beginning; file is C:\Program Files\SAS\SAS 9.1\autoexec.sas. NOTE: AUTOEXEC processing completed. 1 options ls=100 ps=70; 2 data one; infile 'e:\documents\e.csv' dsd firstobs=3 missover; 3 input loc$ winery$ wine$ vin e1-e3; 4 length trt$ 30; 5 trt=compress(winery||wine||vin); 6 title 'epicatechin'; NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 5:28 NOTE: The infile 'e:\documents\e.csv' is: File Name=e:\documents\e.csv, RECFM=V,LRECL=256 NOTE: 39 records were read from the infile 'e:\documents\e.csv'. The minimum record length was 6. The maximum record length was 57. NOTE: The data set WORK.ONE has 39 observations and 8 variables. NOTE: DATA statement used (Total process time): real time 0.23 seconds cpu time 0.23 seconds 7 proc print; 8 NOTE: There were 39 observations read from the data set WORK.ONE. NOTE: The PROCEDURE PRINT printed page 1. NOTE: PROCEDURE PRINT used (Total process time): real time 0.14 seconds cpu time 0.14 seconds 9 data two; set one; 10 e=e1; bot=1; output; 11 e=e2; bot=2; output; 12 e=e3; bot=3; output; 13 14 *proc print; NOTE: There were 39 observations read from the data set WORK.ONE. NOTE: The data set WORK.TWO has 117 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 15 proc glm; class trt; 16 model e=trt;
75
17 means trt/lsd tukey lines; 18
76
2 The SAS System 11:38 Monday, October 19, 2009 NOTE: The PROCEDURE GLM printed pages 2-7. NOTE: PROCEDURE GLM used (Total process time): real time 0.18 seconds cpu time 0.17 seconds 19 data three; set two; 20 if wine='Norton'; NOTE: There were 117 observations read from the data set WORK.TWO. NOTE: The data set WORK.THREE has 60 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 21 proc glm; class loc; 22 model e=loc; 23 means loc/lsd tukey lines; 24 NOTE: The PROCEDURE GLM printed pages 8-11. NOTE: PROCEDURE GLM used (Total process time): real time 0.06 seconds cpu time 0.04 seconds 25 data foure; set two; NOTE: There were 117 observations read from the data set WORK.TWO. NOTE: The data set WORK.FOURE has 117 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 26 proc sort; by trt bot; 27 28 run; NOTE: There were 117 observations read from the data set WORK.FOURE. NOTE: The data set WORK.FOURE has 117 observations and 10 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.06 seconds cpu time 0.06 seconds 29 30 31 data one; infile 'e:\documents\c.csv' dsd firstobs=3 missover; 32 input loc$ winery$ wine$ vin c1-c3; 33 length trt$ 30; 34 trt=compress(winery||wine||vin); 35 title 'catechin'; NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 34:28 NOTE: The infile 'e:\documents\c.csv' is: File Name=e:\documents\c.csv, RECFM=V,LRECL=256 NOTE: 38 records were read from the infile 'e:\documents\c.csv'. The minimum record length was 42. The maximum record length was 60. NOTE: The data set WORK.ONE has 38 observations and 8 variables. NOTE: DATA statement used (Total process time):
77
3 The SAS System 11:38 Monday, October 19, 2009 real time 0.03 seconds cpu time 0.03 seconds 36 proc print; 37 NOTE: There were 38 observations read from the data set WORK.ONE. NOTE: The PROCEDURE PRINT printed page 12. NOTE: PROCEDURE PRINT used (Total process time): real time 0.00 seconds cpu time 0.00 seconds 38 data two; set one; 39 c=c1; bot=1; output; 40 c=c2; bot=2; output; 41 c=c3; bot=3; output; 42 43 *proc print; NOTE: There were 38 observations read from the data set WORK.ONE. NOTE: The data set WORK.TWO has 114 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 44 proc glm; class trt; 45 model c=trt; 46 means trt/lsd tukey lines; 47 NOTE: The PROCEDURE GLM printed pages 13-18. NOTE: PROCEDURE GLM used (Total process time): real time 0.11 seconds cpu time 0.10 seconds 48 data three; set two; 49 if wine='Norton'; NOTE: There were 114 observations read from the data set WORK.TWO. NOTE: The data set WORK.THREE has 60 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 50 proc glm; class loc; 51 model c=loc; 52 means loc/lsd tukey lines; 53 54 NOTE: The PROCEDURE GLM printed pages 19-22. NOTE: PROCEDURE GLM used (Total process time): real time 0.04 seconds cpu time 0.04 seconds 55 data fourc; set two; NOTE: There were 114 observations read from the data set WORK.TWO. NOTE: The data set WORK.FOURC has 114 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
78
4 The SAS System 11:38 Monday, October 19, 2009 56 proc sort; by trt bot; 57 58 run; NOTE: There were 114 observations read from the data set WORK.FOURC. NOTE: The data set WORK.FOURC has 114 observations and 10 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.03 seconds cpu time 0.03 seconds 59 60 61 data one; infile 'e:\documents\b1.csv' dsd firstobs=3 missover; 62 input loc$ winery$ wine$ vin b1-b3; 63 length trt$ 30; 64 trt=compress(winery||wine||vin); 65 title 'procyanidin B1'; NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 64:28 NOTE: The infile 'e:\documents\b1.csv' is: File Name=e:\documents\b1.csv, RECFM=V,LRECL=256 NOTE: 40 records were read from the infile 'e:\documents\b1.csv'. The minimum record length was 6. The maximum record length was 60. NOTE: The data set WORK.ONE has 40 observations and 8 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 66 proc print; 67 NOTE: There were 40 observations read from the data set WORK.ONE. NOTE: The PROCEDURE PRINT printed page 23. NOTE: PROCEDURE PRINT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 68 data two; set one; 69 ba=b1; bot=1; output; 70 ba=b2; bot=2; output; 71 ba=b3; bot=3; output; 72 73 *proc print; NOTE: There were 40 observations read from the data set WORK.ONE. NOTE: The data set WORK.TWO has 120 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 74 proc glm; class trt; 75 model ba=trt; 76 means trt/lsd tukey lines; 77 NOTE: The PROCEDURE GLM printed pages 24-29. NOTE: PROCEDURE GLM used (Total process time):
79
5 The SAS System 11:38 Monday, October 19, 2009 real time 0.12 seconds cpu time 0.12 seconds 78 data three; set two; 79 if wine='Norton'; NOTE: There were 120 observations read from the data set WORK.TWO. NOTE: The data set WORK.THREE has 60 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 80 proc glm; class loc; 81 model ba=loc; 82 means loc/lsd tukey lines; 83 84 NOTE: The PROCEDURE GLM printed pages 30-33. NOTE: PROCEDURE GLM used (Total process time): real time 0.04 seconds cpu time 0.04 seconds 85 data fourb1; set two; NOTE: There were 120 observations read from the data set WORK.TWO. NOTE: The data set WORK.FOURB1 has 120 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 86 proc sort; by trt bot; 87 88 run; NOTE: There were 120 observations read from the data set WORK.FOURB1. NOTE: The data set WORK.FOURB1 has 120 observations and 10 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 89 90 91 92 data one; infile 'e:\documents\b2.csv' dsd firstobs=3 missover; 93 input loc$ winery$ wine$ vin b1-b3; 94 length trt$ 30; 95 trt=compress(winery||wine||vin); 96 title 'procyanidin b2'; NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 95:28 NOTE: The infile 'e:\documents\b2.csv' is: File Name=e:\documents\b2.csv, RECFM=V,LRECL=256 NOTE: 38 records were read from the infile 'e:\documents\b2.csv'. The minimum record length was 42. The maximum record length was 57. NOTE: The data set WORK.ONE has 38 observations and 8 variables. NOTE: DATA statement used (Total process time): real time 0.03 seconds
80
6 The SAS System 11:38 Monday, October 19, 2009 cpu time 0.03 seconds 97 proc print; 98 NOTE: There were 38 observations read from the data set WORK.ONE. NOTE: The PROCEDURE PRINT printed page 34. NOTE: PROCEDURE PRINT used (Total process time): real time 0.00 seconds cpu time 0.00 seconds 99 data two; set one; 100 bb=b1; bot=1; output; 101 bb=b2; bot=2; output; 102 bb=b3; bot=3; output; 103 104 *proc print; NOTE: There were 38 observations read from the data set WORK.ONE. NOTE: The data set WORK.TWO has 114 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds 105 proc glm; class trt; 106 model bb=trt; 107 means trt/lsd tukey lines; 108 NOTE: The PROCEDURE GLM printed pages 35-40. NOTE: PROCEDURE GLM used (Total process time): real time 0.10 seconds cpu time 0.09 seconds 109 data three; set two; 110 if wine='Norton'; NOTE: There were 114 observations read from the data set WORK.TWO. NOTE: The data set WORK.THREE has 60 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 111 proc glm; class loc; 112 model bb=loc; 113 means loc/lsd tukey lines; 114 115 NOTE: The PROCEDURE GLM printed pages 41-44. NOTE: PROCEDURE GLM used (Total process time): real time 0.04 seconds cpu time 0.04 seconds 116 data fourb2; set two; NOTE: There were 114 observations read from the data set WORK.TWO. NOTE: The data set WORK.FOURB2 has 114 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.00 seconds cpu time 0.00 seconds
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7 The SAS System 11:38 Monday, October 19, 2009 117 proc sort; by trt bot; 118 119 run; NOTE: There were 114 observations read from the data set WORK.FOURB2. NOTE: The data set WORK.FOURB2 has 114 observations and 10 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 120 121 122 data one; infile 'e:\documents\r.csv' dsd firstobs=3 missover; 123 input loc$ winery$ wine$ vin r1-r3; 124 length trt$ 30; 125 trt=compress(winery||wine||vin); 126 title 'resveratrol'; NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 125:28 NOTE: The infile 'e:\documents\r.csv' is: File Name=e:\documents\r.csv, RECFM=V,LRECL=256 NOTE: 38 records were read from the infile 'e:\documents\r.csv'. The minimum record length was 48. The maximum record length was 63. NOTE: The data set WORK.ONE has 38 observations and 8 variables. NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds 127 proc print; 128 NOTE: There were 38 observations read from the data set WORK.ONE. NOTE: The PROCEDURE PRINT printed page 45. NOTE: PROCEDURE PRINT used (Total process time): real time 0.00 seconds cpu time 0.00 seconds 129 data two; set one; 130 r=r1; bot=1; output; 131 r=r2; bot=2; output; 132 r=r3; bot=3; output; 133 134 *proc print; NOTE: There were 38 observations read from the data set WORK.ONE. NOTE: The data set WORK.TWO has 114 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 135 proc glm; class trt; 136 model r=trt; 137 means trt/lsd tukey lines; 138 NOTE: The PROCEDURE GLM printed pages 46-51. NOTE: PROCEDURE GLM used (Total process time): real time 0.12 seconds
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8 The SAS System 11:38 Monday, October 19, 2009 cpu time 0.09 seconds 139 data three; set two; 140 if wine='Norton'; NOTE: There were 114 observations read from the data set WORK.TWO. NOTE: The data set WORK.THREE has 60 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 141 proc glm; class loc; 142 model r=loc; 143 means loc/lsd tukey lines; 144 145 NOTE: The PROCEDURE GLM printed pages 52-55. NOTE: PROCEDURE GLM used (Total process time): real time 0.06 seconds cpu time 0.06 seconds 146 data fourr; set two; NOTE: There were 114 observations read from the data set WORK.TWO. NOTE: The data set WORK.FOURR has 114 observations and 10 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 147 proc sort; by trt bot; 148 149 NOTE: There were 114 observations read from the data set WORK.FOURR. NOTE: The data set WORK.FOURR has 114 observations and 10 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 150 data all; merge foure fourc fourb1 fourb2 fourr; by trt bot; 151 drop e1-e3 c1-c3 b1-b3 r1-r3; NOTE: There were 117 observations read from the data set WORK.FOURE. NOTE: There were 114 observations read from the data set WORK.FOURC. NOTE: There were 120 observations read from the data set WORK.FOURB1. NOTE: There were 114 observations read from the data set WORK.FOURB2. NOTE: There were 114 observations read from the data set WORK.FOURR. NOTE: The data set WORK.ALL has 120 observations and 11 variables. NOTE: DATA statement used (Total process time): real time 0.04 seconds cpu time 0.04 seconds 152 proc print; NOTE: There were 120 observations read from the data set WORK.ALL. NOTE: The PROCEDURE PRINT printed pages 56-57. NOTE: PROCEDURE PRINT used (Total process time): real time 0.00 seconds cpu time 0.00 seconds
83
9 The SAS System 11:38 Monday, October 19, 2009 153 proc corr; 154 var e c ba bb r; 155 156 run; NOTE: The PROCEDURE CORR printed page 58. NOTE: PROCEDURE CORR used (Total process time): real time 0.03 seconds cpu time 0.03 seconds NOTE: SAS Institute Inc., SAS Campus Drive, Cary, NC USA 27513-2414 NOTE: The SAS System used: real time 3.31 seconds cpu time 4.24 seconds Epicatechin 11:38 Monday, October 19, 2009 4 epicatechin 11:38 Monday, October 19, 2009 3 The GLM Procedure Dependent Variable: e Sum of Source DF Squares Mean Square F Value Pr > F Model 37 3989.436042 107.822596 595.99 <.0001 Error 76 13.749533 0.180915 Corrected Total 113 4003.185575 R-Square Coeff Var Root MSE e Mean 0.996565 10.36929 0.425341 4.101930 Source DF Type I SS Mean Square F Value Pr > F trt 37 3989.436042 107.822596 595.99 <.0001 Source DF Type III SS Mean Square F Value Pr > F trt 37 3989.436042 107.822596 595.99 <.0001 The GLM Procedure t Tests (LSD) for e NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 0.180915 Critical Value of t 1.99167
84
Least Significant Difference 0.6917 Means with the same letter are not significantly different. t Grouping Mean N trt A 29.4767 3 Winery 2Chambour2004 B 16.2333 3 St.JameNorton2004 B B 15.9067 3 AdamPucNorton2003 C 11.6867 3 St.JameNorotn2003 C C 11.6633 3 St.JameChambour2003 D 10.4533 3 BaltimorChambour2004 E 6.4133 3 AdamPucNorton2002 F 5.6267 3 St.JameSchoolH2006 F F 5.2833 3 AdamPucNorton2004 G 3.4933 3 StoneHiNorton2006 G G 3.3700 3 MountPlNorton2006 G G 3.3500 3 St.JameBlackber2006 G H G 3.1867 3 StoneHiNorton2007 H G H G 3.1833 3 BaltimorNorton2003 H G H G I 2.8400 3 Winery 2Chambour2003 H I H I 2.5933 3 Winery 2Norton2004 H I H I 2.5800 3 St.JameNorton2002 I J I 2.2033 3 St.JameChambour2004 J I J I 2.1767 3 BaltimorNorton2004 J K J 1.8800 3 MountPlNorton2005 K J K J L 1.6300 3 St.JameNorton2000 K L K M L 1.3367 3 St.JameChambour2005 K M L K M L 1.2933 3 BaltimorChambour2003 M L N M L 1.1267 3 StoneHiNorton2004 N M N M O 0.9333 3 St.JameSchoolH2005 N M O N P M O 0.8667 3 MountPlNorton2004 N P M O N P M O 0.8233 3 LesBourChambour2005 N P M O N P M O 0.6767 3 MountPlNorton2003 N P O N P O 0.5500 3 Winery 2Norton2003 N P O N P O 0.4767 3 LesBourPremium2005 P O P O 0.4233 3 LesBourPremium2003 P O P O 0.4200 3 St.JameCherry2006
85
P O P O 0.4033 3 StoneHiNorton2005 P O P O 0.3133 3 AdamPucNorton2001 P O P O 0.3000 3 StoneHiNorton2003 P O P O 0.2433 3 LesBourPremium2002 P P 0.2333 3 LesBourR.Norto2005 P P 0.2233 3 St.JameStrawber2005 epicatechin 11:38 Monday, October 19, 2009 9 The GLM Procedure Dependent Variable: e Sum of Source DF Squares Mean Square F Value Pr > F Model 3 171.489465 57.163155 3.12 0.0331 Error 56 1025.880169 18.319289 Corrected Total 59 1197.369633 R-Square Coeff Var Root MSE e Mean 0.143222 118.6172 4.280104 3.608333 Source DF Type I SS Mean Square F Value Pr > F loc 3 171.4894648 57.1631549 3.12 0.0331 Source DF Type III SS Mean Square F Value Pr > F loc 3 171.4894648 57.1631549 3.12 0.0331
86
epicatechin 11:38 Monday, October 19, 2009 6 The GLM Procedure Tukey's Studentized Range (HSD) Test for e NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 0.180915 Critical Value of Studentized Range 5.68949 Minimum Significant Difference 1.3972 Means with the same letter are not significantly different. Tukey Grouping Mean N trt A 29.4767 3 Winery 2Chambour2004 B 16.2333 3 St.JameNorton2004 B B 15.9067 3 AdamPucNorton2003 C 11.6867 3 St.JameNorotn2003 C C 11.6633 3 St.JameChambour2003 C C 10.4533 3 BaltimorChambour2004 D 6.4133 3 AdamPucNorton2002 D D 5.6267 3 St.JameSchoolH2006 D D 5.2833 3 AdamPucNorton2004 E 3.4933 3 StoneHiNorton2006 E E 3.3700 3 MountPlNorton2006 E E 3.3500 3 St.JameBlackber2006 E F E 3.1867 3 StoneHiNorton2007 F E F E 3.1833 3 BaltimorNorton2003 F E F E G 2.8400 3 Winery 2Chambour2003 F E G F H E G 2.5933 3 Winery 2Norton2004 F H E G F H E G 2.5800 3 St.JameNorton2002 F H E G I F H E G 2.2033 3 St.JameChambour2004 I F H E G I F H E G 2.1767 3 BaltimorNorton2004 I F H G I F H J G 1.8800 3 MountPlNorton2005 I H J G I K H J G 1.6300 3 St.JameNorton2000 I K H J I K H J L 1.3367 3 St.JameChambour2005 I K H J L I K H J L 1.2933 3 BaltimorChambour2003 I K J L I K J L 1.1267 3 StoneHiNorton2004
87
I K J L I K J L 0.9333 3 St.JameSchoolH2005 I K J L I K J L 0.8667 3 MountPlNorton2004 I K J L I K J L 0.8233 3 LesBourChambour2005 K J L K J L 0.6767 3 MountPlNorton2003 K J L K J L 0.5500 3 Winery 2Norton2003 K L K L 0.4767 3 LesBourPremium2005 K L K L 0.4233 3 LesBourPremium2003 K L K L 0.4200 3 St.JameCherry2006 K L K L 0.4033 3 StoneHiNorton2005 K L K L 0.3133 3 AdamPucNorton2001 K L K L 0.3000 3 StoneHiNorton2003 K L K L 0.2433 3 LesBourPremium2002 K L K L 0.2333 3 LesBourR.Norto2005 L L 0.2233 3 St.JameStrawber2005
88
epicatechin 11:38 Monday, October 19, 2009 10 The GLM Procedure t Tests (LSD) for e NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 18.31929 Critical Value of t 2.00324 Least Significant Difference 3.6897 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. t Grouping Mean N loc A 6.814 9 St. Jame A B A 4.047 27 Herman B B 2.680 6 Waverly B B 1.656 18 Winery 2 epicatechin 11:38 Monday, October 19, 2009 11 The GLM Procedure Tukey's Studentized Range (HSD) Test for e NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 18.31929 Critical Value of Studentized Range 3.74475 Minimum Significant Difference 4.8771 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. Tukey Grouping Mean N loc A 6.814 9 St. Jame A B A 4.047 27 Herman B A B A 2.680 6 Waverly B B 1.656 18 Winery 2
89
catechin 11:38 Monday, October 19, 2009 14 The GLM Procedure Dependent Variable: c Sum of Source DF Squares Mean Square F Value Pr > F Model 37 2901.723775 78.424967 162.44 <.0001 Error 76 36.691467 0.482782 Corrected Total 113 2938.415242 R-Square Coeff Var Root MSE c Mean 0.987513 16.10158 0.694825 4.315263 Source DF Type I SS Mean Square F Value Pr > F trt 37 2901.723775 78.424967 162.44 <.0001 Source DF Type III SS Mean Square F Value Pr > F trt 37 2901.723775 78.424967 162.44 <.0001
90
catechin 11:38 Monday, October 19, 2009 15 The GLM Procedure t Tests (LSD) for c NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 0.482782 Critical Value of t 1.99167 Least Significant Difference 1.1299 Means with the same letter are not significantly different. t Grouping Mean N trt A 20.7867 3 St.JameSchoolH2006 B 15.9100 3 MountPlNorton2006 B B 15.7067 3 StoneHiNorton2007 C 14.2167 3 Winery 2Chambour2003 D 12.0433 3 Winery 2Norton2004 E 7.7967 3 MountPlNorton2005 E E 7.6967 3 St.JameNorton2000 E E 7.4633 3 StoneHiNorton2006 F 5.7800 3 St.JameChambour2005 F F 5.7000 3 St.JameBlackber2006 F F 5.4833 3 BaltimorChambour2003 F G F 4.9100 3 St.JameSchoolH2005 G G H 3.9100 3 St.JameStrawber2005 H I H 3.7400 3 MountPlNorton2004 I H I H 3.6400 3 Winery 2Norton2003 I H I H 3.4500 3 MountPlNorton2003 I H I H 2.9967 3 BaltimorChambour2004 I H I H J 2.8733 3 Winery 2Chambour2004 I J I K J 2.6433 3 StoneHiNorton2003 K J L K J 1.8000 3 St.JameCherry2006 L K J L K J 1.7600 3 AdamPucNorton2002 L K L K M 1.6833 3 LesBourChambour2005 L K M L N K M 1.5667 3 AdamPucNorton2001 L N K M L N K M 1.5433 3 St.JameNorton2002 L N M
91
L N O M 1.1467 3 LesBourR.Norto2005 catechin 11:38 Monday, October 19, 2009 16 The GLM Procedure t Tests (LSD) for c Means with the same letter are not significantly different. t Grouping Mean N trt L N O M L N O M 1.1367 3 StoneHiNorton2005 L N O M L N O M 1.0367 3 St.JameNorton2004 L N O M L N O M 0.8833 3 St.JameNorotn2003 L N O M L N O M 0.8800 3 St.JameChambour2003 L N O M L N O M 0.8767 3 BaltimorNorton2004 N O M N O M 0.6267 3 AdamPucNorton2003 N O M N O M 0.5933 3 AdamPucNorton2004 N O N O 0.5033 3 StoneHiNorton2004 O O 0.4133 3 LesBourPremium2003 O O 0.2367 3 St.JameChambour2004 O O 0.1900 3 LesBourPremium2005 O O 0.1867 3 BaltimorNorton2003 O O 0.1700 3 LesBourPremium2002 catechin 11:38 Monday, October 19, 2009 17 The GLM Procedure Tukey's Studentized Range (HSD) Test for c NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 0.482782 Critical Value of Studentized Range 5.68949 Minimum Significant Difference 2.2824 Means with the same letter are not significantly different. Tukey Grouping Mean N trt A 20.7867 3 St.JameSchoolH2006 B 15.9100 3 MountPlNorton2006 B
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B 15.7067 3 StoneHiNorton2007 B C B 14.2167 3 Winery 2Chambour2003 C C 12.0433 3 Winery 2Norton2004 D 7.7967 3 MountPlNorton2005 D E D 7.6967 3 St.JameNorton2000 E D E D 7.4633 3 StoneHiNorton2006 E D E D F 5.7800 3 St.JameChambour2005 E D F E G D F 5.7000 3 St.JameBlackber2006 E G F E G F 5.4833 3 BaltimorChambour2003 G F G H F 4.9100 3 St.JameSchoolH2005 G H F I G H F 3.9100 3 St.JameStrawber2005 I G H F J I G H F 3.7400 3 MountPlNorton2004 J I G H F J I G H F 3.6400 3 Winery 2Norton2003 J I G H J I G H 3.4500 3 MountPlNorton2003 J I H J I H K 2.9967 3 BaltimorChambour2004 J I H K J I L H K 2.8733 3 Winery 2Chambour2004 J I L H K J I L H K M 2.6433 3 StoneHiNorton2003 J I L K M J I L N K M 1.8000 3 St.JameCherry2006 J I L N K M J I L N K M 1.7600 3 AdamPucNorton2002 J I L N K M J I L N K M 1.6833 3 LesBourChambour2005 J L N K M J L N K M 1.5667 3 AdamPucNorton2001 J L N K M J L N K M 1.5433 3 St.JameNorton2002 L N K M L N K M 1.1467 3 LesBourR.Norto2005 L N K M L N K M 1.1367 3 StoneHiNorton2005 L N K M L N K M 1.0367 3 St.JameNorton2004 L N K M L N K M 0.8833 3 St.JameNorotn2003 L N K M L N K M 0.8800 3 St.JameChambour2003 L N K M L N K M 0.8767 3 BaltimorNorton2004 L N M L N M 0.6267 3 AdamPucNorton2003 L N M L N M 0.5933 3 AdamPucNorton2004 N M N M 0.5033 3 StoneHiNorton2004 N M N M 0.4133 3 LesBourPremium2003 N N 0.2367 3 St.JameChambour2004 N N 0.1900 3 LesBourPremium2005 N N 0.1867 3 BaltimorNorton2003 N N 0.1700 3 LesBourPremium2002
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catechin 11:38 Monday, October 19, 2009 20 The GLM Procedure Dependent Variable: c Sum of Source DF Squares Mean Square F Value Pr > F Model 3 320.646468 106.882156 5.34 0.0026 Error 56 1121.512972 20.027017 Corrected Total 59 1442.159440 R-Square Coeff Var Root MSE c Mean 0.222338 99.53638 4.475156 4.496000 Source DF Type I SS Mean Square F Value Pr > F loc 3 320.6464678 106.8821559 5.34 0.0026 Source DF Type III SS Mean Square F Value Pr > F loc 3 320.6464678 106.8821559 5.34 0.0026 catechin 11:38 Monday, October 19, 2009 21 The GLM Procedure t Tests (LSD) for c NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 20.02702 Critical Value of t 2.00324 Least Significant Difference 3.8578 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. t Grouping Mean N loc A 7.763 18 Winery 2 B 3.556 27 Herman B B 3.426 9 St. Jame B B 0.532 6 Waverly
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catechin 11:38 Monday, October 19, 2009 22 The GLM Procedure Tukey's Studentized Range (HSD) Test for c NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 20.02702 Critical Value of Studentized Range 3.74475 Minimum Significant Difference 5.0994 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. Tukey Grouping Mean N loc A 7.763 18 Winery 2 A B A 3.556 27 Herman B A B A 3.426 9 St. Jame B B 0.532 6 Waverly procyanidin B1 11:38 Monday, October 19, 2009 25 The GLM Procedure Dependent Variable: ba Sum of Source DF Squares Mean Square F Value Pr > F Model 37 20043.92573 541.72772 126.65 <.0001 Error 76 325.08340 4.27741 Corrected Total 113 20369.00913 R-Square Coeff Var Root MSE ba Mean 0.984040 11.69571 2.068191 17.68333 Source DF Type I SS Mean Square F Value Pr > F trt 37 20043.92573 541.72772 126.65 <.0001 Source DF Type III SS Mean Square F Value Pr > F
95
trt 37 20043.92573 541.72772 126.65 <.0001 procyanidin B1 11:38 Monday, October 19, 2009 26 The GLM Procedure t Tests (LSD) for ba NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 4.277413 Critical Value of t 1.99167 Least Significant Difference 3.3633 Means with the same letter are not significantly different. t Grouping Mean N trt A 52.160 3 MountPlNorton2006 B 40.057 3 St.JameNorton2002 B C B 38.310 3 St.JameSchoolH2006 C B C B 37.983 3 StoneHiNorton2007 C C D 35.060 3 StoneHiNorton2004 D E D 32.967 3 MountPlNorton2005 E D E D 32.557 3 MountPlNorton2004 E E F 31.117 3 St.JameNorton2000 E F E F 30.380 3 AdamPucNorton2004 F G F 27.950 3 BaltimorNorton2004 G G 26.690 3 MountPlNorton2003 G G H 25.227 3 St.JameNorotn2003 G H G H 25.110 3 StoneHiNorton2006 H I H 22.330 3 BaltimorNorton2003 I H I H 22.120 3 StoneHiNorton2003 I I J 19.907 3 StoneHiNorton2005 J J 18.190 3 AdamPucNorton2003 K 13.813 3 St.JameChambour2004 K K 13.497 3 Winery 2Chambour2003 K L K 13.443 3 St.JameChambour2005 L K L K M 12.517 3 LesBourPremium2003 L K M L K M 11.887 3 Winery 2Norton2003 L K M
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L N K M 11.197 3 St.JameNorton2004 L N K M L N K M 10.620 3 AdamPucNorton2002 L N M L N M 10.130 3 BaltimorChambour2004 N M N O M 9.310 3 LesBourPremium2002 N O P N O 8.467 3 LesBourPremium2005 P N O P N O 8.423 3 Winery 2Norton2004 P N O P N O 7.923 3 LesBourR.Norto2005 P O P O Q 6.083 3 Winery 2Chambour2004 P Q P Q 5.360 3 BaltimorChambour2003 Q R Q 4.200 3 LesBourChambour2005 R Q S R Q 3.807 3 St.JameStrawber2005 S R S R T 1.483 3 St.JameChambour2003 S T S T 0.807 3 AdamPucNorton2001 S T S T 0.590 3 St.JameBlackber2006 T T 0.297 3 St.JameSchoolH2005 T T 0.000 3 St.JameCherry2006 The GLM Procedure Tukey's Studentized Range (HSD) Test for ba NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 4.277413 Critical Value of Studentized Range 5.68949 Minimum Significant Difference 6.7937 Means with the same letter are not significantly different. Tukey Grouping Mean N trt A 52.160 3 MountPlNorton2006 B 40.057 3 St.JameNorton2002 B C B 38.310 3 St.JameSchoolH2006 C B C B 37.983 3 StoneHiNorton2007 C B C B D 35.060 3 StoneHiNorton2004 C D C E D 32.967 3 MountPlNorton2005 C E D C E D 32.557 3 MountPlNorton2004 E D F E D 31.117 3 St.JameNorton2000 F E D
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F E D 30.380 3 AdamPucNorton2004 F E F E G 27.950 3 BaltimorNorton2004 F E G F H E G 26.690 3 MountPlNorton2003 F H G F H G 25.227 3 St.JameNorotn2003 F H G F H G 25.110 3 StoneHiNorton2006 H G H I G 22.330 3 BaltimorNorton2003 H I G H I G 22.120 3 StoneHiNorton2003 H I J H I 19.907 3 StoneHiNorton2005 J I J I K 18.190 3 AdamPucNorton2003 J K J L K 13.813 3 St.JameChambour2004 J L K J L K 13.497 3 Winery 2Chambour2003 J L K J L K 13.443 3 St.JameChambour2005 L K M L K 12.517 3 LesBourPremium2003 M L K M N L K 11.887 3 Winery 2Norton2003 M N L M N L 11.197 3 St.JameNorton2004 M N L M N L O 10.620 3 AdamPucNorton2002 M N L O P M N L O 10.130 3 BaltimorChambour2004 P M N L O P M N L O 9.310 3 LesBourPremium2002 P M N L O P M N L O 8.467 3 LesBourPremium2005 P M N L O P M N L O 8.423 3 Winery 2Norton2004 P M N L O P M N L O Q 7.923 3 LesBourR.Norto2005 P M N O Q P M N R O Q 6.083 3 Winery 2Chambour2004 P N R O Q P N R O Q 5.360 3 BaltimorChambour2003 P R O Q P R O Q 4.200 3 LesBourChambour2005 P R Q P R Q 3.807 3 St.JameStrawber2005 R Q R Q 1.483 3 St.JameChambour2003 R R 0.807 3 AdamPucNorton2001 R R 0.590 3 St.JameBlackber2006 R R 0.297 3 St.JameSchoolH2005 R R 0.000 3 St.JameCherry2006 procyanidin B1 11:38 Monday, October 19, 2009 31 The GLM Procedure Dependent Variable: ba Sum of Source DF Squares Mean Square F Value Pr > F Model 3 366.703916 122.234639 0.79 0.5060
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Error 56 8693.162369 155.235042 Corrected Total 59 9059.866285 R-Square Coeff Var Root MSE ba Mean 0.040476 50.08677 12.45934 24.87550 Source DF Type I SS Mean Square F Value Pr > F loc 3 366.7039165 122.2346388 0.79 0.5060 Source DF Type III SS Mean Square F Value Pr > F loc 3 366.7039165 122.2346388 0.79 0.5060 procyanidin B1 11:38 Monday, October 19, 2009 32 The GLM Procedure t Tests (LSD) for ba NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 155.235 Critical Value of t 2.00324 Least Significant Difference 10.741 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. t Grouping Mean N loc A 27.457 9 St. Jame A A 27.447 18 Winery 2 A A 25.140 6 Waverly A A 22.242 27 Herman procyanidin B1 11:38 Monday, October 19, 2009 33 The GLM Procedure Tukey's Studentized Range (HSD) Test for ba NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ.
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Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 155.235 Critical Value of Studentized Range 3.74475 Minimum Significant Difference 14.197 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. Tukey Grouping Mean N loc A 27.457 9 St. Jame A A 27.447 18 Winery 2 A A 25.140 6 Waverly A A 22.242 27 Herman procyanidin b2 11:38 Monday, October 19, 2009 36 The GLM Procedure Dependent Variable: bb Sum of Source DF Squares Mean Square F Value Pr > F Model 37 2363.902745 63.889263 114.64 <.0001 Error 76 42.354733 0.557299 Corrected Total 113 2406.257478 R-Square Coeff Var Root MSE bb Mean 0.982398 14.98914 0.746525 4.980439 Source DF Type I SS Mean Square F Value Pr > F trt 37 2363.902745 63.889263 114.64 <.0001 Source DF Type III SS Mean Square F Value Pr > F trt 37 2363.902745 63.889263 114.64 <.0001 procyanidin b2 11:38 Monday, October 19, 2009 37 The GLM Procedure t Tests (LSD) for bb NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate.
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Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 0.557299 Critical Value of t 1.99167 Least Significant Difference 1.214 Means with the same letter are not significantly different. t Grouping Mean N trt A 23.9500 3 BaltimorChambour2004 B 15.6933 3 StoneHiNorton2007 C 12.9467 3 StoneHiNorton2006 D 10.5567 3 St.JameBlackber2006 E 8.6800 3 St.JameSchoolH2006 F 7.2333 3 St.JameChambour2004 F G F 6.8367 3 St.JameNorotn2003 G F G F H 6.7633 3 MountPlNorton2006 G F H G I F H 6.7367 3 MountPlNorton2004 G I H G I J H 6.0167 3 BaltimorNorton2004 I J H K I J H 5.5500 3 Winery 2Norton2004 K I J K I J 5.5433 3 MountPlNorton2005 K J K J 5.2467 3 St.JameChambour2003 K J K J L 4.9900 3 Winery 2Chambour2003 K J L K J L 4.9100 3 Winery 2Norton2003 K J L K J L 4.8867 3 LesBourChambour2005 K J L K J L 4.8500 3 AdamPucNorton2001 K L K M L 4.5200 3 AdamPucNorton2004 K M L K N M L 4.3700 3 LesBourPremium2003 N M L N M L 3.9833 3 LesBourPremium2002 N M L N M L 3.9533 3 AdamPucNorton2003 N M L N M L 3.7900 3 St.JameSchoolH2005 N M O N M 3.4033 3 AdamPucNorton2002 O N O N 3.2000 3 BaltimorNorton2003 O O P 2.5533 3 St.JameChambour2005 O P O P 2.4733 3 BaltimorChambour2003 O P O P Q 2.3900 3 MountPlNorton2003 P Q R P Q 1.9700 3 St.JameNorton2002 R P Q R S P Q 1.7000 3 LesBourPremium2005 R S P Q
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R S P Q 1.5733 3 St.JameNorton2004 R S P Q R S P Q 1.4133 3 LesBourR.Norto2005 R S P Q R S P Q 1.3967 3 St.JameNorton2000 R S Q R S Q 1.2267 3 StoneHiNorton2004 R S Q R S Q 1.2267 3 StoneHiNorton2005 R S R S T 1.1400 3 St.JameStrawber2005 R S T R S T 1.0067 3 StoneHiNorton2003 S T S T 0.5767 3 Winery 2Chambour2004 T T 0.0000 3 St.JameCherry2006 procyanidin b2 11:38 Monday, October 19, 2009 39 The GLM Procedure Tukey's Studentized Range (HSD) Test for bb NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 0.557299 Critical Value of Studentized Range 5.68949 Minimum Significant Difference 2.4522 Means with the same letter are not significantly different. Tukey Grouping Mean N trt A 23.9500 3 BaltimorChambour2004 B 15.6933 3 StoneHiNorton2007 C 12.9467 3 StoneHiNorton2006 C D C 10.5567 3 St.JameBlackber2006 D D E 8.6800 3 St.JameSchoolH2006 E F E 7.2333 3 St.JameChambour2004 F E F E G 6.8367 3 St.JameNorotn2003 F E G F H E G 6.7633 3 MountPlNorton2006 F H E G F H E G 6.7367 3 MountPlNorton2004 F H G F H I G 6.0167 3 BaltimorNorton2004 F H I G J F H I G 5.5500 3 Winery 2Norton2004 J F H I G J F H I G 5.5433 3 MountPlNorton2005 J F H I G J F H I G 5.2467 3 St.JameChambour2003 J F H I G J F H I G K 4.9900 3 Winery2Chambour2003 J F H I G K
102
J F H I L G K 4.9100 3 Winery 2Norton2003 J F H I L G K J F H I L G K 4.8867 3 LesBourChambour2005 J F H I L G K J F H I L G K 4.8500 3 AdamPucNorton2001 J H I L G K J M H I L G K 4.5200 3 AdamPucNorton2004 J M H I L K J M H I L N K 4.3700 3 LesBourPremium2003 J M I L N K J M O I L N K 3.9833 3 LesBourPremium2002 J M O I L N K J M O I L N K 3.9533 3 AdamPucNorton2003 J M O I L N K J M O P I L N K 3.7900 3 St.JameSchoolH2005 J M O P L N K J M O P Q L N K 3.4033 3 AdamPucNorton2002 J M O P Q L N K J M O P Q L N K 3.2000 3 BaltimorNorton2003 M O P Q L N K R M O P Q L N K 2.5533 3 St.JameChambour2005 R M O P Q L N R M O P Q L N 2.4733 3 BaltimorChambour2003 R M O P Q N R M O P Q N S 2.3900 3 MountPlNorton2003 R O P Q N S R O P Q N S 1.9700 3 St.JameNorton2002 R O P Q S R O P Q S 1.7000 3 LesBourPremium2005 R O P Q S R O P Q S 1.5733 3 St.JameNorton2004 R P Q S R P Q S 1.4133 3 LesBourR.Norto2005 R P Q S R P Q S 1.3967 3 St.JameNorton2000 R Q S R Q S 1.2267 3 StoneHiNorton2004 R Q S R Q S 1.2267 3 StoneHiNorton2005 R Q S R Q S 1.1400 3 St.JameStrawber2005 R Q S R Q S 1.0067 3 StoneHiNorton2003 R S R S 0.5767 3 Winery2Chambour2004 S S 0.0000 3 St.JameCherry2006
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procyanidin b2 11:38 Monday, October 19, 2009 42 The GLM Procedure Dependent Variable: bb Sum of Source DF Squares Mean Square F Value Pr > F Model 3 104.8602165 34.9534055 2.63 0.0591 Error 56 744.8340019 13.3006072 Corrected Total 59 849.6942183 R-Square Coeff Var Root MSE bb Mean 0.123409 76.87875 3.647000 4.743833 Source DF Type I SS Mean Square F Value Pr > F loc 3 104.8602165 34.9534055 2.63 0.0591 Source DF Type III SS Mean Square F Value Pr > F loc 3 104.8602165 34.9534055 2.63 0.0591 procyanidin b2 11:38 Monday, October 19, 2009 43 The GLM Procedure t Tests (LSD) for bb NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 13.30061 Critical Value of t 2.00324 Least Significant Difference 3.1439 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. t Grouping Mean N loc A 5.425 27 Herman A A 5.316 18 Winery 2 A B A 4.608 6 Waverly B B 1.647 9 St. Jame
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procyanidin b2 11:38 Monday, October 19, 2009 44 The GLM Procedure Tukey's Studentized Range (HSD) Test for bb NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 13.30061 Critical Value of Studentized Range 3.74475 Minimum Significant Difference 4.1557 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. Tukey Grouping Mean N loc A 5.425 27 Herman A A 5.316 18 Winery 2 A A 4.608 6 Waverly A A 1.647 9 St. Jame resveratrol 11:38 Monday, October 19, 2009 47 The GLM Procedure Dependent Variable: r Sum of Source DF Squares Mean Square F Value Pr > F Model 37 19.53164060 0.52788218 38.08 <.0001 Error 76 1.05354667 0.01386246 Corrected Total 113 20.58518727 R-Square Coeff Var Root MSE r Mean 0.948820 22.23031 0.117739 0.529632 Source DF Type I SS Mean Square F Value Pr > F trt 37 19.53164060 0.52788218 38.08 <.0001 Source DF Type III SS Mean Square F Value Pr > F
105
trt 37 19.53164060 0.52788218 38.08 <.000 resveratrol 11:38 Monday, October 19, 2009 48 The GLM Procedure t Tests (LSD) for r NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 0.013862 Critical Value of t 1.99167 Least Significant Difference 0.1915 Means with the same letter are not significantly different. t Grouping Mean N trt A 1.52187 3 MountPlNorton2004 A A 1.49617 3 Winery 2Norton2003 A B A 1.33053 3 AdamPucNorton2004 B B C 1.21950 3 Winery 2Norton2004 B C B C 1.14123 3 BaltimorNorton2003 C C 1.11993 3 MountPlNorton2003 D 0.89220 3 StoneHiNorton2003 D E D 0.80503 3 BaltimorNorton2004 E D E D 0.80160 3 Winery 2Chambour2004 E D E D 0.79557 3 BaltimorChambour2003 E D E D 0.78050 3 St.JameChambour2004 E D E D 0.78027 3 StoneHiNorton2004 E E F 0.66390 3 StoneHiNorton2006 E F E F 0.65063 3 MountPlNorton2006 F G F 0.56903 3 StoneHiNorton2005 G G H 0.44063 3 St.JameNorton2004 G H G H I 0.39810 3 St.JameNorton2002 G H I G H I 0.39283 3 St.JameNorotn2003 G H I G H I 0.38253 3 StoneHiNorton2007 G H I G J H I 0.38177 3 MountPlNorton2005 J H I J H I 0.36750 3 BaltimorChambour2004 J H I K J H I 0.32367 3 St.JameChambour2003 K J H I
106
K J H I 0.32113 3 Winery 2Chambour2003 K J H I K J H I 0.31627 3 LesBourPremium2003 K J H I L K J H I 0.30863 3 St.JameChambour2005 L K J H I 0.30293 3 AdamPucNorton2003 L K J I L K J M I 0.20740 3 AdamPucNorton2001 L K J M L K J M 0.19067 3 LesBourPremium2005 L K M L K M 0.16860 3 LesBourChambour2005 L K M L K M 0.16717 3 St.JameSchoolH2005 L K M L K M 0.16607 3 LesBourPremium2002 L K M L K M 0.14493 3 AdamPucNorton2002 L K M L K M 0.14440 3 LesBourR.Norto2005 L M L M 0.11893 3 St.JameNorton2000 L M L M 0.11883 3 St.JameBlackber2006 M M 0.07947 3 St.JameStrawber2005 M M 0.06910 3 St.JameSchoolH2006 M M 0.04650 3 St.JameCherry2006 resveratrol 11:38 Monday, October 19, 2009 50 The GLM Procedure Tukey's Studentized Range (HSD) Test for r NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 76 Error Mean Square 0.013862 Critical Value of Studentized Range 5.68949 Minimum Significant Difference 0.3868 Means with the same letter are not significantly different. Tukey Grouping Mean N trt A 1.52187 3 MountPlNorton2004 A B A 1.49617 3 Winery 2Norton2003 B A B A 1.33053 3 AdamPucNorton2004 B A B A C 1.21950 3 Winery 2Norton2004 B A C B D A C 1.14123 3 BaltimorNorton2003 B D C B D C 1.11993 3 MountPlNorton2003 D C D E C 0.89220 3 StoneHiNorton2003 D E
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F D E 0.80503 3 BaltimorNorton2004 F D E F D E 0.80160 3 Winery 2Chambour2004 F D E F D E 0.79557 3 BaltimorChambour2003 F D E F D E G 0.78050 3 St.JameChambour2004 F D E G F D E G 0.78027 3 StoneHiNorton2004 F E G F H E G 0.66390 3 StoneHiNorton2006 F H E G F H E G 0.65063 3 MountPlNorton2006 F H E G I F H E G 0.56903 3 StoneHiNorton2005 I F H G I F H J G 0.44063 3 St.JameNorton2004 I H J G I K H J G 0.39810 3 St.JameNorton2002 I K H J I K H J 0.39283 3 St.JameNorotn2003 I K H J I K H J 0.38253 3 StoneHiNorton2007 I K H J I K H J 0.38177 3 MountPlNorton2005 I K H J I K H J 0.36750 3 BaltimorChambour2004 I K H J I K H J 0.32367 3 St.JameChambour2003 I K H J I K H J 0.32113 3 Winery 2Chambour2003 I K H J I K H J 0.31627 3 LesBourPremium2003 I K H J I K H J 0.30863 3 St.JameChambour2005 I K H J I K H J 0.30293 3 AdamPucNorton2003 I K J I K J 0.20740 3 AdamPucNorton2001 I K J I K J 0.19067 3 LesBourPremium2005 K J K J 0.16860 3 LesBourChambour2005 K J K J 0.16717 3 St.JameSchoolH2005 K J K J 0.16607 3 LesBourPremium2002 K J K J 0.14493 3 AdamPucNorton2002 K J K J 0.14440 3 LesBourR.Norto2005 K J K J 0.11893 3 St.JameNorton2000 K J K J 0.11883 3 St.JameBlackber2006 K J K J 0.07947 3 St.JameStrawber2005 K J K J 0.06910 3 St.JameSchoolH2006 K K 0.04650 3 St.JameCherry2006 resveratrol 11:38 Monday, October 19, 2009 53 The GLM Procedure Dependent Variable: r Sum of
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Source DF Squares Mean Square F Value Pr > F Model 3 4.45305467 1.48435156 10.44 <.0001 Error 56 7.96481780 0.14222889 Corrected Total 59 12.41787247 R-Square Coeff Var Root MSE r Mean 0.358600 51.77712 0.377132 0.728377 Source DF Type I SS Mean Square F Value Pr > F loc 3 4.45305467 1.48435156 10.44 <.0001 Source DF Type III SS Mean Square F Value Pr > F loc 3 4.45305467 1.48435156 10.44 <.0001
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resveratrol 11:38 Monday, October 19, 2009 54 The GLM Procedure t Tests (LSD) for r NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 0.142229 Critical Value of t 2.00324 Least Significant Difference 0.3251 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. t Grouping Mean N loc A 1.0650 18 Winery 2 A A 0.9731 6 Waverly B 0.5860 27 Herman B B 0.3192 9 St. Jame resveratrol 11:38 Monday, October 19, 2009 55 The GLM Procedure Tukey's Studentized Range (HSD) Test for r NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 0.142229 Critical Value of Studentized Range 3.74475 Minimum Significant Difference 0.4297 Harmonic Mean of Cell Sizes 10.8 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. Tukey Grouping Mean N loc A 1.0650 18 Winery 2 A B A 0.9731 6 Waverly B B C 0.5860 27 Herman C C 0.3192 9 St. Jame
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resveratrol 11:38 Monday, October 19, 2009 58 The CORR Procedure 5 Variables: e c ba bb r Simple Statistics Variable N Mean Std Dev Sum Minimum Maximum e 114 4.10193 5.95201 467.62000 0 29.90000 c 114 4.31526 5.09938 491.94000 0 21.30000 ba 114 17.68333 13.42597 2016 0 53.69000 bb 114 4.98044 4.61458 567.77000 0 24.72000 r 114 0.52963 0.42681 60.37810 0.01160 1.77340 Pearson Correlation Coefficients, N = 114 Prob > |r| under H0: Rho=0 e c ba bb r e 1.00000 -0.07840 -0.08906 0.08330 -0.00507 0.4070 0.3460 0.3783 0.9573 c -0.07840 1.00000 0.40166 0.34016 -0.07112 0.4070 <.0001 0.0002 0.4521 ba -0.08906 0.40166 1.00000 0.14418 0.28842 0.3460 <.0001 0.1259 0.0019 bb 0.08330 0.34016 0.14418 1.00000 -0.01708 0.3783 0.0002 0.1259 0.8568 r -0.00507 -0.07112 0.28842 -0.01708 1.00000 0.9573 0.4521 0.0019 0.8568
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REFERENCES
Adams LD. 1973. The Wines of America. Boston: Houghton Mifflin Company.p465.
Agriculture WaGBoMDo. 2007. The Economic Impact of Wine and Grapes on the Missouri Economy. In: LLC, M. R., editor). p. 1-26. http://iccve.missouri.edu/publications/mo-winery-impact.pdf
Anli E, Vural N, Demiray S, Ozkan M. 2006. Trans-resveratrol and Other Phenolic Compounds in Turkish Red Wines with HPLC. J Wine Research 17(2):117-25.
Appeldoorn MM, Vincken J-P, Gruppen H, Hollman PCH. 2009. Procyanidin Dimers A1, A2, and B2 are Absorbed Without Conjugation or Methylation From the Small Intestine of Rats. J Nutr 139(8):1469-73.
Azios NG, Krishnamoorthy L, Harris M, Cubano LA, Cammer M, Dharmawardhane SF 2001. Estrogen and Resveratrol Regulated Rac and Cdc42 Signaling to the Actin Cytoskeleton and Metastic Breast Cancer Cells. Neoplasia 9(2):147-58.
Baba S, Osakabe N, Natsume M, Terao J. 2002. Absorption and Urinary Excreation of Procyanidin B2 [Epicatechin - (4β-8)-Epicatechin] in Rates. Free Rad Biol Med 33(1):142-8.
Bertelli AAE, Giovannini L, Stradi R, Bertelli A, Tillement J-P. 1996a. Plasma, Urine and Tissue Levels of Trans- and Cis- Resveratrol (3,4',5-Trihydroxystilbenne) After Short-Term or Prolonged Administration of Red Wine. Intl J of Tissue Reactions 18(2-3):67-71.
Bertelli AAE, Giovannini L, Stradi R, Urien S, Tillement J-P, Bertelli A. 1996b. Kinetics of Trans- and Cis-Resveratrol (3, 4', 5-Trihydroxystilbene) After Red wine Oral Administration in Rats. Intl J of Clinical Pharmacology Research 16(4-5):77-81.
Casper RF, Quesne M, Rogers IM, Shirota T, Jolivet A, Milgrom E, Savouret J-F. 1999. Resveratrol Has Antagonist Activity on the Aryl Hydrocarbon Receptor: Implications for Prevention in Dioxin Toxicity. Molecular Pharmacology 56(4):784-90.
Chanvitayapongs S, Draczynska-Lusiak B, Sun AY. 1997. Amelioration of Oxidative Stress by Antioxidants and Resveratrol in PC12 Cells. Neuropharmacology and Neurotoxicology 8(6):1499-502.
Cho ES, Lee KW, Lee HJ. 2008. Cocoa Procyanidins Protect PC12 Cells From Hydrogen-Peroxide-Induced Apoptosis by Inhibiting Activation of p38 MARK and JNK. Mutation Research 649(1-2):123-30.
112
Cho ES, Jang YJ, Kang NJ, Hwang MK, Kim YT, Lee KW, Lee HJ. 2009. Cocoa Procyandins Attenuate 4-hydroxynonenal-induced Apoptosis of PC12 Cells by Directly Inhibiting Mitogen-Activated Protein Kinase 4 Activity. Free Rad Biol Med 46(10):1319-27.
Clement M, Chawdhury S, Pervaiz S. 1998. Chemopreventive agent resveratrol, a natural product derived from grapes, triggers CD95 signaling-dependent apoptosis in human tumor cells. Blood 92(3):996-1002.
Control CFD. 2009. Heart Disease. FastStats. http://www.trade.gov/td/ocg/wine2008.pdf
Mariani, J. Corder Makes New Scientific Case for Red Wine's Healthy Effects. 2007. 9-18-2007.
Cortell JM, Halbleib M, Gallagher AV, Righetti TL, Kennedy JA. 2005. Influence of Vine Vigor on Grape (Vitis vinifera L. Cv. Pinot Noir) and Wine Proanthocyanidins. J Agric Food Chem 53(14):5798-808.
da Silva Porto PA, Laranjinha JAN, de Freitas VAP. 2003. Antioxidant Protection of Low Density Lipoprotein by Procyandins: Structure/Activity Relationships. Biochem Pharmacol 66(6):947-54.
de Lange DW, Higmering ML, Lorsheyd A, Scholman WLG, Kraaijenhagen RJ, Akkerman J-WN, van de Wiel A. 2004. Rapid Intake of Alcohol (Binge Drinking) Inhibits Platelet Adhesion to Fibrogen Under Flow. Alcoholism: Clinical and Experimental Research 28(11):1562-8.
Deprez S, Brezillon C, Rabot S, Philippe C, Mila I, Lapierre C, Scalbert A. 2000. Polymeric Proanthocyanidins are Catabolized by Human Colonic Microflora in Low-Molecular-Weight Phenolic Acids. J Nutr 130(11):2733-8.
Dugo G, Dugo P, Vilasi F, Magnisi R, Mondello L, La Torre GL. 2006. Determination of the Polyphenolic Content in Sicilian Red Wines of Protected Geographical Indication. Ital J Food Science 18(4):409-22.
Elattar TMA, Virji AS. 1999. The Effect of Red Wine and its Components on Growth and Proliferation of Human Oral Squamous Carcinoma Cells. Anticancer Research 19(6B):5407-14.
Foerster M, Marques-Vidal P, Gmel G, Daeppen J-B, Cornuz J, Hayoz D, Pècoud A, Mooser V, Waeber G, Vallenweider P, Paccaud F, Rodondi N. 2009. Alcohol Drinking and Cardiovascular Risk in a Population With a High Mean Alcohol Consumption. American Journal of Cardiology 103(3):361-8.
Fremont L. 2000. Minireview - Biological Effects of Resveratrol. Life Sciences 66(8):663-73.
113
Friedman LA, Kimball AW. 1986. Coronary Heart Disease Mortality and Alcohol Consumption in Framingham. Am J Epidemiology 124(3):481-9.
Fushs FD, Chambless LE, Folsom AR, Eigenbrodt ML, Duncan BB, Gilbert A, Szklo M. 2004. Association Between Alcoholic Beverate Consumption and Incedence of Coronary Heart Disease in Whites and Blacks. Am J Epidemiology 160(5):466-74.
Gao L, Girard B, Mazza G, Reynolds AG. 1997. Changes in Anthocyanins and Color Characteristics of Pinot Noir Wines during Different Vinification Processes. J Agric Food Chem 45(6):2003-8.
Gawel R. 1998. Red Wine Astringency: A Review. Australian Journal of Grape and Wine Research 4(2):74-96.
Gerogiannaki-Christopoulou, M., Athanasopoulos, P., Kyriakidis, N., Gerogiannaki, I. A., Spanos, M. 2006. trans-Resveratrol in Wines from the Major Greek Red and White Grapes. Food Control 17 (9), 700-6.
Goldberg DM, Ng E. 1996. Regional Differences in Resveratrol Isomer Concentratins of Wines From Various Cultivars. J of Wine Research 7(1):13-24.
Goldberg DM, Yan J, Soleas GJ. 2003. Absorption of Three Wine-related Polyphenols in Three Different Matricies by Healthy Subjects. Clinical Biochemistry 36(1):79-87.
Gomez-Alonso S, Garcia-Romero E, Hermosin-Gutierrez I. 2007. HPLC Analysis of Diverse Grape and Wine Polyphenolics Using Direct Injection and Multidetection by DAD and Flourescence. Journal of Food Composition & Analysis 20(7):618-26.
Gonthier M-P, Donovan JL, Texier O, Felgines C, Remesy C, Scalbert A. 2003. Metabolism of Dietary Procyanidins in Rats. Free Rad Biol Med 35(8):837-44.
Gresele P, Pignatelli P, Guglielmini G, Carnevale R, Mezzasoma AM, Ghiselli A, Momi S, Violi F. 2008. Resveratrol, at Concentrations Attainable with Moderate Wine Consumption, Stimulates Human Platelet Nitric Oxide Production. J Nutr 138(9):1602-8.
Gu X, Creasy L, Kester A, Zeece M. 1999. Capillary Electrophoretic Determinatin of Resveratrol in Wines. J Agric Food Chem 47(8):3223-7.
Gürbüz O, Göçmen D, Dağdelen F, Gürsoy M, Aydin S, Şahin İ, Büyükuysal L, Usta M. 2007. Determination of flavan-3-ols and trans-resveratrol in Grapes and Wine Using HPLC with Fluorescence Detection. Food Chemistry 100(2):518-25.
Haslam E. 1982. Proanthocyanidins. In: Harborne, JB, Mabry, TJ, editors. The Flavonoids: Advances in Research. New York: Chapman and Hall. p. 417-447.
114
Hodgen D. 2008. U.S. Wine Industry - 2008. U. S. Department of Commerce. http://www.trade.gov/td/ocg/wine2008.pdf
Hornsey I. 2007. The Chemistry and Biology of Winemaking. Cambridge: The Royal Society of Chemistry.p161-307
Hsieh TC, Wang Z, Hamby CV, Wu JM. 2005. Inhibition of Melanoma Cell Proliferation by Resveratrol is Correlated with Upregulation of Quinone Reductase 2 and p53. Biochem Biophys Res Commun 334(1):223-30.
Hung L-M, Su M-J, Chen J-K. 2004. Resveratrol Protects Myocaridal Ischemia-Reperfusion Injury Through Both NO-Dependent and NO-Independent Mechanisms. Free Rad Biol Med 36(6):774-84.
Jang J-H, Surh Y-J. 2001. Protective Effects of Resveratrol on Hydrogen Peroxide-induced Apoptosis in Rat Pheochromocytoma (P12) Cells. Mutation Research 496(1-2):181-90.
Jeandet P, Bessis R, Gautheron B. 1991. The Production of Resveratrol (3, 5, 4'-trihydrocystilbene) by Grape Berries in Different Developmental Stages. Am J Enol Vitic 42(1):41-6.
Jo J-Y, De Mejia EG, Lila MA. 2005. Effects of Grape Cell Culture Extracts on Human Topoisomerase II Catalytic Activity and Characterizaiton of Active Fractions. J Agric Food Chem 53(7):2489-98.
Jo J-Y, De Mejia EG, Lila MA. 2006. Catalytic Inhibition of Human DNA Topoisomerase II by Interactions of Grape Cell Culture Polyphenols. J Agric Food Chem 54(6):2083-7.
Jorgensen EM, Marin AB, Kennedy JA. 2004. Analysis of the Oxidative Degradation of Proanthocyanidins Under Basich Conditions. J Agric Food Chem 52(8):2292-6.
Kallithraka S, Mamalos A, Makris D. 2007. Differentiation of Young Red Wines Based on Chemometrics of Minor Polyphenolic Constituents. J Agric Food Chem 55(9):3233-9.
Kampa M, Hatzoglou A, Notas G, Damianaki A, Bakogeorgou E, Gemetzi C, Kouroumalis E, Martin P-M, Castanas E. 2000. Wine Antioxidant Polyphenols Inhibit the Proliferation of Human Prostate Cancer Cell Lines. Nutrition and Cancer 37(2):223-33.
Kang NJ, Lee KW, Lee DE, Rogozin EA, Bode AM, Lee HJ, Dong Z. 2008. Cocoa Procyanidins Suppress Tansformation by Inhibiting Mitogen-Activated Protein Kinase. J Biol Chem 283(30):20663-73.
115
Kauhanen J, Kaplan GA, Goldberg DE, Salonen R, Salonen JT. 1999. Pattern of Alcohol Drinking and Progression of Atherosclerosis. Journal of the American Heart Association: Arterioslerosis, Thrombosis, and Vascular Biology 19(12):3001-6.
Kennedy JA. 2008. The Chemistry of Red Wine Color. American Chemical Society 983:168-84.
Kimura Y Okuda H. 2001. Resveratrol Isolated from Polygonum cuspidatum Root Prevents Tumor Growth and Metastasis to Lung and Tumor-Induced Neovascularization in Lewis Lung Carcinoma-Bearing Mice. J Nutr 31(6):1844-9.
Kiziltepe U, Turan ND, Han U, Ulus AT, Akar F. 2004. Resveratrol, a Red Wine Polyphenol, Protects Spinal Cord From Ischemia-Reperfusion Injury. J of Vascular Surgery 40(1):138-45.
Klatsky AL, Chartier D, Udaltsova N, Gronningen S, Brar S, Friedman GD, Lundstrom RJ. 2005. Alcohol Drinking and Risk of Hospitalization for Heart Failure With and Without Associated Coronary Artery Disease. Am J Cardiology 96(3):346-51.
Kolouchová-Hanzliková I, Melzoch K, Filip V, Šmidrkal J. 2004. Rapid Method for Resveratrol Determination by HPLC with Electrochemical and UV Detections in Wines. Food Chemistry 87(11):151-8.
Kuhnle G, Spencer JPE, Chowrimootoo G, Shroeter H, Debnam ES, Srai SKS, Rice-Evans C, Hahn U. 2000a. Resveratrol is Absorbed in the Small Intestine as Resveratrol Glucuronide. Biochem Biophys Res Commun 272(2):212-7.
Kuhnle G, Spencer JPE, Schroeter H, Shenoy B, Debnam ES, Srai SKS, Rice-Evans C, Hahn U. 2000b. Epicatechin and Catechin are O-Methylated and Glucuronidated in the Small Intestine. Biochem Biophys Res Commun 277(2):507-12.
Landrault N, Ravel P, Gasc F, Cros G, Teissedre PL. 2001. Antioxidant Capacities and Phenolics Levels of French Wines from Different Varieties and Vintages. J Agric Food Chem 49(7):3341-8.
Langcake P, Pryce RJ. 1976. The Production of resveratrol by Vitis vinifera and Other Members of the Vitaceae as response to infection or injury. Physiological Pathology 9(1):77-86.
Langcake P, Pryce RJ. 1977. The Production of Resveratrol and the Viniferins by Grapevines in Response to Ultraviolet Irradiation. Phytochemistry 16(8):1193-6.
Lee J, Rennaker C. 2007. Antioxidant Capacity and Stilbene Contents of Wines Produced in the Snake River Valley of Idaho. Food Chemistry 105(11):195-203.
116
Lee KW, Kang NJ, Oak M-H, Hwang MK, Kim JH, Schini-Kerth V, Lee HJ. 2008. Cocoa Procyanidins Inhibit Expression and Activation of MMP-2 in Vascular Smooth Muscle Cells by Direct Inhibition of MEK and MT1-MMP Activities. Cardiovascular Research 79(1):34-41.
Leikert JF, Rathel T R, Wohlfart P, Cheynier V, Vollmar AM, Dirsch VM. 2002. Red Wine Polyphenols Enhance Endothelial Nitric Oxide Synthase Expression and Subsequent Nitric Oxide Release From Endothelial Cells. Circulation 106(13):1614-7.
Li M-H, Jang J-H, Sun B, Surh Y-J. 2004. Protective Effects of Oligomers of Grape Seed Polyphenols Against β-Amyloid-Induced Oxidative Cell Death. Annals New York Academy of Sciences 1030:317-29.
Lin J-F, Lin S-M, Chih C-L, Nien M-W, Su H-H, Hu B-R, Huang S-S, Tsai S-K. 2008. Resveratrol Reduced Infact Size and Improves Ventricular Function After Myocardial Ischemia in Rats. Life Sciences 83(9-10):313-7.
Lopez-Sepulveda R, Jimenez R, Romero M, Zarzuelo MJ, Sanchez M, Gomez-Guzman M, Vargas F, O'Valle F, Zarzuelo A, Perez-Vizcaino F. 2008. Wine Polyphenols Improve Endothelial Function in Large Vessels of Female Spontaneously Hypertensive Rats. Hypertension 51(1):1088-95.
Lu J, Ho C-T, Ghai G, Chen KY. 2001. Resveratrol Analog, 3,4,5,4'-Tetrahydroxystilbene, Differentially Induced Pro-apoptotic p53/Bax Gene Expression and Ihhibits the Growth of Transformed Cells but Not Their Normal Counterparts. Carcinogenesis 22(2):321-8.
Mackenzie GG, Adamo AM, Decker NP, Oteiza PI. 2008. Dimeric Procyanidin B2 Inhibits Constitutively Active NF-κB in Hodgkin's Lymphoma Cells Independently of the Presence of IκB Mutations. Biochem Pharm 75(7):1462-71.
Main G. 2004. Leaf-removal effects on Cynthiana yield, juice composition, and wine composition. Am J Enol Vitic. 55(2):147-52.
Management NCfIP. 2000. Crop Profile for Grapes in Missouri. In: University, N. C. S., editor). www.ipmcenters.org/cropprofiles/docs/mograpes.pdf
Mariani J. 2007. Corder Makes New Scientific Case for Red Wine's Healthy Effects. Bloomberg.http://www.bloomberg.com/apps/news?pid=20601093&sid=anxakt70Gn7U&refer=home Marier J-F, Vachon P, Gritsas A, Zhang J, Moreau J-P, Ducharme MP. 2002. Metabolism and Disposition of Resveratrol in Rats: Extent of Absorption, Glucuronidation, and Enterohepatic Recirculation Evidenced by a Linked-Rat Model. Pharmacology and Experimental Therapeutics 302(1):369-73.
117
Monagas M, Bartolome B, Laureano O, Ricardo-Da-Silva JM. 2003. Monomeric, oligomeric, and polymeric flavan-3-ol composition of wines and grapes from Vitis vinifera L. cv. Graciano, Tempranillo, and Cabernet Sauvignon. J Agric Food Chem 51(22):6475-81.
Moreno-Manas M, Plexats R. 1985. Dehyroacetic Acid Chemistry. A New Synthesis of Resveratrol, a Phytoalexin of Vitis vinifera. Anal Quim 81:157-61.
Mukamal KJ, Conigrave KM, Mittleman MA, Camargo CA, Stampfer MJ, Willet WC, Rimm EB. 2003. Roles of Drinking Pattern and Type of Alcohol Consumed in Coronary Heart Disease in Men. N Engl J Med 348(2):109-18.
Mukamal KJ, Jensen MK, Grønbaek M, Stampfer MJ, Manson JE, Pischon T, Rimm EB. 2005. Drinking Frequency, Mediating Biomarkers, and Risk of Myocardial Infarction in Women and Men. Circulation 112(10):1406-13.
Nikfardjam MSP, Márk L, Avar P, Figler M, Ohmacht R. 2006. Polyphenols, Anthocyanins, and trans-resveratrol in Red Wines From the Hungarian Villány Region. Food Chemistry 98(3):453-62.
Noble AC. 1990. Bitterness and Astringency in Wine. In: Rouseff, R. L., editor. Bitterness in Foods and Beverages. New York: Elsevier. p. 145-158.
Norton/Cynthiana. 2005. Missouri State University-Mountain Grove. http://mtngrv.missouristate.edu/MWFHU/Norton.htm
Pace-Asciak CR, Rounova O, Hahn SE, Diamandis EP, Goldberg DM. 1996. Wines and Grape Juices as Modulators of Platelet Aggregation in Healthy Human Subjects. Clinica Chimica Acta 246(1-2):163-82.
Pignatelli P, Ghiselli A, Bushetti B, Carnevale R, Natella F, Germano G, Fimognari F, Di Santo S, Lenti L, Violi F. 2006. Polyphenols Synergistically Inhibit Oxidative Stress in Subjects Given Red and White Wine. Atherosclerosis 188(1):77-83.
Prior R, Cao G, Muccitelli H, Hammerstone J. 2001. Identification of Procyanidins and Anthocyanins in Blueberries and Cranberries (Vaccinium spp.) Using High-Performance Liquid Chromatography/Mass Spectrometry. J Agric Food Chem 49(3):1270-6.
Rastija V, Srecnik G, Marica Medic S. 2009. Polyphenolic composition of Croatian wines with different geographical origins. Food Chemistry 115(1):54-60.
Renaud S, De Lorgeril M. 1992. Wine, Alcohol, Platelets, and the French Paradox for Coronary Heart Disease. Lancet 339(8808):1523-6.
Renaud SC, Ruf JC. 1996. Effects of Alcohol on Platelet Functions. Clinica Chimica Acta 246(1-2):77-89.
118
Renaud SC, Beswick AD, Fehily AM, Sharp DS, Elwood PC. 1992. Alcohol and Platelet Aggregation: the Caerphilly Prospective Heart Disease Study. Am J Clinical Nutr 55(5):1012-7.
Renaud SC, Guèguen R, Conard P, Lanzmann-Petithory D, Orgogozo J-M, Henry O. 2004. Moderate Wine Drinkers Have Lower Hypertension-related Mortality: a Prospective Cohort Study in French Men. Am J Clinical Nutr 80(3):621-5.
Revel A, Raanani H, Younglai E, Xu J, Rogers I, Han R, Savouret J-F, Casper RF. 2003. Resveratrol, a Naturel Aryl Hydrocarbon Receptor Antaonist, Protects Lung From DNA Damage and Apoptosis Caused by Benzo[a]pyrene. J of Applied Toxicology 23(4):255-61.
Rimm EB, Biovannucci EC, Willet WC, Colditz GA, Asherio A, Rosner B, Stampfer MJ. 1991. Prospective Study of Alcohol Consumption and Risk of Coronary Disease in Men. Lancet 338(8765):464-8.
Rios LY, Bennett RN, Lazarus SA, Remesy C, Scalbert A, Williamson G. 2002. Cocoa Procyanidins are Stable During Gastric Transit in Humans. Am J Clinical Nutr 76(5):1106-10.
Roncoroni L, Elli L, Dolfini E, Erba E, Dogliotti E, Terrani C, Doneda L, Grimoldi MG, Bardella MT. 2008. Resveratrol Inhibits Cell Growth in a Human Cholangiocarcinoma Cell Line. Liver International 28(10):1426-36.
Rubin R. 1999. Effect of Ethanol on Platelet Function. Alcoholism: Clinical and Experimental Research 23(6):1114-8.
Russell M, Chul Chu B, Banerjee A, Fan AZ, Trevisan M, Dorn JM, Gruenewald P. 2009. Drinking Patterns and Myocardial Infarction: A Linear Dose-Response Model. Alcoholism: Clinical and Experimental Research 33(2):324-31.
Sakano K, Murata M, Oikawa S, Hiraku Y, Kawanishi S. 2005. Procyanidin B2 has Anti- and Pro-Oxidant Effects on Metal-Mediated DNA Damage. Free Rad Biol Med 39(8):1041-9.
Sano A, Yamakoshi J, Tokutake S, Tobe K, Kubota Y, Kikuchi M. 2003. Procyanidin B1 is Detected in Human Serum After Intake of Proanthocyanidin-Rich Grape Seed Extracts. Bioscience Biotechnology and Biochemistry 67(5):1140-3.
Soleas GJ, Diamandis EP, Goldberg DM. 1997. Wine as a Biological Fluid: History, Production, and Role in Disease Prevention. Journal of Clinical Laboratory Analysis 11(5):287-313.
Soleas GJ, Angelinin M, Grass L, Diamandis EP, Goldberg DM. 2001. Absorption of trans-Resveratrol in Rats. Methods in Enzymology 335:145-54.
119
Somers TC. 1971. The Polymeric Nature of Wine Pigments. Phytochemistry 10(9):2175-86.
Stef G, Csiszar A, Lerea K, Ungvari Z, Veress B. 2006. Resveratrol Inhibits Aggregation of Platelets from High-risk Cardic Patients with Aspirin Resistance. J Cariovascular Pharmacology 48(2):1-5.
Steffen Y, Gruber C, Schewe T, Sies H. 2008. Mono-O-methylated Flavanols and Other Flavonoids as Inhibitors of Endothelial NADPH Oxidase. Biochem Bioshys 469(2):209-19.
Stervbo U. 2007. A Review of the Content of the Putative Chemopreventive Phytoalexin Resveratrol in Red Wine. Food Chemistry 101(2):449-57.
Subbaramaiah K, Chung WJ, Michaluart P, Telang N, Tanabe T, Inoue H, Jang M, Pezzuto JM, Cannenberg AJ. 1998. Resveratrol Inhibits Cyclooxygenase-2 Transcription and Activity in Phorbol Ester-treated Human Mammary Epithelial Cells. J of Biol Chem 273(34):21875-82.
Suh I, Shaten J, Cutler JA, Kuller LH. 1992. Alcohol Use and Mortality from Coronary Heart Disease: The Role of High-Density Lipoprotein Cholesterol. Annals of Internal Medicine 116(11):881-7.
Tarara J. 1991. Norton and Cynthiana - Premium Native Wine Grapes. Fruit Varieties Journal 45(2):66-69.
Tarola AM, Milano F, Giannetti V. 2007. Simultaneous Determination of Phenolic Compounds in Red Wines by HPLC-UV. Analytical Letters 40(?12):2433-45.
Threlfall RT, Morris JR, Mauromoustakos A. 1999. Effects of Fining Agents on trans-resveratrol Concentration in Wine. Australian J of Grape and Wine Research 4(1):22-6.
Tseng S-H, Lin S-M, Chen J-C, Su Y-H, Huang H-Y, Chen C-K, Lin P-Y, Chen Y. 2004. Resveratrol Suppresses the Antiogenesis and Tumor Growth of Gliomas in Rats. Clinical Cancer Research 10(6):2190-202.
Van Den Hoogen PW, Feskens EJM, Nagelkerke NJD, Menotti A, Nissinen A, Kromhout D. 2000. The Relation Between Blood Pressure and Mortality Due to Coronary Heart Disease Among Men in Different Parts of the World. N Engl J Med 342(1):1-8.
Verstraeten SV, Keen CL, Schmitz HH, Fraga CG, Oteiza PI. 2003. Flavan-3-ols and Procyanidins Protect Liposomes Against Lipid Oxidation and Disruption of the Bilayer Structure. Free Rad Biol Med 34(1):84-92.
120
Vidal S, Noble A, Kwiatkowski M, Cheynier V, Waters E. 2004. Taste and Mouth-Feel Properties of Different Types of Tannin-Like Polyphenolic Compounds and Anthocyanins in Wine. Analytica Chimica Acta 513(1):57-65.
Vitrac X, Desmouliere A, Brouillaud B, Krisa S, Deffeiux G, Barthe N, Rosenbaum J, Merillon J-M. 2003. Districution of [14C]-trans-resveratrol, a Cancer Chemopreventative Polyphenol, in Mouse Tissue after Oral Administration. Life Sciences 72(20):2219-33.
Walker T, Morris J, Threlfall R, Main G. 2002. pH Modification of Cynthina Wine Using Cationic Exchange. J Agric Food Chem 50(22):6346-52.
Wallerath T, Poleo D, Li H, Forstermann U. 2003. Red Wine Increases the Expression of Human Endothelial Nitric Oxide Synthase. J Am College of Cardiology 41(3):471-8.
Yang Y-M, Chen J-Z, Wang X-X, Wang S-J, Hu H, Wang H-Q. 2008a. Resveratrol Attenuates Thromboxane A2 Receptor Agonist-Induced Platelet Activation by Reducing Phospholipase C Activity. European J of Pharm 583(1):148-55.
Yang Y-M, Wang X-X, Chen J-Z, Wang S-J, Hu H, Wang H-Q. 2008b. Resveratrol Attenuates Adenosine Diphosphate-Induced Platelet Activation by Reducing Protein Kinase C Activity. Am J of Chinese Medicine 36(3):603-13.
Zairis MN, Ambrose JA, Lyras AG, Thoma MA, Psarogianni PK, Psaltiras GP, Kardoulas AD, Bibis GP, Pissimissis EG, Batika PC, DeVoe MC, Prekates AA, Foussas SG. 2003. C Reactive Protein, Moderate Alcohol Consumption, and Long Term Prognosis After Successful Coronary Stenting: Four Year Results From the GENERATION Study. Heart 90(4):419-24.
Zhang W-Y, Liu H-Y, Xie K-Q, Yin L-L, Li Y, Kwik-Uribe CL, Zhu X-z. 2006. Procyanidin Dimer B2 [epicatechin-(4β-8)-epicatechin] Suppresses the Expression of Cyclooxygenase-2 in Endotoxin-Treated Monocytic Cells. Biochem Biophys Res Commun 345(1):508-15.
Zhang W, Fei Z, Zhen H-N, Zhang J-N, Zhang X. 2007. Resveratrol Inhibits Cell Growth and Induces Apoptosis of Rat C6 glioma cells. Journal of Nero-Oncology 81(3):231-40.