MULTIVARIATE STATISTICAL
PATTERN RECOGNITION OF
CURIE-POINT PYROLYSIS-GAS
CHROMATOGRAPHIC FINGERPRINTS
FROM RANGELAND SHRUBS
D. N. Stevenson
R. V. Valcarce
G. G. Smith
B.A. Haws
E. D. McArthur
B. L. Welch
H. C. Stutz
ABSTRACT
The application of pattern recognition to the analysis of
Py-GC data generally consists of two parts: unsupervised
exploratory data analysis and supervised classification
model development (Meglen 1988). Unsupervised exploratory data analysis detects outliers or abnormal measurements and provides information about the intrinsic data
structure through classification. The goal of classification is to categorize a set of data as members of a class
or classes without a prior or assumed knowledge of the
data (Sharaf and others 1986; Wold and others 1984;
Jerman-Blazic and others 1989). Unsupervised exploratory data analysis is an iterative routine that uses a
variety of multivariate statistical methods such as cluster
analysis, factor analysis, and principal component analysis, all of which are based on finding structural relationships or classifications among N-dimensional data
(Meglen 1988; Tabachnick and Fidel11983). The three
multivariate statistical programs used for exploratory
data analysis in this study are: (a) LINK hierarchical
cluster analysis (HCA); (b) MVSP principle component
analysis (PCA); and (c) Fuzzy c-varieties pattern recognition (FCV). In general, these three techniques complement each other, and when used together provide a
powerful tool for exploratory data analysis.
Supervised classification model development is used
to test the classification hypothesis determined in the
exploratory data analysis phase by developing classification and prediction rules. These rules are used to predict
class membership for new samples or to test the classification hypothesis by evaluating the performance of the
rules on the data set (Sharaf and others 1986). Supervised classification model development relies heavily upon
prior or assumed knowledge about class membership of
the samples in the data set. Measurements or features
of known samples are then used to construct a model that
best represents the classification. Subsequent samples to
be classified are compared with the classification model
and assigned to an appropriate class (Knudson and others
1977; Meglen 1988).
It is often desirable to reduce the number of features
(pyrogram peaks) in the data set; this is accomplished
The application of multivariate statistics to chemistry
(chemometrics), using pattern recognition (PR) techniques,
is shown to be a rapid and efficient method for the analysis of complex pyrolysis-gas chromatographic (Py-GC) data
obtained from biomaterials. Results of two studies using
various multivariate pattern recognition programs are
presented. In one study, pyrograms obtained from accessions of big sagebrush (Artemisia tridentata) were correlated with differential palatability of the sagebrush to
sheep. In the other study, Py-GC-PR was used to differentiate levels of ploidy in shadscale (Atriplex confertifolia).
INTRODUCTION
Pyrolysis-gas chromatographic (Py-GC) fingerprinting
of complex biological materials has been shown to be a
rapid and reliable chemotaxonomic technique (Soderstrom
and Frisvad 1984; Torell and others 1989; Valcarce and
Smith 1989a, 1989b). In pyrolysis, small amounts (usually micrograms) of directly sampled, underivitized material are fragmented by heating in the absence of oxygen.
The resulting pyrolyzates are resolved by gas chromatography, producing a pyrogram. Pyrograms from biological
samples, such as sagebrush and shadscale, are complex,
and overall patterns of variation are not easily detected
by visual examination. Multivariate pattern recognition
techniques can be used to statistically evaluate and interpret·the data (Irwin 1982; Jurs 1986).
Paper presented at the Symposium on Cheatgrass Invasion, Shrub DieOff, and Other Aspects of Shrub Biology and Management, Las Vegas, NV,
April 5-7, 1989.
D. N. Stevenson, R. V. Valcarce, and G. G. Smith are students and Professor Emeritus, Department of Chemistry and Biochemistry, Utah State
University, Logan, UT 84322-0300; B. A. Haws is Professor Emeritus,
Department of Biology, Utah State University, Logan, UT 84322-5305;
E. D. McArthur and B. L. Welch are Project Leader and Research Plant
Physiologist, Intermountain Research Station, Forest Service, U.S. Department of Agriculture, Shrub Sciences Laboratory, Provo, UT 84606;
H. C. Stutz is Professor Emeritus, Department of Botany and Range
Science, Brigham Young University, Provo, UT 84602.
325
This file was created by scanning the printed publication.
Errors identified by the software have been corrected;
however, some errors may remain.
using an additional procedure known as feature selection,
which ascertains the minimum number of variables
(pyrogram peaks) necessary to correctly classify the
training set samples (Duewer and Kowalski 1976; Sharaf
and others 1986). In this study, feature selection was performed using the multivariate statistical program CART
(Classification and Regression Trees).
Applications of unsupervised exploratory data analysis
and supervised classification and model development for
the interpretation ofPy-GC data provide a powerful analytical tool for plant materials. The following two studies
conducted on big sagebrush (Artemisia tridentata) and
shadscale (Atriplex confertifolia) are presented to demonstrate the applicability of this technique for the analysis
of genetically different but morphologically similar rangeland plants.
In this study, pyrogram peaks that correlate with palatability of sagebrush to sheep were sought using supervised pattern recognition methods to classifY sagebrush
pyrograms into clusters corresponding to three classes:
low palatability (<25 percent), medium palatability (25
percent-75 percent), and high palatability (>75 percent).
Shadscale
Shadscale is also abundant in the Intermountain region
of the western United States, from central Arizona
and southwestern California to southern and eastern
Montana. Although it is easily distinguished from all
other species of saltbush (Atriplex sp.) populations are
highly variable. Some variation may be attributed to
environmental conditions, but the majority appears to
be genetic, coming mostly from polyploidy and from introgression from other species (Stutz and Sanderson 1983).
The chromosome numbers of shadscale plants are determined by cytological examination of meiotic cells in male
flower buds. Collections for these studies can be made
only during a few weeks each year when the plants are
flowering. Being able to determine the ploidy level of
shadscale at any time during the year would be useful.
Since Py-GC-PR has been shown to be an effective
method for discrimination of plant and insect materials
(Soderstrom and Frisvad 1984; Torell and others 1989;
Valcarce and Smith 1989a, 1989b), it was decided to attempt to characterize shadscale using these same methods. Preliminary Py-GC-PR studies were conducted using
a limited data set of shadscale consisting of 16 plants representing eight locations and four chromosome races
(table 2).
Big Sagebrush
Big sagebrush is among the most widespread shrub
species as well as the most numerous single species in
western North America (McArthur and others 1981;
McArthur and Welch 1982). The big sagebrush complex
is divided into three common subspecies: basin, Wyoming,
and mountain big sagebrush (A t. ssp. tridentata, wyomingensis, and vaseyana) (McArthur and Plummer 1978;
McArthur and Welch 1982), each consisting of various
populations or accessions. Welch and others (1987)
reported that domestic sheep showed differential preference for various accessions of big sagebrush (table 1).
Differential palatability has applications in land rehabilitation, where less-preferred sagebrush could be used for
revegetation in areas subject to overgrazing. Conversely,
the establishment of preferred accessions of big sagebrush
can provide winter forage, improving rangelands for domestic and wild animals (Behan and Welch 1986; Welch
and others 1987).
Table 1-Utilization of big sagebrush accessions by wintering sheep
Table 2-Sample numbers, ploidy, identification numbers, and
(Welch and others 19a7)
Sample
number
1-6
7-12
13-1a
19-24
25-30
31-36'
37-42
43-4a
49-54
55-60
Accession 1
Hobble Creek (v)
Salina Canyon (v)
Dove Creek (t)
Petty Bish'ops Log (v)
Clear Creek Canyon (v)
Hobble Creek II (v)
Clear Creek Canyon (t)
Evanston (t)
Milford (w)
Evanston (w)
location of shadscale samples used in this study
Percent of current year's
vegetative growth eaten
ao.6
10.9
0.0
4a.3
21.6
ao.6
1.9
.5
a2.7
44.2
1
v =A. t. ssp. vaseyana, t =A. t. ssp. tridentata, w = A. t. ssp.
wyomingensis.
326
Sample
number
Ploidy
ID
number
1-3
4-6
7-9
10-12
13-15
16-1a
19-21
22-24
26-27
2a-30
31-33
34-36
37-39
40-42
43-45
46-4a
2x
2x
2x
2x
2x
2x
4x
4x
4x
4x
ax
ax
ax
ax
10x
10x
79777
79777
a2246
a2246
a3244
a3244
a2272
a2272
a2261
a2261
a31aO
a31aO
a2239
a2239
a3133
a3133
Location
Hardin, MT
Hardin, MT
Antelope Island
Antelope Island
Horse Canyon, UT
Horse Canyon, UT
Emery,UT
Emery, UT
Rock Springs, WY
Rock Springs, WY
Alkali Flats, OR
Alkali Flats, OR
Scipio, UT
Scipio, UT
Eskdale, UT
Eskdale, UT
EXPERIMENTAL
represented by a pyrogram, can be considered as a
N -dimensional data vector (Meglen 1988). Essentially,
cluster analysis searches the distance matrix for the
two data vectors with the smallest distance of separation.
They are then treated as a single point, positioned at the
center of gravity of the pair, and a new distance matrix
is computed. This process continues with the number of
groups reduced by one at each step, until all data vectors
have been assigned to a single cluster (Dunn and Everitt
1982; Lavine 1988).
A variety of methods to calculate the distance between
a single point and a cluster, or between two clusters, are
available (Romesburg 1984). Single linkage (SLINK),
complete linkage (CLINK), and average linkage (UPGMA)
between groups were the methods used in this study.
Euclidean distances were used for generating the dissimilarity coefficient matrix. The results ofHCA are illustrated using a two-dimensional dendrogram that displays
the multidimensional relationships among all samples
(for example, fig. 1).
Materials
Big sagebrush samples, grown in uniform gardens established by the U.S. Department of Agriculture, Forest
Service, Shrub Sciences Laboratory, Provo, UT, consisted
of 10 accessions representing three different subspecies
of big sagebrush (basin, mountain, and Wyoming big sagebrush) (Welch and others 1987). Shadscale samples taken
from nursery-grown plants at Brigham Young University,
Provo, UT, consisted ofleaves from 16 plants, representing eight different locations and four different chromosome races (2x, 4x, 8x, and lOx).
Sample Preparation
Each big sagebrush and shadscale sample was uniformly dried and ground to a fine powder. Fifteen milligrams of powder was suspended in 1.5 mL of spectralgrade methanol, and the resulting mixture was sonicated
for 30 minutes. Portions (5-10 J..LL) of the sonicated mixtures were applied to 510 °C ferromagnetic pyrolysis
wires and uniformly dried.
MVSP Principal Component Analysis (PCA)PCA is a standard statistical technique used in numerical
taxonomy (Dunn and Everitt 1982; Tabachnick and Fidell
1983). It reduces the dimensionality of multidimensional
data but retains as much of the variation in the data as
Pyrolysis-Gas Chromatography
Analysis
Each ferromagnetic sample wire was heated by induction, under helium, to 510 oc for 8 seconds using a F.O.M.
XL Curie-point pyrolyzer. The resulting pyrolysis products were resolved on a 27-m (0.32-mm ID, 0.25-J..Lm film)
Supelco SPB-5 fused-silica capillary column using a
Hewlett-Packard 5880A gas chromatograph equipped
with a flame ionization detector. Helium was used as
the carrier gas, and the peak areas were determined with
a Hewlett-Packard series 5880A level4 integrator. The
pyrolyzer head was maintained at 85 °C, the gas chromatograph oven was heated from 50 °C to 200 oc at a rate
of 5 °C/min, and the detector temperature was maintained
at 200 °C.
Dissimilarity Value
10
Evanston
(t)
15
0.5'l'.
Clear Creek Canyon (t) 1.9%
L
Dove Creek
r
(t) 0.0'1.
L
I
l
Data Processing
Hobble Creek (vl 80.6'l'.
The resulting pyrograms (retention time versus peak
areas) were compiled into m X n data matrices, consisting
of i = 1, 2, ... m samples andj = 1, 2, ... n features. Each
data matrix was the starting point for further chemometric investigation by: LINK (HCA), MVSP (PCA), and
FCVI?C-87 run on an IBM-AT-compatible equipped with
a math coprocesser and an Orchid TurboPGA video card,
and CART using a Digital Equipment Corporation VAX
Model 8650. Data standardization was performed by normalizing each column of features in the data matrix to the
sum of the values in the column.
I
Petty Bishop's Log (vl 48.3'1.
I
Clear Creek Canyon (v) 21.6'1. ·
'~
Salina Canyon (v) 10.9'1o
L
I
Milford (w) 82.7'1.
---+---------....1
L
Pattern Recognition Programs
Figure 1-Dendrogram from hierarchical cluster
analysis of sagebrush using single linkage
(SLINK) between groups and Euclidean distance
measure.
LINK Hierarchical Cluster Analysis (HCA)-HCA
uses techniques that search for unbiased natural groupings among samples inN-dimensional space. A sample,
327
20
25
possible. This enables direct examination of relative
positions of the data points (pyrograms) in the highdimensional space. This is accomplished by transforming
the original variables (pyrogram peaks) into a set of new
uncorrelated variables known as principal components
(PC's).
The resulting PC's are linear combinations of the original variables and are arranged in order of decreasing
variance, relative to the variation originally present in the
data (Tabachnick and Fidel11983). If the axes of the first
two or three principal components account for most of the
variation, plots can be generated (PC1 vs. PC2 or PC3) to
represent the relative positions of the data points in the
high-dimensional space.
among the three palatability classes in table 3. A variety
of other methods of supervised classification are available,
including discriminant analysis or soft independent modeling of class analogy (SIMCA). In this study, supervised
classification was performed by normalizing the three
palatability classes separately. Figure 2 shows the threedimensional "false color" plot of the data structure determined using this supervised procedure. An additional
weighting feature relating the palatability of the sagebrush to sheep was added to the data set. This resulted in
a "forced clustering" where the palatability features were
unnormalized (values from 0-100), and the pyrogram peak
areas were normalized (global average value = 1.00), producing the three classes of sagebrush samples: low palatability (30 samples), medium palatability (12 samples), and
high palatability (18 samples). FCV allows the samples to
have a shared class membership. This was particularly
Fuzzy c-Varieties Pattern Recognition (FCV)FCV pattern recognition consists of two parts: multi class
principal component modeling (MPCM) and false-color
data imaging. The objective of the MPCM algorithm
is to obtain disjoint principal component models of the
classes within the data (Gunderson 1984; Jacobsen
and Gunderson 1987). Multiclass principal component
modeling is an unsupervised agglomerative method that
determines the membership of a sample class within a
preselected number of classes using a variance-based optimization routine (Vogt and others'1989). In the MPCM
algorithm, each sample data vector plays a weighted role
in defining each class represented by the data. The result
is a membership matrix, containing membership values
for each sample data vector in each class. The output of
the MPCM algorithms, a set of principal components, one
per class, and a membership coefficient matrix (membership values), can be used for cluster analysis, classification of new samples, and feature selection.
False-color data imaging (Gunderson and others 1988),
a plotting subroutine, makes it possible to evaluate model
validity using three-dimensional "false color" images and
is useful for evaluating the results of the algorithm.
Table 3-Ciassification of big sagebrush accessions according to
palatability
Palatability
level
Accession
Subspecies
Percent
used
High
Hobble Creek
Milford
A t. ssp. vaseyana
A t. ssp. wyomingensis
80.6
82.7
Medium
Petty Bishop's Log
Evanston
A t. ssp. vaseyana
A t. ssp. wyomingensis
48.3
44.2
Low
Evanston
Clear Creek Canyon
Dove Creek
Clear Creek Canyon
Salina Canyon
A
A
A
A
A
Classification and Regression Trees (CART)CART classifies samples according to tree-structured
rules (Breiman and Friedman 1984). The classification
is performed according to a probability model. On a test
set, all features are used to construct a large tree, which
is then pruned to the minimal tree necessary to perform
the classification. A cross-validation procedure can be
used to determine the significance of the features or variables. The success or significance of the classification is
expressed as a misclassification rate. CART can be used
to develop a test set and to analyze unknowns.
t. ssp.
t. ssp.
t. ssp.
t. ssp.
t. ssp.
tridentata
tridentata
tridentata
vaseyana
vaseyana
0.5
1.9
.0
21.6
10.9
y
Moderate Palatability
&~~~-~
RESULTS
z
Big Sagebrush
Using the single-linkage (SLINK) clustering method,
HCA was applied to the big sagebrush data set where
the clustering was found to follow a hierarchical pattern
(fig. 1). Figure 1 shows that classes representing different
sagebrush accessions were detected, although no relationship with palatability could be established.
However, using the supervised approach of the FCVMPCM algorithm, a good discrimination was obtained
Figure 2-Three-dimensional false color data
image plot of the three sagebrush palatability
FCV classes determined using supervised
classification.
328
X
useful in the construction of the palatability classes because it allowed samples with 20 percent palatability
to sheep to be partly in the "low" class and partly in the
"medium" class.
Of particular interest are the pyrogram peaks that
show a stepwise decrease or increase relative to low-,
medium-, and high-palatability classes(* in fig. 3). The
FCV center values (average value of each class center in
37 dimensional spaces) are graphed in figure 3. A slight
increase with increasing palatability is observed for features 2 and 27, and a more pronounced increase for features 20, 22, and 35. A stepwise decrease is observed for
features 7, 15, and 31. Figure 3 also reveals that several
other peaks have much higher discriminating power than
the peaks mentioned, such as, 4, 9, 13, 14, 17, 19, 26, and
36 for the low class, and 12, 18, 23, 24, 25, and 32 for the
medium class. Since the different classes contain different accessions of sagebrush, these peaks may represent
chemical compounds useful in numerical taxonomy, but
not necessarily correlated with palatability.
CART is designed for classification (supervised data
analysis); therefore, an exploratory data analysis cannot
be performed. CART was applied to the data set in which
the palatability value was added as a class variable (low =
1, medium= 2, high= 3), and the chemical data were used
to classify the samples into the three classes. The resulting tree (fig. 4) consists of three nodes where samples are
split into two groups according to the value of a certain
feature. At node 1 of the tree, the samples were classified
according to feature 20 S 0.905. Thirty-six samples were
found with feature 20 S 0.905, as shown in figure 4.
These samples consist of both high-palatability class samples and medium-palatability class samples. Node 2 in
the classification tree was used to classify the 36 samples
according to feature 32 S 4.51. At this node, 30 samples
were found with feature 32 S 4.51, and were assigned to
the high-palatability class; six samples were found with
feature 32 ~ 4.51 and were assigned to the mediumpalatability class. The 24 samples at node 1 with feature
20 ~ 0.905 consisted ofboth medium- and low-palatability
classes. Node 3 was used to split the 24 samples into
medium- or low-palatability classes according to feature
25 ~ 4.57.
In addition to the construction of the classification tree,
CART also evaluates the importance of the different features. Some features compete with those used in the tree.
A ranking according to importance may, therefore, give
high priority to features not used in the classification
step. Table 4 shows the relative importance of the different features determined by CART. Feature 20 was highest; all other features that show a stepwise increase with
increasing palatability have an importance greater than
54 percent. Figure 5 shows the 20 pyrogram peaks best
used for distinguishing palatability.
5
•
4
-
3
-
Low Palatability
0 Med Palatability
IJ High Palatability
*
2
*
*
1
•
>
*
*
*
:
*
:
:
:
0
~
.
~
l
.
.I
:
.
J
~
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Pyrogram Peak
Figure 3-Center values for the three sagebrush palatability FCV classes determined using
supervised classification. The .. above the bars indicates the pyrogram peaks with a stepwise
increase or decrease relative to the low-, medium-, and high-palatability classes.
329
Table 4-Relative importance of features according to
CART
Feature
number
Relative
Importance
Feature 20 ~ 0.905
v
3(
rf \s\4
r-~~--.
~~----~
Medium
Palatability
Low
Palatability
High
Palatability
20
24
25
18
3
23
22
27
2
1
6
29
35
Feature 25 ~ 4.57
'6
Relative
importance
Percent
36/ '24
Feature 32 ~ 4.51
Feature
number
Percent
100
75
75
75
74
71
68
65
63
60
59
55
54
48
48
48
47
47
47
47
42
41
39
28
26
<25
12
32
21
4
13
19
26
28
5
34
10
31
Remaining
......
·.-
Figure 4-Ciassification according
to CART. At each of the nodes, one
feature is used to split the data set into
two subsets. Final classification of
sagebrush samples according to palatability is: high, 30 samples; medium,
12 samples; and low, 18 samples.
6
II Low Palatability
~ Med Palatability
5 - IIJ High Palatability
4
.
4
-
3
-
14
3
15
v
v
v
v
6
v
17
2
2
19
18
1
7
1
.
10
9
11
5
v
v
v
v
v
v
0
1
2
3
1/
4
6
v
v
v
v
v
v
v._
12 13
v
20
16
v
v
v
-
v
,....
v
v
v
,
- •
18 19 20
~-I
v
v
,
v
~/
,
8
13
P' ~
I
v
v
v
v._ v
~' li:;
v
v.
J
~ ,~
/
~
21 22 23 24 25 26 27 29 32 35
Pyrogram Peak
Figure 5-Center values for the three sagebrush palatability FCV classes determined
using supervised classification. Numbers above the bars indicate the 20 most important
features according to CART.
330
12
that HCA was capable of discriminating all nine locations;
however, no apparent correlation to ploidy was found.
Because exploratory data analysis did not reveal any relationship with ploidy, supervised modeling was used on the
data set to obtain chemical models corresponding to the
four euploids. A weighted feature was added to the data
set which corresponded to the four euploids. The results
from HCA on the supervised data set (figs. 10 and 11)
show that HCA correctly classified the shadscale samples
into one of four classes corresponding to 2x, 4x, 8x and lOx.
The dendrogram also indicated similarities and dissimilarities between the groups of shadscale pyrograms, with
4x and 8x samples the most similar, and the 2x the most
dissimilar.
MVSP principal component analysis was applied to
the shadscale data set in table 2. Figure 12 shows a plot
of the first and second principal components. Results
corroborated previous results obtained from HCA.
Shadscale
The shadscale samples used for this study are shown
in table 2. The resulting pyrograms from the Py-GC
analyses of these samples, consisting of approximately
20 peaks, were compiled into a 48 x 28 data matrix.
Representative pyrograms for each different ploidy level
are shown in figures 6 and 7.
HCA was applied to the resulting data matrix using
two different clustering methods: (UPGMA) and (CLINK).
The resulting dendrograms are shown in figures 8 and 9,
respectively. Except for the distance values at which
the samples formed clusters, the order in which samples
merged to form the dendrograms is essentially the same
for both clustering methods. Because UPGMA and
CLINK are based on different philosophies, the resulting
clusters can be considered as well defined and not artifacts of the clustering method. Figures 8 and 9, show
8x
2x
lOx
4x
Figure &-Representative pyrograms from
Figure 7-Representative pyrograms from
Py-GC analysis of shadscale chromosome
races 2x and 4x.
Py-GC analysis of shadscale chromosome
races ax and 1Ox.
331
Ohsia11arity Value
Ohsia11arity Value
10
IS
20
10
25
15
20
25
l~ry,UT87
21
2
~~ock
f
Springs, IIY
1---------,
3
~~88
31
3
32
3
3
3
37
3
4
3
41
42
4
4
4
4
47
4
Figure &-Dendrogram from unsupervised
hierarchical cluster analysis of shadscale
using average linkage (UPGMA) between
groups and Euclidean distance measure.
Figure 10-Dendrogram from supervised
hierarchical cluster analysis of shadscale
using average linkage (UPGMA) between
groups and Euclidean distance measure.
D1ssia11arity Yalu1
10
15
Ohsia11arity Value
20
25
10
15
1
, 87
21 r : h r yUT
2
~~ock
Springs, IIY
~7
1--------~
3
~~
Ellery,
Uf
88
31
3
32
3
3
3
37
3
4
3
41
42
4
4
4
4
47
4
Figure 9-Dendrogram from unsupervised
hierarchical cluster analysis of shadscale
using complete linkage (CLINK) between
groups and Euclidean distance measure.
Figure 11-Dendrogram from supervised
hierarchical cluster analysis of shadscale
using complete linkage (CLINK) between
groups and Euclidean distance measure.
332
20
25
0.025
0
~
0.015
The FCV algorithm was used to determine the chemical
features that differentiate the four classes found by HCA
and PCA. Figure 13 plots the contribution of each class
center (average values of each class) to each of the 28
Euclidean dimensions in the data set. Several peaks
have high discriminating power among the four shadscale
euploids: peaks 16 and 20 for discriminating lOx, peak
24 for discriminating 2x, peak 9 for discriminating 8x, and
the absence of any peaks for discriminating 4x from the
other three.
6. 2x
0
0
0
4x
Sx
lOx
'---
~
N
'E
Cl)
c
0
a.
0.005
E
0
(.)
asa.
'(j
@
6
~
-0.005
.5
~
0:.
g
-0.015
§
DISCUSSION
0
Big Sagebrush
A feature may be important to the class structure in
two ways. It may show a high variation within a class,
thus high "modeling power" for that particular class, or
it may be a good discriminator among classes. In unsupervised clustering, one class is split so that the variations within the new classes are minimized relative to
the global variation. A feature with global modeling
-0.025
-0.1
0.0
0.1
0.2
Principal Comoonent 1
Figure 12-First and second supervised principal
components analysis of shadscale samples.
~
.18
2x
~
7
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Figure 13-Center values of the four chromosome races (2x, 4x, ax, 10x) FCV classes
determined using supervised classification.
333
25
2A
power should, therefore, be a good discriminator at the
subset or class level. Consequently, features with high
modeling power according to this model should be good
discriminators for taxonomy classes but not for palatability classes.
The class centers from FCV, shown in figure 3, illustrate the chemical differences among the three palatability classes. Eight features of possible importance to
the palatability can be identified visually as shown in
figure 3 (features with a stepwise increase or decrease
with palatability).
The combined use of FCV and CART, shown in figure 5,
resulted in a subset of 20 peaks from figure 3 ranked as
important for classification (features ; : : 4 7 percent in
table 4). Numbers above the bars indicate CART's ranking. The eight peaks with a stepwise increase or decrease
with palatability are among the 20 peaks in the subset,
with peak 20 being the most important if the samples
are to be classified according to palatability. In a related
study, Welch and McArthur (1986) have shown that
coumarin compounds are good taxonomic indicators,
as well as palatability indicators for mule deer. Further
studies on peak identification should begin with a comparative study of known taxonomic and palatability indicators (such as coumarin compounds) and peak 20. This
result demonstrates that Py-GC-PR is a viable method for
the determination of big sagebrush palatability.
its relationship to the abundance and species of insects
on shadscale. A survey ofinsects associated with native
shrubs during 1986 to 1989 has included collections from
areas where the ploidy of shadscale is known. It should
be possible to determine if insects and ploidy are correlated. If insects are shown to be to be associated (directly
of indirectly) with shrub dieoff, and with ploidy, understanding the causes of dieoff would be increased. Similar
correlations could be calculated when quantitative, biological data are available about plant diseases, edaphic
factors, and range management (grazing, etc.).
It may well be that some of these associations will
be shown to be random. However, if these associations
are real by using shadscale as a test case, the principles
would have widespread use. For example, as guidelines
for plant materials centers, production and commercialization of native seeds, range management, gathering
information about poisonous range plants, and a host
of other applications.
CONCLUSIONS
Big Sagebrush
Hierarchical application of FCV discriminated among
the 10 accessions. However, neither model resulting from
unsupervised data analysis correlated with palatability
classes.
The two programs used for supervised classification
employed different approaches to evaluate the chemical
features. The FCV class centers identified eight features
that increased or decreased stepwise with increasing
palatability. When these results were combined with
the feature evaluation performed in CART, one chemical
feature (peak 20) proved to discriminate best for palatability; further studies on peak identification should begin
with this feature.
Of the three pattern-recognition programs, the most
definitive information was obtained by combining FCV
and CART. Given an adequate training set, the combined
application of these algorithms is recommended for interpretation of complex data sets such as those resulting
from Py-GC analysis of big sagebrush.
Shadscale
The three multivariate pattern-recognition programs
(HCA, PCA, and FCV) applied to the pyrolysis-gas chromatographic data correctly classified the shadscale samples according to location, with the supervised approach
showing each of the ploidy levels. Output of the FCV
algorithm was used to determine pyrogram peaks responsible for discriminating among the four ploidy levels. Five
features (pyrogram peaks 6, 9, 16, 20, and 24) or their
absence were important in discriminating each of the
ploidy levels of shadscale.
The biological questions that gave rise to these chemical
tests of ploidy were: Knowing that fingerprinting techniques similar to those described here have previously
discriminated among range grasses with different susceptibilities to insect feeding (Windig and others 1983), can
ploidies of shadscale (2x, 4x, etc.) be classified using similar methods? Can ploidy be identified all seasons of the
year? Is ploidy related to the kinds and abundance of
insects found in nativ~ rangelands? Are insects directly
or indirectly related to dieoff of native shrubs?
Results presented here answer some of these questions.
Yes, shadscale ploidy can be classified using Py-GC.
Discrimination of plant location was also determined,
a very important observation that may assist in identifying plants that should or should not be grown in certain
areas because of their adaptation characteristics.
Data now on hand and being analyzed may provide
answers to some of the other questions about ploidy and
Shadscale
The results of this study using a limited data set from
four ploidy levels of shadscale (2x, 4x, 8x, and lOx) demonstrated that Py-GC-PR is capable of discerning minute
biochemical differences among morphologically similar
accessions of shadscale. In addition, this study demonstrates that Py-GC-PR, given a large enough training
set, could classify and differentiate unknown samples
of shadscale according to their ploidy levels. Determination of the chemical identity of the discriminating pyrogram peaks allows development of rapid screening
methods for shadscale identification through the use
of Py-GC-PR.
334
ACKNOWLEDGMENTS
McArthur, E. D.; Welch, B. L. 1982. Growth rate differences among big sagebrush (Artemisia tridentata) accessions and subspecies. Journal of Range Management.
35: 396-401.
Meglen, R. R. 1988. Chemometrics: its role in chemistry
and measurement sciences. Chemometrics and Intelligent Laboratory Systems. 3: 17-29.
Romesburg, H. C. 1984. Cluster analysis for researchers.
Belmont, CA: Lifetime Learning Publications.
Soderstrom, B.; Frisvad, J. C. 1984. Separation of closely
related asymmetric penicillia by pyrolysis gas chromatography and mycotoxin production. Mycologia. 76:
408-419.
Sharaf, M. A.; Tilman, D. L.; Kowalski, B. R. 1986. In:
Elving, P. J.; Winefordner, J.D., eds. Chemometrics
Vol. 82 in chemical analysis. New York: John Wiley
and Sons: Chapter 6.
Stutz, H. C.; Sanderson, S. C. 1983. Evolutionary studies
of Atriplex: chromosome races of A. confertifolia (shadscale). American Journal of Botany. 70(10): 1536-1547.
Tabachnick, B. G.; Fidell, L. S. 1983. In: Using multivariate statistics. New York: Harper and Row.
Torell, J.; Evans, J.; Valcarce, R.; Smith, G. G. 1989.
Chemical characterization of leafy spurge (Euplwrbia
esula L.) by Curie-point pyrolysis-gas chromatographypattern recognition. Journal of Analytical Applied
Pyrolysis. 14: 223-236.
Valcarce, R.; Smith, G. G. 1989a. Chemical characterization of honey bees by Curie-point pyrolysis-gas
chromatography-pattern recognition. Chemometrics
and Intelligent Laboratory Systems. 6: 157-166.
Valcarce, R.; Smith, G. G. 1989b. Pattern recognition
studies of Curie-point pyrolysis-gas chromatographic
data from materials important to agriculture. Journal
of Analytical Applied Pyrolysis. 15: 357-372.
Vogt, N. B.; Bye, E.; Thrane, K. E.; Jacobsen, T.;
Benestad, C. 1989. Composition activity relationshipsCARE: Part 1. Exploratory multivariate analysis of
elements, polycyclic aromatic hydrocarbons and mutagenicity in air samples. Chemometrics and Intelligent
Laboratory Systems. 6:31-47.
Welch, B. L.; McArthur, E. D. 1986. Wintering mule deer
preference for 21 accessions of big sagebrush. Great
Basin Naturalist. 46: 281-286.
Welch, B. L.; McArthur, E. D.; Rodriguez, R. L. 1987.
Variation in utilization of big sagebrush accessions
by wintering sheep. Journal of Range Management.
40: 113-115.
Windig, W.; Meuzlaar, H. L. C.; Haws, B. A.; Campbell,
C. F.; Asay, K. H. 1983. Biochemical differences observed in pyrolysis mass spectra of range grasses with
different resistance to Labops hesperus Uhler attack.
Journal of Analytical and Applied Pyrolysis. 5: 183-198.
Wold, S.; Albano, C.; Dunn, W. J., II; Edlund, U.;
Esbensen, K.; Geladi, P.; Hellberg, S.; Johansson, W.;
Lindberg, W.; Sjostrom, M. 1984. In: Kowalski, B. R.,
ed. Chemometrics: mathematics and statistics in chemistry. NATO ASI Series: NATO Science Affairs Division.
New York: Reidel Publishing.
The authors wish to thank R. W. Gunderson for the
use of his programs FCVPC-87 and LINK; T. Jacobsen,
on leave from the Brewing Industry Research Institute,
Oslo, Norway, for help and useful discussions during data
processing; and J. Robinson for help with the Py-GC
analyses. Portions of this study were funded by the Utah
Agricultural Experiment Station, Logan, UT; the Biotechnology Center, Logan, UT; Utah State University; and
the USDA Forest Service, Intermountain Research Station. Journal paper No. 3885 of the Utah Agricultural
Experiment Station.
REFERENCES
Behan, B.; Welch, B. L. 1986. Winter nutritive content
of black sagebrush (Artemisia nova) grown in a uniform
garden. Great Basin Naturalist. 46: 161-165.
Breiman, L.; Friedman, J. H.; Olshen, R. A.; Stone,
C. J. 1984. In: Bickel, P. J., ed. Classification and
regression trees. Belmont CA: Wadsworth International: Chapter 8.
Duewer, D. L.; Kowalski, B. R.; Fasching, J. L. 1976.
Improving the reliability of factor analysis of chemical
data by utilizing the measured analytical uncertainty.
Analytical Chemistry. 48: 2002.
Dunn, G.; Everitt, B.S. 1982. An introduction to mathematical taxonomy. New York: Cambridge University
Press.
Gunderson, R. W. 1984. FCV-manual. Logan, UT: Utah
State University, Dept. of Electrical Engineering.
Gunderson, R. W.; Thrane, K.; Nilson, R. D. 1988. A falsecolor technique for display and analysis ofmultivariable
chemometric data. Chemometrics and Intelligent Laboratory Systems. 3: 119-131.
Irwin, W. J. 1982. In: Analytical pyrolysis: a comprehensive guide. Chromatographic Science Series Vol. 22.
New York: Marcel Dekker.
Jacobsen, T.; Gunderson, R. W. 1987. In: Piggot, J. J.,
ed. Statistical procedures in food research. Elsevier:
Chapter 10.
Jerman-Blazic, B.; Fabic-Petrac, I.; Randic, M. 1989.
Evaluation of the molecular similarity and property
prediction for QSAR purposes. Chemometrics and
Intelligent Laboratory Systems. 6: 49-63.
Jurs, P. C. 1986. Pattern recognition used to investigate
multivariate data in analytical-chemistry. Science. 232:
1219-1224.
Knudson, E. A.; Duewer, D. L.; Christian, G. D.; Larson,
T. V.. 1977. In: Kowalski, B. R., ed. Chemometrics: theory and application. Washington, DC: ACS Symposium
Series 52.
McArthur, E. D.; Plummer, A. P. 1978. Biogeography
and management of native western shrubs: a case
study, Section Tridentatae of Artemisia. Great Basin
Naturalist Memoirs. 2: 229-243.
McArthur, E. D.; Pope, C. L.; Freeman, D. C. 1981. Chromosomal studies of subgenus Tridentatae of Artemisia:
evidence for autoploidy. American Journal of Botany.
68: 589-605.
335