Quality Assurance Applications of Pattern ... - ACS Publications

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Downloaded by NATL UNIV OF SINGAPORE on May 6, 2018 | https://pubs.acs.org Publication Date: November 6, 1985 | doi: 10.1021/bk-1985-0292.ch006

Quality Assurance Applications of Pattern Recognition to Human Monitoring Data Philip E. Robinson, Joseph J. Breen, and Janet C. Remmers Office of Toxic Substances, U.S. Environmental Protection Agency, Washington, D.C. 20460

Principal Component Analysis (PCA) is performed on a human monitoring data base to assess its ability to identify relationships between variables and to assess the overall quality of the data. The analysis uncovers two unusual events that led to further investigation of the data. One, unusually high levels of chlordane related compounds were observed at one specific collection site. Two, a programming error is uncovered. Both events had gone unnoticed after conventional univariate statistical techniques were applied. These results illustrate the usefulness of PCA in the reduction of multi-dimensioned data bases to allow for the visual inspection of data in a two dimensional plot. Data have been c o l l e c t e d since 1970 on the prevalence and l e v e l s of various chemicals i n human adipose ( f a t ) tissue. These data are stored on a mainframe computer and have undergone r o u t i n e quality assurance/quality control checks using univariate s t a t i s t i c a l methods. Upon completion of the development of a new analysis f i l e , multivariate s t a t i s t i c a l techniques are applied to the data. The purpose of t h i s analysis i s to determine the u t i l i t y of pattern recognition techniques i n assessing the quality of the data and i t s a b i l i t y to a s s i s t i n their i n t e r p r e t a t i o n . T

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Background Under the Toxics Substances Control Act, the Environmental Protection Agency (EPA) i s mandated to gather data on the exposure of the general population to toxic substances. Toward t h i s end, the Office of Toxic Substances within the EPA has undertaken several long term monitoring programs. These programs involve the c o l l e c t i o n of human tissue specimens from a s t a t i s t i c a l l y representative This chapter not subject to U.S. copyright. Published 1985, American Chemical Society Breen and Robinson; Environmental Applications of Chemometrics ACS Symposium Series; American Chemical Society: Washington, DC, 1985.

ENVIRONMENTAL APPLICATIONS OF CHEMOMETRICS


84

sample of the United States population and the subsequent chemical analysis f o r a select group of toxic substance residues and their metabolites. The data generated by these studies are used to establish the prevalence and l e v e l s of human exposure, to i d e n t i f y trends i n t h i s exposure, and to assess the e f f e c t s of regulatory action on exposures to these chemicals. The National Human Adipose Tissue Survey (NHATS) (_1) i s an on-going program conducted annually since 1970. Human adipose tissue specimens are collected during either post-mortem examinations or e l e c t i v e surgical procedures at 40 locations across the c o n t i nental United States. Demographic c h a r a c t e r i s t i c s of each tissue donor are also reported. Since the program's inception, over 20,000 specimens have been chemically analyzed at seven a n a l y t i c a l laboratories. The adipose tissue specimens are chemically analyzed using a packed column gas chromotography/ electron capture detector method and the M i l l s Onley Gaither procedure (2). Data were gathered on 19 organochlorine compounds and PCB s. A l i s t of the residues measured i n adipose tissue i s found i n Table I. f

TABLE I. 1

ρ,ρ DDT o,p DDT ρ,ρ' DDE o,p DDE ρ,ρ DDD o,p DDD alpha BHC beta BHC gamma BHC delta BHC f

f

1

f

Chemical Residues Measured i n Adipose Tissue Aldrin Dieldrin Endrin Heptachlor Heptachlor Epoxide PCB's Oxychlordane Mirex trans-Nonachlor Hexachlorobenzene

The survey design used by NHATS i s based on a multi-stage selection process i n which the f i r s t stage involves the random selection of a specified number of population centers (SMSA s) from each geographical region of the country. At the second stage, a l o c a l medical examiner or pathologist from each SMSA i s i d e n t i f i e d and asked to contribute tissue specimens according to demographic quotas based on age, race and sex. This study provides EPA with human monitoring data to assess the l e v e l of exposure of the general population to various toxic substances. S t a t i s t i c a l analyses of these data have primarily involved a description of the d i s t r i b u t i o n of these chemicals i n the population. S p e c i f i c a l l y , the proportion of specimens for which a particular residue l e v e l was quantified and the l e v e l of the chemical detected have been reported for various age, race, sex and geographical s t r a t a . f

Approach Exploratory data analysis (3) i s performed on the data base using multivariate s t a t i s t i c a l techniques. The objectives of

Breen and Robinson; Environmental Applications of Chemometrics ACS Symposium Series; American Chemical Society: Washington, DC, 1985.


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Human Monitoring Data

85

t h i s analysis are to assess the a p p l i c a b i l i t y of pattern recognition techniques i n the quality control of human monitoring data and to assess i t s a b i l i t y to i d e n t i f y relationships between variables contained i n the data base. Previous analyses were confined to the use of univariate techniques applied to the individual chemical residue l e v e l s . In contrast, t h i s analysis focuses on the evaluation of relationships between and among a l l quantitative variables simultaneously. To simplify the e f f o r t , various subsets of the data base are examined. The intent of t h i s action i s to allow for model v a l i d a t i o n or confirmation should relationships of interest be i d e n t i f i e d . The i n i t i a l data set consisted of 3800 records r e l a t i n g to specimens collected during the years 1977 to 1981 for those chemical residues having a greater than 10% detection rate. Table I I l i s t s those variables and residues included i n the analysis. As the analysis progressed changes were made to t h i s data set to either f a c i l i t a t e interpretation of the results or to further investigate hypotheses generated by the data.

TABLE I I .

Variable L i s t of I n i t i a l Data Set

Variable Name Date of C o l l e c t i o n Date of Analysis Lab Code Geographical Region Age Sex Race Length of Storage Medical Diagnosis Code

Residue Hexachlorobenzene trans-Nonachlor Oxychlordane p,p - DDT p,p - DDE alpha Benzene Hexachloride beta Benzene Hexachloride gamma Benzene Hexachloride Heptachlor Epoxide Dieldrin PCB's !

f

An examination of summary s t a t i s t i c s was conducted to determine which variables to include i n the i n i t i a l analysis. Measures of association between variables, i . e . , correlations, were investigated to ensure that a high degree of m u l t i c o l l i n e a r i t y did not exist between any pair of variables. The need for data scaling, transformation, or dimensionality reduction was also evaluated. For example, body burden data tend to be lognormally distributed. Whether these data need to be transformed prior to using techniques such as p r i n c i p a l component analysis (PCA) i s c r i t i c a l to the development of a basic strategy for the analysis of t h i s and other data sets containing human monitoring data. The i n i t i a l multivariate analysis consisted of a p r i n c i p a l component analysis on the raw data to determine i f any obvious relationships were overlooked by univariate s t a t i s t i c a l analysis. The data base was reviewed and records containing missing data elements were deleted. The data was run through the S t a t i s t i c a l Analysis System (SAS) procedure PRINCOMP and the results were evaluated.



86 Results

Figure 1 i s a plot of the f i r s t two p r i n c i p a l components (PCI and PC2) of data from c o l l e c t i o n year 1981. The symbols on the plot (1 to 9) designate the Census Division number i n which the specimen was collected (1=NE, 2=MA, 3=ENC, 4=WNC, 5=SA, 6=ESC, 7=WSC, 8=Mo, 9=Pa). A map of the continental United States which graphically i l l u s t r a t e s the Census Divisions i s provided i n Figure 2. A s t r i k i n g observation from Figure 1 i s the concentration of 6 s on the l e f t of the p l o t . A 6 represents the South Central Census D i v i s i o n . Table III l i s t s the loading factors associated with the f i r s t and second p r i n c i p a l components for several chemical residues included i n the analysis. The negative loadings provided by the chlordane-related compounds, Oxychlordane and Heptachlor Epoxide, i n the second p r i n c i p a l component are of s p e c i f i c interest since they are i n d i r e c t contrast to the other loading scores. Subsequent analysis of these data found that high l e v e l s of these compounds were confined to one sampling location within the Census D i v i s i o n . Two possible explanations for t h i s phenomena are (1) the samples were contaminated at the c o l l e c t i o n s i t e or (2) there exists an exposure problem to these chemicals i n t h i s geographical area. The Census Division 6 data were produced over an extended period of time and include results with non-elevated l e v e l s of chlordane-related compounds. This suggests that the specimens and not the laboratory are the source of the problem. An investigation i s being conducted by the EPA to determine the cause of these l e v e l s .


f

TABLE I I I . Residue P r i n c i p a l Component Loading Factors Data i s from C o l l e c t i o n Year 1981 Variable p,p DDT p,p DDE alpha BHC beta BHC gamma BHC Dieldrin Oxychlordane Heptachlor Epoxide f

f

PrinComp 1 .414 .437 .009 .395 .039 .144 .465 .268

PrinComp 2 .075 .169 .033 .006 .067 .155 -.104 -.335

Figure 3 i s another plot of output from the PRINCOMP procedure on data collected between 1977 and 1979. The symbols on the plot represent the year of c o l l e c t i o n of the specimens ( 7=1977, 8=1978, 9=1979). A pattern related to the dispersion of the 7 s, 8's, and 9*s i s v i s i b l e but any conclusion at t h i s point i s tentative due to the large number of hidden (unplotted) observations. Examination of the loadings for p r i n c i p a l components 1 and 3 (PCI and PC3) i n Table IV note the contribution of the residues, p,p DDE and p,p DDT, to p r i n c i p a l component 3. To better assess the e f f e c t of these variables on the group f

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T



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4

3

3

9 3 6 8 5 75 85 5 * 6 32 99 58 8 1 5 5 363 3 3 16 15 4 5 4 4 22 59 8 9 74 4 6 9 Τ 5 83 3 87 3 2 34 372 3 34 3 9 β 7 27 7 2 1 5 4 2 8 5 7 7532654 9 9 573788 15 18 1 2 6 β 5 77 32 35 943 9 2772 3 3 1 1 53 37 713 17 5 β 5 3 3 9/7334 374 9β47 33 1 9 53 661 825 7 5 3 33 5 1 **β ί » 2 · 2 8 41 3 2 2 2 * 5

5

5

s

2 2

4 3

9 4 *2

β

3

Data is from Collection fear 1981

Figure 1. Plot of PCI vs. PC2. Division)

(Symbol i s Number of Census




Figure 2.

U.S. Census Divisions



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ROBINSON ET AL.


ft 97 » 7ΐίβο««> 9 9 99 « 7 9 7 » 9 « 9 99· 9 9 < '.777777*77 " 7 7 7 7 7 * 7 7 7 7 7 9 9 ' 1999979 -.77777777*7*91 I99Q99 77*7777777*9' 47**777777 * 7779777' β77777 97799'

Data Is from Collection Vtars 1977-1979

Figure 3.

Plot of PCI vs. PC3 - Uncorrected Data (Symbol i s Year of C o l l e c t i o n : 7=1977, 8=1978, 9=1979)


89


90 f

of 9 s outside the central cluster, the scale on the plot was changed and the data replotted. Figure 4 i s the revised plot.

TABLE IV.

Variable p,p DDT ο,ρ' DDT p,p DDE ο,ρ' DDE beta BHC Dieldrin trans-Nonachlor Oxychlordane Heptachlor Epoxide Hexachlorobenzene PCB's f

f


Residue P r i n c i p a l Component Loading Factors Data i s from C o l l e c t i o n Years 1977 - 1979 PrinComp 3 .452 .064 .419 -.036 .272 -.083 -.240 -.282 -.171 .305 -.351

PrinComp 1 .358 -.012 .410 .021 .078 .124 .376 .453 .157 .082 .294

T

The concentration of 9 s i n the right side of the plot i n Figure 4 indicates a potential bias i n the 1979 data for those variables with the large positive loading scores i n p r i n c i p a l component 3. In an e f f o r t to explain these factors, the data were sorted by the value of the t h i r d p r i n c i p a l component and a printout of the data was examined. The majority of the high scores for PC3 were associated with specimens collected i n 1979. Further analysis indicated that the ρ,ρ DDE residue l e v e l s are unusually high for a large number of specimens i n t h i s year. Although, i n d i v i d u a l l y , each of the data points passed range checks normally used to screen for o u t l i e r s , the frequency of such high l e v e l s i s highly unlikely given the wide variety of demographic and geographic strata from which these specimens were c o l l e c t e d . In addition, as these specimens were chemically analyzed over the course of a year, the problem could not have resulted from the a n a l y t i c a l technique used to quantify these levels. A review of the raw data resulted i n the discovery of an error i n the computer program which created the analysis f i l e . A l l residue l e v e l s greater than 1.0 were coded i n the analysis f i l e with an extra 0 between the decimal point and the f i r s t unit*s place. For example 2.46 was recorded as 20.46. The limited number of such l e v e l s did not s i g n i f i c a n t l y a f f e c t previously computed univariate s t a t i s t i c s and these a r t i f i c i a l o u t l i e r s remained undetected. Figure 5. presents a plot of the PRINCOMP output after the analysis f i l e was corrected. This plot shows a more uniform d i s t r i b u t i o n of data points for specimens collected in each of the three years. 1


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ROBINSON ET A L .

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9

9 Μ Ί 9 9 Β 88 99 9 9 9 9 9 β 9 h 8 9 9 9 9 9 9 9 78687 97* Τ» 9 Λ9 9 7 < 7 β 7 7889*97 87797*8(19 99 99 9 9 β 7* 778 77997 798*9*779 779 999 9 9 « 8 7777 7778797*79*9*989*97798999 9 9 999 9 9 9 «77 7 " · 7 β » * » * 7 9 7 7 8789* 9*9 98779 9 9 9 9 7 8* 87*777** 9S7799997 77**9 998 8 99 9Ç99 9 9 8*797 7 97

β

Data Is from Collection Wart 1977-1979

Figure 4. Rescaled Plot of PCI vs. PC3 - Uncorrected Data (Symbol i s Year of C o l l e c t i o n : 7=1977, 8=1978, 9=1979)

7 7

7 9 „ r

77 7

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H 7/H Λ ii A**? 7

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Quality Assurance Applications of Pattern ... - ACS Publications

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