SELEX: An Expert System for Evaluating Published Data on Selenium
D.W. Bigwood (1) , S.R. Heller (*)(2) , W.R. Wolf (3), A.
Schubert (3), and J.M. Holden (3), USDA, ARS (1) Program
Resources, Inc., (2) Model and Database Coordination Laboratory,
Nutrient Composition Laboratory (3), Beltsville, MD 20705, USA.
* Author to whom correspondence should be addressed.
Providing consistent and objective evaluation of published
nutrient composition data is critical for planning future
analytical studies and effective use of data. Using a commercial
expert system shell, a computer system of approximately 200 rules
has been created to evaluate and quantitatively rate published
data on selenium in foods. The evaluation scheme uses five
general categories for its rule-making process: number of
samples, analytical method, sample handling, sampling plan, and
analytical quality control. For each selenium value to be
evaluated, ratings are assigned in each category by the expert
system based on input which is derived from the information
reported in a given paper. A Quality Index (QI), which is
derived from the ratings, is a measure of the reliability of a
given selenium value over all categories for a given study. The
concepts used in developing SELEX have the potential of
establishing criteria for assisting journal editors and their
reviewers in their evaluation of many manuscripts submitted for
Increasing interest in the selenium intake of Americans due to the
potential relationship of selenium to cancer prevention has generated a
need for the compilation, evaluation, and improvement of data on
selenium in foods. Reasons for undertaking this work include the
concern with the uneven quality of the data and lack of support
documentation. A set of criteria were developed to evaluate the quality
of existing, peer-reviewed, published selenium data (1). A manual
system for post publication evaluation of selenium data (2) using these
criteria proved successful in identifying foods for which the quality of
data was poor or for which there were no acceptable data. However, this
manual system was more tedious, more time consuming, and less consistent
than desired. Consequently an expert system, SELEX, was developed to
automate the evaluation process. Developed directly from the previously
established criteria, this expert system provides users with several
advantages over the manual system. These include speeding the
evaluation process and production of more consistent numeric ratings.
Development of the expert system also allows users who have less
expertise than the domain experts to generate ratings.
Figure 1 shows the overview of the entire evaluation procedure, including the selection of the selenium core foods as well as the individual rating process which is addressed by SELEX. For each food within a study, a rating is assigned in each of five different categories. These five categories are: number of samples, analytical method, sample handling, sampling plan, and analytical quality control. The ratings assigned by SELEX, the selenium mean, and ancillary information from the publication are written into a computer file which can be read by a SAS (Statistical Analysis System) program which determines the Quality Index (QI), selenium mean, and Confidence Code (CC) for each particular food. The QI is determined from the five ratings, and with a few exceptions, is equal to the simple mean of the five numbers. The ratings and QI range from 0 to 3. A QI of 1.0 or greater indicates that the selenium mean is considered acceptable. All acceptable means for a particular food are averaged to yield a grand selenium mean for that food. The CC (A, B, or C), derived from the sum of the QI's, represents the confidence that can be attributed to the grand selenium mean.
Using the concepts and methods created for the development of the
process of evaluating published selenium data, we have considered the
broader implications of these methods. It is hoped that the concepts,
principles, and rules developed for the selenium data evaluation system
will be considered by journal editors and their reviewers for use in
their pre-publication review process. At the least, this work indicates
that better defined procedures are possible for analytical chemical data
evaluation. By employing such techniques it is anticipated that a
better dialog could be developed between the journal editors and
It is well known that the quality of much of the scientific literature
is often lower than desired. There is probably far more poor and
irreproducible research being published than there should be. As Lide
(4) rather bluntly points out that the "scientific literature contains
vast amounts of data collected for a specific purpose and presented by
authors to support their conclusions... Unfortunately, the quality of
the data preserved in the literature leaves much to be desired. This
becomes apparent when data on a much-studied subject are systematically
retrieved... The measurements for (about 200 values of the thermal
conductivity of copper as a function of temperature) were analyzed by
the Center for Information and Numeric Data Analysis and Synthesis at
Purdue University. The scatter of these data illustrates the pitfalls
of relying on a single value retrieved from the literature." Can the
scientific community find a way to improve the peer review process?
Based upon this system for published data on selenium in foods, it
appears this is a goal that is achievable, at least in certain cases.
DATA QUALITY CRITERIA
For each of the five areas or categories used in the evaluation process
(1), a detailed description of the criteria was prepared using knowledge
of accepted analytical methodology, sample handling procedures, and
quality control measures for selenium, as well as a knowledge of
statistical methods, including statistically based sampling methods. As
stated above, the ratings ranged from 3 (highest and most desirable) to
0 (lowest and unacceptable). For example, the evaluation criteria for
the analytical method category are:
Rating 3 (Highest)
The official fluorometric method (reference provided) or other method
was used and is documented by a complete write-up with validation
studies for the foods analyzed. This includes use of an appropriate
Standard Reference Material where available, 95-105% recoveries on a
food similar to the samples analyzed which were reported in the same or
another paper, and the selenium concentration above the quantitation
limit of the method.
A modified fluorometric or other method was used and is partially
documented, but validation studies for the foods analyzed are
incomplete. There must be as least 90-110% recoveries on a food similar
to the samples analyzed which were reported in the same or another
paper, or good recoveries but no statistics are given in the paper,
and/or the authors have used another method (official fluorometric,
isotope dilution, or neutron activation analysis) on the same sample
with good agreement (which is defined as within 10%).
A non-fluorometric method was used and is only partly described.
Recoveries were either 80-90% or > 110% on a food similar to the samples
analyzed, or even better recoveries were obtained or a comparison method
was used on food samples with only a somewhat related nature to the
sample in question.
Rating 0 (Lowest)
The method used for selenium analysis was not documented or referenced or the reference was inaccessible. No validation studies were performed or selenium levels found in the food sample by the test method compared poorly to those found by the comparison method (>10%).
With the above definitions it is expected that trained evaluators will
derive the same ratings. Table 1 reproduces the manual worksheet for
raw egg white which shows the ratings assigned to each of 8 selenium
values found in the literature.
Manual Worksheet for Rating Raw Egg Whites
<--------- Data Quality Criteria Ratings --------->
Number Analytical (b)
Descrip- of Analytical Sample Sampling Quality Quality Comments
tion Samples Method Handling Plan Control Index
White 1 2 1 2 0 1.2 Duplicates,
Albumen 1 1 1 0 0 0.6 No Sampling
Plan or QC
White 2 2 2 2 0 1.6 No QC
White 1 0 1 2 0 0 No Method
White 3 2 1 0 0 1.2 Canadian
White 1 2 2 0 0 1.0 Canadian;
White 2 0 0 1 0 0 No Method
White 1 1 0 0 0 0 Mercury
Confidence Code = B.(a)
(a) The Confidence Code is derived from the sum of the Quality Indexes of the acceptable studies (QI > 1). In this case the sum is equal to 5.0.
(b) It is interesting and disappointing to see that analytical quality
control measures are almost universally not reported.
The initial SELEX implementation was written in ART (the Automated
Reasoning Tool) on a VAXStation II. The main inferencing mechanism was
backward-chaining (deductive reasoning), although approximately 10% of
the rules were forward-chaining (inductive reasoning). The system was
driven backwards from the so-called "rating rules" which generated an
integer rating from 0 to 3 for each of 5 major categories. The system
was rewritten as completely forward-chaining due to the fact that the
automatic goal generating mechanism of ART produced unacceptable
slowness in response time to users. The forward-chaining ART version
was then converted to CLIPS (the C Language Interfacable Production
System) (3), a forward-chaining rule-based system which uses the Rete
pattern-matching algorithm also used by ART and the computer language
OPS5. Examples of two rules and their English translations are given in
CLIPS was written by NASA's Artificial Intelligence Section, Mission
Planning and Analysis Division at the Johnson Space Flight Center (3).
CLIPS provided three immediate benefits. First, the CLIPS syntax is
based closely on ART syntax so that SELEX could be ported quickly.
Second, because CLIPS was written in standard C, it will run on any
machine which has a suitable C compiler. This is particularly important
in light of the fact that ART runs on a limited number of computers.
Third, the source code was provided along with a built-in mechanism for
adding functions so that extending and customizing CLIPS for SELEX was
easily accomplished. For example, two extensions to CLIPS provide SELEX
with the capabilities of verifying user input and keeping an audit trail
file which contains the sequence of questions and the user's input for
each session. The final system consists of approximately 200 rules and
currently is implemented on VAX VMS and IBM PC MS-DOS machines, such as
the IBM AT.
As already stated, SELEX derives ratings for five major categories of
evaluation: number of samples, analytical method, sample handling,
sampling plan, and analytical quality control. Information is gathered
by SELEX by a process of intelligent questioning of the user. The
system was designed so that only pertinent questions are asked. The
responses are provided in accordance with information derived from the
publication containing the selenium value to be rated. Depending upon
the responses, SELEX can produce a rating for each category from as few
as 6 and as many as 65 answers. Approximately 90% of the questions
require only a yes or no response with the remaining 10% requiring
numeric input. A portion of a sample session with SELEX is shown in
Figure 3. As soon as SELEX has enough information to determine a rating
for each of the five categories, the ratings are written to a file along
with associated information such as a publication reference number and a
description of the food. Periodically, this file is merged with a
master file containing information from previously evaluated data. The
master file is then analyzed with a SAS program which calculates a QI, a
mean selenium value for each food, and a Confidence Code (CC) for that
mean. The CC is derived from the QI's for all acceptable selenium
values pertaining to a particular food.
During development, SELEX was validated in two distinct ways. First,
several of the 65 post-1960 selenium publications which reported
original analytical selenium data for foods (from 33 different journals,
reports, proceedings, and books) which have been manually evaluated by
the domain experts were run through SELEX. In instances where there was
a difference between the manual rating assignments and the computer
expert system ratings, the differences were compared. When necessary,
existing rules were clarified or changed. Also, if needed, additional
rules were written to assure a correct evaluation. Second, hypothetical
cases were run through the system to validate decision paths which were
not encompassed by actual data from the publications. Ongoing
validation will continue until the domain experts are satisfied that
SELEX performs at an acceptable level.
BENEFITS and CONCLUSIONS
SELEX has several benefits over the original manual rating system. They
1. The manual system and the rules developed for SELEX incorporate
knowledge from several domain experts who have complementary expertise.
Therefore, the knowledge base is both broader and deeper than if only
one expert had been used. With these rules incorporated in SELEX,
publications can be rated by users who have less expertise than the
2. During the process of formally defining the rating criteria as a rule
set for SELEX, it was necessary to refine or restate some of the
original criteria in more detail. Therefore, SELEX should produce more
3. The formalization of the knowledge base facilitates its transfer to
4. SELEX speeds the evaluation process and automatically maintains
detailed records (audit trail) for each session.
5. SELEX reduces the "human error" factor by minimizing transcription,
data entry, and calculation errors. The determination of a rating for a
category, e.g., analytical method, results from the synthesis of several
pieces of information. SELEX minimizes the errors that may be caused by
the omission of information.
6. Since new publications with selenium data are evaluated
intermittently, SELEX eliminates the need for the users to continually
refamiliarize themselves with the complex set of heuristics.
The overall benefit, of course, is that SELEX will improve the definition and evaluation of the quality of the information available to identify any selenium-cancer correlation, since the results will be more accurate using an automated (objective method) rather than a manual one.
Although SELEX reduces the need for domain expertise, the user must have
a certain level of understanding of analytical chemistry and nutrition
science. Further refinement should reduce the level of expertise
required by the user. SELEX will be generalized so that it is valid for
the evaluation of published data from areas outside the United States.
SELEX will provide the foundation of an expert system which can be
adapted to evaluate data for a variety of nutrients. Part of this
effort will include the engineering of an expert system which will
automatically build rule sets for each nutrient. Such an expert system
is possible because the structure of SELEX can be utilized as the
template for new rules; the five categories nd the rating process for
each will be similar for many, if not most, nutrients. For example, the
rating criteria for analytical method will always include the use of a
standard analytical method or methods, the description of non-standard
analytical methods, validation of these analytical methods, and the use
of reference materials. The expert system will be able to query the
user about the specific details for each nutrient and generate a rule
set which is analogous to the SELEX rule set.
1. J.M. Holden, A. Schubert, W.R. Wolf, and G.R. Beecher, Food and
Nutrition Bulletin, 9 (Suppl. - Food Composition Data: The User's
2. A. Schubert, J. Holden, W. R. Wolf, J. Am. Diet. Assoc., 87 (1987)
3. Gary Riley or Chris Culbert, NASA/Johnson Space Center, Mission Planning & Analysis Division, Artificial Intelligence Section - FM72, Houston, TX 77058.
4. D. R. Lide, Jr., Science, 212 (1981) 1343.
Two rules used to determine a rating for sample handling. The first
rule asserts a rating from information that has been obtained from the
user. The second rule is an example of a rule which queries the user
for information. Each rule is followed by an English translation.
(declare (salience 100))
(assert (rating sample-handling 2)))
Translation of rule Rating-sample-handling-10:
If you are seeking a rating for sample handling and the homogenization validation data is optimal and the moisture level was not documented, then the rating for sample handling is 2.
NOTE: This rule has a declared salience of 100. The system will "fire"
this rule ahead of rules with lower salience. In this case we want
rating rules to fire ahead of information gathering rules such as the
one below (rules with no declared salience are assigned a default
salience of 0) because once SELEX can determine a rating, no further
information is needed. This exemplifies one key element of expert
systems - intelligent questioning.
(or (perishable-food false)
(not (food-preparation-documented ?))
(if (y-or-n-p 3060 0 "Was the food preparation documented")
then (assert (food-preparation-documented true))
else (assert (food-preparation-documented false))
(assert (food-preparation-appropriate true))))
English translation for rule Food-preparation-documented:
If you are seeking a rating for sample handling and either the food is not perishable or the shipping and storage procedures were appropriate or the shipping and storage procedures were not documented and it is not known whether or not the food preparation was documented, then ask the yes-or-no question "Was the food preparation documented?". If the answer is yes then assert that the food preparation was documented or else assert that the food preparation was not documented and assume that the food preparation was appropriate.
Figure 3. Part of a typical session with SELEX. This portion
represents the rating process for sample handling for a hypothetical
Now seeking a rating for sample-handling for selenium.
Was the sample handling procedure documented?
Response (Y or N): y
Was the sample food perishable?
Response (Y or N): y
Were the shipping and storage procedures documented?
Response (Y or N): n
Was the food preparation documented?
Response (Y or N): y
Was the method of food preparation appropriate?
Response (Y or N): y
Was only the edible portion of the food analyzed?
Response (Y or N): y
Was homogenization of the sample required?
Response (Y or N): n
Was the sample moisture level documented?
Response (Y or N): y
Was the moisture level of the sample appropriate?
Response (Y or N): y
The rating for sample-handling is 2.
Caption for Figure 1:
System overview of the selection of selenium core foods and evaluation of published data.