Principles of Quality Assurance of

Chemical Measurements

John K. Taylor

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

Center for Analytical Chemistry

Gaithersburg, MD 20899

February 1985

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100

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85-3105

1985

'f. DEPARTMENT OF COMMERCE

'lONAL BUREAU OF STANDARDS

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NATIONAL BUREAU OF STANDARDS

LIBRARY

NBSIR 85-3105

PRINCIPLES OF QUALITY ASSURANCE OF

CHEMICAL MEASUREMENTS

John K. Taylor

U S. DEPARTMENT OF COMMERCE

National Bureau of Standards

Center for Analytical Chemistry

Gaithersburg, Maryland 20899

Feburary 1 985

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, EmMt Ambi«r. Director

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ABSTRACT

The general principles of quality assurance of chemical measurements are discussed. They may be classified as quality control — what is done to

control the quality of the measurement process, and quality assessment —

what is done to evaluate the quality of the data output. Quality assurance

practices are considered as a hierarchy with levels progressing from the

analyst, the laboratory, the project, to the program. The activities of each

level are different and depend upon the ones beneath it. Recommendations are presented for developing credible quality assurance practices at each level.

An appendix contains outlines that may be used to develop the various

documents associated with a quality assurance program.

iii
PREFACE

This document was prepared as part of a cooperative program between the

National Marine Pollution Program Office and the Center for Analytical

Chemistry, National Bureau of Standards. It discusses the general principles

of quality assurance of measurements and contains comprehensive generic

guidelines for all aspects of quality assurance efforts ranging from those of

an individual laboratory to those of a monitoring program.

In preparing this document, it was recognized that specific guidelines are

not only virtually impossible but may be counterproductive for quality data.

Quality assurance is not like a software package that can be purchased and

utilized but rather needs to be developed for each individual measurement

situation and by each individual laboratory for its own use. What might be

superfluous in one case could be inadequate for another purpose. Actually,

the process of development of a credible quality assurance program is

educational and could even be a major benefit in itself.

Most well-managed laboratories will find that what they are now doing may be

most of what is necesary to be done. However, most will benefit by adopting

a more systematic approach to measurement, by improvement of their documentation, and by a more rigorous statistical evaluation of their data outputs.

The result will be measurement data to which statistically supported limits

of uncertainty can be assigned, and this should be an overall objective of

every laboratory.

It is hoped that these guidelines will be helpful to all laboratories as they

strive to develop their individual quality assurance programs. It is

realized that all of the suggestions contained may not be applicable to every

laboratory but it is hoped that there are no serious omissions that would

seriously affect data quality. Accordingly, each laboratory is urged to

study the contents of this report, to accept those parts which are pertinent

to its own measurement situation and which will be most useful to it in its

efforts to produce sound and dependable data.

The financial support of the National Marine Pollution Program and the

encouragement of its Director, Dr. Andrew Robertson, during the preparation

of this document are gratefully acknowledged.

iv
TABLE OF CONTENTS

Pages

1. Introduction 1 2. Measurement and Calibration 3

2.1 Selection of Methodology 3

3. The Quality Assurance Concept 5

4. Statistical Control 8

5. Quality Control Techniques 9

5.1 Competence 9

5.2 Suitable Facilities and Equipment 9

5.3 Good Laboratory Practices and Good Measurement Practices . 10

5.4 Standard Operations Procedures 10

5.5 Protocols for Specific Purposes 12

5.6 Other Quality Control Techniques 12

5.6.1 Inspection 12

5.6.2 Documentation 13

5.6.3 Training 14

6. Quality Assessment Techniques 15

7. Control Charts 18

8. Samples and Sampling 20

8.1 Uncertainty of the Sample 20

8.2 Quality Control of Sampling 20

8.3 Quality Assessment of Sampling 21 8.4 Chain-of-Custody 21

9. Levels of Quality Assurance 22

10. Quality Assurance Policy 23

11. Planning for Quality Assurance 24

12. Development of a Quality Assurance Program 26

13. Quality Assurance Manual 28

14. Quality Assurance of a Measurement Program 29

15. Protocols for Specific Purposes 31

15.1 Designation of Principal Investigator 31 15.2 Definition of Problem 31 15.3 Specification of Model 31 15.4 Specification of Samples 31 15.5 Specification of Data Base 32

15.6 Specification of Methodology 32

15.7 Specification of Controls 32

15.8 Release of Data 32

16. Quality Assurance of a Monitoring Program 34

17. Functions of a Reference Laboratory 37

18. Bibliography of Selected Quality Assurance Documents 3'

V
TABLE OF CONTENTS (Continued)

Pages

Appendix A. Program Documentation 47

A.1 Example-Quality Assurance Program Document 47

A. 2 Outline-Development of a Quality Assurance Protocol for a Specific Purpose (PSP) 55

A. 3 Outline-Content of a Quality Assurance Protocol for a Specific Purpose 56

A. 4 Outline-Organizing and Implementing a Monitoring Program . 59

A. 5 Outline-Functions of a Reference Laboratory 60

Appendix B. Data Quality Objectives 62

Appendix C. Terminology Used in Quality Assurance ... 64
LIST OF FIGURES

Pages

Figure 1. Production Process Quality Assurance 6

Figure 2. Measurement Process Quality Assurance 6

Figure 3. Methodology Selection 11

Figure 4. Quality Control by Inspection 12

Figure 5. Quality Assurance Perspective 25

Figure 6. Quality Assurance of a Measurement Program 29

Figure 7. Analytical Problem Solving 30

Figure 8. Cooperative Measurement Program 39

Figure 9. Quality Assurance Function of a Reference Laboratory . . A1

vii
LIST OF TABLES

Pages

Table 1 . Basic Elements of Quality Control 9

Table 2. Quality Assessment Techniques 15

Table 3. Protocols for Specific Purposes 33

Table Functions of a Reference Laboratory in National

Measurement/Monitoring Programs 38

viii
1 . INTRODUCTION

The demand for measurement data is ever increasing. Decisions need to be

made on such questions as the suitability of a material for an intended

purpose, the quality of the environment, and the health of individuals. The

vast majority of such measurements entail chemical analyses in which some chemical property of the material or object of interest is measured.

Dramatic changes have taken place in chemical analysis during the past

several decades. Most measurements are now being made using complex

instruments in sophisticated processes. Many of the operations previously

done manually are now automated. Moreover, measurements made by several

analysts and/or laboratories often need to be interrelated for use in a decision process. Monitoring programs of regional, national, or international scope may be involved. All of this places stringent requirements for

the reliability and compatibility of the data.

The quality of data is ordinarily evaluated on the basis of their uncertainty

when compared with end-use requirements. If data have consistency and their

uncertainty is small when compared to the requirements, they are considered

to be of adequate quality. When excessively variable or the level of uncertainty exceeds the needs, the data may be said to be of low or inadequate

quality. The evaluation of data quality is thus a relative determination.

What is high quality for one situation could be unacceptable in another.

The quality of chemical data may be judged on the basis of two aspects: the

accuracy of identification of the parameter measured; and, the numerical

accuracy. The qualitative identification must be without reasonable doubt.

The analyst must state precisely what was measured and be able to prove it.

While empirical measurements, defined by a specific apparatus or methodology

may be useful in some situations, they must be specified as such and used

with due consideration of their limitations.

Quantitative measurements are always estimates of the value of the measurand

and involve some level of uncertainty. The measurements must be made so that

this uncertainty can be assigned within a stated probability. Without such an assignment, no use can be made of the data in any logical sense. To achieve

this, measurements must be made in such a way to provide statistical

predictability. The experience of metrologists has demonstrated that such is

achieved best by a well-designed and consistently implemented quality assurance program.

The above discussion concerns the quality of measurement data, and indeed the

quality of individual measured values. When considered collectively as a body of numerical information for regulatory use, for discussion, or inference, additional quality descriptors may be useful. Thus the

Environmental Protection Agency proposes [45]:

Completeness - A measure of the amount of data obtained from a measurement process compared to the amount that was expected to be

obtained under the conditions of measurement.

1
Representativeness - The degree to which the data accurately and

precisely represent a characteristic of a population parameter,

variation of a property, a process characteristic, or an operational

condition.

Comparability - Expresses the confidence with which one data set can be

compared to another.

While difficult to quantify, such characteristics need to be considered when

evaluating the usefulness of a set of data for scientific interpretation.

The production of data of adequate quality is a common commitment of most

laboratories. However, it is pursued differently:

• in various organizations;

• within laboratories in the same organization;

• within groups in the same laboratory;

• from time-to-time by the same individual.

It is understandable why this is so. The kind and amount of quality assurance effort is related to the kind of work that is done, the knowledge,

skill and expertise of analysts, and the quality requirements for the data.

In many measurement situations, it is assumed that measurement uncertainty is

insignificant compared to the end use of the data. In such cases, the

measurement process may not be optimized, and the uncertainty of the data is

not fully evaluated. The adoption and implementation of a uniform credible

quality assurance policy within organizations and across organizational

units, should minimize much of the data uncertainties that exist and

harmonize data outputs.

It should be recognized that quality assurance is not a system of software

that can be installed but must be developed to satisfy individual needs.

W. Edwards Deming, one of the acknowledged leaders of the quality assurance

effort of industry, has said, ”An obstacle that ensures disappointment is the

supposition, all too prevalent, that quality control is something you

install.... Actually, to be successful in any company, it must be a learning

process.... with accumulation of knowledge and experience under competent

tutelage” (1^).

With due appreciation of the above, one can profit from the experience of

others, while developing a quality assurance program for a specific need.

The information that follows summarizes the experience and thinking of the

measurement community on the requirements for reliable measurements. It is

presented with the premise that it will provide guidance to laboratories in

the development of unique, individualized quality assurance programs that at

the same time, will have interlaboratory consistency.

2
2. MEASUREMENT AND CALIBRATION

Measurement is essentially a comparison process. Units are defined by

convention or consensus agreement and are established by suitable standards.

Comparators in the form of graduated scales or measuring devices incorporating sensors are used to compare an unknown for which some property needs to

be measured with a standard for which that property is believed to be known

with sufficient reliability.

While all measurement involves some kind of comparison, the measurement may

be described as absolute when only basic standards are involved and/or are interrelated by fundamental relationships. When derived standards are involved and/or related by empirical or ihferrential relationships, the measurement may be described as comparative. The majority of modern analytical

measurements are comparative, although absolute measurements such as gravimetry and coulometry are actively used.

In every measurement situation, several assumptions are explicitly or implicitly involved. The first is that the property of interest is

measurable by the measurement process. A second assumption is that suitable

standards are available. A third assumption is that the measurement or intercomparison is meaningful and can be reproduced within describable

limits- The reliability of the measurement process will depend upon the

validity of these assumptions. Judgement of such will require a comprehensive knowledge of the methodology, of the matrix measured, and demonstration

of the stability and performance characteristics of the measurement process.

The influence of the matrix on the measurement process always needs

clarification. The analytical chemist must demonstrate that this is negligible or is adequately compensated, else the measured values can hardly have

significance. Calibration procedures including the use of matrix-matching

standards, spiking, and standard-additions may be employed but their adequacy

must be demonstrated. Matrix modification can also be used in some cases. Removal of the analyte from the matrix and transferring it to a more benign

one can provide increased sensitivity and minimize matrix effects. In each

case, the analytical uncertainties introduced must be understood and taken

into account in reporting results.

Too often, calibration is done in a perfunctory manner. Analysts are urged

to give greater attention to the calibration process and to carefully con- sider the bias and random error that it may introduce. Bias can result if

the calibrants used are inappropriate (matrix-mismatch) or if their compositions are inaccurately defined. Drift of a measurement system between calibrations is a source of bias, hence calibration frequency must be carefully

considered. Calibrations are often considered as "errorless" and rarely

repeated except at intervals. As a result, each individual calibration may

be based on a very few (sometimes zero) degrees of freedom. When calibration

uncertainty becomes significant, as in ultra-trace analysis, it may become a major component of variance of the measurement system.

2.1 SELECTION OF METHODOLOGY

The methodology used must be appropriate and adequate for the measurements

that are required. In order to decide the appropriateness of the methodollogy, advance information is needed on the parameters to be measured, tht

3
level (a) of concentration expected, and the minimum differences among samples

that should be distinguishable. The operational characteristics of the proposed methodology must be known and should equal or exceed the requirements

for the data. In addition, the repeatibility and reproducibility should be

sufficient to minimize the number of replicate measurement that should be

made. The selectivity should be adequate or ways to eliminate interference

from other constituents of the sample must be available.

The limit of detection is an important consideration since it sets a lower

limit for qualitative use of the methodology. The limit of quantitation

defines the lower limit where quantitative measurements can be made with rea- sonable (± 30%) confidence (41). The limit of detection must be suitable in

most trace situations and the limit of quantitation should be less than the

levels at which quantitative data are required.

In addition to the above, it is desirable that the methodology is easy to

use, has a low unit-cost of operation, provides a rapid rate of analysis, is

useful over a wide range of concentrations, and is relatively insensitive

(rugged) to reasonable variations of its operational parameters.

A first requirement for a valid method (49) is that its operational

characteristics are adequate for the data requirements. A second requirement

is that a standard operations procedure (SOP) has been developed so that it can be used in a specified manner by a competent analyst. Both of these factors, and especially the latter, may be judged by collaboratively testing the

methodology.

It is obvious that valid data cannot be expected unless valid methodology is

used, but this does not guarantee reliable data. The laboratory/ analyst must

demonstrate competence and must use an appropriate quality assurance program

to support the quality of the data.
3. THE QUALITY ASSURANCE CONCEPT

Every experimenter knows that the absolute accuracy of any measurement cannot

be guaranteed. Yet data are for use, so they must be trustworthy. Their

quality must be defined, which means that the limits of confidence must be

evaluated and stated. The objective of a quality assurance program is to

provide a statistical basis for the assignment of such limits.

Quality assurance consists of two separate but related activities, quality

control and quality assessment. Both must be operational and coordinated.

The following definitions are offered.

Quality assurance: A system of activities whose purpose is to provide

to the producer or user of a product or a service the assurance that it

meets defined standards of quality with a stated level of confidence.

Quality control: The overall system of activities whose purpose is to

control the quality of a product or service so that it meets the needs

of users. The aim is to provide quality that is satisfactory, adequate,

dependable, and economic.

Quality assessment: The overall system of activities whose purpose is

to provide assurance that the overall quality control job is being done

effectively. It involves a continuing evaluation of performance of the

production system and the products produced.

The objective of quality control is to fine tune and maintain a measurement

process in a desired state of stability and reproducibility. In such, the

process can be considered to have a high degree of similarity to an industrial production process, with the ability to produce objects (in this

case data) with a high degree of reproducibility. Once this is established,

the precision may be defined and any biases can usually be identified and

eliminated or compensated, so that requisite data quality is achieved.

Quality assessment provides the evidence that this has been accomplished.

The general approach to quality assurance of the measurement process

parallels that for the industrial production process and borrows concepts and

techniques proven trustworthy for the latter, to the extent possible. In

industry, the procedure may be called statistical product control. In

metrology, the procedure may be called statistical measurement control.

The quality assurance procedure followed in a typical production process is

illustrated in figure 1. Experience dictates the kind and degree of control

that must be achieved and maintained to obtain products of desired quality.

Random samples of the output are tested or inspected with respect to specifications, in order to release or accept the production lot, to reject it, or

to take corrective actions in the process.

The quality assurance aspects of a typical measurement process are illustrated in figure 2. Quality control procedures are used to tune and

maintain the system in a state of statistical control in which it may be con- sidered as capable of generating an infinite number of measurements of any

material, of which the data of the moment are a typical sample. Quality

assessment procedures are then used to evaluate the quality of the data that

are produced. Unfortunately, it is generally impossible, intrinsically, t

5
evaluate fully the quality of the data on unknown test materials. However,

if the measurement system is in a state of statistical control, known test

samples such as reference materials (RM) may be measured concurrently and the

results compared with the reference values. Such a comparison can be used to

evalutate the performance of the measurement system and permit inferences to

be made on the quality of the data for test samples.

Figure 1. Production Process Quality Assurance

Figure 2. Measurement Process Quality Assurance

Quality assurance activities may be considered as a hierarchy progressing in

an ascending order [51] from the analyst -»• laboratory -»• project * program.

Each level requires different activities and has different responsibilities

which will depend on those below it. The analyst has the closest control of

the measurement process and the responsibilites are largely technical in

6
nature. The laboratory provides facilities and human resources and has

supervisory and managerial responsibilities. The success of a project will

depend on all of these and in addition will hinge on the soundness of the

technical concepts and planning to execute a valid experimental approach.

The project leader, of course, will have major responsibilities in this

regard. A program may be considered as a collection of projects which must be

properly conceived, coordinated, and managed for which the program manager

has prime responsibility. Intercalibration of the data-producing activities

is a major requirement for such a program. It is obvious that the quality

assurance procedures will be different for each level of activity and they

should be specifically designed. Each level may be considered as a subcontractor for the ones above it. If its quality assurance practices are appropriate and well executed, a minimal amount of inspection or checking

should be required by the upper levels, which then can devote their efforts

largely to the quality assurance activities peculiar to their specific

responsibilities. Each level must inspire confidence in the levels above it

and provide supporting evidence for the quality of its outputs. The reader

is referred to reference 51 where the concepts mentioned above are discussed

in greater detail.

7
STATISTICAL CONTROL

Everyone requiring or producing measured values would like them to be

accurate. Yet, it is generally recognized that measurement is a process of

estimation and that some degree of uncertainty is involved. It is axiomatic

that a meaningful measurement is reproducible yet it is common experience

that repetitive measurements will disagree and that the means of such, while

agreeing better, will also disagree. In fact, too close agreement often

indicates a rounding-off either by the measurement process or in the

reporting of the data.

It is becoming increasingly recognized that limits of uncertainty (error

bars) must be established and associated with every data point so that the

strength and weakness of every decision based on them may be evident. This

supports valid interpretation and prevents over-interpretation of data. The

experimenter or user of data may need to answer such questions as:

• are differences in measured values significant?

• how close would an additional value be expected to agree with the data

already obtained?

• what limits of confidence can be assigned to a measured value?

• are the performances of two laboratories/analysts significantly

different?

• are the results obtained using one method/ instrument significantly

different from those obtained by another?

• do estimates of precision obtained on several occasions differ?

These and similar questions cannot be answered categorically but only on a probability basis, which requires that the related measurements must be in a state of statistical control.

Statistical control means that the output of a measurement process is

predictable by statistical concepts. The measurement errors have attained a state of randomness so that predictive statistical models validly represent

the data. (Biases may or may not be present, but their identification will

be facilitated, as well).

The prerequisite for statistical control is a stable measurement process. In

such, the assignable causes of variation are kept in control by the quality

control program and the measurement operations are optimized and implemented

by standardized procedures so that chance causes of random error are minimized and/or acceptable. The output of such a system will be expected to

have a limiting mean and a definite distribution from which statistical

predictions can be made.

Eisenhart has stated, "To qualify as a measurement process, a measurement

operation must have attained what is known in industrial quality control

language as a state of statistical control. Until a measurement operation

has been 'debugged* to the extent that it has attained a state of statistical

control, it cannot be regarded in any logical sense as measuring anything at

all" ( 15 ).

8
5. QUALITY CONTROL TECHNIQUES

Quality control techniques include all practices and procedures that lead to

statistical control of the measurement process. The basic elements of

quality control are listed in table 1. The list proceeds from the general to

the specific and each element depends on the ones above it [52],

Table 1. Basic Elements of Quality Control

Technical Competence of Staff

Suitable Facilities and Equipment

Good Laboratory Practices (GLPs)

Good Measurement Practices (GMPs)

Standard Operations Procedures (SOPs)

Protocols for Specific Purposes (PSPs)

Inspection

Documentation

Training

5.1 COMPETENCE

The competence and expertise of the technical staff is the first requirement

for quality measurements. Despite the increasing sophistication of modern

instrumentation, technical judgement, experience, skill, and even the

professional attitudes of the staff are very important for reducing and

maintaining measurement variability at acceptable levels. The laboratory

staff must have at least a minimum level of competence commensurate with the

skill requirements of the analytical discipline. Only in the most repetitive

of measurement situations can the chemical measurement process be reduced to

a mechanical routine. It is common knowledge that measurement competence

increases as experience is gained with most methods, and that laboratories,

seemingly equal with respect to facilities and equipment, can differ

significantly in the quality of their data output.

5.2 SUITABLE FACILITIES AND EQUIPMENT

Modern chemical analysis typically requires specialized facilities,

apparatus, and equipment, and success or failure often can be traced to their

adequacy and mode of utilization. Thus ultra- trace analysis hardly can be

done without ultra-clean laboratories, and toxic substance analysis may

require the use of containment areas. Humidity and temperature control are recognized prerequisites for reliable measurements in some areas of analysis

and modern data processing and management systems are indispensible for some

and are fast becoming so for most other analytical techniques.

Specialized apparatus and equipment are rapidly displacing general-purpose

equipment so that certain kinds of analytical measurements are limited to

laboratories where such are available. In some cases, apparatus dedicated to

a specific analytical problem may be needed to minimize contamination and

memory effects.

9
Not only are such facilities and equipment required, but they must be

adequately maintained. Prescribed maintenance schedules, calibrations, and

good housekeeping practices must be followed to ensure the quality of the

work output.

With such dependency on facilities and equipment, their use can engender

unmerited reliance and even false security by both the producer and user of

the data. It is a fair statement that suitable facilities and equipment are necessary but not sufficient for the production of quality data. They must

be backed up by the other components of a quality assurance system, specifically designed for the analytical problems of concern. The complex nature of

the problems that can be solved by such facilities and equipment increases,

rather than decreases, the need for quality assurance of the measurement

process

.

5.3 GOOD LABORATORY PRACTICES AND GOOD MEASUREMENT PRACTICES

Good laboratory practices (GLPs) and good measurement practices (GMPs)

embrace the total experience of analysts in the making of good measurements.

GLPs refer to the general practices that relate to many if not all of the

measurements made in a laboratory. They are technique-independent and

address such subjects as maintenance of facilities, records, sample handling,

reagent control, and cleaning of laboratory glassware. Good measurement

practices (GMPs) are technique specific. They can be extensions of GLPs to

the requirements of specific measurement techniques or they may be completely

independent of such.

The subjects for GLPs and GMPs are best identified and the contents developed

by the laboratory personnel on the basis of their experience and that of

their peers. GLPs and GMPs must be documented and formally implemented in a laboratory if good measurements are to result. Too often, GLPs and GMPs are assumed to be common knowledge and unnecessary subjects for documentation. It

is true that many of the things done in a laboratory do not need to be formalized. Indeed, it would be virtually impossible to document every procedure and every step of an operation. However, critical operations can be

identified and should be optimized. Any operation or sub-operation that is

discovered to be an assignable cause of significant variance or bias is a prime subject for optimization and documentation.

5.4 STANDARD OPERATIONS PROCEDURES

Standard operations procedures (SOPs) describe the way specific operations

and methods are performed. These include sampling operations, sample preparation, calibrations, measurement procedures, and any operation that is done

on a repetitive basis. "Standard" means that it specifies the way the operation is to be done on each occasion, which may or may not be the procedure

developed by a standards-writing organization. However, when such are available, laboratories are well advised to consider them since they represent

peer judgement and provide a basis for comparability of data among user laboratories

.

When methods are repeatedly used, there is incentive to write them up in a formal manner, but those used on an infrequent basis are often based on an analyst’s "know-how". Such methodology is seldom optimized and frequently is

used differently on each occasion of use.

10
The practice of using unwritten methods should be discouraged. Documentation

of what was done is almost always needed. When the writing is done afterthe-fact, it is likely to be brief and sketchy and possibly inaccurate.

Describing a method before its use is part of planning and should be done

with as much care as that used for all other aspects of a chemical analysis.

Colleague review of all methodology is a minimum requirement to insure understandability and hence continuity of a measurement process, as well as to

receive the benefits of technical criticism. The writing need not be difficult and time-consuming if a standard format is used. This also provides a mechanism for screening for possible omissions in the descriptions and for

intercompar isons in some cases.

While the use of SOPs may provide a continuity of measurement experience, no methodology should be used blindly. Its appropriateness should be considered

at each time of use. If used infrequently, it may be necessary for the analyst to make sufficient preliminary measurements to validate competence and to

demonstrate attainment of statistical control of the measurement process (^49) on each occasion.

A recommended pattern for increasing the inventory of useful methodology of a laboratory is illustrated in figure 3* Certain of its methods will be

Figure 3. Methodology Selection
in category A and B. When new measurement challenges arise, methodology in

categories C, D, or E may be useful, but some developmental work may be

required before it can be used in a trustworthy manner. If an SOP is prepared concurrent with the use of such, the methodology can move to an A or B

classification for future use.

5.5 PROTOCOLS FOR SPECIFIC PURPOSES (PSPs)

PSPs precisely define the way a specific measurement sequence, project, or program is to be conducted. The premise in the development of a PSP is that

cost-effective and reliable data require a well-considered experimental plan.

While quality control is the primary objective, the PSP will prescribe the

quality assessment procedures to be used as well. Because of their dual

nature, the concept, content, and procedure for development of PSP’s will be

discussed elsewhere (see Section 15) in this document.

5.6 OTHER QUALITY CONTROL TECHNIQUES

5.6.1 Inspection

Inspection is widely used in industrial operations as a quality assurance

procedure. Because of its non-quantitative nature, it is most useful for

quality control in the measurement process, and less useful for quality

assessment purposes.

Inspection is largely a subjective examination for malfunction or for the

detection of abnormal conditions. A good measurement process usually will

have several places where inspection may be used beneficially as suggested by

Figure 4. Samples may be inspected for missing or conflicting information

^ Discard

^ Reject

c

Sampling

I

Inspection -

I

Measurement

i

Inspection

I

Data

Inspection —

I

Accept

Figure 4. Quality Control by Inspection
about the sampling process which could be cause for rejection. Also, the

samples may need to meet requirements for physical appearance or phase

homogeneity, for example.

The measurement process can have performance specifications for the

instrumentation (stability, for example), the laboratory environment, and

validity of calibrations before acceptable data are possible. Data checks

may be made for reasonableness and expression in proper units, for example.

Inspections do not control quality but may prevent the reporting of faulty or questionable data. A good inspection program may make it possible to obtain

additional samples or remeasurements when required and thus reduce gaps in

data that might otherwise occur.

5.6.2 Documentation

Documentation is an important aspect of every measurement program. Data must

be both technically sound and defensible. While technical soundness is a prerequisite, inadequate documentation can cast suspicion on the technical

merits

.

Documentation must address all aspects of the measurement process: the

model, the plan, the methodology, the calibration, the sample, and the data

reduction. All of these need to be tied together in a comprehensive report

that shows the interrelation of each phase and summarizes the data, including

all of its limitations. Conclusions, when presented, need to be fully

supported by documentation.

Details of the documentation process are best addressed in the GLPs. Special

instructions for documentation of the data output of measurement techniques

should be included in the GMPs. The documentation requirements for a specific project or monitoring program should be planned as carefully as all

other aspects of the measurement program and specified in the PSPs. In doing

so, the documentation practices of all participating laboratories should be

examined not only for adequacy but for compatibility. The PSPs may need to

specify the format in which the data are to be presented. This may include

the developement of special reporting forms.

Documentation and reporting of data must be done accurately. Transcription

errors are a common problem that can be virtually eliminated by care and

attention to detail. Systematic inspection and periodic review of notebooks

and similar primary records are recommended to ensure the general quality of

their contents. Changes or revisions of notebook entries must be justified

and documented. Any changes should be made by crossing out the original

entry and substituting the new value. The person making the change should

initial the entry and state why the change was made. No erasures of records

or data are permissible. A system for review of technical reports including

countersigning by higher authority is recommended. Also, the staff members

responsible for the measurements should sign reports to attest the validity

of the technical contents.

13
5.6.3 Training

Adequate education and training are prerequisites for reliable measurement

capability. The latter can be distinguished from the former by the degree of

specificity. The complexity of modern chemical analysis has increased so much that undergraduate education is no longer sufficient for analytical

chemists. Even highly educated analytical chemists find the need from timeto-time for additional training due to advances in technology and to the

adoption of new technology. On-the-job training in the quality assurance

aspects of a laboratory’s program is a necessity, as well. New employees

should be indoctrinated in the specific quality assurance practices they must

follow and refresher courses may be needed by the continuing staff. A laboratory’s quality assurance manual can provide a good basis for quality assurance training.

All training should be direct; no serial training should be permitted.

14
6. QUALITY ASSESSMENT TECHNIQUES

Quality assessment techniques consist of ways by which the measurement

process may be monitored in order to infer the quality of the data ouput.

Table 2 lists a number of approaches that may be used. The central theme is

the concept of redundancy. Precision can be evaluated only by replication

using stable samples of known and even unknown composition. Evaluation of

bias requires the repetitive measurement of samples of known properties

although fewer measurements may be required when statistical control of the

measurement process is demonstrated by other means. The quality assessment

techniques listed in Table 2 are classified as internal or external according

to the source of the assistance needed to implement the assessment technique.

Repetitive measurement of the samples actually tested is the classical way to

evaluate precision. However, this is a time consuming process if not care- fully planned and it is the objective of other quality assessment techniques

to minimize the number of such measurements. Duplicate measurement of an appropriate number of test samples often can provide much of the evaluation

of precision that is needed and eliminate all questions of the

appropriateness of the quality assurance samples.

Table 2. Quality Assessment Techniques

Internal

Repetitive Measurements

Internal Test Samples

Control Charts

Interchange of Operators

Interchange of Equipment

Independent Measurements

Definitive Method Measurements

Audits

External

Collaborative Tests

Exchange of Samples

External Reference Materials (ERM's)

Standard Reference Materials (SRM’s)

Audits

Internal test samples may consist of internal reference materials (IRM's),

split samples, spiked samples, and surrogates which are measured in suitable

test routines to evaluate the precision of the measurement process. Measurements of all such samples and indeed of all quality assurance samples are

best interpreted by the use of control charts. In this way, a few measurements of the moment can join the experience of the past to demonstrate

statistical control and bolster confidence in the measurement process.

Measurement bias can be operator, equipment, and/or methodology dependent.

Internal approaches that may be used to investigate such bias include interchange of operators and apparatus/equipment, measurement of selected samples

by independent techniques, and comparisons of measurement results with those

15
obtained, using a definitive method. The latter approach is useful when

suitable methodology is available and when external aids, such as reference

materials, are not available.

External evidence for the quality of the measurement process is important

since it avoids the dangers of introspection. Several procedures may be followed to provide external evidence of the quality of a measurement process.

These can confirm the internal evaluation of precision and provide independent assessment of any bias (or lack thereof). Activities include participation in collaborative test exercises, exchange of samples with other

laboratories, and the analysis of reference materials obtained from external

sources (ERMs). The National Bureau of Standards Standard Reference Materials (SRMs) [^3,^^] are unexcelled as test materials to evaluate the mea- surement process when they are properly used [12,50]. They have the distinct

advantage of wide acceptance, hence provide a basis for intercomparisons of

measurement systems and test data taken under diverse conditions and by

various laboratories.

From the above discussion, it should be clear that every laboratory has

within-house capability to estimate the precision of its measurements. The

evaluation of bias is difficult, internally, but facilitated by the use of

externally available techniques. No matter what the source of information,

any evidences of mal-performance , whether it be lack of precision or intolerable bias, should be carefully investigated and appropriate corrective

actions should be taken as necessary. After such actions, follow-up measurements should be undertaken to verify that the problems have been eliminated.

Assessment of the accuracy of qualitative identification is difficult. The

concept of control charts is not readily applicable and certified reference

materials for qualitative identification are not available at detection

levels where the problem of identification is most acute. Ordinarily, reliance is based on the known selectivity of the methodology and knowledge of the

absence of potential interferents. The knowledge and experience of the

analyst can be a key factor in decisions on detection.

Identification using independent techniques and/or several modifications of

the same technique (e.g., different columns) is useful for verifying the qualitative identification of a measured analyte. Spot checks by a reference

laboratory can be useful both for verification of measured analytes and to ensure confidence in question of "detect" and "non-detect"

.

Audits are listed as both internal and external quality assessment

techniques. Two kinds of audits may be used. Systems audits consist of both

systematic and spot checks of equipment, facilities, and procedures for com- pliance with the quality assurance plan. Performance audits depend on the

use of test samples to evaluate laboratory proficiency. Monitoring networks

and accrediting agencies often include audits as part of their procedures to

evaluate laboratories. Laboratory management is well advised to develop its own auditing program and to conduct internal audits on both an announced and

unannounced basis, whether or not required by someone else.

The use of internal test samples, particularly with a control chart, is a type of on-going performance audit. Operators should be encouraged to

maintain their own control charts with real-time response capability.

Supervisors and higher management levels may submit samples and keep their

16
own control charts. Monitoring programs should distribute control samples

and maintain control charts on a real-time basis to assure intercalibration

of stations in a network. The effectiveness of the quality assurance process

at any level will minimize the need and amount of effort required at higher

levels to monitor the measurement process.

Systems audits should be conducted in a similar manner. Bench-level audits

provide the potential for consistent implementation of the quality assurance

program and again should minimize the need for higher level inspection. While

the former can prevent faulty operations, the latter can usually only detect

them. The approach for conducting a quality audit, described by Gaft and

Richards (18), is recommended for guidance in developing such a program.

17
7. CONTROL CHARTS

The development and use of appropriate control charts (13) is one of the most

important techniques for monitoring a measurement process. The most common

type involves the scheduled repetitive measurement of a typical stable test

sample and plotting the results. Control limits are established within which

the results are expected to lie due to random fluctuations of the measurement

process. As long as they do so, the system is said to be in a state of statistical control. When the results are plotted on a sequence or time (preferable) basis, trends may be identified before "out-of-control” occurs. Similarly, the standard deviation or range of a series of measurements iftay be

plotted as a control chart and kept in parallel with the above chart to indicate when decrease in precision occurs and hence assist in the identification

of measurement problems.

It is recommended that laboratories maintain charts for the mean (X) and

difference (R) of duplicate determinations. Furthermore, it is recommended

that both warning (2s) and control (3s) limits be utilized. Such charts

should be developed for samples typical of each class of tests performed by

the laboratory, as a minimum. As long as the control samples are stable,

knowledge of their actual composition is of lesser interest. While samples

of unknown composition cannot monitor bias, they can monitor stability of the

measurement process, and bias can be evaluated otherwise. Accordingly, some

of the control samples used should be reference materials to facilitate the

evaluation of bias.

Laboratories are urged to consider also the use of R (duplicate) charts

utilizing the actual samples tested. While any control sample can be questioned as to its similarity to test samples, the duplicate test sample control chart can unquestionably monitor precision. By the use of a number of

the above to monitor short term precision, limited use of control samples to

monitor long-term precision, and occasional use of control reference samples

to monitor for bias (particularly when developing a measurement process), the

performance of the measurement process can be satisfactorily monitored.

All control charts, to be most effective, should be maintained in a real-time

mode, to the extent possible. Responsive corrective actions then can be

taken as necessary and questions of the validity of the data, due to out-ofcontrol operation, can be minimized. The control chart thus changes from a historical record (only useful if the system remains in control) to an active

monitor of the measurement process, and provides the opportunity for feedback process control.

The question of the number of control charts to be maintained needs to be

addressed. A practical answer should consider: the historical stability of

the measurement process; the risk taken when the process is operated out-ofcontrol; and the variety of the work load.

Experience has shown that 5 to 10 percent of the sample load should consist

of control samples of all kinds. More than this produces little extra information unless the system is excessively variable. In such a case, the causes should be sought and corrective actions taken to provide a more stable system. If less than the above percentage is analyzed, too high a risk factor

may be involved.

18
The kinds of control samples to be used have already been discussed briefly.

Since they will be used to draw inferences about the measurement process,

they should simulate the actual test samples or challenge the measurement

process in the same way as the normal test samples. The professional judgement of competent analysts is required in making decisions as to the number

and type of control charts and control samples that will be most beneficial

in monitoring the measurement process.

In addition to control samples, control charts of other kinds may be found to

be very beneficial. Examples of such include; calibration functions;

extraction efficiencies; chemical blanks; and critical factors of a measurement process. When a critical factor is identified and monitored, measurement instability can be identified and corrected before the measurements

themselves are affected.

19
8. SAMPLES AND SAMPLING

The term "sample” as used in chemical analysis denotes a portion of a material selected in some manner to represent a larger body of material. It

is that portion, and sometimes a sub-portion (sub-sample) which is analyzed

in order to draw conclusions about the body of material. Unfortunately,

there are often sample-related uncertainties that can vitiate the conclusions

or even prevent them.

Obviously, the selection of the sample is a critical operation. Questions of

what samples to take, when, where, and how to take them, and how many to take

and analyze are intimately related to the model (concept) of the problem and

also depend on knowledge of the variance of the sample population and the

variance of measurement. These questions are very important but are largely

beyond the scope of the present document. Guidance for various aspects of

sampling will be found in references 26,27,28,39,^1.

Questions concerning the integrity of samples will be discussed in the

following sections.

8.1 UNCERTAINTY OF THE SAMPLE

The uncertainties of the sample may be classified into two categories:

(a) those due to the natural variability of the sample; (b) those due to

changes in the sample resulting from operations and conditions to which it

may be subjected on removal from the population to which it belongs, during

the course of analysis, and events that may occur in the ensuing interval.

The uncertainties of category (a) ordinarily must be evaluated and the

measurement plan must be devised so that population variance and measurement

variance may be de-coupled and estimated. The results of measurement should

include estimates of uncertainty due to these two sources.

The uncertainties of category (b) are related directly to the quality assurance of the measurement process and fall into two groups, those due to sources of random error and those of a systematic nature. Even systematic

errors can have a random component. For example, temperature may have a systematic (and even predictable and correctable) effect but its variability

could produce random uncertainties. Factors which may affect the sample

include: the sampling operation; contamination; degradation; interaction

with, the container; mixing.

8.2 QUALITY CONTROL OF SAMPLING

The best way to deal with sources of sampling errors is to eliminate them or compensate for them to the extent possible. Often, this can be done by a well-designed and implemented quality control program. Just as in measurement, the use of GLP's, GMP*s, and SOP’s is the approach to quality control.

PSP's for measurement programs should include protocols for all aspects of

sampling, related to the specified measurement program.

In some cases, the quality control program for sampling can reduce sampling

uncertainty to insignificant levels. When this is not achieved, estimates of

the associated variances should be made and included in the total analytical

uncertainty (see Appendix B).

20
8.3 QUALITY ASSESSMENT OF SAMPLING

Because there are no "sampling reference materials", this approach to quality

assessment is not possible. However, field blanks and known samples can be

carried through various phases of sampling, including containment and storage, to monitor such operations. Monitoring of the cleaning operation for

containers and of grinding and other kinds of sample preparation is possible.

A valid and precisely implemented chain of custody can document that certain

steps in a sample quality control program have been implemented.

8.4 CHAIN-OF-CUSTODY

The concept of "chain-of-custody" most often is viewed as a means for legal

validation of samples, but its use for quality assurance is equally if not

more important. An adequate system provides both assurance of identification

of the samples that are analyzed and that all aspects of quality control

required for them have been observed.

The simplest system for use in a chain-of-custody involves an appropriate

label or tag that can be attached securely to the sample or its container.

Such identifiers should be designed to trace the history of a sample, including the operations and conditions to which it has been subjected, and indicate places of residence together with times, dates, and the responsible

individuals. The complexity of the system will depend upon the critical

nature of the samples and the risk associated with the uncertainty of any

significant information. In large and/or sensitive measurement programs, a sample custodian with responsibility for safeguarding samples and ensuring

that the chain-of-custody is unbroken may be needed.

Whether or not a sample custodian is required, the management of samples can

be a problem, even in small-scale operations. Often, samples must be subdivided when various parameters are measured by different analysts. Such

sub-divisions must be made carefully to ensure the identity of sub-portions

and without sample deterioration or introduction of contamination. In some

cases, physical or chemical operations such as sieving and blending or dissolution and extraction are performed in a central location and the products

are distributed to analysts. Obviously, these operations can be critical to

the measurement data and must be subjected to appropriate quality control.

The use of dedicated facilities (such as clean rooms) and even reserved areas within them may be required to carry out sample-preparation operations. In

ultra trace analysis, the hazard of cross-contamination by on-going samples

and memory-effects of previous samples and even from laboratory reagents need

to be considered and appropriate precautions must be exercised. Sample

handlers must be thoroughly trained to perform their respective duties

effectively and responsibly.

21
9. LEVELS OF QUALITY ASSURANCE

Formally or informally, quality assurance is a necessary part of the

production of data. No one will quarrel with the concept that measurements

should be made with appropriate care and that limits of uncertainty are necessary for their logical use. Only the questions of how the goals are to

be accomplished are debatable.

For complex operations, for those involving a number of participants, and for

numerous repetitive measurements, a formal quality assurance program is virtually a necessity and the time and effort devoted to its development should

be more than compensated by cost-effective and highly reliable analytical

data.

Indeed, the cost of operation should not exceed 10 percent of the total

operational effort and "do-overs" and "lost" data should be minimized. In

production operations, it has been said that "quality is free" (11).

In research investigations, an informal quality assurance program may be

adequate. However, the principles of quality control and quality assessment

apply and their implementation, even in an informal way, could constitute a major portion of the experimental effort. Accordingly, research programs can formalize appropriate parts of their programs and substantially reduce the

overall quality assurance effort in many cases.

22
10. QUALITY ASSURANCE POLICY

The adoption and enunciation of a quality assurance policy is a prerequisite

for an effective quality assurance program. This will reflect the degree of

commitment of management to quality outputs and the resources that will be

made available for this purpose. Based on the policy, a program can be

developed and implemented. The policy should define the respective responsibilities of the various organizational levels in implementing the program.

23
1 1 . PLANNING FOR QUALITY ASSURANCE

Some degree of quality assurance is practiced in most laboratories. Often

this is most of what is needed if the past measurement process has been well

planned. Ordinarily, there is little opposition by laboratory personnel to

doing a good Job. However, there is a natural aversion to regulations and

requirements, even when they define what is ordinarily done. When regulations include trivia or incredible requirements, they not only discredit the

important ones, but engender contempt for the entire program. Accordingly,

the quality assurance program must be both realistic and perceived as such.

The building blocks for a quality assurance program have been described

earlier [51] and are illustrated in figure 5. The specificity increases in

descending order. The policy of a laboratory reflects its basic measurement

philosophy and the goals and objectives it desires to meet. It will reflect

the type of service it strives to render and the desires and needs of the

clientele* it serves. As already discussed, the GLPs and GMPs describe in

rather specific terms the ways certain operations will be conducted. SOPs

define how specific measurements will be made and the PSPs encompass all of

the above for specific measurement activities.

A laboratory's quality assurance program should be internally motivated by

the general desire to produce high quality outputs but it is externally motivated as well by specific requirements for a specific project. The former is

thus program oriented and usually results in the development of a quality assurance manual. The latter is problem oriented and requires the development of project plans. The density of the tie-lines in the figure indicates

the degree of relatedness of the central elements to the ongoing program or to a specific project.

A laboratory's quality assurance program, as delineated in its quality assurance manual, should be sufficiently comprehensive to define most of the

measurement services which it can provide. Also, it may include PSPs for its

recurring measurement activities. Ordinarily, PSPs will need to be developed

for a specific project of a type not previously encountered, and especially

for monitoring programs. Even when PSPs exist, they may need to be re- examined for their adequacy at each time of re-use. The figure suggests

that, when a laboratory has a well-developed quality assurance program, PSP

development can consist of the selection of elements appropriate to the project of concern with only minor modifications being required to adapt them to

the specific program.

24
Program

Orientation

Internal

Motivation

External

Motivation

Project

Orientation

Figure 5. Quality Assurance Perspective

25
12. DEVELOPMENT OF A QA PROGRAM

The development of a quality assurance program should Involve all levels of

laboratory personnel. The management must make the decision to develop a formal program, and establishes the policy that will be followed. In doing

so, it should commit appropriate resources for both its development and

implementation. Ordinarily, management designates a leader to develop the

details of the program. Management will also need to approve the plans at

various stages of its development and establish a mechanism for oversight and

implementation of the program.

The leader may, or may not chair a committee, depending on the size of the

organization. In either case, the leader must get the cooperation and

involvement of the staff in developing the details and will follow

appropriate procedures to obtain concensus approval.

The staff must provide technical advice and guidance, particularly in the

development of the GLP’s, GMP’s, and SOP's. The staff may actually write

appropriate parts of these documents or review what is written. Consensus

approval of the staff is a necessity for general acceptability and for the

preclusion of trivial and unnecessary requirements.

Staff involvement will be found to be educational for them and this may be one of the most important features of the development of a formal quality assurance program. The development process will require a careful consideration of the measurement process as it is practiced, internally, and as it is

in the state-of-the-art. Parallelisms and contrasts between the practices of

various groups and measurement techniques within a laboratory, can be identified, and the opportunity is provided to elevate all operations to the

highest common factor. When the quality assurance program is assembled into

the form of a manual, it can provide future guidance as well as documentation

of present activities.

The process of development of the quality assurance programs need not be

complicated. The developers should identify the goals to be accomplished and

the motivation behind its development. This will include an examination of

the internal policy and any QA requirements of external programs with which

there is appreciable interaction.

The next step is to identify and document the quality assurance procedures

already implemented in the on-going program. These should be compared with

any requirements and also with the experience of others. Generic guidance is

provided in the present paper and in several of the articles in the

bibliography [2, 3, 6, 7, 8, 25, ^2]

.

The consensus approval, already referred to, is the next step. This will

include concurrence by the internal participants of the program, by management, and by any external group or organization requiring a formal QA

program, in the order mentioned.

Implementation of the quality assurance program is as important as its

development. All levels of the laboratory staff are involved and the responsibilities of each should be clearly defined in the quality assurance program

document. Management may designate a quality assurance officer or coordinator, to provide oversight of the program. The responsibilities of such an

26
officer may range from periodic reviewing of the program and conducting systems audits to providing test materials and conducting internal performance

audits on a routine and continuing basis. The review and approval of proposed work and development of the PSPs for such may also be a responsibility

of this person/office. The quality assurance office should be independent of

daily operations and report directly to management so that unbiased

appraisals of operations can result.

Middle management (the supervisory staff) has the responsibility for direct

implementation of the quality assurance program. Work of subordinates is

reviewed for its technical excellence and conformance with quality assurance

requirements. Middle management provides training related to both aspects

and makes decisions on corrective actions, including the identification of

assignable causes for measurement-process deviations.

The individual members of the technical staff have key responsibilities for

the quality of the data output and for all related laboratory operations. The

staff must be adequately trained to carry out both their technical and quality assurance responsibilities. Staff members often have the first opportunity to detect and correct malfunctions of the measurement system and must

be trained to do this. Both the technical and supervisory staff must be able

to distinguish between normal random fluctuations and abnormal deviations.

Unnecessary over-corrections can often increase the variance of the

measurement process and should be avoided.

The quality assurance program should be strictly enforced yet there should be

flexibility to deviate from it as necessary. If deviations are necessary too

frequently, the program may be defective and need to be amended. Deviation

may be required due to both under- and over-specification of the quality

assurance program or by changes in the measurement program that were not

anticipated or are not covered by the program as originally developed. For

such reasons the program should be continually reviewed and updated as required. While flexibility should be provided, the objective should be to

develop a quality assurance document that can be followed with confidence and

for which deviations would be exceptional and infrequent.

QA PROGRAM DOCUMENTATION

Should be clearly written

Should explain clearly what it is intended to accomplish

Should be accessible

Should be ’’user friendly” otherwise it will be ignored

27
13. QUALITY ASSURANCE MANUAL

The quality assurance program and practices of a laboratory should be

documented. Ordinarily they will take the form of a manual which will

include:

The QA Program Document

GLPs

GMPs

SOPs

Implementation Directives

The quality assurance manual may be viewed as an instruction kit that the

laboratory itself has developed to guide its laboratory operations in the

production of quality data. It can provide internal guidance on policy and

procedures and serve as a training manual for new employees. It should

satisfy most external requirements, as well, so that its existence will

minimize effort wherever PSP's need to be developed.

The quality assurance manual should be comprehensive but concise, and

descriptive but brief. Because some parts will be general and others specific, it will need to be well organized and indexed. QA manuals may be made

more useful when they are put on computers. Careful indexing can facilitate

computer retrieval of information pertinent to a specific measurement

problem, and thus enhance their usefulness.

28
14. QA OF A MEASUREMENT PROCESS

The general operational aspects of the quality assurance of a measurement

process are shown in Figure 6. Laboratory management is responsible for

establishing policy and for managing both the technical operations and the

quality assurance of the measurement program. The laboratory staff has the

responsibility for conducting all operations and for their quality control in

compliance with the associated GLPs, GMPs and SOPs. The staff provides also

the major input to the development of the GLPs, GMPs, and SOPs. The protocols to be followed (PSPs) will depend on the nature of the problem to be

solved and will encompass appropriate GLPs, GMPs and SOPs. Inputs from other sources such as the client, regulations, or specifications also influence the

PSPs which will also contain the quality assessment procedures that are to be

used

.

In the problem solving mode, samples from the problem area are analyzed by

technical operations that are in a state of statistical control, defined by

the quality control program. The data output is screened for acceptability

as defined by the PSP and released when found to be in conformance.

Figure 6 is not intended to be complete since some essential details have

been omitted for simplicity. In fact, it should be considered in connection

with figure 7 which depicts the overall process of analytical problem

solving. This latter figure emphasizes the importance of an adequate model

and of appropriate experimental plans in addition to the measurement aspects

which constitute the bottom line.

Figure 6. Quality Assurance of a Measurement Program

29
Initial planning

Subsequent planning

Data flow

Figure 7. The chemical measurement system. A problem is represented by a model which defines the data requirements and utilization. Based

on the model, a measurement program may be planned which addresses

sampling, calibration, methodology, and the quality assurance

procedures to be utilized. The design and implementation of the

chemical measurement system is often an interactive process and

every measurement program should include a feed-back mechanism

which provides for the identification of deficiencies and the

initiation of corrective actions for all parts of the system, as required

.

All components of the system are critically dependent upon each

other and are implicitly or explicitly present in every analytical

measurement activity. Failure to recognize this and to design

the system properly is a common defect of many analytical

programs.

30
15. PROTOCOLS FOR SPECIFIC PURPOSES (PSP’s)

The PSP is the most specific aspect of a quality assurance program. It

defines explicitly what is to be done in a given measurement situation. The

situation may be as small as a simple analytical service offered by a laboratory or it may be as broad as an international monitoring program. The scope

of a PSP is outlined in table 3.

The PSP is prepared by the authority responsible (or designee) for the

measurement operation. This may be the laboratory director in the case of

work to be done wholely within a single organization or the principal official of a monitoring program. When a PSP is developed for a particular

recurring problem it becomes the way the pertinent data are to be obtained.

When data are obtained for an external purpose, all interested parties may

need to be involved in developing and/or approving the protocol. Needless to

say, input should be sought at the bench level to make the protocol

requirements meaningful and available.

The PSP is both a planning and an operational document and each aspect is a worthy reason for its careful preparation. It may be very brief with references to other documents as possible (and this is preferable) or it may be

very detailed for major programs for which little precedent exists. The

subjects to be addressed in the content are outlined in the following.

15.1 DESIGNATION OF PRINCIPAL INVESTIGATOR

The person (s) with overall responsibility for the implementation of the work

should be designated together with the responsibilities that are delegated.

A quality assurance officer should also be designated, as appropriate,

together with the responsibilities thereof. Other subordinate responsibilities should be specified as appropriate, if not otherwise defined, such as in

an organizational structure for example.

15.2 DEFINITION OF PROBLEM

The problem to be solved should be clearly defined. This will include a concise statement of the objective and the scope (limits) of the work to be

undertaken. An overview of the activities may be included, as appropriate,

with references to other related ones not included in the scope of the

present project.

15.3 SPECIFICATION OF THE MODEL

The model refers to the concept of the problem, the assumptions that are made, and the hypotheses that are to be tested. The general data requirements are also set forth together with the precision and accuracy that should

be achieved.

15.il SPECIFICATION OF SAMPLES

This section will include details as to the number and kind (including

location, time, etc.) of samples as required by the model. The methods of

sampling, storage, preservation, subsampling, preparation for analysis and

other pertinent information should be prescribed. The manner of distribution

and chain-of-custody procedures may need to be specified in some cases.

31
15.5

SPECIFICATION OF DATA BASE

This section will specify the data that should be obtained including that for

analytical samples and for quality control and quality assessment. Measurement sequences may also be specified. The specification of the data base

required to qualify a participant (or contractors) in a monitoring program as peers, prior to their participation, is a part of this section.

15.6 SPECIFICATION OF METHODOLOGY

The measurement methods to be used should be specified in sufficient detail

so that no misunderstandings should arise as to what is to be done. Even

when standard methods are specified, there may need to be additional

information such as explanatory notes, for example. Any GLPs and GMPs, not

given explicitly in the methods, but deemed as necessary to obtain measurements of requisite quality, should be included. Reference to appropriate

GLPs and GMPs described elsewhere may be sufficient, but they should be

readily available and reviewed for their applicability and adequacy in the

present situation. Likewise, where deviations or exceptions from standard

methodology are permitted, they should be indicated clearly.

15.7 SPECIFICATION OF CONTROLS

This section will describe all activities that will be undertaken to assure

the quality of the data. Any necessary preliminary activities such as instrument warm up and performance checks should be described if not included

in the SOP's. The corrective actions to be taken and the procedures used to

indicate when these are necessary should be prescribed.

The control charts that should be maintained and the rationale for their

interpretation should be specified. This will include those to be maintained

by the measurement laboratory and those by others, such as a reference

laboratory for example. All control charts should be kept on as close to a real-time mode as possible so that deviations from statistical control can be

quickly detected.

The control samples to be used such as replicates, internal reference

samples. Standard Reference Materials, blinds, and double blinds should be

specified. In doing so, due consideration should be given to the model (to ensure its appropriateness) and the data base (to ensure its adequacy).

15.8 RELEASE OF DATA

This section should specify the requirements that the data must meet before

it may be released by the originating laboratory and the criteria that the

receiving organization will use to accept data. The persons authorized to

judge the above should be designated also. When sensitive data are involved, the chain of official release should be defined.

32
Table 3. Protocols for Specific Purposes*

What they are

protocols to define what is to be done in a specific problem/

project/ task/program.

How prepared

by responsible authority

may concern recurring problems

may be tailored to a specific problem; approval by management/

client desirable in this case

Content

designation of principal investigator

definition of problem

specification of model

specification of sample

specification of data base

specification of methodology

references to GLPs, SOPs, other PSPs as appropriate

specification of any deviations or exceptions from above

specification of controls

control charts to be maintained

quality assessment procedures

Release of Data

data quality requirements

quality assurance responsibilities

*Sometimes called project QA plans [^5].

33
16. QUALITY ASSURANCE FOR A MONITORING PROGRAM

This section describes the quality assurance aspects of a monitoring program

[51]. Monitoring programs may range in scope from those involving a number

of participants over an extended period of time to individual laboratories

seeking limited objectives. In any case, the program or project needs to be

well planned and the data must be reliable if conclusions are to be drawn.

The following discussion is written with large programs in mind but applies

in principle to all such programs, regardless of size.

An adequate quality assurance program is a critical requirement for any

monitoring program and the need increases with the number of participants and

the complexity of the data base. Experience has shown that adequate quality assurance practices cannot be assumed, even for a single laboratory. By the

laws of probability, the chance of faulty data will increase rapidly as the

number of participants increases. Monitoring programs are often initiated in

response to what is believed to be an urgent problem. The assumed urgency

may override prudent planning in the haste to obtain information. However,

if the planning and quality assurance procedures are faulty, the resulting

data can be questionable, misleading, and even worthless.

The planning for any project and especially for a major monitoring program

should proceed through the steps outlined in the following and illustated in

figure 7 (page 30).

The problem which is intended to be solved by the monitoring data must be

carefully defined. Precisely worded questions should be formulated that need

quantitative answers. At the same time, the related questions which are not

expected to be answered (that are not addressed) by the measurement program

may need to be stated. In other words, there should be no uncertainty, at

the start, as to what is hoped to be accomplished. This will not preclude

inconclusive results but such, as they occur, should clarify the directions

that should be pursued to obtain conclusive answers. In the case of a controversy, all parties concerned should agree that the questions are the

right ones. Until such agreement is reached, it may be futile to obtain data

since it will be impossible to reach any agreement as to what has been

accomplished.

The model for the monitoring program also must be established in advance.

This defines the data base as to kind and quality and specifies the samples

that are to be analyzed. If comparisons are to be made with control sites,

such comparisons should be based on samples in sufficent number, analyzed

preferably by the participants, to make the results statistically

inter pretable. The model should describe how all data are to be reduced and

evaluated.

Preliminary information ordinarily will be required to assist in framing

appropriate questions and developing an adequate model. If such information

is incomplete (and even if not) the questions and model will need to be

re-examined, on the basis of initial data, to confirm their credibility, or to indicate revisions as necessary. In brief, the design of this as well as other parts of the program needs to be an iterative process and will need

periodic re-examination to ensure its credibility and effectiveness. The

worst thing that can be done is to hastily conceive a measurement program,

blindly carry it out, and then try to make something of the data.

34
The next logical step is planning. Based on the model, plans should be made

for all aspects of the measurement process, including; sampling; measurement; calibration; and quality assurance. The PSP should be developed at

this stage, however, this too should be an iterative process and revisions

may be necessary, based on operating experience.

The PSP should clearly define what control charts are to be maintained and

the requirements for demonstration of statistical control of the measurement

process at the time that data are obtained for submission. The PSP should

also address the subject of qualitative identification of the measurand and

should state what confirmatory evidence is needed to support it.

When the limit of detection is of concern, the maximum acceptable value for

the method limit of detection (MDL) (19) should be specified in the PSP and

the frequency that the laboratory should confirm its MDL should be stated.

Because problem solving is essentially hypothesis testing, competent

statistical assistance may be needed in problem definition, modeling, and

planning. It is a common mistake to seek such advice after all data have

been collected and attempts are being made to interpret them.

Intercalibration is an essential part of any multilaboratory monitoring

program. That is to say, formal activities should be undertaken to ensure comparability and compatibility of the data produced. The first requirement is that the participating laboratories must have basic demonstrated competence in the specific measurement area (51). Then, they must maintain this

competence throughout the program. Systems and performance audits before and

during the program are essential. The latter need to be well planned with a rapid turn-around of the results so that appropriate corrective actions can

be taken as they are required to eliminate measurement problems. The program

authority has the responsibility to conduct the intercalibration program and

it should provide technical assistance, as appropriate, to correct significant measurement problems. The use of a reference laboratory (see Section

17) is an excellent way to accomplish this purpose.

An absolute requirement for all monitoring programs is peer review, prior to

their initiation. At any time that major changes are required, peer concui

rence should be obtained as well. All aspects of the program the model,

the plans, the measurement, the PSP's—should be scrutinized and all questions resolved before the program is activated. In the case of adversarial

situations, all opposing factions should be represented in the review

process

.

A procedure similar to that known by the legal profession as "stipulation"

may be followed to advantage in developing monitoring programs. In this, the

attorneys involved agree in advance on how legal proceedings are to be con- ducted in a particular litigation. Similarly, in monitoring programs, all

parties concerned should have the opportunity to agree on what is to be done

also (what cannot be done in some cases) and on any limitations based on such

considerations as time, cost, availability of samples, and the state-of-theart of measurement and interpretation of the data. Unless there can be a reasonable consensus on such matters, in advance, the outcome of the work, even when properly done, can be disappointing and raise questions ranging

from minor to major. In the case of the latter, one should ask whether th^^

35
proposed work is worthwhile and whether research or effort should be devoted

to delineate the problem and methods for its solution, before a monitoring

program is activated.

A monitoring program must recognize that quality of data is a paramount

consideration and must devote sufficient resources to accomplish this goal.

When purchasing measurement services, realistic quality performance specifications and quality assurance practices should be established and vendors

should be held accountable for following them. Laboratories should not be

paid for data of insufficient or undocumented quality [51].

36
17. FUNCTIONS OF A REFERENCE LABORATORY

Large monitoring programs may find it advantageous to use a reference

laboratory (RL) to provide services which would increase confidence in the

data, intercalibrate the measurement laboratories within the network,

maintain continuous surveillance of the measurement program, and advise on corrective actions as required. The reference laboratory may provide a gamut

of services ranging from advice to full responsibility for operation of a monitoring program. The reference laboratory concept is presented in outline

form in appendix A. 5 and is summarized in the following.

Several possible interrelationships of a reference laboratory to others in a monitoring program are illustrated in figure 8. Obviously, the intercalibration lines must be strong if all data are to have peer status and are to be

compatible. The strength of the other bonds will depend on the degree of

responsibility delegated to the reference laboratory. The latter may have

full responsibility for the entire program, in which case the direct lines

between the user and the measurement laboratories could be virtually non existent. When the user assumes major management responsibilities, the lines

between the reference laboratory and the measurement laboratories accordingly

will be weaker.

An effective reference laboratory should have the following qualifications.

It should have recognized competence and a dedication to high quality work.

Preferably, it should be a recognized center of excellence with broad

interdisciplinary capability and experience. It should have a reputation for

impartiality and objectivity. Furthermore, and as a major requirement, it

must have an effective ongoing quality assurance program to serve as a model

for the measurement laboratories, to back-up its technical opinions, and to

provide authoritative support for its own data outputs.

A well-qualified reference laboratory can provide some or all of the

following functions to a monitoring program (see table 4). It may provide

research and development support with respect to both methodology and

reference materials. With respect to the former, it may validate proposed

methodology for use in the monitoring program, identify gaps in the

measurement process, and develop new or improve exisiting methods as needed.

It may conduct collaborative tests to do some of the above. Concerning

reference materials, it can investigate various options and validate and

recommend materials for uses ranging from routine measurement control to

proficiency testing of participating laboratories. Again, when suitable

reference materials are not available, the reference laboratory may be able

to develop ones appropriate for the specific monitoring program.

The reference laboratory may serve in a referee laboratory capacity. As

such, it can analyze (or re-analyze) a fraction of the total samples to

verify measurement consistency and to corroborate key data to increase

confidence in it. The reference laboratory may engage in trouble shooting,

as necessary, with respect to sampling, methodology, or other aspects of the

program.

37
Table Functions of a Reference Laboratory in National

Measurement/Monitoring Programs

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1 u u 38
Sample Flow, Distribution by User

Data Flow

QA Sample Flow

Alternate Sample Flow, Distribution by Ref. Lab.

Intercalibration Program

Figure 8. Cooperative Measurement Program

39
The quality assurance function can range from furnishing advice to oversight

of the entire activity (see figure 9). The reference laboratory may be used

to review and pre-validate the quality assurance program and to demonstrate

that it is credible and can be expected to be effective. Likewise the PSP

for the entire monitoring program can be designed and/or validated by a competent reference laboratory. It may conduct proficiency tests of the

measurement laboratories including those to qualify potential participants

and to verify their continuing competence throughout the program. The

circulation of samples and evaluation of inter-calibration test results can

be a prime responsibility. Belatedly but possibly separately, it may provide

statistical services.

The reference laboratory may be given a training responsibility. It is

recommended that larger monitoring programs should conduct workshops, before

and at intervals during a monitoring program, to indoctrinate participants,

to maintain awareness of programmatic goals and progress, and to introduce

any necessary changes in the program. The reference laboratory could assume responsibilities for such activities.

The reference laboratory and/or the user should maintain a continuous alert

for deficiencies in the monitoring program that would be correctible by

training and provide such on an individual laboratory or group basis as appropriate. Such training conceivably could be in the use of the methodology for measurement and sampling or in quality assurance aspects of the

monitoring program.

In a continuing monitoring program, the reference laboratory also could have

the responsiblity to groom a successor or to develop a line of succession, as described below, to provide for internal surveillance of the program.

Because of its important function, the reference laboratory should be

selected with great care. It may be chosen from laboratories not regularly

engaged in the monitoring program or it may be one of the measurement

laboratories. The former approach is recommended in the case of extensive

programs and the latter is possible in small programs or as a mechanism for

maintaining a larger program after it is well established. In such a case,

the reference laboratory could be given the responsibility to develop a training program that would provide for internal surveillance by the

participants on a rotational basis.

The initial selection of a reference laboratory and of any internal

successors is based on finding a laboratory with unquestionable qualifications and this must receive consensus approval. The reference laboratory

must maintain a high degree of credibility throughout its tenure since it

will be quite influential in developing and implementing the monitoring

policy

.

40
Reference Laboratory

^•igure 9. Quality Assurance Function of a Reference Laboratory

41
18 . BIBLIOGRAPHY

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44
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45
INDEX TO BIBLIOGRAPHY

Accreditation

8 , 10 , 38

Calibration

15 , 24 , 40 , 46

General

15 , 20 , 23 , 30 , 32 , 33 , 41 , 45

Methodology

1 , 5 , 9 , 15 , 17 , 19 , 23 , 30 , 33 . 49

Quality Assurance Systems

2 , 3 , 6 , 7 , 9 , 13 , 17 , 18 , 21 , 25 , 38 , 42 , 45 , 46 , 47 , 48 , 52 , 53

Samples, Sampling

26 , 27 , 28 , 39

Standard Reference Materials

12 , 43 , 44 , 50

Statistics

1 , 4 , 5 , 13 , 14 , 22 , 29 , 31 , 34 , 35 , 36 , 37 , 39 51 , 52 , 53 , 54 , 55 , 56 , 57

46
APPENDIX A. PROGRAM DOCUMENTATION

This appendix contains supplemental material which may be useful in the

development of documents related to the quality assurance program of a specific laboratory. The material is generic in content and is believed to

be widely useful. However, each laboratory should consider its applicability

and will need to develop the specific content most useful to its own measurement program.

The material in this appendix consists largely of outlines that can be

followed when preparing specific documentation. It is recommended that a laboratory or organization which uses such should address each item in an outline, and prepare text as appropriate for its own situation.

An exception to the outline format is Appendix A.1 which contains the text of

a program document that one laboratory has found to be appropriate to its

measurement situtation. This document is presented as an example of such a document and is presented only for guidance.

A.1 EXAMPLE QUALITY ASSURANCE PROGRAM DOCUMENT

1. Policy

The objective of the measurement program of the

is to produce high-quality research and

analytical measurements and data which are accurate, reliable, and adequate

for the intended purpose in a cost-effective manner. To this end, the

management is dedicated to the encouragement of excellence in measurement and

to provide the physical and mental environment conducive to its achievement.

2. Purpose

The purpose of this document is to formalize the quality assurance practices

that have evolved during the past and which are currently operative within

the . A further purpose is to inform users of the services of

the conditions and practices related to the services and data provided.

Due to the diversity of programs, it may not be possible to set forth all

inclusive statements nor to establish a rigid quality assurance plan. The

management encourages the developement and use of the best measurement

practices as dictated by each measurement situation. However, it is the

policy to follow the procedures set forth herein to the extent possible and

to document the experimental details in each individual case and maintain

them as a matter of record.

3. General Aspects

3.1 Preliminary Requirements

All analytical measurements will be done according to a plan individually

developed and optimized for each situation. The requirements of each problem

will be studied and thoroughly understood beforehand, in order to assure that

the measurements planned will be adequate both in kind, number and quality.

All measurements are made using standard methods, methods with peer

47
recognition, methods developed in the specific laboratory, or those mandated

by legal or other requirements. No method will be used for data output

purposes until it is known to be applicable and competence has been acquired

in its use. If or when it appears that available techniques are not adequate

to solve a particular problem, the user of the services will be informed of

any preliminary investigations or research required, including the estimated

costs

.

Demonstration and documentation of attainment of statistical control of the

measurement process shall be a prerequisite for reporting any data, whether

of a research or routine nature.

All laboratory equipment shall be maintained in a reasonable and proper state

of repair. Space shall be maintained according to established rules of

cleanliness, order, and efficiency to facilitate the measurements and to

protect the health and safety of the laboratory personnel. No equipment is

operated until it is first ascertained that it is in a safe and reliable

state and then only by personnel who have been thoroughly trained and have

been duly qualified as operators.

Work done for another organization will adhere to any specified project plan

or quality assurance program to the extent possible. Whenever such directives differ from on-going policy, such differences will be reconciled before

work is initiated or whenever they are first recognized.

3.2 Experimental Plan

All experimental work is to be done according to a written plan developed in

advance of the actual work. Where applicable and appropriate, the plan is

developed in consultation with the end-user of the analytical data and the

statistician who will assist in interpreting the data or reviewing the con- clusions. The plan includes the sampling strategy, the sampling procedure,

the calibration process, the measurement procedure, and the data handling and

validation process. All procedures used should be documented and the

documentation should be available for review by users of the data. Standard

Operations Procedures (SOP's) are used to the extent practical. When other

procedures are required, their adequacy is verified before use in a measurement process.

3.3 Reagents

Reagents are defined as any substances used to dissolve, disperse, extract,

react with, or dilute any sample or analytical component of a sample. Strict

control of reagents shall be maintained to minimize their contamination or degradation. Dilutions or solutions prepared from such reagents shall be

clearly labeled and dated. The shelf life shall be designated and strictly

observed

.

The highest quality analytical reagents, consistent with the end use, shall

be used in all analytical work. Records will be kept of all reagents used in

every critical analysis and each analytical plan shall include procedures to

carefully control the quantities used and to minimize and correct for the

analytical blank to the extent possible.

48
3.4 Laboratory Safety

The policy of the is as follows: General safety rules as developed by will be followed. Special safety rules in addtion

to the above will be part of any GMP for a specific measurement competence.

3.5 Disposal of Toxic Substances

The potential hazard of each substance used regularly or occasionally will be

investigated as a prerequisite for its use. No potentially hazardous

material will be used until it has been approved for use by at least one level of supervision above the user. The Safety Office will be

consulted in each case as appropriate and disposal will be by that office or

in accordance with its approved procedures.

4. Samples

It is recognized that sampling is one of the most critical operations in

analytical measurement. The analyst can only report on the measurement made

on a specific sample at a specific time. Often this is not the information

ultimately desired but rather the projection of that information in time and

space and its relation to the population from which the sample was drawn.

The sampling strategy, methodology, subsampling, chemical and/or physical

treatment, preservation, and storage are logically parts of the analytical

plan and must be developed and specified for each situation.

Sampling plans and measurement procedures must be adequate to permit

evaluation of sample homogeneity and/or population distributions when these

are important considerations.

Any limitations on the analytical results due to the sample must be clearly

specified in the analytical report. It is the policy of the

that the integrity of samples should be unquestionable. All samples are to

be identified in an unambiguous manner and fully documented in the respective

record books. Samples shall be stored to minimize contamination and/or deterioration while in the and shall be retained as long as necessary in the judgement of the supervisor or delegated authority. Adequate

records shall be kept to establish a chain of custody and to ensure their

integrity, identification, and locatability at all times. When necessary or mandated, a legal chain-of-custody shall be established including the

designation of a sample custodian.

5. Analytical Methodology

5.1 General

All analytical measurements shall be made using methodology appropriate for

the specific purpose. Each measurement method will be described in writing

before use in the generation of analytical data. This serves the purpose of

recording the procedure used and also ensures that a sequence of measurements

will be made using the same procedure, which is a prerequisite for statistical treatment of the resulting data. Any significant changes in a measurement

procedure shall be documented and notebook reference shall make clear when

such changes are made.
When changes are made to an SOP, the specific changes, together with the

rationale for them, shall be documented and clearly Indicated whether the

change Is permanent or pertains to a specialized use. Any significant changes

will be considered as generating a new method which will need to be validated

before It may be used for definitive measurements.

The written procedure will Include the following Information, as applicable:

a. Sample preparation and treatment

b. Chemical operations

c. Calibration procedure

d. Measurement procedure

1. Instrumental conditions

2. Instrumental adjustments

e. Data handling (See 4.3)

References to Standard Operations Procedures (SOP’s) when they are used will

satisfy this requirement for documentation.

5.2 Standard Operations Procedures (SOPs)

Standard operations procedures (SOPs) designate those measurement procedures

or sub-steps which have been established by consensus actions of standards

organizations or those reduced to a standard format and demonstrated by con- sistent use in the laboratory to be the preferred way to do a measurement or operation of concern.

Measurement procedures or subsets thereof which are used in a recurring

manner should be written as SOPs to which reference can be made in the event

of future use.

SOPs will be written in a standard format when possible. The format of

preference is that used by ASTM.

5.3 Calibration

5.3.1 Calibration generally refers to physical measurements made on objects,

apparatus, or equipment to verify or provide corrections to the experimental

data it provides. Examples include masses, volumetric apparatus, and

flowmeters. Records of such calibrations, whether made externally or internally, will be maintained in a central location within the corresponding

division. Copies are also made available to the users of the calibrated

articles and supervisors will be responsible to see that the latest

calibration information is applied in each pertinent situation.

5.3.2 Standardization is a type of chemical calibration and refers to those

operations in which instrument response (in its broadest interpretation) is

related to analytical data. Examples include the titer of a solution, an absorbance-concentration curve, and the spanning of a linear-direct reading

chemical analyzer. Except for the latter, such data are ordinarily recorded

in notebooks. The reliability of many measurements is critically dependent

on the quality of the chemical calibrants used in the standardization

50
process. This may involve obtaining primary standards and the preparation of

secondary standards. In such cases, labeling, record of preparation,

expiration date, and special precautions for use are prime considerations.

In some cases, calibrants may be obtained from external sources such as a gas

manufacturer

.

5. 3. 3 It is the responsibility of each analyst to see that the

concentrations of all chemical standards used are verified by reliable methods. The procedures used for such verification, as well as the use of

standards shall be fully documented.

5.3.^ Calibrations and standardizations shall be made with sufficient

frequency to minimize uncertainties in the analytical results due to

uncertainties of calibration and/or standardization.

5.3.5 All calibrations and standardizations shall be fully documented and

the data shall provide clear traceability to the calibrations related

thereto

.

5.

^ Data Handling

Descriptions of each analytical method shall include procedures for handling

data. Because the procedures may be common to many of the measurements in a given laboratory, this may be best described as a GLP. The following

subjects should be addressed, as appropriate:

• Procedures used in the calculation of data; corrections required;

adjustment to standard conditions; normalization of data; computer

programs

.

• Statistical procedures used to report data; method for evaluating

limits of uncertainty.

• Corrections for sytematic errors.

• Source of all constants used in calculations, such as Faraday

constant, atomic weights, molecular weights.

• Checks on accuracy of calculations; use of significant figures;

propagation of errors; analysis of variance.

6. Laboratory Records

6.1 General

It is the responsibility of each individual in the to maintain

complete records of her/his work in the detail and format prescribed by the

particular organizational unit. The minimum requirement shall be that all

significant experimental work shall be described in sufficient detail to make

it understandable to one versed in the art, yet not directly connected with

the work.

All laboratory record books are the property of the and may not be

removed from the premises without permission of higher authority. Records

are to be suitably protected and stored to conform with the general regulations of the . Individuals who wish to maintain personal records

may do so, but these shall be in addition to the complete and permanent

records mentioned above.

51
6.2

Measurement Data

All experimental data will be recorded in bound laboratory record books.

Transcriptions shall be avoided wherever possible. The data shall be suitably

annotated with reference to experimental details, conclusions and/or future

directions as needed to positively identify it. The data record may be kept

in a general laboratory record book or in a separate book, as approved by

supervisors, but cross references shall be made as appropriate. Corrections

to recorded data shall be documented and no erasures will be permitted. The name of the analyst shall be clearly indicated so that there is no question

as to who is responsible for the work.

Data in the form of charts, instrument recordings, and printouts will be

given suitable identification numbers and maintained in a manner consistent

with that indicated for written records. Laboratory record books will refer

to the location and identification number of such records.

In view of the possible legal implications of much of the data, all data

shall be recorded in such a way as to preclude its discreditation at any

time. Any data obtained for use of patent applications or for other legal

purposes may need to be substantiated by witnesses. The rules promulgated by

the legal advisor will be followed in such cases.

6.3 Maintenance Records

Records of maintenance of equipment shall be suitably recorded. Routine

maintenance may be indicated by labels on equipment. Maintenance which

results in modification of equipment must be described in sufficient detail

and recorded in the operational manual for the particular equipment (see

6.4).

6.4 Instrument Manuals

A copy of the operational manual supplied with equipment shall be maintained

in the or other locations as approved. In the latter case,

will maintain a record of where the manuals are located. In

addition, requires that a copy be placed in the Library. The file

will contain descriptions of any modifications that will alter either the

operation or servicing instructions furnished by the supplier. As appropriate, the file will contain additional information on operational

procedures and/or servicing as may be generated as a result of laboratory

experience with the equipment.

Records similar to those described above will be prepared and maintained for

equipment built within the laboratory or within .

7. Control Charts

Control charts are recognized as a prime means for documenting the

statistical control of a measurement process.

It is the policy of the that each measurement competence shall

develop and maintain appropriate control charts for sub-operations and/or

overall operations, where feasible.

52
It is the responsibility of the senior scientist in each competence area to

identify the kinds of control charts that will be most useful. When control

charts are specified in a given protocol, they shall be maintained in a real-time mode as far as possible and shall be the basis for corrective

actions when indicated.

8. Quality Assessment

8.1 Data Verification

All data reported must include a statement of the uncertainty pertaining to

it, and the work plan must include a means for the determination of such

limits. In addition, all data will be verified by one or more of the

following methods:

1. Simultaneous measurement of an SRM or other certified reference

sample

2. Measurement by an independent technique

3. Measurement by an independent operator/apparatus

4. Spiking/recovery technique

5. Replicate measurements by a recognized technique.

The verification data or a summary thereof will become a part of the test

report

.

The signer of each report, in doing so, verifies that the quality assurance

requirements associated with it have been fully met.

8.2 Any significant uncertainties in the measurement process or in the data

produced by it will be resolved before data may be reported or released.

9. Data Review/Reporting

All data will be subject to peer review before external release. All

releases will be in writing. Oral, including telephone, releases are permitted when appropriate, provided they are subsequently (within 24 hours)

confirmed in writing, and provided the data have been previously reviewed and

approved as herein set forth. The lowest level of review for release of data

is by the supervisor or his designate. In the case of sensitive data, a higher level of review may be required. Publication of data is predicated

upon editorial review. Data obtained for a sponsor is considered to be the

property of the sponsor and its dissemination is subject to the sponsor's

release (within the Freedom of Information Act principles).

Reports will be prepared in a format suited to the particular end use. Each

report will contain, as far as practical, the following information:

a. Summary of the problem investigated

b. Complete description of samples

c. Description of methodology (or reference)

d. Summary of data and reference to original data set(s)

e. Interpretation/conclusions

f. Attestation

Signatures of analysts(s). Supervisor (or designate(s) )

.

53
10. Implementation

Each and every member of the staff has responsibility for the implementation

of the quality assurance program, commensurate with her/his technical and/or

supervisory and administrative responsibilities. Specific responsibilities

are described throughout this document. The general aspects are summarized

below.

Individuals ; It is the responsibility of each individual to support the

quality assurance program in principle and detail. Each member of the

staff is responsible for the technical quality of her/his work. All

work must be done with the highest level of integrity and professional

competence. Records must be kept meticulously. Each member of the staff

must be ever alert to problems or sources of error that could compromise

the quality of technical work.

Management Chain ; The management has the responsibility for supervising

and administrating the quality assurance program and is responsible for

the overall administration of the program with responsibilities detailed

down through the supervisory chain. Specific responsibilities are critical elements of the performance agreements of each supervisory and

administrative official.

Quality Assurance Coordinator ; The has

established the position of Quality Assurance Coordinator for oversight

of its quality assurance program. The Coordinator reports to the

Director and has the following responsibilities;

On-going review of the individual quality assurance procedures

within the various units; provides assistance in the development

and implementation of specific quality assurance plans for special

analytical programs such as work done for interagency agreements;

review of interim and final reports with respect to quality assurance aspects of analytical data.

11. Research Investigations

All research investigations shall be carried out efficiently, effectively,

and with a high degree of technical excellence. Accurate data are essential

for reliable research conclusions. The requirements for acquisition of such

data in research situations are similar to those already described. Accordingly, all research investigations shall be carried out consistent with the

general principles described above. Quality control during such investigations is primarily the responsiblity of the individual researchers and their

supervisors, according to established management chains of supervision and

oversight

.

Research outputs are in the form of reports and publications. The quality

assurance for such outputs is by means of the editorial review process.

The respective responsibilities in this process are as follows;

54
Investigator/Author ; Accuracy of data; adequate support for all

conclusions; full disclosure and description of all significant

technical details; maintenance of records of all experimental work,

including data; safeguarding all data and records for reference in the case of future questions.

Middle Manager ; Technical review of all work submitted as reports or publications for scientific/technical adequacy and reliability;

certification to higher authority by signing of test reports or publication records that the technical aspects are sound and that

supporting data and records are maintained according to prescribed

policy.

Upper Manager ; Editorial review of all manuscripts, directly or by

delegation, to ascertain that their quality meets minimum standards,

that all reasonable care has been taken to insure technical validity,

and that the contents may be released for external dissemination.

Approved;

Date

:

A. 2 D EVELOPMENT OF A QA PROTOCOL FOR A SPECIFIC PURPOSE

Outline of the Development/Implementation Process

Study/Review Analytical Problem

Develop/Review Model

Develop/Review Experimental Plan

Sampling

Measurement /Cali brat ion

Data handling

Develop/Choose/Review Appropriate Technology

Sampling/Calibration/Measurement

Test Proposed Technology for Appropriateness

Validation of methodology

Identify Discrete Steps in Technology

In-depth Analysis of Technology/Methodology for Sources of Error

Bias

Variance

Develop/Review Procedures to Reduce Uncertainties to Acceptable Limits

Develop/Specify/Review Quality Control Procedures

55
GLPs

GMPs

SOPs

Develop/Specify/Review Quality Assessment Techniques

Reference Materials (RMs)

Control Samples (CSs)

IRMs

Duplicates/Replicates

Spikes

Surrogates

Frequency of Use of CSs, SRMs

Control Charts

Develop/Specify/Review Overall QA plan

Develop/Specify/Review Criteria for

Qualification/Selection of Contract

Laboratories

Monitor Performance

Reference Laboratory Function

Trouble Shoot Follow-up R & D as Required

A. 3 OUTLINE-CONTENT OF A QUALITY ASSURANCE PROTOCOL FOR A SPECIFIC

PURPOSE (PSP)

1 . Introduction

2.0 Project Description

2.1 Statement of problem

Brief statement of problem with reference to contract, proposal, or other documents where further information can be found.

2.2 Model

Brief discussion of rationale, approach, assumptions

2.3 Major Elements

The basic activities including sampling, measurement, data

processing and any other items that will be pursued concurrently.

2.4 Organization

2.4.1 Sponsoring organization

2.4.2 Principal investigator

56
2.H.3 Organizational chart and/or other documentation that will

describe the respective responsibilities of project/

organizational personnel with respect to present project.

2.4.4 Priorities/Scheduling

A statement of any special priorities and/or scheduling

pertinent to the present project.

3.0 Work Plan

3.1 Sampling

Kinds and numbers of samples; sampling plan; sampling schedule;

sampling methodology; sample storage, preservation, processing;

chain of custody. Refer to section 7.1.1 as appropriate.

3.2 Calibration

Kinds; frequency; calibrants to be used refer to section 7.1.2 as appropriate.

3.3

Measurement

Kinds of measurements; measurement plan; measurement schedule;

measurement methodology; refer to section 7.1.2 as appropriate.

4.0 Quality Assurance Objectives

4.1 Expected precision and accuracy

4.2 Implementation Summary

A brief summary of the various aspects of the quality assurance

plan and how it is to be carried out. Designation of quality assurance responsibilities. Designation of a Quality Assurance Officer

as pertinent and responsibilities.

5.0

Quality Assurance Procedures

5.1 Preliminary Exercises

5.1.1 Statement of any specific training required and how it will

be given and evaluated.

5.1.2 Workshop or other indoctrination.

5.1.3 Performance Checks

Statement of the measurements/audits that will be required

to qualify a laboratory and its personnel and verify their

capability to perform or to meet minimum qualification

requirements

.

57
5.2

Quality Assessment Samples

5.2.1 Description

Description of QA samples that will be used with associated

documentation.

5.2.2 Use

5 . 2 . 2 . 1 Mode

5. 2. 2.2 Frequency

5.3 Control Charts

5.3.1 Specification

Specification of all charts to be maintained and the

respective responsibilities for them.

5.3.2 Strategy

How control chart information is to be used to provide

quality assurance; remedial actions to be taken when necessary.

5.4 Data Validation

A statement of the requirements that must be met before data is

considered to be valid and may be released for internal or external

use, together with who is authorized to make such decisions.

6.0 Management Practices

6.1 Chain-of-Custody

Procedures to be followed to safeguard samples; retention

procedures; positive identification requirements.

6.2 Document Control

Records required; numbering/identification scheme; distribution;

document control; storage of records and data.

6.3 Data Management

Responsibilities for acquisition; review procedures; release

procedures

.

6.4 Reporting

Scope of reports; schedule for reporting; responsibilities for

reporting; responsibilities for release.

58
7.0

Appendices

7.1 Methodology

7.1.1 Sampling

Specific methodology to be used by reference; specific

modifications of existing methods; complete details for

unreferenced methods. This shall include; equipment for

sampling; method of sampling; pretreatments such as sieving

or blending; drying; preparation of analytical samples if

not included in 7.1.2. (see 3.1).

7.1.2 Measurement

Include the same type of information as described in 7.1.1.;

describe calibrations, calibrants to be used, (see 3.2.;

3.3).

7.2 Maintenance of Facilities and Equipment

7.2.1 Maintenance Schedules

7.2.2 Calibration Schedules

7.2.3 Trouble Shooting

This shall include general directions or diagnosis of

trouble and corrective actions to be followed.

A. 4 OUTLINE - ORGANIZING AND IMPLEMENTATION OF A MONITORING PROGRAM

Contract/Participant Laboratory Requirements

Pre-Contract /Participation Requirements*

In-house QA Program

Developed, in-place, and implemented

Availability of appropriate facilities and equipment**

Availability of trained personnel

Prior experience with methodology**

Prior experience with similar samples***

Pr e-ini ti at ion-of-work Requirements*

Pre-inspection/review of facilities and plans

* General specification should be included in RFP

** Specific specifications and/or options included in RFP

*** Contractor should submit evidence of such with his proposal. This can be

based on laboratory history or on tests done especially for this purpose.

Initiator should have samples available for distribution free or at cost

for use of prospective contractor/participants.

59
Development of specific QA plan

Demonstration of competence on typical samples

Monitoring Contractor/Participant Laboratory Performance*

Adherence to Project QA Plan

Performance on replicates/split samples

Performance on prof iciency-testing samples

Maintenance of facilities equipment

Retention of personnel or suitable replacement

Adherence to all aspects of contractual/programmatic requirements

Consistency of results with those of referee laboratory****

Consistency with other labs in monitoring groups

Retention of Status

Retention of qualification status and payment for services (as

pertinent) contingent on providing such of required quality

A. 5 OUTLINE-FUNCTIONS OF A REFERENCE LABORATORY (RL)

The following outline is furnished to provide guidance when the need for a reference laboratory (RL) in a monitoring program is considered. The

requirements that an RL must satisfy are largely independent of the functions

it may serve. The latter will depend on the complexity of the program and

extent to which the various functions outlined can be furnished adequately by

others in the program.

Purpose of an RL

• To provide confidence in data

• To provide continuing surveillance of measurement program

and to advise on corrective actions as required

Functions of an RL

• R and D services

-Validate existing methodology

-Identify gaps in methodologies

-Develop or improve methodology

• Reference Materials

-Validate existing RMs

-Develop new RMs

f Referee Laboratory Services

-Analyze percentage of samples

-Corroborate key data

-Trouble shoot problems

****Referee laboratory should have clear and indisputable peer

qualifications. It should do sufficient work to be able to clearly

defend all judgments, including both acceptability and inacceptability of

performance/ data

.

60
• Quality Assurance Services

-Advise/design QA program

-Validate QA program

-Design/Test PSP (Program Plans)

-Conduct proficiency tests

-Qualify participants

-Provide statistical services

• Training

-Wor kshops

-Specific training

-Methodology

-QA program

-Succession training

Requirements for an RL

• Recognized competence

-Dedication to excellence

-Center of excellence

• Broad interdisciplinary capability and experience

• Recognized impartiality

• On-going QA program to back-up its

technical findings

Selection of an RL

• Independent laboratory having required qualifications

• Group member

-Rational role

-Must meet minimum requirements

61
APPENDIX B. DATA QUALITY OBJECTIVES

The setting'of realistic data quality requirements is a pre-requisite for

planning a measurement program. Data of insufficient quality will have

little value for problem solving while data of excess quality may provide

little if any additional advantages, and may be counter-productive if extra

cost is involved. Accordingly, data quality objectives must be established

based on cost-effective considerations of needs of the problem and capability

of the measurement process. In addition, knowledge of the expected variability of the samples is needed to estimate the number of samples to be taken

and the number of measurements to be made of each sample. While the details

of the planning process are beyond the scope of the present paper, the

considerations involved are presented for general information in the

following.

Qualitative data are commonly considered to require an accuracy of about ±30

percent; quantitative data used for hypothesis testing should have a minimum

accuracy requirement of about ±10 percent; semi -quantitative data will lie

between these limits. In questions of distinguishing differences, a numerical factor of 3 will be found to be useful. For example, a Standard Reference Material certified to ±\% is expected to be useful for identifying

biases of at least ±3?. Multiple independent measurements will reduce the

latter value by a factor of l//n. The same principle may be applied to the

identification of small differences in other measurement situations. In general, the minimum detectable difference of two measured values (with 95$ con- fidence) is 3 U//n, where U represents the uncertainty of the measured

results and n is the number of measurements.

In the previous discussion, the uncertainty, U, represents that due to both

the measurement process (Ujjj) and the sample variability (Ug). Ordinarily,

U = (u| + u2)l/2 ^iii apply. A detailed discussion of the propagation of the

uncertainties due to sampling and measurement, together with the cost con- siderations in designing sampling and measurement plans is given by Provost

(35A).

The uncertainty of the sample composition, Ug, is largely a statistical

problem, although individual samples may be biased, as well. The latter can result from contamination, sample deterioration, and discriminatory sampling

procedures. A well-designed sampling plan should minimize all of the above

sources of bias.

The uncertainty of the measurement process contributes both systematic (B)

and random (R) uncertainties. The former results from such things as improper calibration, blanks, and non-selectivity of the methodology. The latter

includes variability in control of critical parameters. All biases should be

corrected to the extent possible so that only uncertainties in the

corrections remain.

The total uncertainty, U^, in either the sample composition or in its

measurement may be estimated as follows:

U^ = ± (Ug + Ug)

62
(Ut + uB2

+ )

Ur

1/2

The subscripts indicate the several sources of uncertainty in the bias or random error as they are individually identifiable. All uncertainties are ordinarily estimated at the 95$ confidence level. In other than simple or well-defined measurement problems, the collaboration of a competent

statistician is recommended to assist in the development of realistic data

quality objectives and in the design of adequate measurement and sampling

plans

.

63
APPENDIX C. TERMINOLOGY USED IN QUALITY ASSURANCE»f

John K. Taylor

National Bureau of Standards

Absolute Method - A method in which characterization is based entirely on physically (absolute) defined standards.

Accreditiation - A formal process by which a labortory is evaluated, with

respect to established criteria, for its competence to perform a specified kind(s) of measurement. Also, the decision based upon such

a process. When a certificate is issued, the process is often called

certification

.

Accuracy - The degree of agreement of a measured value with the true or expected value of the quantity of concern.

Aliquant - A divisor that does not divide a sample into a number of equal

parts without leaving a remainder; a sample resulting from such a divisor

.

Aliquot - A divisor that divides a sample into a number of equal parts,

leaving no remainder; a sample resulting from such a divisor.

Analyte - The specific component measured in a chemical analysis; also called

analate

.

Assignable cause - A cause believed to be responsible for an identifiable

change of precision or accuracy of a measurement process.

Blank - The measured value obtained when a specified component of a sample is

not present during the measurement. In such a case, the measured

value/signal for the component is believed to be due to artifacts,

hence should be deducted from a measured value to give a net value

due to the component contained in a sample. The blank measurement

must be made so that the correction process is valid.

Blind Sample - A sample submitted for analysis whose composition is known to

the submitter but unknown to the analyst. A blind sample thus is one way to test proficiency of a measurement process.

Bias - A systematic error inherent in a method or caused by some artifact or idiosyncrasy of the measurement system. Temperature effects and

extraction inefficiencies are examples of this first kind. Blanks,

contamination, mechanical losses and calibration errors are examples

of the latter kinds. Bias may be both positive and negative and

several kinds can exist concurrently, so that net bias is all that

can be evaluated, except under special conditions.

*For other definitions, and particularly those related to industrial quality

assurance, see; "Quality Systems Terminology", ANSI/ASQC Standard A3"1978,

64
Bulk sampling - Sampling of a material that does not consist of discrete,

identifiable, constant units, but rather of arbitrary, irregular

units

.

Calibrant - A substance used to calibrate or to establish the analytical

response of a measurement system.

Calibration - Comparison of a measurement standard or instrument with another

standard or instrument to report or eliminate by adjustment any

variation (deviation) in the accuracy of the item being compared.

Cause-Effect Diagram - A graphical representation of the causes that can produce a specified kind of error in measurement. A popular one is

the so-called fish bone diagram, first described by Ishikawa, given

this name because of its suggestive shape.

Certification - See accreditation.

Central Line - The long-term expected value of a variable displayed on a control chart

.

Certified Reference Material (CRM) - A reference material one or more of

whose property values are certified by a technically valid procedure,

accompanied by or traceable to a certificate or other documentation

which is issued by a certifying body.

Certified Value - The value that appears in a certificate as the best

estimate of the value for a property of a reference material.

Chance Cause - A cause for variability of a measurement process that occurs unpredictably , for unknown reasons, and believed to happen by chance,

alone

.

Check Standard - In physical calibration, an artifact measured periodically,

the results of which typically are plotted on a control chart to

evaluate the measurement process.

Coefficient of Variation - The standard deviation divided by the value of the

parameter measured.

Common Cause - A cause of variability of a measurement process, inherent in

and common to the process itself, as contrasted to a special cause

(defined)

.

Comparative Method - A method which is based on the intercompar ison of the

sample with a chemical standard.

Confidence Interval - That range of values, calculated from an estimate of

the mean and the standard deviation, which is expected to include *:n*' population mean with a stated level of confidence. Confidence intervals in the same context also may be calculated for standard

deviations, lines, slopes, points.

65
Control Limit - The limits shown on a control chart beyond which it is highly

improbable that a point could lie while the system remains in a state

of statistical control.

Control Chart - A graphical plot of test results with respect to time or sequence of measurement together with limits within which they are expected to lie when the system is in a state of statistical control.

Control Sample - A material of known composition that is analyzed

concurrently with test samples to evaluate a measurement process

(see also Check Standard).

Composite Sample - A sample composed of two or more increments selected to

represent a population of interest.

Cross Sensitivity - A quantitative measure of the response obtained for an undesired constituent (interferrent ) as compared to that for a constituent of interest.

Detection Limit - The smallest concentration/amount of some component of

interest that can be measured by a single measurement with a stated

level of confidence.

Double Blind - A sample, known by the submitter but submitted to an analyst

in such a way that neither its composition nor its identification as

a check sample are known to the latter

.

Duplicate Measurement ~ A second measurement made on the same (or identical)

sample of material to assist in the evaluation of measurement

variance

.

Duplicate Sample - A second sample randomly selected from a population of

interest (see also split sample) to assist in the evaluation of

sample variance.

Education - Disciplining the mind through instruction or study. Education is

general and prepares the mind to react to a variety of situations.

Error - Difference betwen the true or expected value and the measured value

of a quantity or parameter.

Figure of Merit - A performance characteristic of a method believed to be

useful when deciding its applicability for a specific measurement

situation. Typical figures of merit include: selectivity;

sensitivity; detection limit; precision; bias.

Good Laboratory Practice (GLP) - An acceptable way to perform some basic

operation or activity in a laboratory, that is known or believed to

influence the quality of its outputs. GLP's ordinarily are essentially independent of the measurement techniques used.

Good Measurement Practice (GMP) - An acceptable way to perform some operation

associated with a specific measurement technique, and which is known

or believed to Influence the quality of the measurement.

66
Gross Sample (also called bulk sample, lot sample) - One or more increments

of material taken from a larger quantity (lot) of material for assay

or record purposes.

Homogeneity - The degree to which a property or substance is randomly

distributed throughout a material. Homogeneity depends on the size

of the subsaraple under consideration. Thus a mixture of two minerals

may be inhomogeneous at the molecular or atomic level, but

homogeneous at the particulate level.

Incrememt - An individual portion of material collected by a single operation

of a sampling device, from parts of a lot separated in time or space.

Increments may be either tested individually or combined (composited)

and tested as a unit.

Individuals - Conceivable constituent parts of a population.

Informational Value - Value of a property, not certified but provided because

it is believed to be reliable and to provide information important to

the certified material.

Intercalibration - The process, procedures, and activities used to ensure

that the several laboratories engaged in a monitoring program can produce compatible data. When compatible data outputs are achieved

and this situation is maintained, the laboratories can be said to be

intercalibrated

.

Laboratory Sample - A sample, intended for testing or analysis, prepared from

a gross sample or otherwise obtained. The laboratory sample must

retain the composition of the gross sample. Often reduction in

particle size is necessary in the course of reducing the quantity.

Limiting Mean - The value approached by the average as the number of

measurements, made by a stable measurement process, increases

indefinitely.

Limit of Linearity (LOL) - The upper limit of concentration or amount of

substance for which incremental additions produce constant increments

of response.

Limit of Quantitation (LOQ) - The lower limit of concentration or amount of

substance that must be present before a method is considered to provide quantitative results. By convention LOQ = lOs^, where s^ is the

estimate of the standard deviation at the lowest level of

measurement

.

Lot - A quantity of bulk material of similar composition whose properties are under study.

Method - An assemblage of measurement techniques and the order in which they

are used.

Outlier - A value which appears to deviate markedly from that for other

members of the sample in which it occurs.

67
Pareto Analysis - A statistical approach to ranking assignable causes according to the frequency of occurrence.

Performance Audit - A process to evaluate the proficiency of an analyst/

laboratory by evaluation of the results obtained on known teat

materials.

Population - A generic term denoting any finite or infinite collection of

individual things, objects, or events; in the broadest concept, an aggregate determined by some property that distinguishes things that

do and do not belong.

Precision - The degree of mutual agreement characteristic of independent

measurements as the result of repeated application of the process

under specified conditions. It is concerned with the closeness

together of results.

Primary Standard - A substance or artifact, the value of which can be

accepted (within specific limits) without question when used to

establish the value of the same or related property of another material. Note that the primary standard for one user may have been a secondary standard of another.

Probability - The likelihood of the occurrence of any particular form of an event, estimated as the ratio of the number of ways or times that the

event may occur in that form to the total number of ways that it

could occur in any form.

Procedure - A set of systematic instructions for using a method of

measurement or of sampling or of the steps or operations associated

with such.

Protocol - A procedure specified to be used when performing a measurement or related operation, as a condition to obtain results that could be

acceptable to the specifier.

Protocol for a Specific Purpose (PSP) - Detailed instructions for the

performance of all aspects of a measurement program. This is

sometimes called a project QA plan.

Quality - An estimation of acceptability or suitability for a given purpose

of an object, item, tangible, or intangible thing.

Quality Assessment - The overall system of activities whose purpose is to

provide assurance that the quality control activities are being done

effectively. It involves a continuing evaluation of performance of

the production system and the quality of the products produced.

Quality Assurance - A system of activities whose purpose is to provide to the

producer or user of a product or a service the assurance that it

meets defined standards of quality. It consists of two separate but

related activities, quality control and quality assessment (defined).

68
Quality Circle - A small group of individuals with related interests that

meets at regular intervals to consider problems or other matters

related to the quality of outputs of a process and to the correction

of problems or to the improvement of quality.

Quality Control - The overall system of activities whose purpose is to

control the quality of a product or service so that it meets the

needs of users. The aim is to provide quality that is satisfactory,

adequate, dependable, and economic.

Random Sample - A sample selected from a population, using a randomization

process

.

Reduction - The process of preparing one or more subsamples from a sample.

Reference Material (RM) - A material or substance one or more properties of

which are sufficiently well established to be used for the calibration of an apparatus, the assessment of a measurement method, or for

the assignment of values to materials.

Reference Method - A method which has been specified as capable, by virtue of

recognized accuracy, of providing primary reference data.

Relative Standard Deviation - The coefficient of variation, expressed as a percentage

.

Replicate - A counterpart of another, usually referring to an analytical

sample or a measurement. It is the general case for which duplicate

is the special case consisting of two samples or measurements.

Routine Method - A method used in recurring analytical problems.

Sample - A portion of a population or lot. It may consist of an individual

or groups of individuals. It may refer to objects, materials, or to

measurements, conceivable as part of a larger group that could have

been considered.

Secondary Standard - A standard whose value is based upon comparison with

some primary standard. Note that a secondary standard, once its

value is established, can become a primary standard for some other

user

.

Segment - A specifically demarked portion of a lot, either actual or hypothetical

.

Selectivity - The ability of methodology or instrumentation to respond to a desired substance or constituent and not to others. It is sometimes

quantified as cross sensitivity, which see.

Sensitivity - Capability of methodology or instrumentation to discriminate

between samples having differing concentrations or containing

differing amounts of an analate.

Significant Figure - A figure (s) that remains to a number or decimal after

the ciphers to the right or left are cancelled.

69
Special Cause - A cause of variance or bias that is external (not inherent)

to the measurement system.

Split Sample - A replicate portion or sub-sample of a total sample obtained

in such a manner that it is not believed to differ significantly from

other portions of the same sample.

Standard - A substance or material, the properties of which are believed to

be known with sufficient accuracy to permit its use to evaluate the same property of another. In chemical measurements, it often

describes a solution or substance, commonly prepared by the analyst,

to establish a calibration curve or the analytical response function

of an instrument.

Standard Addition - A method in which small increments of a substance under

measurement are added to a sample under test to establish a response

function or, by extrapolation, to detemine the amount of a constituent orginally present in the test sample.

Standard Method - A method (or procedure) of test developed by a standardswriting organization, based on consensus opinion or other criteria,

and often evaluated for its reliability by a collaborative testing

procedure

.

Standard Operations Procedure (SOP) - A procedure adopted for repetitive use when performing a specific measurement or sampling operation. It may

be a standard method or one developed by the user.

Standard Reference Material - A reference material distributed and certified

by the National Bureau of Standards.

Strata - Segments of a lot that may vary with respect to the property under

study.

Subsample - A portion taken from a sample. A laboratory sample may be a subsample of a gross sample; similarly, a test portion may be a subsample of a laboratory sample.

Technique - A physical or chemical principle utilized separately or in

combination with other techniques to determine the composition

(analysis) of materials.

Test Portion (also called specimen, test specimen, test unit, aliquot) - That quantity of a material of proper size for measurement of the

property of interest. Test portions may be taken from the gross

sample directly, but often preliminary operations, such as mixing or further reduction in particle size, are necessary.

Tolerance Interval - That range of values, calculated from an estimate of the mean and the standard derivation, within which a specified percentage

of Individual values of population (measurements or sample) are expected to lie with a stated level of confidence

70
Traceability - The ability to trace the source of uncertainty of a measurement or a measured value.

Training - Formal or informal instruction designed to provide competence of a specific nature.

Uncertainty - The range of values within which the true value is estimated to

lie. It is a best estimate of possible inaccuracy due to both random

and systematic error.

Validation - The process by which a sample, measurement method, or a piece

of data is deemed to be useful for a specified purpose.

Variance - The value approached by the average of the sum of the squares of

deviations of individual measurements from the limiting mean. Mathematically, it may be expressed as

I(x^ - m)^

n as n ^ ®

p Ordinarily it cannot be known but only its estimate, s , which is

calculated by the expression

2 _

- x)^

n-1

Warning Limits - The limits shown on a control chart within which most of the

test results are expected to lie (within a 95 % probability) while the

system remains in a state of statistical control.

Youden Plot - A graphical presentation of data, recommended first by W. J.

Youden, in which the result (s) obtained by a laboratory on one sample

is plotted with respect to the result(s) it obtained on a similar

sample. It helps in deciding whether discrepant results are due

to random or systematic error.

71
U.S. DEPT. OF COMM. 1. PUBLICATION OR 2. Performing Organ. Report No. BIBLIOGRAPHIC DATA

REPORT NO.

SHEET (See Instructions) NBSIR 85-3105

3. Publication Date

February 1985

4. TITLE AND SUBTITLE

Principles of Qpallty Assurance of Chemical Measurements

5. AUTHOR(S)

Dr. John K. Taylor

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions)

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

7. Contract/Grant No.

8. Type of Report & Period Covered

9.

SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street, City. State, ZIP)

Center for Analytical Chemistry

Bldg. 222, Rm. A309

National Bureau of Standards

Gaithersburg, MD 20899

10.

SUPPLEMENTARY NOTES

I I Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11.

ABSTRACT (A 200‘word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here) The general principles of quality assurance of chemical measurements are

discussed. They may be classified as quality control — what is done to

control the quality of the measurement process, and quality assessment — what

is done to evaluate the quality of the data output. Quality assurance

practices are considered as a hierarchy with levels progressing from the

analyst, the laboratory, the project, to the program. The activities of each

level are different and depend upon the ones beneath it. ' Recommendations are presented for developing credible quality assurance practices at each level.

An appendix contains outlines that may be used to develop the various

documents associated with a quality assurance program.

12. KEY WORDS (S/x to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

chemical analysis; calibration; control charts; good laboratory practices;

good measurement practices; quality assessment; quality assurance; quality

r.nntrnl; standard operations procedures: statistical measurement, control

13. AVAILABILITY

[y] Unlimited

I I For Official Distribution. Do Not Release to NTIS

I I Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402.

Q31 Order From National Technical Information Service (NTIS), Springfield, VA. 22161

14. NO. OF

PRINTED PAGES

80

15. Price

$11.50

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