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terms of the scatter of the results, e.g. in round-robin tests. In considering the role of AQA in the higher education sector it is necessary to differentiate between the various university activities which include services, research and development and teaching, as follows: • Routine chemical analyses (including ad hoc analyses) performed for external clients and for the university's own measurement campaigns (e.g. investigations of the quality of waste-water and air) requiring full documentation. • Routine chemical analyses carried out for internal clients as a service to research in other Chemistry Departments such as Inorganic, Organic, and Biochemistry. • Chemical analyses performed as part of research and development work not only in Analytical Chemistry but also in other chemical disciplines such as Inorganic, Organic and Biochemistry. • Chemical analyses carried out within the framework of research projects having pre-eminent goals which are analytically-based (e.g. studies of the temporal and spatial variations in metal-species concentrations in riverwater; determination of the gas composition in a waste incinerator as a function of the operating parameters). These considerations also apply to the whole range of scientific disciplines in which chemical measurements are made, such as Biology, Geology, Medicine, Microbiology, Mineralogy, Ecology, Pharmacy, Toxicology etc.



Importance of Analytical Quality Management and Quality Assurance in Industry, Academia and Research Projects


Why do we need Good Results?

Industrial Chemistry is involved with the production of chemicals to make a profit. Any process must be commercially viable to survive. However, whilst this is a necessary requirement, it is not sufficient. Chemical production must produce materials of the required specification, they must be produced safely and lawfully and the environment should suffer no harmful effects. Forward-looking companies need to be involved in research and to be aware of new developments so that improved production methods can be introduced. This leads to the requirement for commercially sensitive information to be protected.
All the above activities are to some degree dependant upon analytical chemistry and quality in industrial analytical chemistry is essential if the various aspects of production operations are to be achieved successfully.
It is necessary to recognise that there are different aspects of analytical quality. Quality requirements depend upon the end use of the analysis whether that be ensuring the efficiency of a production operation or protecting the environment from any fugitive emissions.
A teaching lecture relating to the above topic should:
  • Establish by the use of examples why the different aspects of responsible industrial chemical production depend upon analysis.
  • Define different aspects of analytical quality (eg - accuracy, repeatability, timely).
  • Describe why analytical quality is related to the end-use to which the results are to be put.
  • Establish the advantages of good quality results.
BP Chemicals produces a range of bulk chemicals and polymers at several sites throughout the world. The Hull site specialises in ethanoic acid and related organic acids, a range of esters and phthalic anhydride and related esters. Raw materials are brought on to site from a variety of suppliers and products are supplied to a wide range of customers in many different industry sectors. This description would have similarities to other chemical manufacturing facilities throughout the chemical industry.
J. D. Green

The Importance of ‘Good’ Measurements on Industrial Manufacturing Efficiency and Profit

Assessment and assurance of quality within the field of pharmaceutical manufacture is established by the development of appropriate specifications for raw materials and products. The importance of suitable specifications will first be established and examples will be given of the development of a typical specification for a pharmaceutical drug substance. This will include discussion of the types of parameters which need to be controlled and an indication of the numerical values or ranges expected for most modem materials. The analytical methodology which needs to be applied to demonstrate compliance (or otherwise) with the specification will be discussed, along with the implications of method performance necessary in order to distinguish between variations in product quality and simple analytical variability.
A workshop session will also be included which will allow students to simulate the typical discussions which might take place in any large multi-national pharmaceutical organisation when establishing specification ranges or values for a modern drug substance based on a review of batch data generated during the development and early manufacture of the drug.
As a result, students will understand the importance of the control which appropriate specifications bring, the implications of the level of analytical method performance required and the combination of good science and pragmatism used by industry in developing such specifications and methodology for assurance of the quality of modem pharmaceutical drug substances and products.
D. Rudd

Concepts of Quality Management and Quality Assurance in Analytical Research Projects and Non Routine Analysis

Contrary to routine analysis, where the sample matrix and concentration range of the analyte are known, the analytical method has been validated and is tried - the method is fit-for-purpose - analysis in non-routine situations and in R&D may be a tortuous problem solving process. Although many basic quality elements are the same as in routine analysis, unknown factors such as unpredictable behaviour of the sample, unknown composition of the matrix and interdependance with the analyte, even uncertainty about the choice of the instrumental method, require not only a new focus on existing quality elements, but - due to the unpredictability of the analytical procedure and extent of effort - also additional organisational quality elements.
The uncertainties in non-routine analysis and R&D require careful consideration of the problem and required or available analytical techniques, planning and organisation of work, supervision of the analytical progress and adequate presentation and quality of results. Project management allows structuring of all aspects - both technical and organisational - of non-routine and R&D work and defines responsibilities and competences of the personnel.
Analytical task quality elements are:
Phase 1
Preparation and planning before starting work
Definition of task and project design
Project design and research plan
Resource management of task
Phase 2
Work in progress
Progress review/monitoring analysis
The novelty and uncertainties of analytical research and non-routine analysis require additional effort to assure quality. Acceptance of established concepts of quality management and project management is urged.
P. Radvila

Worked Examples of Teaching Analytical Quality Concepts


Quality Systems for Non-routine and R&D Analytical Work - Accreditation of Non-routine Laboratories

These days many testing laboratories are familiar with formal QM-Systems like EN 45001 (ISO Guide 25), ISO 900X (EN 29000 ff) or GLP.
Laboratories engaged in R&D face several QM-systems which are suitable to deal with Non-Routine and R&D analytical work. To make the appropriate choice it is however necessary that the laboratories know the application fields, scopes, benefits and drawbacks of the various systems. Hence this presentation tries to give both survey and insight into the QM-systems concerned like EN 45001, ISO 9001 and GLP as well as the new ISO 17 025. The pros and cons for more or less harmonization of the various standards will also be discussed.
While from the beginning ISO 9001 and GLP allowed more or less efficiently to deal with Non-Routine and R&D the accreditation scheme according to EN 45001 has become flexible enough not until a few years ago. By using the type of test approach to define the scope several accreditation bodies developed a very promising way, which has since then been increasingly practised successfully, e.g. in Switzerland or Germany.
As for laboratories seeking third party recognition of their technical competence accreditation looks more appropriate than the other systems this lecture also addresses some aspects of the accreditation of Non-Routine analysis. Based on own experience the author will give some practical hints on how to prepare a multidisciplinary Non-Routine laboratory according to the type of test approach.
W. Steck

Evaluation of Uncertainty in Analytical Measurement

The following contribution deals with the contents of two lessons of 45 minutes each. Basic knowledge of the evaluation of measurement uncertainty in analytical chemistry is imparted. After the two lessons the audience should know the new concept according to GUM/EURACHEM [1,2] in its fundamentals and be able to calculate the measurement uncertainty of a simple analytical procedure. The content is structured into three parts:
The first part starts with a short description of random and systematic influences. Based on this information the old but still applied concept of uncertainty evaluation is presented. That concept distinguishes between the measurement uncertainties “typeA = random errors”, and “type B = systematic errors”. Then the teacher leads over to the new concept according to GUM/EURACHEM. It is indicated that the new concept allows the transformation of nonstatistically evaluated uncertainties into standard uncertainties. Thus the calculation of the combined standard uncertainty is possible.
In the second paragraph it is shown how to calculate the combined standard uncertainty. In addition the required theoretical knowledge is interposed. The common procedure is demonstrated by means of the corresponding flow chart of the EURACHEM Guide. Each step is illustrated separately. For the identification and the analysis of the uncertainty sources the cause and effect diagram from Ellison and Barwick is initiated. The triangular- and rectangular distribution is presented without deriving the formulas of the variance. The comments about the calculation of the combined standard uncertainty refer only to independent variables. They show the law of propagation of uncertainty and derive two simple mathematical rules for addition/subtraction and multiplication/division. The case of correlated variables is only mentioned without treating. Finally, it is indicated how the results and their measurement uncertainties are put on record correctly.
The lectures require only basic mathematical know-how from the students. It is a qualified instrument for the introduction of the measurement uncertainty at universities and technical colleges. However, it has to be taken into consideration that only an introduction can be given in a double lesson and exercises of at least eight hours should follow.
M. Rösslein, B. Wampfler

Traceability / Trackability

Traceability is a key metrological concept which is defined by the International Vocabulary of Basic and General Terms in Metrology as „the-property of a result of a measurement or the value of a standard whereby it can be related to stated references, usually national or international, through an unbroken chain of comparisons all having stated uncertainties“ [1]. From this definition, it is clearly stated that any experimental measure is based on a comparison with references and that the knowledge of the uncertainties of the values to be compared is absolutely necessary. Therefore, traceability allows the comparability and the harmonisation of results between analytical laboratories. Hence, demonstration of traceability is the primary objective in chemical measurements.
Recently, a wider meaning of the traceability concept has been recognized. In fact, there is an etymological meaning related to the history of the generation of a product or the behaviour of a system. Thus, the ISO 8402-94 („Quality Management and Quality Assurance Vocabulary”) defines traceability as “the ability to trace the history, application or location of an entity by means of recorded identifications”. This additional facet adds to a richer concept of traceability. Some authors also proposed complementary terms, like for example trackability [2, 3]. They reported the following definition: “the property of a result of a measurement whereby the result can be unique related to the sample”. It means that the result of a measurement can be linked unambiguously to the sample to which it refers.
All these tracing connotations will be presented and discussing their implications from the quality point of view of the results provided by the laboratories, as well as the quality of the activities involved in the production of analytical reports.
A. Ríos

Validation: an Example

Validation of analytical methods can be regarded one of the most central topics in teaching analytical chemistry. While in the past it might have been feasible to demonstrate principles and practices of the most common analytical techniques to the students, the proliferating wealth of modem methodologies does not lend itself to this traditional approach due to restrictions in time and cost. Irrespective of the experimental possibilities of a particular analytical department, a couple of fundamental abilities and skills will always be presupposed by future employers of young analytical chemists. Among those abilities are critical thinking, problem solving capacity, selection and implementation of analytical strategies.
In implementing validation strategies the stepwise approach from an initial appraisal of a candidate method, to the discussion of alternative methods, selection of minimum quality criteria to meet the requirements of the customer (or scientific goal), the actual laboratory work to provide experimental evidence that these goals are met, to the analysis of the unknown(s) including the final review of the data obtained. If required, this leads to the selection of improved procedures, to the further optimisation of the originally targeted method and to control experiments at the end of the run in order to assure sufficient stability of the measurement system during the entire analytical process.
If organised in this manner, education in validation related matters amounts to a genuinely advanced training in analytical practice that is widely expected from graduates by industry and government. The actual demonstration of quality criteria such as limit of detection, limit of quantitation, working/linear range, selectivity, robustness, sensitivity, repeatability, etc. can add important extra value to the study of the underlying concepts. If validation is taught to undergraduates the examples should be related to relatively simple standards from water (or similar) control, for graduates the interaction between validation and (further) optimisation can be a real challenge.
A worked example is provided by an industrial analytical probl em for demonstrating the principles outlined above using the Excel macro “ValiData” [1] that has widely been accepted by industry as it is based on the pertinent ISO and DIN Standards.
W. Wegscheider

Metrology in Chemistry

There are two main aspects of the quality of analytical information provided by analytical laboratories. On the one hand, results must be based on metrological principles and, on the other hand, they may be coherent with the requested information by “clients” (solving analytical problems). The first approach will be implemented in a practical way.
Metrology is the science of the measurements. Traditionally, this term has been used to describe measurements of physical parameters, but there is no reason why it should not be used to refer to chemical measurements.
Metrological principles are unique, but the practical connotations of metrology are rather different. There are many important differences between chemical (CMPs) and physical (PMPs) processes which will be emphasised through pertinent examples in order to demonstrate that immediate extrapolations are dangerous and there is a need to carefully adapt the great developments of metrology in physics to the chemical field.
Because most of the written standards and guides are focussed to physical measurements, there is a need to develop documents understandable and useful for analytical chemists which give answers to questions such as “how to be traceable?”, “how to validate a method?”, etc. In this way metrology will be closer to the chemical bench level.
M. Valcárcel

Experiments as Tools to Demonstrate Principles of Quality Assurance


Basic Course Experiments to Demonstrate Validation

Undoubtedly, nowadays modem instrumental Analytical Chemistry (AC) is one of the most important interdisciplinery sciences. Therefore teaching AC at university should have a very high priority - also in undergraduate and graduate courses. In this connection both fundamental analytical strategies - accurate results and obtaining their quality - should be implemented from the very beginning into the chemistry curriculum. One approach in this context is the introduction of fundamental analytical terms in such a manner that the results of measurements performed by students are used to highlight a specific analytical problem and the concept - condensed in the selected term - to solve it. One pivotal analytical problem can be formulated as “How can one be sure that a method produces accurate, i.e. precise and true results?”. This problem is embedded in the context of the term “validation of a method” which is linked with the question “What has to be done to assure that a method is fit for the intended analytical purpose?”
The ability of an analytical method to produce true results can be evaluated by means of the application of independent comparison methods. In this case subsamples of a sufficient homogeneous material are analysed using the method under investigation and methods based on different measurement principles. To illustrate this approach a simple experimental arrangement was worked out utilizing gravimetric determinations of inorganic ions (Fe3+, AI3+, SO4 2-and PO 4 3-) as methods under investigation and photometry resp. flow injection analysis as independent comparison methods. In order to avoid the use of a “black box” method and to enhance the learning effect a “didactic” photometer was constructed which enables the students to have a close look at all relevant components of the instrument and to realize the measurement process in detail. The whole exercise is aimed at students in the first year and can be performed e.g. within the first laboratory course. The students can work together in small groups and in a subsequent tutorial the obtained data can be used to draw conclusions about the specific performance characteristics of the employed methods.
H. Albus

Basic Course Experiments to Demonstrate Intercomparisons

Experiments are presented that can be used to demonstrate intercomparisons in a pratical course for first-year students. These experiments include volumetric determinations to be performed by every student, hand-over of data to a supervisor, data evaluation with help of a microcomputer and discussion of underlying statistical principles in a subsequent tutorial. The students learn how to present an analytical result.
At the Philipps-University (Marburg/Germany) undergraduate chemistry students in their first year attend a practical course in inorganic/analytical chemistry. In order to demonstrate the parameters affecting the repeatability and precision of analytical results, a volumetric method is repeatedly applied to aliquots of the same homogeneous sample by every student of this course. Results are compared after statistical treatment of the data by a supervisor.
The sample to be analysed is an aqueous solution of NaOH (c ≃ O.O1mol L-1 ). Two solutions of the titrator (H2SO4) are used (C1 = O.1mol L-1, C2 = O.O1mol L-1). Each student repeats the titration three times with each standard solution (six titrations per student). The results are handed out to the supervisor in volume units.
The statistical treatment of the data material includes: verification of Gaussian normal distribution, calculation of arithmetic means, calculation of standard deviations for single measurements and for the mean of three measurements. The results are visualized by diagrams, shown as overhead transparencies.
The presented data is used in a subsequent tutorial:
  • to visualize the effects of random and systematic errors
  • to introduce statistical methods for evalution of analytical results
  • to discuss the effect of the concentration of the standard solution on the precision of a method
  • to introduce a definition of the terms precision and accuracy
  • and to explain, why repetition of the method and calculation of the mean enhances the precision of analytical results.
U. Pyell

Assessment of Test Kits in Terms of Time, Cost and Quality

The aim of the assignment is to introduce students to the quality assurance aspects of a site survey which need to be considered to ensure client expectations are met. It is designed to enable students to gain experience in formulating a problem, sampling, analysing and reporting. In addition they will also compare laboratory analytical methods and on-site test kits, balancing the three factors of time, cost and quality.
The assignment is to carry out a water survey of a building, comparing use of on-site test kits with traditional methods for measuring specified analytes in the water.
A water quality survey consists of taking a representative number of samples throughout a building and measuring the concentration of an agreed range of analytes to determine whether the levels of analytes measured meet an agreed specification. The cost and time taken to undertake the survey will depend on the purpose of the survey. On-site test kits provide a relatively cheap alternative to traditional sampling and analytical techniques at the expense possibly of quality.
The presentation will describe the components of the assignment which will in general be carried out by small teams of students. The various components will include:
  • deciding what analytes to measure in water
  • determining the precision of laboratory analytical methods and test kits
  • determining the bias of laboratory analytical methods and test kits
  • designing a sampling plan suitable for surveying a building
  • creating sample information records
  • identifying the sampling equipment required to carry out the survey.
P. Houlgate

Estimation of Random Deviations in Analytical Methods using Analysis of Variance

Analytical methods can vary from extremely simple one-step procedures to those which include a number of steps, often involving complex sample treatments and manipulations, and it can often prove extremely difficult to isolate the variability associated with each step in the method. Proper experimental design can yield more information than an ad hoc approach, and subsequent statistical analysis can reveal the magnitude of random deviations in the steps of a multi-step method, which are not always obvious during a cursory examination. In this exercise, Analysis of Variance (ANOVA) is used to estimate the variance components in a multi-step analytical procedure, namely a sequential extraction of trace elements from soil, with subsequent analysis by flame atomic absorption spectrometry. The exercise will be used to demonstrate the following:
  • the principles of good experimental design
  • the importance of replication and randomisation
  • the use of F-tests and hypothesis testing
  • the estimation of random deviations in an analytical method.
The exercise is aimed at final year undergraduate students, or postgraduate students. The students are required to perform the analytical procedure as part of a laboratory exercise, working in a group and pooling the data at the end of the lab. A subsequent tutorial will then be used to analyse the data and draw conclusions about the analytical method.
E. H. Evans

Course Structures, Contents and Experiences


PT Scheme for Pre-university Students

As part of the Valid Analytical Measurement (VAM) programme and in conjunction with Nuffield Science, an organisation involved with vocational and academic qualifications, LGC has developed a proficiency testing (PT) scheme appropriate for pre-university students.
All PT schemes share two key objectives:
  • the provision of a regular, objective and independent assessment of the accuracy of analytical laboratory’s results on routine test samples
    the promotion of improvements in the quality (accuracy) of routine analytical data.
In broad terms these are the objectives of the PT scheme organised by LGC for schools and colleges. The students, working in groups, have to determine the concentration of ethanoic acid samples using good laboratory practice. The PT scheme is conducted in a similar manner to PT schemes used by professional analytical chemists and as such
  • vialed samples of ethanoic acid are distributed to all participating centres
    the homogeneity and stability of the acid solutions are measured by analysts at LGC
    statistical analysis is performed on the students experimental data and their calculations of the acid concentrations.
In this way, the performance of students in one centre is compared with that of students at the other centres and this information is relayed back to the participating centres.
The PT scheme has been run on two occasions and from teacher’s feedback, the PT scheme is a ‘user friendly’ activity for both students and teachers. Participating centres also receive teachers notes, student information sheets (practical details) and student reporting sheets. In the light of teachers comments, the difficulties encountered by students performing the analysis and the student reports a ‘Guide to Improving Analytical Quality in Chemistry’ has been produced to help students with analytical techniques such as titration.
The PT scheme for schools and colleges runs over an entire school term (approximately 3 months) and so this allows students to perform the analysis when it best fits in with their studies. It provides a friendly analytical challenge for students and, at the same time, it has helped to improve the way in which students perform their measurements.
E. Lee

Teaching of the Concept of Valid Analytical Measurement: Integration of Quality Assurance (QA) Issues or Separate QA Courses for Higher Education

The means by which the topic of Quality Assurance (QA) is introduced and taught in higher education courses, particularly in analytical chemistry, depends very much on the academic background of the‘ students’, the nature of the courses within which the teaching is to take place, and the timescale available. The choice between provision of dedicated QA courses or integration of QA principles into courses on other aspects of analytical science must be carefully considered. This presentation will address these aspects in order to help ‘teachers’ select the most suitable approach for their purpose. The discussion will be based mainly on experience at the University of Hull where QA concepts are taught to disparate groups of students, viz. conventional undergraduates studying analytical science, environmental science students, specialised analytical masters course students and those studying analytical chemistry by ‘open learning’.
In all cases the students must be left in no doubt that analytical measurements should be valid and fit for purpose. They should be clear how such qualities are achieved and monitored, and should be convinced that such QA procedures are an integral part of analytical science. Of course, the discussion is equally relevant to other measurement disciplines, and some thought will be given to these wider connotations.
G. M. Greenway, A. Townshend

Special Requirements for Interlaboratory Proficiency Tests

Interlaboratory tests are very important and very effective tools for proficiency testing. In order to fulfill this task they must meet some special requirements. Collusion between participants must be avoided, special efforts in the laboratories must be prevented, special attention must be paid to the definition of the assigned value and the quality requirements for the analysis must be stipulated.
In a proficiency testing scheme in Germany the University of Stuttgart developed a special design and evaluation method for interlaboratory tests that meet the above mentioned requirements.
M. Koch


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