Course Details

Course Overview:
This course offers technical guidance on the
evaluation of measurement uncertainty for
quantitative test results. While fully compliant
with the principles of the Guide to the
Expression of Uncertainty in Measurement
(GUM), the document also includes alternative
approaches to the “bottom-up” approach,
based on a comprehensive mathematical model
of the measurement process, as emphasized in
the GUM.
These are “top-down” approaches utilizing
whole-method performance data from interlaboratory comparisons (collaborative method
validation, proficiency testing) and from within laboratory validation and quality control data
(precision, bias).
This course covers the advanced topics in Good
Laboratory Practice (GLP). All the practices that
ensure that laboratory testing is planned,
performed, monitored, recorded and reported
in an organized and controlled manner, will be
discussed in general and the Uncertainty
Measurement (GUM) in particular.
The course addresses the requirements of EN
ISO / IEC 17025: 2005 General Requirements for
the Competence of Testing and Calibration
Laboratories as well as the common
requirements of GLP Compliance (OECD, USA,
Canada, and Europe & Japan) as applicable to
QC/QA/Production Control Laboratories.
The course will use the Statistical Process
Control (SPC) module as a tool to validate the
uncertainty
measurement. It covers control charts and their
applicability to uncertainty before covering a
step-by-step process of calculating uncertainty
for a typical application. Methods of reporting
uncertainty, the uncertainty budget and
applicable standards and guidelines for
expressing measurement uncertainty are
covered in some detail. Successful completion
of this course will enable participants to
understand, evaluate and express measurement
uncertainty.

Course duration
3 Days

Learning outcome:
Upon the successful completion of this course, participants will be able to:-
• Describe the Concepts involved in the
calculation of measurement uncertainty
in a practical and pragmatic manner.
• Describe Analytical chemistry methods
and Instruments technique
• Defining measurement processes.
• Identifying sources of measurement
error.
• Create method validation
• Use all methods for calibration.
• Understand Statistical Process Control
(SPC)
• Use statistical method according to ISO
17025 requirements
• Determine the cost of GLP noncompliance
• Describe the critical GLP compliance
issues
• Describe the GLP requirements related
to organization, personnel, facilities,
equipment, operations, testing, control,
protocols, records and reports
• Describe what is the Good Laboratory
Practice (GLP) and its importance in
modern laboratories and in technology
transfer
• Identify uncertainty sources and employ
the process of quantifying uncertainty
• Understand the Statistical Process
Control (SPC) and identify its
applications
• Apply method validation techniques in
accordance with ISO17025

Who should attend
This course is aimed at all staff of Laboratories. This includes laboratory managers, superintendents, supervisors, scientists,
doctors, physicians, chemists, analysts, officers, coordinators and technicians. R&D and government statutory employees are
encouraged to attend this outstanding course. Further, this course is very important for QA/QC employees and Third-Party
Inspection and certification companies.

Course contents
This course will cover the following topics
• Scope, field of application, components
& sources of uncertainty
• Conduct analytical measurement &
experimental studies of method
performance
• Process of measurement uncertainty
estimation
• Data validation
• Method validation
• Carry-out the process of calculating the
combined uncertainty and be able to
review the procedure on reporting
uncertainty
• Statistical Process Control (SPC) and its
applications
• GLP compliance issues
• Introduction to laboratory technique
• Guide to the expression of uncertainty in
measurement
• Measurement uncertainty
• Statistical process control (spc)
• Method validation
• Good laboratory practice
• Stepwise implementation

Day (1)
I. INTRODUCTION TO LABORATORY
TECHNIQUE
• Methods of analysis
• Titration methods
• Chromatography technique (GC and
HPLC)
• Spectroscopy technique
• Instrument analysis data
• Interpolated graph calibration using
external/ internal standards
• Standard addition method extrapolated
graph
• Errors in quantitative analysis
• Random and systematic errors in titration
analysis
• Standard deviation of repeated
measurements
• Distribution of errors
• Confidence limit of the mean of replicate
measurements
• Confidence limit of one measurement
using calibration curve
II. GUIDE TO THE EXPRESSION OF UNCERTAINTY
IN MEASUREMENT
• Scope
• Definitions
• The term “uncertainty”
• Terms specific to this Guide
•
• Basic concepts
• Measurement
• Errors, effects, and corrections
• Uncertainty
• Practical considerations
•
• Evaluating standard uncertainty
• Modeling the measurement
• Type A evaluation of standard
uncertainty
• Type B evaluation of standard
uncertainty
• Graphical illustration of evaluating
standard uncertainty
• Determining combined standard
uncertainty
• Uncorrelated input quantities
• Correlated input quantities
• Determining expanded uncertainty
• Expanded uncertainty
• Choosing a coverage factor
• Reporting uncertainty
• General guidance
• Specific guidance
• Summary of procedure for evaluating and
expressing uncertainty

Day-2
III.MEASUREMENT UNCERTAINTY
1. Errors and uncertainties
a) Types of uncertainty
b) Standard deviation and calibration
uncertainty
2. The difference between type A and type
B estimates
3. Indirect measurements
4. Sensitivity coefficients
5. Preliminary uncertainty budget
6. Higher levels of confidence — the
coverage factor k
7. Degrees of freedom
8. Combining effective degrees of freedom
9. Final uncertainty budget
10. Rounding
11. Correlations in uncertainty
12. Practical use of uncertainty budgets
IV.STATISTICAL PROCESS CONTROL (SPC)
13. Quality Control Today
14. Steps Involved In Using Statistical
Process Control
15. Specific SPC Tools And Procedures
16. Methodology & Tools of Statistical
Process Control
17. Control Charts
18. Types Of Charts Available For The Data
Gathered
19. Attribute Data Charts
20. Case Studies of Statistical Process
Control
Day-3
V. METHOD VALIDATION
21. What is method validation?
22. Why is method validation necessary?
23. Importance of analytical measurement
24. The professional duty of the analytical
chemist
25. When should methods be validated?
26. How should methods be validated?
27. Who carries out method validation?
28. Deciding what degree of validation is
required
29. The analytical requirement
30. Method development
31. The different performance parameters
of a method and what they show
32. The tools of validation
33. Using validated methods
34. Using validation data to design QC
35. Documentation of validated methods
36. Implications of validation data for
calculating results and reporting
VI. GOOD LABORATORY PRACTICE
37. The fundamental points of GLP
38. Resources and characterization
39. Results – raw data and data collection
40. Quality assurance unit
VII. STEPWISE IMPLEMENTATION OF GLP
41. Implementation as a project
42. Stepwise implementation of GLP
requirements

Course Assessment
• Pre & Post Test to be conducted before and after the course.
• Action plan for each participant
• Evaluation to be conducted after the course at last day