This document discusses quality assurance and within-laboratory analytical quality control programs. It emphasizes the need for quality assurance due to errors found in analytical results. A quality assurance program includes sample control, standard procedures, equipment maintenance, calibration, and analytical quality control. Within-laboratory quality control focuses on precision using statistical control charts to monitor performance over time and identify issues. Regular quality control practices help ensure reliability of laboratory results.
Workshop On Risk Assesment by Palash Ch DasPalash Das
Risk management principles are effectively utilized in many areas of business and government including finance, insurance, occupational safety, public health, pharmacovigilance, and by agencies regulating these industries. Although there are some examples of the use of quality risk management in the pharmaceutical industry today, they are limited and do not represent the full contributions that risk management has to offer. In addition, the importance of quality systems has been recognized in the pharmaceutical industry and it is becoming evident that quality risk management is a valuable component of an effective quality system.
Workshop On Risk Assesment by Palash Ch DasPalash Das
Risk management principles are effectively utilized in many areas of business and government including finance, insurance, occupational safety, public health, pharmacovigilance, and by agencies regulating these industries. Although there are some examples of the use of quality risk management in the pharmaceutical industry today, they are limited and do not represent the full contributions that risk management has to offer. In addition, the importance of quality systems has been recognized in the pharmaceutical industry and it is becoming evident that quality risk management is a valuable component of an effective quality system.
ANALYZING THE PROCESS CAPABILITY FOR AN AUTO MANUAL TRANSMISSION BASE PLATE M...ijmvsc
The industry today is working intensively on a goal-oriented way towards introducing regular studies in
manufacturing. The current study is part of a large overall spanning project aiming towards an increase in
productivity, i.e. more products produced per year with availability. In this paper we have analyze what
Process Capability is and how it is implemented on a current process. All the steps are listed out in an easy
to understand manner. In current scenario, specifications for products have been tightened due to
performance competition in market. Statistical tools like control charts, process capability analysis and
cause and effect diagram ensure that processes are fit for company specifications while reduce the process
variation and improve product quality characteristic. Process capability indices (PCIs) are used in the
manufacturing process to provide numerical measures on whether a process is capable of producing items
within the predetermined limits. For the analysis purpose MINITAB 16.0 is used and is found that the
process is placed exactly at the centre of the control limits. Analysis also shows that process is not
adequate. The cause and effect diagram is prepared to found out the root cause of variation in diameter of
work. In this study, a process-capability analysis was also carried out in a medium-sized company that
produces machine and spare parts.
Know the Best Cost Reduction & Performance Management StrategiesNext Level Purchasing
You will learn :
1. Learn to Leverage Total Cost to Serve to Pinpoint Cost Reduction Opportunities
2. Understand how Effective Product Development Mechanisms can Yield Cost Reduction
3. Understand how the Balanced Scorecard Methodologies enhance Performance Management Strategies
Quality Management
& Control
Seung-Kuk Paik, Ph.D.
Systems and Operations Management
CSU, Northridge
What is Quality?
"Quality" can be defined in many ways.
1.Quality is defined as “FITNESS FOR USE”: How well a service or product performs its intended purpose.
2.Quality is also defined as “CONFORMANCE TO REQUIREMENTS”: How a service or product conforms to performance specifications.
What is Quality?
3.In a wider sense, "QUALITY" is often considered the degree of excellence whereby products and services may be ranked against each other on a relative basis for selected features and characteristics.
American Society of Quality (ASQ) has accepted the following definition:
QUALITY: The totality of features and characteristics of a product or service that bear on its ability to satisfy stated or implied needs.
What is Quality?
DIMENSIONS OF QUALITY
1.Performance - A product´s primary operating characteristics.
2.Features - Supplements to a product´s basic functioning characteristics
3.Reliability – Consistency of performance
What is Quality?
DIMENSIONS OF QUALITY
4.Durability - A measure of product life.
Serviceability - The speed and ease of repair
6.Aesthetics - Appearance of a product
7.Safety - Will the product perform its function without unnecessarily endangering the user?
Quality and Productivity
Historically, quality was viewed by some as a controlling activity which took place somewhere near the end of a production process, an after-the-fact measurement of production performance.
Efforts to obtain quality products increased the costs associated with making that product.
Thus, quality and productivity were viewed as conflicting; one was increased at the expense of the other.
Costs of Poor
Process Performance
Defects: Any instance when a process fails to satisfy its customer.
Prevention costs are associated with preventing defects before they happen.
Appraisal costs are incurred when the firm assesses the performance level of its processes.
Internal failure costs result from defects that are discovered during production of services or products.
External failure costs arise when a defect is discovered after the customer receives the service or product.
7
Deming’s Chain Reaction
Quality and Costs
Costs decrease because of less rework, fewer mistakes, fewer delays, and better use of time and materials
Improve quality
Productivity improves
Stay in Business
Provides jobs and more jobs
Capture the market
Quality Improvement
DEMING´S 14 POINTS
1.Create constancy of purpose toward improvement of products
2.Adopt a quality philosophy
3.Cease dependence on mass inspection
4.End the practice of selecting suppliers on the basis of price alone
5.Improve constantly
6.Institute training on the job
7.Institute leadership
8.Drive out fear
DEMING´S 14 POINTS
9.Break down barriers between departments
10. Eliminate slogans and targets
11. Eliminate work standards that prescribe numerical quotas
12. Remove bar ...
PROJECT STORYBOARD: Reducing Learning Curve Ramp for Temp Employees by 2 WeeksGoLeanSixSigma.com
GoLeanSixSigma.com Black Belt Sean Halpin successfully used Lean Six Sigma methods in speeding up learning — with potential applications throughout the private and public sectors. He was able to not only reduce the time to develop employee capability, but was able to show achievement of higher capability levels than before the project.
Sean did a particularly thorough job in analyzing potential root causes and determining appropriate actions. He identified eight potential root causes, half of which proved to be real. A key finding was that training in how to deal with problems was particularly effective. Much training focuses on how things should be — not always considering common problems.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
1. World Bank & Government of The Netherlands funded
Training module # WQ - 49
Quality Assurance and within
Laboratory AQC
New Delhi, September 2000
CSMRS Building, 4th Floor, Olof Palme Marg, Hauz Khas,
New Delhi – 11 00 16 India
Tel: 68 61 681 / 84 Fax: (+ 91 11) 68 61 685
E-Mail: dhvdelft@del2.vsnl.net.in
DHV Consultants BV & DELFT HYDRAULICS
with
HALCROW, TAHAL, CES, ORG & JPS
2. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 1
Table of contents
Page
1. Module context 2
2. Module profile 3
3. Session plan 4
4. Overhead/flipchart master 5
5. Evaluation sheets 18
6. Handout 20
7. Additional handout 26
8. Main text 28
3. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 2
1. Module context
This module discusses the need for Quality Assurance programme and describes the
procedure for setting up a within-laboratory AQC programme.
While designing a training course, the relationship between this module and the others,
would be maintained by keeping them close together in the syllabus and place them in a
logical sequence. The actual selection of the topics and the depth of training would, of
course, depend on the training needs of the participants, i.e. their knowledge level and skills
performance upon the start of the course.
Modules in which prior training is required to complete this module successfully are listed in
the table below.
No. Module Code Objectives
1. Basic Statistics WQ – 47 • Understand difference between
accuracy and precision
• Calculate, descriptors of frequency
distribution
2. Applied Statistics WQ – 48 • Apply common statistical tests for
evaluation of the precision of data
4. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 3
2. Module profile
Title : Quality Assurance and within Laboratory AQC
Target group : HIS function(s): Q2, Q3, Q5, Q6
Duration : one session of 60 min
Objectives : After the training the participants will be able to:
• Understand the need for QA programmes
• Set up with-in laboratory AQC programme
Key concepts : • Quality Assurance
• With-in laboratory AQC
Training methods : Lecture, exercises, OHS
Training tools
required
: Board, flipchart
Handouts : As provided in this module
Further reading
and references
: • Standard Methods: for the Examination of Water and
Wastewater, APHA, AWWA, WEF/1995. APHA Publication
• Statistical Procedures for analysis of Environmenrtal monitoring
Data and Risk Assessment’, Edward A. Mc Bean and Frank A.
Rovers, Prentice Hall, 1998.
5. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 4
3. Session plan
No Activities Time Tools
1 Preparations
2 Introduction:
• Ask participants how can they be sure of the correctness of
their results.
• Discuss the need for quality assurance programmes
10 min OHS
3 Review
• Precision, bias and accuracy
• Definitions from statistics
15 min OHS
4 With-in laboratory AQC
• Discuss precision and statistical control
• Shewhart charts
• Interpretation of results
30 min OHS
5 Conclusion 5 min OHS
6. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 5
4. Overhead/flipchart master
OHS format guidelines
Type of text Style Setting
Headings: OHS-Title Arial 30-36, with bottom border line (not:
underline)
Text: OHS-lev1
OHS-lev2
Arial 24-26, maximum two levels
Case: Sentence case. Avoid full text in UPPERCASE.
Italics: Use occasionally and in a consistent way
Listings: OHS-lev1
OHS-lev1-Numbered
Big bullets.
Numbers for definite series of steps. Avoid
roman numbers and letters.
Colours: None, as these get lost in photocopying and
some colours do not reproduce at all.
Formulas/Equat
ions
OHS-Equation Use of a table will ease horizontal alignment
over more lines (columns)
Use equation editor for advanced formatting
only
7. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 6
Quality Assurance
• Need for QA
- Analytical results are subject to errors
- EPA studies showed ± 50% and ±100% errors in results of
ammonia and nitrate analyses
- Only 34% of SPCB laboratories reported acceptable EC results
for standard sample.
- Actions taken on such results become questionable.
8. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 7
Quality Assurance
Figure 1: The overall performance of all the 4 rounds of exercises carried out by CPCB in 8 slots during 1992 to 1997 covering 19
parameters. Laboratories found within the acceptable limits for all the 19 parameters.
9. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 8
QA Programme
• Sample control and documentation
• Standard analytical procedures
• Equipment maintenance
• Calibration procedures
• AQC, within-laboratory and inter-laboratory
10. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 9
Within-Laboratory AQC
• Suitability of analytical methods
• Purity of chemicals
• Sampling techniques
• Sample preservation
• Data reporting
• Method precision, Shewhart charts
11. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 10
Shewhart Control Chart (1)
• Focuses on precision – state of ‘Statistical Control’
• Construction of charts
- Make 20 replicate analyses on a standard solution
- Calculate mean and standard deviation
- Establish warning limits at x ± 2S and control atx ± 3S
• Repeat analysis of control after 20 to 50 routine samples and
plot results
12. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 11
Shewhart Control Chart (2)
• Evaluate performance
- Loss of statistical control
- Newly introduced bias
- Revised control limits
13. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 12
Shewhart Control Chart (3)
Shewhart control chart for TH
(out of control: UCL)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 3: Example of loss of statistical control by the Control Limit criterion
14. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 13
Shewhart Control Chart (4)
Shewhart control chart for TH
(out of control: Warning)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 4: Example of loss of statistical control by the Control Limit criterion
15. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 14
Shewhart Control Chart (5)
Shewhart control chart for TH
(out of control: Standard Deviation)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 5: Example of loss of statistical control by the Standard Deviation criterion
16. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 15
Shewhart Control Chart (6)
Shewhart control chart for TH
(out of control: Trend)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 6: Example of loss of statistical control by the Trend criterion
17. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 16
Shewhart Control Chart (7)
Shewart control chart for TH
(out of control: Average)
80
85
90
95
100
105
110
115
120
03Oct97
04Oct97
05Oct97
06Oct97
07Oct97
08Oct97
09Oct97
10Oct97
11Oct97
12Oct97
13Oct97
14Oct97
15Oct97
16Oct97
17Oct97
18Oct97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 7: Example of loss of statistical control by the Average (Central Line) criterion
18. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 17
Conclusion
• Within-laboratory AQC measures precision
• An internal mechanism to check performance
• Practised by responsible chemists
• It is not much additional work
• It should not be a one time exercise
19. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 18
5. Evaluation sheets
20. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 19
21. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 20
6. Handout
22. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 21
Quality Assurance
• Need for QA
- Analytical results are subject to errors
- EPA studies showed ± 50% and ±100% errors in results of ammonia and nitrate
analyses
- Only 34% of SPCB laboratories reported acceptable EC results for standard sample.
- Actions taken on such results become questionable.
Figure 1: The overall performance of all the 4 rounds of exercises carried out by CPCB
in 8 slots during 1992 to 1997 covering 19 parameters. Laboratories found
within the acceptable limits for all the 19 parameters.
QA Programme
• Sample control and documentation
• Standard analytical procedures
• Equipment maintenance
• Calibration procedures
• AQC, within-laboratory and inter-laboratory
23. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 22
Within-Laboratory AQC
• Suitability of analytical methods
• Purity of chemicals
• Sampling techniques
• Sample preservation
• Data reporting
• Method precision, Shewhart Control
Shewhart Control Chart
• Focuses on precision – state of ‘Statistical Control’
• Construction of charts
- Make 20 replicate analyses on a standard solution
- Calculate mean and standard deviation
- Establish warning limits at x ± 2S and control at x ± 3S
• Repeat analysis of control after 20 to 50 routine samples and plot results
• Evaluate performance
- Loss of statistical control
- Newly introduced bias
- Revised control limits
Figure 3: Example of loss of statistical control by the Control Limit criterion
Shewhart control chart for TH
(out of control: UCL)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
24. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 23
Figure 4: Example of loss of statistical control by the Control Limit criterion
Shewhart control chart for TH
(out of control: Warning)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 5: Example of loss of statistical control by the Standard Deviation criterion
Shewhart control chart for TH
(out of control: Standard Deviation)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 6: Example of loss of statistical control by the Trend criterion
Shewhart control chart for TH
(out of control: Trend)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 7: Example of loss of statistical control by the Average (Central Line) criterion
25. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 24
Shewart control chart for TH
(out of control: Average)
80
85
90
95
100
105
110
115
120
03Oct97
04Oct97
05Oct97
06Oct97
07Oct97
08Oct97
09Oct97
10Oct97
11Oct97
12Oct97
13Oct97
14Oct97
15Oct97
16Oct97
17Oct97
18Oct97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Conclusion
• Within-laboratory AQC measures precision
• An internal mechanism to check performance
• Practised by responsible chemists
• It is not much additional work
• It should not be a one time exercise
26. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 25
Add copy of Main text in chapter 8, for all participants.
27. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 26
7. Additional handout
These handouts are distributed during delivery and contain test questions, answers to
questions, special worksheets, optional information, and other matters you would not like to
be seen in the regular handouts.
It is a good practice to pre-punch these additional handouts, so the participants can easily
insert them in the main handout folder.
28. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 27
29. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 28
8. Main text
Contents
1. Need for quality Assurance 1
2. Quality assurance programme 1
3. Review of basic statistics 3
4. Shewhart control charts 4
5. Discussion of results 4
30. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 1
Quality Assurance and within Laboratory AQC
1. Need for Quality Assurance
Many studies have shown that analytical results are often subject to serious errors,
particularly at the low concentrations encountered in water analysis. In fact, the errors may
be so large that the validity of actions taken regarding management of water quality may
become questionable.
Nutrients, N and P, in very small concentrations can cause eutrophication of waterbodies. An
analytical quality control exercise (AQC) exercise conducted by United States Environmental
Protection Agency (US-EPA) showed a wide variation in results when identical samples
were analysed in 22 laboratories:
Nutrient Concentration,
mg/L
Range of results,
mg/L
Ammonia 0.26
1.71
0.09 - 0.39
1.44 - 2.46
Nitrate 0.19 0.08 - 0.41
Total phosphorus 0.882 0.642 - 1.407
It is seen that the range of values reported are significantly large, ±50% for ammonia and
±100% for nitrates, compared to the actual concentrations. Therefore, the need for nutrient
control programme and its results become difficult to assess.
Many laboratories under Hydrology Project (HP) report total dissolved salts (TDS) calculated
from the electrical conductivity (EC) value:
TDS, mg/L = A x EC, µS/cm
where A is a constant ranging between 0.55 and 0.9 depending on the ionic composition of
salts dissolved in the water.
An inter-laboratory AQC exercise conducted by Central Pollution Control Board (CPCB)
showed that for measurement of EC of a standard solution, out of 44 participating
laboratories only 34% reported values in the acceptable range. Figure 1.
Thus, the reliability of iso-concentrations of TDS in groundwaters, drawn based on data of
several laboratories may become questionable on two counts; use of an arbitrary value for
the constant A and variation in inter-laboratory measurements.
These examples amply demonstrate the need for quality assurance (QA) programmes.
2. Quality assurance programme
The QA programme for a laboratory or a group of laboratories should contain a set of
operating principles, written down and agreed upon by the organisation, delineating specific
functions and responsibilities of each person involved and the chain of command. The
following sections describe various aspects of the programmes
31. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 2
Sample control and documentation: Procedures regarding sample collection, labelling,
preservation, transport, preparation of its derivatives, where required, and the chain-of-
custody.
Standard analytical procedures: Procedures giving detailed analytical method for the
analysis of each parameter giving results of acceptable accuracy.
Analyst qualifications: Qualifications and training requirements of the analysts must be
specified. The number of repetitive analyses required to obtain result of acceptable accuracy
also depends on the experience of the analyst.
Equipment maintenance: For each instrument, a strict preventive maintenance programme
should be followed. It will reduce instrument malfunctions, maintain calibration and reduce
downtime. Corrective actions to be taken in case of malfunctions should be specified.
Calibration procedures: In analyses where an instrument has to be calibrated, the
procedure for preparing a standard curve must be specified, e.g., the minimum number of
different dilutions of a standard to be used, method detection limit (MDL), range of
calibration, verification of the standard curve during routine analyses, etc.
Analytical quality control: This includes both within-laboratory AQC and inter-laboratory
AQC.
Under the within-laboratory programme studies may include: recovery of known additions to
evaluate matrix effect and suitability of analytical method; analysis of reagent blanks to
monitor purity of chemicals and reagent water; analysis of sample blanks to evaluate sample
preservation, storage and transportation; analysis of duplicates to asses method precision;
and analysis of individual samples or sets of samples (to obtain mean values) from same
control standard to check random error.
Inter-laboratory programmes are designed to evaluate laboratory bias.
It may be added that for various determinands all of the AQC actions listed may not be
necessary. Further, these are not one time exercises but rather internal mechanisms for
checking performance and protecting laboratory work from errors that may creep in.
Laboratories who accept these control checks will find that it results in only about 5 percent
extra work.
In Summary:
AQC is:
• an internal mechanism for checking your own performance
• protecting yourself from a dozen of errors that may creep into analytical work
• to avoid human errors in routine work
• practiced by responsible chemists
• not useless work
• common practice in certified laboratories
AQC is NOT:
• much work
• to be carried out for each and every routine sample
• checking and reporting the quality of your work
• a one time exercise to be forgotten soon
32. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 3
Data reduction, validation and reporting: Data obtained from analytical procedures,
where required, must be corrected for sample size, extraction efficiency, instrument
efficiency, and background value. The correction factors as well as validation procedures
should be specified. Results should be reported in standard units. A prescribed method
should be used for reporting results below MDL.
An important aspect of reporting the results is use of correct number of significant figures. In
order to decide the number of significant digits the uncertainty associated with the reading(s)
in the procedure should be known. Knowledge of standard deviation will help in rounding off
the figures that are not significant. Procedures regarding rounding off must be followed.
3. Review of basic statistics
Bias: Bias is a measure of systematic error. It has two components, one due to method and
the other due to laboratory use of method.
Precision: Precision is a measure of closeness with which multiple analyses of a given
sample agree with each other.
Random error: Multiple analyses of a given sample give results that are scattered around
some value. This scatter is attributed to random error.
Accuracy: Combination of bias and precision of an analytical procedure, which reflects the
closeness of a measured value to the true value.
Frequency distribution: Relation between the values of results of repetitive analyses of a
sample and the number of times (frequency) that a particular value occurs.
Mean: Mean is the central value of results of a set of repetitive analyses of a sample. It is
calculated by summing the individual observations and dividing it by the total number of
observations.
Normal distribution: Normal distribution is a frequency distribution, which is symmetrical
around the mean. In a normal distribution 95.5% and 99.7% of the observations lie in ± two
times standard deviation and ±three times standard deviation range around the mean,
respectively.
Standard deviation: Standard deviation is a measure of spread of results of repetitive
analyses of a sample around its mean value. It is a measure of precision of the analytical
method. It is calculated by taking square root of sum of squares of deviation of the
observations from the mean divided by the number of observations minus one. Figure 2.
Coefficient of variation: Comparison of standard deviation values for results of repetitive
analysis, of two samples having different concentration of the determinand, may sometimes
give wrong conclusion regarding precision of the measurement. Coefficient of variation (CV),
which is calculated as CV = standard deviation/mean X 100, is a better parameter for such
comparison. For example, for results of two sets of analyses, performed on two different
samples, if the mean values are 160 and 10 mg/L and standard deviations are 8 and 1.5
mg/L, respectively, comparison of standard deviation would indicate lower precision for the
first set of observations (standard deviation 8 mg/L), while the CV values work out to be 5
(8/160 X 100) and 15 (1.5/10 X 100) percents respectively. Indicating a lower precision for
the second set of observations.
33. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 4
4. Shewhart control charts
If a set of analytical results is obtained for a control sample under conditions of routine
analysis, some variation of the observed values will be evident. The information is said to be
statistically uniform and the analytical procedure is said to be under statistical control if this
variation arises solely from random variability. The function of a control chart is to identify
any deviation from the state of statistical control.
Shewhart control chart is most widely used form of control charts. In its simplest form, results
of individual measurements made on a control sample are plotted on a chart in a time series.
The control sample is analysed in the same way as the routine samples at fixed time
intervals, once or twice every week, or after 20 to 50 routine samples.
Assuming the results for the control sample follow the Normal frequency distribution, it would
be expected that only 0.3% of results would fall outside lines drawn at 3 standard deviations
above and below the mean value called upper and lower control limits, UCL and LCL,
respectively. Individual results would be expected to fall outside these limit so seldom (3 out
of 1000 results), that such an event would justify the assumption that the analytical
procedure was no longer in statistical control, i.e., a real change in accuracy has occurred.
Two lines are inserted on the chart at 2 standard deviations above and below the mean
value called upper and lower warning limits, UWL and LWL, respectively. If the method is
under control, approximately 4.5% of results may be expected to fall outside these lines.
This type of chart provides a check on both random and systematic error gauged from the
spread of results and their displacement, respectively. Standard Methods lists the following
actions that may be taken based on analysis results in comparison to the standard deviation.
Control limit: If one measurement exceeds the limits, repeat the analysis immediately. If the
repeat is within the UCL and LCL, continue analyses; if it exceeds the action limits again,
discontinue analyses and correct the problem.
Warning limit: If two out of three successive points exceeds the limits, analyse another
sample. If the next point is within the UWL and LWL, continue analyses; if the next point
exceeds the warning limits, discontinue analyses and correct the problem.
Standard deviation: If four out of five successive points exceed one standard deviation, or
are in increasing or decreasing order, analyse another sample. If the next point is less than
one standard deviation away from the mean, or changes the order, continue analyses;
otherwise discontinue analyses and correct the problem.
Central line: If six successive points are on one side of the mean line, analyse another
sample. If the next point changes the side continue the analyses; otherwise discontinue
analyses and correct the problem.
Figure 3 to Figure 7 illustrate the cases of loss of statistical control for analysis of individual
samples based on the above criteria.
5. Discussion of results
5.1 Precision
The most important parameter to evaluate in the results is the precision. The statistical term
to evaluate precision is standard deviation. The numerical value of the standard deviation
depends on the average concentration (standard deviation also has the unit of
34. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 5
concentration). Numerical values of standard deviations of low concentration solutions are
usually smaller than those of solutions with higher concentrations. Therefore the coefficient
of variation, defined earlier, should be used to evaluate precision. This is particularly useful
when comparing results of analysis for samples having different concentrations. Before
evaluating the results one should answer the question ‘what is the desired precision for an
analyses?’. In fact this question should be answered by the so called ‘data users’. The use of
the data determines the required precision, e.g. detection of trends may require more
precise results (in order to actually detect small changes in the cause of time) than checking
water for use, say for irrigation. Laboratory staff should always ask for the purpose for which
they are performing the requested test.
As a minimum goal for precision, however, the precision that can be obtained by correctly
and adequately following the method prescribed by the APHA Standard Methods for the
examination of water and wastewater may be adopted
5.2 Calculating revised limits when continuing the exercise
Warning and control limits should be recalculated periodically. Especially when new
techniques are introduced, the precision improves when experience is gained with the
technique. A good time for recalculating the control and warning limits is at the time when
the control chart is full and a new graph has to be created anyway. At this point, use the 20
most recent data on the old chart for construction of LCL, LWL, average, UWL and UCL.
5.3 Errors that cannot be detected by within-laborartory AQC
The within-laboratory AQC exercise focusses mainly on precision. A laboratory on its own
cannot detect many sources of bias. A good example to illustrate this is the total hardness
method. If the analytical balance in a lab always reads 10% too much all solution prepared
will have a 10% higher concentration: the Standard CaCO3 solution, the EDTA titrant and
also the control sample containing CaCO3. This error can only be detected by analysing a
sample prepared by a laboratory with a correctly functioning balance. The current laboratory
will underestimate the concentration of such a inter-laboratory sample by 10% because their
EDTA titrant is ’10% too strong’.
In some cases freshly introduced bias may be detected. For example, if the measurements
consistently fall on one side of the previously calculated mean, it indicates a freshly
introduced bias.
An inter-laboratory AQC exercise should be conducted for detecting bias or accuracy for
analysis.
35. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 6
Figure 1: The overall performance of all the 4 rounds of exercises carried out by CPCB in 8 slots during 1992 to 1997 covering 19
parameters. Laboratories found within the acceptable limits for all the 19 parameters.
36. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 7
Normal distribution with high precision
0
5
10
15
20
25
16-18 18-20 20-22 22-24 24-26 26-28 28-30 30-32 32-34 34-36 36-38 38-40
TH (mg/L)
noofobservations
Norm aldistribution w ith low precision
0
5
10
15
20
25
16-18 18-20 20-22 22-24 24-26 26-28 28-30 30-32 32-34 34-36 36-38 38-40
TH (m g/L)
noofobservatio
Figure 2: Example of two normal distributions with the same mean value, the
upper one being more precise (having a lower standard deviation and
CV)
37. Hydrology Project Training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version 06/11/02 Page 8
Shewhart control chart for TH
(out of control: UCL)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 3: Example of loss of statistical control by the Control Limit criterion
38. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 9
Shewhart control chart for TH
(out of control: Warning)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 4: Example of loss of statistical control by the Control Limit criterion
39. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 10
Shewhart control chart for TH
(out of control: Standard Deviation)
80
85
90
95
100
105
110
115
120
03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 5: Example of loss of statistical control by the Standard Deviation criterion
40. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 11
Shewhart control chart for TH
(out of control: Trend)
80
85
90
95
100
105
110
115
120 03-Oct-97
04-Oct-97
05-Oct-97
06-Oct-97
07-Oct-97
08-Oct-97
09-Oct-97
10-Oct-97
11-Oct-97
12-Oct-97
13-Oct-97
14-Oct-97
15-Oct-97
16-Oct-97
17-Oct-97
18-Oct-97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 6: Example of loss of statistical control by the Trend criterion
41. HP training Module File: “ 49 Quality Assurance and within Laboratory AQC.doc” Version September 2000 Page 12
Shewart control chart for TH
(out of control: Average)
80
85
90
95
100
105
110
115
120 03Oct97
04Oct97
05Oct97
06Oct97
07Oct97
08Oct97
09Oct97
10Oct97
11Oct97
12Oct97
13Oct97
14Oct97
15Oct97
16Oct97
17Oct97
18Oct97
Sample date
Concentration(mg/L)
Control Limit
Control Limit
Warning Limit
Warning Limit
Expected Concentration
Figure 7: Example of loss of statistical control by the Average (Central Line) criterion