This document discusses quality assurance and quality control procedures for chemical test laboratories to meet ISO/IEC 17025:2017 requirements. It covers establishing quality assurance plans, differentiating quality assurance and quality control, applying quality control practices like blanks, replicates, and laboratory controls. Quality control charts are presented as a tool to monitor analytical accuracy and precision over time.

1. Ensuring the validity of results
Meeting the ISO/IEC 17025:2017 clause 7.7
requirements for chemical test laboratories
• https://consultglp.com
Webinar contents:
 The laboratory QA/QC protocols – What and Why
 Implementing the laboratory QA strategies
 What are the elements of QC to be applied?
 Let’s learn how to build up a quality control chart
Tuesday 18 August 2020 * Virtual Zoom

2. Learning Outcome
• Upon the end of presentation, the
participants are able to:
• know the importance of having a
robust laboratory QA/QC system
in place
• differentiate the meaning of QA
and QC
• implement elements of QC
protocols
• construct laboratory quality
control charts to monitor routine
analyses

3. Introduction
• ISO/IEC 17025:2017 accreditation standard
outlines the requirements of a good laboratory
quality management system (LQMS)
• It covers various quality assurance / quality
control (QA/QC) principles and procedures
ensuring:
 the lab technical competence is up to an
acceptable standard
 its test results are precise and reliable for
the intended purposes.

4. Effectiveness of a LQMS is checked by ….
• having documentation of QA/QC procedures amongst other quality
management documents
• planning regular internal audits, covering all aspects of the lab
operations;
• conducting regular management reviews of the suitability and
effectiveness of the quality management system;
• analyzing potential and actual problems as reflected in customer
complaints, and through supplier and subcontractor assessments
• seeking recognition of a national or international laboratory
accreditation scheme, such as SAC-SINGLAS in Singapore, or SAMM
in Malaysia.

5. Quality Assurance (QA) and Quality Control (QC)
• Quality assurance and quality control are two aspects of quality
management.
• While some QA and QC activities are interrelated, the two are
defined differently.
• Quality Assurance QA
• all those planned and systematic actions necessary to provide
adequate confidence that a product, process, or service will
satisfy given requirements for quality;
• Quality Control QC
• operational techniques and activities that are used to fulfill
requirements for quality.

6. Laboratory Quality System
Quality Assurance
Quality Control
Good QC results
indicates adequate QA
system
Quality Assurance focuses on
providing confidence that quality
requirements will be fulfilled by
laying down policies, actions and
strategies to be taken.
Quality Control focuses on the
steps to be taken in fulfilling these
quality requirements
Relationship of Quality System, Quality
Assurance & Quality Control
We need to have various QA strategies and QC procedures in place to
ensure our data quality.

7. QA strategies or plans for ….
• Laboratory layout planning and construction
• Compliance to construction codes
• Fire prevention measures
• Environment, occupational health & safety considerations
• Factors which may influence analytical quality – staffing,
instrumentation, management
• Correct use of instruments and apparatus
• Keeping equipment records & maintenance
• Regular re-calibrations at scheduled intervals
• Setting criteria and frequency for instrument accuracy checking
• Following the re-calibration trend by control chart

8. QA strategies or plans for …
• Equipment Qualification (EQ)
• To ensure that the instrument used is ‘fit for purpose’
• The laboratory is responsible to provide this evidence to the
end users or to the technical auditors
• The laboratory can confirm the instrument performance by
carrying out appropriate calibration and performance checks,
but ...
• you must start looking at the instrument at the time you
want to put in your Purchase Order, i.e. defining the
minimum level of performance criteria and specification
(Design Qualification)

9. QA strategies or plans for …
• Instrument qualification (IQ)
• you should try to purchase the instrument with specification exceeds your
needs by a certain margin, as instrument does deteriorate in performance over
time.
• Operation qualification (OQ)
• check ‘pass/fail’ performance by the supplier before handing over to the lab
• Operation qualification must be also be done when there is a major
component replacement
• Performance qualification (PQ)
• an ongoing routine process of ‘pass/fail’ performance by the lab itself
• Go back to the supplier before payment if you find the PQ unsatisfactory, until
the suppler rectify and show that the instrument is performing as expected.

10. QA strategies or plans for ….
• Test Sample Controls
• Sampling and sub-sampling protocols - representative
• Sample labeling – unambiguous sample source identification
• Sample transportation – preserve sample integrity
• Sample preparation – following test procedures faithfully
• Lab reagents and glassware meeting test requirements
• Choice of test methods
• Rational and empirical methods – the difference
• Empirical method : measurand defined in terms of method (different
methods give different results)
• Use of standard methods and official methods recommended
• In-house developed methods need to go through full method
validation process

11. Applying QC practices
• Having laid-down QA policies and guidelines, you need to apply QC
practices to ensure measurement accuracy and acceptable precision.
• How to achieve acceptable test results?
• Samples are properly drawn, labeled and preserved before analysis
• Using validated methods to reduce analytical errors
• Training staff for technical competence
• Use of reference materials and laboratory control samples as an
internal quality assurance
• Participation of inter-laboratory or PT programs as an external quality
assurance measure
• Expressing the confidence limits of a reported figure by estimating its
measurement uncertainty, which covers the analyte’s true value with
certain degrees of confidence

12. How much routine QC measures
are to be carried out?
That would depend on how rigorous the quality of works
that your laboratory or your customers would ask for.

13. ISO/IEC 17025:2017 Clause 7.7 requirements
• 7.7.1 The laboratory shall have a procedure for monitoring the
validity of results. The resulting data shall be recorded in such a
way that trends are detectable and, where practicable, statistical
techniques shall be applied to review the results.
• Clause 7.7.1 further lists out non-exhaustive 11 ways to monitor
the measurement reliability.
• In addition, extra assurance comes in clause 7.7.2 which suggests
to participate in proficiency testing or inter-laboratory comparison
studies to monitor the laboratory’s performance by comparison
with results of other laboratories, where appropriate and available.

14. QC of analytical accuracy & precision - Blanks
• For the sake of accuracy, determine blank value for any
background correction to the analyte because of interference
• Reagent Blank
• Matrix Blank
• Subject the Blank to go through the whole analytical process
before measurement
• Why do we need blank values?
• Reagents used may not be completely free of the analyte to be
measured;
• Poor method selectivity; (Contd….)
For some environmental test parameters, taking a
Trip Blank is a must if field sampling is involved.

15. QC of analytical accuracy & precision - Blanks
• Reagents or sample matrix may possess a color of
their own to interfere in absorbance;
• Systematic contamination of reaction vessels and
measuring instruments
• Differences between cuvettes due to aging,
environmental effects, etc.
• The environment during sampling may cause errors
in sampling before laboratory analysis; e.g.
measuring volatile organic compounds in
groundwater

16. QC of analytical accuracy & precision - Replicates
• Multiple determinations show the spread of
results around the mean value – how precise are
the measurements:
• Repeatability : Results obtained from repeated
analyses on a sample by a test method carried
out by a single analyst on the same equipment
over a short period of time
• Intermediate Repeatability or Intermediate
Precision : Results obtained from repeated
analyses on a sample by a test method carried
out by different analysts over different days using
different equipment
0.0
5.0
10.0
15.0
20.0
25.0
15.80 15.90 16.00 16.10 16.20
x
y
n = 15
n = 3

17. Pooling of Standard Deviations for intermediate precision
• Estimates of the standard deviation obtained at several times may
be combined (pooled) to obtain a better estimate based upon more
degrees of freedom:
• where sp is based on
degrees
of freedom
       
       1...111
1...111
321
22
33
2
22
2
11



k
kk
p
nnnn
snsnsnsn
s
       1...111 321  knnnn
17
Different number (n)
of repeats gives rise
to different standard
deviations

18. QC of analytical accuracy & precision - Replicates
• For normal QC practice, one may choose to carry out a
“Batch Duplicate” on each occasion of analysis, such as one
sample taken for duplicate analysis in every batch of 10 or
20 samples.
• Collection of duplicated data over a period of time can be
used to determine the method precision in terms of
standard deviation.
• The laboratory sets its own frequency in batch duplicate
analysis

19. QC of analytical accuracy & precision – Lab Controls
• Use of reference materials or quality control materials for accuracy
monitoring, based on comparison with their certified or assigned
values.
• An ideal laboratory control sample should :
• be representative with respect to matrix and concentration;
• have its content chosen so that analytical important regions such as limit
regions are secured;
• be available in sufficient quantity for long time analysis
• have proven long term stability;
• no influence of shelf-life on the storage containers
• the regular removal of partial samples for control analyses must not lead
to changes in the control sample (e.g. evaporation of highly volatile
organic compounds through frequent opening of the container).

20. QC of analytical accuracy & precision – Lab Controls
• Laboratory control samples (LCS) can also be standard solutions ‘spiked’
with a known amount of the analyte standard of known purity.
• They can be a member of the working standard solutions for instrument
calibration but must be prepared from a different source or batch number
of the pure analyte standard.
• Such LCS must be prepared in sufficient quantity so that many analyses can
be performed regularly over a period of time, within the stability of the
analyte upon storage.
• LCS can be run like a normal sample for every batch of 20 sample analyses.
The recovery of the results is then compared with the pre-set acceptance
criteria. How to set your acceptance criteria?

21. QC of analytical accuracy & precision – Lab Controls
• Treat the LCS sample as a routine sample, going through the whole
analytical process
• Establish its repeatability in terms of standard deviation through n
replicates
• Calculate the % Recovery as:
• Check if the mean value of measurement is significantly different
from the known concentration (True Value, ) by Student’s t-test:
100
spike
tested
C
C
𝜇 = 𝑥 ± 𝑡(𝛼=0.05,𝑑𝑓)
𝑠
𝑛

22. QC of analytical accuracy & precision – Lab Controls
• Having collected some 25-30 data over a period of time and these data
had been shown to be not significantly different from the ‘spiked’
value, a Shewhart control chart can be constructed with lower/upper
warning and action limits set.
• Prior to constructing the QC chart, use some statistical normality tests
(e.g. Anderson-Darling, Shapiro-Wilk, etc.) to confirm the data
collected are random and independent.
• Ideally ensure daily QC data fall within the lines of lower and upper
warning limits
• If the average value =  , standard deviation =  over all sample data,
• Upper Control Limit UCL:  + 3𝜎
• Upper Warning Limit UWL:  + 2𝜎
• Central line: 
• Lower Warning Limit LWL:  − 2𝜎
• Lower Control Limit LCL:  − 3𝜎
The use of Shewhart control chart is
to control analytical precision and
accuracy.

23. Use of Shewhart Control Chart
• A visual presentation example of Shewhart quality control chart
We can use a MS Excel
spreadsheet to prepare
a control chart
UWL = 10.593
LWL = 9.523
 = 0.267

24. Control chart interpretations
• If data are found outside the UCL or LCL, it indicates the system is
out of control. Action must be taken to examine the system and
rectify it as soon as possible.
• The following scenario will highlight possible change of the
measurement system:
• Continuous 9 data points at one side of the central line
• Continuous 7 data points showing an increasing or decreasing trend
• Continuous 5 data points lying on one side and beyond 1
• 2 of the 3 continuous data points lying on one side and beyond 2
(i.e. UWL or LWL).

25. Determining  to construct a Shewhart chart
• For the case of one QC measurement per run,  and  are
generally obtained by calculating the mean and standard deviation of
a number of results obtained from the analysis of this QC material in
different runs, normally the number of runs, N > 20.
• For the case where more than one QC result is averaged per run,
the standard deviation  must be calculated from separate estimates
of within- (sw) and between-run (sb) variances, obtained by ANOVA.
• The calculation equation is : 𝜎 = 𝑠 𝑏
2
+
𝑠 𝑤
2
𝑛
where n = number of QC
replicates.
• For a single QC result taken per run, n = 1,  is estimated directly
from the standard deviation of single results obtained in different
runs, i.e. sb only.

26. Example for construction of control chart
Week 1 Week 2 Week 3
23.8 25.3 24.1
24.6 24.7 24.6
24.8 25.6 24.9
24.2 25.2 25.4
25.2 24.6 24.6
24.9 25.3 25.3
Anova: Single
Factor
SUMMARY
Groups Count Sum Average Variance
Week 1 6 147.5 24.583 0.258
Week 2 6 150.7 25.117 0.150
Week 3 6 148.9 24.817 0.238
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Weeks 0.858 2 0.429 1.995 0.171 3.682
Within Weeks 3.225 15 0.215
Total 4.08288 17
Variance (between) = 0.429
Variance (within) = 0.215
𝜎 = 0.429 + 0.215/6) = 0.682
𝜎 = 𝑠 𝑏
2
+
𝑠 𝑤
2
𝑛
Mean Square = Variance

27. Another worked example
Week # 1 2 3 4 5 6
x1 1.96 2.02 2.00 2.02 2.01 1.99
x2 2.00 2.00 1.99 2.00 2.04 1.95
x3 1.97 1.97 1.98 2.00 2.00 2.02
x4 1.96 2.02 1.96 1.98 2.02 2.00
1.97 2.00 1.98 2.00 2.02 1.99
Std Dev 0.019 0.024 0.017 0.016 0.017 0.029
𝑥
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Week 1 4 7.890 1.973 0.000358
Week 2 4 8.010 2.003 0.000558
Week 3 4 7.930 1.983 0.000292
Week 4 4 8.000 2.000 0.000267
Week 5 4 8.070 2.018 0.000292
Week 6 4 7.960 1.990 0.000867
ANOVA
Source of
Variation SS df MS F P- value F crit
Between
Weeks 0.005083 5 0.001017 2.316 0.086 2.773
Within
Weeks 0.0079 18 0.000439
Total 0.012983 23
𝜎 = 𝑠 𝑏
2
+
𝑠 𝑤
2
𝑛
= 0.001017 +
0.000439
4
=0.0336
Using the default Excel setting for ANOVA, the
display of data must be in this manner.

28. Usefulness of QC samples in control charts
Regular analysis of a single
control sample will identify if a
system is in or out of control
conditions
Target value
Daily QC data
Step change
Bias
Drift
In control

29. CuSum (Cumulative Sum) Chart
• CuSum charts have been shown to be
more efficient in detecting small shifts
in the mean value before obvious
deviations occur in the Shewhart
charts.
Example data for CuSum calculation
Target value = 80; standard deviation = 2.5
No. #
Sample
mean
Sample mean -
target value CuSum
1 82 2 2
2 79 -1 1
3 80 0 1
4 78 -2 -1
5 82 2 1
6 79 -1 0
7 80 0 0
8 79 -1 -1
9 78 -2 -3
10 80 0 -3
11 76 -4 -7
12 77 -3 -10
13 76 -4 -14
14 76 -4 -18
15 75 -5 -2374
75
76
77
78
79
80
81
82
83
0 2 4 6 8 10 12 14 16
Shwhart control chart
Center
Line 80
UWL 2
75
2 + (-1) = 1
82 – 80 = 2

30. Interpretation of CuSum chart
-25
-20
-15
-10
-5
0
5
0 2 4 6 8 10 12 14 16
CUSUM CHART
The CuSum chart shows
that the mean changes
seems to fall after the
7th observation, so the
CuSum becomes more
and more negative
before the Shewhart
chart reveals any
uncontrollable trend.

31. QC of analytical accuracy & precision – Lab Spikings
• More advanced QC checks:
• Matrix spikes – adding known amount of analyte onto similar
sample matrix with zero analyte
• Surrogate spikes – adding known amount of compound which
has similar structure of the analyte (e.g. deuterated
compounds)
• Surrogate spikes as recovery controls are useful in
chromatographic analysis, as the surrogate will behave similarly
during the course of sample preparation and chromatographic
separation

32. QC of analytical accuracy & precision – Intra-laboratory
comparison
• Intra-laboratory comparison study is conducted to compare the
accuracy and precision of laboratory analysts. This is used as a
form of internal audit after training before signing off as a
competent analyst
• Two or more analysts are given a similar sample to repeat a
number of times and the raw data collated are to be analyzed by
one-factor ANOVA (Analysis of Variance) for checking accuracy
and precision based on the F-test result found.

33. QC of analytical accuracy & precision – ‘Blind’ Samples
• The laboratory analysts are provided with
test samples which have assigned values
(true values) which are only known to
their supervisor.
• This is to test the analysts’ technical
competence in obtaining accurate results.
• The analysts are to carry out replicated
analyses.
• Data analysis is done by the Student’s t-
test.
Student t-Test
Compare the calculated t value against the
critical t value of the t-distribution table at
(n-1) degrees of freedom.
If t ≥ t(critical) : the mean value obtained
is significantly different from the spiked
value (μ), i.e. the mean value is ‘biased’.
If t < t(critical) : the mean value obtained
is not significantly different from the spiked
value (μ), i.e. the mean value is not biased
and falls within the variation of the
analytical method.
s
nx
t
|| 



34. QC of analytical accuracy & precision – Data correlation
• If a quicker or more economical test method has been
developed, it has to show its performance is equivalent to a
known standard method, if not better, in terms of precision
• A series of analysis is carried out over the normal range of
concentration using the said in-house method. The results are
compared with those data obtained by the standard method
with the same sample.

35. External QC : Proficiency Testing (PT) Programs
• One of the many ways for Accuracy
control is by to participate in
interlaboratory test programs
• Identical samples are sent to
participating laboratories to analyze
an analyte using same (or different)
methods at or about the same
period.
• The collated results are then
statistically analyzed.
PT Program – Objectives and Roles
1) to determine the performance of individual
lab & monitoring lab’s performance
2) to identify problems in the lab for it to
initiate remedial action
3) to establish effectiveness & comparability of
new test or measurement methods
4) to monitor established methods and its
performance
5) to provide additional confidence to clients
6) to identify inter-laboratory differences
7) to assign values to reference materials (RM)
and assess their suitability for use in specific
test and measurement procedure

36. External QC : Proficiency Testing (PT) Programs
• Performance is judged by “z” scores
• z-score measures the laboratory bias against
the assigned values of samples analyzed;
• |z| < 2 : satisfactory;
• 2 < |z| < 3 : warning (questionable;
opprotunity);
• |z| > 3 : unacceptable (concern)
• A laboratory’s performance is normally
judged on its standing in PT programs

X

37. Conclusion
• There is indeed no short cut for producing accurate and
precise analytical data.
• One must make it a standard practice to carry out regular
quality control checks to ensure reliability of results
reported to the clients.
• Understanding and practicing the various QA and QC
principles and procedures will help the chemist to report
test results with much confidence.