Transcript CH250 Lecture notes

CH250 Interactive Notes
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CH250 Intermediate Analysis
1. Review of CH115/CH108 material (ELO)
2. Analytical Design (ELO)
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method selection
validation
3. Sampling (ELO)
4. Sample preparation (ELO)
5. Detection and identification
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Spectroscopy (AW)
Electrochemistry (AW)
Spectrometry (Mass Spectrometry and NMR, ELO)
Materials and Nanotechnology (RW)
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1. Review of CH115/CH108
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The analytical process – designing analyses
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Errors and uncertainty
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Sample collection, preparation and storage
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Calibration methods, spiking and recovery, LOD, LOQ
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Classical Analyses: Gravimetry and Titrimetry
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Instrumental Analyses:
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Chromatography (HPLC, GC, TLC)
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Spectroscopy (UV, AAS)
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Electrochemistry (ISE)
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The analytical process
Analysis must be: “Fit for purpose”
i.e. Representative, selective and reliable
Process
 Formulating the question (Aims)
 Selecting an analytical technique (Introduction)
 Conducting the analysis (Methods)
 Reporting the data (Results)
 Answering the question (Discussion/Conclusion)
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Errors and Uncertainty
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Accuracy vs precision, significant figures
Types, Sources and methods of avoiding uncertainty,
Measures of location and spread
Gaussian distribution & student’s t, populations and
samples, degrees of freedom
Comparison of Means: Student’s t-test (3 variants)
Comparison of precision/s : f-test
Investigation of Outliers: Dixon’s Q and Grubbs
Propagation of errors
Reproducibility, repeatability, interlaboratory trials
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Sample collection, preparation and storage
Sampling
 Ensuring selective, reliable and representative sampling
 Factors influencing choice of method
 Homogeneous vs heterogeneous samples
Storage
 Understand and discuss methods for avoiding
 Contamination
 Decomposition
Preparation
 Choose and describe simple methods of preparation for
given examples
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Calibration
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External Standard, Standard Addition, Internal
Standards
Spiking and recovery
Draw appropriate graphs and calculate the
concentration of an unknown from them
Calculation errors in linear regression, and thus
in the unknown
LOD, LOQ
Convert measured concentration into amount in
original sample, with uncertainty.
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Classical Analyses:
Gravimetry and Titrimetry
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Correct methods of weighing and
measuring
Unit conversion calculations, yields
Sample preparation and dilution
Types of analysis
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Instrumental analyses
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Chromatography
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Spectroscopy
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Physicochemical principles of separation
Formats: plate vs gel
Types: HPLC, GC,TLC
Parameters, quantitation (peak shape, size, resolution)
UV – Beer-Lambert, Practical applications
AAS – simple inorganic analysis
Electrochemistry –ISEs, Nernst equation
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CH250 Intermediate Analysis
1. Review of CH115/CH108 material (ELO)
2. Analytical Design (ELO)
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method selection
validation
3. Sampling (ELO)
4. Sample preparation (ELO)
5. Detection and identification
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Spectroscopy (AW)
Electrochemistry (AW)
Spectrometry (Mass Spectrometry and NMR, ELO)
Materials and Nanotechnology (RW)
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2. Analytical Design
Method selection
Validation
Background Reading: Quality Assurance in Analytical Chemistry, Chapter 4
VAM leaflet: Introduction to method validation (on Studentcentral)
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Method selection vs Validation
Define analytical
requirements
Select/Develop
candidate method
NO
Validation
document
YES
Criteri
a met?
Plan validation
experiments
Conduct validation
experiments
Assess fitness for
purpose
Based on: Quality Assurance in Analytical Chemistry, Chapter 4, Fig. 4.2
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Principles of method selection (1)
Components
Practicalities
Accuracy
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Principles of Method Selection (2)
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What are the analytes?
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What else is present (matrix)?
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Components
Will it interfere – false positive or negative?
Over repeated runs?
How accurate do the results have to be?
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What properties do they have?
How much is present?
How big/small an effect am I looking for?
What are the minimum amounts that must be detected?
Which must be eliminated – false positives or negatives?
What are the consequences of incorrect results?
How many samples must be analysed?
What resources are available?
Accuracy
Resources
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Components: Analyte and Matrix
The physico-chemical properties of components affect:
 Separation methods: depends on all components
(exploiting differences between analyte and matrix)
 Detection method: must use analyte property
 Level of selectivity needed: depends on scale and type
of differences between analyte and matrix
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Accuracy
Successful analysis means setting, testing and meeting
performance criteria
 Qualititative or quantitative
 Depend on the acceptable level of uncertainty (risk)
 Criteria allow objective selection of sampling,
separation and detection methods
 Common criteria include measures of:
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Precision
Selectivity/specificity
Bias
Ruggedness
Linearity (working range)
Limit of Detection / Quantitation
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Resources
Cost, availability of instruments, materials and
staff will affect:
 Number of samples
 Available methods
 Accuracy of measurements
 Rate of turnaround
Must be balanced against the consequences of
an incorrect result
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Finding a suitable method
Sources of potential methods include:
 Primary scientific literature
 Patents
 British & international standards (via UoB online library)
 Manufacturer’s technical information
You may find several potential methods, but:
 It is rare to find one which is perfect
 Objective criteria are needed to select the best method
 Adapted methods must then be validated to make sure
they are…
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FIT-FOR-PURPOSE
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Thought Exercise –In groups
1) Write down five chemical properties
a) Name a separation technique that exploits differences
in each of these
b)Name a detection method for each
c) Add an example of matrix and analyte for each of the
five
2) Give an example of an analysis where each of the
following might occur:
a) High risk (minimal consequence) false positive result
b)Low risk, false positive result
c) High risk, false negative result
d)Low risk, false negative result
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Validation
“Confirmation by the examination and the
provision of objective evidence, that the
particular requirements for a specific
intended use are fulfilled.”
Reference: ISO/IEC 17025:2005. General requirements for the competence of testing and
calibration laboratories
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How to validate?
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Purpose
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Performance
Criteria
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Test Plan
Interpretation
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What exactly are we analysing for?
Assay, iD, limit (impurities)
What are limits to the conditions the
analysis covers?
What objective parameters will show
whether the goals have been met?
Will these detect failures?
How best can the parameters be
measured?
How should the data be compared to
the specified parameters ?(statistics?,
blind trials?)
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Performance criteria
Type of Analysis
Parameter
Qualitative
Major
(Identification) Component
Precision
Specificity/Selectivity
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Bias
Ruggedness
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Linearity/working
range
Limit of Detection
Limit of Quantitation
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Trace
Analysis
Physical
Properties
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Precision
Definition: “The closeness of agreement
between independent test results obtained
under specified conditions”. Includes
reproducibility and repeatability.
Affected by: Number of measurements,
uncontrolled random errors.
Measurement: Measures of spread (s, 95% CI
etc.) Should include effect of factors that
will not be consistent during normal use of
method.
Evaluation: Acceptable levels of precision
depend on the levels of variability tolerated.
Effect of concentration may be large. Bias
also affects precision requirements.
Student’s t and f tests.
Are you hitting the bullseye?
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Specificity
Definition: “The extent to which a method can be
used to determine particular analytes in mixtures or
matrices without interference from the presence of
components with similar behaviour.”
Affected by: Types of components routinely present.
Lack of specificity can give a false negative or
positive.
Measurement: Increasing concentrations of potential
interferents added to samples. Can quantify at
what concentrations interference becomes
significant.
Spot the Oak leaf?
Evaluation: Most important in trace analysis as
contaminants can be significant. False positives
can be neglected in screening assays, if followed by
second confirmatory technique.
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Bias
Definition: “The difference between the
calculated value and the accepted reference
standard”. A Measure of trueness
Affected by: Systematic errors.
Measurement: Spiking and recovery (how much of
a known amount of analyte added at to the
starting sample is measured by the analysis).
Measurement with alternative validated method.
Interlaboratory trials – to establish causes of bias.
Evaluation: Simple t-test (compare result to known
value).
Bias and precision combine to give accuracy
Altered gravity – or
systematic building error?
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Ruggedness
Definition: The degree to which a
method is affect by small changes in
the operating conditions. Associated
with both precision and bias.
Affected by: type of technique,
number of variable parameters
Measurement: Deliberately vary
conditions to quantify their effect on
results, and identify critical
parameters.
Evaluation: Focused on identifying
prime causes of variability, and
setting controls for these.
Don’t get stuck in the mud?
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Linearity
Definition: “The ability to produce
test results that are proportional to
the analyte concentration within a
given range.”
Measurement: Calibration, ideally
with CRMs or spiked samples.
Concentrations must be evenly
spaced. LOQ is often lower limit.
Evaluation: May use visual
inspection, r, runs test.
20
18
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Measured Vaule
Affected by: Technique,
interferents, recovery.
Working range
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2
0
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25
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Concentration
LOD
LOQ
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Limit of Detection (LOD)
Definition: “The minimum concentration of
analyte that can be detected with statistical
confidence.”
Affected by: method, uncertainty…the kitchen
sink
Measurement: Concentration (calculated from
line of best fit) at which either (a) signal is
equivalent to blank + 3 x sd of blank or (b)
intercept (y0) + (3 x Sxy)
Evaluation: No analysis should rely on a value
below this. All but qualitative analyses
should use the higher LOQ.
Is this glass empty, or not?
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Limit of Quantitation
Definition: “The lowest concentration of
analyte that can be determined with an
acceptable level of uncertainty.”
Affected by: Method performance…no really!
Measurement: As for LOD but 10 x sd of
blank
Evaluation: This is the point at which
quantitative analysis can be considered
valid. May require multiple assays
(alongside reproducibility studies to set
limits appropriately).
The world’s smallest ruler?
1 Division = 1.25µm
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Thought exercise 2
You are to validate a new method for the analysis of calcium in infant
formula:
1) What are the key features of this analysis?
2) What interferents might be important?
3) How would you decide what limits should be set for each of these
parameters?
a) Precision
b) Selectivity/specificity
c) Bias
d) Ruggedness
e) Linearity (working range)
f) Limit of Detection / Quantitation
4) How would you determine if your analysis met the criteria
specified?
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Validation Documentation and QC
Key Features include:
 Description of method, including scope (what
can it do, and what can it not do?)
 All important technical details (how do I do it?)
 Expected performance criteria (how well does
it do it?)
 Warning limits – normally 2-3 times within lab
precision (how can you tell if it is not working?)
 Responsible signatory, dated versions and
revisions, document control to ensure currency
See also end of Chapter 4: Quality Assurance in Analytical Chemistry
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Typical Documentation
1.
Scope and applicability
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Samples
Analytes
Ranges
Description and principle of the
method
Equipment
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4.
Specification
Calibration and qualification
Range of operability
Reference materials and reagents
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5.
6.
Specification
Preparation
Storage
Health & Safety
Sampling
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Methods
Storage
Limitations
7. Analytical Procedure
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Preparation of samples
Preparation of standards
Critical factors
Detailed description of all steps
Typical outputs; chromatograms,
spectra, etc.
8. Recording and reporting of data
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Method
Rounding and significant figures
Data treatments
9. Calculation of results
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Calibration model
Calculation methods
Assumptions and limitations
10.Method performance
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Statistical measures
Control charting
11.References & Bibliography
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Validation proposal activity 1
Make a Word (or equivalent) template covering each of the items
from the suggested table of contents for a validation document on the
previous slide
 Add your ideas from the thought exercise about calcium in infant
formula
 Decide which areas require further research and which may be
answered during the practical sessions
 Use the scientific literature to research possible answers to
questions
 Make notes on how you might decide which of two methods is more
suitable – can you prioritise performance criteria?
You will add to this document during the course of the module – it is
designed to form the basis of your final assessment – the “validation
proposal”.
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CH250 Intermediate Analysis
1. Review of CH115/CH108 material (ELO)
2. Analytical Design (ELO)


method selection
validation
3. Sampling (ELO)
4. Sample preparation (ELO)
5. Detection and identification
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
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Spectroscopy (AW)
Electrochemistry (AW)
Spectrometry (Mass Spectrometry and NMR, ELO)
Materials and Nanotechnology (RW)
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3. Sampling (ELO)
“A defined procedure whereby a part of a substance,
material or product is taken to provide for testing or
calibration a representative sample of the whole. Sampling
may also be required by the appropriate specification for
which the substance, material or product is to be tested or
calibrated.”
ie The sample(s) must be REFLECTIVE of the TRUE situation
Background Reading: Quality Assurance in Analytical Chemistry, Chapter 3
For an example relevant to your analyses, see BS EN ISO 707:2008 Milk and
milk products – Guidance on sampling
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Sampling Strategies (1)
Depend on:
 Types of parent material
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Concentration
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Trace (prone to heterogeneity and contamination)
Principal component (% uncertainty much lower)
Packaging
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Homogeneous or heterogeneous
Static (stable and contained) or dynamic (cannot be resampled)
Bulk (eg a silo)
Packaged (eg cornflakes)
Items (eg tablets)
Results required
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Quantitative (how much)
Qualitative or compliance (yes/no answer) – “acceptance sampling”
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Sampling Strategies (2)
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Criteria?
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attributes (x% of items must conform)
variables (a specified average and sd)
Selective sampling OK? (eg fruit but not stone)?
Overall strategy determined by purpose:
Client requirements
 Statutory requirements (legal obligations)
 Trade definitions (contractual)
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Types of Sampling
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100% sampling (all items monitored)
Probability sampling (statistical chance of
being representative)
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Judgement (guided by the reason for
analysis)
Quota (stratified judgement sampling)
Convenience (as and when available)
Non-probability sampling (selective)
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Sample size and number
Must be specified as part of the method.
 Sample Size
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Enough for method
Enough to be reflective (esp for trace
analysis)
Sample number
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enough to reduce uncertainty (esp that from
sampling) to acceptable levels
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Sampling Uncertainty
Overall uncertainty in analysis is composed of:
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Measurement uncertainty (quantifiable using standards)
Sample uncertainty which is caused by
 Population uncertainty (real wobble in the system)
 Sampling uncertainty (due to the process of collection)
Sampling uncertainty must be reduced until:
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it does not obscure population uncertainty
the resource required outweighs the risk of an incorrect result
More samples and smaller particles reduce sampling uncertainty
See also CH250:1 Causes of uncertainty
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How many samples are needed?
Number of samples needed can be estimated by rearranging the
equation for confidence intervals:
ConfidenceInterval 
ts
n
 ts 
 n   
E
2
n is the number of samples
 t is Student’s t (routinely approximated to 2 for 95% confidence)
 s is the standard deviation for the method (measured using reference
standards, and inter-laboratory trials)
 E is the size of the effect that must be measurable (in the same
units as s).
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Number of samples - worked eg
Cadmium concentrations in soil were measured at a
brownfield site during preliminary surveying. The mean ±
standard deviation were (16 ± 4) ppm.
a) Calculate the number of samples required to obtain a
total uncertainty of 20%.
b) How many samples would be required to reduce this
uncertainty to 10%?
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Number of samples - Answer
Cadmium concentrations in soil were
measured at a brownfield site during
preliminary surveying. The mean ±
standard deviation were (16 ± 4) ppm.
a)
E
24
 

 3 .2 
 6.25
 n
a) Calculate the number of samples
required to obtain a total uncertainty of
20%.
Between 6 and 7, assuming t=2
b)
b) How many samples would be required
to reduce this uncertainty to 10%?
25, assuming t=2
 n
 20% of
 0.2  16
 3.2 ppm
E
2
 10% of
 0.1  16
 1.6 ppm
24
 

 1 .6 
 25
16
16
2
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How big should the sample be?
There are various statistical calculations based on a) the
proportion of analyte present and b) the particle size to
approximate sample mass needed. The simplest of these
is as follows:
Ks

%CV  m
2
%CV

100 s
x
Where Ks is the sampling constant, m is the mass in g and
%CV is the Coefficient of Variation (which must be
calculated from measurements on several test portions).
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How big should the sample be?
Once Ks is known the equation can be rearranged
to calculate:
a) The test portion mass (m) required to achieve
a specified %CV
m

Ks
(%CV ) 2
b) The likely %CV from a given test portion mass
%CV

Ks
m
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CH250 Intermediate Analysis
1. Review of CH115/CH108 material (ELO)
2. Analytical Design (ELO)
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method selection
validation
3. Sampling (ELO)
4. Sample preparation (ELO)
5. Detection and identification
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Spectroscopy (AW)
Electrochemistry (AW)
Spectrometry (Mass Spectrometry and NMR, ELO)
Materials and Nanotechnology (RW)
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4. Sample preparation (ELO)
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Dissolution and digestion
Derivatisation
Separation Strategies
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Extraction methods
Chromatography
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Sample Preparation
Need to get the bulk sample into the correct:
 form (solution, gas etc)
 concentration (high enough to detect, and
low enough to be in linear range)
 purity (away from interferents)
…without losing any analyte
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Separation vs Recovery
Separation
Recovery
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Dissolution and digestion
Δ
i.e. the process of turning messy, often complex gunge into a nice
clean solution. Δ may involve:
 heat
 microwave radiation
 ultrasonic baths
 pressure
 acids and other solvents (chemical energy)
The important feature is that it converts the analyte into an
accessible form (sometimes purifying it as a result), without losing or
destroying any of it. (See also extraction)
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Derivatisation
Chemically converting the analyte into a form that renders
it easier to separate or detect. It often involves:
 Selective chemical reactions
 Change in physical behaviour (eg boiling point or spectra)
Requires careful optimisation and monitoring to ensure
recovery (ie ~100% conversion)
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Extraction
Dissolution
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Extraction
Chromatography
Purification based on relative affinity for extracting media
May isolate analyte from solid, liquid or gaseous matrices
Can involve extraction to solvent or solid phase media
The following are readable reviews of this stage of sample preparation:
1. Roger M Smith. (2003) Before the injection – modern methods of
sample preparation for separation techniques J. Chromatography A,
1000, 3-27.
2. Kathy Ridgway et. al.(2007) Sample Preparation techniques for the
determination of trace residues and contaminants in food. J.
Chromatography A, 1153, 36-53.
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Analytes in Solid Samples
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Particle size is important – so may need pre-extraction
homogenisation
Solvent is chosen to enhance selectivity
May also require
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Recycling (Soxhlet)
Heating (boiling, or superheating under pressure)
Supercritical fluids
Microwave and sonic radiation (difficult to automate)
Aqueous pastes (eg meat or plant tissue) require special care –
sometimes using powdered matrix
Insoluble analytes may be pyrolysed (to produce soluble fragments)
Volatile analytes may be “thermally desorped” and then treated as
for gases
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Analytes in Solution
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Biphasic (aqueous/organic) liquid-liquid extraction but
has problems
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Purge and Trap (volatile analyte flushed out by gas)
Dialysis
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Costly in solvent
Waste/environmental concerns increae cost
Concentration of solvent gives loss of volatile analytes and
concentration of solvent impurites
Uses a membrane to separate layers / increase selectivity
Solid phase extraction/microextraction
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Solid-Phase Extraction
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Adsorbent coated on
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packed powder
fibre/capillary
beads
stirrer bar
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low waste
automatable
effective concentration
May be “in-line”
Performance is affected by
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flow rate
preconditioning
Brand
matrix
Most chromatographic phases
available
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Advantages include
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C18
ion exchange
size exclusion
chiral, immunoaffinity and
molecular imprinted
Requires desorption post
extraction
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by solvent
thermally (GC injection port)
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Gaseous Analytes
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Problems
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Sample preparation focuses on trapping
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low concentration
difficult to store
cooling
bubbling through solution
adsorbing to fibre
Need to control for misting and desolvation
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