CH915: Elemental Analysis
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Module leader: Dr. Claudia Blindauer
Lecturers:
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Lab classes:
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Dr. Claudia Blindauer
Dr. John Fenlon (Statistics)
Dr. Andrew Mead (Warwick HRI)
Dr. Abraha Habtemariam
Book recommendations, e.g.:
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D.C. Harris: Quantitative Chemical Analysis
Vogel’s textbook of quantitative chemical analysis
For the entire course: Skoog, Holler, Nieman: Principles
of Instrumental Analysis
Aims of the module
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Introduce the Analytical Process
Introduce concepts for quantitative analysis
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Including Statistics for Data Analysis
Enable professional data analysis
Introduce important methods for elemental
analysis of liquid and solid samples
Enable selection of the best possible method
for a given analysis problem
Enable to design experiments
Module Overview
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5 sessions on chemical aspects of
quantitative and elemental analysis (C.
Blindauer, see handout)
4 lab classes (A. Habtemariam)
8 sessions on understanding data and
statistical aspects of quantitative analysis
(J. Fenlon, A. Mead, J. Lynn) – together
with MAOC and Systems Biology students
What is elemental analysis
and where is it applied ?
What is Elemental Analysis ?
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Determine the elemental composition of
material
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Qualitative
Quantitative
CHNX: Combustion analysis for
verification of compound identity
Other elements
Elemental Analysis is applied in:
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Materials Sciences
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Metallurgy, glass, ceramics, cements, superconductors,
microelectronics…
Geosciences
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geochemistry, mineralogy, geochronology…
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Environmental Sciences
Biological Systems and Medicine
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In Industry:
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Quality control: Establish that produced material conforms
in terms of composition and purity
Process control
Food safety incl. packaging
Forensics:
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Determine composition of soil, fibres, plastic, paint etc to
establish origin
Trace analysis of Firearms Projectile Lead (FBI procedure)
Elemental Analysis – Method
overview
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Classical methods:
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Instrumental trace analysis in solution
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Spectroscopic methods: AAS, ICP-AES/OES
Mass spectrometry: ICP-MS
Electrochemical methods ( CH914)
Instrumental methods for solid materials
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Qualitative Inorganic Analysis (Fresenius, Treadwell)
Quantitative: Gravimetry,Titrimetry, Colorimetry…
X-ray methods (also spectroscopic)
Mass spectrometry methods: SIMS and many other
NB: Most instrumental methods are based on
physics, not chemistry of element
Solid
state
methods
Analysis
in liquid
state
Select method
Acquire/define
sample
Acquire/define
sample
Process
sample
Process
sample
Chemical
dissolution
The analytical
process
General
considerations
and steps
No
Soluble?
Yes
Measurable
property?
Yes
Eliminate
interferences
Measure X
Calculate result
Determine error
No
Change
chemical
form
Method selection - considerations
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Destructive/non-destructive ?
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Non-destructive methods of analysis
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Destructive methods of analysis
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X-ray fluorescence, emission, etc.
Combustion analyses
Volumetric, gravimetric, electroanalytical analyses
Atomic absorbance (AA) and inductively coupled plasma (ICP)
spectroscopy
Mass spectrometry
Expected analyte concentrations and
performance characteristics of method must
match
Sample must be compatible with required
processing and measurement
Quantitative Analysis - Principles
1) Define sample amount (mass or volume)
2) Measure quantity proportional to analyte
concentration
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Measured property must vary in a defined way:
calibration with known standards necessary
Analysis must be specific: Interferences must
be known and if possible be eliminated
Accuracy: Proximity of measured value to accepted
(or "true") value: must be determined
Precision: Closeness of measured values to one
another: must be defined and reported
Performance characteristic of
quantitative analytical methods
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Accuracy
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Precision
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For definitions see:
http://www.nmschembio.org.uk/
GenericArticle.aspx?m=98&amid=445
Reproducibility and Repeatability
Detection capability
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Bias
Recovery
Sensitivity
Limit of Detection (LoD)
Limit of Quantitation (LoQ)
Selectivity and Specificity
Linearity
Working Range
Robustness/Ruggedness
All these characteristics are intimately linked to the experimental error
Experimental error
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Systematic error:
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Sources:
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Can be discovered and corrected
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Instrumental
Method
Personal
Standard reference materials
Blanks
Controls, e.g. spiked samples
Handle error by proper standardisation/calibration or
application of a correction factor
Systematic errors impact on Bias
Experimental error
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Random error:
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Always present, can't be corrected
Consequence of uncertainty of measurements
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electrical noise from instrument, causing fluctuations in
reading
uncertainties in measurements of mass and volume
Ultimate limitation in quantitation
Must be aware of error and deal with it
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Repeated measurements
Random errors impact on Precision,
Reproducibility, Repeatability, LOD and LOQ
Both systematic and random errors affect accuracy
Reporting quantitative data
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Errors can be defined via:
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Standard deviation (SD)
Variance
Relative std. deviation
Coefficient of variation
N
SD 
2
(
x

x
)
 i
i 1
N 1
All quantitative data must
V  SD 2
be reported with error
– SD and RSD most common
SD
RSD 
Propagation of errors
x
must be considered
CV  RSD 100%
Sampling errors: dealing with
heterogeneity
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“Real” samples are usually heterogeneous
Examples: Foodstuffs, soils, water samples…
Lot
Sampling
Representative
bulk sample
Sample
preparation
Homogeneous
lab sample
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Random sampling:
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Sample fractions selected randomly
Composite sampling:
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Samples taken at regular intervals and mixed
Aliquots
Sampling error
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Overall error is composed of the errors introduced by
the analytical procedure (including sample preparation
and actual measurement(s)) and the sampling error:
SDo2 = SDa2 + SDs2
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SDo = overall standard deviation, SDa = sd of analytical
procedure, SDs = SD of sampling procedure
If SDa << SDs or SDs << SDa, there is little point in trying
to reduce the smaller one
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Eg. If sa = 5% and ss = 10%, then so = 11%. Using a more
expensive and time consuming method whose sa = 1% will only
reduce so to 10%
Summary
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Elemental Analysis is important in a range
of sectors
The analytical process consists of many
steps
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Meaningful analysis must consider all steps
together
Meaningful experimental design requires
understanding data
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Awareness of performance characteristics of
methods
Awareness of statistics