1. CH915: Elemental Analysis
Module leader: Dr. Claudia Blindauer
Lecturers:
Dr. Claudia Blindauer
Dr. John Fenlon (Statistics)
Dr. Andrew Mead (Warwick HRI)
Lab classes:
Dr. Abraha Habtemariam
Book recommendations, e.g.:
D.C. Harris: Quantitative Chemical Analysis
Vogel’s textbook of quantitative chemical analysis
For the entire course: Skoog, Holler, Nieman: Principles of Instrumental Analysis

2. Aims of the module Introduce the Analytical Process
Introduce concepts for quantitative analysis
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

3. Module Overview
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

4. What is elemental analysis and where is it applied ?

5. What is Elemental Analysis ?
Determine the elemental composition of material
Qualitative
Quantitative
CHNX: Combustion analysis for verification of compound identity
Other elements

6. Elemental Analysis is applied in:
Materials Sciences
Metallurgy, glass, ceramics, cements, superconductors, microelectronics…
Geosciences
geochemistry, mineralogy, geochronology…
Environmental Sciences
Biological Systems and Medicine
In Industry:
Quality control: Establish that produced material conforms in terms of composition and purity
Process control
Food safety incl. packaging
Forensics:
Determine composition of soil, fibres, plastic, paint etc to establish origin
Trace analysis of Firearms Projectile Lead (FBI procedure)

7. Elemental Analysis – Method overview
Classical methods:
Qualitative Inorganic Analysis (Fresenius, Treadwell)
Quantitative: Gravimetry,Titrimetry, Colorimetry…
Instrumental trace analysis in solution
Spectroscopic methods: AAS, ICP-AES/OES
Mass spectrometry: ICP-MS
Electrochemical methods ( CH914)
Instrumental methods for solid materials
X-ray methods (also spectroscopic)
Mass spectrometry methods: SIMS and many other
NB: Most instrumental methods are based on physics, not chemistry of element

8. The analytical process General considerations
Solid state methods
Select method
Analysis in liquid state
Acquire/define sample
Acquire/define sample
Process
sample
Process
sample No
Soluble?
Chemical
dissolution
Yes
The analytical process
General considerations
and steps
Measurable
property?
Yes No
Eliminate
interferences
Change
chemical
form
Measure X
Calculate result
Determine error

9. Method selection - considerations
Destructive/non-destructive ?
Non-destructive methods of analysis
X-ray fluorescence, emission, etc.
Destructive methods of analysis
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

10. Quantitative Analysis - Principles
Define sample amount (mass or volume)
Measure quantity proportional to analyte concentration
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

11. Performance characteristic of quantitative analytical methods
Accuracy
Bias
Recovery
Precision
Reproducibility and Repeatability
Detection capability
Sensitivity
Limit of Detection (LoD)
Limit of Quantitation (LoQ)
Selectivity and Specificity
Linearity
Working Range
Robustness/Ruggedness
For definitions see:
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All these characteristics are intimately linked to the experimental error

12. Experimental error Systematic error: Systematic errors impact on Bias
Sources:
Instrumental
Method
Personal
Can be discovered and corrected
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

13. Both systematic and random errors affect accuracy
Experimental error
Random error:
Always present, can't be corrected
Consequence of uncertainty of measurements
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
Repeated measurements
Random errors impact on Precision, Reproducibility, Repeatability, LOD and LOQ
Both systematic and random errors affect accuracy

14. Reporting quantitative data
Errors can be defined via:
Standard deviation (SD)
Variance
Relative std. deviation
Coefficient of variation
All quantitative data must be reported with error – SD and RSD most common
Propagation of errors must be considered

15. Sampling errors: dealing with heterogeneity
“Real” samples are usually heterogeneous
Examples: Foodstuffs, soils, water samples…
Random sampling:
Sample fractions selected randomly
Composite sampling:
Samples taken at regular intervals and mixed
Lot
Sampling
Representative bulk sample
Sample preparation
Homogeneous lab sample
Aliquots

16. Sampling error
Overall error is composed of the errors introduced by the analytical procedure (including sample preparation and actual measurement(s)) and the sampling error:
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
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%
SDo2 = SDa2 + SDs2

17. Summary Elemental Analysis is important in a range of sectors
The analytical process consists of many steps
Meaningful analysis must consider all steps together
Meaningful experimental design requires understanding data
Awareness of performance characteristics of methods
Awareness of statistics