CH250 Interactive Notes

1. CH250 Interactive Notes  

3. CH250 Intermediate Analysis   • Review of CH115/CH108 material (ELO) • Analytical Design (ELO) • method selection • validation • Sampling (ELO) • Sample preparation (ELO) • Detection and identification • Spectroscopy (AW) • Electrochemistry (AW) • Spectrometry (Mass Spectrometry and NMR, ELO) • Materials and Nanotechnology (RW)

4. 1. Review of CH115/CH108 • The analytical process – designing analyses • Errors and uncertainty • Sample collection, preparation and storage • Calibration methods, spiking and recovery, LOD, LOQ • Classical Analyses: Gravimetry and Titrimetry • Instrumental Analyses: • Chromatography (HPLC, GC, TLC) • Spectroscopy (UV, AAS) • Electrochemistry (ISE)

5. 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)

6. Errors and Uncertainty • 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

7. 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

8. Calibration • 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.

9. Classical Analyses: Gravimetry and Titrimetry • Correct methods of weighing and measuring • Unit conversion calculations, yields • Sample preparation and dilution • Types of analysis

10. Instrumental analyses • Chromatography • Physicochemical principles of separation • Formats: plate vs gel • Types: HPLC, GC,TLC • Parameters, quantitation (peak shape, size, resolution) • Spectroscopy • UV – Beer-Lambert, Practical applications • AAS – simple inorganic analysis • Electrochemistry –ISEs, Nernst equation

11. CH250 Intermediate Analysis   • Review of CH115/CH108 material (ELO) • Analytical Design (ELO) • method selection • validation • Sampling (ELO) • Sample preparation (ELO) • Detection and identification • Spectroscopy (AW) • Electrochemistry (AW) • Spectrometry (Mass Spectrometry and NMR, ELO) • Materials and Nanotechnology (RW)

12. 2. Analytical Design Method selection Validation Background Reading: Quality Assurance in Analytical Chemistry, Chapter 4 VAM leaflet: Introduction to method validation (on Studentcentral)

13. Define analytical requirements Select/Develop candidate method Plan validation experiments Conduct validation experiments NO Criteria met? YES Validation document Assess fitness for purpose Method selection vs Validation Based on: Quality Assurance in Analytical Chemistry, Chapter 4, Fig. 4.2

14. Principles of method selection (1) Components Practicalities Accuracy

15. Principles of Method Selection (2) • What are the analytes? • What properties do they have? • How much is present? • What else is present (matrix)? • Will it interfere – false positive or negative? • Over repeated runs? • How accurate do the results have to be? • 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? Components Accuracy Resources

16. 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

17. 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: • Precision • Selectivity/specificity • Bias • Ruggedness • Linearity (working range) • Limit of Detection / Quantitation

18. 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

19. 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…

20. FIT-FOR-PURPOSE

21. Thought Exercise –In groups • Write down five chemical properties • Name a separation technique that exploits differences in each of these • Name a detection method for each • Add an example of matrix and analyte for each of the five • Give an example of an analysis where each of the following might occur: • High risk (minimal consequence) false positive result • Low risk, false positive result • High risk, false negative result • Low risk, false negative result

22. 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

23. How to validate? Purpose • 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?) Performance Criteria Test Plan Interpretation

24. Performance criteria

25. 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?

26. 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. 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. Spot the Oak leaf?

27. 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?

28. 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?

29. Linearity Definition: “The ability to produce test results that are proportional to the analyte concentration within a given range.” Affected by: Technique, interferents, recovery. 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.

30. 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?

31. 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

32. 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? • Precision • Selectivity/specificity • Bias • Ruggedness • Linearity (working range) • Limit of Detection / Quantitation 4) How would you determine if your analysis met the criteria specified?

33. 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

34. Typical Documentation • Analytical Procedure • Preparation of samples • Preparation of standards • Critical factors • Detailed description of all steps • Typical outputs; chromatograms, spectra, etc. • Recording and reporting of data • Method • Rounding and significant figures • Data treatments • Calculation of results • Calibration model • Calculation methods • Assumptions and limitations • Method performance • Statistical measures • Control charting • References & Bibliography • Scope and applicability • Samples • Analytes • Ranges • Description and principle of the method • Equipment • Specification • Calibration and qualification • Range of operability • Reference materials and reagents • Specification • Preparation • Storage • Health & Safety • Sampling • Methods • Storage • Limitations

35. 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”.

36. CH250 Intermediate Analysis   • Review of CH115/CH108 material (ELO) • Analytical Design (ELO) • method selection • validation • Sampling (ELO) • Sample preparation (ELO) • Detection and identification • Spectroscopy (AW) • Electrochemistry (AW) • Spectrometry (Mass Spectrometry and NMR, ELO) • Materials and Nanotechnology (RW)

37. 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

38. Sampling Strategies (1) Depend on: • Types of parent material • Homogeneous or heterogeneous • Static (stable and contained) or dynamic (cannot be resampled) • Concentration • Trace (prone to heterogeneity and contamination) • Principal component (% uncertainty much lower) • Packaging • Bulk (eg a silo) • Packaged (eg cornflakes) • Items (eg tablets) • Results required • Quantitative (how much) • Qualitative or compliance (yes/no answer) – “acceptance sampling”

39. Sampling Strategies (2) • Criteria? • 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)

40. Types of Sampling • 100% sampling (all items monitored) • Probability sampling (statistical chance of being representative) • Judgement (guided by the reason for analysis) • Quota (stratified judgement sampling) • Convenience (as and when available) • Non-probability sampling (selective)

41. Sample size and number Must be specified as part of the method. • Sample Size • Enough for method • Enough to be reflective (esp for trace analysis) • Sample number • enough to reduce uncertainty (esp that from sampling) to acceptable levels

42. Sampling Uncertainty Overall uncertainty in analysis is composed of: • 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: • 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

43. How many samples are needed? Number of samples needed can be estimated by rearranging the equation for confidence intervals: • 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).

44. 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%?

45. 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) Calculate the number of samples required to obtain a total uncertainty of 20%. Between 6 and 7, assuming t=2 b) How many samples would be required to reduce this uncertainty to 10%? 25, assuming t=2

46. 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: 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).

47. 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 b) The likely %CV from a given test portion mass

48. CH250 Intermediate Analysis   • Review of CH115/CH108 material (ELO) • Analytical Design (ELO) • method selection • validation • Sampling (ELO) • Sample preparation (ELO) • Detection and identification • Spectroscopy (AW) • Electrochemistry (AW) • Spectrometry (Mass Spectrometry and NMR, ELO) • Materials and Nanotechnology (RW)

49. 4. Sample preparation (ELO) • Dissolution and digestion • Derivatisation • Separation Strategies • Extraction methods • Chromatography

50. 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