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CHEMOMETRICS IN CHROMATOGRAPHY AND SPECTROSCOPY Richard G. Brereton

CHEMOMETRICS IN CHROMATOGRAPHY AND SPECTROSCOPY Richard G. Brereton School of Chemistry, University of Bristol R.G.Brereton@bris.ac.uk. 1986. Founding of Chemolab RB Assoc editor. Founding of UKCDG : RB Newsletter editor. 1986. 1987. First chemometrics workshop. 1988.

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CHEMOMETRICS IN CHROMATOGRAPHY AND SPECTROSCOPY Richard G. Brereton

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  1. CHEMOMETRICS IN CHROMATOGRAPHY AND SPECTROSCOPY Richard G. Brereton School of Chemistry, University of Bristol R.G.Brereton@bris.ac.uk

  2. 1986 Founding of Chemolab RB Assoc editor Founding of UKCDG : RB Newsletter editor 1986 1987 First chemometrics workshop 1988 First European Chemometrics workshop First independent postdocs / research students 1989 1990 Publication Chemometrics text with E Horwood 1992 Custom built area in Dept for chemometrics 1992 Publication of Edited book on Pattern Recognition Significant expansion in research ca. 50 papers published in this period. 1993- 8 ChemWeb chemometrics column started 1999 1999 RSC award to student 2002 New custom built area in Dept for chemometrics Publication book with Wiley 2002

  3. WHO WORKS IN THE GROUP 2001-2 Postdoc Hailin Shen  Ph.D. students Christian Airiau Petra Vacas Tom Thurston Hassan Sukri Mohammed Wasim Antonio Carvalho  Visitors Jirut Watoom (Bangkok, Thailand) Alexis Flaquiere (Dijon, France) Maryse Lugez (Dijon, France) Project students Ian Stott Dan Burbidge Adam Simson Martin May-Hall

  4. CHROMATOGRAPHY • SPECTROSCOPY • SOFTWARE

  5. CHROMATOGRAPHY • DAD-HPLC • LC-MS • LC-NMR • Resolution • Quantification • Pattern Recognition

  6. Coupled chromatography – a revolution in the analytical laboratory. • Cheaper. • Benchtop. • Coupling many techniques e.g. DAD-LC-MS-NMR.

  7. Resolution • How many peaks characterise a cluster? • Impurities • What are their characteristics? • Spectra and identities

  8. DIODE ARRAY HPLC How many peaks in a cluster and where? Derivatives Chlorophyll degradation products

  9. 1,2 Benzanthracene Chrysene 6.5 7 time/min What are the spectra of each component? Isomer in synthesis 1% PAH impurity

  10. 6 7 4 2 5 3 0 30 60 90 120 150 180 210 240 270 300 330 360 932 931 933 934 962 948 963 949 947 961 945 946 964 941 892 977 978 939 914 979 940 915 918 938 894 917 896 893 895 LC-MS (ESI and MS/MS) What are characteristic ions of complex mixtures? Chlorophyll degradation products 2 Scores Loadings 3 7 4 6 5 916

  11. Obtaining spectra by deconvolution LC-ESI-MS of isomers of hydroxypyridine

  12. DETECTING DOPE IN HORSE URINE Quinine metabolites : detect breakdown / oxidation products GCMS

  13. PEAK CLUSTER

  14. Quinine metabolites 1146 Quinine metabolites 224 1147 1140 1139 1145 PC2 PC2 1148 1141 225 252 255 361 253 1149 540 0 254 0 1144 434 0 0 433 435 523 1133 1134 Background 1138 1137 1136 Background 1135 PC1 PC1

  15. 224 1 1.2 1 0.8 0.8 0.6 1 Realtive intensity 0.6 Relative intensity 0.4 0.4 2 252 0.2 170 3 0.2 0 0 100 200 300 400 500 600 1130 1135 1140 1145 1150 1155 1160 m/z Scan number 253 1 0.8 433 0.6 Realtive intensity 0.4 361 523 0.2 0 100 200 300 400 500 600 m/z

  16. On-flow LC-NMR • Now quite easy • Potential routine use as an analytical technique of mixtures • Emphasis on speed and conservation of deuterated solvents • Mixture analysis • On-flow e.g. embedded peaks of isomers • Balance between chromatography and spectroscopy

  17. LC-NMR of mixtures Overlapping chromatographic and spectroscopic peaks Water

  18. 13 11 log10(Eigenvalue) 9 7 How many compounds in the mixture? Seven components identified by Window factor analysis

  19. 3 Dimensional scores and loadings

  20. B D C G A E F 0 10 20 30 40 50 60 70 80 Time (scans) Obtaining pure spectra

  21. Chromatograms Concentrations 100 30 20 20 QUANTIFICATION. Can we measure accurately chromatographic impurities? Multiway calibration

  22. Predicting 0.1 – 0.5% impurities: pharmaceutical impurity monitoring and regulations

  23. Chromatographic pattern recognition • Chromatographic columns and tests • Industrial importance as new columns arrive • Different columns (8) • Different test compounds (9) • Different pHs (2) • Different mobile phases (3) • Different peakshape parameters (4) • A lot of experimental work.

  24. Aims •    Determine the relationship between the columns. •    Determine the relationship between the test parameters – which measure similar properties so can the number of test compounds or chromatographic peakshape parameters be reduced. •     Compare directly the results of using different conditions e.g. different pHs or different mobile phases. •    Compare directly the results using the full set of parameters and a subset – hence what information is lost by reducing the number of tests.

  25. Scores and loadings plots

  26. Comparing conditions Procrustes analysis Methanol and acetonitrile ; methanol and THF

  27. PATTERN RECOGNITION – BADGER URINE • GCMS OF BADGER URINE • 84 Badgers • Sex • Time of year • Age • Diet / social group

  28. SCORES OF OVERALL GROUP INCONCLUSIVE

  29. SUB-GROUPS E.G. SUMMER ADULTS – SOME DISCRIMINATION

  30. LOADINGS – CHEMICAL FINGERPRINT Aldehydes, aromatics, ketones

  31. CLASSIFICATION – PCA FOLLOWED BY MAHALANOBIS DISTANCE (SUMMER)

  32. Can we group badgers by their chemical fingerprint? • Which chemicals are most diagnostic? • What combination of factors? • Is there are relationship between occurrence of chemicals?

  33. PYROLYSIS GCMS • Pharmaceutical tablets • Method of production • Wet granulation • Direct compression • Can we distinguish origin? • Patent protection law.

  34. Variable selection – by aligning GCMS and finding common peaks. By performing PCA and looking at scores and loadings. Reject some samples and variables.

  35. Some discrimination, replication

  36. Classification by Mahalanobis distance Cross-validation important

  37. SPECTROSCOPY • Spectroscopy of mixtures e.g. uv/vis • Reaction monitoring by uv/vis, Raman and potentially NIR • Off-line • On-line

  38. Spectroscopy of mixtures How much is known about the system? Are all pure standards available? How to validate the model? Cross-validation, test sets and experimental designs.

  39. UV/vis spectroscopy of mixtures of 10 PAHs. Can the amount of a PAH be measured using spectroscopy? How reliably?

  40. COAL TAR PITCH VOLATILES FROM INDUSTRY • Factories : place filter in factory – 8 hours • Conventional : extract and look at weight • Problem : active compounds (mainly PAHs) are on the surface of the particle. • Analyse the composition of the particles.

  41. Conventional approach GCMS : problem – expensive for routine monitoring Alternative : uv/vis (EAS) of mixtures. Calibrate to GCMS Validation to models. Can a suitably wide range of industries be spanned?

  42. REACTION MONITORING • What do we want to know? • End-points. • When the product is at a maximum. • Spectra of unknown degradation products. • Exact kinetics. • Deviation from ideal behaviour. • Concentration profiles.

  43. What do we know? • Spectra of all or some of the compounds. • Initial concentrations of all or some of the compounds. • Expected kinetics.

  44. Off-line methods. FIA and uv/vis. Compare to HPLC.

  45. PCA for reaction profiling

  46. PLS and MLR for obtaining profiles 1.2 0.8 Concentration of component / M 0.4 0 0 20 40 60 80 100 120 140 time// mins

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