Multivariate analysis, cost data

CONTENTS 1. Chemometrics and the Analytical Process. 2. Precision and Accuracy. 3. Evaluation of Precision and Accuracy. Comparison of Two Procedures. 4. Evaluation of Sources of Variation in Data. Analysis of Variance. 5. Calibration. 6. Reliability and Drift. 7. Sensitivity and Limit of Detection. 8. Selectivity and Specificity. 9. Information. 10. Costs. 11. The Time Constant. 12. Signals and Data. 13. Regression Methods. 14. Correlation Methods. 15. Signal Processing. 16. Response Surfaces and Models. 17. Exploration of Response Surfaces. 18. Optimization of Analytical Chemical Methods. 19. Optimization of Chromatographic Methods. 20. The Multivariate Approach. 21. Principal Components and Factor Analysis. 22. Clustering Techniques. 23. Supervised Pattern Recognition. 24. Decisions in the Analytical Laboratory. 

Normal human urine contains both squamous cells from the distal urethra, and urothelial cells from the Uning of the bladder. The former ceU type may be further subdivided into glycogen-containing and glycogen-free squamous cells. Here, we aim to establish that IR spectral methods, followed by multivariate data analysis, can distinguish between these cells types. Our ultimate intention is to establish this methodology as a low-cost, automated test for bladder cancer. 

Each sample was analysed by means of NIR spectroscopy and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy. In particular, NIR and MIR spectra were collected in the spectral regions from 10000 to 4150 cm (with a resolution of 4 cm ) and from 4000 to 600 cm (with a resolution of 4 cm ), respectively. These techniques are very common in food and raw materials characterisation, since they are fast, non-destructive, low cost, and do not require a preliminary sample preparation [54,55]. Moreover, multivariate data analysis is extensively and fruitfully applied on NIR and MIR signals with the aim to extract and visualise relevant information [56,57]. 

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