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Lab 6: Saliva Practical Beer-Lambert Law

Lab 6: Saliva Practical Beer-Lambert Law. University of Lincoln presentation. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License. This session…. . Overview of the practical… Statistical analysis….

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Lab 6: Saliva Practical Beer-Lambert Law

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  1. Lab 6: Saliva Practical Beer-Lambert Law University of Lincoln presentation This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  2. This session…. • Overview of the practical… • Statistical analysis…. • Take a look at an example control chart… This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  3. The Practical • Determine the thiocyanate (SCN-) in a sample of your saliva using a colourimetric method of analysis • Calibration curve to determine the [SCN-] of the unknowns • This was ALL completed in the practical class • Some of your absorbance values may have been higher than the absorbance values of your top standards… is this a problem???? This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  4. Types of data QUALITATIVE Non numerical i.e what is present? QUANTITATIVE Numerical: i.e. How much is present? This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  5. Beer-Lambert Law Beers Law states that absorbance is proportional to concentration over a certain concentration range A = cl A = absorbance  = molar extinction coefficient (M-1 cm-1 or mol-1 Lcm-1) c = concentration (M or mol L-1) l = path length (cm) (width of cuvette) This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  6. 1 Beer-Lambert Law • Beer’s law is valid at low concentrations, but breaks down at higher concentrations • For linearity, A < 1 This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  7. If your unknown has a higher concentration than your highest standard, you have to ASSUME that linearity still holds (NOT GOOD for quantitative analysis) Unknowns should ideally fall within the standard range 1 Beer-Lambert Law This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  8. Quantitative Analysis • A < 1 • If A > 1: • Dilute the sample • Use a narrower cuvette • (cuvettes are usually 1 mm, 1 cm or 10 cm) • Plot the data (A v C) to produce a calibration ‘curve’ • Obtain equation of straight line (y=mx) from line of ‘best fit’ • Use equation to calculate the concentration of the unknown(s) This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  9. Quantitative Analysis This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  10. Statistical Analysis This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  11. Mean The mean provides us with a typical value which is representative of a distribution This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  12. Normal Distribution This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  13. MEAN Mean and Standard Deviation This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  14. Standard Deviation • Measures the variation of the samples: • Population std () • Sample std (s) •  = √((xi–µ)2/n) • s =√((xi–µ)2/(n-1)) This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  15.  or s? In forensic analysis, the rule of thumb is: If n > 15 use  If n < 15 use s This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  16. Absolute Error and Error % • Absolute Error Experimental value – True Value • Error % This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  17. 1  = 68% 2  = 95% 2.5  = 98% 3  = 99.7% Confidence limits This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  18. Control Data • Work out the mean and standard deviation of the control data • Use only 1 value per group • Which std is it?  or s? • This will tell us how precise your work is in the lab This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  19. Control Data • Calculate the Absolute Error and the Error % • True value of [SCN–] in the control = 2.0 x 10–3 M • This will tell us how accurately you work, and hence how good your calibration is!!! This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  20. 2  2.5  Control Data Plot a Control Chart for the control data This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  21. Significance • Divide the data into six groups: • Smokers • Non-smokers • Male • Female • Meat-eaters • Rabbits • Work out the mean and std for each group ( or s?) This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  22. Significance • Plot the values on a bar chart • Add error bars (y-axis) • at the 95% confidence limit – 2.0  This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  23. Significance This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  24. Identifying Significance • In the most simplistic terms: • If there is no overlap of error bars between two groups, you can be fairly sure the difference in means is significant This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

  25. Acknowledgements • JISC • HEA • Centre for Educational Research and Development • School of natural and applied sciences • School of Journalism • SirenFM • http://tango.freedesktop.org This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License

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