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Lab 6: Saliva Practical

Lab 6: Saliva Practical. Beer-Lambert Law. This session…. . Overview of the practical… Statistical analysis…. Take a look at an example control chart…. The Practical. Determine the thiocyanate (SCN - ) in a sample of your saliva using a colourimetric method of analysis

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Lab 6: Saliva Practical

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  1. Lab 6: Saliva Practical Beer-Lambert Law

  2. This session…. • Overview of the practical… • Statistical analysis…. • Take a look at an example control chart…

  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????

  4. Types of data QUALITATIVE Non numerical i.e what is present? QUANTITATIVE Numerical: i.e. How much is present?

  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)

  6. Beer-Lambert Law • Beer’s law is valid at low concentrations, but breaks down at higher concentrations • For linearity, A < 1 1

  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 Beer-Lambert Law

  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)

  9. Quantitative Analysis

  10. Statistical Analysis

  11. Mean The mean provides us with a typical value which is representative of a distribution Mean= the sum (å) of all the observations the number (N) of observations

  12. Normal Distribution

  13. Mean and Standard Deviation MEAN

  14. Standard Deviation • Measures the variation of the samples: • Population std () • Sample std (s) •  = √((xi–µ)2/n) • s =√((xi–µ)2/(n-1))

  15.  or s? In forensic analysis, the rule of thumb is: If n > 15 use  If n < 15 use s

  16. Absolute Error and Error % • Absolute Error • Experimental value – True Value • Error % • Experimental value – True Value x 100% True value

  17. Confidence limits 1  = 68% 2  = 95% 2.5  = 98% 3  = 99.7%

  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

  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!!!

  20. Control Data • Plot a Control Chart for the control data 2  2.5 

  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?)

  22. Significance • Plot the values on a bar chart • Add error bars (y-axis) • at the 95% confidence limit – 2.0 

  23. Significance

  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

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