1 / 24

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

kiril
Télécharger la présentation

Lab 6: Saliva Practical

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related