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

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License Beer-Lambert Law 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

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

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

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

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

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

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

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

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

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