lab 6 saliva practical l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Lab 6: Saliva Practical PowerPoint Presentation
Download Presentation
Lab 6: Saliva Practical

Loading in 2 Seconds...

play fullscreen
1 / 24

Lab 6: Saliva Practical - PowerPoint PPT Presentation


  • 290 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Lab 6: Saliva Practical' - kiril


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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
lab 6 saliva practical

Lab 6: Saliva Practical

Beer-Lambert Law

this session
This session….
  • Overview of the practical…
  • Statistical analysis….
  • Take a look at an example control chart…
the practical
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????
slide4

Types of data

QUALITATIVE

Non numerical i.e what is present?

QUANTITATIVE

Numerical: i.e. How much is present?

beer lambert law
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)

beer lambert law6
Beer-Lambert Law
  • Beer’s law is valid at low concentrations, but breaks down at higher concentrations
  • For linearity, A < 1

1

beer lambert law7
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
quantitative analysis
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)
slide11

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

standard deviation
Standard Deviation
  • Measures the variation of the samples:
    • Population std ()
    • Sample std (s)
  •  = √((xi–µ)2/n)
  • s =√((xi–µ)2/(n-1))
slide15
 or s?

In forensic analysis, the rule of thumb is:

If n > 15 use 

If n < 15 use s

absolute error and error
Absolute Error and Error %
  • Absolute Error
    • Experimental value – True Value
  • Error %
    • Experimental value – True Value x 100%

True value

confidence limits
Confidence limits

1  = 68%

2  = 95%

2.5  = 98%

3  = 99.7%

control data
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
control data19
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!!!
control data20
Control Data
  • Plot a Control Chart for the control data

2 

2.5 

significance
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?)
significance22
Significance
  • Plot the values on a bar chart
  • Add error bars (y-axis)
    • at the 95% confidence limit – 2.0 
identifying significance
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