Download
Skip this Video
1 / 24
Lab 6: Saliva Practical - PowerPoint PPT Presentation
- By kiril
- Follow User
- 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
Download Presentation 
An Image/Link below is provided (as is) to 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
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
- 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????
QUALITATIVE
Non numerical i.e what is present?
QUANTITATIVE
Numerical: i.e. How much is present?
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 Law
- Beer’s law is valid at low concentrations, but breaks down at higher concentrations
- For linearity, A < 1
1
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 LawQuantitative 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)
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
- Measures the variation of the samples:
- Population std ()
- Sample std (s)
- = √((xi–µ)2/n)
- s =√((xi–µ)2/(n-1))
Absolute Error and Error %
- Absolute Error
- Experimental value – True Value
- Error %
- Experimental value – True Value x 100%
True value
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 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!!!
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?)
Significance
- Plot the values on a bar chart
- Add error bars (y-axis)
- at the 95% confidence limit – 2.0
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
