Lab 6: Saliva Practical
Beer-Lambert Law
University of Lincoln
presentation
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This session….
• Overview of the practical…
• Statistical analysis….
• Take a look at an example control chart…
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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????
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Types of data
QUALITATIVE
Non numerical i.e what is present?
QUANTITATIVE
Numerical: i.e. How much is present?
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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)
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Beer-Lambert Law
• Beer’s law is valid at low concentrations, but
breaks down at higher concentrations
• For linearity, A < 1
1
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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)
1
• Unknowns should ideally
fall within the standard
range
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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)
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Quantitative Analysis
Absorbance ( no units)
Calibration curve showing absorbance as
a function of metal concentration
1.2
y = 0.9982x
1
R2 = 0.9996
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
Concentration (mg L-1)
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Statistical Analysis
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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
the sum (å) of all the observations
Mean 
the number (N) of observations
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Normal Distribution
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Mean and Standard Deviation
MEAN
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Standard Deviation
• Measures the variation of the samples:
– Population std ()
– Sample std (s)
•  = √((xi–µ)2/n)
• s =√((xi–µ)2/(n-1))
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 or s?
In forensic analysis, the rule of thumb is:
If n > 15 use 
If n < 15 use s
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Absolute Error and Error %
• Absolute Error
Experimental value – True Value
• Error %
Experimental value – T rue Value
 100%
T rue value
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Confidence limits
1
= 68%
2
= 95%
2.5  = 98%
3
= 99.7%
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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
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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!!!
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Control Data
Plot a Control Chart for the control data
Quality Control Chart
4.00E-03
Control thiocyanate concentration (mol/L)
3.50E-03
3.00E-03
Control value
inner limit
inner limit
outer limit
outer limit
group values
2.50E-03
2.00E-03
2
1.50E-03
2.5 
1.00E-03
1
6
11
16
21
26
31
Measurement number
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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?)
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Significance
• Plot the values on a bar chart
• Add error bars (y-axis)
– at the 95% confidence limit – 2.0 
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Significance
Mean [SCN-] (M)
Variation in [SCN-] in Saliva for Various Groups of
Forensic Science Students (not REAL data)
9
8
7
6
5
4
3
2
1
0
Smokers
NonSmokers
Male
Female
Lions
Rabbits
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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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Acknowledgements
•
•
•
•
•
•
•
JISC
HEA
Centre for Educational Research and Development
School of natural and applied sciences
School of Journalism
SirenFM
http://tango.freedesktop.org
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