Chemical and Physical Properties: Practical Session

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Lab 6: Saliva PracticalBeer-Lambert Law

University of Lincoln presentation

http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

http://chemistryfm.blogs.lincoln.ac.uk/


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????




Types of data

QUALITATIVENon numerical i.e what is present?

QUANTITATIVENumerical: i.e. How much is present?




Beer-Lambert LawBeers 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)




Beer-Lambert Law

• Beer’s law is valid at low concentrations, but breaks down at higher concentrations

• For linearity, A < 1

1




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




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)




Quantitative AnalysisCalibration curve showing absorbance as

a function of metal concentration

y = 0.9982x

R2 = 0.9996

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

Concentration (mg L-1)

Abso

rbanc

e ( no

uni

ts)




Statistical Analysis




Mean

The mean provides us with a typical value which is representative of a distribution

nsobservatio of (N) number thensobservatio theall of (å) sum the

Mean

nsobservatio of (N) number thensobservatio theall of (å) sum the

Mean




Normal Distribution



http://en.wikipedia.org/wiki/Image:Normal_approximation_to_binomial.png


Mean and Standard Deviation

MEAN




Standard Deviation

• Measures the variation of the samples:– Population std ()– Sample std (s)

= √((xi–µ)2/n)

• s =√((xi–µ)2/(n-1))




or s?

In forensic analysis, the rule of thumb is:

If n > 15 use If n < 15 use s




Absolute Error and Error %

• Absolute ErrorExperimental value – True Value

• Error %100%

value True Value True– value alExperiment




Confidence limits

1 = 68%

2 = 95%

2.5 = 98%

3 = 99.7%




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!!!




Control DataPlot a Control Chart for the control

dataQuality Control Chart

1.00E-03

1.50E-03

2.00E-03

2.50E-03

3.00E-03

3.50E-03

4.00E-03

1 6 11 16 21 26 31

Measurement number

Con

trol

thio

cyanate

con

centr

ation

(m

ol/L

)

Control valueinner limitinner limitouter limitouter limitgroup values

2.5 2




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




Significance

Variation in [SCN-] in Saliva for Various Groups of Forensic Science Students (not REAL data)

0123456789

Smokers Non-Smokers

Male Female Lions Rabbits

Mea

n [S

CN

-] (M

)




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




Acknowledgements

• JISC• HEA• Centre for Educational Research and

Development• School of natural and applied sciences• School of Journalism• SirenFM• http://tango.freedesktop.org


Chemical and Physical Properties: Practical Session

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