Lab 6: Saliva Practical

1 / 24

# Lab 6: Saliva Practical - PowerPoint PPT Presentation

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

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

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

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????

Types of data

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 Law
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)

### Statistical Analysis

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
• 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 Error
• Experimental value – True Value
• Error %
• Experimental value – True Value x 100%

True value

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 Data
• Plot a Control Chart for the control data

2 

2.5 

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