Lab Information: Lab 2

Educational Objectives: This lab is probably the second most intense lab of the course.  Being prepared for this lab will allow you to complete the lab on time with as little stress as possible!!  The educational goals of this lab include the knowledge of how to prepare solutions from either a solid or from a stock solution; how to prepare a parallel and a serial dilution and the use of a spectrophotometer to measure the absorbance of a solution.  In addition, you will create a standard curve and use linear regression analysis (called a trendline in excel) to determine the concentration of an unknown solution. 

Experimental Objectives:  In this lab, you will determine at which wavelength of light KMnO3  absorbs the best – this wavelength is known as λmax.   You will then determine if the relationship between absorbance and concentration is linear (using trendline analysis in excel, see excel quiz 3) and if this relationship is the same whether you use a parallel or a serial dilution.  Finally, you will calculate the concentration of an unknown KMnO3  solution using trendline analysis.

Before Coming to Lab:

  1. Read and complete the pre-lab activities.  In this lab, it is very important that this is done so that you will be able to have enough time to complete the procedures in the time provided.  You should, for example, already have set up the procedures for making the parallel and serial dilutions.  You will lose points on your lab report if you do not complete the lab in time.
  2. Read the procedures of the lab and prepare your lab note book with the Introduction and Methods and Materials, as you should every lab.  In this lab, we will not be altering the procedures.

After Lab:

  1. Analyze your data.  You will need to plot the effect of changing the wavelength on the absorbance of a single sample.  You should discover that there is a wavelength at which KMnO3  absorbs best.
  2. You will need to plot, on the same graph (see my homepage for how to do this), absorbance as a function of concentration for the parallel and serial dilutions.  You then need to perform a trendline analysis on each plot and determine the equation for each line (see excel quiz 3).  Be sure to calculate the r2 value – for trendlines, you want the r2 to be at least greater than 0.90 to demonstrate linearity.
  3. Using the equations from 2, calculate the concentration of the unknown solution (you should have two values – what do you think is the best thing to do with these two values?)
  4. Again, you lab report is only to contain the Results and Discussion sections.  In the Results, you should have properly labeled (and titled – including “Figure 1” and an appropriate title.  Each graph should be sequentially numbered) and description of your results in paragraph form and the concentration of the unknown.   The Discussion should include what you learned from the data, linearity of the plots, does it make a difference if the standards are from the parallel or serial dilution, etc.

 

Sample Data Analysis:

The following data is from a similar set of experiments in which Bottu (a newly discovered compound) was used instead of potassium permanganate.

  1. Finding λmax for Bottu:

Table 1: Absorbance Spectrum for Bottu

 

Wavelength (nm)

Absorbance

 

 

400

0.14

 

 

420

0.22

 

 

440

0.45

 

 

460

0.78

 

 

480

0.43

 

 

500

0.34

 

 

520

0.21

 

 

540

0.21

 

 

560

0.13

 

 

580

0.11

 

 

600

0.1

 

 

 

 

Figure 1 was constructed using the data shown in Table 1.  Using this data, the λmax  of Bottu is 460 nm.  You should format your tables and graphs like these.  Be sure to completely label them (including units where applicable) and you should make sure that the graphs are at least one half of a printed page (this one is a bit small).

  1. Relationship between absorbance and concentration

Table 2 shows the data from a hypothetical experiment using a serial dilution and a parallel dilution of Butta (what wavelength should have been used to get this data?) and Figure 2 is a plot of this data and this plot includes a trendline analysis of the data.

Table 2: Concentration of Butta vs. Absorbance

Concentration (Mm)

Absorbance

Serial Dilution

Parallel Dilution

10

0.133

 

20

0.235

 

30

0.294

 

40

0.425

 

50

0.534

 

60

0.599

 

70

0.721

 

3.5

 

0.029

7

 

0.099

14

 

0.161

28

 

0.267

56

 

0.6

112

 

1.009

 

 

 

First of all, the trendline analysis of the data (note that there is no line that ‘connects’ the dots – since you are doing a trendline, you do not want to clutter up the graph with extra lines) indicates that both sets of data demonstrate a linear relationship because the r2 for both the serial and the parallel dilutions is greater than 0.90.    In addition, the trendline analysis calculates the equation  of the line of best fit for each plot.  For serial the equation is

y = 0.0098x + 0.0299

Since the x variable is the concentration and the y variable is the absorbance, this equation can be rewritten as:

Absorbance = 0.0098 · concentration + 0.0299

The line of best fit using the parallel data is:

Absorbance = 0.009 · concentration + 0.03

  1. Calculate the Value of an Unknown Concentration:

The absorbance of the unknown sample is 0.600.  Replace this value for the Absorbance term in the equations and calculate the concentration.  If you do this, the serial dilution with give a concentration of 60.9 mM while the equation from using the parallel dilution with give a concentration of  66.3 mM.  We should report the concentration as the average of the two values: 63.6 mM.

 

Real Data:

1. The following chart was published in the scientific journal Arquivos Brasileiros de Endocrinologia & Metabologia in June, 2006.  l;though I am not sure why,  they were trying to relate the waist-hip ratio of individuals with the rate of albumin excretion by the kidney.

 



 

 

In this instance, the r2 for this relationship was 0.28 - does this indicate that the relationship between the waist-hip ratio and the urinary albumin excretion rate is linear?  Does the regression line appear to 'fit' the data well?

 

2. The following chart was published in the scientific journal Nucleic Acid Research in July, 2006.  The paper attempts to compare a model to predict the rate of folding of a newly synthesize protein with the actual value.  They want to convince you that their model can accurately predict the rate of folding.

The r2 for this was 0.96 - does this indicate that the relationship between the experimental values and the calculated values of the rate are  linear?  Does the regression line appear to 'fit' the data well?  Do you feel that their model appears accurate?