Introduction
One of the most important part of protein assay is the determination of protein concentration. There are diverse methods used for protein concentration determination, but the most widely used are the spectroscopic methods of analysis or spectroscopy. Spectroscopy usually involves the determination between the correlations of the degree of interaction of light with the analyte, which is the proteins in this particular experiment. There are different types light to analyte interactions and one of these is the absorbance of light or electromagnetic radiation by the analyte. This type of spectrometry is called absorbance spectrometry.
Principle involved in absorbance spectrometry and spectrometers
The majority of substances absorb electromagnetic radiation, most especially colored substances (Broekaert, 1998).The color of the substance is dependent on the frequency of light it absorbs. As a rule the complement of the color absorbed is the visible color of the substance. For example, if a substance is colored green, then it means that it absorbs the color red and vice versa. Note that the complement of the color is the color that appears opposite to it in the color wheel (Lighting Research Center, 2014). The amount of light absorbed by the substance is directly proportional to its concentration (Welz and Sperling, 1999). This means that if you have more of the substance in a container then the more the intense its color is (Ingle and Crouch, 1988).Therefore, if we can determine the amount of light that a substance absorbs, then we can have an estimate of its real concentration (L’vov, 1984). This can be done effectively with the use of standard solutions and an absorbance spectrometer, which measure the intensity of light absorbed (L’vov, 2005). Accordingly, the corresponding absorbance of each of the standard solutions can be graphed with their corresponding concentrations. The best fit curve can then be determined that best describes the line created from the concentration vs. absorbance graph. After determining the best fit curve, its equation can be derived (Walsh, 1955). The concentrations of unknowns or analytes can then be determined by simply inputting their absorbance values into the said equation. There is however one problem with proteins used in this experiment – they are colorless. This means that, alone it will not absorb electromagnetic radiation at the visible region. There is, however, one way of going around this problem. This way is the addition of another chemical species to the proteins to form a colored complex. One of these reagents of chemical species is the Biuret Reagent (BR). It is a common practice in laboratory experiments to put the said reagent in excess as it will not affect the absorbance of the complex significantly. The absorbance of the complex is then determined and the aforementioned curved is constructed (Biuret Protein Assay, n.d.).
The best fit calibration curve and the standard calibration curve
A standard calibration curve is a curve created from the absorbance readings of standard solutions. Standard solutions are solutions with known concentrations of certain chemical species, which is in this case, the protein standards. The protein standards are considered accurate in terms of their protein concentration content. These standards are usually both or are made from other standards. The determination of the best fit curve for the standard calibration curve can be done in two ways. The first is manually using pen, graphing paper and ruler; and the other is through the use of applications, such as the Microsoft Excel application.
In manual best fit curve concentration, the absorbance values are plotted into a graphing paper. The usual convention is the setting of the independent variable (the concentration of the standard solutions) at the horizontal or x-axis and the dependent variable (the corresponding absorbance of each concentration) at the vertical or y-axis. A straight line is then drawn across the pints so that there are as much points that are touched by the straight line. The straight line is then considered as the best fit curve of the calibration curve. The concentrations of the unknown samples are then determined by extrapolating them through their absorbance values. The alternative way of constructing the calibration curve is through the use of the Microsoft Excel application or other similar applications (Potgieter, 2004). In Excel, a scatter plot of the points (concentration, absorbance) is created. Using the built-in functions of the said application the best fit curve is determined. The Pearson Product Moment Correlation Value (R2) and the equation of the best fit curve can then be automatically generated. Ideally, a best fit standard calibration curve is one that has an (R2) value of greater than 0.9. Note that the best value for (R2) is “1,” which means that all points are touched by the best fit curve. The equation of the standard calibration curve can then be sued to determine the concentration of the unknowns (Galton, 1886).
Overview of methods used
In this experiment, five standard solution of a known proteins species were used to create a standard calibration curves. Accordingly, Biuret reagent was added to each of the standard solutions and their corresponding absorbance at 540nm was determined using an absorbance spectrometer. The manual and the automated (use of Excel application) ways of constructing the standard calibration curves and determining the protein concentrations of two unknown samples (labeled Z and Y) were then employed.
Results
For the manually generated standard calibration curve is shown in figure 1. The best fit curve is a straight line that lies in the first quadrant of the Cartesian plain. The extrapolated values for unknowns Y and Z are: 3.74ppm 0.25ppm respectively.
Figure 1: Manually constructed standard calibration curve and the concentrations of the unknowns Y and Z
Figure 2 is for the automated or Microsoft Excel generated standard calibration curve. The best fit curve is also a straight line lying at the first quadrant of the Cartesian plain. The equation of the line is y = 0.104x + 0.049 with a Pearson Correlation value of R² = 0.964 indicating a strong correlation between the two variables: absorbance and protein concentration.
Figure 2: Excel generated standard calibration curve for protein standards
Using the equation the concentration were computed as follows:
Absorbance: Unknown Y = 0.556
y = 0.104x + 0.049;
x = (y – 0.049)/0.104
x = (0.556 – 0.049)/0.104
y = 4.85ppm
Absorbance Unknown Z = 0.298
y = 0.104x + 0.049;
x = (y – 0.049)/0.104
x = (0.298 – 0.049)/0.104
y = 2.39ppm
Concentrations of unknown Y and Z are 4.85ppm and 2.39ppm, respectively.
Discussion
Accordingly, it should be acknowledge that there are many types of errors that contribute to the overall errors of the experimental results. One of these types of errors is called parallax error. Parallax error is the error generated by human limitations and bias. In the manual method, this type of error is more likely to occur as the entire process of generating the standard curve and the extrapolation of concentrations was done first-hand the thee experimenter (Carlson, 2002). The Excel-generated standard calibration curve, however is machine generated. This means that parallax error is not likely to occur. The only error that could be associated with the Excel application is the algorithm which generates the graphs and the equations. It can be argued, therefore, that the results generated through the Excel application are less bias and are there more reliable and valid than those generated manually. Perhaps another important feature of the Excel generated standard curve is that we can see the Pearson Product Moment Correlation value, which actually tells us how accurate the straight line is in describing the relationship between the two variables. This is not done in the manual method of making the standard calibration curve. The high Pearson Correlation value means that the line generated is highly accurate in describing the relationship between the two variables, while we cannot be sure in the manual method whether the arbitrarily drawn straight line accurately describes the said relationship. Hence, there is a much higher level of uncertainty in the manual method compared to the automated or Excel-generated curve. This means that, in both accuracy and certainty, the automated method is more advantageous over the manual method.
Summary and Conclusion
In this experiment the spectrometry was employed to analyze the two unknown samples for their protein concentrations. There were a total of five standard protein solutions used in order to generate the standard calibrations curve. The standard calibration curve was generated in two ways or methods. The first method is the manual method and the second is the automated or Excel-based method. These two methods were then compared. Results and findings showed that the automated method produced higher concentration values for both unknowns, consistently compared to the manual method. There could never be an actual evaluation and comparison of the accuracy of the methods used because the actual concentrations of the two unknowns were not revealed to the experimenter. Nevertheless, using theoretical evaluations, it can be concluded that the automated method of constructing the standard calibration curve has higher accuracy and certainty compared to the manual method. The main reason for this conclusion is the avoidance of parallax error and bias in the automated method, as well as the ability of the Excel application to generate the Pearson Product Moment Correlation value and the equation of the best fit curve easily. It is therefore concluded that in future endeavors, it will be more advantageous to utilize the automated method of generating the standard curve in tandem with spectrometry in analyzing the concentration of unknown protein samples.
Bibliography
Broekaert, J.A.C. (1998), Analytical Atomic Spectrometry with Flames and Plasmas, 3rd Edition, Weinheim: Wiley-VCH.
“Biuret Protein Assay.” (n.d.). Available at: <http://www.gru.edu/colleges/scimath/biology/courses/BIOL_1107/biuretproteinassay.pdf>.
Carlson, G.A. (2002). Experiment Errors and Uncertainty. Available at: <http://www.ece.rochester.edu/courses/ECE111/error_uncertainty.pdf>.
Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15(1), pp. 246–263.
Ingle, J.D.Jr. and Crouch, S.R. (1988). Spectrochemical Analysis. NY: Prentice Hall.
L’vov, B.V. (1984), Twenty-five years of furnace atomic absorption spectroscopy, Spectrochimica Acta 39(B, pp. 149–157.
L’vov, B.V. (2005). Fifty years of atomic absorption spectrometry. Journal of Analytical Chemistry 60(1), pp. 382–392.
Lighting Research Center (2014). How do we see color. Available at: <http://www.lrc.rpi.edu/programs/nlpip/lightinganswers/lightsources/seeColor.asp>.
Potgieter, L. (2004). Manual vs. Automated Performance Management. Available at: <http://www.pmia.org.au/white/manvauto.pdf>
Walsh, A. (1955). The application of atomic absorption spectra to chemical analysis, Spectrochimica Acta 7(1), pp. 108–117.
Welz, B. and Sperling, M. (1999). Atomic Absorption Spectrometry, Weinhem: Wiley-VCH, Weinheim.
