Red blood cell samples A and B were lysed. Then 0. 1 cm3 of the sample solutions were mixed with 2. 9 cm3 of a protein precipitant to separate the glucose molecules from the other proteins. After mixing and centrifugation to separate the precipitated proteins from the soluble glucose/sugar mixture, 1 cm3 of the supernatant was transferred to a fresh tube and mixed with 3 cm3 of glucose oxidase reagent. This reagent contains the aminophenazone and peroxidase. The mixture was incubated in a 37 °C water bath for 15 minutes, after which the absorbance was read at 515 nm.

A blank containing all reagents but without the samples was used to zero the spectrophotometer. Preparation of the standard curve Standard solutions of glucose were prepared by pipetting 1 cm3 of the stock solution and adding it to 9 cm3 of distilled water. Serial dilutions were performed by pipetting 1 cm3 of the previous solution and adding it to 9 cm3 distilled water. This was done serially up to six dilutions. At each dilution, 0. 1 cm3 was transferred and mixed with protein precipitant.

From the clear supernatant, 1 cm3 was mixed with glucose oxidase reagent, incubated in a 37 °C water bath and the absorbance read at 510 nm wavelength. The glucose concentration of the standards and their corresponding absorbances were plotted on a scatterplot, and the trendline was generated with the regression equation using the MS Excel function for graphs and trendlines. Determination of the number of replicates necessary To determine the number of replicates necessary, glucose samples of decreasing concentrations were pipette and subjected to the glucose oxidase reaction.

Then the absorbances of the resulting reactions were measured at 510 nm. Three replicates were made. The raw data were entered into an MS Excel worksheet. The variance, standard deviation, and averages were determined for the three replications of each glucose concentration using the MS Excel Program. Coefficients of variation for the whole measurements were determined by getting the ratio of the overall standard deviation to the overall mean and multiplying the resulting value with 100. Results The results of the glucose determinations are presented in the following tables and figure.

Tables 1, 2 and 3 presents the raw values obtained for the determination and construction of the standard curve, the determination of the coefficient of variation, standard deviation, variances and means, and the data obtained for the unknown samples, A and B. Figure 1 presents the graph that was generated from the values obtained for the standard curve. It shows that the values clustered at the bottom left corner of the plot. This is due to the serial dilutions that were made which clustered more data points near the more dilute concentrations (those with higher dilutions).

Nevertheless, the regression equation, with the intercept set at zero, showed a high R2 value of 0. 974. Since this value is near to 1, it means that the fit of the regression line was acceptable. The slope was 18. 75, which means that a change in 1 unit of glucose concentration corresponds to a change of 18. 75 units of the absorbance (Y-axis). The slope of 18. 75 was used to determine the blood glucose concentration of the samples A and B, given their absorbance at 510 nm.

Table 4 presents the computed coefficients of variation which showed the number of replicates, and which replicates are, necessary to get a value. The mean for all determinations (Reps I-III) were calculated, so with the standard deviation. The ratio of SD to mean was the coefficient of variation. The results showed that if all replications were to be considered the cv would be very high at 70%. Considering any two replications, it was found that using Reps I and II gave the least cv, while Reps II and II had the highest cv at 89%.

This means that the results are not reliable because the cv was very high. Coefficients of variation should be lower than 15% for the experimenter to say that the data obtained is reliable or near the true value. The variances and standard deviation for each concentration were also calculated. Results show that variances and the deviation were only small when the glucose concentrations were higher, but as the solutions became more dilute, the variance and deviation increased (Table 5).

Based on the standard curve, the relationship between glucose and absorbance should be linear (meaning that as glucose concentration increases, so should absorbance). A close inspection shows that this is not the case. Table 6 shows the blood glucose concentration of the two samples. Sample A has a higher blood glucose concentration, while Sample B has a very low glucose concentration. The second absorbance value for Sample B was not included in the calculation of the mean because it was highly different from the values of the two replications.