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In conclusion, it appears that both sets of results can used to investigate how concentration of substrate effects the rate of reaction. However, the data does not necessarily fit the predicted pattern perfectly (whilst at the same time the data does not disprove the prediction outlined in the plan). This is explained in the next section Numerical evidence To from a mathematical idea of how accurate my data is and an equation linking the rate against the concentration of the substrate (whether it be orange juice or glucose solution).
We can use regression formula to calculate the formula in terms of y = a + bx; where y= rate, x= substrate concentration and a & b are constants. Orange juice formula: Y=-0. 80+0. 86x Glucose trial: Y=-0. 03+0. 54x Thus, from these equations we can deduce that the relationship was linear and that the results passed through close to (0,0)- the reason why this did not occur I believe was because of errors in my data. However the “a” value is so small in each case that it is hardly worth noticing. Thus, I believe that numerically the evidence is strong for supporting my theory.
(all measurements in this section are correct to 2 decimal places) Scientific Theory in explaining trends Although not all the theory outlined in my prediction came to be supported in the data collected, I believe that this can be discussed scientifically so that the results do not disprove what my theory predicted. The trends shown by my graphs appear to show a linear relationship between the rate of reaction and the substrate concentration for an infinite amount of time, whereas, in my prediction I predicted a linear relationship until a certain point from which the rate of reaction would plateau.
I can explain why the results did not appear to plateau and thus agree with my theory and prediction because the range of the substrate concentrations used was not great enough to incorporate the plateau area of the graph. Therefore, all that appeared on the graph was the linear relationship. I would have expected the levelling of the graph because of two reasons in particular, these are: As the substrate concentration increases then an osmotic pressure builds between the cell and the exterior environment, thus to stabilise the water potential on each side the Yeast would be forced to release water into the exterior environment.
In doing this once enough water has been released from the cell it becomes incapable of supporting the functions that it requires for respiration. Therefore, enzyme activity will be reduced significantly. Thus, the rate will appear to plateau. Also, there are only a certain number/ concentration of enzymes present and they have an optimal rate of reaction, i. e. there is a maximum number of reactions that they can catalyse per second.
Thus, when the concentration increases to a certain value, regardless of the abundance of molecules for the enzyme to catalyse it can only go at its peak rate, thus the rate will appear to plateau because it is catalysing as fast as possible. Nevertheless, the initial linear relationship that I predicted and observed was compatible with my initial theory, this can be explained by the lock and key theory of enzymes and also the collision theory of determining rate (these theorem are explained in the planning section in detail). Conclusions to data.
After carefully analysing the data obtained from this experiment I am confident that the data obtained offers a fair and strong case for the theory outlined both in quantitative and qualitative terms. From my data my initial theory is strongly supported and all the data appears to align closely with a line of best fit apart from one anomalous reading. Evaluation section: Suitability of experimental procedure In terms of the suitability of the experimental procedures that I adopted in completing the investigation I believe that they were highly suitable.
I state this because the end objective of finding a relationship between substrate concentration and rate was obtained in both quantitative and qualitative terms, The results were accurate enough to draw firm conclusions and all the experiments were able to be completed within the allotted time period. Therefore, since the procedures that I adopted satisfied all my scientific criteria with no injuries and few anomalies I believe strongly that the procedures adopted were highly successful and appropriate.
Anomalous Results After carefully analysing the data obtained from my investigation, I strongly believe that there was one substantial anomalous result that occurred on trial 2 at 6. 7% substrate concentration on the glucose test. I state this because the result was more than 10% outside the mean value obtained and also the other results in that particular concentration. To combat this I have repeated the result and left this value out of the mean calculation.
Reasons behind anomalous results I believe that that this particular result could have been anomalous due to the test tube being contaminated with an inhibitor which attached itself to a proportion of the Yeast’s enzymes that were present- thus rendering them useless. The data backs up this theory because the rate was significantly lower than the others in that field, thus suggesting that some of the yeast enzymes present were not available for catalysing the reaction.
The contaminant could have met with yeast when it was first introduced to the test tube due to insufficient cleaning or pre-cleaning contamination, or it maybe that the substrate concentration was contaminated with the inhibitor 9this is unlikely because other tests that used the substrate batch were unaffected). Limitations in experiment and the effect on data After completing this experiment and evaluating all the aspects of the investigation I believe that this particular investigation suffered from numerous limitations which ultimately lead to an increase in errors and the accuracy that could be obtained in the data section.
In particular I think the following areas were the locations of the limitations: Measuring devices for both time and quantities of substances were only accurate to 1/100th of a second and 1ml. Also, the burette, which measured the gas released, was also only accurate to 1/10 cm3. There was no method available to ensure that each experiment was subjected to the same degree of homogenisation. Thus, although a necessary procedure, we could not determine how much was given to each, thus, the effect that it had on the rate as well as the substrate concentration. Accuracy of data PMCC = n n.
The product moment correlation co-efficient (PMCC) is a statistical method that allows each result to be taken into account and compared with all the surrounding data to establish how closely the data pattern fits the predicted line/ desired line. This is given in a value between -1 and +1, where -1=perfect negative correlation, +1=perfect positive correlation and 0= no correlation. From my data they have the following values:
Orange Juice triCC value: 0. 999 % accuracy: 99. 8% % accuracy: 99. 9% Main sources of error After completing this investigation I believe that although my data was accurate to a high degree and showed signs of reliability the methods that I used did however offer themselves to error. Therefore, I believe that the following were reasons why my data was not as accurate as possible. Method 1: Inaccurate method of taking into account the volume used by the air-bubble encapsulated in the test tube. Measuring cylinders, thermometers and stop watch accuracy, i.e. measuring cylinders were accurate to 1ml, thermometers 1i?? c and stop watches to 1/100th second.
None specific list of contents for the orange juice, thus onl qualitative and quantitative conclusion can be drawn from the results. Method 2 Large length of tubing required from water bath to burette, thus, greater chance of pressure loss thus the achieving of required gas levels Fine filament tube for bubbling gas- problem because hard to locate in right areas and a safety hazard due to glass breaking easily Measuring cylinders, thermometers and stop watch accuracy, i. e.measuring cylinders were accurate to 1ml, thermometers 1i?? c and stop watches to 1/100th second. Improvements to procedure/ strategy and justification Method 1 For the future and the repeating of this experiment I would be inclined to make the following alterations:
I would conduct the initial homogenisation of the yeast and substrate in excess amounts of each and then dip in a sample test tube to up turn and do all of this beneath the water. The advantage in terms of accuracy I believe would be significant because the air bubble created in up turning the test tube would be eliminated.
Method 2 If the experiment was to be repeated using the apparatus and method specified in “method two” to ensure an improvement in the accuracy and reliability I would make the following adjustments: The water-bath and bath underneath the burette keeping the water in the burette become one and the same. I believe that this would increase the accuracy because the delivery tube length would be significantly cut. Therefore the loss of pressure also, thus more accurate data could be obtained. Improvements applying to both methods.
In terms of improvements that would improve the accuracy and reliability of both the experiments I would recommend that the following changes be adopted in future experiments: Increased range and number of repeats so that we can be more certain of obtained data and the pattern that it is showing. A device that enables the yeast and substrate to homogenise quicker and the same in each experiment, i. e. something better than just the hand shaking to produce the required mixing and reaction start. * Improved accuracy on measuring devices.
This would help improve the accuracy because errors caused by measuring cylinders and other measuring instrumentation for recording data would be greatly reduced. Significance of uncertainties Although my experiments were effected by numerous sources of errors with one result in particular being effected enough to be deemed as anomalous I do not believe that the results on the whole were large enough to be classed as significant. I state this because, both sets of data were over 99% accurate, my data supported my theory and the results were constant in terms of repeats.