The hypothesis stated that if sucrose was added to the yeast, then the greatest amount of CO2 would be produced because sucrose contains glucose as one of its individual sugar units, which is the primary food source for eukaryotic cells undergoing aerobic cellular respiration. This hypothesis was supported by data from the group and class averages. According to the group data, sucrose had the greatest respiration rate at 35.94 ppm/s, then agave at 20.22 ppm/s, honey at 13.69 ppm/s, and the lowest respiration rate water, at 3.63 ppm/s. The class data was as follows: sucrose with 13.66 ppm/s, Honey with 11.24 ppm/s, agave with 11.09 ppm/s, and finally water with the lowest respiration rate again at 3.03 ppm/s. The group’s data for sucrose was …show more content…
The believability of the group data is arguable. An error that occurred in the group was due to the faulty carbon dioxide sensor. When testing sugars, such as sucrose, the carbon dioxides would rise and fall randomly. This explain why the group data for sucrose was 35.94 ppm/s when the class data for sucrose was 13.66 ppm/s, which is a little more than half the amount. In order to fix this problem, carbon dioxide sensor’s could have been switched to one that worked more efficiently and properly. Another issue that potentially occurred was that the sugar to yeast ratio was not completely accurate. There is a great possibility that when measuring the yeast and sugar, more sugar was added or more yeast accidentally. Next time, materials need to measured out more accurately by slowly pouring substances and not being too lazy to remeasure. A third error that most likely caused different data was overtiming the respiration chamber data collection. Often times the group forgot to set a thirty second timer before pressing collect on logger pro which obviously had an effect on the results. In order to avoid common mistakes like this, one person should be assigned as the timer to make sure each trial is as accurate as possible, and no trials have extra respiration time. Accurate labs and data are crucial in providing valid results and forming
We are doing a gummy bear lab. Gummy bears come in different sizes, shapes, and colors. Gummy bears are squishy, chewy, and sticky, they are made of sugar which are glucose and glucose are carbohydrate. We predict that during this stage of lab the solute and solvent will go through the stages of hypertonic, hypotonic and isotonic. We get 4 gummy bears and 4 cups filled half way with different types of liquid, such as Salt-Water, Coffee-Creamer, Vinegar, and Soda.
Next was the second trial slide from the 10 grams and found two moving microorganisms. Lastly I found zero moving microorganism in the third 10 grams slide. I then moved on to the 1 gram trial slides and found zero moving microorganisms, however I did find six moving microorganisms on the second trial slide for the 1 gram sample. For the last trial for 1 gram I found three moving microorganisms. I moved onto the 0.1 gram sample and found four moving microorganisms in the first trial, zero microorganisms in the second trial, and two in the third 0.1 gram trial.
My hypothesis was confirmed because the yeast took a little amount of time to find the sugar since there was more yeast in the mixture than sugar. Once the yeast finds the sugar we saw a big jump in the CO2 production, meaning the yeast is consuming the sugar at a fast rate. But, once all of the available sugar is gone from the solution, we saw an abrupt stop. This abrupt stop was because there was no longer any sugar remaining for the yeast to consume so the production of the yeast by-product, CO2, halted and we no longer saw an increase in the air space. The standard deviation of the first part of the experiment compared to the standard deviation of the second experiment that we created are surprisingly accurate to what they should be.
Some ways to improve the lab are to make sure that the error sources are fixed. Next time, it should be imperative that the table being used is perfectly balanced and that the tape is not placed on the inside
The cause of this is likely that the protein was already broken down so much when used for cooking that Biuret’s test was unable to detect it. While the results from this experiment seem appropriate for the experiment, there could have been a few issues that could have taken place during the experiment. One of these could be that the solutions used for testing (such as Biuret’s solution) could have at out for too long since we did the experiment in the afternoon. This could lead to an incorrect data. Also, the materials may have not been completely clean, such as the test tubes, which could have also affected the data.
Macromolecule test 1 differs from the second chart by testing non-reducing sugars in the first test and proteins in the second. In depth the lab required to heat the sample at times, mix them, and add them to a warm water bath of 100 Celsius. The following graphs were obtained by following the guidelines within the
The final mass could be far off due to the water and chunks of expanded gummy bears found in the beaker, leading to an inaccurate result. As well, for the sugar solution, the result could have been different if a more accurate measurement of the sugar needed was made. For the specific result, the hypothesis stated, the sugar solution needed to have an equal amount of sugar content to the gummy bear which did not occur. Ensuring that the beaker contained 10 grams of sugar was off, due to prerequisite calculations that lead to too much liquid in the beaker that needed to be removed. To be correct, the hypotheses that were wrong could
Dependent Variable amount of product (glucose and fructose) produced 2. Independent Variable temperature 3. Controlled Variables pH, amount of substrate (sucrose) present, sucrase + sucrose incubation time Effect of Substrate Concentration on Enzyme Activity 1. Dependent Variable amount of product (glucose and fructose) produced 2.
Restate your predictions that were correct and give the data from your experiment that supports them. Restate your predictions that were not correct and correct them, giving the data from your experiment that supports the corrections. At the end of this experiment, it showed that I was right. Sucrase activity was at its optimal at ph6 and at 40 Celsius. The graph above shows my prediction of pH is correct but my temperature prediction is slightly off since I said it would be at 50 Celsius.
I predicted that the control would have a higher alcohol content than the experimental since beta and alpha amylase are working together. Since only Alpha-Amylase worked in the experimental, there was probably bigger carbohydrates present in the flask, therefore, there was a lower alcohol percentage since yeast can’t digest bigger sugars. b. My results also matched my prediction regarding mean reducing carbohydrate levels during the mashing process between the control and the experimental. My prediction stated that there would be less reducing carbohydrate ends in the experimental, which was proven in the data table.
5 water bath were set up each to10 °C. (5 were used do the experiment faster) 5 cm3 of starch solution were added into the 5 test tubes that were labeled test tubes. Then 5 cm3 of amylase enzyme was added into the other 5 test tubes that were labeled. Put one of the starch solution test tube (preferably the one labeled 1) and one of the test tube containing amylase into the water bath (10 °C).
The results of the phenol-sulfuric acid analysis conducted in this experiment suggest that the data acquired was relatively precise but inaccurate with respect to the given carbohydrate concentrations of the soda and Gatorade samples. Using a standard curve generated from a glucose solution with a known concentration, the carbohydrate concentration of the samples was determined (in terms of glucose) and a low coefficient of variation was calculated. However, a high percent relative error was apparent in the analysis of both samples. This may have been due to the fact that the analysis was conducted assuming glucose was the carbohydrate of interest, while, in fact, a significant portion of the monosaccharides would have existed as fructose (a
The Effect of Sugar Concentration on CO2 Production by Cellular Respiration in Yeast Introduction In this lab, our main focus was to find how sugar concentration affect yeast respiration rates. This was to simulate the process of cellular respiration. Cellular respiration is the process that cells use to transfer energy from the organic molecules in food to ATP (Adenosine Tri-Phosphate). Glucose, CO2, and yeast (used as a catalyst in this experiment) are a few of the many vital components that contribute to cellular respiration.
Joshua Miller 12/18/17 Fermentation Lab report Introduction The term fermentation refers to the chemical breakdown of a substance by bacteria, yeasts, or other microorganisms, typically involving effervescence and the giving off of heat (wikipedia). Sugars are converted to ethyl alcohol when fermentation happens. In this experiment we determined if yeast cells undergo fermentation when placed in a closed flask with no oxygen. Glucose and yeast are mixed together in a closed flask and allowed to incubate for about one hour.
This was not sufficient enough, as with only one sample size, the random errors couldn’t be identified, as there were no other results to compare to. The higher the sample size, the more accurate the final average and result is, as it shows that the data is consistently showing up a certain way. Random errors are easily spotted with higher sample sizes, and they also taint the final results less than they would with less sample sizes. With this experiment, it was assumed that some errors were random or systematic, but no evidence apart from the variations in differences of results of various independent