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2.2.6: Review - Biology

2.2.6: Review - Biology


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Summary

After completing this chapter you should be able to...

  • Describe three different population dispersion patterns.
  • Differentiate between population size and density.
  • Explain how ecologists measure population size.
  • Calculate population growth rate (r) and doubling time (t).
  • Distinguish between exponential and logistic population growth models, explaining the role of the carrying capacity in logistic growth.
  • Provide examples of density-dependent and density-independent factors that regulate populations.
  • Compare K-selected and r-selected reproductive strategies.
  • Interpret life tables.
  • Compare type I, type II, and type III survivorship curves.

Populations are interacting, interbreeding groups of individuals from the same species. Ecologists measure characteristics of populations: dispersion pattern, population size, and population density. Populations with unlimited resources grow exponentially—with an accelerating growth rate. When resources become limiting, populations follow a logistic growth curve in which population size will level off at the carrying capacity. Density-dependent factors limit population growth as they reach their carrying capacity and include biotic factors such as predation, competition, and disease. Density-independent factors, such as storms and fires, are abiotic and decrease population size regardless of density.

Several frameworks explain how life history can influence population dynamics. K-selected species tend to have long life spans and produce few offspring with much parental care whereas r-selected species mature and reproduce rapidly, producing many offspring and offering little parental care. Life tables are useful to calculate life expectancies of individual population members. Survivorship curves show the number of individuals surviving at each age interval plotted versus time.


Interactive Exploration of How We Get Our Skin Color

The added information provided at pause points within the animation How We Get Our Skin Color allows for a richer exploration of the topic of human skin structure and function.

How We Get Our Skin Color explains the connections between the anatomy and function of our skin, particularly in relation to our health. Students learn about the roles of the different layers of cells in skin and dive deeper into the functions of melanocytes, a type of cell that produces the pigment melanin. The animation also explores the relationships between melanin, skin color, vitamin D synthesis, and protection against the harmful effects of UV radiation.

This version of the animation contains automatic pause points, during which students access additional information in the form of text and illustrations, videos, questions, and fun interactive widgets.

The “Resource Google Folder” link directs to a Google Drive folder of resource documents in the Google Docs format. Not all downloadable documents for the resource may be available in this format. The Google Drive folder is set as “View Only” to save a copy of a document in this folder to your Google Drive, open that document, then select File → “Make a copy.” These documents can be copied, modified, and distributed online following the Terms of Use listed in the “Details” section below, including crediting BioInteractive.


Review of the algal biology program within the National Alliance for Advanced Biofuels and Bioproducts

In 2010, when the National Alliance for Advanced Biofuels and Bioproducts (NAABB) consortium began, little was known about the molecular basis of algal biomass or oil production. Very few algal genome sequences were available and efforts to identify the best-producing wild species through bioprospecting approaches had largely stalled after the U.S. Department of Energy's Aquatic Species Program. This lack of knowledge included how reduced carbon was partitioned into storage products like triglycerides or starch and the role played by metabolite remodeling in the accumulation of energy-dense storage products. Furthermore, genetic transformation and metabolic engineering approaches to improve algal biomass and oil yields were in their infancy. Genome sequencing and transcriptional profiling were becoming less expensive, however and the tools to annotate gene expression profiles under various growth and engineered conditions were just starting to be developed for algae. It was in this context that an integrated algal biology program was introduced in the NAABB to address the greatest constraints limiting algal biomass yield. This review describes the NAABB algal biology program, including hypotheses, research objectives, and strategies to move algal biology research into the twenty-first century and to realize the greatest potential of algae biomass systems to produce biofuels.


2.2.6: Review - Biology

Colon targeted drug delivery is an active area of research for local diseases affecting the colon, as it improves the efficacy of therapeutics and enables localized treatment, which reduces systemic toxicity. Targeted delivery of therapeutics to the colon is particularly advantageous for the treatment of inflammatory bowel disease (IBD), which includes ulcerative colitis and Crohn's disease. Advances in oral drug delivery design have significantly improved the bioavailability of drugs to the colon however in order for a drug to have therapeutic efficacy during disease, considerations must be made for the altered physiology of the gastrointestinal (GI) tract that is associated with GI inflammation. Nanotechnology has been used in oral dosage formulation design as strategies to further enhance uptake into diseased tissue within the colon. This review will describe some of the physiological challenges faced by orally administered delivery systems in IBD, the important developments in orally administered nano-delivery systems for colon targeting, and the future advances of this research.

From the Clinical Editor

Inflammatory Bowel Disease (IBD) poses a significant problem for a large number of patients worldwide. Current medical therapy mostly aims at suppressing the active inflammatory episodes. In this review article, the authors described and discussed the various approaches current nano-delivery systems can offer in overcoming the limitations of conventional drug formulations.


Conclusions

Removing batch effects is essential for the analysis of data from multiple scRNA-seq experiments and multiple technical platforms. Here we introduce BERMUDA, a novel batch correction method for scRNA-seq data based on deep transfer learning. We use an autoencoder to learn a low-dimensional representation of the original gene expression profiles while removing the batch effects locally by incorporating MMD loss on similar cell clusters. BERMUDA provides several improvements compared to existing methods. Firstly, by introducing three different metrics to evaluate the batch correction performance we demonstrate that BERMUDA outperforms existing methods in merging the same cell types, preserving cell types not shared by all the batches, and separating different cell types. Secondly, BERMUDA can properly remove batch effects even when the cell population compositions across different batches are vastly different. Thirdly, BERMUDA can preserve batch-specific biological signals and discover new information that might be hard to extract by analyzing each batch individually. Finally, BERMUDA can be easily generalized to handle multiple batches and can scale to large datasets.


Effect of sugar concentration on transfer of water molecules across a semi-permeable membrane

Effect of temperature on Vitamin C concentration in Solanum lycopersicum
How does altering temperature affect the concentration of Vitamin C in Solanum lycopersicum as measured by a solution with 2,6-Dichlorophenol Indophenol?
Background information
Vitamin C refers to ascorbic acid and its salts. It is essential nutrient for humans as it is not synthesized by the body. Vitamin C acts as a cofactor in many enzymatic reactions, most notable of which is the synthesis of collagen. Consequently, lack of Vitamin C leads to the disease called scurvy.
Vitamin C is present in present in plants (such as strawberries, oranges, and in tomatoes) and in most animal meat (specifically the liver). However, the Vitamin C content in these foods is proportionate to the time and temperature they are stored or cooked at. This because oxidation occurs: 2 ascorbic acid + O2 2 dehydroascorbate + 2 H2O
The rate of this reaction increases with temperature. It is also further increased as fruits such as tomatoes have an enzyme called L-ascorbate oxidase which catalyzes the previous chemical reaction.
Nature of titration used: 2,6-Dichlorophenol Indophenol is a blue redox dye. When it is reduced by ascorbic acid, it turns colourless:
DCPIP (blue) + ascorbic acid→ DCPIPH2 (colorless) + dehydroascorbic acid
This reaction occurs in a 1:1 ratio ie 1 molecule of DCPIP reacts with 1 molecule of ascorbic acid.
Vitamin C content in tomatoes: According to the USDA, tomatoes have 13.7 mg of Vitamin C per 100 grams.
Hypothesis
I expect the volume of tomato solution needed to titrate the DCPIP to increase with temperature. This is because I predict Vitamin C oxidation to increase with temperature and thus Vitamin C content of the tomato will be inversely proportional to temperature. I expect the Vitamin C will decrease fairly constantly with temperature as the denaturing of the L-ascorbate oxidase will be compensated by the large increase in kinetic activity of particles.
Variables
Independent Variable:
Temperature to which tomato juice sample was heated: 20 °C, 40 °C, 60 °C, 80 °C, 100 °C.
These temperatures were obtained by heating all the tomato juice samples in a water bath and removing them as they reached the desired temperature.
Dependent Variable:
Volume of tomato solution required to neutralize 0.5 cm3 2,6-Dichlorophenol Indophenol (DCPIP). DCPIP becomes colorless when it is reduced. Thus, when all of the molecules of DCPIP are reduced by ascorbic acid, there is a visible color change. From this data, I then calculated the amount of Vitamin C (g) in 100 cm3 of the solution.
Control Variables Effect if left uncontrolled How it was controlled
Type of tomato Using different tomato cultivars or tomatoes at different ripeness would alter the amount of Vitamin C in the tomato solution. This would pose a problem when comparing it to literature values. I used tomatoes connected to the same vine, ensuring that the tomatoes came from the same plant and are of the same cultivar. These were all red and ripe.
Conditions under which tomatoes were stored after purchase. Storing tomatoes under sun and heat decreases their Vitamin C content. If tomatoes were stored at temperatures above 20 °C, it would also make the first set of results meaningless as the tomatoes had already been heated above that temperature. I purchased these tomatoes from an outdoor market. As it was winter time the temperature was well below 20 °C. Then they were stored in a refrigerator.
However, I do not know if these tomatoes were stored at higher temperatures before purchase.
Turbidity of tomato juice Greater turbidity would cause end point determination in titration to be more difficult. Solution had to be homogeneous so all trials had same amount of Vitamin C. I crushed the tomatoes into purée and I added water to dilute it. Then I filtered the juice to remove solid material. I did all the solution at once so that all the solutions used had the same level of turbidity. The solution was red/ orange so the endpoint was decided not when the solution was colorless but when it was the original red/ orange color.
Light intensity in room The endpoint was determined using my eyesight, which is subjective to light intensity. Lower light intensity would cause solution to appear darker thus I would titrate more tomato solution. I did the titration in the same room on the same day and at equal distance to window.
Temperature at which titration took place Temperatures alters the kinetic energy of particles, causing the reaction between DCPIP and ascorbic acid to occur at different rates. Therefore, titre would alter. Heated tomato juice was let to cool down to 20 °C. Temperature was measure using a temperature probe ±0.1°C .
Time over which tomato solution was heated Duration of heating affects overall oxidation. Longer time periods causes greater amounts of oxidation to take place, decreasing the ascorbic acid content.

Boiling tubes were heated together in the same water bath so that equal heat was applied. The solutions were then removed as they reached the required temperature ensuring that each consecutive sample was heated for the same time as the previous sample plus whatever time it for the next temperature to be reached. Although, I did not heat all the solutions for the same time, this was the greatest level of control that could have been done using lab apparatus. Once heated, they were placed in a beaker of water at 14 °C ±0.1°C to cool down quickly.
Volume of 1% DCPIP solution in test tube Greater volume of DCPIP would require greater quantities of tomato juice to be titrated for neutralization to occur. I fixed this volume at 0.5 cm3 for all of the trials.
Concentration of DCPIP and tomato solution Using different concentrations at each trial would alter the amount of volume needed for titration endpoint to be reached. Concentration of DCPIP was maintained at 1% and the tomato solution was made using 750 g of tomato and 250 cm3 of water.
Control Variables Effect if left uncontrolled How it was controlled
Rate at which tomato juice was added with burette The redox reaction requires time. Greater rate of addition would decrease the time allowed for reaction to occur. As a result, more tomato juice would be added. I fixed the rate to a steady stream of drops. I used the same burette and I marked the degree of opening on the burette so it would remain constant.
Amount of mixing during titration Mixing during the titration increases the kinetic energy of the particles, increasing the rate of reaction and decreasing the volume of tomato solution needed. No stirring would not produce accurate results as the reactants would not become in contact as easily as they would rely on diffusion. I mixed the solution of test tube using a steady movement of my hand throughout the trials for all of the trials. I realize that this is inaccurate. However, I did not have access to a magnetic stirrer.
Apparatus
2 cm3 glass pipette ±0.006 cm3
50 cm3 burette ±0.05 cm3
1000 cm3 beaker
5 boiling tubes
50 cm3 graduated cylinder ± 1 cm3
Filter paper
Data logger thermometer probe ±0.1°C
Digital balance ± 0.01 g
Materials
How it was made Uncertainty
1% DCPIP (2,6-Dichlorophenol Indophenol) solution It was produced by technician Unknown as it was produced by technician
75% Tomato solution Grind fresh tomatoes into a purée.
Measure 750.00 g of tomato purée using digital balance.
Measure 250 g of water using digital balance
Filter solution with filter paper Uncertainty of digital balance ±0.01 g
Uncertainty of Tomato purée: ±0.01 g
Uncertainty of Water: ±0.01 g
Overall uncertainty of Tomato solution:
(±0.02 g )/(1000 g)=±0.002 %
Method
Heating of tomato solutions
Check room is at 20 °C ±0.1°C with thermometer probe.
Transfer 50 cm3 of tomato solution using a 50 cm3 graduated cylinder into five boiling tubes.
Set one of the boiling tubes aside.
Combine 300 cm3 of water and 50 g of salt (NaCl) in a 500 cm3 beaker
Place 4 boiling tubes in beaker and heat with a Bunsen burner.
Place a thermometer in each boiling tube.
Remove the first boiling tubes when the thermometer reads 40°C ±1°C. Then place the boiling tube in the cold water bath until the thermometer reads 20 °C ±1°C.
Repeat step 7 as the boiling tubes respectively reach 60°C, 80°C, and 100°C.
Titration:
Check room is at 20 °C ±1°C with thermometer.
Transfer 0.5 cm3 of DCPIP using a 2 cm3 glass pipette into a test tube.
Decant 50 cm3 of the tomato solution heated to 20 °C solution into burette.
Titrate DCPIP into test tube using a burette ±0.1 cm3 until DCPIP loses dark blue color and becomes red/orange like original tomato solution.
Repeat steps 1-4 to obtain 5 trials.
Repeat step 1-5 using tomato solution heated to 40 °C, 60 °C, 80 °C and 100 °C.
Quantitative data
Table 1 to show the volume of tomato juice, which has been heated to different temperatures, needed to neutralize 0.5 cm3 of DCPIP in a titration
Temperature to which tomato solution was heated ±1 °C Start point of volume of tomato solution
±0.1 cm3 (1 DP) End point of volume of tomato solution
±0.1 cm3 (1 DP) Titre of tomato solution
±0.1 cm3 (1 DP) Average ±0.1 cm3 (1 DP) Standard deviation ±0.1 cm3 (1 DP)
20 Trial 1 0.0 4.2 4.2 5.0 0.8
Trial 2 11.5 17.6 6.1
Trial 3 17.6 23.0 5.4
Trial 4 23.0 27.8 4.8
Trial 5 35.0 39.5 4.5
40 Trial 1 13.0 18.6 5.6 6.3 0.6
Trial 2 18.6 24.8 6.2
Trial 3 24.8 31.5 6.7
Trial 4 31.5 37.4 5.9
Trial 5 37.4 44.5 7.1
60 Trial 1 14.0 19.8 5.8 6.5 0.7
Trial 2 19.8 26.7 6.9
Trial 3 26.7 33.2 6.5
Trial 4 33.2 39.2 6.0
Trial 5 39.2 46.7 7.5
80 Trial 1 13.8 21.0 7.2 7.4 0.9
Trial 2 21.0 27.8 6.8
Trial 3 27.8 34.1 6.3
Trial 4 34.1 42.5 8.4
Trial 5 41.5 49.8 8.3
100 Trial 1 11.1 18.6 7.5 7.7 0.3
Trial 2 18.6 26.7 8.1
Trial 3 26.7 34.0 7.3
Trial 4 34.0 41.7 7.7
Trial 5 41.7 49.5 7.8

Sample calculation:
Using the data from the 5 trials of tomato solution heated to 20 °C:
Average titre of tomato solution:(4.2 〖cm〗^3+6.1〖cm〗^3+5.4〖cm〗^3+4.8〖cm〗^3+4.5〖cm〗^3)/5=5.0〖cm〗^3
Standard deviation: σ=√(((4.2-5.0)^(2 ) 〖+(6.1-5.0)〗^2 〖+(5.4-5.0)〗^2 〖+(4.8-5.0)〗^2+(4.5-5.0)^2 )/5)= 0.75828≈0.8 cm3 (1 DP)
Qualitative Data:
Temperature to which tomato solution was heated (°C) Observations during heating Observations during titration
20 – End point was red/orange. However, there was still a faint tint from DCPIP. Furthermore, after some time, the solution darkened again.
40 Colour of tomato juice did not change. Bubbles were not produced.
60 Colour of tomato solution did not change. Some bubbles were produced in the water of the water bath.
80 More bubbles were produced in the water of the water bath. Slight darkening of tomato solution.
100 The tomato solution turned brown and produced foam. Water in beaker boiled.
Processed data
Although the following are chemical calculations, they allow me to understand the significance of my biology experiment:
I know from my background information that 1 mol of DCPIP reacts with 1 mol of Vitamin C. Since I know the concentration of DCPIP, I can calculate the mol of DCPIP that reacted and thus the mol of Vitamin C in every tomato solution.
1% DCPIP solution suggests there is 1 g DCPIP per 100 g of water. Thus,
1/100g=x/0.5g→x=0.005 g DCPIP in 0.5 cm3 of 1% solution

To find the moles one divides the mass by the molar mass : moles=mass/(molar mass)
Mr of DCPIP: 268.1 g mol-1
moles of DCPIP which reacted in the titration=(0.005 g)/(268.1 g 〖mol〗^(-1) )=1.865 x 10-5 mol
Thus, 1.865 x 10-5 mol Vitamin C also reacted in the titration.
To find mass of Vitamin C which reacted in the titration I use the formula:
mass reacted= Mr (vitamin C) x mol (of vitamin C reacted)
Mr of Vitamin C: 176.12
1.865 ×〖10〗^(-5) mol ×176.1 g mol〖 g〗^(-1)= 0.00328 g

The following is a sample calculation for trial 1 of the 20°C solution to explain how the g of Vitamin C in 100 g of pure tomato heated to 20 °C was obtained. I chose 100 grams as it is the standard amount used in the food industry allowing me to compare my results to literature values.
Titre: 4.2 cm3
Therefore there were 0.00328 g Vitamin C in 4.2 cm3 of 75% tomato solution.
To find g of Vitamin C in 100 cm3 of 75% tomato solution:
(0.00328 g)/(4.2 〖cm〗^3 )=x/(100 〖cm〗^3 )→x= 0.078 ≈ 0.08 g (2 DP)

75% tomato solution refers to the % of tomato mass in the solution. In order to obtain a ratio of mass/ volume just as the g of vitamin C in 100 cm3, I calculated the density of the tomato solution. Tomato puree has a density of 1.12 g per cm3 and water has a density of 1 g per cm3. 100 g of 75% tomato solution consists of 75g tomato puree and 25 g water:
├ █(75 g of tomato puree would occupy (75.00 g)/(1.12 g cm^(-3) )=66.96 cm^[email protected] g of water has a volume of 25 〖cm〗^[email protected])>25+66.96=91.96 〖cm〗^(3 ) is the volume of 100 g of 75% tomato solution
Therefore, (75 g)/(91.96 〖cm〗^3 ) is the ratio of tomato mass to volume of solution.

To find the grams of Vitamin C in 100 g of pure tomato, I divided the g of vitamin C in 100 cm3 of the 75% solution by the ratio of tomato to volume of the 75% solution:
0.078 g/(75 g)/(91.96 〖cm〗^3 ) = 0.09563 g ≈ 0.10 g ±0.01g (2 DP) of Vitamin C in 100 grams of solution.

The g of Vitamin C per 100 cm3 of the solutions in each trial are summarized in the table on the next page:
Table 2 to show the calculated amount of Vitamin C in 100 cm3 of the 100% solution. It also includes the average value along with the standard deviation, standard error and 2 standard error. The values shown are rounded to two decimal places as this was the precision of the balance used. However, as this level of precision does not allow one to appreciate the differences in standard deviation or standard error, I added a table in the appendix with the values to 4 decimal places.
Temperature to which tomato solution was heated ±1 °C
Mass of Vitamin C (g) in 100 cm3 of pure solution
±0.01 g (2 DP) Average grams of Vitamin C in 100 g of solution pure solution
±0.01g (2 DP) standard deviation
±0.01g
(2 DP) standard error
±0.01g
(2 DP) 2 standard error
±0.01g (2 DP)

20
Trial 1 0.10 0.08 0.01 0.01 0.01
Trial 2 0.07
Trial 3 0.07
Trial 4 0.08
Trial 5 0.09

Trial 1 0.07 0.06 0.01 0.00 0.01
Trial 2 0.06
Trial 3 0.06
Trial 4 0.07
Trial 5 0.06

Trial 1 0.07 0.06 0.01 0.00 0.01
Trial 2 0.06
Trial 3 0.06
Trial 4 0.07
Trial 5 0.05

Trial 1 0.06 0.06 0.01 0.00 0.01
Trial 2 0.06
Trial 3 0.06
Trial 4 0.05
Trial 5 0.05

Trial 1 0.05 0.05 0.00 0.00 0.00
Trial 2 0.05
Trial 3 0.06
Trial 4 0.05
Trial 5 0.05

Sample calculation:
Using the data from the 5 trials of tomato solution heated to 20 °C:
Average g of Vitamin C in 100 g of solution: (∑▒x)/N:(0.09858 g+0.0659g+0.0745g+0.0838g+0.0894g)/5=0.0819g≈0.08 g
Standard deviation: σ=√((∑▒〖(X-X ̅)〗^2 )/N) σ=√(((0.0958-0.0819)^(2 ) 〖+(0.0659-0.0819)〗^2 〖+(0.0745-0.0819)〗^2 〖+(0.0838-0.0819)〗^2+(0.0894-0.0819)^2 )/5)=0.0118≈0.01 (2 DP)
Standard error:(0.0118)/√5 =0.0053≈0.01 (2 DP) 2 standard error : 0.0053x 2=0.0106≈0.01
Graph
Graph 1 showing the mass of Vitamin C in pure tomato heated to different temperatures with error bars representing 2 standard error
The curve of best fit in this graph shows a negative correlation between the temperature to which the tomato solution was heated and g of Vitamin C per 100 g of tomato in the solution. The error bars represent 2 standard errors, thus there is 95% chance that the true values lie within the bars.
Conclusion
Qualitative data:
My qualitative data did not indicate a change in the amount of Vitamin C. Nonetheless, the darkening of the color of the tomato solution could be due to the fact that the red carotenoids in tomato degrade at high temperatures.
Quantitative Data:

My quanititative data saw an increasing volume of tomato solution react with DCPIP as the temperature increased. As vitamin C and DCPIP react in a 1:1 ratio, an increasing volume per the same amount of DCPIP signified that vitamin C concentration decreased with increasing temperature. Thus my hypothesis that ‘Vitamin C content of the tomato will be inversely proportional to temperature’ was supported

I used my titration results to calculate the g of vitamin C per 100 g of pure tomato pure. These chemical calculations indicated that the pure tomato heated to 20 °C had 0.08 g per 100 g whilst tomato heated to 100 °C had 0.05 g per 100g. That constitutes a 33% decrease in Vitamin C content.
Plotting the data in a graph shows that there is an overall decreasing trend throughout the 5 trials. However, whilst I hypothesized that the ‘Vitamin C will decrease fairly constantly with temperature as the denaturing of the L-ascorbate oxidase will be compensated by the large increase in kinetic activity of particles’, in the graph obtained is not linear one can observe the most significant decrease in Vitamin C content is between 20 °C and 40 °C and after that, there is no significant difference between 40 °C to 60 °C and 80 °C to 100 °C as the error bars overlap. This could be due to the fact that the L-ascorbate oxydaze enzyme present in tomatoes, which catalyzes Vitamin C oxidation, works optimally between 20 °C and 40 °C, and denatures soon after. In fact, according to the fact file on the enzyme provided by SEKISUI ENZYMES, L-ascorbate oxydaze’s optimum temperature is 45 °C and it’s activity declines rapidly afterwards . Overall, as temperatures above 20 °C are quite common, my data would indicate that it is very important for tomatoes to be stored at a cool temperature to avoid a significant reduction in vitamin C.
The data of g of vitamin C per 100 g of tomato was not dispersed as can be seen by the 2 standard error of 0.1 cm3 for 4 of the trials. This is insignificant as the uncertainty was ±0.1 cm3. Nonetheless, the values were not accurate, this is evidenced by the fact that at 20 °C I calculated a Vitamin C content of 0.08g per 100g whilst the literature value, according to the USDA , is 0.0137 g. This is a difference of which leads to a very high percentage error of 484%. As the uncertainty of ±0.01 g is much smaller than the error of 0.07 g I can conclude that the largest errors in my experiment were systematic (such as the water) and not random. I will explain these errors further in the evaluation. Nonetheless, the predominance of systematic error lead me to conclude that the overall trend is accurate as a systematic error affects the trials equally.
The decreasing trend in my results is coherent with an experiment done by Lucia Sánchez-Moreno, published in an article called ‘Impact of high-pressure and traditional thermal processing of tomato purée on carotenoids, Vitamin C and antioxidant activity’ for The Journal of the Science of Food and Agriculture. A paper by the Federal of University of Technology in Owerri, Nigeria, called ‘Temperature Effects on Vitamin C Content in Citrus Fruits’ also found that the most significant decreases in Vitamin C content occurred between 30 °C and 40 °C and 70 °C and 80 °C. Allthough these experiment were done on citrus fruits, the nature of the ascorbic acid present is the same.

Problems that occurred during the experiment Effect these had on experiment Improvements to be made if experiment was repeated
The titration method had inaccuracies due to the fact that the end-point was hard to determine as the tomato solution was not colorless. This was a systematic error in my method as the end-point was not accurate. This can be seen by the large error compared to literature values of 484% or which is not accounted for by the small uncertainty of ±0.01 g. The tomato solution would be filtered using a less permeable filter or a centrifuge to produce a clearer solution. This would allow one to observe the color change in the DCPIP more clearly. A colorimeter could be used to obtain a more precise endpoint. A different method altogether could be used. For example, measuring the absorbance of the solution using a UV-spectroscophotometer. However, the school lab did not have this apparatus.
During my titration I tried to swirl the test tubes consistently throughout the trials. Nonetheless, as it was done with my hand, it was not controlled properly as it is subject to human error. Therefore, the amount of mixing that occurred in each trial was different. Different amounts of mixing will cause different rates of reaction. This is important as a slower rate will cause more tomato solution to be titrated in, decreasing its value for Vitamin C content. Swirling a solution increases the particles’ kinetic energy, increasing the number of successful collisions between particles. Additionally, mixing allows the two solutions to come in contact easier. If there is no mixing, the solutions will take longer to come in contact with each other as they will rely solely on diffusion. If the experiment were repeated, I would use a magnetic stirrer. As it is mechanical, it will allow the solutions in each trial to be mixed equally and thus reducing the random error in my experiment.
The DCPIP was saturated. Some solid DCPIP deposited in the flask. I assumed the fact that DCPIP was a 1% solution. However, some DCPIP had precipitated out of the solution, the actual percentage of DCPIP in the solution was slight lower. This increased the values of Vitamin C. This could account for part of the 484% percentage error compared to literature values. If the experiment were repeated I would use a more diluted DCPIP solution. This would assure that the solution was not saturated so no DCPIP was deposited on the bottom. Furthermore, this would allow solution to be more homogeneous. Additionally, with a more diluted solution I could use greater volumes in the titration. This would reduce the effect of the uncertainties. Additionally, I would do the solution myself and not rely on the lab technicians.
When calculating the mass of vitamin C, I assumed that when the solution turned colourless, all of the molecules of DCPIP had reacted. However, this could not have been the case. If not all of the molecules of DCPIP reacted then, due to my assumption that ascorbic acid and DCPIP reacted in a 1:1 ratio, a inaccurately low mass of vitamin C was calculated. However, this error is unlikely to have been significant as my calculated masses were much greater than the literature values. If the experiment were repeated I would use a different method to calculate vitamin C. Instead of using chemical mol calculations, I could have titrated the DCPIP with a known Vitamin C solution (using an ascorbic acid tablet). The known mass of Vitamin C in this Vitamin C solution will be equal to the volume of the Vitamin C in the tomato solution titrated. Consequently, I could find the masses of Vitamin C in each solution.
Problems that occurred during the experiment Effect these had on experiment Improvements to be made if experiment was repeated
In my experiment, it was necessary to dilute and filter tomato purée in order for the titration to be accurate.
However, as I was interested in knowing the Vitamin C content of 100 g of pure tomato purée in order to compare it to known values, I used the value of Vitamin C in the dilution and I divided it by the mass of tomato puree in the solution to take into account for the dilution. By doing this calculation, I assumed that the water had had no effect on the decrease in Vitamin C content. This is questionable as water is needed for Vitamin C to oxidize. Nevertheless, there was already water present in the pure tomato. Additionally, the water affected all the trials equally so it did not affect the overall trend. If the experiment were repeated I would not dilute the tomato puree in water. This would nonetheless not allow the titration method to be useful so a different method would be necessary such as the spectroscopic technique explained previously.
Further investigation:
It would be interesting to investigate what happens to Vitamin Content above 100 °C as cooking temperatures normally range between 140 °C to 165 °C due to the nature of the Maillard reactions that occur .
Additionally, it would be noteworthy to investigate the effects of long term storage at below zero temperatures compared to normal room temperatures. This is important because from my experiment I was learnt that at the lowest temperature was when most oxidation occurred. Thus, if storage at these temperatures also leads to significant levels of oxidation, maybe freezing produce should be considered in order to maintain Vitamin C content.


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