spectrophotometric determination of iron in brocolli using visible spectrophotometer
DESCRIPTION
Research work done by my IB student Ken Loon. Please cite and give proper referencing to him on his work if you use this materialTRANSCRIPT
International Baccalaureate Diploma Program
Sri KDU Smart School
Extended Essay
-Chemistry-
Spectrophotemetric determination of iron content in
broccoli plants Brassica oleracea
Which part of the broccoli plant contains the highest iron content and
how different incubating temperature affects its iron content?
3990 Words Only
By
Choo Ken Loon
002206-004
2
Abstract
This extended essay is on spectophotometric determination of iron found in different parts of
the broccoli, Brassica oleracea and the effects of incubating temperature on the iron content.
Spectophotometry quantification was adopted. Iron(II) was reacted with orthophenanthroline
solution to produce orange-red colour complex exhibiting a maximum absorption peak, λmax at 510nm.
Beer-Lambert’s law is applied where the absorbance reading is directly proportional to the
concentration of the sample. A standard calibration curve for iron(II) solution was plotted which
shows a significant (P<0.05) and good correlation (R2 = 0.9929) between concentration and
absorbance. Samples from different parts of the broccoli (buds, stems and base) was obtained, heated
to ash and dissolved in hydrochloric acid(4.0M) to form the required solution. Broccoli bud samples
were incubated in water of different temperatures (24.0°C, 60.0°C, 80.0°C and 100.0°C) for the
second part of the investigation.
Statistical results show a significantly higher (P<0.05) iron content at the buds compared to the
stems and the base of the broccoli. The amount of iron in the buds and the stems are 2.90mg dm-3
g-1
to
0.30mg dm-3
g-1
respectively, indicating 90% higher iron content in the buds. Investigation on different
incubating temperature shows a negative correlation between incubating temperature and iron content.
Statistical results showed a significant decrease in iron content observed only when the incubating
temperature is at 100°C. Iron content for control sample(24.0°C), 8.30mg dm-3
g-1
decreases to 2.65mg
dm-3
g-1
at 100.0°C, implying a significant 61% drop. Incubating temperatures of 60°C to 80°C result to
a range of decrease of iron content between 1.8% and 25%. Statistical tests were carried out for all
results to support the significant difference between the samples (P<0.05).
The conclusion was that the iron content is highest at the buds of the broccoli and
increasing incubating temperature decreases iron content.
(296 words only)
3
Acknowledgement
I would like to thank:
Mr Lawrence Kok for his unending support and guidance
My parents for supporting me and enduring my needs
My friends for everything they have done
And
All the other people who helped in this investigation
4
Table of Contents
Abstract ............................................................................................................................................................................2
Acknowledgement ........................................................................................................................................................... 3
1.0 Introduction ..................................................................................................................................................... 5
1.1 Rationale of study ......................................................................................................................................... 5
2.0 Hypotheses ....................................................................................................................................................... 7
2.1 Investigation on the different parts of the broccoli plant .............................................................................. 7
2.2 Investigation on the different incubation temperature................................................................................... 9
3.0 Methodology ................................................................................................................................................... 10
4.0 Plotting a standard calibration curve for Iron(II) ...................................................................................... 12
4.1 Requirements for the plotting of the standard calibration curve for Iron(II) ............................................... 12
4.2 Procedure to prepare iron(II) phenanthroline complex and plotting the calibration curve .......................... 13
4.3 Data Collection ........................................................................................................................................... 14
4.4 Data Processing ........................................................................................................................................... 15
5.0 Methodology for iron extraction from different parts of the broccoli plants ........................................... 16
5.1 Preparing samples from different parts of the broccoli plant ...................................................................... 16
5.2 Reducing iron(III) and measuring its absorbance ....................................................................................... 17
5.3 Data Collection ........................................................................................................................................... 18
5.4 Data Processing ........................................................................................................................................... 20
6.0 Methodology adopted to investigate the effects of different incubation temperature on the amount of
iron in broccoli buds ..................................................................................................................................... 23
6.1 Preparing samples incubated in distilled water of different temperatures ................................................... 23
6.2 Reducing iron(III) and measuring absorbance ............................................................................................ 24
6.3 Data Collection ........................................................................................................................................... 24
6.4 Data Processing ........................................................................................................................................... 27
7.0 Data Presentation .......................................................................................................................................... 30
8.0 Data Processing: ANOVA and Tukey’s HSD Test ..................................................................................... 32
9.0 Data Analysis ................................................................................................................................................. 35
9.1 Parts of the broccoli plant ........................................................................................................................... 35
9.2 Incubating temperature ............................................................................................................................... 37
10.0 Evaluation ...................................................................................................................................................... 39
10.1 Uncertainties and Limitations ..................................................................................................................... 39
10.2 Ways of Improvement ................................................................................................................................. 40
10.3 Further Investigations ................................................................................................................................. 41
11.0 Conclusion ...................................................................................................................................................... 42
12.0 Reference ........................................................................................................................................................ 43
13.0 Appendix.........................................................................................................................................................45
5
1.0 Introduction
1.1 Rationale of Study
Iron, one of the most abundant metals on Earth, is essential as it maintains good
health [1]
including growth, reproduction and the human immune system. Iron is vital as a
component to form haemoglobin for oxygen transportation.
Iron deficiency is the number one nutritional disorder [2]
, it causes fatigue and anaemia
where red blood cells with low concentration of haemoglobin fail to supply adequate amount
of oxygen to the body tissues. Thus, it was found worthy to study iron because it is directly
related to our daily diet and health.
Questions were devised on how to maximize the iron intake when consuming
particular vegetable where certain variables were manipulated.
The iron content in different parts of a specific vegetable was initially investigated.
Broccoli, Brassica oleracea was chosen due to high iron content (0.73mg/100g)[4]
and a
source of many useful nutrition including high amount of calcium, beta-carotene, vitamin C
and fibre. This is to raise awareness regarding the proper part of the plant to be consumed
which gives us more iron. Further investigation was planned on the effects on iron content
due to the cooking procedure. It was proposed that the temperature of water used to incubate
vegetable would affect the nutrient content in the vegetable significantly. It is important to
create awareness on how simple preparation of vegetable would affect the nutrients found in
the particular vegetable as very often vegetables are overcooked and this affects the valuable
nutrients found in it.
6
The research is planned to be carried out on distinct parts of the broccoli plant
Brassica oleracea, namely the buds, stems and the base. The preparation method by soaking
broccoli in water was selected because it is a common and simple method of preparation.
Hence, the precise research question is:
“Which part of the broccoli plant contains the highest amount of iron and how
different incubating temperature affects its iron content?”
Figure 1: The different parts of the broccoli plant the buds, the stems and the base.
7
2.0 Hypotheses
2.1 Investigation on the different parts of the broccoli plant [5]
Chlorophyll found in the chloroplast is responsible for light absorption during
photosynthesis. Ferredoxin1, cytochromes b6f
2 and other electron-carrier proteins are involved
in the photosynthetic pathway while cytochrome c3, Fe –S clusters and other electron-carrier
proteins are present in mitochondrial electron-transport chain all which function as electron
carriers. All the listed protein complexes above have iron as part of its component.
It is hypothesized that broccoli buds contains the highest amount of iron because of
higher density of iron-containing electron-carrier proteins:
Photosynthetic pathway
1. Broccoli buds are greener due to higher density of Chlorophyll a4 compared to the
stems and base since it is needed for photosynthesis.
2. More electrons are excited due to higher amount of energy absorbed by chlorophyll.
3. More ferrodexin, cytochrome b6f proteins and other electron-carrier proteins are
required for electron transportation.
Mitochondrial electron-transport chain
1. Mitochondria are more abundant in broccoli buds compared to the stems and base of
the broccoli plant because the stems and base consist of mostly xylem and phloem
which functions in food and water transport.
1 Acidic, low molecular weight, soluble iron-sulphur proteins
2 Monomeric unit of the complex that contains six bound prosthetic groups, three hemes (f, two hemes b, bp
and bn), one [2Fe-2S] cluster, and one molecule each of chlorophyll-a and carotene 3 Able to transition between ferrous and ferric states within the cell, therefore functioning efficiently.
4 Magnesium-containing substituted tetrapyrroles.
8
2. More cytochrome c, Fe –S clusters and other electron-carrier proteins are required to
function for the transportation of electrons for the complex processes occurring in the
mitochondria which involves electron transfer for energy production.
Figure 2: Electron-carrier proteins in the form of ferrodexin and cytochrome b6f located in the
chloroplast.
Figure 3: Mitochondrial electron-transport chain where cytochrome c and Fe –S clusters are located.
9
2.2 Investigation on the different incubating temperature
It is hypothesized that iron content in the broccoli plant decreases as the incubating
temperature increases because at high temperature:
1. Cell walls break down easily.
2. Increased rate of denaturation of the protein membrane present at the cell membrane.
3. Increased kinetic energy5 increases probability of the electron-carrier proteins
diffusing out of the cell membrane.
4. Proteins in the plant might denature and react with each other forming unknown
products of altered physical properties such as the increased solubility and ability to
diffuse through the cell membrane, resulting to iron leaching out of the plant.
5. The number of molecules having higher energy increases, increasing rate of reaction
between molecules present in the plant according to the Maxwell–Boltzmann
distribution, thus iron-containing proteins reacts at a higher rate, resulting to a greater
loss of iron from the plant through leaching.
Figure 4: Maxwell-Boltzmann distribution on the distribution of molecular speed at different
temperature.
5 Average kinetic energy, EK is directly proportional to the absolute temperature. Emean = 3⁄2kBT.
Frac
tio
n o
f m
ole
cule
s
wit
h a
giv
en s
pee
d
10
3.0 Methodology
Since the iron content in broccoli is in small amount, a sensitive and less complicated method
was used – Spectrophotometry [6]
. The theory involved relates concentration of the solution
containing iron to its absorbance of light at a specific wavelength. A visible
spectrophotometer which passes a light beam with wavelength complement to the colour of
the sample was use to measure the amount of light each iron(II) sample of different
concentrations would absorb.
For this investigation, the product was orange-red in colour, thus a light beam of
specific wavelength which exhibits an absorbance peak at 510nm, λmax was used.
Beer’s Law[7]
was applied, it can be expressed as:
A = εlc6
The same type of cuvette was used in all the measurements and the molar absorptivity is
constant for all the samples, therefore both ε and l are constants. Hence, this indicates that the
absorbance of a sample is directly proportional to its concentration, A c and the relationship
between variables A and c is linear.
In this research, iron solution is reacted with orthophenanthroline (ο-phen) (figure 7)
to form an orange-red iron(II) orthophenanthroline complex (figure 8) at optimum pH of 3.5
where the complex would be stable for at least 20 hours at pH 2 – 9 [8]
. The chemical
equation is given as:
Fe2+
+ 3Phen ↔ Fe(Phen)3 2+
6 A is the absorbance of the sample. ‘ε’ is the molar absorptivity, which is a constant. ‘l’ is the thickness of the
cuvette holding the sample. ‘c’ is the concentration of the absorbing species.
11
The stoichiometric ratio is 1: 3.
Figure 5: The structure of o-phenanthroline.
Figure 6: The structure of iron(II) phenanthroline complex and the solution.
Figure 7: Visible Spectrophotometer
12
4.0 Plotting a standard calibration curve for Iron(II)
4.1 Requirements for the plotting of the standard calibration curve for Iron(II)
Iron(II) solution with known concentration were prepared. The solutions were reacted with o-
phennanthroline to form an iron complex and the absorbance of each complex measured. The
required solutions7 are:
1. Iron(II) standard solutions8 of concentrations 25.0x10
-5M, 10.0x10
-5M, 7.50x10
-5M,
6.00x10-5
M, 5.00x10-5
M and 2.50x10-5
M.
2. 10% hydroxylammonium chloride9, NH3OHCl solution.
3. 5% of trisodium citrate10
, Na3C6H5O7 solution.
4. 0.01M Orthophenanthroline solution of 0.01M.
7 See appendix 1 for procedures to prepare the required solutions.
8 See appendix 2 for dilution of iron(II) standards
9 Excess reducing agent.
10 Buffer reagent.
13
4.2 Procedure to prepare iron(II) phenanthroline complex and plotting the
calibration curve
1. 1.0cm3 of 25.0x10
-5M iron(II) standard was transferred into a 100.0cm
3 container
using a micropipette.
2. 0.5cm3 of 5% trisodium citrate solution was added followed by 0.5cm
3 of 10%
hydroxylammonium chloride solution and finally 1.0cm3 of 0.01M phenanthroline
solution.
3. The mixture was left for 60 minutes for the orange-red complex to form completely.
4. 1.0cm3 of the solution was transferred into a cuvette.
5. The instrument was calibrated using a blank solution11
with wavelength setting at
510nm.
6. The cuvette was inserted into the cuvette holder inside the visible spectrophotometer.
7. The absorbance reading was recorded at 510nm, λmax12
, again after 90 and 120
minutes.
8. Steps 1-7 were repeated using iron(II) standards of 10.0x10-5
M, 7.50x10-5
M,
6.00x10-5
M, 5.00x10-5
M and 2.50x10-5
M.
Figure 8: Iron(II) phenanthroline complex at different concentration.
11
A solution containing all reagents except iron. 12
A wavelength that exhibits maximum absorption peak.
14
4.3 Data Collection
Concentration, c /
mol dm-3
Absorbance reading, A after Mean Absorbance(a)
± (95%) Confidence
Interval13
60 minutes 90 minutes 120 minutes
25.0x10-5
0.801 0.761 0.721 0.761 ± 0.100
10.0x10-5
0.337 0.373 0.302 0.337 ± 0.090
7.50x10-5
0.228 0.233 0.232 0.231 ± 0.007
6.00x10-5
0.189 0.206 0.196 0.197 ± 0.020
5.00x10-5
0.059
0.202 0.107 0.123 ± 0.600
2.50x10-5
0.118 0.062 0.089 0.090 ± 0.070
Table 1: The concentration of iron(II) standards, the corresponding absorbance reading after 60, 90 and 120
minutes respectively and the mean absorbance.
(a) Mean absorbance ± (95%) Confidence Interval obtained at 60, 90 and 120 minutes.
Standard deviation for standard iron(II) concentrations were not recorded as uncertainty due
to the apparatus used for the preparation of the standards is assumed to be insignificant.
Due to the instability of the instrument, the absorbance readings fluctuate throughout the
absorbance measuring process. Mean absorbance was calculated using the absorbance
measured at intervals of 60, 90 and 120 minutes and the confidence interval was calculated.
13
See appendix 3 for further calculations on (95%) confidence interval.
15
4.4 Data Processing
Graph 1: The calibration curve for iron(II) plotted from the collected data.
(a) Error bars denote (95%) confidence interval.
(b) Significant correlation between concentration and absorbance (R
2 = 0.9976, α = 0.05, P<0.05)
14
14
See appendix 4 for regression analysis.
y = 0.3045x + 0.0055R² = 0.9929
(P<0.05)
-0.200
0.000
0.200
0.400
0.600
0.800
1.000
0 0.5 1 1.5 2 2.5 3
Ab
sorb
ance
, A
Concentration of iron, c / x10-4 mol dm-3
Graph of Absorbance, A against Concentration, c / x10-4 mol dm-3
(a)
16
5.0 Methodology for iron extraction from different parts of the broccoli plants
The samples undergo dry ashing so that iron would be oxidized by air. Ash was dissolved in
(4.0M) hydrochloric acid15
to obtain an aqueous solution for absorbance measurement.
5.1 Preparing samples from different parts of the broccoli plant
1. Random samples from the buds, stems and base were collected from a broccoli plant
(Brassica oleracea). Triplicate samples were obtained from different broccoli plants.
2. The samples were heated for 30 minutes in an oven on a heating tray to obtain dry
mass16
.
3. 4.000g of samples from the buds, stems and base respectively was weighed using and
electronic balance (±0.001g) and placed into a crucible. Triplicate samples were
prepared.
4. The crucibles without the lids on were heated directly in a crucible using a Bunsen
burner for six hours.
5. 1.0cm3 of concentrated hydrochloric acid (4.0M) was added to each of the crucible to
dissolve the ash to form aqueous iron(III) solution.
Figure 9: Samples from the broccoli buds, stems and base on a heating tray and during dry ashing.
15
See appendix 5 for pictures of the ash dissolved in HCl acid. 16
Mass of the dried matter which does not contains water.
17
5.2 Reducing iron(III) and measuring its absorbance
1. 1.0cm3 of solution together with ash was transferred into a microcentrifuge tube using
a micropipette (100 – 1000)µl. The centrifuge machine is used to precipitate
unwanted ash in the microcentrifuge tube.
2. 1.0cm3 of solution is transferred from the microcentrifuge tube into a 100.0cm
3
container.
3. 0.5cm3 of 5% trisodium citrate solution was added followed by 0.5cm
3 of 10%
hydroxylammonium chloride solution and finally 1.0cm3 of 0.01M phenanthroline
solution.
4. Absorbance of the samples was measured at time intervals 60, 90, 120 minutes.
18
5.3 Data Collection:
Part of the plant
Random
samples
(triplicates)
Absorbance reading, A after Mean
absorbance for
each sample
Mean absorbance(a)
±
(95%) Confidence
Interval 60 minutes 90 minutes 120 minutes
Buds
1 0.596 0.669 0.621 0.629 0.639±0.100
2 0.602 0.730 0.616 0.649
3(b)
- - - -
Stems
1 0.077 0.152 0.033 0.087
0.079±0.100 2 0.060 0.089 0.064 0.071
3(b)
- - - -
Base
1 0.054 0.118 0.012 0.061
0.070±0.020 2 0.089 0.055 0.075 0.073
3 0.014 0.145 0.070 0.076
Table 2: Parts of the plant, random samples (n=3), the absorbance reading after 60, 90 and 120 minutes respectively and the mean absorbance with (95%) confidence
interval for triplicate samples.
(a) Mean absorbance ± (95%) Confidence Interval obtained for the random triplicate samples.
(b) / - Results were not included into the mean absorbance due to inconsistencies and irregularities. A 90% confidence interval Q test (rejection test) is performed to determine
the outliners in the data collected.17
17
See appendix 7 for further calculations on Q test.
19
Qualitative Observations
Some brown-black coloured substance18
was observed sticking on the side of the inner wall
of the crucible after heating. The black substance was scraped off using a sharp pointed
wooden splinter and was dissolved in hydrochloric acid (4.0M) together with the ash. The
intensity of the orange-red coloured complex decreases in the sequence of buds, stems and
base.
18
See Appendix 5 for picture of the brown-black substance sticking on the wall.
20
5.4 Data Processing With reference to the standard calibration curve, the concentration of iron(II) in the different
part of the broccoli plant was determined.
Parts Of The
Broccoli
Plant
Mean Absorbance(a)
± (95%) Confidence
Interval
(n = 3)
Concentration of
iron(II) ± SD19
, c /
x10-4
mol dm-3
Concentration of
iron(II) ± SD, c /
mg dm-3
Amount of
iron(II) per
gram of sample
± SD, c /
mg dm-3
g-1
Buds 0.639±0.100 2.08±0.78
11.61±0.44 2.90±0.11
Stems 0.079±0.100 0.241±0.068 1.35±0.38 0.34±0.09
Base 0.070±0.020 0.212±0.061
1.18±0.34 0.30±0.09
Table 3: Parts of the broccoli plant, mean absorbance with (95%) confidence interval and the amount of iron(II)
per gram of sample.
(a) Mean absorbance ± (95%) Confidence Interval obtained for the random triplicate samples.
SD = Standard Deviation
19
See Appendix 8 for calculations for the standard deviation of the concentration of iron(II).
21
Example calculation on the amount of iron(II) found in broccoli buds
From the standard calibration curve of iron(II), the regression equation relating absorbance to
concentration is given as A = 0.3045c + 0.0055 (refer to page 16). Since the graph is plotted
as absorbance, A against concentration, c x10-4
, the final value of c is multiplied by 10-4
. The
mean absorbance for samples from the buds is 0.639.
Therefore: c = ( 𝐴−0.0055
0.3045 ) x 10
-4
= ( 0.639−0.0055
0.3045 ) x 10
-4 = 2.08x10-4
M
The value in 2.08x10-4
mol dm-3
is converted to units of mg dm-3
by multiplying with the
relative atomic mass of iron, 55.85g mol-1
.
Therefore: c = 2.08x10-4
M x 55.85g mol-1
= 11.61mg dm-3
Finally, calculations were made to determine the amount of iron per gram sample.
Therefore: c = 11.61
4 = 2.90mg dm
-3g
-1
Similar calculations were performed for different parts of the plants.
22
Example calculation on the standard deviation of amount of iron(II) found in broccoli buds
Calculations for standard deviation were performed using Windows Excel 2007. The
formula20
used is as shown below:
SX = sr
𝑚 {
1
𝑀 +
1
𝑁 +
(𝑌 𝑋− 𝑦 )2
𝑚2 (𝑥𝑖− 𝑥 )2}
1/2
= 0.02305
3044.14 {
1
2 +
1
6 +
(0.639− 0.290)2
(3044.14)2(3.26x10−8)}
1/2
= 7.83x10-6
mol dm-3
The value in 7.83x10-6
mol dm-3
is converted to units of mg dm-3
by multiplying with the
relative atomic mass of iron, 55.85g mol-1
.
Therefore: SX = 7.83x10-6
M x 55.85g mol-1
= 0.437mg dm-3
Finally, calculations were made to determine the standard deviation for concentration, SX per
gram sample.
Therefore: c = 0.437
4 = 0.109mg dm
-3g
-1
Similar calculations for standard deviation were performed for different parts of the broccoli
plant.
The results show that the broccoli buds contained the highest amount of iron, therefore,
broccoli bud was selected for the second part of the research question which investigates the
effects of incubating temperature on the amount of iron in broccoli buds.
20
See appendix 6 for the explanations of the symbols used in the formula.
23
6.0 Methodology adopted to investigate the effects of different incubating
temperature on the amount of iron in broccoli buds
6.1 Preparing samples incubated in distilled water of different temperatures
1. Random samples from the buds were collected from broccoli plant (Brassica
oleracea). Triplicate samples were obtained from different broccoli plants.
2. Water baths at room temperature 24.0°C, 60.0°C, 80.0°C and 100.0°C were prepared
using electric water baths.
3. 4.000g of samples from the buds was weighed using an electronic balance (±0.001)
into separate water baths. Triplicate samples were prepared for each temperature.
4. The samples were soaked for 15 minutes and then filtered.
5. The filtered samples were carefully transferred to crucibles without the lids on and
then heated directly using a Bunsen burner for six hours.
6. 1.0cm3 of concentrated hydrochloric acid (4.0M) was added to each of the crucible to
dissolve the ash to form aqueous iron(III) solution.
Figure 10: Water baths used to incubate the samples.
24
6.2 Reducing iron(III) and measuring absorbance
Same procedures from part 5.2 (page 18) was carried out to reduce the iron(III) in the
samples and to measure the absorbance.
6.3 Data Collection
Due to the intense colour formed by the complex, the samples were diluted by a factor of 5 to
reduce the orange-red colour intensity formed, enabling the visible spectrophotometer to
measure the absorbance21
.
1.0cm3 of sample was pipette out and diluted with 4.0cm
3 of distilled water to create a 5.0cm
3
solution.
21
A deviation would occur if the intensity of the sample is too high.
25
Incubating
temperature
(±0.1)°C
Random samples
(triplicates)
Absorbance reading, A after Mean absorbance
for each sample
Mean absorbance(a)
±
(95%) Confidence
Interval 60 minutes 90 minutes 120 minutes
Room
Temperature,
24.0°C
1 0.289 0.457 0.371 0.372
0.369±0.020 2 0.356 0.408 0.365 0.376
3 0.342 0.463 0.270 0.358
60.0°C
1 0.340 0.429 0.373 0.381
0.362±0.050 2 0.316 0.400 0.310 0.342
3 0.316 0.438 0.334 0.363
80.0°C
1(b)
- - - -
0.280±0.030 2 0.221 0.415 0.226 0.287
3 0.241 0.319 0.258 0.273
100.0°C
1 0.067 0.050 0.082 0.066
0.121±0.200 2 0.068 0.119 0.096 0.094
3 0.180 0.270 0.181 0.204
Table 4: Incubating temperature, the sample number, the absorbance reading after 60, 90 and 120 minutes respectively and the mean absorbance with (95%) confidence
interval for triplicate samples, all of which the samples were diluted by a dilution factor of 5.
(a) Mean absorbance ± (95%) Confidence Interval obtained for the random triplicate samples.
(b) / - Results were not included into the mean absorbance due to inconsistencies and irregularities.
26
Qualitative Observations
After incubating the broccoli buds in water of 100.0°C, it is observed that the water turns
greenish in colour. The intensity of the greenish colour decreases as the temperature of the
water decreases. However, the intensity of the orange-red coloured complex decreases as the
temperature of the water for the sample increase.
Figure 11: The greenish colour filtrate for the samples at 100.0°C incubating temperature.
27
6.4 Data Processing
The absorbance for the diluted concentration and its actual concentration expressed in mg dm-3
g-1
for samples incubated at different temperature:
Incubating
temperature
(±0.01)°C
Diluted concentration(a) Actual concentration
Mean absorbance(b)
± Confidence Limit
Diluted concentration
of iron(II)
± SD22
, c /
x10-4
mol dm-3
Concentration of
iron(II) ± SD, c /
mg dm-3
Amount of
iron(II) in one
gram of sample
± SD, c /
mg dm-3
g-1
Amount of iron(II) in
one gram of sample ±
SD, c /
mg dm-3
g-1
Room
Temperature, 24.0 0.369±0.020 1.19±0.06
6.64±0.31 1.66±0.08 8.30±0.38
60.0 0.362±0.050 1.17±0.05
6.53±0.30 1.63±0.08 8.15±0.38
80.0 0.280±0.030 0.901±0.062
5.03±0.35 1.25±0.09 6.25±0.43
100.0 0.121±0.200 0.379±0.058
2.12±0.33 0.53±0.08 2.65±0.40
Table 5: Incubating temperature, mean absorbance with (95%) confidence interval for triplicate samples, its corresponding concentration of iron(II) when compared to the
calibration curve, concentration of iron(II) in mg dm-3
and the concentration of iron(II) in one gram of sample. The data are separated as diluted concentration and actual
concentration.
(a) diluted by a factor of 5
(b) Mean absorbance ± (95%) Confidence Interval obtained for the random triplicate samples.
SD Standard Deviation
22
See Appendix 6 for calculations for the standard deviation of the concentration of iron(II).
28
Example calculation on the amount of iron(II) found after incubating at 100.0°C
From the standard calibration curve of iron(II), the regression equation relating absorbance to
concentration is given as A = 0.3045(c x 10-4
) + 0.0055 (refer to page 16). Since the graph is
plotted as absorbance, A against concentration, c x10-4
, the final value of c is multiplied by 10-4
.
The mean absorbance for samples incubated at 100.0°C is 0.121.
Therefore: c = ( 𝐴−0.0055
0.3045 ) x 10
-4
= ( 0.121−0.0055
0.3045 ) x 10
-4 = 0.379x10-4
M
The value in 0.379x10-4
mol dm-3
is converted to units of mg dm-3
by multiplying with the
relative atomic mass of iron, 55.85g mol-1
.
Therefore: c = 0.379x10-4
M x 55.85g mol-1
= 2.12mg dm-3
Calculations were made to determine the amount of iron per gram sample.
Therefore: c = 2.12
4 = 0.53mg dm
-3g
-1
Finally, the value obtained was multiplied by 5 because the samples were diluted by a factor
of 5.
Therefore: c = 0.53 x 5 = 2.65mg dm-3
g-1
Similar calculations were performed for different incubating temperature.
29
Example calculation on the standard deviation of amount of iron(II) found after incubating at
100.0°C
Calculations for standard deviation were performed using Windows Excel 2007. The
formula23
used is as shown below:
SX = sr
𝑚 {
1
𝑀 +
1
𝑁 +
(𝑌 𝑋− 𝑦 )2
𝑚2 (𝑥𝑖− 𝑥 )2}
1/2
= 0.02305
3044.14 {
1
3 +
1
6 +
(0.121− 0.290)2
(3044.14)2(3.26x10−8)}
1/2
= 5.84x10-6
mol dm-3
The value in 5.84x10-6
mol dm-3
is converted to units of mg dm-3
by multiplying with the
relative atomic mass of iron, 55.85g mol-1
.
Therefore: SX = 5.84x10-6
M x 55.85g mol-1
= 0.33mg dm-3
Calculations were made to determine the standard deviation for concentration, SX per gram
sample.
Therefore: c = 0.33
4 = 0.08mg dm
-3g
-1
Finally, the value obtained was multiplied by 5 because the samples were diluted by a factor
of 5.
Therefore: c = 0.33 x 5 = 0.40mg dm-3
g-1
Similar calculations were performed for different incubating temperature.
23
See appendix 6 for the explanations of the symbols used in the formula.
30
7.0 Data Presentation
Graph 2: The graphical representation of the amount of iron(II) and in various parts of the broccoli plant.
(a) = Error bars denote (95%) confidence interval of triplicate samples of each part of the broccoli plant.
0
0.5
1
1.5
2
2.5
3
3.5
Buds Stems Base
Co
nce
ntr
atio
n o
f ir
on
(II)
in o
ne
gra
m o
f sa
mp
le,
c/
mg
dm
-3g-1
Parts of the broccoli plant
The parts of the broccoli and the corresponding concentration of iron(II) per gram of sample, c / mg dm-3 g-1
(a)
31
Graph 3: The graphical representation of the amount of iron(II) and at different incubating temperature.
(a) = Error bars denote (95%) confidence interval of triplicate samples for each incubating temperature.
0
1
2
3
4
5
6
7
8
9
10
24 60 80 100
Am
ou
nt
of
iro
n(I
I) in
on
e gr
am o
f sa
mp
le,
c/
mg
dm
-3g-1
Incubating temperature, T / °C
The incubating temperature, T / °C and the corresponding amount of iron(II) per gram of sample, c / mg dm-3 g-1
(a)
32
8.0 Data Processing: ANOVA and Tukey’s HSD Test24
Data processing is carried out using Analysis of Variance (ANOVA) and Tukey’s HSD
(honestly significant difference) test. ANOVA shows the variance between or within each
group.
A null hypothesis is first assumed where there is no difference between the means of different
groups. Then the F ratio is calculated.
F ratio = mean square between groups
mean square within groups
The result of ANOVA shows whether the F ratio is greater than the F critical value at the
significance level of 0.05 (α=0.05), if so, the null hypothesis is rejected and there is one
group that is significantly different from others.
Three assumptions were made to carry out ANOVA:
1. The observations are independent (the value of one observations is not correlated with
the value of another).
2. The observations in each group are normally distributed.
3. Equal variance for all groups.
Tukey’s HSD post hoc analysis is then conducted to test the hypothesis that all possible pairs
of means are equal. Pairs with differences exceeding the HSD are considered to be
significantly different.
24
See appendix 9 for ANOVA and Tukey’s HSD Test.
33
ANOVA is performed using Microsoft Excel 2007 while Tukey’s HSD25
test is calculated
manually. The results are tabulated as shown below:
Variable F-value F-critical Indication
Parts 2012.24 6.94 ANOVA tests on both sets of
data show F-value > F-critical.
This indicates that there is a
group which is significantly
different from the others in
their own respective set of
data.
Temperature 23.80 4.35
Table 6: Results of ANOVA on two sets of data to determine whether there is significant difference between
the mean absorbance reading of samples obtained from different parts of the broccoli and for the variable of the
temperature of water which the broccoli buds were soaked in.
25
See appendix 9 for ANOVA and Tukey’s HSD Test.
34
Tukey’s HSD multiple comparison test was carried out and the results are tabulated as shown
below:
Group Combination
Mean difference of
absorbance reading HSD critical value Implication
Mean absorbance
reading of different
parts of the broccoli
plant
Buds Stems 0.560 0.029 Significant difference
Buds Base 0.569 0.029 Significant difference
Stems Base 0.009 0.029 No Significant difference
Table 7: Results of Tukey’s HSD test on the mean absorbance reading of samples from each part of the broccoli
plant to determine which group is significantly different than the other.
Significance test at α = 0.05.
Group Combination, °C Mean
difference
absorbance
reading
HSD critical
value Implication
Mean absorbance reading of
broccoli plant soaked in different
temperature of water
100 80 0.159 0.111 Significant difference
100 60 0.241 0.111 Significant difference
100 24 0.247 0.111 Significant difference
80 60 0.082 0.111 No significant difference
80 24 0.089 0.111 No significant difference
60 24 0.007 0.111 No significant difference
Table 8: Results of Tukey’s HSD test on the mean absorbance reading of samples that have been incubated in
water of different temperature to determine which group is significantly different than the other.
Significance test at α = 0.05.
35
9.0 Data Analysis
9.1 Parts of the broccoli plant
Graph 2 indicates that iron content is highest in the broccoli buds with 2.90mg dm-3
g-1
followed by the stems and base which is 0.34mg dm-3
g-1
and 0.30mg dm-3
g-1
respectively
indicating that the iron content in the broccoli buds is 88% ~ 90% higher when compared to the
stems and base.
Statistical tests performed at α=0.05 showed a significant difference in iron content
found in broccoli buds and stems and broccoli buds and base as the mean difference of each
group exceeds the critical value (Table 7). There is no significant difference between the iron
content in broccoli stems and base. Therefore, results show that iron content is highest at the
broccoli buds.
There are two possible explanations on why iron content is highest at the broccoli buds:
The broccoli buds are greener compared to the stems and the base due to higher
chlorophyll density. Therefore, more light energy is absorbed for electron excitation during
photosynthesis. Specific iron-containing electron-carrier proteins like ferredoxin and
cytochrome b6f are required for electron transportation. In conclusion, broccoli buds are
greener due to high chlorophyll density which absorbs more energy for more electron
excitation. Thus, more iron-containing electron-carrier proteins are required for electron
transportation, resulting to highest iron content in the broccoli buds.
Another possible explanation is that electron-transport chains are present in the
mitochondria. Mitochondria is found abundantly in the broccoli buds because they are
involve in energy production while the stems and base contains xylem and phloem are
involved in food and water uptake. Iron-containing electron-carrier proteins such as
36
cytochrome c and Fe –S clusters transports electrons in the mitochondrial electron-trasport
chain. In conclusion, this process occurs more frequently in the broccoli buds due to higher
abundance of mitochondria, resulting in a greater number of iron-containing electron-carrier
proteins found in the broccoli buds. Thus, iron content is highest in the broccoli buds.
37
9.2 Incubating temperature
Graph 3 indicates that the iron content in broccoli buds samples incubated at 100.0°C
is the lowest followed by samples incubated at 80.0°C, 60.0°C and room temperature 24.0°C.
Results show that iron content in the control sample incubated at room temperature 24.0°C
and sample incubated at 100.0°C is 8.30mg dm-3
g-1
and 2.65mg dm-3
g-1
respectively
indicating a 68% decrease in iron content after incubating at 100.0°C.
Table 5 shows that some data have been omitted due to inconsistencies and
irregularities compared to the other data. However, for this part of the data collected, Q
rejection test was not performed. The omission was based solely on observations as the
outlier data would influence the results of the investigation.
Statistical tests performed at α=0.05 showed a significant difference in iron content of
broccoli buds samples incubated at 100.0°C when compared to samples incubate at 80.0°C,
60.0°C and room temperature 24.0°C where the mean difference of each group exceeds the
critical value (Table 8).
Preliminary data suggest that iron content is the lowest when broccoli buds samples
are incubated at 100.0°C. Given a few possible explanations:
Cell walls break down easily and rate of denaturation of the protein membrane present
at the cell membrane increases when exposed to high temperature. This enables proteins
found in the buds to leach or diffuse out of the buds more easily which includes the diffusion
of iron-containing electron-carrier protein complexes such as ferredoxin, cytochromes b6f,
cytochrome c and Fe –S clusters. High temperature increases kinetic energy of the electron-
carrier proteins, resulting to the increased probability of the protein molecules to diffuse out
of the cell membrane. This is further suggested by the qualitative observations as the sample
38
filtrate of 100.0°C incubating temperature is greener compared to other sample filtrate of
lower incubating temperature. This indicates that maybe chlorophyll diffuses out of the
sample, causing the green colouration. In conclusion, increased incubation temperature
causes increased rate of diffusion.
It is also possible that the proteins present in the buds are denatured and reacts with
each other forming unknown products of possible physical property changes such as
increased solubility or the ability to diffuse through the cell membrane, resulting to higher
quantity of iron diffusing out of the broccoli buds. Maxwell–Boltzmann distribution indicates
that rate of reaction increases as temperature increases, thus temperature directly influences
the rate of iron diffusing out of the broccoli bud. Therefore, iron content is lowest after
broccoli buds samples are incubated at 100.0°C when compared to other lower incubating
temperatures.
39
10.0 Evaluation
10.1 Uncertainties and Limitations
Errors might occur during dry ashing of the samples using crucibles due to incomplete
combustion. This is evident as some black substance suspected as carbon and unoxidized iron
is still visibly sticking to the crucible. Since no lids were used, iron which is considered a
volatile metallic compound may be lost during combustion.
When reducing iron(III) and measuring the absorbance, solutions were added in the
order: trisodium citrate solution, hydroxylammonium chloride solution and phenanthroline
solution. It is unknown whether the sequence in which the solutions were added would affect
the intensity of the orange-red complex formed. Simplified and modified procedures were
carried out for this investigation due to time constraint as it is solely for iron content
comparison and it does not reflects the actual amount of iron in the broccoli.[8]
There might be interfering ions present in broccoli plants which would react with
phenanthroline solution to produce a colour complex, interfering with the intensity of the
orange-red complex produced by iron-phenenahroline complex.
Due to time constraint, only triplicate random samples were performed in this
investigation (n=3). Although random sampling was performed, the sample size (n=3) is too
small to be a good representation of the general population of Brassica oleracea.
Contamination in the samples might occur due to impurities left on the broccoli plants
after washing. This is evident as some of the outlier data were omitted due to inconsistencies
when compared to other samples.
40
Systematic error also arises due to the visible spectrophotometer. Fluctuations occur
frequently when the absorbance of the samples were being measured, giving inconsistent
absorbance readings.
The dry mass of the samples might vary as there is difficulty in obtaining the dry mass.
Samples heated in the over for too long might be charred but samples heated for a short
duration might still contain water. Hence, this uncertainty affects the accuracy on the iron
content in the samples.
10.2 Ways of Improvement
Methods can be carried out to improve dry ashing method used. Microwave furnaces
can be used to replace the crucibles used. Samples are placed in small chamber to undergo
combustion quickly and preventing the loss of volatile metallic compounds. Although
expensive, it provides an alternative method of dry ashing using crucible which would reduce
the systematic error present. [9]
Atomic-absorption spectroscopy (AAS) can be used for more accurate quantification
of iron in various samples. Samples are first vaporized and absorption of visible light excites
the electrons to a higher electronic energy level, enabling the concentration of iron to be
accurately quantified. Although AAS instrument would provide accurate data, it is an
expensive equipment. [10]
Other colorimetric reagents can be used to increase the sensitivity of iron
quantification such as 4,7-Diphenyl-1,10-phenanthroline as it extracts the iron in the reagents
and water used in the test and thus of reducing the blank to zero. [11]
41
Sample size should be increased in order to improve the accuracy and reliability of the
data. Increasing sample size will reduce the standard deviation. The larger the sample size,
the more likely it is able to represent the general population of Brassica oleracea.
10.3 Further Investigations and Unresolved Questions
Prior investigation with o-phenanthroline on spinach, Amaranthus gangeticus on iron
content in different coloured part of the leaves: red and green failed due to interfering ions
and inconsistent data collected. Thus, method of quantification other than using o-
phenanthroline will be needed for this investigation.
Investigation on the effects of different cooking methods on the iron content of the
vegetable such as stir-frying, steaming and microwaving should be explored in order to raise
awareness. It is believed that iron content would not decrease as significantly if broccoli is
steamed or microwaved because water is not directly in contact with the broccoli, thus
reducing leaching of iron from the broccoli.
An investigation can be carried out on how pH condition affects the iron content in
broccoli. Broccoli can be incubated in water of different pH value for this investigation to
determine the effects of pH on the iron content.
42
11.0 Conclusion
With enough supporting statistical evidence, it can be concluded that broccoli
(Brassica oleracea) buds has the highest iron content at 88% ~ 90% higher than the iron
content at the stems and buds.
After being incubated at temperature of 100.0°C, the iron content in the broccoli buds
significantly decreases by 68% when compared to samples incubated at room temperature
24.0°C. It is suggested that iron content decreases as the incubating temperature increases.
43
12.0 Reference
[1] National Institutes of Health, 2004. Dietary Supplement Fact Sheet: Iron. [Online]
(Updated 24 August 2007)
Available at: http://ods.od.nih.gov/factsheets/iron.asp
[Accessed 14 December 2008]
[2] Office of Dietary Supplements, 2005. Facts About Iron. [Online] (Updated 7
September 2005)
Available at: http://ibdcrohns.about.com/cs/nutrition/a/fdairon_2.htm
[Accessed 14 December 2008]
[3][4] USDA National Nutrient Database, 19??. Nutrient Data Labotory. [Online]
Available at: http://www.nal.usda.gov/fnic/foodcomp/search/
[Accessed 15 December 2008]
[5] Garrett, R. H., & Grisham, C. M., 2005. Biochemistry. 3rd
ed. Belmont (CA): Thomas
Learning, Inc.
Koolman, J. & Roehm K. H., 2006. Color Atlas of Biochemistry. 2nd
ed. New York:
Thieme Stuttgart.
Institute for Protein Research, Osaka University, 200?. Ferredoxins... Ubiquitous Iron-
Sulfur Proteins. [Online]
Available at: http://www.protein.osaka-u.ac.jp/enzymology/Fd_Model/Ferredoxin.html
[Accessed 27 March 2009].
Carnegie Mellon University, 200?. Research Projects. [Online]
Avalibale at: http://www.chem.cmu.edu/groups/hendrich/research/index.html
[Accessed 27 March 2009].
Darragh, F., 200?. Kinetics: Collision Theory, Maxwell-Boltzmann Distribution. [Online]
Available at: http://www.bustertests.co.uk/studies/kinetics-collision-theory-maxwell-
boltzmann-distribution.php
[Accessed 1 July 2009].
[6] Daniel C. H., 2002. Quantitative Chemical Analysis, 6th
ed. s.l. s.n.
[7] Anon. Spectrophotometric Determination of Iron in Vitamin Supplement Tablets.
[Class Handout]
[8] Krishna Murti, G. S. R.,Volk, V. V. & Jackson M. L., 196?. Colorimetric
Determination of Iron of Mixed Valency by Orthophenanthroline, [online]. Abstract
only.
Available at: http://soil.scijournals.org/cgi/content/abstract/30/5/663
[Accessed 30 March 2009]
[9] Skoog, D. A, West, D. M., Holler, F. J., Crouch, S. R., 2004. Fundamentals of
Analytical Chemistry, 8th
ed. US: Thomas Learning, Inc.
44
[10] Tissue, B. M., 1996. Atomic-Absorption Spectroscopy (AA). [Online] (updated 21
August 1996)
Available at: http://elchem.kaist.ac.kr/vt/chem-ed/spec/atomic/aa.htm
[Accessed 8 July 2009]
[11] GFS Chemicals, 199?. Organics - Phenanthrolines and Bipyridines. [Online]
Available at:
http://www.gfschemicals.com/statics/documents/technical/technical300af5f5bcce4870
85c1ff0229dcac49.html
[Accesed 8 July 2009]
Kuzma, J. W. & Bohnenblust, S. E., 2001. Basic Statistics for the Health Sciences, 4th
ed. s.l.,
Mayfield Publishing Company.
Christian, G. D., 2004. Analytical Chemistry, 6th
ed. s.l., Matrix Publising Services.
Lim, J. A., 2008. Antimicrobial Activity, extended essay.
45
13.0 Appendix
Appendix 1: Procedure to prepare the required solutions
1. 1000.0cm3 of 25.0x10
-5M iron(II) solution was prepared using (0.049 ± 0.001)g of
hydrated iron(II) sulphate, Fe(SO4)2(NH4)2∙6H2O, the solution contained 1.0cm3 of
concentrated hydrochloric acid (4.0M). A series of dilution was carried out to obtain the
iron(II) solution of concentrations 10.0x10-5
M, 7.50x10-5
M, 6.00x10-5
M, 5.00x10-5
M
and 2.50x10-5
M.
2. 10% hydroxylammonium chloride, NH3OHClsolution was prepared by dissolving
(10.000 ± 0.001)g of solid in 100.0cm3 of distilled water.
3. 5% of trisodium citrate, Na3C6H5O7 solution was prepared by dissolving (5.000 ±
0.001)g of solid in 100.0cm3 of distilled water.
4. 0.01M Orthophenanthroline solution was prepared by dissolving (0.198±0.001)g of the
solid in 10.0cm3 of ethanol
26 and 90.0cm
3 of distilled water was added to form a
100.0cm3 solution.
26
Ethanol was added to dissolve the phenanthroline solids as it consists of polar molecules while ethanol is a polar solvent.
46
Appendix 2: Dilution of iron(II) standards
Steps were carried out to dilute the 25.0x10-5
M iron(II) stock solution to the solutions of
lower concentration 10.0x10-5
M, 7.50x10-5
M, 6.00x10-5
M, 5.00x10-5
M and 2.50x10-5
M. The
dilution table is as shown below:
Concentration of
iron(II) standards /
mol dm-3
Volume of 2.5x10-4
M iron(II) solution
transferred using pipette / cm3
(±0.03)cm3
Volume of distilled
water added to form
the 100cm3 solution /
cm3
(±0.08)cm3
10.0x10-5
40.00 60.00
7.50x10-5
30.00 70.00
6.00x10-5
24.00 76.00
5.00x10-5
20.00 80.00
2.50x10-5
10.00 90.00
Table 9: Concentration of iron(II) standards, volume of 25.0x10-5
M iron(II) solution transferred to a 100.0cm3
volumetric flask using a graduated pipette and the volume of water added to form a 100.0cm3 solution.
According to the table above, the required volume of 25.0x10-4
M iron(II) solution is
transferred to a 100.0cm3 volumetric flask using a graduated pipette. Distilled water is added
until the bottom of the meniscus reaches the calibration mark.
47
Appendix 3: Confidence Limit
Confidence limit allows us to estimate the range within which the true value might fall,
within a given probability, defined by the experimental mean and the standard deviation. The
confidence limit is given by:
Confidence limit = 𝑥 ± 𝑡𝑠
𝑁
where t is a statistical factor that depends on the number of degrees of freedom, v and the
confidence level desired. The number of degrees of freedom is one less than the number of
measurements, s is the standard deviation of the mean and N is the number of samples.
v Confidence Level
1 12.701
2 4.303
3 3.182
4 2.776
5 2.571
6 2.447
7 2.365
8 2.306
9 2.262 Table 10: Values of t for v Degrees of Freedom for confidence level of 95%.
48
Appendix 4: Regression Analysis
Regression analysis was carried out to determine that there is a significant correlation
between the concentration of iron and absorbance used to plot the standard calibration curve
for iron(II) in section 4.0. The regression analysis is carried out with α = 0.05.
Regression Statistics
Multiple R 0.996
R Square 0.993
Adjusted R Square 0.991
Standard Error 0.0232
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 0.302 0.302 559.25 1.896x10-5
Residual 4 0.00216 5.403x10-4
Total 5 0.304
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.00554 0.0153 0.362 0.736 -0.0370 0.0481 -0.0370 0.0481
Concentration 0.305 0.0129 23.65 1.90x10-5 0.269 0.340 0.269 0.340
Table 11: Results for the regression analysis of standard calibration curve for iron(II) with α = 0.05.
Notation Meanings
df Degree of freedom
MS Mean square
SS Sum of squares
The analysis shows that the P-value is 1.90x10-5
(P<0.05). Therefore, there is a significant
correlation between the concentration of iron and absorbance used to plot the standard
calibration curve for iron(II).
49
Appendix 5: The ash was dissolved in acid. Black coloured substance can be sticking on the
side of the inner wall
50
Appendix 5: Q Test
Q test is carried out to determine whether a particular measurement should be rejected. The Q
test is a statistical test which is used when only a small number of results are obtained.
Qcalc = | outlier −nearest neighbour |
range
The value of Qcalc is compared to a set of Q test rejection coefficients for 90% confidence
level.
N 3 4 5 6 7 8 9 10 11 12
Q 0.94 0.76 0.64 0.56 0.51 0.47 0.44 0.41 0.39 0.37 Table 12: Number of samples and the corresponding Q test rejection coefficients for 90% confidence level.
The outlying data can be rejected when Qcalc > Q test rejection coefficient.
51
Appendix 6: Standard Deviation for concentration of iron(II)
The standard deviation for concentration of iron(II) is calculated using a statistic formula. An
assumption was made such that there is negligible systematic errors during preparation. The
formula is given as shown:
SX = sr
𝑚 {
1
𝑀 +
1
𝑁 +
(𝑌 𝑋− 𝑦 )2
𝑚2 (𝑥𝑖− 𝑥 )2}1/2
where: SX = Standard deviation of the concentration of the sample
sr = Standard deviation of the standard calibration curve
m = Slope of the standard calibration curve
n = Number of calibration standards
𝑌 𝑋 = Mean absorbance of the samples
𝑦 = Mean absorbance of all standards
𝑥𝑖 = Concentration of the standards
𝑥 = Mean concentration of the standards
52
Appendix 7: Calculations of ANOVA and Tukey’s HSD Test
A null hypothesis is first assumed where there is no difference between the means of different
groups.
H0: μ1 = μ2 = μ3 = ... = μk
The theoretical basis for performing ANOVA is the partitioning of the variance of all
observations into two sources of variation: variation between the group means and variation
within each group. The sampling distribution used for testing is called the F distribution.
The notations of ANOVA and meanings are tabulated as shown below:
Notation Meanings
MSw or 𝑠𝑤2
Within-group variance / mean square within
MSb or 𝑠𝑏2 Between-group variance / mean square
between
df Degree of freedom
k Number of groups
n Number of observation in each group
N Total number of observation
α Significance level
SSb Sum of squares between group
SSw Sum of squares within group
Table 13: Notations of ANOVA and meanings.
If MSB > MSW, the variance between group is greater than the variance within group, then
there is treatment effect. There is no treatment effect when MSB ≈ MSW.
F ratio = MS b
MS w
MSb has k – 1 degree of freedom, dfb.
MSw has N – k degree of freedom, dfw.
Source of
variation
Sum of
Squares
df Mean
Squares,
(s2)
F ratio Critical F
(α = 0.05)
P value
Between SSb k - 1 MSb = 𝑆𝑆𝑏
𝑘−1 MS𝑤
MS𝑏
Fk-1,N-k Computer
generated
Within SSw N - k MSw = 𝑆𝑆𝑤
𝑁−𝑘
Total SSt N - 1
Table 14: One-way ANOVA table.
53
Post hoc Tukey’s HSD test is performed when the F ratio is greater than the critical F
which means that there is significant difference between at least one pair. Tukey’s HSD test
is carried out to perform multiple comparisons. The formula for calculating the HSD value is
as shown below:
HSD = q (α, k, N-k) MS𝑤
𝑛
There is significant difference when the difference between the mean of the groups is
greater than the HSD value.
54
The table below shows the ANOVA table generated using Microsoft Excel 2007.
1. The part of the broccoli which contains the highest iron content
Groups Count Sum Average Variance
Buds 2 1.278 0.639 0.0002
Stems 2 0.158 0.079 0.0001
Base 3 0.21 0.070 0.00006
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 0.457 2 0.228 2012.2 9.86x10-7
6.94
Within Groups 0.0005 4 0.0001
Total 0.4572 6
Table 15: ANOVA table
HSD = 5.04 0.0001
3
= 0.029
55
2. The effects of incubating temperature on the amount of iron.
Groups Count Sum Average Variance
Room Temperature
(24°C) 3 1.106 0.369 8.933x10
-5
60°C 3 1.086 0.362 0.0004
80°C 2 0.560 0.280 9.80x10-5
100°C 3 0.364 0.121 0.005
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 0.1192 3 0.040 23.80 0.0005 4.35
Within Groups 0.0117 7 0.0017
Total 0.1308 10
Table 16: ANOVA table
HSD = 4.68 0.0017
3
= 0.111