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Experiment 7 FTIR
Margaret Schnell
maschnell 00784786
Partner: No Partner
March 29, 2016 April 7, 2016
Analytical Chemistry CHEM:3430:0A01 Prof. Scott Shaw
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Introduction:
In this experiment, we first used Fourier Transform infrared spectroscopy (FTIR) to
obtain absorbance spectra of different organic solvents and oils. FTIR works by measuring
characteristic vibrational frequencies (or wavenumbers) through absorption of IR-radiation from
about 4000 to 400 cm-1. Although many peaks may be observed, only the most intense few say
something about the component you are testing.
Principal Component Analysis (in this case Matlab) was then used to reduce redundant
information from the obtained data sets. The PCA helps to find combinations of strongly
correlated factors in the spectra from the sets of data. PCA Scores were used to identify the
unknown oil by choosing which oil’s PCA Scores resembled the unknowns the best.
PCA Scores are plotted against each other to define clusters of unique components. If a
PCA plot has no defined clusters, the PCA Scores at those values do not truly contribute any
distinguishing features of the individual samples. PCA plots can have clustering in either specific
points or elongated areas, so long as each component has a defined cluster separate from the
other components.
Experimental Methods:
For this experiment, there were no deviations from the experimental procedure. The only
manipulation of data was plugging results from FTIR into the Matlab software; this was
explained in the appendices for the experiment.
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Data and Results:
The first part of this experiment was performed to practice working with the Fourier
Transform infrared spectroscopy (FTIR) and the Principal Component Analysis (PCA), Matlab.
The practice was done with
three different organic
solvents. The FTIR spectra
collected from acetone,
methyl ethyl ketone, and
ethyl acetate are shown in
Figure 1. The overlay shows
the spectra obtained for each
of the individual solvents.
Overall, the three spectra
are about the same, but unique peaks for each solvent can be seen. The wavenumber regions
containing the unique peaks are the ones that give good PCA plots, setting solvents in their own,
well defined clusters.
Five wavenumbers
for different wavenumber
regions from each of the
runs were recorded (five
sets of values for each
solvent and fifteen sets
total; Appendix A, Table
-0.1
0.4
0.9
1.4
1.9
2.4
700 1700 2700 3700
Abs
orpt
ion
Wavenumber (cm-1)
Overlay of Absorption at a Range of Wavenumbers for Organic Solvents
Acetone
Methyl Ethyl Ketone Ethyl Acetate
Figure 1 This overlay plot shows the different absorptions of different organic solvents at various wavenumbers. There are some peaks that are unique to a solvent, such as that at ~1100 being unique to ethyl acetate, and some that all of the solvents have in common, such as ~1700.
-150
-100
-50
0
50
100
150
0 1 2 3 4 5
PCA
Sco
res
Wavenumber Region
Comparison of PCA Scores for Organic Solvents
Acetone
Methyl Ethyl Ketone Ethyl Acetate
Figure 2 This figure shows the different PCA Scores obtained for the assigned wavenumbers obtained from the FTIR data. The wavenumber values chosen for the first wavenumber region were unique to each solvent. With the last three wavenumber regions, the solvents cannot even be distinguished from one another.
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Figure 3 This chart shows the difference when Score 2 is plotted against Score 1. The three compounds can be clearly distinguished from on another when we compare the data in this way.
1.2). Then the values were plugged into Matlab. This produced a set of numbers; these were the
PCA Scores, and there was one for each of the values (Appendix A, Table 1.1). Comparison of
the PCA Scores are shown in Figure 2. The PCA Scores reflect the difference in the Score values
calculated for each wavenumber region. For example, the peak values chosen for the first
wavenumber region vary significantly between the solvents compared to the values chosen for
the fifth wavenumber region. We can see this because the points at wavenumber region one can
be distinguished from one another, where the points at wavenumber region five all overlap and
cannot be separated from one another. This makes sense when compared with Figure 1 since the
first wavenumber region corresponds to the lowest wavenumber chosen (where the peaks were
most unique). The variation in Figure 1 is much more substantial than at the higher
wavenumbers. This is a consistency seen between both of the figures.
Although some differences can be seen from Figure 2, the PCA Scores give the most
information about different compounds when the scores are plotted against each other. Figures 3-
5 are examples of this. Figure 3 shows Score 2 plotted against Score 1. The separation indicates
that the two Scores vary significantly between the compounds. Comparisons of different sets of
scores give different degrees of variation. For
example, Figure 4 still has separation between
the compounds, but in Figure 5, we would not
be able to distinguish between the data sets
were it not or the different marker styles. The
differences in the clustering of data have to do
with the uniqueness for each set of Scores
plotted. Any sort of separated clustering,
-25 -20 -15 -10 -5 0 5
10 15 20 25
-200 -100 0 100
Scor
e 2
Valu
e
Score 1 Value
Score 2 vs. Score 1 for Organic Solvents
Acetone Data
Methyl Ethyl Ketone
Ethyl Acetate
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whether it be at single point or an elongated area, indicates that the compounds possess some
degree of uniqueness from the others.
After practicing with the
organic solvents, we ran FTIR
and computed PCA Scores for
six different oils, five known and
one unknown. We were trying to
identify the unknown oil by
comparing its Score values with
the Scores of the known oil
samples. Figure 6 shows the
overlay of the six absorption
spectra from the FTIR. These
overlaid spectra were different from those in Figure 1 because they did not contain any obvious
-30
-20
-10
0
10
20
30
-2 -1 0 1 2 3
Scor
e 2
Valu
e
Score 3 Value
Score 2 vs. Score 3 for Organic Solvents
Acetone
Methyl Ethyl Ketone
Ethyl Acetate -0.4 -0.3 -0.2 -0.1
0 0.1 0.2 0.3
-0.5 0 0.5 1
Scor
e 5
Valu
e
Score 4 Value
Score 5 vs. Score 4 for Organic Solvents
Acetone
Methyl Ethyl Ketone
Ethyl Acetate
Figure 4 This comparison between Score 2 plotted against Score 3 for the organic solvents still shows good definition between the various compounds. Linear clusters are not as ideal as the point clusters seen in Figure 3, but the compounds can still be distinguished from one another.
Figure 5 The values for Score 4 and Score 5 are very similar between the three compounds (Appendix A, Table 1.1 and 1.2). Since the values are very similar for all three compounds, they are not easily distinguished when comparing these two Scores.
-0.1
0.4
0.9
1.4
1.9
2.4
700 946 1,192 1,438 1,684 1,929 2,175 2,421 2,667 2,913 3,159 3,405 3,651 3,897
Abs
orba
nce
Wavenumber (cm-1)
Overlay of Absrbance of Oil Samples at Various Wavenumbers
Canola Oil
Olive Oil
Corn Oil
Safflower Oil
Soybean
Unknown
Figure 6 The overlay of the oils is hard to interpret, as almost all of the peaks are exactly the same with only slightly varying intensities. With this overlay it is impossible to determine the differing compounds, and hence, shows why PCA analysis is very useful.
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unique peaks. The only difference that can be seen from the various components in these spectra
is that some have more absorbance at certain wavenumbers.
An overall comparison of
the Score values at each
Wavenumber region was also made
for the six oil trials. Figure 7 shows
the results. Just as we saw for the
organic solvents, the most unique
Scores occupy the first
wavenumber region. This also
shows that Scores for regions 1 and
2 are going to give us the best spearation between the oils when plotted against each other. In
Appendix A, Tables 2.1 and 2.2, you can see the actual values of the wavenumber used for each
wavenumber region and the PCA Scores associated with those values.
Various PCA Scores needed
to be plotted against each other to
see the relationships between the oil
samples. Figure 8 shows a plot of
Score 5 versus Score 4. It is clear
from this plot, along with Figure 7,
that the oils cannot be distinguished
with only these two Score values.
All of the trials had Scores too
-3
-2
-1
0
1
2
3
0 1 2 3 4 5
PCA
Sco
re
Wavenumber Region
Comparison of PCA Scores for Oils
Canola Oil
Olive Oil
Corn Oil
Safflower Oil
Sobean Oil
Unknown Oil
Figure 7 The Scores for each oil at the various wavenumber regions are showed in this graph. The uniqueness of the oils at region 1 is much greater than that at region 5.
-0.3 -0.25 -0.2
-0.15 -0.1
-0.05 0
0.05 0.1
0.15
-0.12 -0.07 -0.02 0.03 0.08
Scor
e 4
Valu
es
Score 5 Values
Score 5 vs. Score 4 Values for Oils
Canola Oil
Olive Oil
Corn Oil
Safflower Oil
Soybean Oil
Unknown Oil
Figure 8 The plot of Score 5 vs. Score 4 goes to show that the different oil samples cannot be confidently distinguished between only these two scores. Without the different marker styles, we would be unable to identify individual groups.
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similar in these wavenumber regions. The similarities in these regions are due to chemical
components that react the same in the FTIR.
Two plots made very it very easy to identify different groupings of Score values. Figure 9
and Figure 10 show Score value 1 plotted against Score value 2 and Score value 3. These plots
were much clearer and straight
forward; this was varified by what
we saw in Figure 7. The Score
value for the first wavenumber
region had the most variation
between the different oils. That
Score value was what would
prove to be the most important
point in determining the unknown
oil. Notice how these groupings
are elongated as compared to
those groups obtained from the
organic solvents in Figure 3. The
way the points group together also
had to do with how unique each
oil’s chemical components acted
in the FTIR. For this experiment, any sort of grouping was acceptable in determining the
unknown oil. From the unknown oil’s similarities with safflower oil, it was determined that this
-3
-2
-1
0
1
2
3
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
Scor
e 1
Valu
es
Score 2 Values
Score 2 vs. Score 1 Values for Oils
Canola Oil
Olive Oil
Corn Oil
Safflower Oil
Soybean Oil
Unknown Oil
-0.3
-0.2
-0.1
0
0.1
0.2
-3 -2 -1 0 1 2 3
Scor
e 3
Valu
es
Score 1 Values
Score 1 vs. Score 3 Values for Oils
Canola Oil
Olive Oil
Corn Oil
Safflower Oil
Soybean Oil
Unknown Oil
Figure 9 This plot of Score 2 vs. Score 1 was helpful in the determination of our unknown oil. The clear separation between groups proved that Score values for these two wavenumber regions were somewhat unique for each oil.
Figure 10 This plot of Score 1 vs. Score 3 was also helpful in the determination of our unknown oil. The clear separation between groups proved that Score values for these two wavenumber regions were somewhat unique for each oil.
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was the identification of the unknown. Additional Score plots that helped to determine the
unknown oil can be found in Appendix B, Figure set 1.
Discussion:
The first thing we did in this experiment was collect the FTIR spectra for the three
organic solvents. According to the structures of these solvents (Appendix C, Question 1), they
should each have absorptions for alkane carbon-hydrogen bonds from 2850-2960 cm-1 and
carbon-oxygen double bonds. The locations of the carbon-oxygen double bonds changed for
each solvent’s spectra because each of the carbon-oxygen double bonds was different due to the
various arrangements of molecules, specifically those also bonded to the carbon. Acetone is an
aldehyde, and so it was expected to absorb at ~1725 cm-1. Methyl ethyl ketone is a ketone and so
it was expected to absorb around ~1715 cm-1. Ethyl acetate is neither of those, but was expected
to be found around ~1750 cm-1. Other intense absorptions in the spectra are due to stretching of
carbon-carbon bonds that are caused by the stretching of the carbon-oxygen double bond. These
individual spectra can be found in Appendix B, Figure set 3.
A PCA plot is considered good when points from each sample are separated into their
own, unique clusters. These clusters can form a single point or an elongated shape. If the shapes
are elongated, it is because one of the Scores is better than the other. For the organic solvents, the
plot of Score 1 (from wavenumber region one) and Score 2 (obtained from wavenumber region
two), Figure 3, is the best plot. Each solvent’s Scores are arranged into close point groups. This
makes sense in accordance to the wavenumbers chosen for each of these regions. In Appendix A,
Table 1.1, we can see that the wavenumbers for regions one and two give much varying Scores
for each solvent. The difference types of stretching for the absorptions in regions one and two are
highly dependent on the types of carbon-oxygen double bonds in the molecule. Since all three
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have different types of carbon-oxygen double bonds (aldehyde, ketone, and acetate), it makes
sense that these regions would be very unique to each solvent.
The PCA plot in Figure 5, on the other hand, does not give such good results. All three of
the samples’ results are clustered together into one big group. This plot was dependent on the
Scores obtained from the wavenumber regions four and five. If we look at Appendix A, Table
1.1 again, we can see that for wavenumber regions four and five, the Score values are not as
diverse between the three solvents as they were for Score 1 and Score 2. The wavenumbers in
these regions are associated with chemical properties all three of the solvents have in common,
the carbon-carbon stretching.
Next we worked with the oil samples. The main objective for this part of the experiment
was to create and compare PCA plots to determine which oil the unknown oil is most similar to.
We needed to utilize the PCA plots because it is impossible to distinguish the spectra in Figure 6
from one another. If we cannot distinguish the individual spectra, it is clear that a determination
of the unknown oil due to similarities with a known cannot be made. The identity of the
unknown oil could not be determined by the spectrum alone either because all six of the oils’
spectra were too similar. As can be seen in Figure 7, the Score associated with the wavenumbers
chosen for region one was the only one that has any real uniqueness between the oils. This peak
was ~3000 cm-1 for all of the oils; this indicates that all of the oils are structurally very similar.
The first PCA plot we are talking about is the one that had no effect on the determination
of our unknown oil. This is the plot of Score 4 against Score 5 depicted in Figure 8. If it were not
for the different markers, it would be impossible to tell the different clusters from one another.
Because of the uncertainty related to this plot, we cannot make a realistic determination of our
unknown oil.
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We did determine that the unknown oil was safflower oil. There are multiple PCA plots
that support this conclusion. If we first look at Figure 9, the plot of Score 1 against Score 2, we
can see that the unknown’s elongated cluster is very close to that of the safflower oil. This same
trend was observed in Figure 10, the plot of Score 1 against Score 3.
To verify this data, extra PCA plots were made. In Appendix B, Figure set 1, we see three
additional PCA plots. 1.1 and 1.2 are both plots including the Score value of wavenumber region
one. There plots both have good distinction between the five known oils and their clusters. As we
saw in both Figure 9 and Figure 10, the unknown oil is still found closely related to the safflower
oil sample. From Appendix B, Figure 1.3, we can see that Score 1 is not used for the plot. The
plot is also chaotic and has no defined clusters for the individual oils.
The PCA Score found for the wavenumber region one was the most influential in
determining the unknown oil. The defined clusters were only found in PCA plots including this
values. We can see from Figure 8 and Appendix B, Figure 1.3 that PCA plots without the Score
value for the first wavenumber region, there were no reliable conclusions that could be made in
relation to the determination of the unknown oil.
Conclusion:
PCA is a very useful method that can be use in everyday determination of distinction
between closely related molecules. One example in which PCA is useful is in food quality
analysis. FTIR and PCA were used to determine different organic solvents to show that different
chemical arrangements cause different stretching trends in all bonds in the molecules. We then
used PCA to determine that our unknown oil was safflower oil. We were able to make this
conclusion by comparing multiple PCA plots. The only PCA Score that had a real effect on the
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results in determining our unknown was the Score value associated with the first wavenumber
region.
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References:
1. Esmaeili, A. (2011) Assessing the effect of oil price on world food prices: Application of
principal component analysis . Energy Policy 39, 1022–1025.
2. Jolliffe, I. T. (2002) Principal Component Analysis (81-85). Springer, New York City.
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Appendix A: Supplemental Tables
Trial PCA 1 PCA 2 PCA 3 PCA 4 PCA 5 Sample
1 85.99384 -18.1519 -1.21013 0.305259 0.105256 Acetone 2 85.36627 -18.4377 0.949813 -0.36953 -0.09302 Acetone 3 85.71092 -18.2844 0.641868 0.54878 0.123294 Acetone 4 85.88461 -18.2149 -0.51644 -0.05536 -0.01827 Acetone 5 85.57996 -18.3483 0.127086 -0.43483 -0.11032 Acetone 1 47.46907 22.26426 -0.89432 -0.06152 -0.01634 Methyl Ethyl Ketone 2 47.37731 22.27939 -1.03859 -0.16025 -0.0406 Methyl Ethyl Ketone 3 47.14742 22.17287 0.858494 0.004033 -0.02076 Methyl Ethyl Ketone 4 46.96499 22.1063 2.085866 0.124959 0.058749 Methyl Ethyl Ketone 5 47.37594 22.11052 -0.99127 0.10062 0.008254 Methyl Ethyl Ketone 1 -133.13 -4.07511 -0.09284 0.098519 -0.10827 Ethyl Acetate 2 -132.861 -3.75874 -0.09264 -0.07032 0.107214 Ethyl Acetate 3 -132.751 -3.69477 0.062337 -0.13338 0.116807 Ethyl Acetate 4 -133.602 -4.51021 0.079813 0.342741 -0.32931 Ethyl Acetate 5 -132.526 -3.45746 0.030953 -0.23973 0.217308 Ethyl Acetate
Table 1.1 This table shows the specific values obtained for the PCA Scores for each trial of the organic solvents.
Trial Wavenumber 1 (cm-1)
Wavenumber 2 (cm-1)
Wavenumber 3 (cm-1)
Wavenumber 4 (cm-1)
Wavenumber 5 (cm-1) Sample
1 3004.49 1713.79 1420.71 1362.86 1222.23 A 2 3004.45 1715.96 1419.76 1362.74 1222.14 A 3 3004.43 1715.68 1420.78 1362.78 1222.22 A 4 3004.44 1714.46 1420.28 1362.92 1222.28 A 5 3004.45 1715.11 1419.79 1362.83 1222.2 A 1 2979.95 1716.21 1417.28 1366.18 1172.36 MEK 2 2979.94 1716.07 1417.17 1366.14 1172.28 MEK 3 2979.88 1717.99 1417.18 1366.14 1172.3 MEK 4 2979.89 1719.24 1417.21 1366.14 1172.26 MEK 5 2980.03 1716.15 1417.4 1365.97 1172.35 MEK 1 2984.97 1741.78 1373.91 1242.82 1047.73 EA 2 2985.01 1741.72 1373.91 1243.31 1047.69 EA 3 2984.99 1741.85 1373.88 1243.46 1047.75 EA 4 2984.96 1742.05 1373.91 1242.13 1047.69 EA 5 2984.97 1741.77 1373.89 1243.81 1047.76 EA
Table 1.2 The values for each of the wavenumber regions for the organic solvents are tabulated above. A represents acetone, MEK represents methyl ethyl ketone, and EA represents ethyl acetate.
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Trial Wavenumber 1(cm-1)
Wavenumber 2(cm-1)
Wavenumber 3cm-1)
Wavenumber 4(cm-1)
Wavenumber 5 (cm-1) Sample
1 3007.4 1746.22 1465.66 1377.72 1163.76 Canola 2 3007.37 1746.06 1465.96 1377.7 1163.83 Canola 3 3007.38 1746.13 1465.67 1377.72 1163.77 Canola 4 3007.33 1746.16 1465.77 1377.72 1163.8 Canola 5 3007.37 1746.08 1465.66 1377.7 1163.5 Canola 1 3005.04 1746.31 1465.88 1377.78 1163.81 Olive 2 3005.04 1746.3 1466 1377.7 1163.73 Olive 3 3004.94 1746.32 1465.96 1377.77 1163.79 Olive 4 3005 1746.44 1465.95 1377.81 1163.56 Olive 5 3004.96 1746.31 1465.73 1377.8 1163.8 Olive 1 3008.96 1746.07 1465.97 1377.8 1163.69 Corn 2 3008.91 1746.1 1465.96 1377.8 1163.58 Corn 3 3008.89 1745.79 1465.85 1377.77 1163.66 Corn 4 3009.04 1746.11 1465.86 1377.71 1163.8 Corn 5 3008.93 1746.07 1465.96 1377.8 1163.77 Corn 1 3005.45 1746.35 1465.82 1377.77 1163.78 Saff. 2 3005.42 1746.36 1465.84 1377.79 1163.77 Saff. 3 3005.42 1746.31 1465.92 1377.76 1163.81 Saff. 4 3005.39 1746.31 1465.92 1377.78 1163.78 Saff. 5 3005.42 1746.29 1465.98 1377.75 1163.87 Saff. 1 3009.32 1745.96 1465.82 1377.77 1163.59 Soyb. 2 3009.31 1745.95 1465.65 1377.72 1163.31 Soyb. 3 3009.28 1745.91 1465.7 1377.72 1163.54 Soyb. 4 3009.37 1746.09 1465.6 1377.75 1163.66 Soyb. 5 3009.54 1746.08 1465.94 1377.64 1163.76 Soyb. 1 3005.6 1746.32 1465.9 1377.77 1163.96 Unk. 2 3005.59 1746.28 1465.92 1377.79 1163.71 Unk. 3 3005.6 1746.37 1466.03 1377.78 1163.7 Unk. 4 3005.61 1746.3 1465.86 1377.75 1163.81 Unk. 5 3005.57 1746.31 1465.79 1377.8 1163.81 Unk.
Table 2.1 This table shows the exact wavenumber values selected for each wavenumber region of each oil trial run
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Trial PC 1 PC 2 PC 3 PC 4 PC 5 Sample 1 0.450475 -0.08461 0.168097 0.072873 -0.01828 Canola 2 0.424522 0.152925 0.007115 -0.12248 -0.04334 Canola 3 0.437278 -0.085 0.164144 -0.01915 -0.01182 Canola 4 0.381739 0.008976 0.117781 -0.00577 -0.01923 Canola 5 0.441482 -0.28323 -0.02342 -0.03401 -0.0477 Canola 1 -1.91407 -0.00313 0.019598 -0.03399 0.01376 Olive 2 -1.91238 0.026467 -0.11252 -0.04937 -0.07968 Olive 3 -2.01544 0.036893 -0.04953 -0.03642 -0.00461 Olive 4 -1.95728 -0.1047 -0.20594 0.120078 0.009999 Olive 5 -1.99018 -0.12168 0.112206 -0.02445 0.043531 Olive 1 2.01094 0.151865 -0.08899 0.030148 0.049649 Corn 2 1.962897 0.071965 -0.16053 0.071273 0.039738 Corn 3 1.967973 -0.00013 -0.03859 -0.23818 0.046413 Corn 4 2.086627 0.156364 0.077588 0.063326 -0.02587 Corn 5 1.978413 0.197284 -0.02501 0.018209 0.056302 Corn 1 -1.50654 -0.03915 0.047455 0.043891 0.004578 Safflower 2 -1.53748 -0.03141 0.024552 0.05263 0.021364 Safflower 3 -1.5364 0.045422 -0.00074 -0.01074 -0.00762 Safflower 4 -1.56537 0.023761 -0.02475 -0.0075 0.00985 Safflower 5 -1.53819 0.126103 0.001355 -0.04411 -0.01596 Safflower 1 2.385549 -0.02129 -0.05503 -0.02766 0.032609 Soybean 2 2.390703 -0.33573 -0.13516 0.009308 -0.02496 Soybean 3 2.354684 -0.1515 -0.00631 -0.06677 -0.0085 Soybean 4 2.427366 -0.11037 0.154728 0.111488 0.024933 Soybean 5 2.587187 0.207857 0.006412 0.064145 -0.10017 Soybean 1 -1.36301 0.143748 0.12263 -0.00497 0.015008 Unknown 2 -1.36135 -0.01741 -0.07477 -0.01217 0.017393 Unknown 3 -1.36071 0.068568 -0.15225 0.068258 -0.0078 Unknown 4 -1.34497 0.01073 0.04352 -0.0022 -0.01172 Unknown 5 -1.38447 -0.0396 0.086353 0.01431 0.042122 Unknown
Table 2.2 this table shows the Score values associated with each wavenumber region for each oil trial run. These values were found in Matlab.
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Appendix B: Supplemental Figures
Figure 1.1 This figure shows the Score values of wavenumber region 1 and 5. The grouping is
due to the uniqueness of the points in the wavenumber region 1.
Figure 1.2 This figure shows the score values from wavenumber regions one and four. There are
clea r clusters for each individual oil.
-0.12
-0.08
-0.04
0
0.04
0.08
-3 -2 -1 0 1 2 3
Scor
e 5
Valu
es
Score 1 Values
Score 1 vs. Score 5 Values for Oils
Canola Oil
Olive Oil
Corn Oin
Safflower Oil
Soybean Oil
Unknown Oil
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
-3 -2 -1 0 1 2 3
Scor
e 4
Valu
es
Score 1 Values
Score 1 vs. Score 4 PCA Plot
Canola Oil
Olive Oil
Corn Oil
Safflower Oil
Soybean Oil
Unknown Oil
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Figure 1.3 This figure is the PCA plot for the Score values obtained from the wavenumber
regions two and three. There is no clear clustering or distinction between the six oil samples.
Figure 2.1 This figure shows the peak intensities of the safflower oil sample.
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
Scor
e 3
Valu
e
Score 2 Value
Score 2 vs. Score 3 PCA plot
Canola Oil
Olive Oil
Corn Oil
Safflower Oil
Soybean Oil
Unknown Oil
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000
Abs
orba
nce
Wavenumber (cm-1)
Safflower Oil Spectra
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Figure 2.2 This figure shows the peak intensities of the unknown
Figure 3.1 The peak intensity for the acetone can be seen more clearly here. The most intense
peak is around ~1725 cm -1.
-0.5
0
0.5
1
1.5
2
700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000
Abs
orba
nce
Wavenumber (cm-1)
Unknown Spectrum
-0.5
0
0.5
1
1.5
2
2.5
700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000
Abs
orba
nce
Wavenumber (cm-1)
Acetone Spectra
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Figure 3.2 The peak intensities of the methyl ethyl ketone sample can be seen more clearly here.
The most intense peak is around ~1715 cm-1.
Figure 3.3 The peak intensities for the ethyl acetate sample can be seen more clearly here. The
most intense peak is around ~1750 cm-1.
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Wavenumber (cm-1)
Methyl Ethyl Ketone Spectra
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Ethyl Acetate Spectra
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Appendix C: Questions
1. The most intense absorptions for all three organic solvents will be in relation to their
carbon-oxygen double bonds stretching. According to the Figure 3 set in Appendix B, we can see
that these vibrational frequencies (~1725 for acetone, ~1715 for methyl ethyl ketone, and ~1750
for ethyl acetate) from their spectra make sense with their structures.
For acetone, there were two other intense absorptions. These absorptions were at 1225
cm-1 and 1350 cm-1. These are a result of carbon-carbon bond stretching due to the carbon-
oxygen double bonds stretching at the two different carbons.
Methyl ethyl ketone also had a few other intense peaks. These peaks were around ~3000
cm-1, ~1400 cm-1, and ~1200 cm-1. The peaks around ~3000 cm-1 were due to the stretching
between the carbons and the hydrogens they were bonded to. The other two peaks are due to
stretching in the carbon-carbon bonds that happen as a result from the stretching of the carbon-
oxygen bond.
Ethyl acetate also had other prominent peaks in its spectra. These peaks were at about
~1250 cm-1 and ~1050 cm-1. Both of these peaks have to do with the differing stretching due to
the stretching at the carbon-oxygen double bond.
2. It would not be appropriate to analyze rubbing alcohol, vodka, and methanol solutions in the
FTIR. Oxygen-hydrogen bonds, characteristic of all of these sample, give a strong broad band in
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the FTIR spectra. We cannot compare these three to each other because with a wide peak comes
larger area.
3. The three most intense vibrational frequencies from a safflower oil spectra can be determined
from the spectra below. The three vibrational frequencies are ~2925 cm-1, ~1750 cm-1, and
~2850 cm-1. These correlate to alkane carbon-hydrogen bonds, carbon-oxygen double bonds, and
another set of carbon-hydrogen bonds, respectively.
4.
