Category Archives: Spring 2017

Pigmentation and Color Spectroscopy

Simone K. Johnson
Yuhong Chen


Our project looked at the color spectroscopy of pigmented substances such as makeup, food coloring, and paint. Using the color spectrometer, we tested each sample for the wavelengths red (645 nm), yellow (585 nm), green (560 nm), and blue (470 nm). Doing this gives a more thorough understanding of the color composition of each sample, and what certain pigmentations reflect and absorb.

This was done using a Vernier ALTA® II Reflectance Spectrometer and logging the data into Excel. We swatched each substance onto a 10 cm by 10 cm square. The spectrometer emits a colored LED light of a certain wavelength onto the sample of swatched pigment and measures the absorbance based on wavelength.

Results: Absorption Readings

(lower number = higher absorbance, higher number = higher reflectance)

560 nm (GREEN) 585nm (YELLOW) 470 nm (BLUE) 645 nm (RED)
Oil Paint (Alizarin Crimson | Pigment: Anthroquinone) 135 173 45 525
Acrylic Paint (Artist’s Loft – Crimson Red) 370 272 271 779
Acrylic Paint (Artist’s Loft – Vermillion) 217 416 48 842
Oil Paint (Burnt Sienna | Pigment: Synthetic Iron Oxide Red) 253 247 118 406
Oil Paint (Cadmium | Pigment: Cadmium Zinc Sulfide + Cadmium Sulpho Selemide) 750 741 171 712
Oil Paint (Cadmium Yellow Hue | Pigment: Zinc Chromate) 675 674 69 728
Oil Paint (Cadmium Yellow Hue | Pigment: Arylide Yellow) 840 777 65 731
Food Coloring  (Red | RED 40 + RED 3 ) 176 248 42 835
Food Coloring  (Blue | BLUE 1 + RED 40 ) 128 79 124 162
Food Coloring (Yellow | YELLOW 5 ) 580 730 32 1005
Lipstick (Rose Lancome) 757 588 60 841
Lipstick (Gazpacho – Amuse Bouche) 36 40 711 885
Lipstick (Tarte Cheerleader) 368 280 84 625
Lipstick (So Sofia) 78 121 78 918
Lipstick (Core Cora) 31 31 441 730
Lipstick (Urban Decay Crimson) 730 750 660 610
Lipstick (Bobbi Brown Russian Doll) 670 563 666 823
Lipstick (Make Up Forever Artist Rouge) 360 571 266 751

Hypothesis, Analysis of Results and Conclusions


The original hypothesis was that the more expensive products would have more pigmentation and return higher numbers on average. However, the conclusion reached was that more expensive products did not necessarily lead to higher pigmentations. The blue wavelengths were noticeably low in every sample, especially the red pigmented colors. There were high variations in lipstick colors. In seven of the nine lipstick colors tested, there was a very low amount of yellow, green and/or blue. There was high red reflectance in all of the samples, as they were red lipsticks. The Urban Decay Crimson lipstick and the Artist Rouge lipstick were the only two lipsticks that had high levels of blue, green, yellow, and red. This high mix of multiple colors could suggest that the pigmentation is stronger or more complex, as it is composed of more colors.

In regards to the cadmium family of oil paint, ‘true’ cadmium pigment is toxic and more expensive. As a result, we acquired two substitutes of ‘cadmium yellow hue’ that use different, often cheaper pigments to emulate the color of cadmium. However, the colors from both brands had slight differentiation on paper and this was evident in the lower reflectance of blues, and the too-high-or-too-low reflectances of yellow and green. The shapes of the spectrums appear similar enough for the imitation shades to be considered good substitutes, but provide further implications on the effect of pigment on color payoff as well as safety of use.


This project taught us about color spectroscopy and interaction between light and color, while giving us an understanding of color dynamics, wavelengths, and properties. We learned about reflective and absorbed lights in the visible light part of the electromagnetic spectrum and how that affects how people interact with color and pigmentation in commercial products. This project also taught us how to work with color spectrometers and equipment usually used for physics projects.

What we learned was that high end did not necessarily translate to higher pigmentation, and higher pigmentation did not necessarily translate to higher quality — that there were more factors that contributed to price and quality, such as the safety, availability, and formulation of the vehicle carrying the pigment all contributed to the ‘quality’ of a pigmented substance. Also, specific shades made up of varying color compositions are often sought after more than shades that are highly concentrated in one color.

Repetition and Continuation

If we did this experiment again, we would probably organize by the pigments in the ingredients list. Unfortunately, due to the makeup industry’s regulations, companies do not have to release all specific information on their pigments and formulas — this can be something that can be further investigated by acquiring pure pigments and trying to match the color composition through the use of more spectrophotometry and some chemistry.

If we were to do this project again, we would probably collect more samples to have more varied data with different colors. We would also be able to buy pure pigments to mix for new colors and make comparisons to the collected samples.

These results are related to current technological developments because it can be used in various ways to advance pigment manufacturing. Color spectroscopy can be used in quality control to ensure consistency during manufacture. Further, color spectroscopy can be used in the analysis of many more pigments, organic, inorganic, or synthetic, which can be used by chemists as well. Last, the emergence of different methods — such as makeup printers and color scanning ink pens — combined with the use of color spectroscopy might revolutionize the industries of art supply, makeup, and other pigmented substances.


Analyzing Rock Salt Runoff with Spectroscopy and Finding the Purest Water Fountain on Campus

An Analysis of Rock Salt Runoff

Michael Eacobacci


As someone who is interested in environmental chemistry, I designed a project where I could empirically analyze the environmental effects of something that is done every winter at Vassar, salting the roads and sidewalks.  After the snow melts the water flows down into rivers and lakes, and it carries the dissolved rock salt with it.  This leads to a significantly higher salt concentrations in the water which can be very harmful to many fish/plant species.  Over the course of 16 days I took 12 water samples the river running under the bridge building.  I was careful to sample from around the same area each time and tried to control as many variables as possible.  I then analyzed these samples with a Vernier Spectrometer.  I first calibrated the spectrometer with distilled water taken from one of the Chemistry labs.  Then, to get each data point, I would shake up the water from a specific day, then dip a cuvette into the holding jar, and run the spectrometer.   I analyzed each day’s water two times to insure against a bad sampling.


Figure 1.  Raw Data from Spectrometer

Figure 2.  Data organized into a chart of Mean Absorbance vs. Days After the Storm *Discontinuities on the X-Axis are a result of no data being taken on absent days.


I am very pleased with the results my study revealed.  I saw different absorbance for each sample I took, with the highest mean absorbance being 8 days after the storm, which was around the time the snow was melting rapidly.  I also observed a small, but consistent peak at around 390 nm on the readings for days 3, 8, 9, 10, 11 and 15.  Upon further research, I found that salt can absorb light at around this wavelength, so this peak could very well be rock salt runoff polluting the river.  I would also note there is one line (day 0) has a small, broad peak from 500-700 nm where all of the other lines just sloped down through the spectrum.  This could be the result of a sampling error, or something that was only present in the river on that day due to the active snowfall.

Although the data is not perfect, I observed an upward trend in the mean absorption as more days passed after the storm.  This suggests that as snow from the storm melts, rock salt and other pollutants are increasing in concentration in the river due to runoff.  This supports my original hypothesis that runoff from the snow would carry pollutants into the river.


Science that I learned during this experiment is mainly the inner workings of a UV-Vis spectrometer.  It mainly functions by being able to disperse light into its individual wavelengths (380-950 nm) and shooting it through the sample in the cuvette.  The detector on the far side gives a reading based on how much light reaches it, which gives us a measurement of how much light was absorbed by the sample and at which wavelength it was absorbed.  When I loaded a blank with distilled water, I calibrated the spectrometer to have that be 0 absorbance (or 100% transmittance) and compared the river water samples to that.  This allows researchers to determine what compounds are in a sample I also learned about Beer’s law which takes into account cell path length (b) in cm,  concentration (C) in mols/L and molar absortivity (e) in L/mol*cm to give Absorbance, which is unit less.

This project fits into current science by exposing a negative environmental impact of our actions, which is a growing concern in the modern scientific community.  It is becoming ever more important that we are conscious of the way we are affecting our surroundings, and making changes to lessen our impact.

If I were to repeat this experiment, I would have started taking samples earlier than 1 day before the storm to get a better baseline to compare the changes to.  I would also take samples from more than one river so I could draw a more general conclusion, as this one river could be an outlier in either direction.  If I had 6 more weeks, I would continue to take these samples as to see when the river returns to its original state, as when I finished, it was still above where it started.  I would also try to identify what some of the pollutants are by spiking the samples with different compounds (like NaCl) and seeing where the peaks were enhanced.

Finding the Purest Water Fountain on Campus

Joseph Griffen

For my project I went to decided to examine the water quality of different drinking fountains on campus to see if the water quality across campus was the same or if certain dorms had better or worse drinking water than others. Since it would be difficult to test the quality of Vassar’s water in general, I decided a better approach was to test each dorm’s water quality compared with the other dorms, rather than determining the quality of Vassar’s water as a whole. This means that, while my results can point to one dorm having slightly better water than another, I won’t be making any assertions as to the quality of water at Vassar as a whole. For my experiment I ended up testing the water from 7 different drinking fountains, each from a different dorm. The dorms I chose were Main, Lathrop, Davidson, Joss, Jewett, Strong, and Raymond.

To conduct my experiment I took the water samples and placed them in a spectrometer. This shows how much light from specific wavelengths the water absorbs, which can point to there being minerals or other compounds in the water which may pollute it. If a water sample is absorbing significantly more light of a certain spectrum then it generally means that there is some particle in that water that is causing that increase in absorption. One issue with this project, however, was determining what this increase in absorption means. Some extra particles that absorb light could be perfectly harmless or the difference could be so miniscule that it wouldn’t have any noticeable effect in water quality. On the other hand, the increase in absorption could indicate a contaminant that is affecting our drinking quality, perhaps not enough to make it unsafe to drink, but enough for you to stop and think twice about drinking the water. In order to determine whether certain contaminants are present in the water in significant quantities one would have to do detailed specific analysis into the spectrums of color that these contaminants absorb. This would require different, more precise equipment and testing than was able to be conducted. These results therefore, are intended to be a jumping off point, and can indicate which water fountains may have poorer water quality and which further examination of may be able to indicate pollutants in the water more certainly.

Graph 1: Absorbance vs. Wavelength (with visible spectrum)

Graph 2: Absorbance vs. Wavelength (without visible spectrum)

Here are two graphs that display the results of the spectrometer. They show each water sample and how absorbent each was with regard to the different wavelengths of light. The first graph shows this information with the visible light spectrum so that one can easily see what the wavelengths correspond to. The second more clearly shows the different water samples so that they can be easily compared. Below is a key that matches the color of the line on the graph to the location the water sample was taken from.


Strong:        Pink

Lathrop:    Black (Darker Olive Green in Graph 2)

Davidson:     Blue

Raymond:    Green

Joss:        Maroon

Jewett:        Orange

Main:        Red

The data clearly shows that there are some significant differences between the water samples from different locations on campus. Initial examination reveals that at some points in the spectrum there is no discernible difference in absorption between any of the samples. For example in the wavelengths of visible light that corresponds to the color red (approximately 600-800nm) there is no significant difference and each sample follows approximately the same pattern. In the spectrum that corresponds to yellow and green light (approx. 500-600nm) as well as most of the infrared spectrum (approx. 800-900nm) the samples split into distinct levels of absorbency. Although there is a little fluctuation between the different samples at other points in the spectrum, we will use these two parts of the spectrum for our comparison because in these places the patterns of the graphs roughly mirror each other which shows that the device is measuring well and the samples are similar, except for that some are absorbing more of the light than others. Additionally these two parts of the spectrum match each other in results and provide clear results that give us a good picture of which water samples are most absorbent.

So, in examining the results we find that we can easily rank the 7 locations in terms of how absorbent they are. The results look like this:

Most Absorbent

  1. Strong
  2. Lathrop
  3. Davidson
  4. Raymond
  5. Joss
  6. Jewett
  7. Main

Least Absorbent

So, what exactly does this tell us? First, we need to realize the limitations of this analysis. We are simply comparing the 7 water samples against each other, not necessarily against water samples that are drinkable or non-drinkable. Hopefully, we can assume that all of the water Vassar provides us to drink with is of a permissible quality. Still, as the results clearly show, not all the water is identical, and there are obvious variations across campus. Since we are simply comparing the samples against each other a ranking of the 7 is a better way to display the results than figures of their actual absorbency. Indeed, in order to get any figure of absorbency we would have to pick one (or several) wavelengths to examine, and the selections would end up being more or less arbitrary. Therefore, a more holistic examination of the results will yield more relevant information and the ranking of the dorms is the best way to display the results. It is, however, important to notice that there is a significant jump between the absorbency of Raymond and Joss. The rest of the samples are relatively close to each other, but split into two distinct groups. Strong, Lathrop, Davidson, and Raymond are significantly more absorbent than Joss, Jewett, and Main.

Now that we’ve discussed exactly what the results show us we need to know how to interpret them. They show absorbency, but what exactly does this mean in terms of water quality? As I explained earlier, this part isn’t as clear as one would hope. Based on the way the spectrometer works, we know that there are some sort of particles that are more present in the water from Strong than the water from Main. What these particles are is hard to tell though. More careful and precise analysis of the specific wavelengths where we see disparities could perhaps reveal more about which particles are present and whether they are present in large enough quantities to be harmful, or if the particles themselves are even harmful at all. Water quality is difficult to measure objectively and often requires multiple tests to check for different possible contaminants. What we do have evidence of, however, is that there are significant differences in the water across campus. In particular, the evidence suggests that Strong has the most extra particles in it, while Main has the least. Further examination could reveal the nature of these extra particles and whether they are good or bad or neutral, but they are present and their presence suggests that further examination may indeed be warranted.

I would say that the results are roughly what I had predicted. Although I didn’t make any actual predictions about which houses would have better or worse water quality, I expected that I would find significant differences between their absorbency in some spectrums and that in others the samples would yield nearly identical results and this ended up being the case.

The science I learned by doing this project mostly related to wavelengths of light and water quality. I learned a lot about how a spectrometer works and what it can tell us about what is in a liquid. By measuring how much light of each wavelength is able to pass through we can learn a lot about the makeup of a substance. Since certain particles in water are known to absorb certain wavelengths of light, knowing which wavelengths are absorbed more significantly can indicate the presence of different materials. It’s interesting to see how something like light can tie into water quality. The two seem like fairly separate fields, so it’s interesting to see how they can overlap like this. It’s also neat to see how we can use light to detect the presence of different particles.

Additionally the application of this for making water quality testing easier is interesting. By using these comparatively simple means of analyzing the contents of water samples, scientists can save significant amounts of time and resources on more complex methods of detecting contaminants in water. Spectroscopy is a promising tool for analyzing water quality that hopefully can contribute to improvements in water quality across the world.

If I were to do this project again I would conduct further research into spectrometers and how they can be used to analyze water quality. I would find spectrometers that can specifically analyze spectrums that correspond to contaminants commonly found in drinking water. By focusing precisely on these spectrums I could check for the presence of contaminants in our drinking water and get more specific results on the overall quality of drinking water at Vassar. By focusing specifically on drinking water and checking for specific contaminants, I would be able to more make concrete conclusions about the quality and how safe it is for drinking instead of just comparing the samples against each other.

If I were to conduct the experiment for another six weeks without significantly changing my approach in the way I discussed in the previous paragraph, I think I would just focus on increasing the sample size. I only took one sample from each dorm and if I took five or ten from different drinking fountains in each building and averaged them I could gather more evidence about the water quality in the entire building as a whole. I would also expand the experiment to the rest of the houses and possibly other buildings on campus. An expanded survey of water quality could yield more interesting results and analysis (i.e. is the water quality better in dorms than in academic buildings?) However, before I expanded the experiment I would want to make sure to work out more precise measurements which would allow me to make better conclusions about what the results can actually tell us about the water quality as I’ve discussed several times already.
In conclusion, while this experiment encountered some errors regarding how much the results can actually tell us about the water quality itself, I think it was successful in providing evidence that there are significant differences in water quality in different places on campus. Moreover, it was able to suggest the water quality was perhaps better in some locations (like Main) than in others (like Strong). While these results will remain inconclusive in exactly how good the water quality is they do point to something that should be explored. Further, more precise research into the water quality at Vassar would be interesting. In particular, as this experiment showed research into the differences in quality between locations yields interesting results that should be explored further.

Migraines and Physical Conditions

A migraine is a type of severe headache disorder that can last up to 72 hours, and is often associated with nausea, vomiting, numbness, and sensitivity to light, sound or smell; and, more broadly, sleep disturbances, anxiety, or depression. Most migraine sufferers experience migraine attacks one or twice a month, and almost all sufferers are unable to work or function normally during the attacks. Sufferers are sometimes able to detect incoming migraines before the pain hits, through feelings of aura (strange sensory disturbances) or through other premature neurological symptoms (early nausea or sleep disturbances).


Spectroscopy of artificial lighting on Vassar campus



This project aimed to look at light pollution and the effectiveness of artificial lighting across Vassar campus, as well as the differences in the lighting of various areas. There is a particular focus on the presence or absence of blue light (~450-495nm), as blue light is associated with a reduction in the production of melatonin. The data was taken in various buildings using a spectrometer in association with the Logger Pro software.



Above, we can see a graphical representation of the data collected from 6 different areas. The full data is available at:


We can see that the data falls within two general patterns. While the data lines for Raymond MPR and the Shiva work lights are somewhat harder to see, they follow the same trend as the curves for the Raymond room. In fact, all three of these seem to follow the same pattern as the Main College Center lights, which is simply much more pronounced: we see peaks at around 435, 550 and 610 nanometers, corresponding to blue, green and orange respectively. I expected that most of the lighting would be fluorescent lighting, and as such this falls within expectations as this is the typical emission spectrum of a three band lamp. The other peaks in the spectrum of the Main College Center lighting could be due to light pollution from other sources or the presence of foreign agents. We also see that, while the levels of intensity are very similar for the Main College Center spectrum, blue light has lower intensity in the three other spectra.

The other pattern we observe is that of Deece and Villard room lighting. Here, we see a mostly constant curve with two slight peaks at 450 and 610, in the blue and orange. This is typically the spectrum of incandescent lighting. We also see that the Villard room’s lights have higher intensity and seem to contain slightly more blue light. While it was within my expectations to find some incandescent lighting on campus, I did not expect the Deece to use it as incandescent lighting is less efficient and the Deece is one of the main, most populated areas.


Science learned and context:

While I did not use any highly advanced technology during this project, it let me familiarize myself with the use of spectrometers and the Logger Pro software. I did not expect that such a small and unassuming piece of affordable equipment could take data with so much precision, taking readings with intervals of 0.6 nanometers.

I think my project fits in with current technology in two ways. First, technology is making increased use of various mediums of light, visible or slightly outside vision range. Secondly, as screens and artificial lighting have become so prevalent, a number of people have started looking into the different effects of different types of lighting on the human body and brain. The fact that melatonin production is inhibited by blue light is now fairly well known, and there are many very popular apps or programs to reduce the emission of blue light on phones and computers at night in order to try and preserve the circadian sleep cycle. There are also innovations that are being made, such as an electronic headband called the iBand+ that supposedly uses a combination of light signals and vibrations to help the wearer sleep and induce lucid dreaming. While there seems to be limited evidence to support that claim, we see that technology has started focusing on this aspect of light more


Conclusions and looking back:

As said previously, blue light is less conducive to sleeping, and so could be seen as better for studying. Light with higher intensity may also be more helpful when working, but less comfortable when relaxing. As such, in terms of lighting, the Main College Center seems like a good place to work and the Deece seems like a good place to relax.

If I had to do the project again, I would try to control more accurately for light intensity. Indeed, while the spectrum will not change much regardless of where you point the spectrometer, it is a precise measuring tool and being a few degrees off can make a significant change in the perceived intensity of the lighting. I also failed to take into account the number of lights in the room, and in one case the proximity (the Shiva data was taken while sitting on top of the risers, which is atypical of where students would be when the lights are on).

If I had another 6 weeks, I would probably try to measure outside lighting as well. I am curious as to the path lighting, and would also have liked to take measurements from natural lighting in order to see how bright certain areas were and how much artificial lighting influences the brightness in comparison with moonlight.