Using light to describe the ancient world

#Introducing R

R is an incredibly useful programming language that I often use in my research. For example, I use have used R to analyse XRF spectra, to study feather colours, and to look at the possibility of red-feathered dinosaurs.

If you are interested in learning to use R then check out my Introduction to R slides, which can be accessed from Slideshare. These slides provide a basic intro, including a few statistical tests for you to work with. In a future set of slides I will include the code for my spectral analyses.


Raman spectroscopy gives us some amazing insight into ancient life. To pick just example, Raman researchers have analysed ancient microbes on Earth to teach us how to recognise life on other planets (Marshall et al 2006). Rather than highlight the advantages of Raman spectroscopy though, I thought I might focus on one of the biggest drawbacks of this technique: Autofluorescence, which is more commonly just called fluorescence.

“The natural enemy of Raman spectroscopy is fluorescence” – Olaf Hollricher, Raman Instrumentation for Confocal Raman Microscopy.

[Check out this post for a basic refresher on Raman spectroscopy]. Fluorescence is what happens when a substance absorbs and then immediately emits light. In essence, the substance glows when a light is shone on it. This is kind-of but-not-quite like a glow-in-the-dark toy: you shine light on the toy, and when you remove the light you can still see the toy. The difference here is that the toy has stored the light and is slowly releasing it, instead of receiving and releasing the light at nearly the same time. Glow-in-the-dark is an example of phosphorescence. We are talking about fluorescence.

Have you ever been to a museum with rocks and minerals that are being lit with a ‘black light’? Did the minerals look all bright red and green and purple like in the picture from Wikipedia below? The bright colours of these minerals are examples of fluorescence.

Fluorescent minerals. Click to link back to the  Wikipedia source.

Fluorescent minerals. Click to link back to the Wikipedia source.

For a plain-language video explanation of fluorescence (…that uses lasers), check out this Youtube clip:

So the important point here is that a higher energy (shorter wavelength) light source can make substances emit lower energy (longer wavelength) light. A Raman spectrometer that uses a green wavelength (shorter wavelength) might cause a bone to emit near infrared light (longer wavelength). How does this make fluorescence the “…natural enemy…” of Raman spectroscopy? Quite simply, it’s because the business end of a Raman spectrometer is a CCD or CMOS detector, like the one in your camera. The Raman instrument is designed around producing and gathering light, and because of the way Raman scattering  works, the detectors need to be very sensitive. Also, detectors don’t discriminate between Raman scattered light and the light produced by fluorescence. Light emitted during fluorescence is much more intense than Raman scattered light: emitted light drowns out the scattered light.

So, why is fluorescence the “…natural enemy…” of Raman spectroscopy? A sample that fluoresces during a Raman analysis is unlikely to give you a meaningful Raman spectrum. So the trick is to stop a sample from fluorescing…


Marshall CP, Carter EA, Leuko S, Javaux EJ. 2006. Vibrational spectroscopy of extant and fossil microbes: Relevance for the astrobiological exploration of Mars. Vibrational Spectroscopy 41: 182-189

Figure 2: Raman spectra from a fossil feather preserved in amber and from a carbonised compression fossil.

Raman spectra from a fossil feather preserved in amber and from a carbonised compression fossil.

In an earlier post I talked about the ‘insects in amber’ study that Professor Howell Edwards (University of Bradford) and his colleagues reported in 2007. In this study, Edwards et al. used a non-destructive technique (Raman spectroscopy) to analyse the bodies of insects preserved for many millions of years in amber. You can read all about it here and here.

What an amazing idea – amber is not just as a time capsule for ancient life, but also a vault for ancient biomolecules. Edwards and colleagues used this idea to learn more about insect preservation, but what else might be preserved in amber? What about feather pigments?

Birds today are highly coloured – they have colourful skin, scales, eggs, eyes, beaks… and colourful feathers. There are at least six distinct pigments that birds used to add colour to feathers. Carotenoid pigments are the most common colour molecules in the red, orange and yellow feathers of living birds. Were there carotenoids in the feathers in the ancient ancestors of birds? Did dinosaurs have red feathers?

I want an answer to this question!

My first step in getting that answer was to assemble an all-star team. Dr David Grimaldi and Mr Paul Nascimbene are amber experts at the American Museum of Natural History in New York, Dr Carla Dove (Smithsonian Institution) is a plumage expert who describes feathers at a microscopic-level, and Dr Helen James (Smithsonian Institution) is an ornithologist and paleontologist with an amazing understanding of bird evolution. Brilliant.

Next, we had a special Raman microscope shipped to us in the Birds Division at the Smithsonian Institution. This Raman microscope would let us analyse feathers in amber because it had a 1064 nm laser and confocal optics. 1064 nm is is a low energy laser, which means it doesn’t cause amber to  fluoresce (i.e. ‘glow’) – fluorescence is bad for Raman analyses. The confocal optics mean we can analyse a feather through the amber without worrying too much about the surrounding amber. Lynn Chandler at BaySpec did an amazing job of arranging the Raman microscope for us.

OK, I have the right team and the right tools, but do I have the right feathers? Carotenoids are not common in the feathers of living birds – one third of birds have feathers with carotenoid pigments, and most birds only have carotenoids on the outermost feathers. So think about it: if you randomly pluck one feather from any possible bird alive right now, the chances of that feather having carotenoids are pretty low. Imagine also that these feathers were plucked from a bird or dinosaur millions of years ago, they have to survive through deep time, be found by a paleontologist, and then be given to me for analysis. These may be be low odds, but we will never find red feathered dinosaurs if we don’t look.

Feathers in amber are amazingly precious, and we had the opportunity to work with seven ancient feathers from across the world. Alas, we didn’t find evidence for carotenoid pigments in these seven specimens. BUT, we did show that this type of work could be done without damaging the fossil feathers. It’s now just a matter of searching through every feather in amber that is found, to see if we can find our red-feathered dinosaur.

Thomas DB, Nascimbene PC, Dove CJ, Grimaldi DA, James HF. 2014. Seeking carotenoid pigments in amber-preserved fossil feathers. Scientific Reports 4, Article number: 5226 doi:10.1038/srep05226

My academic journey so far.

My academic journey so far.

I have recently started a new position at Massey University in Auckland: Lecturer of Vertebrate Zoology (!!!!!). This is an amazing opportunity and I can’t wait to sink my teeth in. I wanted to take this opportunity to give a big thank you to everyone who has helped me along the way, and to name but a tiny fraction:

University of Otago: Professor Ewan Fordyce, Professor Russell Frew, Dr Marc Schallenberg

University of Cape Town: Professor Anusuya Chinsamy-Turan

Smithsonian Institution National Museum of Natural History: Dr Helen James, Dr Matthew Carrano, Dr Gary Graves, Dr Carla Dove, Christopher Milensky, Christina Gebhard, Brian Schmidt, Jacob Saucier

Arizona State University: Professor Kevin McGraw

And a very special thank you to Cushla McGoverin, Joanne Thomas, Murray Thomas, Hollie Steel, Dan Ksepka, Mark Clements and Brandon Gellis


It’s busy times at the moment and there are big plans afoot. Sorry blog, but we might be trains in the night for a little while longer.

Charlotte Doney during her undergraduate internship at the Museum Conservation Institute. Charlotte is shown here sitting in front of an FT-Raman spectrometer in Dr. Odile Madden's Modern Materials lab. Photo source:

Charlotte Doney during her undergraduate internship at the Museum Conservation Institute. Charlotte is sitting in front of an FT-Raman spectrometer in Dr. Odile Madden’s Modern Materials lab. Photo source.

My colleagues at the Smithsonian Institution and I have recently published an article that explores the preservation of old collagen. I think this is a great methods paper that could lead on to some really interesting applications, and I will get to the details of the article in a little bit. First though, I want to highlight one of the most fun aspects of this paper – a good chunk of the work was done by Charlotte Doney, an undergraduate intern from George Washington University.

In 2012, Dr. Christine France successfully attracted funding for undergraduate students to take up research projects in the Museum Conservation Institute. This Institute is housed within the Smithsonian Institution’s Museum Support Center, in Suitland, Maryland. Charlotte Doney was interested in working with Dr. France and Dr. Odile Madden on a project these senior researchers had discussed some years prior – can Raman spectroscopy tell us if collagen in an ancient bone is well preserved? Charlotte was interested in both the challenge and answer.

Raman spectroscopy provides chemical information about a sample, and in the case of an old bone, is useful for studying both the collagen and the bone mineral. Furthermore, the isotopic compositions of carbon and nitrogen in collagen can tell us about the lifestyle of the person or animal the bone is from. Raman spectroscopy doesn’t report on the isotopic composition of collagen – this is the job of a mass spectrometer. Instead, Raman spectroscopy gives us an idea about how much collagen is present in the bone. As collagen degrades, the isotopic composition becomes less meaningful about the original lifestyle of the person or animal. As collagen degrades, there is less and less of it left in the bone, and we can detect this with Raman spectroscopy. Charlotte collected Raman spectra from bones that had known isotopic compositions.

I was working in Dr. Madden’s lab at the time and had the privilege of training Charlotte to collect Raman spectra, and then later I analysed the data and we each cowrote the now published manuscript.

During my time at the University of Otago and the University of Cape Town I hadn’t worked alongside undergraduate interns, so this was one of the new experiences I encountered at the Smithsonian. I had undertaken (and later, worked with) summer studentships at the University of Otago, and looking back there are many similarities. A dedicated research project, a short and fixed time frame, an opportunity to work with professional researchers. I can’t value these experiences highly enough, and if the student is particularly motivated, like Charlotte, then the work can be recognised as a formal publication. How good is that!

France, C. A. M., Thomas, D. B., Doney, C. R. and Madden, O. 2014. FT-Raman spectroscopy as a method for screening collagen diagenesis in bone. Journal of Archaeological Science 42: 346–355

Continued from the post below

The loadings the thing, wherin I’ll catch the essence of the dataset. Principal component analysis (PCA) is an excellent, and often essential, method for analysing a large amount of data. Our research question centers around the differences between two fossil sites, and the large dataset we have at hand is made up of x-ray fluorescence spectra from fossil bones. These data go into the PCA, and out pour our beautiful results in two forms – scores and loadings.

Let’s look at the loadings.

Loadings let us see the major sources of variation in our dataset. By ‘sources of variation’, here I mean the way in which spectra differ from one another, like having peaks in different places. Different sources of variation are teased out for each principal component, and we can visualise these ‘components’ with the loadings. Take the loadings for PC1, for example:

Principal component one loadings for x-ray fluorescence spectra. Data were collected from fossil antelope bone. Modified from Thomas and Chinsamy 2011.

Principal component one loadings for x-ray fluorescence spectra. Data were collected from fossil antelope bone. Modified from Thomas and Chinsamy 2011.

These loadings show us that the peaks attributed to iron and strontium are positively weighted, and the peaks attributed to calcium are negatively weighted. So this means that some samples in our dataset that have a great deal of Fe and Sr, and some samples have extra calcium, over and above the amount typical amount for bone. Now we jump to the next step, and used our PC1 loadings to interpret our PC1 scores, which are below:

Principal component one and two score values. Each score value represents a fossil antelope bone from South Africa. Modified from Thomas and Chinsamy 2011.

Principal component one and two score values. Each score value represents a fossil antelope bone from South Africa. Modified from Thomas and Chinsamy 2011.

We found that our Fe and Sr peaks were positively loaded. In our scores plot, this means that samples with positive PC1 scores should be rich with Fe and Sr. Likewise, our Ca peaks were negatively weighted, and so our samples with negative PC1 scores probably contain an extra calcite mineral. If we take a look at our PC1 scores we find that the positively weighted samples are all from Elandsfontein Main, and all of the Swartklip 1 have negative score values.

So we have found a chemical difference between the bones of these two sites. Elandsfontein bones have been infiltrated with iron and strontium rich minerals, which actually turn out to be clays and sands deposited by groundwater. The Swartklip bones contain abundant calcite. What does that mean for the burial history of these two sites?

The fossil bones at Elandsfontein Main and Swartklip 1 both accumulated in dune environments during the Pleistocene. The Elandsfontein Main site remained inland, and the slightly acidic groundwater that percolated through the fossils partially dissolved the bones and filled them with sediment. In contrast, sea level change periodically brought the coastline close to the Swartklip 1 site, where it is now, actually. The marine influence introduced calcium carbonate into the environment, which buffered the acidic groundwater and laced it with dissolved carbonates. These carbonates precipitated onto the Swartklip 1 fossils.

So, at Elandsfontein Main we have fossils that have been subjected to acidic groundwater for tens of thousands of years, and at Swartklip 1, we have fossils that have been periodically buffered by soil carbonates. If I was to pick one site to start looking for intact and well preserved bone, even down to isotope-level, I would start with the fossils Swartklip 1.

So yeah, this is the type of information we get from spectroscopy and principal components analysis. Pretty cool eh.

Thomas D B, Chinsamy A, 2011. Chemometric analysis of EDXRF measurements from fossil bone. X-ray Spectrometry 40: 441-445

Still to come: R code for pretreating spectra, performing a PCA analysis, and producing informative graphs…

X-ray diffraction spectrum of calcite, the mineral that makes up most fossil shells. Data from The RRUFF™ Project

X-ray diffraction spectrum of calcite, the mineral that makes up most fossil shells. Data from The RRUFF™ Project

Most of the chemical tools we use to study fossils produce a data spectrum, which is just a set of measurements made over a series of observations. For example, think of the output from an x-ray diffraction analysis – the pattern of peak counts for each two-theta value is a spectrum. Other instruments that produce spectra include x-ray fluorescence, isotope ratio mass spectrometry, Fourier transform infrared spectroscopy and so on. We have become adept at reducing these data spectra to a single or few values of interest – calcite has an intense two-theta value at 29.4 under irradiation from a copper source – and we tend to throw away the rest of the spectrum. While there are study questions that can be addressed with point values (“Is there calcite in my fossil clam shell?”), there are other questions that could be better answered while working with the whole spectrum. Is there more than just calcite in my fossil shell? Does the calcite have an unusual chemistry? Is my fossil shell different to your fossil shell? These are questions that we can address by looking at variation between spectra, which we can do with principal component analysis.

I am going to switch study systems and talk about fossil bones and x-ray fluorescence. I will work this up as a practical  tutorial of sorts, and talk about some data from South African fossils that I collected a couple of years ago. There are a couple of advantages in working with these data – one, this is a real world scenario with a tangible result – two, the spectral data are available online so you can perform your own principal component analyses on fossil data.

Principal component analysis (PCA) on spectra from fossil bones.

PremiseStable isotopes in fossils are an incredibly rich source of information about ancient animals. Isotopes in fossil teeth and bones can tell us what an animal ate and how frequently it visited a source of water. Unfortunately, teeth and bones can become altered when they are buried, and the important biological information in the isotopic compositions can be lost. It would be nice to know ahead of time whether or not a fossil bone is altered, and one approach to assessing alteration is to study the burial history of the fossil bone or tooth. In this scenario we are going to examine the burial history of a fossil by studying the elemental composition of the fossil surface – changes in the environment over time will have changed the chemistry of the fossil. We will study fossils from two different Pleistocene locations in the Western Cape of South Africa, and the end goal is to decide which location has the best-preserved fossils.

Samples: Fossil teeth and bones from two localities were analysed. These bones were the horn cores of Pleistocene antelope – springbok (Antidorcas marsupialis), eland (Taurotragus oryx) and a relative of sable (Hippotragus sp.).

Data collection: Fossil teeth and horn cores from each site were analysed with a portable x-ray fluorescence instrument. We will use the energy spectra that the instrument produced, rather than the elemental ratios.

Swartklip fossil location, outside of Cape Town South Africa

Elandsfontein fossil location, outside of Cape Town South Africa


Results and Discussion: A principal component analysis (PCA) identifies the sources of variation in a dataset, which it sees as individual ‘components’. The largest source of variation is principal component one, the second largest source of variation is principal component two, and so on. By separating out sources of variation, PCA provides us with two very important sets of data about the samples we have analysed.

The first set of data are the ‘score values’ (or eigenvalues). The spectrum from each sample is reduced to a single ‘score’ value for each principal component. Samples will have similar score values when they have similar amounts of a particular variable . So, we can look at a distribution of score values and see which samples are more similar to which other samples by how closely positioned they are. Lets take this image here:

Principal component score values, from XRF analyses of fossil bones and teeth.

Principal component score values, from XRF analyses of fossil bones and teeth.

These are the score values from principal component one and principal component two, for our fossil XRF data. The first thing to notice is that all of the Swartklip samples have similar PC1 values. That is, the fossils from the two sites can be separated by the variation that is being pulled out by principal component one. The fossils from both sites have a range of PC2 values, so there is variation at both sites in whatever this component is. So, what is the source of variation along principal component one that is separating these fossil sites?

More soon, including the R code for pre-treating spectral data, performing PCA, and presenting PCA results…

Tag Cloud