Today I'm going to talk with you about spectrometers, one of my favorite
instruments. There are many different types of spectrometers and the one I'll describe
is centered on visible light with some reach into the ultraviolet and
near-infrared. We're all familiar that a prism decomposes light into a rainbow
spectrum and so does a spectrometer, but a spectrometer then accurately measures
each of potentially a couple thousand colors where each color is a range of
wavelengths. With that spectra resolution material identification and plant health
assessment are a couple of the applications. Now I'll describe how a
spectrometer is constructed, how one is used, and finally demo software that I've
written to help analyze the data. This is an older spectrometer meant to be
installed directly into computer but everything we'll see in this
spectrometer is perfectly valid for more recent spectrometers. This is the actual
spectrometer with everything else here being the interface components to the
computer. Light is delivered to a spectrometer by a fiber cable. We have to
be careful not to exceed the bend radius of the fiber cable otherwise light may
actually break through. The fiber cable is composed of two different types of
glass. There's the core and the cladding and that's surrounded by a jacket. The
core and the cladding are slightly different glass with different
refractive indices to help keep the like cane to the core.
This housing is called the optic bench and it holds all the optic elements rigidly
in correct relation to each other while allowing for some adjustment. The
optic bench may have elements such as light traps and baffles embedded into
its body to help guide and trap stray light. The fiber cable is attached to the
optic bench by an SMA connector and light passes from the fiber cable
through a slit. Choosing a slit size is a compromise between spectral resolution
and throughput of the spectrometer. A spectrometer may also have mode
strippers installed after the slit to remove light which has passed down the
cladding of the fiber cable. [Ideally, use single-mode fiber cable.]
After light has passed through the slit it is
expanding towards a collimating mirror. The collimating mirror takes the
expanding rays of light and turns them into parallel rays. The parallel rays are
then directed toward a diffraction grating which is the dispersive element
in this spectrometer. It serves the same function as a prism that is the break to
light up into its constituent wavelengths but uses a different
mechanism than a prism. The diffraction grating consists of many very small
parallel grooves and reflecting light off of the diffraction grating creates
monochromatic versions of the image from the slit. Those images are directed
toward a focusing mirror which directs the light to a detector which sits over
here. The detector is a linear CCD and may only be a few pixels high but is a few
several thousand pixels long. A linear lens is installed on top of the sensor and
helps to improve the performance. Recall that the light received by the
spectrometer is broke up into its constituent wavelengths and reflected on
to the detector. This means that each pixel of the detector measures a known
range of wavelengths or a particular color. Our spectrometer is sensitive from
about 350 to 1100 nanometers. Using a detector of 2,000 pixels each pixel is
capturing about 3/8 of a nanometer. This isn't quite right because there's non
detecting space between the pixels but it's pretty close. As a quick summary,
light is delivered to the spectrometer by a fiber cable. The light passes
through a slit and is reflected off a collimating mirror. A parallel beam of
light is directed to a diffraction grating. The diffraction grating breaks
the light up into a spectrum. The spectrum is directed to a focusing mirror
and the focusing mirror directs the spectrum to a detector. The detector is a one
pixel high camera and measures the intensity of each color in the spectrum.
And basically that's how a spectrometer works. This is a second spectrometer that
we'll look at. This spectrometer is from CI D Bio-Science. The actual spectrometer
is from Ccean Optics just like the ISA card based spectrometer that we looked
at. The ISA card interface has been replaced with USB and there are pins on
top for digital I/O and power. CID Bio-Science uses those to power their
lighting module. This spectrometer is a nice example of an OEM module being used
for a specialized application, in this case measuring the relative
concentration of plant pigments based on the spectrum of reflected light. I
attached a CID Bio-Science CI-710 spectrometer to a Microsoft Surface
tablet. The spectra of several leaves on two severely droughted geranium plants
were then measured. One plant remained droughted as a control and a second plant
was watered. After 24 hours the data collection process was repeated. This is
a representative spectrum of light reflected from a plant normalized by the
spectrum of the illumination source. There's noise on both sides of this
graph because the light source lacks power in the ultraviolet on the left
hand side of the graph and the detector lacks sensitivity in the near-infrared
or the right-hand side of the graph. CID addresses this partially by using two
light sources, an incandescent light and a blue LED. This is clever and adds power
to the blue region of the spectrum but ultraviolet and near-infrared are still
going to be weak. Analysis could be misled by noise so both sides of the
graph are cropped. Plants absorb blue and red light and use it in photosynthesis
to generate energy. However plants don't use green [much] light and
instead reflect it and that's why plants look green. We can see that a plant is
green by looking at it but a spectrometer reveals the details. The green that we
see is associated with a broad peak at 550 nanometers. Another characteristic
plant signature is called the red edge at 700 nanometers. This is where red
transitions the near-infrared. An interesting property of plants is that
they're highly reflective in the near-infrared. It's important to be aware
of other sources of noise. This plant spectrum was measured under fluorescent
lights. The leaf clip wasn't completely closed so in addition to the plant
spectrum we're measuring the emission lines of mercury and the rare earth phosphors
used by the fluorescent lights. When measuring the visible spectrum
of a plant leaf, we are primarily measuring plant pigments. We can see these directly
by using paper chromatography. This is a neat experiment that's easily done at
home. Grind up the plant leaves and add a solvent. I'm using equal parts acetone
and 70% isopropyl alcohol. Next separate the fibers from the mixture. Vacuum
assist equipment would be nice, but if you don't have that just let the
solution drip through filter paper. Besides being slow, unfortunately a lot
of the acetone will evaporate off if using the drip method. Ideally after the
solution is separated it's concentrated onto chromatography paper. Coffee filters
will work fine too. Micro capillary pipettes are really nice
for transferring small quantities of liquids. A small amount is transferred to
the chromatography paper. The larger dot is from a normal eyedropper. After the
dot dries add additional solution to the dot and repeat this many times until you
have a concentrated dot. I'm not patient enough for that method so I suspend the
entire chromatography paper strip into the solution. The solution is wicked up
the paper and carries with it the plant pigments. The distance traveled and
separation of the pigments is going to be a [complex] function of molecule size and
interaction with the cellulose in the paper. Here the pigments of two leaves
have been separated by a paper chromatography.
Despite the leaf appearing only green there are yellow and green pigment bands.
The yellow indicates a carotenoid pigment and the green indicates a
chlorophyll pigment. Depending upon the species and plant stress and there may also
be blue or red bands. By the way, the easiest way to play with paper
chromatography at home is to try to separate the pigments of water-based
markers. We now have a lot of background information and are ready to look at an
example. I collected leaves from 22 different
tree species and I'm going to predict the tree species based on the color of
or a spectrum of the leaf. I've written browser based software to store and analyze
spectra and I'll explain a few of the screens. Each spectra has an ID and
description and several metrics are calculated on demand. I've added metrics
that CID uses and researchers can add their own metrics and equations to my
software's database. To review quality I verify the flat and dark fields are
sensible and then check each group's spectra. Spectra from three leaves of
each tree species form the groups. Outliers within a group are removed.
These spectra look good with a strong red edge and generally a strong green
peak. A few tree species have red leaves such as a smoked tree. Generally if
replicants agree the data are likely fine unless
systemic errors are present. A predictive model is created using the partial least
squares discriminant analysis machine learning method. Spectra regressed to
group categories and with each iteration the performance of the model to predict
training and disjoint testing datasets is measured. To avoid overtraining I
select a model that maximizes the prediction of testing dataset while
minimizing model complexity. Predictions are summarized in a matrix after each
iteration. True categories are rows and predicted categories are columns. Off
diagonal values are prediction errors. With additional model complexity,
generally, the performance improves and fewer values are off diagonal. Loading
plots show the weight assigned to each wavelength for each latent variable.
Simple loading plots can almost be described in words. This one sort of
compares the near-infrared to the visible range. The loading plots get
progressively more complex until they're likely fitting noise. For reference the
average spectrum of the training data set is shown with shaded regions
containing one standard deviation and the extrema at each wavelength. These
help assess noise and if trimming is needed. This model predicts tree species
of the tests in data set with 55% accuracy that identifies 12 of 22 tree
species by color alone. Leaf shape and bark texture are what people would use
for identification so 55% from color alone is pretty good.
Granted fall colors and stress will confuse the model. Spectrometers are
fascinating instruments, easy to understand and with a lot of depth to
explore. As a winter project, maybe an early spring project, I'd like to design,
simulate, build, and test one. None of the individual pieces are overly complex.
Granted, people earn doctorates studying spectroscopy, but a decent rudimentary
visible light spectrometer is within the abilities of a home shop to produce, at
least I think it is. Subscribe my channel follow along and we'll talk again soon.