Imagine
zooming in on a dried drop of salt solution—each pattern a unique masterpiece,
reminiscent of abstract art, yet no larger than the size of a penny.
New research by scientists in the
Florida State University Department of Chemistry and Biochemistry uses the
patterns formed by a dried salt solution to train a machine learning algorithm
that can identify the chemical composition of different salts. The work was
published in the Proceedings
of the National Academy of Sciences.
"We are taking chemical
fingerprints of different salts," said Oliver Steinbock, Cottrell
Professor of Chemistry. "Thinking of sodium chloride, or table salt, for
example—among all samples of this type, they always look similar. There are differences
from sample to sample, but all examples are distinct enough from other types
that we can tell what kind of salt it is."
When a salt solution dries, all sorts of
forces are at play. Chemists have studied how fluid motion, crystal growth, environmental factors and other processes interact to determine the
deposit pattern. The FSU researchers approached the problem from the opposite
direction: If presented with a pattern of a dried solution droplet, could they
determine what type of salt it was?
Video showing the growth of various salts
captured with a microscope. Credit: Oliver Steinbock
To do that, the researchers
recorded 7,500 photos of 42 different types of salt stains. Using a new
software approach, they translated each image into 16 parameters that can be
rapidly analyzed by machine learning methods. The parameters capture features
such as deposit area, compactness and texture. Each image was translated into
numbers that in subtle ways encode the patterns' arrangement of tiny crystals
in rings, needles and leaf-like shapes.
To test their program's ability to
predict composition, the researchers analyzed additional images that were not
part of the initial dataset. These programs successfully identified the correct
salt in 90% of attempts.
"We were surprised at how well
this worked," Steinbock said. "Who would think that from a photo, you
can tell the difference between sodium chloride and potassium chloride? They
look very similar in the pictures. But the method is very good."
The researchers plan to add to the training dataset by analyzing hundreds of thousands of new images, which would make their tool even more accurate and versatile. This vast number calls for lab automation, and such a robotic drop imager is currently being tested in Steinbock's group.
"The difference between a human doing this and a computer is that the computer and our software can do this quantitatively," Steinbock said. "If I were to present you 7,500 images, you would be confused. But the computer gets better and better with more and more images."
The ability to quickly provide insight
into the chemical composition of a sample from a photograph has many potential
applications. For example, outfitting a rover exploring another planet with a
full chemistry lab would be difficult and expensive, but a camera offers a
cheap and lightweight alternative. Other scenarios, such as testing materials
for lab safety, rapid screening for suspected drugs or low-cost blood analysis
in places without access to hospitals, offer other potential applications.
Another benefit of this approach is that
it only requires a minute amount of material. With just a few milligrams that
make up a salt deposit, users could understand what they are likely handling
and inform their decisions about how to proceed.
"If you want to have a rough idea
of what that stain or spill is on a lab bench, you might use this as a cursory,
first-step analysis," said Bruno Batista, a senior researcher in
Steinbock's lab and the paper's lead author.
A microscope image of sodium sulfite.
Credit: Oliver Steinbock
Co-authors
on this paper were Semhare Tekle, an undergraduate researcher in Steinbock's
lab; Jie Yan, a professor in the Department of Computer Science at Bowie State
University; and Beni Dangi, an associate professor in the Department of
Chemistry at Florida A&M University.
To see more images of salt patterns analyzed by Steinbock's lab, visit the SaltScapes Gallery at his group's website.
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