Florida State University chemists have developed a machine learning tool that can identify the chemical composition of dried salt solutions with 99% accuracy based on images. By utilizing robotics to prepare samples and artificial intelligence for data analysis, this innovative tool presents cost-effective options for chemical analysis, potentially beneficial for applications in space exploration, law enforcement, and home testing. This research builds on a previous study where manual methods were used, now enhanced by the Robotic Drop Imager (RODI) to automate sample preparation and significantly increase the image database for machine learning analysis.
By using robotics to prepare thousands of samples and artificial intelligence to analyze their data, they created a simple, inexpensive tool that could expand possibilities for performing chemical analysis.
"We are living in the age of artificial intelligence and big data," said co-author Oliver Steinbock, a professor in the FSU Department of Chemistry and Biochemistry.
The research could make possible cheaper, faster chemical analysis that could be used in space exploration, law enforcement, home testing and more.
Instead of hand-pipetting samples, the researchers created what they named the Robotic Drop Imager, or RODI, which is capable of preparing more than 2,000 samples per day.
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