AI trained on photos of salt ‘stains’ can predict their chemical composition
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[Source: © 2024 Bruno C Batista et al]


When salt solutions evaporate, deposits of intricate and often beautiful crystallisation patterns are formed which may seem random and unpredictable.

But now a machine learning technique has shown how images of such patterns from aqueous inorganic salt deposits can be used to predict their chemical composition. The approach could find various uses from space exploration to deposit-identifying smartphone apps.

‘The processes that form deposit patterns during drop drying are very complicated and computing the diverse stain patterns from just the crystallising salt type is challenging, perhaps impossible,’ says Oliver Steinbock, whose lab conducted the work at Florida State University, US.

‘We looked at this problem in the opposite direction and asked whether it is possible to find the composition solely from a photo of the drop stain.’
 

 When salt solutions evaporate, deposits of intricate and often beautiful crystallisation patterns are formed which may seem random and unpredictable. But now a machine learning technique has shown how images of such patterns from aqueous inorganic salt deposits can be used to predict their chemical composition. The approach could find various uses from space exploration to deposit-identifying smartphone apps. 

 Researchers have developed an AI program that can predict the chemical composition of salt stains by analyzing photos. The program was trained using 7,500 photos of 42 different types of salt stains, which were translated into 16 parameters capturing features such as deposit area, compactness, and texture. The machine learning algorithm, once trained, was able to correctly identify the composition of salt stains in 90% of attempts, even for images that were not part of the initial dataset