Machine Learning Promoting Extreme Simplification of Spectroscopy Equipment
This work addresses the high cost and complexity of spectroscopy instruments for researchers and scientists, potentially enabling broader access, though it appears incremental as it builds on existing machine learning methods.
The authors tackled the problem of expensive and complex spectroscopy equipment by proposing a machine learning strategy that enables the use of an exceedingly-simplified setup for absorbance curve measurement, with initial verification showing it meets the needs for this approach.
The spectroscopy measurement is one of main pathways for exploring and understanding the nature. Today, it seems that racing artificial intelligence will remould its styles. The algorithms contained in huge neural networks are capable of substituting many of expensive and complex components of spectrum instruments. In this work, we presented a smart machine learning strategy on the measurement of absorbance curves, and also initially verified that an exceedingly-simplified equipment is sufficient to meet the needs for this strategy. Further, with its simplicity, the setup is expected to infiltrate into many scientific areas in versatile forms.