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.
@article{arxiv.1808.03679,
title = {Machine Learning Promoting Extreme Simplification of Spectroscopy Equipment},
author = {Jianchao Lee and Qiannan Duan and Sifan Bi and Ruen Luo and Yachao Lian and Hanqiang Liu and Ruixing Tian and Jiayuan Chen and Guodong Ma and Jinhong Gao and Zhaoyi Xu},
journal= {arXiv preprint arXiv:1808.03679},
year = {2019}
}
Comments
This is the second version. On pages 7 through 8, we have added a new case about the spectral properties of mixtures. Specifically, paragraph 1 on page 8 and Fig.7 is added