Using deep learning algorithms to perform accurate spectral classification

Spectral detection has attracted much attention because it can be used to identify molecular information of various samples. However, since the samples consist of a wide variety of molecules, the spectral data are often complex and difficult to analyze accurately. In our paper, we transform spectral data into two-dimensional matrix data, and further use image-oriented deep learning algorithms to process it. We used this algorithm to analyze the reflectance spectra of six samples and achieved accurate classification through spectral data. Its maximum classification accuracy is 99.56 %, the average accuracy is 96.78 %. Our method makes spectral data analysis compatible with deep learning algorithms. With the continuous improvement of deep learning algorithms, we can carry out more accurate analysis for more complex spectral data in the future.

Automated summary

The study converted spectral data into two-dimensional matrix data and processed it using deep learning algorithms to accurately classify reflectance spectra. The algorithm achieved a maximum classification accuracy of 99.56% and an average accuracy of 96.78%. This method enables the analysis of spectral data with deep learning algorithms, allowing for more accurate analysis of complex spectral data in the future.