Automated material identification with a Raman spectrometer based on the contribution enhancement of small...

There is only a small difference in Raman peaks between two materials, but they also represent different molecular materials. Therefore, the accurate identification ability for similar materials with small differences among their Raman peaks plays a key role in Raman spectrometers for material identification. However, the noises, the baseline (i.e., fluorescence backgrounds), and the requirements, such as fast and automated detection, of excellent user experiences cause many difficulties. In this paper, the target Raman peak is directly subtracted from the detected Raman spectrum by the adaptive minimum root mean square error (RMSE) estimation for a residual spectrum. Unlike the usual methods in which the detected Raman peak needs to be first recovered by removing the baseline from its Raman spectrum and then to be compared with the target Raman peak, our method can effectively enhance the contribution of small differences between the detected and the target Raman peak on the residual spectrum so as to make the RMSE of the residual spectrum more sensitive with increasing differences. On the other hand, the obtained RMSE of the residual spectrum only has a small change for the detected Raman spectrum with various baselines. So the common criteria (i.e., the third-order polynomials describing RMSE) to identify the detected Raman spectrum with various baselines and the target Raman spectrum is presented. Simulation results show that the small difference, where there is only an additional small Raman peak as low as 1/25 of the maximum peak height, can also be accurately identified. Experiments also demonstrate that similar materials can be accurately identified, whereas some commercial Raman spectrometers fail to identify them. Our method effectively deals with the problem in which the error of the complex baseline correction causes erroneous judgement in Raman spectrometers for material identification.