A spectral recovery method for Raman spectroscopy utilizing prior datasets

Spectral-based method has been widely used for the qualitative and quantitative analysis of different substances in various fields. The spectral recovery method is a crucial role in the spectral-based method, which can save the measurement cost and computation time in measuring. In this paper, we introduce a simple and reliable spectral recovery method base on prior datasets, which can tolerate substantial spectral noise. The method has been successfully applied in the quantitative analysis of the pharmaceutical mixture. The SNR of the recovery spectra can be increased by ~100 times.

Quantification of Carbon Nanotubes by Raman Analysis

The increasing prevalence of carbon nanotubes (CNTs) in manufacturing and research environments, together with the potential exposure risks, necessitates development of reliable and accurate monitoring methods for these materials. We examined quantification of CNTs by two distinct methods based on Raman spectroscopy. First, as measured by the Raman peak intensity of aqueous CNT suspensions, and second, by Raman mapping of air filter surfaces onto which CNTs were collected as aerosols or applied as small-area (0.05 cm2) deposits. Correlation (R2 = 0.97) between CNT concentration and Raman scattering intensity for suspensions in cuvettes was found over a concentration range from about 2 to 10 µg/ml, but measurement variance precludes practical determination of a calibration curve. Raman mapping of aerosol sample filter surfaces shows correlation with CNT mass when the surface density is relatively high (R2 = 0.83 and 0.95 above about 5 µg total mass on filter), while heterogeneity of CNT deposition makes obtaining representative maps of lower density samples difficult. This difficulty can be mitigated by increasing the area mapped relative to the total sample area, improving both precision and the limit of detection (LOD). For small-area deposits, detection of low masses relevant to occupational monitoring can be achieved, with an estimated LOD of about 50 ng.

Raman Spectroscopy for Pharmaceutical Quantitative Analysis by Low-Rank Estimation

Raman spectroscopy has been widely used for quantitative analysis in biomedical and pharmaceutical applications. However, the signal-to-noise ratio (SNR) of Raman spectra is always poor due to weak Raman scattering. The noise in Raman spectral dataset will limit the accuracy of quantitative analysis. Because of high correlations in the spectral signatures, Raman spectra have the low-rank property, which can be used as a constraint to improve Raman spectral SNR. In this paper, a simple and feasible Raman spectroscopic analysis method by Low-Rank Estimation (LRE) is proposed. The Frank-Wolfe (FW) algorithm is applied in the LRE method to seek the optimal solution. The proposed method is used for the quantitative analysis of pharmaceutical mixtures. The accuracy and robustness of Partial Least Squares (PLS) and Support Vector Machine (SVM) chemometric models can be improved by the LRE method.