No abstract

The spectral analysis depends heavily on unwanted signals, such as the fluorescent background from the samples or other interfering components. A number of mathematical algorithms have been proposed to remove the background of Raman spectra. However, these methods require the selection of appropriate parameters to correct the of Raman spectra baseline. In this paper, we propose a method of adaptive noise model based on iteratively reweighted penalized least squares (ANM‐IRPLS) for Raman spectrum baseline correction. The algorithm was applied to various artificial spectra containing real forms of baselines and characteristic Raman peaks and then to the spectra of real drug samples with fluorescence obtained on a device equipped with a 532‐nm laser with a resolution of 15 cm−1. The modeling results showed that the proposed ANM‐IRPLS baseline correction method allows for better results in background removal than the airPLS. For real Raman spectra processed by the ANM‐IRPLS method, it is shown that the algorithm handles a complex background well, while maintaining the characteristic Raman signal features, such as a wide water peak for aqueous solutions.

Development of drug quality control methods using non‐invasive methods, in particular Raman spectroscopy, is an important task in the pharmaceutical industry. This paper presents a new approach to the analysis of drug spectra obtained with portable Raman spectrometers. It represents a normalized relative error distribution analysis (nReDA). The proposed method uses the Pearson residuals distribution statistics and, compared with the correlation method, allows increasing the screening sensitivity to foreign components that are not contained in the original sample, with the same resistance to changes in the noise distribution and the peak ratios. On the basis of the statistical data, the nReDA in some way is similar to the techniques such as principal component analysis, however, it requires a considerably less spectra to build a representative model and is suitable for comparing Raman spectra, having a noise other than the Gaussian one. The paper shows the prospects of the algorithm for creating a spectral library and solving the problem of transferring Raman libraries to a large number of spectrometers of the same type.

This paper presents the results of the identification procedure certification and subsequent quantitative determination of the active ingredients of two-component injectable medicines (active ingredient and solvent) using Raman spectroscopy. The main objective of the research was to select approaches for estimating the metrological characteristics of the measurement procedure, which include consideration of the methodological parameters and provide the metrological traceability of measurement results to SI units. According to this purpose, the GVET 176‑1‑2010 State Secondary Measurement Standard for units of mass fraction, mass (molar) concentration of components in solid and liquid substances and materials based on volumetric titration was used. The following substances were chosen as the research objects for estimating the metrological characteristics of the measurement procedure: ascorbic acid, novocaine and sodium thiosulphate. The authors of the work have demonstrated the measurement-procedure certification results, whose accurate determination was confirmed by the results of interlaboratory comparisons. The obtained results confirmed the accuracy of the identification procedure and subsequent quantitative determination, which proves its applicability for the determination of the active ingredients in two-component injectable medicines. In addition, the possibility of developing reference materials based on the medicines under study is indicated. Further development of this study may be directed at the development of an identification procedure and its certification, with subsequent quantitative determination of the active ingredients of injectable medicines having three components as well as those having a more complex composition.