Calibration transfer between modelled and commercial pharmaceutical tablet for API quantification using backscattering NIR, Raman and transmission Raman spectroscopy (TRS).

Backscattering NIR, Raman (BSR) and transmission Raman spectroscopy (TRS) coupled with chemometrics have shown to be rapid and non-invasive tools for the quantification of active pharmaceutical ingredient (API) content in tablets. However, the developed models are generally specifically related to the measurement conditions and sample characteristics. In this study, a number of calibration transfer methods, including DS, PDS, DWPDS, GLSW and SST, were evaluated for the spectra correction between modelled tablets produced in the laboratory and commercial samples. Results showed that the NIR and BSR spectra of commercial tablet corrected by DWPDS and PDS, respectively, enabled accurate API predictions with the high ratio of prediction error to deviation (RPDP) values of 2.33 and 3.03. The most successfully approach was achieved with DS corrected TRS data and SiPLS modelling (161 variables) and yielded RMSEP of 0.72 %, R2P of 0.946 and RPDP of 4.35. The proposed calibration transfer strategy offers the opportunities to analyse samples produced in different conditions; in the future, its implication will find extensively process control and quality assurance applications and benefit all possible users in the entire pharmaceutical industry.

[Rapid identification of Coix seed varieties by near infrared spectroscopy].

Unsupervised learning algorithm-principal component analysis (PCA), and supervised learning algorithm-learning vector quantization (LVQ) neural network and support vector machine (SVM) were used to carry out qualitative discriminant analysis of different varieties of coix seed from different regions. Since nutrient compositions of different varieties coix seed samples from different origins were complex and the contents were similar, characteristic variables of two kinds of coix seed were alike, the scores plot of their principal components seriously overlapped and the categories of coix seed were difficult to distinguish While satisfactory results were obtained by LVQ neural network and SVM. The accuracy of LVQ neural network prediction is 90. 91%, while the classification accuracy of SVM, whose penalty parameter and kernel function parameter were optimized, can be up to 100%. The results show that NIRS combined with chemometrics can be used as a rapid, nondestructive and reliable method to identify coix seed varieties and provide technical reference for market regulation.