Extreme Point Sort Transformation Combined With a Long Short-Term Memory Network Algorithm for the Raman-Based Identification of Therapeutic Monoclonal Antibodies.

Therapeutic monoclonal antibodies (mAbs) are a new generation of protein-based medicines that are usually expensive and thus represent a target for counterfeiters. In the present study, a method based on Raman spectroscopy that combined extreme point sort transformation with a long short-term memory (LSTM) network algorithm was presented for the identification of therapeutic mAbs. A total of 15 therapeutic mAbs were used in this study. An in-house Raman spectrum dataset for model training was created with 1,350 spectra. The characteristic region of the Raman spectrum was reduced in dimension and then transformed through an extreme point sort transformation into a sequence array, which was fitted for the LSTM network. The characteristic array was extracted from the sequence array using a well-trained LSTM network and then compared with standard spectra for identification. To demonstrate whether the present algorithm was better, ThermoFisher OMNIC 8.3 software (Thermo Fisher Scientific Inc., U.S.) with two matching modes was selected for comparison. Finally, the present method was successfully applied to identify 30 samples, including 15 therapeutic mAbs and 15 other injections. The characteristic region was selected from 100 to 1800 cm-1 of the full spectrum. The optimized dimensional values were set from 35 to 53, and the threshold value range was from 0.97 to 0.99 for 15 therapeutic mAbs. The results of the robustness test indicated that the present method had good robustness against spectral peak drift, random noise and fluorescence interference from the measurement. The areas under the curve (AUC) values of the present method that were analysed on the full spectrum and analysed on the characteristic region by the OMNIC 8.3 software's built-in method were 1.000, 0.678, and 0.613, respectively. The similarity scores for 15 therapeutic mAbs using OMNIC 8.3 software in all groups compared with that of the relative present algorithm group had extremely remarkable differences (p < 0.001). The results suggested that the extreme point sort transformation combined with the LSTM network algorithm enabled the characteristic extraction of the therapeutic mAb Raman spectrum. The present method is a proposed solution to rapidly identify therapeutic mAbs.

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.