VasLine: Realize online detection and augmented NIR using deep learning

Abstract

The chemical information acquisition capabilities of near infrared (NIR) have attracted great interests of investigators on exploring its potential as process analytical technologies (PAT) on the traditional Chinese medicine (TCM). However, due to the operation environment of TCM is complex and noisy, the accurate online composition detection and analysis is challenging and remains unsolved. Therefore, in this paper, we propose a new online TCM composition analysis platform for high quality online spectral data acquisition. We design, VasLine, a generative framework based on deep learning to estimate the multiple critical quality attributes (CQAs). To demonstrate the feasibility of the system, the platform was applied to the extraction process of Xiao’er Xiaoji Zhike Oral Liquid (XXZOL) and the framework was trained to estimate the content of the 7 CQAs. In the evaluation, we carried out 16 batches extraction experiments and collected extensive online NIR data, offline NIR data and high-performance liquid chromatography (HPLC) data for TCM composition analysis. The results show that the estimation accuracy of VasLine outperforms the state-of-the-art regression approaches significantly for all the 7 different CQAs, e.g., the R2 metrics of VasLine for the CQAs are all higher than 0.95. This study aims to propose a novel generative framework based on a deep learning model, and the framework is applied in the self-developed platform to estimate CQAs with high-quality generative data from noisy online NIR accurately. The experiments for online TCM composition analysis show that VasLine is a new solution for the quality improvement of the actual pharmaceutical industry.