The team of Professor Ma Jiong from Fudan University and Researcher Li Bei from Changguang Chenying published a paper titled "Visible Particle Identification Using Raman Spectroscopy and Machine Learning" in the AAPS PharmaSciTech journal, using Changguang Chenying's core product - MicroRaman particle detector. This study used a standard solution containing visible particles to identify the full spectrum, fingerprint region, high wavenumber region, and Raman background spectral data of its Raman spectrum, and constructed a spectral dataset. Five classification algorithms were trained to provide a simple and accurate data analysis method for particle recognition.

Abstract

Visible particle identification is a crucial prerequisite step for process improvement and control during the manufacturing of injectable biotherapeutic drug products. Raman spectroscopy is a technology with several advantages for particle identification including high chemical sensitivity, minimal sample manipulation, and applicability to aqueous solutions. However, considerable effort and experience are required to extract and interpret Raman spectral data. In this study, we applied machine learning algorithms to analyze Raman spectral data for visible particle identification in order to minimize expert support and improve data analysis accuracy. We manually prepared ten types of particle standard solutions to simulate the particle types typically observed during manufacturing and established a Raman spectral library with accurate peak assignments for the visible particles. Five classification algorithms were trained using visible particle Raman spectral data. All models had high prediction accuracy of >98% for all types of visible particles. Our results demonstrate that the combination of Raman spectroscopy and machine learning can provide a simple and accurate data analysis approach for visible particle identification.

The paper links：

https://pubmed.ncbi.nlm.nih.gov/35790644/