Visible Particle Identification Using Raman Spectroscopy and Machine Learning.

Sheng, Han; Zhao, Yinping; Long, Xiangan; Chen, Liwen; Li, Bei; Fei, Yiyan; Mi, Lan; Ma, Jiong

Sheng, Han; Zhao, Yinping; Long, Xiangan; Chen, Liwen; Li, Bei; Fei, Yiyan; Mi, Lan; Ma, Jiong.

Afiliación

Sheng H; Institute of Biomedical Engineering and Technology, Academy for Engineer and Technology, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Zhao Y; Institute of Biomedical Engineering and Technology, Academy for Engineer and Technology, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Long X; Institute of Biomedical Engineering and Technology, Academy for Engineer and Technology, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Chen L; Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Green Photoelectron Platform, Department of Optical Science and Engineering, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Li B; Ruidge Biotech Co. Ltd., No. 888, Huanhu West 2nd Road, Lin-Gang Special Area, China (Shanghai) Pilot Free Trade Zone, Shanghai, 200131, China.
Fei Y; State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, No. 3888 Dong Nanhu Road, Changchun, Jilin, 130033, China.
Mi L; Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Green Photoelectron Platform, Department of Optical Science and Engineering, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Ma J; Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Green Photoelectron Platform, Department of Optical Science and Engineering, Fudan University, 220 Handan Road, Shanghai, 200433, China. lanmi@

AAPS PharmSciTech ; 23(6): 186, 2022 Jul 06.

Article en En | MEDLINE | ID: mdl-35790644

RESUMEN

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.

Asunto(s)

Aprendizaje Automático; Espectrometría Raman; Algoritmos; Análisis de Datos

Palabras clave

Injectable; Machine learning; Particle identification; Processing; Raman spectroscopy

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectrometría Raman / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: AAPS PharmSciTech Asunto de la revista: FARMACOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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