拉曼光谱在原研药和仿制药中的实时鉴别与评价分析  

Real-time Identification and Rapid Analysis of Raman Spectroscopy in Innovator and Generic Drugs

作　　者：于永爱 罗智嘉 邓赵斌 陈娟 Yu Yongai;Luo Zhijia;Deng Zhaobing;Chen Juan(college of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China;Shanghai Oceanhood Opto-electronics Tech Co.,Ltd.,Shanghai 201216,China)

机构地区：[1]上海第二工业大学计算机与信息工程学院,上海201209 [2]上海如海光电科技有限公司,上海201216

出　　处：《实验与分析》2024年第4期39-44,共6页LABOR PRAXIS

摘　　要：传统的药物理化分析鉴别方法普遍存在破环性检测、实时效力差以及制备流程复杂等问题。为此,可考虑使用拉曼光谱结合机器学习的方法进行快速建模分析。依据原研药和仿制药等谱图特征,结合机器学习及深度学习等算法,最终使用PSO-RF模型,针对不同批次的原研药片和仿制药片识别准确度达97.5%。同时,在对各类药物进行一致性评价量化时,通过基于傅里叶频域变换的余弦相似度FFT-Coisne及光谱信息散射度SID和光谱角SAM值量化了各类药品和标准原研药的相似度差异。Traditional physical and chemical analysis identification methods generally suffer from problems such as destructive detection,poor real-time effectiveness,and complex preparation processes;Consider using Raman spectroscopy combined with machine learning methods for modeling and analysis.Considering the spectral characteristics of original drugs and generic drugs,combined with machine learning and deep learning algorithms,the PSO-RF model was ultimately used,and the accuracy of tablet recognition for different batches was 97.5%.At the same time,when quantifying the consistency evaluation of various drugs,the similarity differences between various drugs and standard original drugs were quantified using cosine similarity FFT Coisne based on Fourier frequency domain transformation,spectral information scattering degree SID,and spectral angle SAM values.

关 键 词：机器学习 拉曼光谱 药物鉴别 量化一致性指标 

分 类 号：O657.37[理学—分析化学]