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机器学习用于生物制药树脂的高光谱成像数据的解卷和细分

Hong Wei¹, Joseph P Smith¹

¹Process Research & Development, MRL, Merck & Co., Inc., West Point, Pennsylvania 19486, United States.

Molecular pharmaceutics

|September 17, 2024

概括

这项研究引入了无监督机器学习,特别是非负矩阵因子化 (NMF) 和k-means集群,结合拉曼超光谱成像来快速分析生物制药树脂. 这种新的方法可以准确地识别和绘制分子和空间特性,而不会改变样本.

科学领域:

分析化学分析化学
材料科学材料科学材料科学
生物技术是生物技术.

背景情况:

生物制药树脂是药物净化,输送和生物催化剂的关键惰性矩阵.
目前分析树脂性能的方法往往具有破坏性,需要样本改变,并产生有限的信息.
需要先进的,非破坏性的技术来表征这些基本材料.

研究的目的:

开发和验证一种新的,快速的,非破坏性的方法来阐明生物制药树脂的分子和空间特性.
应用无监督机器学习算法,非负矩阵因子化 (NMF) 和k-means集群,与拉曼超谱成像相结合.
为了证明这种综合方法对综合树脂分析的能力,使用Immobead 150P作为代表性的例子.

主要方法:

利用拉曼超光谱成像来收集生物制药树脂的光谱数据.
应用非负矩阵因子化 (NMF) 用于树脂和基板组件的光谱和空间解卷.
在多个激发波长 (532,638和785纳米) 的NMF解卷后用于图像细分的k-平均集群.

主要成果:

在Immobead 150P树脂和玻璃基板中使用NMF成功地解构和空间分解了化学物种.
通过在所有测试的激发波长中通过k-means集群实现了树脂组件的准确图像细分.
证明了结合NMF和k-means方法的有效性,用于对生物制药树脂进行全面,数据丰富的表征.

结论:

无监督机器学习 (NMF和k-means) 与拉曼高光谱成像的集成为分析生物制药树脂提供了强大,快速和非破坏性的工具.
这种方法提供了详细的分子和空间见解,克服了传统分析技术的局限性.
开发的方法代表了生物制药和相关多学科领域的研发的重大进步.

关键词:

拉曼高光谱成像技术有丰富的数据的实验实验.解体解体是一种解体.图像分割图像细分图像细分树脂分析树脂分析无监督的机器学习

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