Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in‐line concentration monitoring. Recent approaches and some theoretical considerations show a correlation between Raman signals and particle sizes but do not determine polymer size from Raman spectroscopic measurements accurately and reliably. With this in mind, we propose three alternative machine learning workflows to perform this task, all involving diffusion maps, a nonlinear manifold learning technique for dimensionality reduction: (i) directly from diffusion maps, (ii) alternating diffusion maps, and (iii) conformal autoencoder neural networks. We apply the workflows to a data set of Raman spectra with associated size measured via dynamic light scattering of 47 microgel (cross‐linked polymer) samples in a diameter range of 208–483 nm. The conformal autoencoders substantially outperform state‐of‐the‐art methods and results for the first time in a promising prediction of polymer size from Raman spectra.

中文翻译：

---

非线性流形学习通过拉曼光谱确定微凝胶尺寸


聚合物粒度是聚合反应中产品质量的一个重要特征。拉曼光谱是一种成熟且可靠的在线浓度监测过程分析技术。最近的方法和一些理论考虑显示了拉曼信号和颗粒尺寸之间的相关性，但没有准确可靠地从拉曼光谱测量中确定聚合物尺寸。考虑到这一点，我们提出了三种替代机器学习工作流程来执行此任务，所有工作流程都涉及扩散图，一种用于降维的非线性流形学习技术：（i）直接来自扩散图，（ii）交替扩散图，以及（iii）共形自动编码器神经网络。我们将工作流程应用于拉曼光谱数据集，并通过动态光散射测量直径范围为 208-483 nm 的 47 个微凝胶（交联聚合物）样品来测量相关尺寸。共形自动编码器的性能远远优于最先进的方法，并且首次在根据拉曼光谱预测聚合物尺寸方面取得了有希望的结果。