REQUIMTE, Departamento de Química, Faculdade de Farmácia, Universidade do Porto, Rua Aníbal Cunha 164, 4099-030 Porto, Portugal.
Talanta. 2010 Nov 15;83(1):130-8. doi: 10.1016/j.talanta.2010.08.051.
Freeze-drying or lyophilisation is a batch wise industrial process used to remove water from solutions, hence stabilizing the solutes for distribution and storage. The objective of the present work was to outline a batch modelling approach to monitor a freeze-drying process in-line and in real-time using Raman spectroscopy. A 5% (w/v) D-mannitol solution was freeze-dried in this study as model. The monitoring of a freeze-drying process using Raman spectroscopy allows following the product behaviour and some process evolution aspects by detecting the changes of the solutes and solvent occurring during the process. Herewith, real-time solid-state characterization of the final product is also possible. The timely spectroscopic measurements allowed the differentiation between batches operated in normal process conditions and batches having deviations from the normal trajectory. Two strategies were employed to develop batch models: partial least squares (PLS) using the unfolded data and parallel factor analysis (PARAFAC). It was shown that both strategies were able to developed batch models using in-line Raman spectroscopy, allowing to monitor the evolution in real-time of new batches. However, the computational effort required to develop the PLS model and to evaluate new batches using this model is significant lower compared to the PARAFAC model. Moreover, PLS scores in the time mode can be computed for new batches, while using PARAFAC only the batch mode scores can be determined for new batches.
冷冻干燥或冻干是一种批量工业过程，用于从溶液中去除水分，从而稳定溶质以进行分配和储存。本工作的目的是概述一种使用拉曼光谱在线实时监测冷冻干燥过程的批量建模方法。在这项研究中，5%（w/v）D-甘露醇溶液被冻干作为模型。使用拉曼光谱监测冷冻干燥过程可以通过检测溶质和溶剂在过程中发生的变化来跟踪产品的行为和一些过程演变方面。此外，还可以实时对最终产品进行固态特性分析。及时的光谱测量允许区分在正常工艺条件下操作的批次和偏离正常轨迹的批次。采用两种策略开发批量模型：使用展开数据的偏最小二乘法（PLS）和并行因子分析（PARAFAC）。结果表明，这两种策略都能够使用在线拉曼光谱开发批量模型，从而能够实时监测新批次的演变。然而，与 PARAFAC 模型相比，开发 PLS 模型和使用该模型评估新批次所需的计算工作量要低得多。此外，对于新批次，可以计算时间模式中的 PLS 得分，而对于新批次，只能确定批处理模式中的得分。
