In many industries, viscosity is an important quality parameter which significantly affects consumer satisfaction and process efficiency. In the personal care industry, this applies to products such as shampoo and shower gels whose complex structures are built up of micellar liquids. Measuring viscosity offline is well established using benchtop rheometers and viscometers. The difficulty lies in measuring this property directly in the process via on or inline technologies. Therefore, the aim of this work is to investigate whether proxy measurements using inline vibrational spectroscopy, e.g., near-infrared (NIR), mid-infrared (MIR), and Raman, can be used to predict the viscosity of micellar liquids. As optical techniques, they are nondestructive and easily implementable process analytical tools where each type of spectroscopy detects different molecular functionalities. Inline fiber optic coupled probes were employed; a transmission probe for NIR measurements, an attenuated total reflectance probe for MIR and a backscattering probe for Raman. Models were developed using forward interval partial least squares variable selection and log viscosity was used. For each technique, combinations of pre-processing techniques were trialed including detrending, Whittaker filters, standard normal variate, and multiple scatter correction. The results indicate that all three techniques could be applied individually to predict the viscosity of micellar liquids all showing comparable errors of prediction: NIR: 1.75 Pa s; MIR: 1.73 Pa s; and Raman: 1.57 Pa s. The Raman model showed the highest relative prediction deviation (RPD) value of 5.07, with the NIR and MIR models showing slightly lower values of 4.57 and 4.61, respectively. Data fusion was also explored to determine whether employing information from more than one data set improved the model quality. Trials involved weighting data sets based on their signal-to-noise ratio and weighting based on transmission curves (infrared data sets only). The signal-to-noise weighted NIR–MIR–Raman model showed the best performance compared with both combined and individual models with a root mean square error of cross-validation of 0.75 Pa s and an RPD of 10.62. This comparative study provides a good initial assessment of the three prospective process analytical technologies for the measurement of micellar liquid viscosity but also provides a good basis for general measurements of inline viscosity using commercially available process analytical technology. With these techniques typically being employed for compositional analysis, this work presents their capability in the measurement of viscosity—an important physical parameter, extending the applicability of these spectroscopic techniques.

中文翻译：

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EXPRESS：比较单个和集成的在线拉曼、近红外和中红外光谱模型来预测胶束液体的粘度

在许多行业中，粘度是一个重要的质量参数，它会显着影响消费者满意度和流程效率。在个人护理行业，这适用于洗发水和沐浴露等复杂结构由胶束液体构成的产品。使用台式流变仪和粘度计可以很好地离线测量粘度。困难在于通过在线或在线技术在过程中直接测量此属性。因此，这项工作的目的是研究使用在线振动光谱（例如近红外 (NIR)、中红外 (MIR) 和拉曼）的代理测量是否可用于预测胶束液体的粘度。作为光学技术，它们是无损且易于实施的过程分析工具，其中每种类型的光谱检测不同的分子功能。使用了在线光纤耦合探头；一个用于 NIR 测量的透射探头，一个用于 MIR 的衰减全反射探头和一个用于拉曼的反向散射探头。模型是使用前向区间偏最小二乘变量选择开发的，并使用对数粘度。对于每种技术，都尝试了预处理技术的组合，包括去趋势、惠特克滤波器、标准正态变量和多重散射校正。结果表明，所有三种技术都可以单独应用来预测胶束液体的粘度，它们都显示出相当的预测误差：NIR：1.75 Pa s；中红外：1.73 帕秒；和拉曼：1.57 Pa s。拉曼模型显示最高的相对预测偏差 (RPD) 值为 5.07，而 NIR 和 MIR 模型分别显示略低的值，分别为 4.57 和 4.61。还探索了数据融合，以确定使用来自多个数据集的信息是否可以提高模型质量。试验涉及基于信噪比的数据集加权和基于传输曲线的加权（仅限红外数据集）。与组合模型和单个模型相比，信噪比加权的 NIR-MIR-Raman 模型表现出最佳性能，交叉验证的均方根误差为 0.75 Pa s，RPD 为 10.62。该比较研究为用于测量胶束液体粘度的三种前瞻性过程分析技术提供了良好的初步评估，但也为使用市售过程分析技术对在线粘度进行一般测量提供了良好的基础。由于这些技术通常用于成分分析，这项工作展示了它们在测量粘度方面的能力——一个重要的物理参数，扩展了这些光谱技术的适用性。