Lyles School of Civil and Construction Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, 550 Stadium Mall Drive, West Lafayette, Indiana 47907-2051, United States.
Pfizer Worldwide Research &Development, Groton, Connecticut 06340, United States.
ACS Sens. 2024 Aug 23;9(8):4186-4195. doi: 10.1021/acssensors.4c01180. Epub 2024 Aug 3.
The moisture content of pharmaceutical powders can significantly impact the physical and chemical properties of drug formulations, solubility, flowability, and stability. However, current technologies for measuring moisture content in pharmaceutical materials require extensive calibration processes, leading to poor consistency and a lack of speed. To address this challenge, this study explores the feasibility of using impedance spectroscopy to enable accurate, rapid testing of moisture content of pharmaceutical materials with minimal to zero calibration. By utilizing electrochemical impedance spectroscopy (EIS) signals, we identify a strong correlation between the electrical properties of the materials and varying moisture contents in pharmaceutical samples. Equivalent circuit modeling is employed to unravel the underlying mechanism, providing valuable insights into the sensitivity of impedance spectroscopy to moisture content variations. Furthermore, the study incorporates deep learning techniques utilizing a 1D convolutional neural network (1DCNN) model to effectively process the complex spectroscopy data. The proposed model achieved a notable predictive accuracy with an average error of just 0.69% in moisture content estimation. This method serves as a pioneering study in using deep learning to provide a reliable solution for real-time moisture content monitoring, with potential applications extending from pharmaceuticals to the food, energy, environmental, and healthcare sectors.
药物粉末的含水量会显著影响药物制剂的物理和化学性质、溶解度、流动性和稳定性。然而，目前用于测量药物材料含水量的技术需要广泛的校准过程，导致一致性差且速度慢。为了解决这一挑战，本研究探讨了使用阻抗谱来实现对药物材料含水量的准确、快速测试的可行性，无需或只需极少的校准。通过利用电化学阻抗谱（EIS）信号，我们发现材料的电学性质与药物样品中不同含水量之间存在很强的相关性。采用等效电路模型来揭示其内在机制，为阻抗谱对水分含量变化的敏感性提供了有价值的见解。此外，该研究还结合了深度学习技术，利用一维卷积神经网络（1DCNN）模型来有效地处理复杂的光谱数据。所提出的模型在水分含量估计方面取得了显著的预测精度，平均误差仅为 0.69%。该方法是使用深度学习为实时水分含量监测提供可靠解决方案的开创性研究，其潜在应用从制药扩展到食品、能源、环境和医疗保健等领域。
