Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd., Oak Ridge, Tennessee 37830, United States.
ACS Sens. 2024 Nov 22;9(11):6257-6264. doi: 10.1021/acssensors.4c02211. Epub 2024 Sep 19.
An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).
一个自动化平台已经被开发出来，以帮助研究人员快速开发光学光谱传感器，从而从光谱数据中定量物种。该平台同时进行校准和验证测量。通过光学光谱实时原位监测复杂系统已被证明是一种有用的工具；然而，构建校准模型需要开发时间，这在放射性或其他危险系统的情况下可能是一个限制因素。虽然通过优化实验设计可以减少校准时间，但本研究通过自动化以不同的方式解决了这一挑战。ATLAS（用于应用传感器的自动瞬态学习）平台使用气动控制储备溶液，通过所需的校准浓度循环流动曲线，用于多元模型构建。此外，基于流量计算的所需浓度之间的瞬变用作验证测量，以了解模型预测能力。与估计的手动样品制备和静态测量相比，这种自动化方法使模型开发时间减少了 76%，样品量减少了 60%。ATLAS 系统在两个系统上进行了演示：一个三镧系元素系统，其中 Pr/Nd/Ho 代表分析物特征之间存在显著重叠或干扰的用例，另一个包含 Pr/Nd/Ni 的替代系统代表宽带腐蚀物种特征干扰更明显镧系元素吸收谱的用例。两个系统都产生了很强的模型预测性能（RMSEP<9%）。最后，ATLAS 被证明是一种模拟过程监测场景（例如，柱分离）的工具，在这些场景中，可以进一步优化模型以根据需要（例如，基线校正）考虑日常变化。最终，ATLAS 提供了一个重要的工具，可以快速筛选监测方法，研究传感器融合，并探索更复杂的系统（即，更多的物种）。
