Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time (OFAT) approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0â1 M) and nitric acid (0.1â10 M) using the near-infrared water band centered at 1,440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a OFAT approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with OFAT while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the OFAT approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.

中文翻译：

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EXPRESS：用于定量硝酸中硝酸盐的化学计量学和实验设计：近红外光谱吸收分析

在热室环境中对放射化学处理流实施远程实时光谱监测需要效率和简单性。用于化学系统中物种定量的光谱学的成功在很大程度上取决于具有代表性的训练集和合适的验证集。使用标准的一次一个因子 (OFAT) 方法来选择训练集（即校准标准）来构建多元回归模型既费时又费资源。本研究描述了使用实验设计生成光谱训练集和使用以 1,440 nm 为中心的近红外水带量化硝酸钠 (0–1 M) 和硝酸 (0.1–10 M) 的验证集。偏最小二乘回归模型是根据 D 和 I 最优实验设计和 OFAT 方法生成的训练集构建的。通过比较预测的偏差和标准误差的统计显着性来评估每个模型的预测性能。与 OFAT 相比，D 和 I 最优设计减少了构建回归模型所需的样本数量，同时还提高了性能。在最小化训练集中的样本数量时，必须根据验证样本集确认模型。通过提高预测能力并将训练集中的样本数量与 OFAT 方法相比，D 最优设计在考虑性能和效率时表现最佳。