Calibration Model Updating to Novel Sample and Measurement Conditions without Reference Values
Calibration Model Updating to Novel Sample and Measurement Conditions without Reference Values
复制标题
校准模型更新为新样品和测量条件，无需参考值
DOI：
10.1021/acs.analchem.1c00578
复制
发表时间：
2021
影响因子：
7.4
通讯作者：
Kalivas, John H.
中科院分区：
文献类型：
--
作者：
Spiers, Robert C.;Kalivas, John H.
更新在原始（初级）样品和光谱测量条件下形成的校准模型以预测新的（次级）条件下的分析物值是分析化学中的一项基本活动，以避免完全重新校准。已建立的模型更新方法需要在更新过程中使用一小部分次级域样本（标记数据）的样本分析参考值。由于获得参考值是耗时的，并且是任何校准的昂贵部分，因此需要不需要标记二次样本的方法，从而允许按需更新模型。本文比较了带标记和不带标记二次样本的模型更新方法。提出并评价了一种混合模型更新方法。不幸的是，在没有二级分析物参考值的情况下适应模型的主要障碍是模型选择。由于模型更新方法通常涉及多个调优参数，形成了成千上万的模型，使得模型选择变得复杂。最近开发的一个框架评估了几种2到3个基于调谐参数的模型更新方法的自动模型选择，没有二次分析参考值（标签）。模型选择方法基于待预测未标记样本的模型多样性和预测相似性（MDPS）。新的待预测的二次样本可以用来组成更新的模型，并再次选择最终的预测模型。由于模型是根据需要形成和选择的，可以直接预测目标样本，因此不需要复杂的交叉验证过程。对涵盖40种模型更新情况的4个近红外数据集进行了评估，结果表明MDPS可以选择可靠的更新模型，其性能优于或优于具有次要参考值的总再校准的预测误差。
Updating a calibration model formed in original (primary) sample and spectral measurement conditions to predict analyte values in novel(secondary) conditions is an essential activity in analytical chemistry in order to avoid a complete recalibration. Established model updating methods require sample analyte reference values for a small set of secondary domain samples (labeled data) to be used in updating processes. Because obtaining reference values is time consuming and is the costly part of any calibration, methods are needed that do not require labeled secondary samples, thereby allowing on demand model updating. This paper compares model updating methods with and without labeled secondary samples. A hybrid model updating approach is also developed and evaluated. Unfortunately, a major impediment to adapting a model without secondary analyte reference values has been model selection. Because multiple tuning parameters are commonly involved in model updating methods, thousands of models are formed, making model selection complex. A recently developed framework is evaluated for automatic model selection of several two to three tuning parameter-based model updating methods without secondary analyte reference values (labels). The model selection method is based on model diversity and prediction similarity (MDPS) of the unlabeled samples to be predicted. The new secondary samples to be predicted can be used to form the updated models and again to select the final predicting models. Because models are formed and selected on demand to directly predict target samples, complicated cross-validation processes are not needed. Four near-infrared data sets covering 40 model updating situations are evaluated showing that MDPS can select reliable updated models outperforming or rivaling prediction errors from total recalibrations with secondary reference values.