Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe S3000ZAA, Argentina.
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290 CABA C1425FQB, Argentina.
Anal Chem. 2020 Sep 15;92(18):12265-12272. doi: 10.1021/acs.analchem.0c01863. Epub 2020 Sep 1.
The use of machine learning for multivariate spectroscopic data analysis in applications related to process monitoring has become very popular since non-linearities in the relationship between signal and predicted variables are commonly observed. In this regard, the use of artificial neural networks (ANN) to develop calibration models has demonstrated to be more appropriate and flexible than classical multivariate linear methods. The most frequently reported type of ANN is the so-called multilayer perceptron (MLP). Nevertheless, the latter models still lack a complete statistical characterization in terms of prediction uncertainty, which is an advantage of the parametric counterparts. In the field of analytical calibration, developments regarding the estimation of prediction errors would derive in the calculation of other analytical figures of merit (AFOMs), such as sensitivity, analytical sensitivity, and limits of detection and quantitation. In this work, equations to estimate the sensitivity in MLP-based calibrations were deduced and are here reported for the first time. The reliability of the derived sensitivity parameter was assessed through a set of simulated and experimental data. The results were also applied to a previously reported MLP fluorescence calibration methodology for the biopharmaceutical industry, yielding a value of sensitivity ca. 30 times larger than for the univariate reference method.
由于信号与预测变量之间的关系通常是非线性的，因此机器学习在与过程监测相关的应用中用于多变量光谱数据分析已经变得非常流行。在这方面，使用人工神经网络 (ANN) 来开发校准模型比经典的多元线性方法更合适和灵活。最常报道的 ANN 类型是所谓的多层感知器 (MLP)。然而，后者模型在预测不确定性方面仍然缺乏完整的统计特征，这是参数对应物的优势。在分析校准领域，关于预测误差估计的发展将导致计算其他分析关键指标 (AFOM)，如灵敏度、分析灵敏度以及检测限和定量限。在这项工作中，推导出了基于 MLP 校准的灵敏度估计方程，并首次在这里进行了报道。通过一组模拟和实验数据评估了推导的灵敏度参数的可靠性。结果还应用于先前报道的用于生物制药行业的 MLP 荧光校准方法，得到的灵敏度值大约比单变量参考方法大 30 倍。
