Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química de Rosario (IQUIR-CONICET) , Suipacha 531, Rosario S2002LRK, Argentina.
Anal Chem. 2016 Aug 2;88(15):7807-12. doi: 10.1021/acs.analchem.6b01857. Epub 2016 Jul 13.
With the proliferation of multivariate calibration methods based on artificial neural networks, expressions for the estimation of figures of merit such as sensitivity, prediction uncertainty, and detection limit are urgently needed. This would bring nonlinear multivariate calibration methodologies to the same status as the linear counterparts in terms of comparability. Currently only the average prediction error or the ratio of performance to deviation for a test sample set is employed to characterize and promote neural network calibrations. It is clear that additional information is required. We report for the first time expressions that easily allow one to compute three relevant figures: (1) the sensitivity, which turns out to be sample-dependent, as expected, (2) the prediction uncertainty, and (3) the detection limit. The approach resembles that employed for linear multivariate calibration, i.e., partial least-squares regression, specifically adapted to neural network calibration scenarios. As usual, both simulated and real (near-infrared) spectral data sets serve to illustrate the proposal.
随着基于人工神经网络的多元校正方法的普及，迫切需要用于估计灵敏度、预测不确定性和检测限等评价指标的表达式。这将使非线性多元校正方法在可比较性方面与线性方法处于同等地位。目前，仅使用平均预测误差或测试样本集的性能偏差比来描述和促进神经网络校正。显然，还需要其他信息。我们首次报告了易于计算三个相关指标的表达式：(1)灵敏度，结果如预期的那样，取决于样本；(2)预测不确定性；和(3)检测限。该方法类似于用于线性多元校正的方法，即偏最小二乘回归，特别适用于神经网络校正情况。与往常一样，模拟和真实（近红外）光谱数据集都用于说明该方法。
