Andries Jan P M, Vander Heyden Yvan
Research Group Analysis Techniques in the Life Sciences, Avans Hogeschool, University of Professional Education, P.O. Box 90116, 4800 RA, Breda, The Netherlands.
Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel-VUB, Laarbeeklaan 103, B-1090, Brussels, Belgium.
Anal Bioanal Chem. 2024 Oct;416(24):5351-5364. doi: 10.1007/s00216-024-05468-6. Epub 2024 Aug 3.
In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X-y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.
在本研究中，提出并评估了一种从原始校准集中选择信息性标准化样本的新方法，用于在近红外仪器之间转移校准模型。首先，在通过最终复杂度自适应模型（FCAM）方法进行变量选择后，以偏最小二乘回归系数的显著性（FCAM-SIG）作为选择标准，开发一个校准模型。然后，将所得模型用于选择具有最佳预测能力的校准样本的最佳拟合子集，称为最佳预测校准子集（OPCS）。接下来，从OPCS中选择标准化样本。通过广泛使用的分段直接标准化（PDS）方法，将从属仪器上的光谱转移到主仪器上的相应光谱。此后，对于从属仪器上的测试集，绘制预测均方根误差（RMSEP）作为OPCS样本数量和用于PDS方法的窗口大小的函数的三维响应表面图。最后，选择提供最佳RMSEP的最小校准样本集与最佳窗口大小相结合，作为标准化集。所提出的用于选择标准化样本的OPCS方法在两个实际近红外数据集上进行了测试，提供了13个X-y组合用于建模。结果表明，基于OPCS获得的标准化样本数量在统计学上显著低于使用广泛使用的肯纳德和斯通代表性样本选择方法获得的数量，而预测性能相似。
