A method for calibration and validation subset partitioning
A method for calibration and validation subset partitioning
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DOI：
10.1016/j.talanta.2005.03.025
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发表时间：
2005-10-15
期刊：
影响因子：
6.1
通讯作者：
Saldanha, TCB
中科院分区：
文献类型：
--
作者：
Galvao, RKH;Araujo, MCU;Saldanha, TCB
本文提出了一种新方法，将样本池划分为校准子集和验证子集，以进行多变量建模。该方法对于涉及复杂基质的分析应用具有价值，其中真实样品的成分变异性不能通过优化的实验设计轻松重现。采用逐步程序根据 x（仪器响应）和 y（预测参数）空间中的差异来选择样本。所提出的技术在一个案例研究中得到了说明，该案例研究涉及通过近红外光谱法和 PLS 建模预测柴油的三个质量参数（10% 和 90% 样品蒸发时的比质量和蒸馏温度）。为了进行比较，PLS 模型也是通过完全交叉验证以及使用 Kennard-Stone 和随机抽样方法进行校准和验证子集划分来构建的。通过使用一组不用于校准或验证的独立样本来比较所获得的模型的预测性能。 95% 置信水平的 F 检验结果表明，所提出的技术可能是其他三种策略的有利替代方案。 (c) 2005 Elsevier B.V. 保留所有权利。
This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies. (c) 2005 Elsevier B.V. All rights reserved.