A method for calibration and validation subset partitioning.

By Roberto Kawakami Harrop Galvão, Mário César Ugulino de Araújo, G. E. José, M. J. C. Pontes, Edvan Cirino da Silva, T. C. B. Saldanha. Talanta: The International Journal of Pure and Applied Analytical Chemistry, 2005

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Nondestructive Detection of Soluble Solids Content of Apples from Dielectric Spectra with ANN and Chemometric Methods (2015, methodology): “Sample set partitioning based on joint x–y distances (SPXY) is an algorithm evolved from Kennard Stone to divide calibration set and prediction set (Galvao et al. 2005).”
Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database (2012, methodology): “In contrast, Kennard and Stone algorithm [40] is aimed at selecting samples that cover the spectral domain as uniformly as possible [41,42].”
Optimal ratio for data splitting (2022, neutral): “Since random sampling and SPlit maintain the distribution, the independence is a reasonable assumption, but not for the other deterministic splitting procedures such as CADEX, DUPLEX, and SPXY.”
Fast Prediction of Sugar Content in Dangshan Pear (Pyrus spp.) Using Hyperspectral Imagery Data (2018, methodology): “The main reason for using SPXY is that it simultaneously considers the spectral matrix and the concentration matrix when dividing the sample set, and its performance is superior to that of frequently used methods such as the Kennard-Stone (KS) method and the random sampling algorithm (Galvão et al. ”