Abstract

Two strategies for selection of samples based on spectral measurements on a large set of samples are tested and compared. A method based on cluster analysis appears to be the best. The same prediction results achieved with the whole calibration set of 114 samples were obtained with only 20 samples selected by this algorithm.

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model

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Rapid detection of talc content in flour based on near-infrared spectroscopy combined with feature wavelength selection

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Optimizing selection of the test color sample set for the CIE 2017 color fidelity index

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Optimized spectral reconstruction based on adaptive training set selection

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Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy

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