Article Dans Une Revue Chemometrics and Intelligent Laboratory Systems Année : 2026

Optimizing calibration sample selection in infrared spectroscopy

Résumé

Highlights

• Models trained on samples selected by the KS and WSP algorithms performed better than those trained on randomly selected samples.

• Both WSP and KS generally benefit from dimensionality reduction and encoding or at least maintain the predictive performance of the models.

• For higher-complexity data, differences in the predictive performance of models were more pronounced between selection algorithms.

• Preprocessing affects the predictive performance of models trained on samples selected by WSP and KS differently.

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Dates et versions

hal-05556247 , version 1 (17-03-2026)

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Carl Charloto, Maxime Metz, Nicolas Grotus, David Esteve, Michelle Sergent, et al.. Optimizing calibration sample selection in infrared spectroscopy. Chemometrics and Intelligent Laboratory Systems, 2026, 272, pp.105678. ⟨10.1016/j.chemolab.2026.105678⟩. ⟨hal-05556247⟩
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