Fearn, T;
Beleites, C;
Fernández Pierna, JA;
Baeten, V;
Lagerholm, M;
Roger, JM;
Koidis, A;
(2025)
Multivariate calibration of non-destructive spectral sensors with a particular focus on food applications: Validation issues and guidelines.
Trac Trends in Analytical Chemistry
, 192
, Article 118410. 10.1016/j.trac.2025.118410.
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Abstract
Multivariate calibration methods have enabled the use of non-destructive spectral sensors in a wide range of applications but carry a risk of overfitting to the available training samples. For this reason, the prediction of unseen samples plays a vital role both in tuning the prediction algorithm and in assessing its performance, two activities that need to be carefully distinguished. Methods employed include data-splitting, cross-validation, and the use of genuinely independent sets of data. These approaches are described and some common issues with them are identified. The focus is on food applications but the methods discussed are widely used in other areas.
| Type: | Article |
|---|---|
| Title: | Multivariate calibration of non-destructive spectral sensors with a particular focus on food applications: Validation issues and guidelines |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.trac.2025.118410 |
| Publisher version: | https://doi.org/10.1016/j.trac.2025.118410 |
| Language: | English |
| Additional information: | © 2025 The Authors. Published by Elsevier B.V. under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10212733 |
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