@Article{KaterineDiaz2017,
author="Katerine Diaz
and Konstantia Georgouli
and Anastasios Koidis
and Jesus Martinez del Rincon",
title="Incremental model learning for spectroscopy-based food analysis",
journal="Chemometrics and Intelligent Laboratory Systems",
year="2017",
volume="167",
pages="123--131",
optkeywords="Incremental model learning",
optkeywords="IGDCV technique",
optkeywords="Subspace based learning",
optkeywords="IdentificationVegetable oils",
optkeywords="FT-IR spectroscopy",
abstract="In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps.",
optnote="ADAS; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3002), last updated on Thu, 04 Apr 2019 12:39:26 +0200",
opturl="https://doi.org/10.1016/j.chemolab.2017.06.002"
}