PT Journal
AU Katerine Diaz
   Konstantia Georgouli
   Anastasios Koidis
   Jesus Martinez del Rincon
TI Incremental model learning for spectroscopy-based food analysis
SO Chemometrics and Intelligent Laboratory Systems
JI CILS
PY 2017
BP 123
EP 131
VL 167
DE Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy
AB 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.
ER