ChemHouse: A research and development centre for chemometrics

Lesnoff M Metz M Roger JM. Comparison of locally weighted PLS strategies for regression and discrimination on agronomic NIR data. J Chemometrics 2020; 34: e3209.

Metz M Lesnoff M Roger JM. Un algorithme big-data pour la local-PLS. France: HélioSPIR, 2019.

Metz M Abdelghafour F Roger JM , et al. RoBoost-PLSR: a novel robust PLS regression method inspired from boosting principles. Anal Chim Acta (under review) 2021; 1179: 338823.

Herrero-Langreo A Gorretta N Beghin A , et al. Orthogonal projection as a spectral pre-treatment method to reduce the interference of polystyrene signal in NIR imaging of agar on petri-dishes. In: 2019 10th workshop on hyperspectral imaging and signal processing: evolution in remote sensing (WHISPERS). Piscataway: IEEE, 2019, pp.1–4.

Boulet JC Sabatier R. Further investigations on the relationship between the OPLS preprocessing and the NAS. Chemometrics Intell Lab Syst 2020; 206: 104159.

Ryckewaert M Gorretta N Henriot F , et al. Reduction of repeatability error for analysis of variance-Simultaneous Component Analysis (REP-ASCA): application to NIR spectroscopy on coffee sample. Anal Chimica Acta 2020; 1101: 23–31.

Mishra P Roger JM Marini F , et al. Parallel pre-processing through orthogonalization (PORTO) and its application to near-infrared spectroscopy. Chemometrics Intell Lab Syst 2020; 212: 104190.

Roger JM Biancolillo A Marini F. Sequential preprocessing through ORThogonalization (SPORT) and its application to near-infrared spectroscopy. Chemometrics Intell Lab Syst 2020; 199: 103975.

Rabatel G Marini F Walczak B , et al. VSN: variable sorting for normalization. J Chemometrics 2020; 34: e3164.

Biancolillo A Marini F Roger JM. SO-CovSel: a novel method for variable selection in a multiblock framework. J Chemometrics 2020; 34: e3120.

Picheny V Servien R Villa-Vialaneix N. Interpretable sparse SIR for functional data. Stat Comput 2019; 29: 255–267.

Chaix G Pires Franco M Chambi Legoas R , et al. Impact of drought on eucalyptus wood chemistry by near-infrared hyperspectral imaging. Pesq.f/01: bras .. Colombo. 2019; 39: e2019020–13. Special issue. p. 1–768.

Mallet A Charnier C Latrille É , et al. Unveiling non-linear water effects in near-infrared spectroscopy: a study on organic wastes during drying using chemometrics. Waste Manage 2021; 122: 36–48.

Rey-Bayle M Bendoula R Caillol N , et al. Multiangle near-infrared spectroscopy associated with common components and specific weights analysis for in-line monitoring. J Near Infrared Spectrosc 2019; 27: 134–146.

Mishra P Roger JM Rutledge DN , et al. Two standard-free approaches to correct for external influences on near-infrared spectra to make models widely applicable. Postharvest Biol Technol 2020; 170: 111326.

Rossard V Boulet JC Gogé F , et al. ChemFlow, chemometrics using Galaxy. In: Galaxy Community Conference – GCC2016, Bloomington, USA, (24 June 2016–29 July 2016.

Zgouz A Héran D Barthès B , et al. Dataset of visible-near infrared handheld and micro-spectrometers – comparison of the prediction accuracy of sugarcane properties. Data Brief 2020; 31: 106013.

Chauvergne C Latrille E Bonnal L , et al. Dataset of organic sample near infrared spectra acquired on different spectrometers. Data Brief 2020; 32: 106264.