An automatic generation of pre-processing strategy combined with machine learning multivariate analysis for NIR spectral data

Autor: Nunik Destria Arianti, Edo Saputra, Agustami Sitorus

Publikováno v: Journal of Agriculture and Food Research, Vol 13, Iss , Pp 100625- (2023)

Pre-processing near-infrared (NIR) spectral data is indispensable in multivariate analysis, since the measured spectra of complex samples are often subject to overwhelming background, light scattering, varying noises, and other unexpected factors. Va

Externí odkaz: https://doaj.org/article/fb7a3c90d889407784ec236aa32437b7

A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit

Autor: Dário Passos, Puneet Mishra

Publikováno v: Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Chemometrics and Intelligent Laboratory Systems, 212
Chemometrics and Intelligent Laboratory Systems 212 (2021)

This study provides an innovative approach to improve deep learning (DL) models for spectral data processing with the use of chemometrics knowledge. The technique proposes pre-filtering the outliers using the Hotelling’s T2 and Q statistics obtaine

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9434d6437985cfa772e5af75cf5c379
https://hdl.handle.net/10400.1/15488