Strategies for multivariate characterization and classification of pulps and papers by near-infrared spectroscopy
Open Access
- 26 June 2024
- journal article
- research article
- Published by Elsevier in Analytica Chimica Acta
- Vol. 1318, 342895
Abstract
No abstract availableKeywords
This publication has 27 references indexed in Scilit:
- Water desorption monitoring of cellulose pulps by NIR spectroscopyIndustrial Crops and Products, 2022
- Artificial neural network and partial least square regressions for rapid estimation of cellulose pulp dryness based on near infrared spectroscopic dataCarbohydrate Polymers, 2019
- Classification of thermally treated wood using machine learning techniquesWood Science and Technology, 2018
- A review of recent application of near infrared spectroscopy to wood science and technologyJournal of Wood Science, 2015
- Chromophores in lignin-free cellulosic materials belong to three compound classes. Chromophores in cellulosics, XIICellulose, 2015
- Thermogravimetric analysis as a new method to determine the lignocellulosic composition of biomassBiomass and Bioenergy, 2011
- Review of the most common pre-processing techniques for near-infrared spectraTrAC Trends in Analytical Chemistry, 2009
- Robust methods for partial least squares regressionJournal of Chemometrics, 2003
- Multivariate calibration models using NIR spectroscopy on pulp and paper industrial applicationsJournal of Chemometrics, 1996
- Partial least-squares regression: a tutorialAnalytica Chimica Acta, 1986