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On-site substrate characterization in the anaerobic digestion context: A dataset of near infrared spectra acquired with four different optical systems on freeze-dried and ground organic waste

Authors :
Pérémé, Margaud
Mallet, Alexandre
Awhangbo, Lorraine
Charnier, Cyrille
Roger, Jean-Michel
Steyer, Jean-Philippe
Latrille, Éric
Bendoula, Ryad
Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE)
Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)
Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP)
BioEnTech
French Agency of National Research and Technology (ANRT) [grant number 2018/0461
National Research Institute for Agriculture, Food and Environment (INRAE)
Source :
Data in Brief, Data in Brief, Vol 36, Iss, Pp 107126-(2021), Data in Brief, Elsevier, 2021, 36, pp.107126. ⟨10.1016/j.dib.2021.107126⟩
Publication Year :
2021
Publisher :
Elsevier, 2021.

Abstract

International audience; The near infrared spectra of thirty-three freeze-dried and ground organic waste samples of various biochemical composition were collected on four different optical systems, including a laboratory spectrometer, a transportable spectrometer with two measurement configurations (an immersed probe, and a polarized light system) and a micro-spectrometer. The provided data contains one file per spectroscopic system including the reflectance or absorbance spectra with the corresponding sample name and wavelengths. A reference data file containing carbohydrates, lipid and nitrogen content, biochemical methane potential (BMP) and chemical oxygen demand (COD) for each sample is also provided. This data enables the comparison of the optical systems for predictive model calibration based for example on Partial Least Squares Regression (PLS-R) [1], but could be used more broadly to test new chemometrics methods. For example, the data could be used to evaluate different transfer functions between spectroscopic systems [2]. This dataset enabled the research work reported by Mallet et al. 2021 [3].

Details

Language :
English
ISSN :
23523409
Volume :
36
Database :
OpenAIRE
Journal :
Data in Brief
Accession number :
edsair.pmid.dedup....c2e29486b2a9450bed09f7b3be1fb1fe