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

Margaud Pérémé; Alexandre Mallet; Lorraine Awhangbo; Cyrille Charnier; Jean-Michel Roger; Jean-Philippe Steyer; Éric Latrille; Ryad Bendoula

doi:10.1016/j.dib.2021.107126

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

Data Brief. 2021 May 11:36:107126. doi: 10.1016/j.dib.2021.107126. eCollection 2021 Jun.

Authors

Margaud Pérémé^{1

2

3}, Alexandre Mallet^{1

4

5

3}, Lorraine Awhangbo^{1

3}, Cyrille Charnier⁵, Jean-Michel Roger^{4

3}, Jean-Philippe Steyer¹, Éric Latrille^{1

3}, Ryad Bendoula⁴

Affiliations

¹ INRAE, Univ Montpellier, LBE, 102 Av des Etangs, Narbonne F-11100, France.
² ENSCM, 240 Av du professeur Emile Jeanbrau, Montpellier F-34090, France.
³ ChemHouse Research Group, Montpellier F-34000, France.
⁴ INRAE, UMR ITAP, Montpellier University, Montpellier F-34000, France.
⁵ BIOENTECH Company, Narbonne F-11100, France.

Abstract

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].

Keywords: Chemometrics; Compact spectrometers; Instrument standardization; Near infrared spectroscopy; Organic waste.