Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects | Caractérisation rapide et robuste des déchets organiques bruts par NIRS : Utilisation de méthodes non linéaires pour gérer les effets de l'eau
2022
Mallet, Alexandre | Charnier, Cyrille | Latrille, Éric | Bendoula, Ryad | Roger, Jean-Michel | Steyer, Jean-Philippe | Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; 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) | Technologies et Méthodes pour les Agricultures de demain (UMR ITAP) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; 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) | BioEnTech | Financial support from the National Research Institute for Agriculture, Food and Environment (INRAE), the French Agency of National Research and Technology (ANRT) [grant number 2018/0461] and the Biogaz-RIO platform [FEDER-FSE Languedoc Roussillon 2014-2020]
International audience
Показать больше [+] Меньше [-]Английский. Fast characterization of organic waste using near infrared spectroscopy (NIRS) has been successfully developed in the last decade. However, up to now, an on-site use of this technology has been hindered by necessary sample preparation steps (freeze-drying and grinding) to avoid important water effects on NIRS. Recent research studies have shown that these effects are highly non-linear and relate both to the biochemical and physical properties of samples. To account for these complex effects, the current study compares the use of many different types of non-linear methods such as partial least squares regression (PLSR) based methods (global, clustered and local versions of PLSR), machine learning methods (support vector machines, regression trees and ensemble methods) and deep learning methods (artificial and convolutional neural networks). On an independent test data set, non-linear methods showed errors 28% lower than linear methods. The standard errors of prediction obtained for the prediction of total solids content (TS%), chemical oxygen demand (COD) and biochemical methane potential (BMP) were respectively 8%, 160 mg(O2).gTS−1 and 92 mL(CH4).gTS−1. These latter errors are similar to successful NIRS applications developed on freeze-dried samples. These findings hold great promises regarding the development of at-site and online NIRS solutions in anaerobic digestion plants.
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