Improved prediction of fuel properties with near-infrared spectroscopy using a complementary sequential fusion of scatter correction techniques
2021
Mishra, Puneet | Marini, Federico | Biancolillo, Alessandra | Roger, Jean-Michel | Wageningen University and Research [Wageningen] (WUR) | Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome] (UNIROMA) | Department of Physical and Chemical Sciences [L'Aquila] (DSFC) ; Università degli Studi dell'Aquila = University of L'Aquila = Université de L'Aquila (UNIVAQ) | Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - 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)
International audience
اظهر المزيد [+] اقل [-]إنجليزي. Near-infrared (NIR) spectroscopy of fuels can suffer from scattering effects which may mask the signals corresponding to key analytes in the spectra. Therefore, scatter correction techniques are often used prior to any modelling so to remove scattering and improve predictive performances. However, different scatter correction techniques may carry complementary information so that, if jointly used, both model stability and performances could be improved. A solution to that is the fusion of complementary information from differently scatter corrected data. In the present work, the use of a preprocessing fusion approach called sequential preprocessing through orthogonalization (SPORT) is demonstrated for predicting key quality parameters in diesel fuels. In particular, the possibility of predicting four different key properties, i.e., boiling point (°C), density (g/mL), aromatic mass (%) and viscosity (cSt), was considered. As a comparison, standard partial least-squares (PLS) regression modelling was performed on data pretreated by SNV and 2nd derivative (which is a widely used preprocessing combination). The results showed that the SPORT models, based on the fusion of scatter correction techniques, outperformed the standard PLS models in the prediction of all the four properties, suggesting that selection and use of a single scatter correction technique is often not sufficient. Up to complete bias removal with 50% reduction in prediction error was obtained. The R2P was increased by up to 8%. The sequential scatter fusion approach (SPORT) is not limited to NIR data but can be applied to any other spectral data where a preprocessing optimization step is required.
اظهر المزيد [+] اقل [-]المعلومات البيبليوغرافية
تم تزويد هذا السجل من قبل Institut national de la recherche agronomique