Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

Journal Article · Wed Aug 14 00:00:00 EDT 2024 · Biotechnology for Biofuels and Bioproducts

DOI:https://doi.org/10.1186/s13068-024-02558-6· OSTI ID:2441468

Tillman, Zofia ^[1]; Gray, Kevin ^[1]; Wolfrum, Edward ^[1]

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1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO)
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 2441468
• Report Number(s): NREL/JA--2800-90863; MainId:92641; UUID:84ec8eb9-e07f-472f-815a-5b044ed0d9e8; MainAdminId:73684
• Journal Information: Biotechnology for Biofuels and Bioproducts, Journal Name: Biotechnology for Biofuels and Bioproducts Journal Issue: 1 Vol. 17; ISSN 2731-3654
• Publisher: BioMed Central
• Country of Publication: United States
• Language: English

Related Subjects

09 BIOMASS FUELS
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
at-line monitoring
bioenergy
bioproducts
multivariate statistics
near-infrared spectroscopy
process monitoring
xylo-oligomers