NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models

Simple item page
dc.contributor.authorShrestha, Bijendra
dc.contributor.authorPosom, Jetsada
dc.contributor.authorSirisomboon, Panmanas
dc.contributor.authorShrestha, Bim Prasad
dc.contributor.authorPornchaloempong, Pimpen
dc.contributor.authorFunke, Axel
dc.date.accessioned2026-08-06T10:45:29Z
dc.date.available2026-08-06T10:45:29Z
dc.date.issued2024-02-01
dc.description.abstractThis study investigates the non-destructive analysis of proximate parameters (moisture content, MC; volatile matter, VM; fixed carbon, FC; ash content) in various chipped and ground biomass using a combination of destructive thermogravimetric analysis (TGA) and non-destructive near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR). The thermogravimetric method determines proximate analysis data through TG and DTG curves, tracking biomass mass loss over time or temperature. NIRS scans chipped biomass in diffuse reflectance, and ground biomass in transflectance mode, covering the wavenumber range from 3595 to 12,489 cm<sup>−1</sup>. PLSR-based models (Full-PLSR, GA-PLSR, SPA-PLSR, MP PLSR 5-range method, and MP PLSR 3-range method) are developed and evaluated based on R<sup>2</sup>P, RMSEP, and RPD. MC and FC models for chip biomass exhibit satisfactory performance, making them cautiously applicable in various applications, including research. Optimal models for MC and FC in chip biomass, constructed using GA-PLSR with the second derivative and Full-PLSR with a constant offset, yield high R<sup>2</sup>P values (0.8654 and 0.8773), low RMSEP values (0.85% and 2.12%), and high RPD values (2.9 and 3.0), indicating applicative capabilities. Other parameters such as MC and FC in ground biomass, as well as VM and ash content in both chip and ground biomass, are found suitable for rough screening. Model sensitivity, assessed by calculating LOQ, indicates high sensitivity for VM in both chip and ground biomass and FC in chip biomass, as the calculated LOQ value is lower than the minimum reference values used during model development. However, for the remaining parameters, LOQ values surpass the established minimum reference value, suggesting limitations in predicting samples below the calibration range. Continuous model enhancement incorporating an ample number of representative biomass samples and consistent validation with unknown samples are imperative for ensuring accurate predictions.
dc.identifier.citationEnergies, 17(4), 2024
dc.identifier.doi10.3390/en17040800
dc.identifier.issn19961073
dc.identifier.other2-s2.0-85185713763
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15482
dc.sourceEnergies
dc.subjectbiomass
dc.subjectnear-infrared spectroscopy
dc.subjectpartial least squares regression
dc.subjectproximate analysis
dc.subjectthermogravimetry
dc.titleNIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models
dc.typeArticle

Files

Collections

Research