A Performance Comparison of Low-Cost Near-Infrared (NIR) Spectrometers to a Conventional Laboratory Spectrometer for Rapid Biomass Compositional Analysis

Edward Wolfrum, Courtney Payne, Alexa Schwartz, Joshua Jacobs, Robert Kressin

Research output: Contribution to journalArticlepeer-review

11 Scopus Citations

Abstract

The performance of a conventional laboratory near-infrared (NIR) spectrometer and two NIR spectrometer prototypes (a Texas Instruments NIRSCAN Nano evaluation model (EVM) and an InnoSpectra NIR-M-R2 spectrometer) are compared by collecting reflectance spectra of 270 well-characterized herbaceous biomass samples, building calibration models using the partial least squares (PLS-2) algorithm to predict five constituents of the samples from the reflectance spectra, and comparing the resulting model statistics. The prediction models developed using spectra from the Foss XDS spectrometer were slightly better than the prediction models developed using spectra from either the TI NIRSCAN Nano EVM and the InnoSpectra NIR-M-R2 as measured by the root mean square error (RMSECV) and the correlation coefficient (R2_cv) for “leave-one-out” cross-validation (CV). The models built from the two prototype units were not statistically significantly different from each other (p = 0.05). The Foss spectrometer has a larger wavelength range (400–2500 nm) compared with the two prototypes (900–1700 nm). When the spectra from the Foss XDS spectrometer were truncated so their wavelength range matched the wavelength range of the two prototype units, the resulting model was not statistically significantly different from the models from either prototype.

Original languageAmerican English
Pages (from-to)1121-1129
Number of pages9
JournalBioenergy Research
Volume13
Issue number4
DOIs
StatePublished - 1 Dec 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

NREL Publication Number

  • NREL/JA-2800-74656

Keywords

  • Biomass feedstocks
  • Chemometrics
  • Instrumentation
  • Multivariate analysis

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