A Performance Comparison of Low-Cost Near-Infrared (NIR) Spectrometers to a Conventional Laboratory Spectrometer for Rap
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A Performance Comparison of Low-Cost Near-Infrared (NIR) Spectrometers to a Conventional Laboratory Spectrometer for Rapid Biomass Compositional Analysis Edward J. Wolfrum 1
&
Courtney Payne 1 & Alexa Schwartz 1 & Joshua Jacobs 2 & Robert W. Kressin 3
# The Author(s) 2020
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” crossvalidation (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. Keywords Biomass feedstocks . Multivariate analysis . Instrumentation . Chemometrics
Introduction Rapid analysis using spectroscopy and chemometrics is considered a secondary analytical technique because it requires extensive calibration with a representative set of samples of known composition to develop a robust predictive model. For rapid analysis using near-infrared (NIR) spectroscopy, NIR spectra are collected from a set of well-characterized samples, and a calibration model is developed using a variety of Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12155-020-10135-6) contains supplementary material, which is available to authorized users. * Edward J. Wolfrum [email protected] 1
National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA
2
Texas Instruments Incorporated, 13532 North Central Expressway, MS 3810, Dallas, TX 75243, USA
3
KS Technologies, 11580 Black Forest Road Suite no. 60, Colorado Springs, CO 80908, USA
multivariate statistical techniques. NIR spectra of new samples are then collected, and the calibration model is used to predict the composition of these new samples. The calibration set must be carefully chosen to reflect the concentration range of the analytes to be measured as well as the nature of the samples to be predicted. This “calibrate-collect-predict” cycle is common across all applications where sp
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