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

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.

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

“…Further work at NREL demonstrated a large variation in the cell wall composition of corn stover from various harvests as predicted by NIRS (Templeton et al, 2009). Lately, the same group has demonstrated that potentially lowcost and portable spectrometers with limited spectral range provide predictions that are nearly as accurate as those of welldeveloped laboratory instruments (Wolfrum et al, 2020). Here, we demonstrate that a comparable "off the shelf" instrument is adequate to predict not only composition, but further extend the predictive capability to anatomical tissue type.…”

“…NIR spectra were collected on a Foss InfraXact 7,500 non-contact spectrometer over wavelengths from 570 to 1850 nm in reflectance with the empty cup (air) serving as a baseline. This is a reduced wavelength range compared to many studies using NIR for chemical composition prediction (typically covering the full NIR range from 800 to 2500 nm), but recent work has confirmed little reduction in model predictive capability when using reduced spectral range (Wolfrum et al, 2020). For correlation of chemical composition (i.e.…”

High temperatures and low soil moisture synergistically reduce switchgrass yields from marginal field sites and inhibit fermentation

“…A diffuse reflectance detector, set to 17.25 mm spot size, scanned the samples at a wavelength of 400–2499.5 nm with 0.5 nm resolution. The dataset was evaluated using a previously developed NIRS composition model based on various biomass types (Templeton et al., 2016; Wolfrum et al., 2020).…”

An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil

“…Yan and Siesler [ 11 ] demonstrated the use of these low-cost FT-NIR, linear variable filters (LVFs), and diffraction NIR systems for both classification and measurement. Wolfrum et al [ 12 ] compared the performance of low-cost NIRS to a conventional laboratory spectrometer. In Section 3 , spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work [ 13 ].…”