Abstract
Advanced chemometric analysis is required for rapid and reliable determination of physical and/or chemical components in complex gas mixtures. Based on infrared (IR) spectroscopic/sensing techniques, we propose an advanced regression model based on the extreme learning machine (ELM) algorithm for quantitative chemometric analysis. The proposed model makes two contributions to the field of advanced chemometrics. First, an ELM-based autoencoder (AE) was developed for reducing the dimensionality of spectral signals and learning important features for regression. Second, the fast regression ability of ELM architecture was directly used for constructing the regression model. In this contribution, nitrogen oxide mixtures (i.e., N2O/NO2/NO) found in vehicle exhaust were selected as a relevant example of a real-world gas mixture. Both simulated data and experimental data acquired using Fourier transform infrared spectroscopy (FTIR) were analyzed by the proposed chemometrics model. By comparing the numerical results with those obtained using conventional principle components regression (PCR) and partial least square regression (PLSR) models, the proposed model was verified to offer superior robustness and performance in quantitative IR spectral analysis.
Highlights
With the development of advanced technologies in medical, industrial, and environmental applications, gas sensing has been applied to play an essential role in many areas [1,2]
We propose an advanced regression model based on extreme learning machine (ELM) and ELM-based auto-encoder (ELM-AE), as described in detail below
Technology simultaneously serving with substrate-integrated hollow waveguide technology simultaneously serving as as highly highly efficient gas cell
Summary
With the development of advanced technologies in medical, industrial, and environmental applications, gas sensing has been applied to play an essential role in many areas [1,2]. Quantitative gas analysis could benefit from a variety of technologies [4,5], among which gas chromatography (GC) and spectroscopic sensing are two frequently applied methods [6]. Spectroscopic methods can identify gases according to their more or less pronounced spectral signatures across the entire electromagnetic spectrum, and especially in near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopy, which are commonly used for real time and in-field gas sensing applications [8,9,10,11]. A commonly-applied method is based on the selection of the suitable wavelength regimes, while avoiding spectral segments that do not provide molecularly relevant signatures, thereby reducing computational expense. Dimension reduction algorithms are usually applied, such as PCA which could be realized by Karhunen–Loeve transform (KLT) [29,30]
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