Abstract
Miniaturized Fourier transform infrared spectrometers serve emerging market needs in many applications such as gas analysis. The miniaturization comes at the cost of lower performance than bench-top instrumentation, especially for the spectral resolution. However, higher spectral resolution is needed for better identification of the composition of materials. This article presents a convolutional neural network (CNN) for 3X resolution enhancement of the measured infrared gas spectra using a Fourier transform infrared (FTIR) spectrometer beyond the transform limit. The proposed network extracts a set of high-dimensional features from the input spectra and constructs high-resolution outputs by nonlinear mapping. The network is trained using synthetic transmission spectra of complex gas mixtures and simulated sensor non-idealities such as baseline drifts and non-uniform signal-to-noise ratio. Ten gases that are relevant to the natural and bio gas industry are considered whose mixtures suffer from overlapped features in the mid-infrared spectral range of 2000–4000 cm−1. The network results are presented for both synthetic and experimentally measured spectra using both bench-top and miniaturized MEMS spectrometers, improving the resolution from 60 cm−1 to 20 cm−1 with a mean square error down to 2.4×10−3 in the transmission spectra. The technique supports selective spectral analysis based on miniaturized MEMS spectrometers.
Highlights
The training of the convolutional neural network (CNN) required the consideration of data with different spectral resolutions and signal-to-noise ratios (SNRs)
CNN architecture enables the processing of the input signal on different layers, where the signal is convolved with many kernels to capture local features around each sample, the output of each layer is fed to the level to capture more abstract features as wider neighborhood of the sample is involved
While miniaturized Fourier transform infrared spectrometers are finding increasing numbers of important applications, such as gas analysis, they generally suffer from the problem of low spectral resolution
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. One notable example for smart industry is the analysis of the biogas and natural gas contents as a primary source of energy in domestic and industrial markets There is a need for realizing a high-resolution spectral analysis over a wide spectral range based on compact spectroscopic devices We extend the results by enhancing the resolution of experimentally measured spectra using both bench-top FTIR spectrometer (bench-top device Tensor II by Bruker) and a miniaturized mid-infrared. The training of the CNN required the consideration of data with different spectral resolutions and signal-to-noise ratios (SNRs).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
