Plastic solid waste management is a growing field in many countries that is supported by governmental legislation. Plastic recycling is widely concerned since the other methods such as, landfilling and burning, harm the environment and are economically inefficient. Different methods have been proposed in literature based on the physical and thermal properties of the plastic but they are labor intensive and time consuming. The most promising method is based on the optical spectral sensing in the infrared range. It is non-destructive and time-efficient especially when coupled with machine learning models such as chemometrics. However, the infrared spectrometers are bulky, expensive and require building a model for each unit. Micro-electro-mechanical-system (MEMS) Fourier-transform infrared spectroscopy (FTIR) spectrometers are designed to be portable and lower in cost and in power consumption than traditional spectrometers. In this work, we present a classification predictor of two plastic categories Polyethylene Terephthalate (PETE) and Polypropylene (PP) using a portable MEMS FTIR spectrometer. PETE and PP samples are collected with different shapes and surface curvatures and the samples diffuse reflectance spectra are measured at 10 different surface spots per sample. The measurements are averaged and processed to mitigate the physical scatter effects. A multivariate linear classification chemometrics model is built and validated using one sample out cross validation. The classification using one principle component reaches a classification success rate of 100 % opening the door for low cost portable device for accurate plastic sorting.
The wide spectral range, compactness, low cost and high measurement speed of the MEMS FTIR spectrometers enable their use in real-time in-line gas analysis. They have the potential of identifying and quantifying several gases simultaneously compared to other infrared technologies such as the NDIR. However, the challenge of real-time spectral background removal from the measured spectrum has to be tackled first. In fact background removal is a common problem to spectroscopic applications relaying on the spectral shape and strength of the absorption lines. In this work, two of the most recent background correction algorithms, namely iterative averaging and morphological weighted penalized least squares, are adapted and applied on the MEMS FTIR spectrometer for gas mixture analysis. These algorithms don’t require prior knowledge about the background or the peaks position and don’t involve any manual selection of a suitable local minimum value. A 10-cm gas cell that contains known concentrations of SO2, C2H4, N2O and N2 was measured using the MEMS FTIR spectrometer. The presence of several spectral absorption lines of these gases in the mid-infrared (MIR) region is considered a challenging case for automatic background correction algorithms. The spectra are measured in the MIR range of 1.6 μm - 4.9 μm with a resolution down to 33 cm-1. The corrected spectra are compared with spectra measured with a standard bench-top spectrometer and the RMS error and Pearson’s correlation coefficient are calculated and good values of 0.8 % and 98 %, respectively, are obtained. Overcoming the spectral background removal paves the way for the use of MEMS FTIR spectrometer in real-time monitoring of multiple gases simultaneously.
Air pollution is used to refer to the release of pollutants into the air, where these pollutants are harmful to the human health and our planet. The main source of these pollutants comes from energy production and consumption that release Volatile Organic Compounds (VOCs) such as BTEX and Aldehydes group. Real time monitoring of these VOCs in factories, stations, homes and in the street is important for analysis of the pollution sources fingerprint and for alerting, when exceeding the harmful limits. In this work we report the use of a MEMS FTIR spectrometer in the mid-infrared for this purpose. The spectrometer works in the wavelength range of 1.6 μm - 4.9 μm with a resolution down to 33 cm-1. This covers the absorption spectrum of water vapour, BTEX, Aldehydes and CO2 around 2.65 μm, 3.27 μm, 3.6 μm and 4.3 μm, respectively. The spectra of Toluene with different concentrations are measured, using a multipass gas cell with a physical length of 50 cm and an optical path length of 20 m, showing excellent sensor linearity. The minimum concentration measured is 350 ppb limited by the interference of the side lobes of the strong absorption of water vapour, which can be overcome in the future by humidity compensation. The SNR is measured and found to be 5000:1, corresponding to a detection limit of about 90 ppb. The achieved results open the door for a compact and low-cost solution targeting air pollution monitoring.