Detecting small microplastics down to 1.3 µm using large area ATR-FTIR.

Large area attenuated total reflectance-Fourier transform infrared spectroscopy (LAATR-FTIR) is introduced as a novel technique for detecting small microplastics (MPs) down to 1.3 µm. Two different LAATR units, one with a zinc selenide (ZnSe) and one with a germanium (Ge) crystal, were used to detect reference MPs < 20 µm, and MPs in marine water samples, and compared with µ-FTIR in transmission mode. The LAATR units performed well in identifying small MPs down to 1.3 µm. However, they were poorly suited for large MPs as uneven particle thickness resulted in uneven contact between crystal and particle, misinterpreting large MPs as many small MPs. However, for more homogeneous matrices, the technique was promising. Further assessment indicated that there was little difference in spectra quality between transmission mode and LAATR mode. All in all, while LAATR units struggle to substitute transmission mode, it provides additional information and valuable information on small MPs.

Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images.

In this work, a random decision forest model is built for fast identification of Fourier-transform infrared spectra of the eleven most common types of microplastics in the environment. The random decision forest input data is reduced to a combination of highly discriminative single wavenumbers selected using a machine learning classifier. This dimension reduction allows input from systems with individual wavenumber measurements, and decreases prediction time. The training and testing spectra are extracted from Fourier-transform infrared hyperspectral images of pure-type microplastic samples, automatizing the process with reference spectra and a fast background correction and identification algorithm. Random decision forest classification results are validated using procedurally generated ground truth. The classification accuracy achieved on said ground truths are not expected to carry over to environmental samples as those usually contain a broader variety of materials.

Lamellar grating optimization for miniaturized fourier transform spectrometers.

Microfabricated Lamellar grating interferometers (LGI) require fewer components compared to Michelson interferotemeters and offer compact and broadband Fourier transform spectrometers (FTS) with good spectral resolution, high speed and high efficiency. This study presents the fundamental equations that govern the performance and limitations of LGI based FTS systems. Simulations and experiments were conducted to demonstrate and explain the periodic nature of the interferogram envelope due to Talbot image formation. Simulations reveal that the grating period should be chosen large enough to avoid Talbot phase reversal at the expense of mixing of the diffraction orders at the detector. Optimal LGI grating period selection depends on a number of system parameters and requires compromises in spectral resolution and signal-to-bias ratio (SBR) of the interferogram within the spectral range of interest. New analytical equations are derived for spectral resolution and SBR of LGI based FTS systems.