Unknown Spectrum Research Articles

Enhancing Confidence in Microplastic Spectral Identification via Conformal Prediction.

Microplastics are an emerging pollutant of concern, with environmental observations recorded across the world. Identifying the type of microplastic is challenging due to spectral similarities among the most common polymers, necessitating methods that can confidently distinguish plastic identities. In practice, a researcher chooses the reference vibrational spectrum that is most like the unknown spectrum, where the likeness between the two spectra is expressed numerically as the hit quality index (HQI). Despite the widespread use of HQI thresholds in the literature, acceptance of a spectral label often lacks any associated confidence. To address this gap, we apply a machine-learning framework called conformal prediction to output a set of possible labels that contain the true identity of the unknown spectrum with a user-defined probability (e.g., 90%). Microplastic reference libraries of environmentally aged and pristine polymeric materials, as well as unknown environmental plastic spectra, were employed to illustrate the benefits of this approach when used with two similarity metrics to compute HQI. We present an adaptable workflow using our open-access code to ensure spectral matching confidence for the microplastic community, reducing manual inspection of spectral matches and enhancing the robustness of quantification in the field.

Single snapshot-based miniaturized mid-infrared spectrometer with a dielectric metasurface

Mid-infrared spectroscopy is critical for applications like environmental monitoring and medical diagnostics, as it includes the fundamental vibrational frequencies of many molecular bonds, enabling detailed chemical analysis. We developed a miniature mid-infrared spectrometer using a fully dielectric metasurface operating in the 3–5 µm range. By combining simple structural units, design flexibility was enhanced, resulting in rich spectral data. The spectrometer was evaluated using narrowband, broadband, and transmission spectra of real materials. We detected the spectral responses of metasurface arrays and determined the unknown spectrum using an algorithm based on Tikhonov regularization with generalized cross-validation. By reconstructing multiple narrow spectra obtained through rotating the filters, spectral resolution intervals of approximately 10 nm were achieved.

Debiasing astro-photometric observations with corrections using statistics (DePhOCUS)

Photometric measurements allow the determination of an asteroid’s absolute magnitude, which often represents the sole means to infer its size. Photometric observations can be obtained in a variety of filters that can be unique to a specific observatory. Those observations are then calibrated into specific bands with respect to reference star catalogs. In order to combine all the different measurements for evaluation, photometric observations need to be converted to a common band, typically V-band. Current band-correction schemes in use by IAU’s Minor Planet Center (MPC), JPL’s Center for Near Earth Object Studies (CNEOS) and ESA’s NEO Coordination Centre (NEOCC) use average correction values for the apparent magnitude derived from photometry of asteroids as the corrections are dependent on the typically unknown spectrum of the object to be corrected. By statistically analyzing the photometric residuals of asteroids, we develop a new photometric correction scheme that does not only consider the band, but also accounts for reference catalog and observatory. We analyzed nearly 500000 observations submitted to the MPC from 468 asteroids with published and independently determined high confidence H and G values. We describe a new statistical photometry correction scheme for asteroid observations with debiased corrections. Testing this scheme on a reference group of asteroids, we see a 36% reduction in the photometric residuals. Moreover, the new scheme leads to a more accurate and debiased determination of the H-G magnitude system and, in turn, to more reliable inferred sizes. We discuss the significant shift in the corrections with this “DePhOCUS” debiasing system, its limitations, and the impact for photometric and physical properties of all asteroids, especially Near-Earth Objects.

Quantitative analysis of acetamiprid and thiacloprid in green tea using enhanced SERS and neural networks

Neonicotinoids (NEOs) are widely used in agricultural cultivation, and the presence of residual NEOs poses a serious threat to consumer health. Therefore, trace detection of NEOs is particularly important. In this study, a response surface experimental design was employed to develop AgNP with high enhancement factor and surface height anisotropy, effectively enhancing the Raman signals of acetamiprid (ACE) and thiacloprid (THI) through densely populated hotspots on the surface. Gaussian simulation calculations were used to assign peaks in the ACE and THI spectra. Subsequently, convolutional neural network, backpropagation neural network, and AlexNet neural network were employed to identify the ACE, THI, and mixed component spectra, successfully distinguishing the spectra of the three compounds and achieving qualitative analysis of unknown spectra. Furthermore, genetic algorithm-partial least squares was used to model the spectral data, achieving a correlation coefficient of 0.97. The method was applied for the detection of spiked tea samples, achieving a precision RSD of 4.84 %. Therefore, this sensor combined with intelligent algorithms can be used for spectral identification and trace detection of mixed pesticide samples with structurally similar compounds.

Direct Identification of the Continuous Relaxation Time and Frequency Spectra of Viscoelastic Materials

Relaxation time and frequency spectra are not directly available by measurement. To determine them, an ill-posed inverse problem must be solved based on relaxation stress or oscillatory shear relaxation data. Therefore, the quality of spectra models has only been assessed indirectly by examining the fit of the experiment data to the relaxation modulus or dynamic moduli models. As the measures of data fitting, the mean sum of the moduli square errors were usually used, the minimization of which was an essential step of the identification algorithms. The aim of this paper was to determine a relaxation spectrum model that best approximates the real unknown spectrum in a direct manner. It was assumed that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. A modified relaxation frequency spectrum was defined as a quotient of the real relaxation spectrum and relaxation frequency and expanded into a series of linearly independent exponential functions that are known to constitute a basis of the space of square-integrable functions. The spectrum model, given by a finite series of these basis functions, was assumed. An integral-square error between the real unknown modified spectrum and the spectrum model was taken as a measure of the model quality. This index was proved to be expressed in terms of the measurable relaxation modulus at uniquely defined sampling instants. Next, an empirical identification index was introduced in which the values of the real relaxation modulus are replaced by their noisy measurements. The identification consists of determining the spectrum model that minimizes this empirical index. Tikhonov regularization was applied to guarantee model smoothness and noise robustness. A simple analytical formula was derived to calculate the optimal model parameters and expressed in terms of the singular value decomposition. A complete identification algorithm was developed. The analysis of the model smoothness and model accuracy for noisy measurements was carried out. The equivalence of the direct identification of the relaxation frequency and time spectra has been demonstrated when the time spectrum is modeled by a series of functions given by the product of the relaxation frequency and its exponential function. The direct identification concept can be applied to both viscoelastic fluids and solids; however, some limitations to its applicability have been pointed out. Numerical studies have shown that the proposed identification algorithm can be successfully used to identify Gaussian-like and Kohlrausch–Williams–Watt relaxation spectra. The applicability of this approach to determining other commonly used classes of relaxation spectra was also examined.

Miniaturized on-chip spectrometer enabled by electrochromic modulation.

Miniaturized on-chip spectrometers with small footprints, lightweight, and low cost are in great demand for portable optical sensing, lab-on-chip systems, and so on. Such miniaturized spectrometers are usually based on engineered spectral response units and then reconstruct unknown spectra with algorithms. However, due to the limited footprints of computational on-chip spectrometers, the recovered spectral resolution is limited by the number of integrated spectral response units/filters. Thus, it is challenging to improve the spectral resolution without increasing the number of used filters. Here we present a computational on-chip spectrometer using electrochromic filter-based computational spectral units that can be electrochemically modulated to increase the efficient sampling number for higher spectral resolution. These filters are directly integrated on top of the photodetector pixels, and the spectral modulation of the filters results from redox reactions during the dual injection of ions and electrons into the electrochromic material. We experimentally demonstrate that the spectral resolution of the proposed spectrometer can be effectively improved as the number of applied voltages increases. The average difference of the peak wavelengths between the reconstructed and the reference spectra decreases from 1.61 nm to 0.29 nm. We also demonstrate the proposed spectrometer can be worked with only four or two filter units, assisted by electrochromic modulation. In addition, we also demonstrate that the electrochromic filter can be easily adapted for hyperspectral imaging, due to its uniform transparency. This strategy suggests a new way to enhance the performance of miniaturized spectrometers with tunable spectral filters for high resolution, low-cost, and portable spectral sensing, and would also inspire the exploration of other stimulus responses such as photochromic and force-chromic, etc, on computational spectrometers.

Benchmarking of Approaches for Gene Copy-Number Variation Analysis and Its Utility for Genetic Aberration Detection in High-Grade Serous Ovarian Carcinomas.

Objective: The goal of this study was to compare the results of CNV detection by three different methods using 13 paired carcinoma samples, as well as to perform a statistical analysis of the agreement. Methods: CNV was studied using NanoString nCounter v2 Cancer CN Assay (Nanostring), Illumina Infinium CoreExome microarrays (CoreExome microarrays) and digital droplet PCR (ddPCR). Results: There was a good level of agreement (PABAK score > 0.6) between the CoreExome microarrays and the ddPCR results for finding CNVs. There was a moderate level of agreement (PABAK values ≈ 0.3-0.6) between the NanoString Assay results and microarrays or ddPCR. For 83 out of 87 target genes studied (95%), the agreement between the CoreExome microarrays and NanoString nCounter was characterized by PABAK values < 0.75, except for MAGI3, PDGFRA, NKX2-1 and KDR genes (>0.75). The MET, HMGA2, KDR, C8orf4, PAX9, CDK6, and CCND2 genes had the highest agreement among all three approaches. Conclusions: Therefore, to get a better idea of how to genotype an unknown CNV spectrum in tumor or normal tissue samples that are very different molecularly, it makes sense to use at least two CNV detection methods. One of them, like ddPCR, should be able to quantitatively confirm the results of the other.

Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301.

This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear artificial neural network (ANN). The response functions of the EJ-301 detector for monoenergetic neutron sources, as well as the light outputs, have been simulated and calculated by Monte Carlo procedure FLUKA. The linear ANN was trained and tested through the simulated data, where response functions were set as the input of ANN and the corresponding neutron spectra were output. Therefore, the neutron spectrum-unfolding model was created. This spectrum-unfolding model was tested through the light outputs induced by monoenergetic neutrons and the random superposition of them. Unfolding results show that this model could identify the information of the neutron spectrum accurately from the light outputs of a liquid scintillation detector. Moreover, the EJ-301 detector was used to measure the radioactivity of 252Cf, and the pulse height distribution induced by neutrons was derived through the charge-comparison method to remove the influence of gamma rays. The measured pulse height distribution was unfolded by the trained model, and measured results show that the unfolded neutron spectrum of 252Cf was consistent with the reference one. This paper presents the feasibility that the unknown neutron spectrum could be identified and confirmed through a linear neural network trained by simulated monoenergetic neutron response functions, which could be a candidate of choice for the determination of the neutron spectrum.

Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging

As hyperpolarized (HP) carbon-13 (13C) metabolic imaging is clinically translated, there is a need for easy-to-implement, fast, and robust imaging techniques. However, achieving high temporal resolution without decreasing spatial and/or spectral resolution, whilst maintaining the usability of the imaging sequence is challenging. Therefore, this study looked to accelerate HP 13C MRI by combining a well-established and robust sequence called two-dimensional Chemical Shift Imaging (2D CSI) with prospective under sampling and SENSitivity Encoding (SENSE) reconstruction. Due to the low natural abundance of 13C, the sensitivity maps cannot be pre-acquired for the reconstruction. As such, the implementation of sodium (23Na) sensitivity maps for SENSE reconstructed 13C CSI was demonstrated in a phantom and in vivo in the pig kidney. Results showed that SENSE reconstruction using 23Na sensitivity maps corrected aliased images with a four-fold acceleration. With high temporal resolution, the kidney spectra produced a detailed metabolic arrival and decay curve, useful for further metabolite kinetic modelling or denoising. Metabolic ratio maps were produced in three pigs demonstrating the technique’s ability for repeat metabolic measurements. In cases with unknown metabolite spectra or limited HP MRI specialist knowledge, this robust acceleration method ensures comprehensive capture of metabolic signals, mitigating the risk of missing spectral data.

Ultra-broadband diffractive imaging with unknown probe spectrum

Strict requirement of a coherent spectrum in coherent diffractive imaging (CDI) architectures poses a significant obstacle to achieving efficient photon utilization across the full spectrum. To date, nearly all broadband computational imaging experiments have relied on accurate spectroscopic measurements, as broad spectra are incompatible with conventional CDI systems. This paper presents an advanced approach to broaden the scope of CDI to ultra-broadband illumination with unknown probe spectrum, effectively addresses the key challenges encountered by existing state-of-the-art broadband diffractive imaging frameworks. This advancement eliminates the necessity for prior knowledge of probe spectrum and relaxes constraints on non-dispersive samples, resulting in a significant extension in spectral bandwidth, achieving a nearly fourfold improvement in bandlimit compared to the existing benchmark. Our method not only monochromatizes a broadband diffraction pattern from unknown illumination spectrum, but also determines the compressive sampled profile of spectrum of the diffracted radiation. This superiority is experimentally validated using both CDI and ptychography techniques on an ultra-broadband supercontinuum with relative bandwidth exceeding 40%, revealing a significantly enhanced coherence and improved reconstruction with high fidelity under ultra-broadband illumination.

Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging.

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used.

Investigation of a hyperspectral Scanning Laser Optical Tomography setup for label-free cell identification

The development of non-destructive, tomographic imaging systems is a current topic of research in biomedical technologies. One of these technologies is Scanning Laser Optical Tomography (SLOT), which features a highly modular setup with various contrast mechanisms. Extending this technology with new acquisition mechanisms allows us to investigate untreated and non-stained biological samples, leaving their natural biological physiology intact. To enhance the development of SLOT, we aimed to extend the density of information with a significant increase of acquisition channels. This should allow us to investigate samples with unknown emission spectra and even allow for label-fee cell identification. We developed and integrated a hyperspectral module into an existing SLOT system. The adaptations allow for the acquisition of three-dimensional datasets containing a highly increased information density. For validation, artificial test objects were made from fluorescent acrylic and acquired with the new hyperspectral setup. In addition, measurements were made on two different human cell spheroids with an unknown spectra, to test the possibilities of label-free cell identification. The validation measurements of the artificial test target show the expected results. Furthermore, the measurements of the biological cell spheroids show small variations in their tomographic spectrum that allow for label-free cell type differentiation. The results of the biological sample demonstrate the potential of label-free cell identification of the newly developed setup.

TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry

Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking.Scientific ContributionWe propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.

Screening Disinfection Byproducts in Arid-Coastal Wastewater: A Workflow Using GC×GC-TOFMS, Passive Sampling, and NMF Deconvolution Algorithm.

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts-with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks-significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks.

Dual-channel air-pulse optical coherence elastography for frequency-response analysis

Microliter air-pulse optical coherence elastography (OCE) has recently been proposed for the characterization of soft-tissue biomechanics using transient, sub-nanometer to micrometer-scale natural frequency oscillations. However, previous studies have not been able to provide real-time air-pulse monitoring during OCE natural frequency measurement, which could lead to inaccurate measurement results due to the unknown excitation spectrum. To address this issue, we introduce a dual-channel air-pulse OCE method, with one channel stimulating the sample and the other being simultaneously measured with a pressure sensor. This allows for more accurate natural frequency characterization using the frequency response function, as proven by a comprehensive comparison under different conditions with a diverse range of excitation spectra (from broad to narrow, clean to noisy) as well as a diverse set of sample response spectra. We also demonstrate the capability of the frequency-response analysis in distinguishing samples with different stiffness levels: the dominant natural frequencies increased with agar concentrations (181–359 Hz, concentrations: 1–2%, and maximum displacements: 0.12–0.47 µm) and intraocular pressures (IOPs) for the silicone cornea (333–412 Hz, IOP: 5–40 mmHg, and maximum displacements: 0.41–0.52 µm) under a 200 Pa stimulation pressure. These frequencies remained consistent across different air-pulse durations (3 ms to 35 ms). The dual-channel OCE approach that uses transient, low-pressure stimulation and high-resolution imaging holds the potential to advance our understanding of sample frequency responses, especially when investigating delicate tissues such as the human cornea in vivo.

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Colorectal cancer is very widespread in developed countries. Its diagnosis partly depends on pathologists’ experience and their laboratories’ instrumentation, producing uncertainty in diagnosis. The use of spectroscopic techniques sensitive to the cellular biochemical environment could aid in achieving a reliable diagnosis. So, we used Raman micro-spectroscopy, combined with a spectral analysis by means of machine learning methods, to build classification models, which allow colon cancer to be diagnosed in cell samples, in order to support such methods as complementary tools for achieving a reliable identification of colon cancer. The Raman spectra were analyzed in the 980–1800 cm−1 range by focusing the laser beam onto the nuclei and the cytoplasm regions of single FHC and CaCo-2 cells (modelling healthy and cancerous samples, respectively) grown onto glass coverslips. The comparison of the Raman intensity of several spectral peaks and the Principal Component Analysis highlighted small biochemical differences between healthy and cancerous cells mainly due to the larger relative lipid content in the former cells with respect to the latter ones and to the larger relative amount of nucleic acid components in cancerous cells compared with healthy ones. We considered four classification algorithms (logistic regression, support vector machine, k nearest neighbors, and a neural network) to associate unknown Raman spectra with the cell type to which they belong. The built machine learning methods achieved median values of classification accuracy ranging from 95.5% to 97.1%, sensitivity values ranging from 95.5% to 100%, and specificity values ranging from 93.9% to 97.1%. The same median values of the classification parameters, which were estimated for a testing set including unknown spectra, ranged between 93.1% and 100% for accuracy and between 92.9% and 100% for sensitivity and specificity. A comparison of the four methods pointed out that k nearest neighbors and neural networks better perform the classification of nucleus and cytoplasm spectra, respectively. These findings are a further step towards the perspective of clinical translation of the Raman technique assisted by multivariate analysis as a support method to the standard cytological and immunohistochemical methods for diagnostic purposes.

Weighting Low-Intensity MS/MS Ions and m/z Frequency for Spectral Library Annotation.

Calculating spectral similarity is a fundamental step in MS/MS data analysis in untargeted metabolomics experiments, as it facilitates the identification of related spectra and the annotation of compounds. To improve matching accuracy when querying an experimental mass spectrum against a spectral library, previous approaches have proposed increasing peak intensities for high m/z ranges. These high m/z values tend to be smaller in magnitude, yet they offer more crucial information for identifying the chemical structure. Here, we evaluate the impact of using these weights for identifying structurally related compounds and mass spectral library searches. Additionally, we propose a weighting approach that (i) takes into account the frequency of the m/z values within a spectral library in order to assign higher importance to the most common peaks and (ii) increases the intensity of lower peaks, similar to previous approaches. To demonstrate our approach, we applied weighting preprocessing to modified cosine, entropy, and fidelity distance metrics and benchmarked it against previously reported weights. Our results demonstrate how weighting-based preprocessing can assist in annotating the structure of unknown spectra as well as identifying structurally similar compounds. Finally, we examined scenarios in which the utilization of weights resulted in diminished performance, pinpointing spectral features where the application of weights might be detrimental.

Experimental investigation of machine-learning-based soft-failure management using the optical spectrum

The demand for high-speed data is exponentially growing. To conquer this, optical networks have undergone significant changes, getting more complex and versatile. The increasing complexity necessitates that the fault management be more adaptive to enhance network assurance. In this paper, we experimentally compare the performance of soft-failure management of different machine learning algorithms. We further introduce a machine-learning-based soft-failure management framework. It utilizes a variational autoencoder-based generative adversarial network (VAE-GAN) running on optical spectral data obtained by optical spectrum analyzers. The framework is able to reliably run on a fraction of available training data as well as identify unknown failure types. The investigations show that the VAE-GAN outperforms the other machine learning algorithms when up to 10% of the total training data are available in identification tasks. Furthermore, the advanced training mechanism for the GAN shows a high F1-score for unknown spectrum identification. The failure localization comparison shows the advantage of a low complexity neural network in combination with a VAE over established machine learning algorithms.

Mass spectra prediction with structural motif-based graph neural networks.

Mass spectra, which are agglomerations of ionized fragments from targeted molecules, play a crucial role across various fields for the identification of molecular structures. A prevalent analysis method involves spectral library searches, where unknown spectra are cross-referenced with a database. The effectiveness of such search-based approaches, however, is restricted by the scope of the existing mass spectra database, underscoring the need to expand the database via mass spectra prediction. In this research, we propose the Motif-based Mass Spectrum prediction Network (MoMS-Net), a GNN-based architecture to predict the mass spectra pattern utilizing the structural motif information of the molecule. MoMS-Net considers both a molecule and its substructures as a graph form, which facilitates the incorporation of long-range dependencies while using less memory compared to the graph transformer model. We evaluated our model over various types of mass spectra and showed the validity and superiority over the conventional models.

A reliable method to determine airborne microplastics using quantum cascade laser infrared spectrometry

The number of studies dealing with airborne microplastics (MPs) is increasing but sampling and sample treatment are not standardized, yet. Here, a fast and reliable method to characterize MPs is presented. It involves the study of two passive sampling devices to collect atmospheric bulk deposition (wet and dry deposition) and three digestion methods (two alkaline-oxidative and an oxidative) to treat the samples. The alkaline-oxidative method based on KOH and NaClO was selected for a mild organic matrix digestion. In addition, some operational parameters of a high-throughput quantum cascade laser-based infrared device (LDIR) were optimized: an effective automatic tiered approach to differentiate fibres from particles (>90 % success in validation) and a criterion to establish positive matches when comparing an unknown spectrum against the spectral database (proposed match index > 0.85). The procedural analytical recoveries were very good for particles (82–90 %) and slightly lower for fibres (62–73 %). Finally, the amount and type of MPs deposited at a sub-urban area NW Spain were evaluated. Most common polymers were Polyethylene (PE), Polypropylene (PP) and Polyethylene terephthalate (PET). The deposition rates ranged 98–1220 MP/m2/day, ca. 1.7 % of the total collected particles. More than 50 % of the total MPs deposited were in the 20–50 μm size range, whereas fibres were mostly in the 50–500 μm size range.