Library-Based Raman Spectral Identification Using Multi-Input Hybrid ResNet

Raman spectroscopy is widely used for its exceptional identification capabilities in various fields. Traditional methods for target identification using Raman spectroscopy rely on signal correlation with moving windows, requiring data preprocessing that can significantly impact identification performance. In recent years, deep-learning approaches have been proposed to leverage data augmentation techniques, such as baseline and additive noise addition, in order to overcome data scarcity. However, these deep-learning methods are limited to the spectra encountered during training and struggle to handle unseen spectra. To address these limitations, we propose a multi-input hybrid deep-learning model trained with simulated spectral data. By employing simulated spectra, our method tackles the challenges of data scarcity and the handling of unseen spectra encountered in traditional and deep-learning methods. Experimental results demonstrate that our proposed method achieves outstanding identification performance and effectively handles spectra obtained from different Raman spectroscopy systems.

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning

Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios.

Whispering gallery mode resonators in continuous flow: spectral assignments and sensing with monodisperse microspheres

Whispering gallery mode resonator (WGMR) microspheres yield highly structured optical spectra that are extremely sensitive to their environment and are of intense interest for use in a variety of sensing applications. Many efforts to leverage the unique sensitivities of WGMRs have relied on stringent experimental requirements to correlate specific spectral shifts/changes to an analyte/stimulus such as (1) precise positional knowledge, (2) reference spectra for each microsphere, and (3) high mechanical stability. Consequently, these factors can hinder adequate mixing or incorporation of analytes and can create challenges for remote sensing. This work describes a continuous flow technique for measuring whispering gallery mode (WGM) spectra of dye-doped microspheres suspended in solution and an accompanying analysis scheme that can extract the local refractive index without a priori knowledge of the microsphere size and position and without a reference spectrum. This measurement technique and analysis scheme was shown to accurately measure the refractive index of a range of alcohol and saline solutions down to a few thousandths of a refractive index unit (RIU). Additionally, a spectral clustering algorithm was applied to the fit results of two batches of microspheres suspended in water and was able to accurately assign spectra back to either batch of microspheres.

Are We Underestimating Anthropogenic Microfiber Pollution? A Critical Review of Occurrence, Methods, and Reporting

Anthropogenic microfibers, a ubiquitous environmental contaminant, can be categorized as synthetic, semisynthetic, or natural according to material of origin and production process. Although natural fibers, such as cotton and wool, originated from natural sources, they often contain chemical additives, including colorants (e.g., dyes, pigments) and finishes (e.g., flame retardants, antimicrobial agents, ultraviolet light stabilizers). These additives are applied to textiles during production to give textiles desired properties like enhanced durability. Anthropogenically modified "natural" and semisynthetic fibers are sufficiently persistent to undergo long-range transport and accumulate in the environment, where they are ingested by biota. Although most research and communication on microfibers have focused on the sources, pathways, and effects of synthetic fibers in the environment, natural and semisynthetic fibers warrant further investigation because of their abundance. Because of the challenges in enumerating and identifying natural and semisynthetic fibers in environmental samples and the focus on microplastic or synthetic fibers, reports of anthropogenic microfibers in the environment may be underestimated. In this critical review, we 1) report that natural and semisynthetic microfibers are abundant, 2) highlight that some environmental compartments are relatively understudied in the microfiber literature, and 3) report which methods are suitable to enumerate and characterize the full suite of anthropogenic microfibers. We then use these findings to 4) recommend best practices to assess the abundance of anthropogenic microfibers in the environment, including natural and semisynthetic fibers. By focusing exclusively on synthetic fibers in the environment, we are neglecting a major component of anthropogenic microfiber pollution. Environ Toxicol Chem 2022;41:822-837. © 2021 SETAC.

Anthropogenic particles (including microfibers and microplastics) in marine sediments of the Canadian Arctic

We report the first Canadian Arctic-wide study of anthropogenic particles (APs, >125 μm), including microfibers (synthetic, semi-synthetic and anthropogenically modified cellulose) and microplastics, in marine sediments from 14 sites. Samples from across the Canadian Arctic were collected between 2014 and 2017 from onboard the CCGS Amundsen. Samples were processed using density separation with calcium chloride (CaCl₂). APs >125 μm were identified and a subset (22%) were characterized using Raman spectroscopy. Following blank-correction, microfiber numbers were corrected using Raman data in a novel approach to subtract possible "natural" cellulose microfibers with no anthropogenic signal via Raman spectroscopy, to estimate the proportion of cellulose microfibers that are of confirmed anthropogenic origin. Of all microfibers examined by Raman spectroscopy, 51% were anthropogenic cellulose, 11% were synthetic polymers, and 7% were extruded fibers emitting a dye signal. The remaining 31% of microfibers were identified as cellulosic but could not be confirmed as anthropogenic and thus were excluded from the final concentrations. Concentrations of confirmed APs in sediments ranged from 0.6 to 4.7 particles g^-1 dry weight (dw). Microfibers comprised 82% of all APs, followed by fragments at 15%. Total microfiber concentrations ranged from 0.4 to 3.2 microfibers g^-1 dw, while microplastic (fragments, foams, films and spheres) concentrations ranged from 0 to 1.6 microplastics g^-1 dw. These concentrations may exceed those recorded in urban areas near point sources of plastic pollution, and indicate that the Canadian Arctic is a sink for APs, including anthropogenic cellulose fibers. Overall, we provide an important benchmark of AP contamination in Canadian Arctic marine sediments against which to measure temporal trends, including the effects of source reduction strategies and climate change, both of which will likely alter patterns of accumulation of anthropogenic particles.

On Selecting a Suitable Spectral Matching Method for Automated Analytical Applications of Raman Spectroscopy

Raman spectra are molecular structure-specific and hence are employed in applications requiring chemical identification. The advent of efficient handheld and smartphone-based Raman instruments is promoting widespread applications of the technique, which often involve less trained end users. Software modules that enable spectral library searches based on spectral pattern matching is an essential part of such applications. The Raman spectrum recorded by end users will naturally have varying levels of signal to noise (SN), baseline fluctuations, etc., depending on the sample environment. Further, in biological, forensic, food, pharmaceuticals, etc., fields where a vast amount of Raman spectral data is generated, careful removal of background is often impossible. In other words, a 100% match between the library spectrum and user input cannot be often guaranteed or expected. Often, such influences are discounted upon developing mathematical methods for general applications. In this manuscript, we carefully examine how such effects would determine the results of spectral similarity-based library search. We show that several popular mathematical spectral matching approaches give incorrect results under the influence of small changes in the baseline and/or the noise. We also discuss the points to be carefully considered while generating a spectral library. We believe our results will be a guiding note for developing applications of Raman spectroscopy that uses a standard spectral library and mathematical spectral matching.

Adaptive Hit-Quality Index for Raman Spectrum Identification

The recognition capability of the identification system using Raman spectroscopy is increasing with the demands in the field. Among the various approaches that determine the identity of a target, signal correlation using a moving window is one of the most effective and intuitive methods. In this paper, we report a new correlation method that is robust to spectral intensity variations. Using the peak distribution of a given spectrum, this method adaptively determines meaningful spectral regions for the identification target. Three commercial Raman spectrometer and a 14 033 library were included in the study, which was used for a library-based chemical discrimination test and mixed material analysis experiments. According to the identification experimental results, the proposed method correctly identified all of the spectra and maintained a mean correlation score above 0.95 while maintaining the correlation score of nontarget materials as low as possible.

Deep learning for 'artefact' removal in infrared spectroscopy

It has been well recognized that infrared spectra of microscopically heterogeneous media do not merely reflect the absorption of the sample but are influenced also by geometric factors and the wave nature of light causing scattering, reflection, interference, etc. These phenomena often occur simultaneously in complex samples like tissues and manifest themselves as intense baseline profiles, fringes, band distortion and band intensity changes in a measured IR spectrum. The information on the molecular level contained in IR spectra is thus entangled with the geometric structure of a sample and the optical model behind it, which largely hinders the data interpretation and in many cases renders the Beer-Lambert law invalid. It is required to recover the pure absorption (i.e., absorbance) of the sample from the measurement (i.e., apparent absorbance), that is, to remove the 'artefacts' caused merely by optical influences. To do so, we propose an artefact removal approach based on a deep convolutional neural network (CNN), specifically a 1-dimensional U-shape convolutional neural network (1D U-Net), and based our study on poly(methyl methacrylate) (PMMA) as materials. To start, a simulated dataset composed of apparent absorbance and absorbance pairs was generated according to the Mie-theory for PMMA spheres. After a data augmentation procedure, this dataset was utilized to train the 1D U-Net aiming to transform the input apparent absorbance into the corrected absorbance. The performance of the artefact removal was evaluated by the hit-quality-index (HQI) between the corrected and the true absorbance. Based on the prediction and the HQI of two experimental and one simulated independent testing datasets, we could demonstrate that the network was able to retrieve the absorbance very well, even in cases where the absorbance is completely overwhelmed by extremely large 'artefacts'. As the testing datasets bear different patterns of absorbance and 'artefacts' to the training data, the promising correction also indicated a good generalization performance of the 1D U-Net. Finally, the reliability and computational mechanism of the trained network were illustrated via two interpretation approaches including a direct visualization of layer-wise outputs as well as a saliency-based method.

Discovery of Several New Families of Saturable Absorbers for Ultrashort Pulsed Laser Systems

Saturable Absorber (SA) is a key element of any passive mode-locked laser system to provide ultrashort laser system. So far various materials have been proposed that could be used for this purpose. However, the field is still looking for new ways to make the fabrication process easier and cost-effective. Another challenge in testing mode-locked laser systems using various SA samples is the lack of knowledge in preparing these by laser physicists given this is outside their remit of expertise. In this study, we have proposed a novel method to produce these SAs from plastic materials and glycol. Our new method relies upon increase in thickness up to a value where the modulation depth is enough to give stable ultrashort pulses. Although we have shown this method for four materials; similar approach could be applied to any material. This will open the door of unlimited families of SAs that could be easily prepared and applied without any prior knowledge in material sciences.