Adaptive Hit-Quality Index for Raman Spectrum Identification

MicroRaman spectroscopy detects the presence of microplastics in human urine and kidney tissue

There is a growing concern within the medical community about the potential burden of microplastics on human organs and tissues. In this study, we investigated by microRaman spectroscopy the presence of microplastics in human kidneys and urine. Moreover, an open-access software was developed and validated for the project, which enabled the comparison between the investigated spectra and a self-created spectral database, thus enhancing the ability to characterize polymers and pigments in biological matrices. Healthy portions of ten kidneys obtained from nephrectomies, as well as ten urine samples from healthy donors were analyzed: 26 particles in both kidney and urine samples were identified, with sizes ranging from 3 to 13 μm in urine and from 1 to 29 μm in kidneys. The most frequently determined polymers are polyethylene and polystyrene, while the most common pigments are hematite and Cu-phthalocyanine. This preclinical study proves the presence of microplastics in renal tissues and confirms their presence in urine, providing the first evidence of kidney microplastics deposition in humans.

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.

Assessment of filter subsampling and extrapolation for quantifying microplastics in environmental samples using Raman spectroscopy

A common method for characterizing microplastics (MPs) involves capturing the plastic particles on a filter after extraction and isolation from the sediment particles. Microplastics captured on the filter are then scanned with Raman spectroscopy for polymer identification and quantification. However, scanning the whole filter manually using Raman analysis is a labor-intensive and time-consuming process. This study investigates a subsampling method for Raman spectroscopic analysis of microplastics (operationally defined here as 45-1000 μm in size) present in sediments and isolated onto laboratory filters. The method was evaluated using spiked MPs in deionized water and two environmentally contaminated sediments. Based on statistical analyses, we found quantification of a sub-fraction of 12.5 % of the filter in a wedge form was optimal, efficient, and accurate for estimating the entire filter count. The extrapolation method was then used to assess microplastic contamination in sediments from different marine regions of the United States.

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.

Baseline correction using a deep-learning model combining ResNet and UNet

A baseline correction method based on deep-learning model is proposed, which combines ResNet and UNet. Compared with the traditional methods, this method has relatively excellent performance.

Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations

Hyperspectral imaging has been widely used for different kinds of applications and many chemometric tools have been developed to help identifying chemical compounds. However, most of those tools rely on factorial decomposition techniques that can be challenging for large data sets and/or in the presence of minor compounds. The present study proposes a pixel-based identification (PBI) approach that allows readily identifying spectral signatures in Raman hyperspectral imaging data. This strategy is based on the identification of essential spectral pixels (ESP), which can be found by convex hull calculation. As the corresponding set of spectra is largely reduced and encompasses the purest spectral signatures, direct database matching and identification can be reliably and rapidly performed. The efficiency of PBI was evaluated on both known and unknown samples, considering genuine and falsified pharmaceutical tablets. We showed that it is possible to analyze a wide variety of pharmaceutical formulations of increasing complexity (from 5 to 0.1% (w/w) of polymorphic impurity detection) for medium (150 x 150 pixels) and big (1000 x 1000 pixels) map sizes in less than 2 min. Moreover, in the case of falsified medicines, it is demonstrated that the proposed approach allows the identification of all compounds, found in very different proportions and, sometimes, in trace amounts. Furthermore, the relevant spectral signatures for which no match is found in the reference database can be identified at a later stage and the nature of the corresponding compounds further investigated. Overall, the provided results show that Raman hyperspectral imaging combined with PBI enables rapid and reliable spectral identification of complex pharmaceutical formulations.