Spectroscopic techniques such as Surface-Enhanced Raman Scattering (SERS), Surface-Enhanced Infrared Absorption (SEIRA), and Surface-Enhanced Fluorescence (SEF) are essential analytical techniques used to study the composition of materials by analyzing the way materials scatter light, absorb infrared radiation or emit fluorescence signals. This provides information about their molecular structure and properties. However, traditional SERS, SEIRA, and SEF techniques can be limited in sensitivity, resolution, and reproducibility, hindering their ability to detect and analyze trace amounts of substances or complex molecular structures. Metasurfaces, a class of engineered two-dimensional metamaterials with unique optical properties, have emerged as a promising tool to overcome these limitations and enhance spectroscopic techniques. This article provides a state-of-the-art overview of metasurfaces for enhanced SERS, SEIRA and SEF, covering their theoretical background, different types, advantages, disadvantages, and potential applications.

Cyanobacterial blooms pose significant environmental and public health risks due to the production of toxins that contaminate water sources and disrupt aquatic ecosystems. Rapid and accurate identification of cyanobacterial species is crucial for effective monitoring and management strategies. In this study, we combined Raman spectroscopy with deep learning techniques to classify four toxic cyanobacterial species: Dolichospermum crassum, Aphanizomenon sp., Planktothrix agardhii and Microcystis aeruginosa. Spectral data were acquired using a confocal Raman microscope with a 532 nm excitation wavelength and subjected to preprocessing and filtering to enhance signal quality. We evaluated a multichannel one-dimensional convolutional neural network (1D-CNN) approach that incorporates raw spectra, baseline estimations, and preprocessed spectra. This multichannel approach improved overall classification accuracy, achieving 86% compared to 74% with a traditional single-channel 1D-CNN using only preprocessed spectra while maintaining low overfitting. Shapley Additive exPlanations (SHAP) were applied to identify critical spectral regions for classification to enhance interpretability. These findings highlight the potential of combining Raman spectroscopy with explainable deep learning methods as a powerful tool for water quality monitoring and the early detection of Harmful Algal Blooms (HABs).

Antimicrobial resistance poses a significant challenge in modern medicine, affecting public health. Klebsiella pneumoniae infections compound this issue due to their broad range of infections and the emergence of multiple antibiotic resistance mechanisms. Efficient detection of its capsular serotypes is crucial for immediate patient treatment, epidemiological tracking and outbreak containment. Current methods have limitations that can delay interventions and increase the risk of morbidity and mortality. Raman spectroscopy is a promising alternative to identify capsular serotypes in hypermucoviscous K. pneumoniae isolates. It provides rapid and in situ measurements with minimal sample preparation. Moreover, its combination with machine learning tools demonstrates high accuracy and reproducibility. This study analyzed the viability of combining Raman spectroscopy with one-dimensional convolutional neural networks (1-D CNN) to classify four capsular serotypes of hypermucoviscous K. pneumoniae: K1, K2, K54 and K57. Our approach involved identifying the most relevant Raman features for classification to prevent overfitting in the training models. Simplifying the dataset to essential information maintains accuracy and reduces computational costs and training time. Capsular serotypes were classified with 96 % accuracy using less than 30 Raman features out of 2400 contained in each spectrum. To validate our methodology, we expanded the dataset to include both hypermucoviscous and non-mucoid isolates and distinguished between them. This resulted in an accuracy rate of 94 %. The results obtained have significant potential for practical healthcare applications, especially for enabling the prompt prescription of the appropriate antibiotic treatment against infections.

This study explores the potential of conventional Raman spectroscopy and commonly used spectral analysis pipelines for rapid and straightforward assessment of degradation in serum samples resulting from storage delays. Serum samples from 18 volunteers were processed within 2 h of extraction, which later on were analyzed via Raman spectroscopy over 4 days, while the corresponding serum vials were kept at room temperature. The resulting spectra were processed, including silicon normalization and a newly proposed outlier detection ensemble method. Next, baseline correction was performed, and spectral unmixing along with Principal Component Analysis (PCA) were applied. Several classification models (KNN, RF, and SVM) were trained and evaluated on three distinct balanced datasets: one including all data, one excluding low signal-to-noise ratio (SNR) data, and one excluding low-SNR data with baseline correction. Feature importance, assessed through random permutations, was used for explainability. Spectral unmixing and PCA indicated limited spectral changes directly attributable to analyte degradation, with inter- and intra-sample variability dominating. Classification results showed that while removing the baseline led to inconclusive results, models trained on datasets retaining the baseline effectively identified non-degraded samples. These findings suggest that while conventional Raman spectroscopy may not be optimally sensitive to subtle analyte variations in serum stored at room temperature, the auto-fluorescence background holds promise as a potential biomarker for monitoring serum storage quality.

Phytoplankton are essential to aquatic ecosystems but can cause harmful algal blooms (HABs) that threaten water quality, aquatic life, and human health. Developing new devices based on spectroscopic techniques offers a promising alternative for rapid and accurate monitoring of aquatic environments. However, phytoplankton undergo various physiological changes throughout their life cycle, leading to alterations in their optical properties, such as autofluorescence. In this study, we present a modification of a low-cost photobioreactor designed to implement fluorescence spectroscopy to analyze the evolution of spectral signals during phytoplankton growth cycles. This device primarily facilitates the characterization of changes in autofluorescence, providing valuable information for the development of future spectroscopic techniques for detecting and monitoring phytoplankton. Additionally, real-time testing was performed on cyanobacterial cultures, where changes in autofluorescence were observed under different conditions. This work demonstrates a cost-effective implementation of spectroscopic techniques within a photobioreactor, offering a preliminary analysis for the future development of functional field devices for monitoring aquatic ecosystems.

Patella vulgata shells preserve geochemical and structural variations that can provide insights into past environmental conditions. Their composition, primarily calcium carbonate with organic residues from the biomineralization process, is influenced by external factors, such as sea surface temperature. Raman spectroscopy has emerged as a rapid, non-destructive tool for studying biogenic carbonates, enabling the identification of crystalline phases, organic components, and ion distribution. In this study, Raman imaging was applied to six shell sections of P. vulgata live-collected from Langre Beach in Cantabria, Spain. Spectral data were acquired using a Raman probe with a 532 nm excitation laser, providing high-resolution mapping of structural and compositional features. The analysis revealed spatial variations in mineralogy, organic matrix distribution, and ion incorporation in the calcium carbonate lattice, suggesting patterns originating during shell formation. Notably, the results suggest a consistent relationship between the organic and mineral components of the shells, with carotenoid distribution and carbonate ion substitution in the calcium carbonate lattice following similar growth patterns. These findings highlight the potential of Raman spectroscopy for studying biomineralization processes and the environmental records preserved in marine mollusk shells.