Environmental diagnosis with Raman Spectroscopy applied to diatoms

On-line adapted islands SERS Chip for quantitatively sensing H2S molecules in food spoilage

Surface-enhanced Raman scattering (SERS) has extraordinary potential in detecting hydrogen sulfide (H2S) gas molecules due to its high sensitivity and specificity. However, constructing SERS substrates with strong Raman signal enhancement effects has been a major challenge. In this study, a gold/silver nanostars (Au/Ag NSs) islands chip was developed, leveraging the phenomenon that the SERS enhancement can be significantly improved by patterning plasmonic metals with micro/nanostructures. Compared to the SERS mapping spectrogram of the conventional homogeneous Au/Ag NSs chip, uniform and dense hot spots were formed on the surface of the island chip, which is the key to remarkable enhancement of Raman signal. Both chips were functionalized with 4-mercaptophenylboronic acid (4-MPBA) for the quantitative detection of H2S. The islands chip displays 1.6 folds and 2 orders higher blank Raman signal intensity and a limit of detection (LOD) for H2S than conventional homogeneous SERS chips, respectively. The islands chip is also used for on-line monitoring of spoilage in high-protein foods. The detection ranges for H2S were 2 × 10-8 M - 2 × 10-3 M, with a LOD of 1.9 nM. This sensing strategy provides not only a novel approach for on-ling gaseous hazards detection for food spoilage but also a potential possibility for monitoring gas molecules in the environment.

Deep-Learning-Assisted Raman Spectral Analysis for Accurate Differentiation of Highly Structurally Similar CA Series Synthetic Cannabinoids

Precise discrimination of the crucial substances, e.g., synthetic cannabinoids (SCs) that are composed of low-active chemical groups and structurally similar to each other with tiny differences, is a pressing need and of great significance for safeguarding public security and human health. The structure-relevant vibrational spectroscopic techniques, e.g., Raman spectroscopy, could reflect structural fingerprint information on the target; however, the algorithm-assisted phrasing is inevitable. This work achieved the accurate identification of CA series SCs by proposing an attention mechanism involving a CNN algorithm to phrase the Raman data. Specifically, these SCs have only one different chemical group compared to each other, the attention mechanism was introduced to intensify the computation on their structural difference from the massive data, realizing the accurate discrimination. Furthermore, how the spectral peaks corresponded to the specific structure was revealed, which plays a decisive role for the algorithm to distinguish these substances, and provides an instructive reference for differentiating other SCs based on Raman spectra. Hence, this work provides a research paradigm for applying the advanced CNN algorithm-aided Raman spectral analysis to sub-differentiate the substances, strengthening the understanding of spectral information from the sub-molecular level and propelling the integration of interdisciplinary areas.

Microfluidic Device on Fused Silica for Raman Spectroscopy of Liquid Samples

Water testing is becoming increasingly important due to dangerous phenomena such as Harmful Algal Blooms (HABs). Commonly, the content of a water sample is measured for the detection, monitoring and control of these events. Raman spectroscopy is a technique for the molecular characterization of materials in solid, liquid or gaseous form, which makes it an attractive method for analysing materials' components. However, Raman scattering is a weak optical process and requires an accurate system for detection. In our work, we present, from design to fabrication, a microfluidic device on fused silica adapted to optimise the Raman spectrum of liquid samples when using a Raman probe. The device features a portable design for rapid on-site continuous flow measurements avoiding the use of large, costly and complex laboratory equipment. The main manufacturing technique used was ultrafast laser-assisted etching (ULAE). Finally, the effectiveness of the microfluidic device was demonstrated by comparing the Raman spectra of a known species of cyanobacteria with those obtained using other conventional substrates in laboratory analysis. The results demonstrate that the microfluidic device, under continuous flow conditions, exhibited a lower standard deviation of the Raman signal, reduced background noise and avoided signal variations caused by sample drying in static measurements.

Baseline Raman Spectral Fingerprints of Zebrafish Embryos and Larvae

As a highly sensitive vibrational technique, Raman spectroscopy (RS) can provide valuable chemical and molecular data useful to characterise animal cell types, tissues and organs. As a label-free, rapid detection method, RS has been considered a valuable asset in forensics, biology and medicine. The technique has been applied to zebrafish for various purposes, including physiological, biochemical or bioaccumulation analyses. The available data point out its potential for the early diagnosis of detrimental effects elicited by toxicant exposure. Nevertheless, no baseline spectra are available for zebrafish embryos and larvae that could allow for suitable planning of toxicological assessments, comparison with toxicant-elicited spectra or mechanistic understanding of biochemical and physiological responses to the exposures. With this in mind, this work carried out a baseline characterisation of Raman spectra of zebrafish embryos and larvae throughout early development. Raman spectra were recorded from the iris, forebrain, melanocytes, heart, muscle and swim bladder between 24 and 168 h post-fertilisation. A chemometrics approach, based on partial least-squares discriminant analysis (PLS-DA), was used to obtain a Raman characterisation of each tissue or organ. In total, 117 Raman bands were identified, of which 24 were well represented and, thus, retained in the data analysed. Only three bands were found to be common to all organs and tissues. The PLS-DA provided a tentative Raman spectral fingerprint typical of each tissue or organ, reflecting the ongoing developmental dynamics. The bands showed frequencies previously assigned to collagen, cholesterol, various essential amino acids, carbohydrates and nucleic acids.

Pilot SERS Monitoring Study of Two Natural Hypersaline Lake Waters from a Balneary Resort during Winter-Months Period

Water samples from two naturally hypersaline lakes, renowned for their balneotherapeutic properties, were investigated through a pilot SERS monitoring program. Nanotechnology-based techniques were employed to periodically measure the ultra-sensitive SERS molecular characteristics of the raw water-bearing microbial community and the inorganic content. Employing the Pearson correlation coefficient revealed a robust linear relationship between electrical conductivity and pH and Raman and SERS spectral data of water samples, highlighting the interplay complexity of Raman/SERS signals and physicochemical parameters within each lake. The SERS data obtained from raw waters with AgNPs exhibited a dominant, reproducible SERS feature resembling adsorbed β-carotene at submicromole concentration, which could be related to the cyanobacteria-AgNPs interface and supported by TEM analyses. Notably, spurious SERS sampling cases showed molecular traces attributed to additional metabolites, suggesting multiplexed SERS signatures. The conducted PCA demonstrated observable differences in the β-carotene SERS band intensities between the two lakes, signifying potential variations in picoplankton abundance and composition or environmental influences. Moreover, the study examined variations in the SERS intensity ratio I245/I1512, related to the balance between inorganic (Cl--induced AgNPs aggregation) and organic (cyanobacteria population) balance, in correlation with the electrical conductivity. These findings signify the potential of SERS data for monitoring variations in microorganism concentration, clearly dependent on ion concentration and nutrient dynamics in raw, hypersaline water bodies.

Raman Spectroscopy Combined with Malaria Protein for Early Capture and Recognition of Broad-Spectrum Circulating Tumor Cells

Early identification of tumors can significantly reduce the mortality rate. Circulating tumor cells (CTCs) are a type of tumor cell that detaches from the primary tumor and circulates through the bloodstream. Monitoring CTCs may allow the early identification of tumor progression. However, due to their rarity and heterogeneity, the enrichment and identification of CTCs is still challenging. Studies have shown that Raman spectroscopy could distinguish CTCs from metastatic cancer patients. VAR2CSA, a class of malaria proteins, has a strong broad-spectrum binding effect on various tumor cells and is a promising candidate biomarker for cancer detection. Here, recombinant malaria VAR2CSA proteins were synthesized, expressed, and purified. After confirming that various types of tumor cells can be isolated from blood by recombinant malaria VAR2CSA proteins, we further proved that the VAR2CSA combined with Raman spectroscopy could be used efficiently for tumor capture and type recognition using A549 cell lines spiked into the blood. This would allow the early screening and detection of a broad spectrum of CTCs. Finally, we synthesized and purified the malaria protein fusion antibody and confirmed its in vitro tumor-killing activity. Herein, this paper exploits the theoretical basis of a novel strategy to capture, recognize, and kill broad-spectrum types of CTCs from the peripheral blood.

Novel Approach to Freshwater Diatom Profiling and Identification Using Raman Spectroscopy and Chemometric Analysis

(1) An approach with great potential for fast and cost-effective profiling and identification of diatoms in lake ecosystems is presented herein. This approach takes advantage of Raman spectroscopy. (2) The study was based on the analysis of 790 Raman spectra from 29 species, belonging to 15 genera, 12 families, 9 orders and 4 subclasses, which were analysed using chemometric methods. The Raman data were first analysed by a partial least squares regression discriminant analysis (PLS-DA) to characterise the diatom species. Furthermore, a method was developed to streamline the integrated interpretation of PLS-DA when a high number of significant components is extracted. Subsequently, an artificial neural network (ANN) was used for taxa identification from Raman data. (3) The PLS interpretation produced a Raman profile for each species reflecting its biochemical composition. The ANN models were useful to identify various taxa with high accuracy. (4) Compared to studies in the literature, involving huge datasets one to four orders of magnitude larger than ours, high sensitivity was found for the identification of Achnanthidium exiguum (67%), Fragilaria pararumpens (67%), Amphora pediculus (71%), Achnanthidium minutissimum (80%) and Melosira varians (82%).