Multicomponent Gas Sensing Fiber Probe System Based on Platinum Coated Capillary Enhanced Raman Spectroscopy.

This paper proposes a novel multicomponent gas-sensing optical fiber probe system. It utilizes a precisely engineered Platinum-coated capillary fabricated via Atomic Layer Deposition (ALD) technology as the core for enhanced Raman spectroscopy, marking the first application of ALD in creating such a structure for gas Raman sensing. The noble metal capillary gas Raman probe demonstrates a low detection limit of 55 ppm for CO2 with a 30 s exposure time and good repeatability in multicomponent gas sensing. The capillary exhibits excellent stability, environmental resistance, and a large core diameter, enabling a rapid gas exchange rate and making it suitable for practical applications.

Identification of fluoroquinolone-resistant Mycobacterium tuberculosis through high-level data fusion of Raman and laser-induced breakdown spectroscopy.

Accurate and rapid diagnosis of drug susceptibility of Mycobacterium tuberculosis is crucial for the successful treatment of tuberculosis, a persistent global public health threat. To shorten diagnosis times and enhance accuracy, this study introduces a fusion model combining laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. This model offers a rapid and accurate method for diagnosing drug-resistance. LIBS and Raman spectroscopy provide complementary information, enabling accurate identification of drug resistance in tuberculosis. Although individual use of LIBS or Raman spectroscopy achieved approximately 90% accuracy in identifying drug resistance, the fusion model significantly improved identification accuracy to 98.3%. Given the fast measurement capabilities of both techniques, this fusion approach is expected to markedly decrease the time required for diagnosis.

Highly sensitive microfluidic sensor using integrated optical fiber and real-time single-cell Raman spectroscopy for diagnosis of pancreatic cancer.

Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer.

Ultrabroadband two-beam coherent anti-Stokes Raman scattering and spontaneous Raman spectroscopy of organic fluids: A comparative study.

Spontaneous Raman spectroscopy is a well-established diagnostic tool, allowing for the identification of all Raman active species with a single measurement. Yet, it may suffer from low-signal intensity and fluorescent background. In contrast, coherent anti-Stokes Raman scattering (CARS) offers laser-like signals, but the traditional approach lacks the multiplex capability of spontaneous Raman spectroscopy. We present an ultrabroadband CARS setup which aims at exciting the full spectrum (300-3700 cm-1) of biological molecules. A dual-output optical parametric amplifier provides a ~7 fs pump/Stokes and a ~700 fs probe pulse. CARS spectra of DMSO, ethanol, and methanol show great agreement with spontaneous Raman spectroscopy and superiority in fluorescent environments. The spectral resolution proves sufficient to differentiate between the complex spectra of L-proline and hydroxyproline. Moreover, decay constants in the sub picosecond range are determined for individual Raman transitions, providing an additional approach for sample characterization.

Quantitative SERS sensor for mycotoxins with extraction and identification function.

The development of new sensors for on-site food toxin monitoring that combine extraction, analytes distinction and detection is important in resource-limited environments. Surface-enhanced Raman scattering (SERS)-based signal readout features fast response and high sensitivity, making it a powerful method for detecting mycotoxins. In this work, a SERS-based assay for the detection of multiple mycotoxins is presented that combines extraction and subsequent detection, achieving an analytically relevant detection limit (∼ 1 ng/mL), which is also tested in corn samples. This sensor consists of a magnetic-core and mycotoxin-absorbing polydopamine-shell, with SERS-active Au nanoparticles on the outer surface. The assay can concentrate multiple mycotoxins, which are identified through multiclass partite least squares analysis based on their SERS spectra. We developed a strategy for the analysis of multiple mycotoxins with minimal sample pretreatment, enabling in situ analytical extraction and subsequent detection, displaying the potential to rapidly identify lethal mycotoxin contamination on site.

Microsphere lens array embedded microfluidic chip for SERS detection with simultaneous enhancement of sensitivity and stability.

Surface enhanced Raman spectroscopy (SERS) utilizes the fingerprint features of molecular vibrations to identify and detect substances. However, in traditional single focus excitation scenarios, its signal collection efficiency of the objective is restricted. Furthermore, the uneven distribution of samples on the SERS substrate would result in poor signal stability, while the excitation power is limited to avoid sample damage. SERS detection system always requires precise adjustment of focal length and spot size, making it difficult for point-of-care testing applications. Here, we proposed a SERS microfluidic chip with barium titanate microspheres array (BTMA) embedded using vacuum self-assembled hot-pressing method for SERS detection with simultaneous enhancement of sensitivity and stability. Due to photonic nano-jets and directional antenna effects, high index microspheres are perfect micro-lens for effective light focusing and signal collecting. The BTMA can not only disperse excitation beam into an array of focal points covering the target uniformly with very low signal fluctuation, but enlarge the power threshold for higher signal intensity. We conducted a proof-of-principle experiment on chip for the detection of bacteria with immuno-magnetic tags and immuno-SERS tags. Together with magnetic and ultrasonic operations, the target bacteria in the flow were evenly congregated on the focal plane of BTMA. It demonstrated a limit of detection of 5 cells/mL, excellent signal reproducibility (error∼4.84%), and excellent position tolerance of 500 µm in X-Y plane (error∼5.375%). It can be seen that BTMA-SERS microfluidic chip can effectively solve the contradiction between sensitivity and stability in SERS detection.

Well-ordered Au@Ag NBPs/SiO2 nanoarray for sensitive detection of chloramphenicol via DNAzyme-assisted SERS sensing.

Misuse of chloramphenicol (CAP) can lead to severe food safety issues. Therefore, the accurate and sensitive detection of CAP residues is important for public health. Herein, a convenient and reliable interfacial self-assembly technique was used to form a uniform Au@Ag nanobipyramids (NBPs) film on an ordered SiO2 nanosphere array (SiO2 NS), which served as a Raman-enhanced substrate. In conjunction with a deoxyribonucleic acid enzyme-induced signal amplification strategy, we developed a novel surface-enhanced Raman scattering (SERS) biosensor for the selective and sensitive detection of CAP. The biosensor exhibited a detection limit of 6.42 × 10-13 mol·L-1 and a detection range of 1.0 × 10-12-1.0 × 10-6 mol·L-1. The biosensor could detect CAP in spiked milk samples with a high accuracy, and its recovery rates ranged from 97.88% to 107.86%. The as-developed biosensor with the advantages of high sensitivity and high selectivity offers a new strategy for the rapid, reliable and sensitive detection of CAP, rendering it applicable to food safety control.

Detection of redox potential evolution during the initial stage of an acute wound based on a redox-sensitive SERS-active optical fiber.

To detect redox potential evolution during the initial stage of an acute wound, a redox-sensitive SERS-active optical fiber was fabricated by integrating redox-sensitive SERS probes in a hole of an optical fiber. The redox-sensitive SERS-active optical fibers carried redox-sensitive SERS probes into the inside of a wound to sense its redox potential. The laser was transmitted to the redox-sensitive SERS probes in the body by optical fibers, and the SERS signals of the redox-sensitive SERS probes were transferred out of the body by optical fibers to indicate the redox potentials in the wound. The redox-sensitive SERS probes dynamically sensed the redox potential in vivo, and their SERS signals were collected constantly to indicate the redox potentials. The assessments in vivo and in vitro proved the responsiveness of redox-sensitive SERS-active optical fibers. The redox potential evolution during the initial stage of an acute wound with the treatments of different concentrations of glucose was detected to verify the feasibility of redox-sensitive SERS-active optical fibers to dynamically detect redox potentials in vivo. The redox-sensitive SERS-active optical fiber would be a versatile tool to explore the roles of redox potentials in living organisms.

Photonic-plasmonic resonator for SERS biodetection.

To improve the laser utilization efficiency and avoid photodamage in surface-enhanced Raman spectroscopy (SERS), it is imperative to introduce photon technology into the field of SERS detection. A major challenge is the inefficient interaction between the substrate and the incident wavelength, resulting in limited Raman enhancement at a relatively low level. Here, we sputtered plasmonic Au nanoparticles (NPs) onto photonic TiOx nanocavities, creating a novel hybrid photonic-plasmonic resonator that achieves a large degree of optical manipulation and long-term localization. By facilely controlling the size of Au NPs, the resonance wavelength of plasmonic Au NPs can be matched with the photonic nanocavity to maximize the light trapping intensity, which leads to a synergistic enhancement of SERS via the electromagnetic and chemical mechanisms, resulting in a SERS enhancement up to 1.75 × 109 under non-resonant excitation. In particular, the substrate can achieve strong absorption and localization for long wavelengths, thus enabling a large SERS enhancement with a small light intensity, which can effectively avoid the photodamage that may occur in Raman testing. The substrate can detect various biomolecules, including biomarkers in serum, thus realizing the differentiation of different cancers. This study provides a powerful and sensitive platform for SERS, facilitating bioanalysis and disease diagnosis in complex systems.

SLE diagnosis research based on SERS combined with a multi-modal fusion method.

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer.

Measuring, recording and analyzing spectral information of materials as its unique finger print using a ubiquitous smartphone has been desired by scientists and consumers. We demonstrated it as drug classification by chemical components with smartphone Raman spectrometer. The Raman spectrometer is based on the CMOS image sensor of the smartphone with a periodic array of band pass filters, capturing 2D Raman spectral intensity map, newly defined as spectral barcode in this work. Here we show 11 major components of drugs are classified with high accuracy, 99.0%, with the aid of convolutional neural network (CNN). The beneficial of spectral barcodes is that even brand name of drug is distinguishable and major component of unknown drugs can be identified. Combining spectral barcode with information obtained by red, green and blue (RGB) imaging system or applying image recognition techniques, this inherent property based labeling system will facilitate fundamental research and business opportunities.

Development of a multi-needle fiberoptic Raman spectroscopy technique for simultaneous multi-site deep tissue Raman measurements in the brain.

We report on the development of a multi-needle fiberoptic Raman spectroscopy (MNF-RS) technique for simultaneous multi-site deep Raman measurements in brain tissue. The multi-needle fiberoptic Raman probe is designed and fabricated using a number of 100 µm core diameter, aluminum-coated fibers under a coaxial laser excitation and Raman collection scheme, enabling simultaneous collection of deep tissue Raman spectra from a number of tissue sites. We have also developed a Raman retrieval algorithm based on the transformation matrix of each individual needle fiber probe projected to different pixels of a charge-coupled device (CCD) for recovering the tissue Raman spectra collected by each needle fiber probe, allowing simultaneous multi-channel detection by a single Raman spectrometer. High-quality tissue Raman spectra of different tissue types (e.g., muscle, fat, gray matter, and white matter in porcine brain) can be acquired in both the fingerprint (900-1800 cm-1) and high-wavenumber (2800-3300 cm-1) regions within sub-second times using the MNF-RS technique. We also demonstrate that by advancing the multi-needle fiberoptic Raman probe into deep porcine brain, tissue Raman spectra can be acquired simultaneously from different brain regions (e.g., cortex, thalamus, midbrain, and cerebellum). The significant biochemical differences across different brain tissues can also be distinguished, suggesting the promising potential of the MNF-RS technique for label-free neuroscience study at the molecular level.

Intraoperative assessment of resection margins by Raman spectroscopy to guide oral cancer surgery.

Patients with oral cavity cancer are almost always treated with surgery. The goal is to remove the tumor with a margin of more than 5 mm of surrounding healthy tissue. Unfortunately, this is only achieved in about 15% to 26% of cases. Intraoperative assessment of tumor resection margins (IOARM) can dramatically improve surgical results. However, current methods are laborious, subjective, and logistically demanding. This hinders broad adoption of IOARM, to the detriment of patients. Here we present the development and validation of a high-wavenumber Raman spectroscopic technology, for quick and objective intraoperative measurement of resection margins on fresh specimens. It employs a thin fiber-optic needle probe, which is inserted into the tissue, to measure the distance between a resection surface and the tumor. A tissue classification model was developed to discriminate oral cavity squamous cell carcinoma (OCSCC) from healthy oral tissue, with a sensitivity of 0.85 and a specificity of 0.92. The tissue classification model was then used to develop a margin length prediction model, showing a mean difference between margin length predicted by Raman spectroscopy and histopathology of -0.17 mm.

Label-Free Quantification of Microscopic Alignment in Engineered Tissue Scaffolds by Polarized Raman Spectroscopy.

Monitoring of extracellular matrix (ECM) microstructure is essential in studying structure-associated cellular processes, improving cellular function, and for ensuring sufficient mechanical integrity in engineered tissues. This paper describes a novel method to study the microscale alignment of the matrix in engineered tissue scaffolds (ETS) that are usually composed of a variety of biomacromolecules derived by cells. First, a trained loading function was derived from Raman spectra of highly aligned native tissue via principal component analysis (PCA), where prominent changes associated with specific Raman bands (e.g., 1444, 1465, 1605, 1627-1660, and 1665-1689 cm-1) were detected with respect to the polarization angle. These changes were mainly caused by the aligned matrix of many compounds within the tissue relative to the laser polarization, including proteins, lipids, and carbohydrates. Hence this trained function was applied to quantify the alignment within ETS of various matrix components derived by cells. Furthermore, a simple metric called Amplitude Alignment Metric (AAM) was derived to correlate the orientation dependence of polarized Raman spectra of ETS to the degree of matrix alignment. It was found that the AAM was significantly higher in anisotropic ETS than isotropic ones. The PRS method revealed a lower p-value for distinguishing the alignment between these two types of ETS as compared to the microscopic method for detecting fluorescent-labeled protein matrices at a similar microscopic scale. These results indicate that the anisotropy of a complex matrix in engineered tissue can be assessed at the microscopic scale using a PRS-based simple metric, which is superior to the traditional microscopic method. This PRS-based method can serve as a complementary tool for the design and assessment of engineered tissues that mimic the native matrix organizational microstructures.

A clinical Raman spectroscopy imaging system and safety requirements for in situ intraoperative tissue characterization.

Raman spectroscopy imaging is a technique that can be adapted for intraoperative tissue characterization to be used for surgical guidance. Here we present a macroscopic line scanning Raman imaging system that has been modified to ensure suitability for intraoperative use. The imaging system has a field of view of 1 × 1 cm2 and acquires Raman fingerprint images of 40 × 42 pixels, typically in less than 5 minutes. The system is mounted on a mobile cart, it is equiped with a passive support arm and possesses a removable and sterilizable probe muzzle. The results of a proof of concept study are presented in porcine adipose and muscle tissue. Supervised machine learning models (support vector machines and random forests) were trained and they were tested on a holdout dataset consisting of 7 Raman images (10 080 spectra) acquired in different animal tissues. This led to a detection accuracy >96% and prediction confidence maps providing a quantitative detection assessment for tissue border visualization. Further testing was accomplished on a dataset acquired with the imaging probe's contact muzzle and tailored classification models showed robust classifications capabilities with specificity, sensitivity and accuracy all surpassing 95% with a support vector machine classifier. Finally, laser safety, biosafety and sterilization of the system was assest. The safety assessment showed that the system's laser can be operated safetly according to the American National Standards Institute's standard for maximum permissible exposures for eyes and skin. It was further shown that during tissue interrogation, the temperature-history in cumulative equivalent minutes at 43 °C (CEM43 °C) never exceeded a safe threshold of 5 min.

Optimization of Surface-Enhanced Raman Spectroscopy Detection Conditions for Interaction between Gonyautoxin and Its Aptamer.

This study aimed to optimize the detection conditions for surface-enhanced Raman spectroscopy (SERS) of single-stranded DNA (ssDNA) in four different buffers and explore the interaction between gonyautoxin (GTX1/4) and its aptamer, GO18. The influence of the silver colloid solution and MgSO4 concentration (0.01 M) added under four different buffered conditions on DNA SERS detection was studied to determine the optimum detection conditions. We explored the interaction between GTX1/4 and GO18 under the same conditions as those in the systematic evolution of ligands by exponential enrichment technique, using Tris-HCl as the buffer. The characteristic peaks of GO18 and its G-quadruplex were detected in four different buffer solutions. The change in peak intensity at 1656 cm-1 confirmed that the binding site between GTX1/4 and GO18 was in the G-quadruplex plane. The relative intensity of the peak at 1656 cm-1 was selected for the GTX1/4-GO18 complex (I1656/I1099) to plot the ratio of GTX1/4 in the Tris-HCl buffer condition (including 30 µL of silver colloid solution and 2 µL of MgSO4), and a linear relationship was obtained as follows: Y = 0.1867X + 1.2205 (R2 = 0.9239). This study provides a basis for subsequent application of SERS in the detection of ssDNA, as well as the binding of small toxins and aptamers.

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling.

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.

Line-scan Raman scattering image and multivariate analysis for rapid and noninvasive detection of restructured beef.

The mean spectral (MS) features were extracted from Raman scattering images (RSI) of beef samples over the region of interest covering the spectral range of 789-1710cm-1 and the spatial offset range of 0-5 mm (for two sides of the incident laser). The RSI monitored the main change in the protein, amide bands, lipids, and amino acid residues. The classification model performance based on MS features compared the conventional Raman spectral features and confirmed the usefulness of RSI. Finally, the results showed that RSI technology is a reliable tool for rapid and noninvasive detection of restructured beef.

In situ SERS detection of quinolone antibiotic residues in a water environment based on optofluidic in-fiber integrated Ag nanoparticles.

In this paper, we present a microstructured optofluidic in-fiber Raman sensor for the detection of quinolone antibiotic residue in a water environment based on Ag surface-enhanced Raman scattering (SERS) substrate grown on the surface of the suspended core of micro-hollow optical fiber (MHF). Here, MHF has a special structure with a suspended core and a microchannel inside, which can become a natural in-fiber optofluidic device. Meanwhile, the self-assembled Ag SERS substrate can be grown on the suspended core's surface through chemical bonds, forming a microstructured optofluidic device with a Raman enhancement effect. Therefore, it can effectively detect the Raman signal of unlabeled trace quinolone antibiotic residue (ciprofloxacin and norfloxacin) inside the optical fiber. The results show that the ciprofloxacin and norfloxacin detection limits (LOD) are 10-10M and 10-11M, respectively. Compared with the maximum residue limit (3.01×10-7mol/L) stipulated by the European Union, the results are much lower, and an ideal quantitative relationship can be obtained within the detection range. Significantly, this study provides an in-fiber microstructured optofluidic Raman sensor for the label-free detection of quinolone antibiotic residue, which will have good development prospects in the field of antibiotic water pollution environmental detection.

Silk fibroin fibers decorated with urchin-like Au/Ag nanoalloys: a flexible hygroscopic SERS sensor for monitoring of folic acid in human sweat.

Surface-enhanced Raman scattering (SERS) spectroscopy has become a powerful and sensitive analytical tool for the detection and assessment of chemical/biological molecules in special scenarios. Herein we propose a flexible hygroscopic SERS biocompatible sensor based on the silk fibroin fibers (SFF) decorated with urchin-like Au/Ag nanoalloys (NAs). The hybrid SFF-Au/Ag NAs with a stronger absorbance capacity (500∼1100 nm) and excellent hygroscopicity provide a remarkable higher near-infrared (NIR)-SERS activity than that of bare urchin-like Au/Ag NAs. The interesting NIR-SERS sensor enables the limit of detection (LOD) of folic acid (FA) to be achieved at nanomolar (nM, 10-9 M) level, facilitating the ultrasensitive monitoring of FA in human sweat and offering reliable real-time personal health management in the near future.