Raman chemical imaging microspectroscopy is evaluated as a technology for waterborne pathogen and bioaerosol detection. Raman imaging produces a three-dimensional data cube consisting of a Raman spectrum at every pixel in a microscope field of view. Binary and ternary mixtures including combinations of polystyrene beads, gram-positive Bacillus anthracis, B. thuringiensis, and B. atrophaeus spores, and B. cereus vegetative cells were investigated by Raman imaging for differentiation and characterization purposes. Bacillus spore aerosol sizes were varied to provide visual proof for corroboration of spectral assignments. Conventional applications of Raman imaging consist of differentiating relatively broad areas of a sample in a microscope field of view. The spectral angle mapping data analysis algorithm was used to compare a library spectrum with experimental spectra from pixels in the microscope field of view. This direct one-to-one matching is straightforward, does not require a training set, is independent of absolute spectral intensity, and only requires univariate statistics. Raman imaging is expanded in its capabilities to differentiate and distinguish between discrete 1-6 microm size bacterial species in single particles, clusters of mixed species, and bioaerosols with interference background particles.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/ac901074c | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.
Transl Vis Sci Technol
January 2025
School of Optometry and Vision Science, University of New South Wales, Sydney, Australia.
Purpose: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.
Methods: The dataset included 2265 participants aged 40 years and above from a population-based study in South India. The dataset included ocular and systemic clinical parameters, dilated retinal fundus images, and hematological data such as complete blood counts and Hb concentration levels.
Probing the effects of dextran-coated CeO nanoparticles on lung fibroblasts using multivariate single-cell Raman spectroscopy.
Nanotoxicology
January 2025
Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia.
In this study, we investigated the cytotoxic effect of highly soluble dextran-coated CeO nanoparticles on human fetal lung fibroblasts MRC-5. We examined individual nanoparticle-treated cells by Raman spectroscopy and analyzed Raman spectra using non-negative principal component analysis and k-means clustering. In this way, we determined dose-dependent differences between treated cells, which were reflected through the intensity change of lipid, phospholipid and RNA-related Raman modes.
View Article and Find Full Text PDFOn-Chip Stimulated Raman Scattering Imaging and Quantification of Molecular Diffusion in Aqueous Microfluidics.
Anal Chem
January 2025
State Key Laboratory of Surface Physics and Department of Physics, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Fudan University, Shanghai 200433, China.
Numerous chemical reactions and most life processes occur in aqueous solutions, where the physical diffusion of small molecules plays a vital role, including solvent water molecules, solute biomolecules, and ions. Conventional methods of measuring diffusion coefficients are often limited by technical complexity, large sample consumption, or significant time cost. Here, we present an optical imaging method to study molecular diffusion by combining stimulated Raman scattering (SRS) microscopy with microfluidics: a "Y"-shaped microfluidic channel forming two laminar flows with a stable concentration gradient across the interface.
View Article and Find Full Text PDFMachine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy.
Sci Rep
January 2025
Department of Internal Medicine and Liver Research Institute, Department of Medical Device Development, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcinoma. Raman spectroscopy-based machine learning was applied in real-time during endoscopy in 19 patients (aged 51-85 years) with high-risk for gastric adenocarcinoma. Raman spectra were captured from suspicious lesions and adjacent normal mucosa, which were biopsied for matched histopathologic diagnosis.
View Article and Find Full Text PDFConfocal Raman Microscopy with Adaptive Optics.
ACS Photonics
January 2025
Institute of Biomedical Physics, Medical University of Innsbruck, Müllerstraße 44, 6020 Innsbruck, Austria.
Confocal Raman microscopy, a highly specific and label-free technique for the microscale study of thick samples, often presents difficulties due to weak Raman signals. Inhomogeneous samples introduce wavefront aberrations that further reduce these signals, requiring even longer acquisition times. In this study, we introduce Adaptive Optics to confocal Raman microscopy for the first time to counteract such aberrations, significantly increasing the Raman signal and image quality.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!