Methods in Raman spectroscopy for saliva studies – a review

Hardy, Mike; Kelleher, Liam; Gomes, Paulo de Carvalho; Buchan, Emma; Chu, Hin On; Oppenheimer, Pola Goldberg

doi:10.1080/05704928.2021.1969944

Cited by 33 publications

(31 citation statements)

References 359 publications

(731 reference statements)

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“…Here, we used highly diluted hydrochloric acid and centrifugation to prepare clarified saliva by removing cellular debris, bacteria, and high molecular weight proteins. [ 60 ] As shown in Figure a, after the centrifugation process, solid waste was isolated. Only the supernatant liquid was extracted for sensing afterward (see details in Experimental Section).…”

Section: Resultsmentioning

confidence: 99%

Superhydrophobic 3D‐Assembled Metallic Nanoparticles for Trace Chemical Enrichment in SERS Sensing

Liu

Zhang

Tua

et al. 2022

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The performance of surface‐enhanced Raman spectroscopy (SERS) is determined by the interaction between highly diluted analytes and boosted localized electromagnetic fields in nanovolumes. Although superhydrophobic surfaces are developed for analyte enrichment, i.e., to concentrate and transfer analytes toward a specific position, it is still challenging to realize reproducible, uniform, and sensitive superhydrophobic SERS substrates over large scales, representing a major barrier for practical sensing applications. To overcome this challenge, a superhydrophobic SERS chip that combines 3D‐assembled gold nanoparticles on nanoporous substrates is proposed, for a strong localized field, with superhydrophobic surface treatment for analyte enrichment. Intriguingly, by concentrating droplets in the volume of 40 µL, the sensitivity of 1 nm is demonstrated using 1,2‐bis(4‐pyridyl)‐ethylene molecules. In addition, this unique chip demonstrates a relative standard deviation (RSD) of 2.2% in chip‐to‐chip reproducibility for detection of fentanyl at 1 µg mL–1 concentration, revealing its potential for quantitative sensing of chemicals and drugs. Furthermore, the trace analysis of fentanyl and fentanyl‐heroin mixture in human saliva is realized after a simple pretreatment process. This superhydrophobic chip paves the way toward on‐site and real‐time drug sensing to tackle many societal issues like drug abuse and the opioid crisis.

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Section: Resultsmentioning

confidence: 99%

Superhydrophobic 3D‐Assembled Metallic Nanoparticles for Trace Chemical Enrichment in SERS Sensing

Liu

Zhang

Tua

et al. 2022

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“…In these tables, Raman shifts for the S and N proteins being also present in the SARS-CoV-2 Raman spectrum are marked by a footnote "a". Saliva Raman spectroscopy is the most promising approach for SARS-CoV2 infection detection [73,74]. Salivary COVID-19 Raman fingerprints were analyzed [75].…”

Section: Respiratory Viruses Detectionmentioning

confidence: 99%

“…Saliva Raman spectroscopy is the most promising approach for SARS‐CoV2 infection detection [73, 74]. Salivary COVID‐19 Raman fingerprints were analyzed [75].…”

Section: Raman Spectroscopy Implementations In Virus and Virus‐associ...mentioning

confidence: 99%

Label‐free laser spectroscopy for respiratory virus detection: A review

Kistenev

Das

Mazumder

et al. 2022

Journal of Biophotonics

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Infectious diseases are among the most severe threats to modern society. Current methods of virus infection detection based on genome tests need reagents and specialized laboratories. The desired characteristics of new virus detection methods are noninvasiveness, simplicity of implementation, real‐time, low cost and label‐free detection. There are two groups of methods for molecular biomarkers' detection and analysis: (i) a sample physical separation into individual molecular components and their identification, and (ii) sample content analysis by laser spectroscopy. Variations in the spectral data are typically minor. It requires the use of sophisticated analytical methods like machine learning. This review examines the current technological level of laser spectroscopy and machine learning methods in applications for virus infection detection.

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“…Multivariate Analysis. Multivariate analysis 46 was performed using the self-optimizing Kohonen index network (SKiNET) based on self-organizing maps (SOMs), described in detail in ref 47. SOMs are single-layer artificial neural networks represented as a two-dimensional (2D) hexagonal array of neurons.…”

Section: Atomic Force Microscopy (Afm)mentioning

confidence: 99%

Optimization of Nanosubstrates toward Molecularly Surface-Functionalized Raman Spectroscopy

et al. 2022

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Diagnostic advancements require continuous developments of reliable analytical sensors, which can simultaneously fulfill many criteria, including high sensitivity and specificity for a broad range of target analytes. Incorporating the highly sensitive attributes of surface-enhanced Raman spectroscopy (SERS) combined with highly specific analyte recognition capabilities via molecular surface functionalization could address major challenges in molecular diagnostics and analytical spectroscopy fields. Herein, we have established a controllable molecular surface functionalization process for a series of textured gold surfaces. To create the molecularly surface-functionalized SERS platforms, self-assembled benzyl-terminated and benzoboroxole-terminated monolayers were used to compare which thicknesses and root-mean-square (RMS) roughness of planar gold produced the most sensitive and specific surfaces. Optimal functionalization was identified at 80 ± 8 nm thickness and 7.2 ± 1.0 nm RMS. These exhibited a considerably higher SERS signal (70-fold) and improved sensitivity for polysaccharides when analyzed using principal component analysis (PCA) and self-organizing maps (SOM). These findings lay the procedure for establishing the optimal substrate specifications as an essential prerequisite for future studies aiming at developing the feasibility of molecular imprinting for SERS diagnostic applications and the subsequent delivery of advanced, highly selective, and sensitive sensing devices and analytical platforms.

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Methods in Raman spectroscopy for saliva studies – a review

Cited by 33 publications

References 359 publications

Superhydrophobic 3D‐Assembled Metallic Nanoparticles for Trace Chemical Enrichment in SERS Sensing

Superhydrophobic 3D‐Assembled Metallic Nanoparticles for Trace Chemical Enrichment in SERS Sensing

Label‐free laser spectroscopy for respiratory virus detection: A review

Optimization of Nanosubstrates toward Molecularly Surface-Functionalized Raman Spectroscopy

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