Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer

Recently there has been upsurge in reports that illicit seizures of cocaine and heroin have been adulterated with fentanyl. Surface-enhanced Raman spectroscopy (SERS) provides a useful alternative to current screening procedures that permits detection of trace levels of fentanyl in mixtures. Samples are solubilized and allowed to interact with aggregated colloidal nanostars to produce a rapid and sensitive assay. In this study, we present the quantitative determination of fentanyl in heroin and cocaine using SERS, using a point-and-shoot handheld Raman system. Our protocol is optimized to detect pure fentanyl down to 0.20 ± 0.06 ng/mL and can also distinguish pure cocaine and heroin at ng/mL levels. Multiplex analysis of mixtures is enabled by combining SERS detection with principal component analysis and super partial least squares regression discriminate analysis (SPLS-DA), which allow for the determination of fentanyl as low as 0.05% in simulated seized heroin and 0.10% in simulated seized cocaine samples.

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Droplet based microfluidics: spectroscopic characterization of levofloxacin and its SERS detection

Physical Chemistry Chemical Physics ◽

10.1039/c4cp04970e ◽

2015 ◽

Vol 17 (33) ◽

pp. 21236-21242 ◽

Cited By ~ 30

Author(s):

I. J. Hidi ◽

M. Jahn ◽

K. Weber ◽

D. Cialla-May ◽

J. Popp

Keyword(s):

Aqueous Solution ◽

Raman Spectroscopy ◽

Silver Nanoparticles ◽

Spectroscopic Characterization ◽

Surface Enhanced Raman Spectroscopy ◽

Surface Enhanced ◽

Surface Enhanced Raman ◽

Sers Detection

The determination of the absorption behavior of levofloxacin (levaquin) on the surface of silver nanoparticles and its determination in aqueous solution by droplet based microfluidics combined with surface enhanced Raman spectroscopy.

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Quantitative detection of dithiocarbamate pesticides by surface-enhanced Raman spectroscopy combined with an exhaustive peak-seeking method

Analytical Methods ◽

10.1039/d0ay01953d ◽

2021 ◽

Vol 13 (12) ◽

pp. 1479-1488

Author(s):

Qiaoling Wei ◽

Liangdong Zhang ◽

Chunfeng Song ◽

Hongfu Yuan ◽

Xiaoyu Li

Keyword(s):

Raman Spectroscopy ◽

Detection Method ◽

Surface Enhanced Raman Spectroscopy ◽

Quantitative Detection ◽

Surface Enhanced ◽

Surface Enhanced Raman ◽

Sers Detection ◽

Good Signal

A surface-enhanced Raman spectroscopy (SERS) detection method for dithiocarbamate (DTC) with good signal repeatability and a wide linear quantitative range is developed combined with an exhaustive peak-seeking method.

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Bacillus thuringiensis Cells Selectively Captured by Phages and Identified by Surface Enhanced Raman Spectroscopy Technique

Micromachines ◽

10.3390/mi12020100 ◽

2021 ◽

Vol 12 (2) ◽

pp. 100

Author(s):

Salvatore Almaviva ◽

Antonio Palucci ◽

Eleonora Aruffo ◽

Alessandro Rufoloni ◽

Antonia Lai

Keyword(s):

Raman Spectroscopy ◽

Bacillus Thuringiensis ◽

Principal Component Regression ◽

Principal Component ◽

Surface Enhanced Raman Spectroscopy ◽

Water Evaporation ◽

Least Squares Regression ◽

Surface Enhanced ◽

Surface Enhanced Raman ◽

Using Data

In this work, the results on the detection and identification of Bacillus thuringiensis (Bt) cells by using surface-enhanced Raman spectroscopy (SERS) are presented. Bt has been chosen as a harmless surrogate of the pathogen Bacillus anthracis (Ba) responsible for the deadly Anthrax disease, because of their genetic similarities. Drops of 200 μL of Bt suspensions, with concentrations 102 CFU/mL, 104 CFU/mL, 106 CFU/mL, were deposited on a SERS chip and sampled after water evaporation. To minimize the contribution to the SERS data given by naturally occurring interferents present in a real scenario, the SERS chip was functionalized with specific phage receptors BtCS33, that bind Bt (or Ba) cells to the SERS surface and allow to rinse the chip removing unwanted contaminants. Different chemometric approaches were applied to the SERS data to classify spectra from Bt-contaminated and uncontaminated areas of the chip: Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA). The first two was tested and trained by using data from both contaminated and un-contaminated chips, the last was trained by using data from un-contaminated chips only and tested with all the available data. All of them were able to correctly classify the SERS spectra with great accuracy, the last being suitable for an automated recognition procedure.

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