Detection and classification of fentanyl and its precursors by surface-enhanced Raman spectroscopy

The Analyst ◽  
2020 ◽  
Vol 145 (9) ◽  
pp. 3440-3446 ◽  
Author(s):  
Rustin Mirsafavi ◽  
Martin Moskovits ◽  
Carl Meinhart
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Surface Enhanced Raman Spectroscopy ◽  
Analytical Technique ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

An analytical technique combining partial least squares-discriminant analysis, surface-enhanced Raman spectroscopy (SERS), and microfluidics for the detection and classification of fentanyl and its chemical precursors.

scholarly journals Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy

Molecules ◽  
2018 ◽  
Vol 23 (8) ◽  
pp. 1949 ◽  
Author(s):  
Pengcheng Nie ◽  
Tao Dong ◽  
Shupei Xiao ◽  
Lei Lin ◽  
Yong He ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Pesticide Residues ◽  
Surface Enhanced Raman Spectroscopy ◽  
Red Soil ◽  
Soil Extracts ◽  
Relative Standard ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Thiabendazole (TBZ) is widely used in sclerotium blight, downy mildew as well as root rot disease prevention and treatment in plant. The indiscriminate use of TBZ causes the excess pesticide residues in soil, which leads to soil hardening and environmental pollution. Therefore, it is important to accurately monitor whether the TBZ residue in soil exceeds the standard. For this study, density functional theory (DFT) was used to theoretically analyze the molecular structure of TBZ, gold nanoparticles (AuNPs) were used to enhance the detection signal of surface-enhanced Raman spectroscopy (SERS) and the TBZ residue in red soil extracts was quantitatively determined by SERS. As a result, the theoretical Raman peaks of TBZ calculated by DFT were basically consistent with the measured results. Moreover, 784, 1008, 1270, 1328, 1406 and 1576 cm−1 could be determined as the TBZ characteristic peaks in soil and the limits of detection (LOD) could reach 0.1 mg/L. Also, there was a good linear correlation between the intensity of Raman peaks and TBZ concentration in soil (784 cm−1: y = 672.26x + 5748.4, R2 = 0.9948; 1008 cm−1: y = 1155.4x + 8740.2, R2 = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation (RSD) ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300–1700 cm−1 SERS of TBZ were analyzed by the partial least squares (PLS) and backward interval partial least squares (biPLS). Also, the prediction accuracy of TBZ in soil (Rp2 = 0.9769, RMSEP = 0.556 mg/L, RPD = 5.97) was the highest when the original spectra were pretreated by standard normal variation (SNV) and then modeled by PLS. In summary, the TBZ in red soil extracts could be quantitatively determined by SERS based on AuNPs, which was beneficial to provide a new, rapid and accurate scheme for the detection of pesticide residues in soil.

scholarly journals Rapid uropathogen identification using surface enhanced Raman spectroscopy active filters

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon D. Dryden ◽  
Salzitsa Anastasova ◽  
Giovanni Satta ◽  
Alex J. Thompson ◽  
Daniel R. Leff ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Bacterial Infections ◽  
Surface Enhanced Raman Spectroscopy ◽  
Active Filters ◽  
Raman Signal ◽  
Rapid Diagnostics ◽  
Bacterial Classification ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

AbstractUrinary tract infection is one of the most common bacterial infections leading to increased morbidity, mortality and societal costs. Current diagnostics exacerbate this problem due to an inability to provide timely pathogen identification. Surface enhanced Raman spectroscopy (SERS) has the potential to overcome these issues by providing immediate bacterial classification. To date, achieving accurate classification has required technically complicated processes to capture pathogens, which has precluded the integration of SERS into rapid diagnostics. This work demonstrates that gold-coated membrane filters capture and aggregate bacteria, separating them from urine, while also providing Raman signal enhancement. An optimal gold coating thickness of 50 nm was demonstrated, and the diagnostic performance of the SERS-active filters was assessed using phantom urine infection samples at clinically relevant concentrations (105 CFU/ml). Infected and uninfected (control) samples were identified with an accuracy of 91.1%. Amongst infected samples only, classification of three bacteria (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) was achieved at a rate of 91.6%.

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