Ultraviolet resonance Raman spectroscopy for the detection of cocaine in oral fluid

D’Elia, Valentina; Montalvo, Gemma; García-Ruiz, Carmen; Ermolenkov, Vladimir V.; Ahmed, Yasmine; Lednev, Igor K.

doi:10.1016/j.saa.2017.07.010

Cited by 26 publications

(17 citation statements)

References 16 publications

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“…Therefore, a method which could assess the quality of a food product and perform a multicomponent characterization at the first level test represents an important issue when dealing with the monitoring of food quality or human health. For this reason, innovative screening systems, able to rapidly process samples without requiring any pretreatment or clean up, in a non-destructive way, are becoming more and more recommended (Dunn et al, 2011 ; Risoluti et al, 2016 , 2019a , b , c ; D'Elia et al, 2018 ). Reference analytical procedures for milk analyses usually require chromatographic techniques, such as High Performance Liquid Chromatography associated to Mass Spectrometry (HPLC-MS) (Chotyakul et al, 2014 ; Aiello et al, 2015 ; Rocchetti et al, 2020 ), Gas Chromatography coupled to Mass Spectrometry (GC-MS) (Marchetti et al, 2002 ; Materazzi and Risoluti, 2014 ; Teng et al, 2017 ), and Nuclear Magnetic Resonance (NMR) (Garcia et al, 2012 ; Crea et al, 2014 ; Santos et al, 2016 ; Aiello et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

2020

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In this work, an innovative screening platform based on MicroNIR and chemometrics is proposed for the on-site and contactless monitoring of the quality of milk using simultaneous multicomponent analysis. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that allows samples to be processed in a rapid and accurate way and to obtain in a single click a comprehensive characterization of the chemical composition of milk. To optimize the platform, milk specimens with different origins and compositions were considered and prediction models were developed by chemometric analysis of the NIR spectra using Partial Least Square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and validation was performed by comparing results of the novel platform with those obtained from the chromatographic analysis. Results demonstrated the ability of the platform to differentiate milk as a function of the distribution of fatty acids, providing a rapid and non-destructive method to assess the quality of milk and to avoid food adulteration.

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Section: Introductionmentioning

confidence: 99%

Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

2020

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“…Ultraviolet resonance Raman (UVRR) spectroscopy is a versatile spectroscopic technique with numerous fundamental and practical applications such as investigating biomolecular structure, [1][2][3][4] pharmaceutical and clinical applications, [5,6] forensics applications, [7,8] and trace explosive detection. [9][10][11] When the Raman excitation wavelength falls within an electronic transition, the Raman band intensities significantly increase and the Raman spectra can be used for trace chemical analysis.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

228‐nm quadrupled quasi‐three‐level Nd:GdVO₄ laser for ultraviolet resonance Raman spectroscopy of explosives and biological molecules

Bykov

Roppel

Mao

et al. 2020

J Raman Spectroscopy

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We describe a new compact diode-pumped solid-state frequency quadrupled quasi-three-level neodymium-doped gadolinium vanadate (Nd:GdVO 4) laser that generates $50 mW of 228-nm quasi-continuous wave light as ns pulses at a tunable kilohertz repetition rate. We developed two generations of this laser. The first generation has a high duty cycle and a tunable repetition rate. The second generation is optimized for maximum output power. We utilize these new lasers to measure ultraviolet resonance Raman (UVRR) spectra of many important chromophores that absorb in deep ultraviolet (UV). We demonstrate the utility of this excitation by measuring the 228-nm absolute differential Raman cross sections of explosives, peptides, aromatic amino acids, and DNA/RNA nucleotides. Deep UV excitation at 228 nm occurs within the π ! π* electronic transitions of these molecules. The 228-nm resonance excitation enhances the Raman intensities of vibrations of NO x groups, peptide bonds, aromatic amino acid side chains, and DNA/RNA nucleotides. The measured 228-nm UVRR cross sections of these molecules are 10 3-10 4 fold greater than those excited in the visible spectral region. These new lasers should be of great interest for UVRR spectroscopy and for other applications that benefit from compact, high average power deep UV laser light sources with low peak powers.

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“…Recently, Khandasammy et al reviewed emerging forensic applications of Raman spectroscopy in the fields of controlled substances, counterfeit pharmaceuticals, toxicology, explosives, gunshot residue, hair, fibers, paint, cosmetics, body fluids and anthropology. Raman spectroscopy has also been used for the identification of cocaine in textiles impregnated with the drug and in saliva . Most recently, Raman spectroscopy has been used for the analysis of biological trace evidence such as bloodstains, semen, vaginal fluid, oral fluid and sweat .…”

Section: Introductionmentioning

confidence: 99%

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

Al‐Hetlani

Halámková

Amin

et al. 2019

Journal of Biophotonics

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Raman spectroscopy has proven to be a valuable tool for analyzing various types of forensic evidence such as traces of body fluids. In this work, Raman spectroscopy was employed as a nondestructive technique for the analysis of dry traces of oral fluid to differentiate between smoker and nonsmoker donors with the aid of advanced statistical tools. A total of 32 oral fluid samples were collected from donors of differing gender, age and race and were subjected to Raman spectroscopic analysis. A genetic algorithm was used to determine eight spectral regions that contribute the most to the differentiation of smokers and nonsmokers. Thereafter, a classification model was developed based on the artificial neural network that showed 100% accuracy after external validation. The developed approach demonstrates great potential for the differentiation of smokers and nonsmokers based on the analysis of dry traces of oral fluid.

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Ultraviolet resonance Raman spectroscopy for the detection of cocaine in oral fluid

Cited by 26 publications

References 16 publications

Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

228‐nm quadrupled quasi‐three‐level Nd:GdVO₄ laser for ultraviolet resonance Raman spectroscopy of explosives and biological molecules

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

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Ultraviolet resonance Raman spectroscopy for the detection of cocaine in oral fluid

Cited by 26 publications

References 16 publications

Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

228‐nm quadrupled quasi‐three‐level Nd:GdVO4 laser for ultraviolet resonance Raman spectroscopy of explosives and biological molecules

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

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228‐nm quadrupled quasi‐three‐level Nd:GdVO₄ laser for ultraviolet resonance Raman spectroscopy of explosives and biological molecules