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In this work, a novel coupled approach MicroNIR/Chemometrics based on a miniaturized and portable spectrometer is proposed for the on site detection of amphetamines (AMP) in non pretreated oral fluids. In particular, the coupling of MicroNIR with chemometrics was investigated with the aim of developing a fast and accurate approach able to perform the on-site prediction of AMP abuse. A predictive model to be used in real cases was developed by collecting specimens from volunteers and spiked samples with increasing amounts of AMP were prepared to optimize calibration. Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLS) were involved to detect and quantify AMP. Results demonstrated that MicroNIR/Chemometric platform is statistically able to identify AMP abuse in simulated oral fluid samples containing, with the accuracy and sensitivity of the actual proposed official reference methods. The method was checked against false positive and true positive response and results proved to be those required for confirmatory analyses. This method would permit to simplify AMP abuse monitoring for roadside drug testing or workplace surveillance and may be of help at first aid points.

A liquid chromatography–tandem mass spectrometry analytical procedure has been developed for the detection and quantitative determination of underivatized amino acids at low concentrations in a Standard Reference Material—urban dust. In order to minimize interferences of other compounds, an accelerated solvent extraction followed by a solid phase extraction on two different cartridges was applied prior to LC–MS–MS. Fourteen amino acids were separated by high resolution liquid chromatography, detected and quantified by multiple reaction monitoring on a triple quadrupole. The proposed methodology has been applied for the first time on Standard Reference Material 1649a (urban dust) from the National Institute of Standards and Technology, that does not report certification values for these compounds. This methodology avoids the derivatization step and allows the amino acid quantification in a complex matrix, such as that of atmospheric particulate matter, and represent a good method suitable to analyze this class of compounds in atmospheric aerosol. The selected strategy demonstrated to be fit-for-purpose, by applying it to a real atmospheric sample with the aim to verify the efficacy of the study and to provide information about the organic matter content.

Airborne microorganisms, as bacteria and fungi, are ubiquitous components of the atmospheric aerosol particles. In this paper, we report a method for the simultaneous extraction, purification, separation, identification and quantification of ergosterol, mannitol and arabitol as biomarkers of fungal spores in bioaerosol particles. After sampling by a low volume sampler, filters were spiked with mannitol-13C and dehydrocholesterol as internal standards. Samples were then extracted by accelerated solvent extraction using pure ethanol. The extract was then passed through an amino cartridge and divided in two parts: the apolar fraction, released from the cartridge, was subjected to liquid liquid extraction (by n-hexane), while polar compounds, retained by the cartridge, were eluted by a mixture of methanol–water. The two fractions were joined and analyzed by HPLC equipped with two different columns in series, and coupled to a triple-quadrupole mass spectrometer with Atmospheric Pressure Chemical Ionization source. In addition, the same fractions were analyzed, after derivatization, by GC–MS. The results obtained by the two techniques were finally compared, showing good agreement between them. Last, the contents of the three biomarkers have been estimated in three atmospheric samples collected in a suburban/rural site and, using literature conversion factors, correlated to fungal biomass.