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.

Analysis of cranberry proanthocyanidins using UPLC-ion mobility-high-resolution mass spectrometry.

Cranberry proanthocyanidin oligomers were investigated using ultra performance liquid chromatography-ion mobility-high-resolution mass spectrometry (UPLC-IM-HRMS). A total of 304 individual A-type and B-type proanthocyanidins, including 40 trimers, 68 tetramers, 53 pentamers, 54 hexamers, 49 heptamers, 28 octamers, and 12 nonamers, were characterized. A-type proanthocyanidins appeared to dominate the cranberry proanthocyanidins. As the degree of polymerization increased, the abundance of molecules with multiple A-type double inter-flavan linkage or having doubly charged ions also increased. Under the same charge state, the drift times of proanthocyanidin ions increased with their degree of polymerization or the number of double inter-flavan linkages. For the same proanthocyanidin molecules, doubly charged ions had shorter drift times compared to their singly charged counterparts, which lead to separated trendlines in the ion mobility-mass plot. While consistent ion mobility was observed for most proanthocyanidins with the same degree of polymerization, coeluted isomeric ions of trimer and tetramer were detected by their unique drift times. Incorporation of ion mobility into HRMS proved to be of great value to characterize and analyze proanthocyanidins from complex sample matrices. Graphical abstract.

Sensitive voltammetric sensor based on isopropanol-Nafion-PSS-GR nanocomposite modified glassy carbon electrode for determination of clenbuterol in pork.

In the present study, poly(sodium 4-styrenesulfonate) (PSS) functionalized graphene (GR) was synthesised via a simple one-step chemical reduction of exfoliated graphite oxides in the presence of PSS. Characterisation of as-made nanocomposite using Fourier transform infrared spectroscopy (FT-IR) and ultraviolet and visible spectroscopy (UV-vis) clearly demonstrate the successful attachment of PSS to graphene sheets. A novel clenbuterol (CLB) electrochemical sensor was fabricated based on isopropanol-Nafion-PSS-GR composite film modified glassy carbon electrode. In the Britton-Robinson buffer (pH 1.2), the sensor exhibited superior electrocatalytic activity towards the oxidation of CLB. Applying linear sweep voltammetry, a good linear relationship of the oxidation peak current with respect to concentrations of CLB cross the range of 7.5 × 10(-8)-2.5 × 10(-5)mol L(-1) and a detection limit of 2.2 × 10(-8) mol L(-1) were achieved. The proposed method was successfully applied for the determination of CLB in pork.

Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks.

Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. Different network configurations that used multiple network models with single output (Uni-TCCCN) and single networks with multiple outputs (Multi-TCCCN) were compared. Comparative studies were made by using Latin-partitions and leave-one-out cross-validation methods. Results showed that multiple networks with single output predicted generally better than single network with multiple outputs. Better results with TCCCN models were obtained compared with conventional back propagation neural networks (BPNNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, NIR spectra from powdered rhubarb samples were classified by a TCCCN model with 100% accuracy.

SIMPLISMA and ALS applied to two-way nonlinear wavelet compressed ion mobility spectra of chemical warfare agent simulants.

Ion mobility spectrometry is a rapid scanning measurement method for which compression methods that facilitate the handling of large collections of data are beneficial. Peak distortion in reconstructed ion mobility spectra from linear wavelet compression is problematic in that artifact peaks may cause false positive alarms. Peak shifting also may cause false alarms if target peaks shift out of or interfering peaks shift into detection windows. Nonlinear wavelet compression (NLWC) preserves peak shape and can lessen the degree of distortion, shifting, and artifact peaks in the reconstructed spectra. NLWC was applied to achieve high compression and fidelity in the reconstructed spectra. Another benefit is that NLWC improves signal-to-noise ratios and thus the models built from compressed data are improved. By compressing both the drift time order and the spectrum acquisition order, greater compressions maybe achieved. A two-way nonlinear wavelet compression method that incorporates alternating least squares (2W-NLWC-ALS) algorithm was devised by applying ALS to partially reconstructed wavelet coefficients generated from two-way NLWC. The number of components in a data set can be determined automatically using ASIMPLISMA. The smaller ALS models are saved as the final compressed data and can be used to reconstruct the entire data set efficiently without maintaining the compressed wavelet coefficient matrix of the original data set. The 2W-NLWC-ALS algorithm provides greater compression ratios compared to regular wavelet compression and interpretable models. Using this method, large volumes of data can be acquired and easily evaluated through a simple compressed model. A compression ratio of 510 ppm, root-mean-square error (E(RMS)) of 6.3 mV (full-scale signal is usually 1 V or larger), and relative root-mean-square error (RE(RMS)) of 1.62% were achieved for data sets collected by CAM. A compression ratio of 46 ppm, E(RMS) of 9.2 mV, and RE(RMS) of 0.42% were achieved for data sets collected with an ITEMISER instrument. The 2W-NLWC-ALS algorithm is an efficient compression method that provides the benefits of a simple model.