A fast and straightforward reversed-phase dispersive liquid-liquid microextraction (RP-DLLME) using a deep eutectic solvent (DES) procedure to determine free tryptophan in vegetable oils was developed. The influence of eight variables affecting the RP-DLLME efficiency has been studied by a multivariate approach. A Plackett-Burman design for screening the most influential variables followed by a central composite response surface methodology led to an optimum RP-DLLME setup for a 1 g oil sample: 9 mL hexane as the diluting solvent, vortex extraction with 0.45 mL of DES (choline chloride-urea) at 40 °C, without addition of salt, and centrifugation at 6000 rpm for 4.0 min. The reconstituted extract was directly injected into a high-performance liquid chromatography (HPLC) system working in the diode array mode. At the studied concentration levels, the obtained method detection limits (MDL) was 11 mg/kg, linearity in matrix-matched standards was R2 ≥ 0.997, relative standard deviations (RSD) was 7.8%, and average recovery was 93%. The combined use of the recently developed DES -based RP-DLLME and HPLC provides an innovative, efficient, cost-effective, and more sustainable method for the extraction and quantification of free tryptophan in oily food matrices. The method was employed to analyze cold-pressed oils from nine vegetables (Brazil nut, almond, cashew, hazelnut, peanut, pumpkin, sesame, sunflower, and walnut) for the first time. The results showed that free tryptophan was present in the range of 11-38 mg/100 g. This article is important for its contributions to the field of food analysis, and for its development of a new and efficient method for the determination of free tryptophan in complex matrices, which has the potential to be applied to other analytes and sample types.

Synthetic cathinones are a class of new psychoactive substances (NPS), an emerging group of analogues to traditional illicit drugs which are functionalized to circumvent legal regulations. The analytical investigation of NPS by traditional methods, such as gas chromatography-mass spectrometry (GC-MS), is challenging because newly emerging NPS may not yet appear in spectral libraries and because of the inability to determine certain positional isomers. Low-field or \"benchtop\" proton nuclear magnetic resonance spectroscopy (NMR) is an alternative that provides significant qualitative information but is particularly susceptible to matrix interferences. To this end, the development of a Sequential Injection Analysis (SIA) method which uses solid-phase extraction (SPE) to remove interfering matrix components prior to NMR determination is described. Factors including the type of SPE sorbent, column dimensions, and sample loading and elution conditions were examined. Several cathinone simulants (primary, secondary, and tertiary amines), \"DEA exempt\" cathinone standards, as well as authentic case samples were studied. The selectivity of the SIA-NMR-UV method was investigated against a broad range of \"cutting agents\" and was found to successfully remove all compounds tested with the exception of other basic drugs (e.g., acetaminophen). The limit of detection and reproducibility of the method were optimized using a Plackett-Burman screening design and Sequential Simplex optimization. Using a UV detector for dual (in series) quantification, the multivariate-optimized method produced a method limit of detection (3σ) for the cathinone simulant Phenylpropanolamine (PPA) of 23 μmol L-1, and a calibration model, in terms of UV peak area, of Area = 0.19 [PPA, mmol L-1] - 0.04. The optimized method generated ~2 mL of waste per day, and had a footprint of ~1 m2 Finally, the multivariate-optimized SIA-NMR-UV method was successfully applied to several more case samples and the cathinones were definitively identified.

An environmentally friendly micro-solid phase extraction (μ-SPE) method utilizing a plant based nanocomposite as a sorbent for determination of trace level beta blockers (ß-blockers) in hospital wastewater prior to Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. μ-SPE extraction conditions were evaluated using a multivariate chemometric approach. Rice husk silica-carbon nanocomposite (5-20Wt.% Fe) in glycerol were synthesized via hydrolytic sol-gel method. The nanosorbent were fully characterized and then evaluated for μ-SPE of trace level ß-blockers in hospital wastewater. To establish the best extraction conditions at minimal experimental cost, multivariate techniques based on fractional factorial (FFD) and central composite designs (CCD) with desirability function (DF) were used to optimize the extraction conditions. Experimental results showed good agreement with predicted values and logical DF was realized at relatively low extraction time. Under optimized conditions, good linearity ranges (0.02-5.0μgL-1) with correlation of determinations higher than 0.9954 were obtained. The limits of detection and quantitation for the five ß-blockers (atenolol, alprenolol, pindolol, acebutolol and propranolol) ranged from 4.0-6.4 and 13.0-19.0ngL-1, respectively. Inter-day and intra-day precision (percent relative standard deviations, n=5) were lower than 8.3% while relative recoveries for hospital wastewater samples (80.6-105.1%) were in satisfactory ranges. This experimental approach therefore, demonstrated simplicity, reduction in the experimental runs, effectively increased sensitivity of LC-MS/MS and was hence suitable for complex matrix sample analysis.