Headspace Adsorptive Microextraction Analysis of Oregano Fragrance Using Polyaniline-Nylon-6 Nanocomposite, GC-MS, and Multivariate Curve Resolution

Essential Oils as Multicomponent Mixtures and Their Potential for Human Health and Well-Being

Essential oils (EOs) and their individual volatile organic constituents have been an inherent part of our civilization for thousands of years. They are widely used as fragrances in perfumes and cosmetics and contribute to a healthy diet, but also act as active ingredients of pharmaceutical products. Their antibacterial, antiviral, and anti-inflammatory properties have qualified EOs early on for both, the causal and symptomatic therapy of a number of diseases, but also for prevention. Obtained from natural, mostly plant materials, EOs constitute a typical example of a multicomponent mixture (more than one constituent substances, MOCS) with up to several hundreds of individual compounds, which in a sophisticated composition make up the property of a particular complete EO. The integrative use of EOs as MOCS will play a major role in human and veterinary medicine now and in the future and is already widely used in some cases, e.g., in aromatherapy for the treatment of psychosomatic complaints, for inhalation in the treatment of respiratory diseases, or topically administered to manage adverse skin diseases. The diversity of molecules with different functionalities exhibits a broad range of multiple physical and chemical properties, which are the base of their multi-target activity as opposed to single isolated compounds. Whether and how such a broad-spectrum effect is reflected in natural mixtures and which kind of pharmacological potential they provide will be considered in the context of ONE Health in more detail in this review.

Random Forest Processing of Direct Analysis in Real-Time Mass Spectrometric Data Enables Species Identification of Psychoactive Plants from Their Headspace Chemical Signatures

The United Nations Office on Drugs and Crime has designated several "legal highs" as "plants of concern" because of the dangers associated with their increasing recreational abuse. Routine identification of these products is hampered by the difficulty in distinguishing them from innocuous plant materials such as foods, herbs, and spices. It is demonstrated here that several of these products have unique but consistent headspace chemical profiles and that multivariate statistical analysis processing of their chemical signatures can be used to accurately identify the species of plants from which the materials are derived. For this study, the headspace volatiles of several species were analyzed by direct analysis in real-time high-resolution mass spectrometry (DART-HRMS). These species include Althaea officinalis, Calea zacatechichi, Cannabis indica, Cannabis sativa, Echinopsis pachanoi, Lactuca virosa, Leonotis leonurus, Mimosa hositlis, Mitragyna speciosa, Ocimum basilicum, Origanum vulgare, Piper methysticum, Salvia divinorum, Turnera diffusa, and Voacanga africana. The results of the DART-HRMS analysis revealed intraspecies similarities and interspecies differences. Exploratory statistical analysis of the data using principal component analysis and global t-distributed stochastic neighbor embedding showed clustering of like species and separation of different species. This led to the use of supervised random forest (RF), which resulted in a model with 99% accuracy. A conformal predictor based on the RF classifier was created and proved to be valid for a significance level of 8% with an efficiency of 0.1, an observed fuzziness of 0, and an error rate of 0. The variables used for the statistical analysis processing were ranked in terms of the ability to enable clustering and discrimination between species using principal component analysis-variable importance of projection scores and RF variable importance indices. The variables that ranked the highest were then identified as m/z values consistent with molecules previously identified in plant material. This technique therefore shows proof-of-concept for the creation of a database for the detection and identification of plant-based legal highs through headspace analysis.

Sampling methods for the study of volatile profile of PDO wine vinegars. A comparison using multivariate data analysis

High-quality wine vinegars have been registered in Spain under protected designation of origin (PDO): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". The raw material, production and aging processes determine their quality and their aromatic composition. Vinegar volatile profile is usually analyzed by gas chromatography-mass spectrometry (GC-MS), being necessary a previous extraction step. Thus, three different sampling methods (Headspace solid phase microextraction "HS-SPME", Headspace stir bar sorptive extraction "HSSE" and Dynamic headspace extraction "DHS") were studied for the analysis of the volatile composition of Spanish PDO wine vinegars. Multivariate curve resolution (MCR) was used to solve chromatographic problems, improving the results obtained. Principal component analysis (PCA) showed that not all the sampling methods were equally suitable for the characterization and differentiation between PDOs and categories, being HSSE the technique that made able the best vinegar characterization.