Cannabis sativa L. is a dioecious plant belonging to the Cannabaceae family. The main phytochemicals that are found in this plant are represented by cannabinoids, flavones, and terpenes. Some biological activities of cannabinoids are known to be enhanced by the presence of terpenes and flavonoids in the extracts, due to a synergistic action. In the light of all the above, the present study was aimed at the multi-component analysis of the bioactive compounds present in fibre-type C. sativa (hemp) inflorescences of different varieties by means of innovative HPLC and GC methods. In particular, the profiling of non-psychoactive cannabinoids was carried out by means of HPLC-UV/DAD, ESI-MS, and MS2. The content of prenylated flavones in hemp extracts, including cannflavins A and B, was also evaluated by HPLC. The study on Cannabis volatile compounds was performed by developing a new method based on headspace solid-phase microextraction (HS-SPME) coupled with GC-MS and GC-FID. Cannabidiolic acid (CBDA) and cannabidiol (CBD) were found to be the most abundant cannabinoids in the hemp samples analysed, while β-myrcene and β-caryophyllene were the major terpenes. As regards flavonoids, cannflavin A was observed to be the main compound in almost all the samples. The methods developed in this work are suitable for the comprehensive chemical analysis of both hemp plant material and related pharmaceutical or nutraceutical products in order to ensure their quality, efficacy, and safety.

Introduction: The growing consumer interest in "naturals" led to an increased application of essential oils (EOs). The market outbreak induced the intensification of EO adulterations, which could affect their quality.Objectives: Nowadays, little is known about the illegal practice of adulteration of EOs with vegetable oils. Therefore, the application of mid-infrared spectroscopy coupled with chemometrics was proposed for the detection of EO counterfeits.Materials and methods: Two EOs, three seed oils, and their mixtures were selected to build the adulteration model. EO-adulterant mixtures for model calibration and validation were analyzed by attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral data were analyzed with principal component analysis (PCA) and partial least-squares (PLS) regression.Results: PCA allowed the discrimination of the EO and adulterant percentages by explaining 97.47% of the total spectral variance with two principal components. A PLS regression model was generated with three factors explaining 97.73% and 99.69% of the total variance in X and Y, respectively. The root mean square error of calibration and the root mean square error of cross-validation were 0.918 and 1.049, respectively. The root mean square error of prediction value obtained from the external validation set was 1.588 and the coefficients of determination R 2 CAL and R 2 CV were 0.997 and 0.996, respectively. Conclusions:The results highlighted the robustness of the developed method in quantifying counterfeits in the range from 0 to 50% of adulterants, disregarding the type of EO and adulterant employed. The present work offers a research advance and makes an important impact in phytochemistry, revealing an easily applicable method for EO quality assessment.

Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen responsible for a wide range of clinical conditions, from mild infections to life-threatening nosocomial biofilm-associated diseases, which are particularly severe in susceptible individuals. The aim of this in vitro study was to assess the effects of an Albanian propolis on several virulence-related factors of P. aeruginosa, such as growth ability, biofilm formation, extracellular DNA (eDNA) release and phenazine production. To this end, propolis was processed using three different solvents and the extracted polyphenolic compounds were identified by means of high performance liquid chromatography coupled to electrospray ionization mass spectrometry (HPLC-ESI-MS) analysis. As assessed by a bioluminescence-based assay, among the three propolis extracts, the ethanol (EtOH) extract was the most effective in inhibiting both microbial growth and biofilm formation, followed by propylene glycol (PG) and polyethylene glycol 400 (PEG 400) propolis extracts. Furthermore, Pseudomonas exposure to propolis EtOH extract caused a decrease in eDNA release and phenazine production. Finally, caffeic acid phenethyl ester (CAPE) and quercetin decreased upon propolis EtOH extract exposure to bacteria. Overall, our data add new insights on the anti-microbial properties of a natural compound, such as propolis against P. aeruginosa. The potential implications of these findings will be discussed.

Cannabis sativa L. is a dioecious plant belonging to the Cannabaceae family. The discovery of the presence of many biologically-active metabolites (cannabinoids) in fibre-type Cannabis (hemp) has recently given rise to the valorisation of this variety. In this context, the present study was aimed at the multi-component analysis and determination of the main non-psychoactive cannabinoids (cannabidiol, cannabidiolic acid, cannabigerol and cannabigerolic acid) in female inflorescences of different hemp varieties by means of 13C quantitative nuclear magnetic resonance spectroscopy (qNMR). The method proposed here for the first time for the determination of cannabinoids provided reliable results in a competitive time with respect to the more consolidated HPLC technique. In fact, it gave sufficiently precise and sensitive results, with LOQ values lower than 750 μg/mL, which is easily achievable with concentrated extracts, without affecting the quality of 13C-qNMR spectra. In conclusion, this method can be considered as a promising and appropriate tool for the comprehensive chemical analysis of bioactive cannabinoids in hemp and other derived products in order to ensure their quality, efficacy and safety.

The consumption of high-nutritional-value juice blends is increasing worldwide and, considering the large market volume, fraud and adulteration represent an ongoing problem. Therefore, advanced anti-fraud tools are needed. This study aims to verify the potential of 1H NMR combined with partial least squares regression (PLS) to determine the relative percentage of pure fruit juices in commercial blends. Apple, orange, pineapple, and pomegranate juices were selected to set up an experimental plan and then mixed in different proportions according to a central composite design (CCD). NOESY (nuclear Overhauser enhancement spectroscopy) experiments that suppress the water signal were used. Considering the high complexity of the spectra, it was necessary to pretreat and then analyze by chemometric tools the large amount of information contained in the raw data. PLS analysis was performed using venetian-blind internal cross-validation, and the model was established using different chemometric indicators (RMSEC, RMSECV, RMSEP, R2CAL, R2CV, R2PRED). PLS produced the best model, using five factors explaining 94.51 and 88.62% of the total variance in X and Y, respectively. The present work shows the feasibility and advantages of using 1H NMR spectral data in combination with multivariate analysis to develop and optimize calibration models potentially useful for detecting fruit juice adulteration.