Radical scavenging activity and metabolomic profiling study of ylang-ylang essential oils based on high-performance thin-layer chromatography and multivariate statistical analysis

Cananga odorata L., Illicium verum, and Foeniculum vulgare essential oils impair the reproduction of the cattle tick Rhipicephalus microplus (Acari: Ixodidae)

This study aimed to evaluate the acaricidal potential of the essential oils of Cananga odorata L. (ylang-ylang), Illicium verum (star anise), and Foeniculum vulgare (sweet fennel) in Rhipicephalus microplus engorged females. The essential oils were extracted by hydrodistillation, and the females were immersed in each essential oil at concentrations ranging from 10 μL/mL to 50 μL/mL, using 10% dimethyl sulfoxide as a solvent. Tick mortality was evaluated after seven and 14 days, and the following parameters were determined: female weight before oviposition, preoviposition period, egg mass weight, egg production index, incubation period, hatching rate, fecundity, percentage of reduction in oviposition, percentage of reduction in hatching and product efficacy. Lethal concentration (LC50) values were 34.42 μL/mL (28.34-40.86) for C. odorata and 40.60 μL/mL (32.58-58.21) for F. vulgare. This parameter was not calculated for I. verum because there was insufficient mortality at the concentrations evaluated. The greatest efficacy was observed in the group treated with 40 μL/mL F. vulgare essential oil (97.17%), followed by I. verum (91.35%) and C. odorata (86.19%) at the same concentration. Efficacy results were associated with a greater reduction in egg mass weight and lower hatching rates. Although the essential oils caused a slight acaricidal impact, they showed significant effects on the reproductive performance of R. microplus engorged females. Thus, the results of this study indicate that these essential oils can be used in the development of new acaricidal formulations.

Quantitative NMR Spectrometry of Phenylpropanoids, including Isoeugenol in Herbs, Spices, and Essential Oils

Isoeugenol (2-methoxy-4-(1-propenyl)phenol) has been recently classified as possibly carcinogenic to humans (Group 2B) by the International Agency for Research on Cancer (IARC). This study conducted an analysis of isoeugenol in common herbs and spices, including basil, cinnamon, ginger, and nutmeg, using 1H nuclear magnetic resonance (NMR) spectrometry. Additionally, over 1300 coffee samples were analysed by 1H-NMR for isoeugenol, but it was not detected in any of the analysed samples. Various essential oils, including nutmeg, basil, clove, sweet flag, and ylang-ylang oils, were examined for isoeugenol content. Out of the twelve nutmeg oils tested, four contained isoeugenol, with concentrations ranging from 3.68 ± 0.09 g/kg to 11.2 ± 0.10 g/kg. However, isoeugenol was not detected in the essential oils of calamus, basil, ylang-ylang, and clove using NMR spectrometry. These findings warrant critical evaluation of the previous literature, given reports of high isoeugenol levels in some of these matrices. A toxicological assessment has determined that there is no risk to human health by exposure to isoeugenol via nutmeg essential oils.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

HPLC-DAD Analysis, SFE-CO2 Extraction, and Antibacterial Activity on Bioactive Compounds from Mosla chinensis Maxim

Mosla chinensis Maxim is an annual herb with many potential purposes in agricultural, industrial, and pharmaceutical fields. At present, the extract of the whole plant from M. chinensis has been proven to demonstrate antifungal, antioxidant, and anti-inflammatory activities. Previous studies focused on the enzyme pretreatment in hydrodistillation from M. chinensis. However, organic solvent or supercritical fluid carbon dioxide extraction (SFE-CO2) methods, which are commonly utilized in industry, have seldom been studied and cannot provide multiple evaluations of yield. In this work, we analysed compounds from M. chinensis by HPLC-DAD, discussed n-hexane extraction, and conducted further investigations on SFE-CO2 through the design of response surface methodology (RSM). The sample obtained from pilot-scale SFE-CO2 was also tested against nine kinds of microorganisms. Single-factor results revealed that the extraction rates from M. chinensis by steam distillation, n-hexane extraction, and SFE-CO2 were 1%, 2.09%, and 3.26%, respectively. RSM results showed a significant improvement in extraction rate through optimising pressure and time, and the interaction of both factors was more important than that of temperature-pressure and temperature-time. A pilot-scale test with an extraction rate of 3.34% indicated that the predicted RSM condition was operable. In addition, samples from the pilot-scale SFE-CO2 showed antibacterial effects against three previously unreported bacteria (Gardnerella vaginalis, methicillin-resistant Staphylococcus aureus, and Propionibacterium acnes). These results fill the gap in previous research and provide more information for the application and development of M. chinensis in the future.

Profiling of Aroma-Active Compounds in Ylang-Ylang Essential Oils by Aroma Extract Dilution Analysis (AEDA) and Chemometric Methods

The aroma-active compounds in the extra, first, and third grades of ylang-ylang essential oils (YYEO) from Comoros and Madagascar were identified by gas chromatography-mass spectrometry with olfactometry (GC-MS/O) using an aroma extract dilution analysis (AEDA) technique. In the previous study, the authors investigated differences in volatile compound profiles between YYEO of different grades and regions using GC coupled with a flame ionization detector (FID) and GC-MS. This study follows up with identification of the aroma-active compounds present in YYEO of various grades from both origins and to profile the aroma of those oils. For the first time, principal component analysis (PCA) on AEDA logarithmic flavor dilution (LFD) data was performed, in comparison with the corresponding PCA on GC-FID-MS data. Based on AEDA data, 21 aroma-active compounds were found across all samples and grades of YYEO, with 8 common ones previously identified by GC-FID. Linalool had the highest odor activity and is the major component of YYEO, followed by geraniol, although the latter only appeared as a much smaller peak in the chromatogram. Other trace compounds such as eugenol and vanillin were also found to be significant to the aroma of YYEO. Using PCA on resulting LFD data, YYEO from Comoros were found to have spicier odor qualities as compared to those from Madagascar. The main contributors that determine the difference in a spicy aroma profile of Comoros and Madagascar oils are vanillin, methyl eugenol, and trans-cinnamyl acetate.