Chemical Composition and Antioxidant Activity of Essential Oils from Populations of Baccharis dracunculifolia DC. in Southern Brazil

HIGHLIGHTS Essential oils from populations of B. dracunculifolia were investigated. β-pinene and (E)-nerolidol were the main compounds in B. dracunculifolia populations. The difference in the chemical profile of the essential oils is quantitative only. There is a negative correlation between the antioxidant activity and spathulenol.

Abstract Baccharis dracunculifolia DC. is a Brazilian native plant, presenting wide chemical diversity and numerous pharmaceutical and industrial applications. This research assessed the yield, antioxidant activity and the chemical similarity of essential oils from 10 populations of B. dracunculifolia in the state of Paraná, southern Brazil. The extraction of the volatile compounds was carried out by hydrodistillation, the chemical composition was determined by GC/FID and GC/MS and the antioxidant activity by the DPPH method. The essential oil yield of wild B. dracunculifolia populations ranged from 0.14 to 0.87%. The oils were predominantly composed of oxygenated sesquiterpenes (34.16 - 51.01%), monoterpene hydrocarbons (18.02 - 46.17%) and sesquiterpenes hydrocarbons (9.60 - 17.70%). The major compounds found in all populations were β-pinene (7.65 - 29.8%) and (E)-nerolidol (9.11 - 21.68%). Essential oil solutions (20%) from different populations presented antioxidant capacity ranging from 27.78 to 91.67%. A negative correlation was found between the antioxidant activity and spathulenol (r = -0.696). Multivariate analyses separated the populations into three groups: (1) low concentrations of α-pinene (2.02 - 2.06%), (2) high concentrations of α-pinene (4.17 - 4.61%) and β-pinene (22.54 - 29.80%), and (3) intermediate concentrations of α-pinene (2.38 - 3.31%), β-pinene (12.77 - 19.03%) and spathulenol (6.02 - 9.06%).

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data.

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant's resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis' chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds (λ = 280-400ηm), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz).

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in ß-carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis-ß-carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (red-fleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.