Abstract
Chemical composition of essential oils (EOs) of
This species is characterized by shrub or arboreal size, inhabiting water courses or dry land forest as in savannas and in the Amazon. This plant is almost completely glabrous, except for some floral characters like bracts (inner face of sepals and petal margins). Its leaves usually have visible tertiary and marginal ribs, and its flowers have chalice with conspicuous appendages, foliaceous, whose greatest width is positioned in the median region of its lengths. From the morphological point of view,
The chemical analysis of essential oils (EOs) obtained from
Results and Discussion
The plant material was collected in Salvaterra (Marajó Island, Brazilian Amazon) (Figure 1). The plant was monitored during an entire day of the months of January (rainy Amazon season) and July (dry Amazon season). For more information, see the Materials and Methods section.

To evaluate the differences in environmental conditions between January and July, the climate data rainfall, relative air humidity, and solar radiation were obtained from the website of the Instituto Nacional de Meteorologia (INMET, http://www.inmet.gov.br/portal/) of the Brazilian Government.
In January, the rainfall index (153.0 mm) was 4 times greater than in the dry period (35.8 mm). Moreover, the relative air humidity was 81.14% in January and 76.46% in July. Additionally, the solar radiation was higher in July (951.58 kJ/m2) than in January (747.54 kJ/m2). Thus, taking into account the climate parameters, the month of January belongs to rainy season and July to dry season.
The Brazilian Amazonian climate is characterized only by dry and rainy seasons. Due to the permanent humid and warm climate, the Amazon presents spatial and seasonal heterogeneity of rainfall. Based on the precipitation data of 2018, the rainy season occurred from December to April and the dry season from June to November. May was a period of transition between these 2 seasons. 3
In the circadian rhythm of the oil yield of
The EO yields from the circadian collections of rainy and dry months showed low correlation with the climate parameters: humidity, solar radiation, and temperature (Table 1). In summary, Pearson’s correlation analysis shows no correlation between quantitative parameters of in
Correlation Between the
A previous analysis of
The constituents of the oils were identified and quantified by gas chromatography coupled to mass spectrometry (GC-MS) and flame ionization detector (GC-FID), respectively. A total of 101 compounds were identified, representing an average of 95.3% of the composition of the total oils (Table 2). Oxygenated monoterpenes were predominant (20.4%-42.8%) followed by sesquiterpene hydrocarbons (16.8%-46.5%), oxygenated sesquiterpenes (13.9%-36.5%), and monoterpene hydrocarbons (6.6%-14.9%). The main constituents were 1,8-cineole (14.5%-33.0%) and limonene (5.4%-11.7%); followed by α-terpineol (3.5%-7.9%); and α-copaene (3.5%-7.3%), (
The oxygenated monoterpene 1,8-cineole was the main compound identified in the samples with amount ranged from 14.4% (January, 6

Circadian rhythm of 1,8-cineole content (left) and average during the collections of rainy and dry seasons (right).
The results of Figure 2 suggest that the quantitative variation in the chemical composition of the circadian rhythm could be attributed to the climatic changes, with a strong correlation observed in January. Also, it is possible to see in Table 3 the correlation with the climatic factors: a strong positive correlation between the temperature and the monoterpene hydrocarbons; a significant negative correlation of the sesquiterpene hydrocarbons and the solar radiation; and a positive correlation between the oxygenated sesquiterpenes and the solar radiation. These results were obtained by Pearson’s correlation coefficient analysis, involving the compound classes of the oils and the climatic parameters.
Correlation Between Chemical Compositions and the Environmental Conditions.
aSignificant at
It is known that, in addition to environmental factors, differences in oil yield and composition can be attributed to genetic, geographical, and invasive predators (herbivory and pathogens).
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For example, some Myrtaceae species have shown qualitative and quantitative variability in their EO compositions according to different collection sites. Oils of
The EO of
In order to evaluate the chemical variability during the circadian rhythm, the multivariate analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) techniques was applied to the primary constituents present in oils (content ≥1.5%). The HCA (Figure 3) performed with complete linkage and Euclidean distance showed the formation of 2 different groups with similarity of 19.70%. The groups were classified as follows: Group I characterized by the lowest amounts of 1,8-cineole (16.03%-18.67%), which is formed for the samples collected from daily cycle of the rainy season (January) and only 1 sample from dry season (July) collected at 6

Hierarchical cluster analysis of essential oils sampled in the circadian rhythms made with Euclidean distance and complete linkage.
These results were confirmed by the PCA (PC1 and PC2, Figure 4) which explained a proportional variance of 51.3% and 22.7%, respectively, representing a total of 73.0% of data variation. In other words, all samples collected from circadian rhythm of the rainy season (January) and 1 sample from dried season (July, 6

Principal component analysis of chemical compositions of essential oils sampled in a daily cycle of the rainy season (January) and dried season of
The chemical composition and biological activities of
The monoterpene 1,8-cineole (eucalyptol or 1,8-epoxy-
Conclusion
The environmental parameters did not show relationship with oil yield and 1,8-cineole content in the
The significant oil yield and higher amounts of 1,8-cineole obtained in the dry period indicates that this plant can be used as a renewable source of this compound. Due to the little qualitative variation of its EO chemical profile, the standardization and its economic exploration can be viable.
Materials and Methods
Plant Material and Climate Data
A wild specimen of
Extraction and Chemical Composition Analysis of the EO
The plant material was air-dried (2 days) at room temperature, after collection. Then, it was ground and submitted to hydrodistillation using a Clevenger-type apparatus (3 hours).
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The oils obtained were dried over anhydrous sodium sulfate and total oil yields were expressed as mL/100 g of the dried material.
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The chemical composition analysis was performed by GC-MS, using a Shimadzu instrument Model QP 2010 ultra (Shimadzu, Tokyo, Japan), equipped with a Rtx-5MS (30 m × 0.25 mm; 0.25 µm film thickness) fused silica capillary column (Restek, Bellefonte, United States). Helium was used as carrier gas adjusted to 1.0 mL/min at 57.5 kPa; split injection (split ratio 1:20) of 1 µL of hexane solution (oil 5 µL:hexane 500 µL); injector and interface temperature were 250°C; oven temperature programmed was 60°C to 240°C (3°C/min), followed by an isotherm of 10 minutes. Electron Impact Mass Spectrometry (EIMS): electron energy, 70 eV; ion source temperature was 200°C. The mass spectra were obtained by automatic scanning every 0.3 seconds, with mass fragments in the range of 35 to 400
Statistical Analysis
The multivariate analysis was performed by using as variables the EO constituents with content above 1.5%. The data matrix was standardized by subtracting the mean and then dividing it by the standard deviation. For HCA, the complete linkage method and the Euclidean distance were used. 9 All analyses were performed using the software Minitab (free 390 version, Minitab Inc., State College, PA, United States).
Footnotes
Acknowledgment
We are grateful to CNPq and CAPES, institutions of support for scientific research of the Brazilian Government.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We are grateful to CNPq and CAPES, institutions of support for scientific research of the Brazilian Government.
