Characterization of Leaf Extracts of Raddi by GC-MS and Chemometric Analysis.

BACKGROUND

Raddi belongs to Anacardiacea family and is widely known as "aroeira." This species originates from South America, and its extracts are used in folk medicine due to its therapeutic properties, which include antimicrobial, anti-inflammatory, and antipyretic effects. The complexity and variability of the chemical constitution of the herbal raw material establishes the quality of the respective herbal medicine products.

OBJECTIVE

Thus, the purpose of this study was to investigate the variability of the volatile compounds from leaves of .

MATERIALS AND METHODS

The samples were collected from different states of the Northeast region of Brazil and analyzed with a gas chromatograph coupled to a mass spectrometer (GC-MS). The collected data were analyzed using multivariate data analysis.

RESULTS

The samples' chromatograms, obtained by GC-MS, showed similar chemical profiles in a number of peaks, but some differences were observed in the intensity of these analytical markers. The chromatographic fingerprints obtained by GC-MS were suitable for discrimination of the samples; these results along with a statistical treatment (principal component analysis [PCA]) were used as a tool for comparative analysis between the different samples of .

CONCLUSION

The experimental data show that the PCA used in this study clustered the samples into groups with similar chemical profiles, which builds an appropriate approach to evaluate the similarity in the phytochemical pattern found in the different leaf samples.

SUMMARY

The leave extracts of were obtained by turbo-extractionThe extracts were partitioned with hexane and analyzed by GC-MSThe chromatographic data were analyzed using the principal component analysis (PCA)The PCA plots showed the main compounds (phellandrene, limonene, and carene), which were used to group the samples from a different geographical location in accordance to their chemical similarity. AL: Alagoas, BA: Bahia, CE: Ceará, CPETEC: Center for Weather Forecasting and Climate Studies, GC-MS: Gas chromatograph coupled to a mass spectrometer, MA: Maranhão, MVA: Multivariate data analysis, PB: Paraíba, PC1: Direction that describes the maximum variance of the original data, PC2: Maximum direction variance of the data in the subspace orthogonal to PC1, PCA: Principal component analysis, PE: Pernambuco, PI: Piauí, RN: Rio Grande do Norte, SE: Sergipe.