Pytochemical profile of Aloe ferox Mill. across different regions within South Africa

Background: Aloe ferox is an indigenous medicinal plant that is widely used for its various medicinal and pharmacological properties. Despite the medicinal importance and various applications of the species, it is surprising that little is known about the extent of geographical differences in its major chemical compounds. Also, the correlation between different geographic regions and variations in plant phytochemicals has received less attention. Aim: This study sought to investigate the presence of biologically active compounds in the leaf extracts of A. ferox from different geographical regions across South Africa. Setting: This study was set in different regions within South Africa. Methods: Phytochemical screening was performed qualitatively using established standard procedures involving chemical reagents such as hexane, chloroform and methanol and a series of reactions to determine the presence of phytocompounds of biological importance. Results: The study revealed that A. ferox leaves possess several classes of phytocompounds such as alkaloids, tannins, terpenoids, glycosides, phenolics, flavonoids, saponins and fixed oils and fats across various samples. Mucilage was absent across the samples. Conclusion: The study revealed eight classes of phytochemical compounds present on A. ferox leaves in three different geographic regions, which is consistent with the previous studies; however, further research is needed to enhance the study through qualitative research, gas chromatography–mass spectrometry and high-performance liquid chromatography analyses to validate phytochemical variations and their therapeutic effects. Contribution: This study contributes to the existing knowledge of the therapeutic Aloe genus

Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Soil visible-near infrared diffuse reflectance spectroscopy (vis-NIR DRS) has become an important area of research in the fields of remote and proximal soil sensing. The technique is considered to be particularly useful for acquiring data for soil digital mapping, precision agriculture and soil survey. In this study, 1581 soil samples were collected from 14 provinces in China, including Tibet, Xinjiang, Heilongjiang, and Hainan. The samples represent 16 soil groups of the Genetic Soil Classification of China. After air-drying and sieving, the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer. All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses. The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification. The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils. The results on the classification of the spectra are comparable to the results of other similar research. Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression (PLSR). This combination significantly improved the predictions of soil organic matter (R 2 = 0.899; RPD = 3.158) compared with using PLSR alone (R 2 = 0.697; RPD = 1.817)