Fingerprint profile and efficacy-associated markers of Nigella sativa oil for geographical origin determination using targeted and untargeted HPTLC-multivariate analysis

Validated High-Performance Thin-Layer Chromatography-Mass Spectrometry Method and Stability Study of Linalool in the Volatile Oil of the Rhizomes of Homalomena aromatica Schott

Homalomena aromatica is a herb of tremendous ethnomedicinal importance to various communities residing in northeast India. In this study, a high-performance thin-layer chromatography-based densitometric method was developed for identification, quantification and stability study of linalool. Mass spectrometry was hyphenated to HPTLC for streamlining the method. The stability of linalool was studied by analyzing the effect of acid, base, UV, sunlight, thermal stress and H2O2 on linalool. The chromatographic plates were developed to a height of 70 mm in toluene:ethyl acetate solvent system at a ratio of 9.5:0.5 and visualized with p-anisaldehyde reagent. The developed method was found to be precise, accurate and reproducible according to International Conference on Harmonization guidelines, and compact bands of linalool were observed at Rf of 0.351 ± 0.001. The content of linalool in the volatile oil of H. aromatica was found to be 58% v/v. By application of the hyphenated MS technique, linalool was identified at m/z 137, (M + H)+. It was observed that acidic pH has the highest effect on linalool with a percentage degradation of 65. The developed method can be used in the analysis and quality control of herbal materials and volatile oils containing linalool and quality control of rhizomes of H. aromatica.

Juniperus horizontalis Moench: Chemical Composition, Herbicidal and Insecticidal Activities of Its Essential Oil and of Its Main Component, Sabinene

The composition, herbicidal, larvicidal, and toxic activities of Juniperus horizontalis Moench essential oil and sabinene, its main component, were evaluated. The seed germination percentage and root length of eight different plant species (crops and weeds) were measured for in vitro herbicidal activity tests. Different doses (100, 50, 10, 5, 1, 0.5 µg/mL) of the samples were applied to seeds for 120 h. The same doses were applied to test the toxicity of the samples on Tubifex tubifex (sludge worm) and Chironomus aprilinus (blood worm). Four doses (435, 652.5, 870, and 1740) of samples were in a larvicidal test on Tenebrio molitor L. (mealworm), and bioassays were checked after 24 and 48 h. The analysis of the oil allowed for the identification of dominant components as sabinene (38.7%), α-pinene (10.0%), elemol (8.6%), γ-terpinene (8.3%), limonene (7.8%) and α-thujene (5.3%). The results showed that the effect of oil on root length inhibition was significant in all crop species. The doses which affected root growth were not toxic to Tubifex tubifex and Chironomus aprilinus. Finally, the obtained results in the larvicidal bioassay indicate that the potential of J. horizontalis in yellow mealworm survivorship limitation can be a starting point for future research.

Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.

Quality control of the traditional herbs and herbal products: a review

Background

Herbal medicinal material and product need is increasing, and with this increase in the need, it is very much an essential requirement to maintain the quality of them.

Main body

The quality of the herbals is altered by various physical, chemical, and geographical aspects which contribute to the quality of these materials. Apart from that, adulteration is also an increasing concern when it comes to herbal material quality. Various chemical and phytochemical test, analytical techniques, and hyphenated analytical techniques are used for determining the quality aspects of the herbal materials in the herbal pharmaceuticals.

Conclusion

These techniques can be used as quality control tool in assessing the quality of herbal materials and herbal pharmaceuticals.

Compositional Analysis of Non-Polar and Polar Metabolites in 14 Soybeans Using Spectroscopy and Chromatography Tools

There has been significant interest in soybean oil, fatty acid, and sugar composition to develop new value-added soybean products. Thus, compositional analysis is critical for developing value-added soybeans. In the present study, we showed simple screening tools (near infrared spectroscopy (NIR) and high-performance thin layer chromatography (HPTLC)) coupled with multivariate analysis for the sample classification of 14 soybeans as a proof-of-concept. We further determined major non-polar and polar metabolites responsible for differences between different soybeans using gas and ion chromatography. These differences in soybean profiles were attributed to lower levels of total oil content in wild soybeans (~9%) versus cultivated soybeans (16%–22%). In addition, higher levels of linolenic acid (~17%) and stachyose (~53%) were determined in wild type, whereas higher levels of oleic acid (~19%) and sucrose (~59%) were detected in cultivated soybeans. Interestingly, one cultivated soybean had a desirable sugar profile with a high amount of sucrose (86%) and a low abundance of stachyose (9%). The correlation studies showed a positive correlation between oil and soluble sugars (R² = 0.80) and negative correlations between methyl linolenate and soluble sugars (R² = −0.79), oil (R² = −0.94), and methyl oleate (R² = −0.94) content. Both polar and non-polar metabolites showed significant differences in wild and cultivated soybeans.