Phytochemical Analysis最新文献

Introduction: Crocin-I, a water-soluble carotenoid pigment, is an important coloring constituent in gardenia fruit. It has wide application in various industries such as food, medicine, chemical industry, and so on. So the content of crocin-I plays a key role in evaluating the quality of gardenia.

Objective: We assessed crocin-I content in gardenia with a rapid, nondestructive, and convenient method.

Method: The data of gardenia samples were scanned under a portable visible-near-infrared (Vis-NIR) hyperspectral imaging (HSI) in the spectral range of 400-1000 nm. Afterward, the spectral data along with image-related information, encompassing color and texture, were extracted from the HSI. Based on a single information and its fusion at different fusion levels (low-level, traditional mid-level fusion, and an improved mid-level fusion), partial least squares regression (PLSR) prediction models were established and compared.

Result: The results demonstrated the superiority of data fusion, which ingeniously combined spectra and image data. Compared with individual information sources, the traditional mid-level fusion model showed a robust predictive ability. The correlation coefficient of the prediction set (R_p), the root mean square error of prediction (RMSEP), and the ratios of performance to deviation (RPDP) of the model were 0.901, 0.962, and 2.262, respectively.

Conclusion: This study highlights the effectiveness of the data fusion method, showcasing its capacity to significantly enhance the prediction accuracy of crocin-I content in gardenia through the integration of hyperspectral mapping data. The findings of this research are anticipated to serve as a valuable reference for predicting the active ingredients of other Chinese herbal medicines.

Introduction: Dangshen (DS) has been used for hundreds of years as a traditional Chinese medicine. It has a wide range of biological activities. Flavonoids are one of the important bioactive components with strong free radical scavenging and antioxidant capacity in DS. However, the biosynthesis process of flavonoids in DS remains unclear.

Objective: The aim of this study was to understand the biosynthesis molecular mechanism of flavonoids in DS.

Methods: In this study, metabolomics research and transcriptome sequencing for DS were carried out. Transcript and metabolite profiles were generated by high-throughput RNA sequencing (RNA-seq) data analysis and liquid chromatography-tandem mass spectrometry, respectively.

Results: In total, 256 metabolites were identified in the root, stem, leaf, and flower of DS using untargeted metabolomics. Among them, 55 flavonoids, including pinobanksin, butein, fustin, pelargonidin, apigenin, luteolin, and eriodictyol, were closely related to flavonoid metabolism, and most of them were upregulated in different tissues of DS. Furthermore, the differentially expressed genes identified by transcriptomics were mainly enriched in the biosynthesis of flavonoid, isoflavonoid, flavone, and flavonol. A number of genes, including ANS, CCOAOMT, CHI, CHS, CYP75B1, CYP75A, CYP93B2_16, CYP98A/C3'H, DFR, F3H, FLS, and HCT, may regulate the production of flavonoids in different tissues of DS. An integrated analysis of transcriptome and metabolome revealed the flavonoid biosynthetic network in DS and elucidated the diversity of flavonoid biosynthetic pathway in roots, stems, leaves, and flowers of DS.

Conclusion: Our findings provide a molecular basis and new insights into flavonoid biosynthesis in DS and lay the foundation for breeding new valuable DS cultivars.

Introduction: Corydalis DC., the largest genus of Papaveraceae, comprises numerous species known for their abundant alkaloid content and historical use in clinical medicine. Recently, a new species of genus Corydalis named Corydalis huangshanensis Lu Q. Huang & H. S. Peng was discovered in the Huangshan Mountains of Anhui Province, China.

Objective: To compare the chemical characteristics of C. huangshanensis and other 13 Corydalis species, aiming to elucidate the potential medicinal value of this new species.

Materials and methods: The chemical constituents of C. huangshanensis and other 13 medicinal plants of genus Corydalis were analyzed using ultra-high-performance liquid chromatography Q-Exactive Plus hybrid quadrupole-Orbitrap mass spectrometer (Q-Orbitrap) mass technology. The differences in the alkaloids in the 14 species were distinguished by chemometrics.

Results: The mass spectrometry fragmentation information and relative content of 72 alkaloids were obtained. Orthogonal partial least squares discriminant analysis (OPLS-DA) and cluster heat mapping analysis showed that these 14 species were divided into two groups. The clustering relationship between C. huangshanensis and C. decumbens (Thunb.) Pers. was similar, exhibiting similar chemical compositions and characteristics. These results indicate the potential pharmacological effects of C. huangshanensis.

Conclusion: This study enhances our understanding of the chemical classification of Corydalis and provides a basis for speculations on the medicinal value of C. huangshanensis.

Introduction: Magnoliae officinalis cortex (MOC) is an important traditional Chinese medicine (TCM), and both raw and stir-fried MOC were commonly used in clinic.

Objectives: This study aimed to discriminate MOC and MOC stir-fried with ginger juice (MOCG) using an integrated approach combining liquid chromatography/mass spectrometry (LC/MS), gas chromatography/mass spectrometry (GC/MS), intelligent sensors, and chemometrics.

Methods: The sensory characters of the samples were digitalized using intelligent sensors, i.e., colorimeter, electronic nose, and electronic tongue. Meanwhile, the chemical profiles of the samples were analyzed using LC/MS and GC/MS methods. Chemometric models were constructed to discriminate samples of MOC and MOCG based on not only the sensory data but also the chemical data.

Results: The differential sensory characters (L* and b* from colorimeter, ANS from electronic tongue, W1S and W2S from electronic nose) and the differential chemical compounds (26 and 11 compounds from LC/MS and GC/MS, respectively) were discovered between MOC and MOCG. Furthermore, twelve differential compounds showed good relations with differential sensory characters. Finally, artificial neural network models were established to discriminate samples of MOC and MOCG, in which W1S, W2S, ANS, b*, and 10 differential compounds were among the top 10 important variables, respectively.

Conclusion: Samples of MOC and MOCG can be discriminated not only by the digitalized data of color, taste, and scent detected by intelligent sensors but also by chemical information obtained from LC/MS and GC/MS using chemometrics. The variations in sensory characters and chemical compounds between MOC and MOCG partially resulted from the Maillard reaction products and the oxidation of some compounds in the stir-frying process.

Introduction: Kratom (leaves from Mitragyna speciosa Korth.; Rubiaceae) is a herbal medicine known for its analgesic properties and psychoactive effects. Kratom in Thailand is currently legal; however, it is prohibited in some countries and considered a narcotic plant.

Objective: Our aim was to establish a reliable, simple, and rapid method for quantifying mitragynine in Kratom leaves and related products through a combination of high-performance thin-layer chromatography (HPTLC) and densitometry.

Methodology: A densitometric HPTLC method was developed and validated in terms of specificity, linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, precision, and robustness. The fingerprints of kratom leaves, Mitragyna spp., and related products were constructed.

Results: For HPTLC, samples were applied to silica gel 60 F₂₅₄ plates, and the mobile phase comprised n-hexane, ethyl acetate, and triethylamine (1:1:0.15, v/v/v). Densitometric detection was carried out under ultraviolet light at a wavelength of 226 nm. The validated method exhibited a range of 14.31-143.10 μg/mL, yielding a correlation coefficient of 0.9993. Spiked recovery rates were within a range of 98.3%-100.9%, and the LOD and LOQ were 3.80 and 11.53 μg/mL, respectively. Kratom samples were analyzed with the developed method, and the correlation coefficient was 0.9641, compared to the high-performance liquid chromatography-diode-array detection (HPLC-DAD) method. The HPTLC fingerprints displayed a distinctive pattern, facilitating discrimination among different plant parts and Mitragyna spp.

Conclusion: The established method offers the advantages of simplicity, ease of use, and speed of analysis, serving as a practical alternative for mitragynine quantification in kratom leaf and its related products.

Introduction: Stemona tuberosa Lour. (ST) is a significant traditional Chinese medicine (TCM) renowned for its antitussive and insecticidal properties. ST is commonly subjected to processing in clinical practice before being utilized as a medicinal substance. Currently, the customary technique for processing ST is honey-fried. Nevertheless, the specific variations in chemical constituents of ST before and after honey-fried remain unclear.

Objective: This work aimed to analyze the variations in chemical constituents of ST before and after honey-fried and to study the distribution of differential markers in the roots.

Methods: UPLC-Orbitrap Fusion MS combined with molecular network analysis was used to analyze the metabolome of ST and honey-fried ST (HST) and to screen the differential metabolites by multivariate statistical analysis. Spatial metabolomics was applied to study the distribution of differential metabolites by desorption electrospray ionization mass spectrometry imaging (DESI-MSI).

Results: The ST and HST exhibited notable disparities, with 56 and 61 chemical constituents found from each, respectively. After processing, the types of alkaloids decreased, and 12 differential metabolites were screened from the common compounds. The notable component variations were epibisdehydro-tuberostemonine J, neostenine, tuberostemonine, croomine, neotuberostemonine, and so forth. MSI visualized the spatial distribution of differential metabolites.

Conclusions: Our research provided a rapid and effective visualization method for the identification and spatial distribution of metabolites in ST. Compared with the traditional method, this method offered more convincing data supporting the processing mechanism investigations of Stemona tuberosa from a macroscopic perspective.

Introduction: Cannabis sativa is a highly versatile plant with a long history of cultivation and domestication. It produces multiple compounds that exert distinct and valuable therapeutic effects by modulating diverse biological systems, including the endocannabinoid system (ECS). Access to standardized, metabolically diverse, and reproducible C. sativa chemotypes and chemovars is essential for physicians to optimize individualized patient treatment and for industries to conduct drug-discovery campaigns.

Objective: This study aimed to characterize and assess the phytochemical diversity of C. sativa chemotypes in diverse ecological regions of Colombia, South America.

Methodology: Ten cannabinoids and 23 terpenes were measured using liquid and gas chromatography, in addition to other phenotypic traits, in 156 C. sativa plants that were grown in diverse ecological regions in Colombia, a hotspot for global biodiversity.

Results: Our results reveal significant phytochemical diversity in Colombian-grown C. sativa plants, with four distinct chemotypes based on cannabinoid profile. The significant amount of usually uncommon terpenes suggests that Colombia's environments may have unique capabilities that allow the plant to express these compounds. Colombia's diverse climates offer enormous cultivation potential, making it a key player in both domestic and international medicinal and recreational C. sativa trade.

Conclusion: These findings underscore Colombia's capacity to pioneer global C. sativa production diversification, particularly in South America with new emerging markets.

Introduction: Andrographolide is a bioactive component found in the medicinal herb Andrographis paniculata (Burm. f.) Wall. ex Nees (Family-Acanthaceae) is well-known for its ability to cure liver disorders and as a bitter tonic.

Objective: In this study, the rate of degradation of andrographolide was examined over the course of a year of storage.

Materials and methods: New and old (1-year storage) A. paniculata powder samples were used in the study. High-performance liquid chromatography (HPLC) was used to assess the concentration of andrographolide after its extraction using ethanol as the solvent.

Results: The findings demonstrated a 69.26% progressive deterioration of andrographolide over the storage period. Temperature and crystallinity are two factors that affect how quickly andrographolide degrades.

Conclusion: The results emphasize how crucial it is to retain the effectiveness of A. paniculata extract by avoiding prolonged storage or by providing ideal storage conditions.

Introduction: The genus Salvia L., a member of the family Lamiaceae, is a keystone genus with a wide range of medicinal properties. It possesses a rich metabolite source that has long been used to treat different disorders.

Objectives: Due to a deficiency of untargeted metabolomic profiling in the genus Salvia, this work attempts to investigate a comprehensive mass spectral library matching, computational data annotations, exclusive biomarkers, specific chemotypes, intraspecific metabolite profile variation, and metabolite enrichment by a case study of five medicinal species of Salvia.

Material and methods: Aerial parts of each species were subjected to QTRAP liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow based on untargeted metabolites. A comprehensive and multivariate analysis was acquired on the metabolite dataset utilizing MetaboAnalyst 6.0 and the Global Natural Products Social Molecular Networking (GNPS) Web Platform.

Results: The untargeted approach empowered the identification of 117 metabolites by library matching and 92 nodes annotated by automated matching. A machine learning algorithm as substructural topic modeling, MS2LDA, was further implemented to explore the metabolite substructures, resulting in four Mass2Motifs. The automated library newly discovered a total of 23 metabolites. In addition, 87 verified biomarkers of library matching, 58 biomarkers of GNPS annotations, and 11 specific chemotypes were screened.

Conclusion: Integrative spectral library matching and automated annotation by the GNPS platform provide comprehensive metabolite profiling through a workflow. In addition, QTRAP LC-MS/MS with multivariate analysis unveiled reliable information about inter and intraspecific levels of differentiation. The rigorous investigation of metabolite profiling presents a large-scale overview and new insights for chemotaxonomy and pharmaceutical studies.

Introduction: Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Chinese herbal medicines are limited by small sample sizes, high dimensionality, and overfitting.

Objectives: This study aims to use metabolomics data to develop HerbMet, a high-performance artificial intelligence system for accurately identifying Chinese herbal medicines, particularly those from different species of the same genus.

Methods: We propose HerbMet, an AI-based system for accurately identifying Chinese herbal medicines. HerbMet employs a 1D-ResNet architecture to extract discriminative features from input samples and uses a multilayer perceptron for classification. Additionally, we design the double dropout regularization module to alleviate overfitting and improve model's performance.

Results: Compared to 10 commonly used machine learning and deep learning methods, HerbMet achieves superior accuracy and robustness, with an accuracy of 0.9571 and an F1-score of 0.9542 for distinguishing seven similar Panax ginseng species. After feature selection by 25 different feature ranking techniques in combination with prior knowledge, we obtained 100% accuracy and an F1-score for discriminating P. ginseng species. Furthermore, HerbMet exhibits acceptable inference speed and computational costs compared to existing approaches on both CPU and GPU.

Conclusions: HerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real-world scenarios, eliminating the need for feature ranking and selection in classical machine learning-based methods.