Comprehensive quality evaluation of two different geography originated Angelica sinensis Radix based on potential production area development and resource protection

Gaining insights into nutrient and metal element distributions in radix Angelicae sinensis using mass spectrometry

Angelica sinensis is a medicine food homology plant. Its dried root (radix Angelicae sinensis, RAS) is rich in nutrients and bioactive ingredients. This study involved a quantitative analysis of 401 RAS material batches harvested from different geographical regions over a time span of four years. Quantitative statistical distributions of 24 nutrients of interest were determined using ultrahigh performance liquid chromatography-tandem mass spectrometry. The distribution features of 15 target metal elements were specified using inductively coupled plasma-mass spectrometry and atomic absorption spectroscopy methods. A new integrated bioaccessibility response method was introduced to address the statistical analysis of content responses of multiple nutrients through one multivariate compact area. This work not only provides new insights into the compositional ingredients of RAS material to assist in quality control but also helps in understanding the potential benefits of RAS material to human health and increasing its applications.

E-Nose and HS-SPME-GC-MS unveiling the scent signature of Ligusticum chuanxiong and its medicinal relatives

Introduction

To explore the origin and evolution of Ligusticum Chuanxiong, we conducted a component analysis of Ligusticum Chuanxiong and its medicinal relatives.

Methods

This study encompassed seven species from various origins, including Chuanxiong (Ligusticum chuanxiong Hort.), Gansu Chuanxiong (Ligusticum chuanxiong cv. Gansu), Yunnan Chuanxiong (Ligusticum chuanxiong cv. Yunnan), Japanese Chuanxiong (Cnidium officinale Makino), Fuxiong (Ligusticum sinense 'Fuxiong'), Gaoben (Ligusticum sinense), and Liaogaoben (Ligusticum jeholense), comprising 27 distinct materials. We employed headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) to identify various odor profiles from these species using electronic nose technology (E-nose). The method effectively identified volatile constituents in the leaves of these seven species.

Results

Results indicated that odor differences between L. chuanxiong and its medicinal relatives were predominantly observed in sensors W1W and W1S. Linear discriminant factor analysis (LDA) successfully distinguished five of the relatives; however, L. chuanxiong and L. sinense exhibited high odor similarity, limiting complete differentiation in some samples. HS-SPME-GC-MS identified a total of 118 volatile constituents, with eight differential volatiles identified: trans-Neocnidilide, β-Caryophyllene, β-Selinene, 5-Pentylcyclohexa-1,3-diene, (E)-Ligustilide, Butylphthalide, Neophytadiene, and Senkyunolide. Hierarchical cluster analysis (HCA) grouped L. chuanxiong, L. sinense, L. jeholense, and L. chuanxiong cv. Gansu together, highlighting the close relationship between L. chuanxiong and L. sinense. Joint analysis revealed a significant positive correlation between sensor W1W and the differential volatile component β-Caryophyllene, suggesting its potential for distinguishing closely related species.

Discussion

This study provides a foundational understanding of volatile components in the leaves of L. chuanxiong and its medicinal relatives using E-nose combined with HS-SPME-GC-MS, contributing to the discussion on their interspecific odor characteristics and relationships.

Visualizing the Spatial Distribution of Metabolites in Angelica sinensis Roots by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

INTRODUCTION

Angelica sinensis is one of the most popular traditional Chinese medicines (TCM) and has been extensively used to treat various diseases. Hundreds of endogenous ingredients have been isolated and identified from this herb, but their spatial distribution within the plant root is largely unknown.

OBJECTIVES

In this study, we tried to investigate and map within-tissue spatial distribution of metabolites in Angelica sinensis roots.

MATERIAL AND METHODS

After optimization of experiment conditions, the 1,5-diaminonaphthalene (1,5-DAN) was chosen as the matrix and was sprayed on the surface of root sections. Then matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was employed to perform in situ detection and obtain detail spatial distribution information of metabolites in Angelica sinensis roots.

RESULTS

The spatial distributions of a wide range of metabolites including organic acids, amino acids, oligosaccharides, and phospholipids were characterized and visualized in Angelica sinensis roots. Majority of these metabolites were located in the phloem and xylem, while ferulic acid was mainly present in the cork layer. The results revealed a dramatic metabolic heterogeneity among different regions of the roots and distinct spatial distribution patterns of different metabolites. Additionally, the metabolic pathways involved in the biosynthesis of choline were also successfully localized and visualized.

CONCLUSION

This study comprehensively characterized the spatial distribution of metabolites in Angelica sinensis roots, which would prompt the understanding of its chemical separation, biosynthesis, and pharmacological activities.

Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network

The authentic traditional Chinese medicines (TCMs) including Angelicae Sinensis Radix (ASR) are the representative of high-quality herbals in China. However, ASR from authentic region being adulterated or counterfeited is frequently occurring, and there is still a lack of rapid quality evaluation methods for identifying the authentic ASR. In this study, the color features of ASR were firstly characterized. The results showed that the authentic ASR cannot be fully identified by color characteristics. Then near-infrared (NIR) spectroscopy combined with Bayesian optimized long short-term memory (BO-LSTM) was used to evaluate the quality of ASR, and the performance of BO-LSTM with common classification and regression algorithms was compared. The results revealed that following the pretreatment of NIR spectra, the optimal NIR spectra combined with BO-LSTM not only successfully distinguished authentic, non-authentic, and adulterated ASR with 100 % accuracy, but also accurately predicted the adulteration concentration of authentic ASR (R2 > 0.99). Moreover, BO-LSTM demonstrated excellent performance in classification and regression compared with common algorithms (ANN, SVM, PLSR, etc.). Overall, the proposed strategy could quickly and accurately evaluate the quality of ASR, which provided a reference for other TCMs.