Rapid Identification of Peucedanum Praeruptorum Dunn and its Adulterants by Hand-Held near-Infrared Spectroscopy

A Review on the Morphology, Cultivation, Identification, Phytochemistry, and Pharmacology of Kitagawia praeruptora (Dunn) Pimenov

Kitagawia praeruptora (Dunn) Pimenov, commonly known as Qianhu in China, is a widely used folk Chinese herbal medicine. This article reviews its botanical traits, ethnopharmacology, cultivation techniques, identification, phytochemical compositions, and pharmacological effects. Over 70 coumarin compounds, including simple coumarins, pyranocoumarins, and furanocoumarins, have been isolated within this plant. Additionally, K. praeruptora contains other components such as flavonoids, fatty acids, benzoic acids, and sterols. This information highlights the importance of utilizing active ingredients and excavating pharmacological effects. With its remarkable versatility, K. praeruptora exhibits a wide range of pharmacological effects. It has been found to possess expectorant and bronchodilator properties, cardiovascular protection, antimicrobial and antioxidant activities, anti-tumor effects, and even antidiabetic properties. It is recommended to focus on the development of new drugs that leverage the active ingredients of K. praeruptora and explore its potential for new clinical applications and holistic utilization.

Rapid and Green Methods for Qualitative Classification of Polygonati Rhizoma and Polygonati Odorati Rhizoma Using a Handheld near Infrared Instrument

The confusing use of Polygonati Rhizoma (PR) and Polygonati Odorati Rhizoma (POR) poses an unpredictable threat to the health of consumers. Sensitive, nondestructive, rapid, and multicomponent techniques for their detection are sought after. In this study, a low-cost, short-wavelength (898–1668 nm), and handheld near-infrared (NIR) spectrometer combined with multivariate spectral evaluation methods was used to establish calibration models for identifying PR and POR. NIR spectra were treated with a standard normal variate (SNV) before performing chemometric approaches. Then principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were tested for calibration model development. The PCA results showed that spectral differences existed between the two herbs. However, the evaluation techniques could not separate them with the required accuracy. The PLS-DA calibration model, on the other hand, could separate the two herbs according to their spectral information with the prediction accuracy of >98.3%. Thus, it has been proven that a rapid, green, and low-cost method to support on-site and practical inspection through a handheld NIR instrument has been established to identify PR and POR and ensure the safety of the clinical medication.

Determination of Characteristic Volatile Component Fingerprint of Peucedanum Praeruptorum Dunn at Different Harvest Periods Based on HS-GC-IMS

BACKGROUND

Peucedanum praeruptorum Dunn (PPD) is a Chinese herbal medicine with medicinal value. Clinical studies have shown that PPD has protective effects against wind-heat, cough, asthma, cardiovascular diseases, and cancer. Therefore, cultivation of PPD is becoming more common. However, it has been difficult to determine the optimal harvest period for botanical Chinese medicines such as PPD.

OBJECTIVES

Odor characteristics are directly related to the chemical components contained in traditional Chinese medicines. In particular, for traditional Chinese medicines such as PPD that are rich in volatile components, higher quality usually means more beneficial volatile components. The purpose of this study was to analyze changes in PPD volatile components across different harvest periods, and provide the basis for the identification of the ideal harvest period to ensure PPD quality.

METHODS

We measured the volatile components of PPD at different harvest periods using HS-GC-IMS to characterize its volatile component fingerprint at different harvest periods.

RESULTS

We identified 80 volatile components in PPD across five harvest periods, and combined complex heatmap and PCA methods distinguish the characteristics of the different harvest periods, and used ion mobility spectrometry to determine the volatile organic compounds (VOCs), which mainly included compounds such as olefins, esters, alcohols, aldehydes, and ketones, and determined that the abundance of volatile components reached a peak in December.

CONCLUSIONS

The fingerprint determination of characteristic volatile components based on HS-GC-IMS can distinguish PPD in different harvest periods.

HIGHLIGHTS

We used HS-GC-IMS to determine the characteristic fingerprint of volatile components from PPD across different harvest periods. This approach differs from past studies, which have determined the optimal harvest time of medicinal materials based on only the content of a single active ingredient.