Identification of Forsythia suspensa (Thunb.) Vahl in different harvest periods using intelligent sensory technologies, HPLC characteristic fingerprint coupled with chemometrics

Introduction Forsythia suspensa (Thunb.) Vahl (FS), the fruit of Oleaceae plants, as a large part of traditional Chinese medicine, is classified as “Qingqiao (Q)” and “Laoqiao (L)” based on the harvest time. Because the maturation of FS is a gradual process, its accurate identification based on diff...

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Bibliographic Details
Published in:Phytochemical analysis 2022-04, Vol.33 (3), p.490-501
Main Authors: Pan, Meng Hua, Su, Ya Fei, Liu, Xue Jing, Xiang, Si Qi, Ding, Yan Xia, Li, Qin
Format: Article
Language:English
Subjects:
Bionics
Chemometrics
Chromatography, High Pressure Liquid - methods
Colorimetry
different harvest times
Dimensional analysis
Drugs, Chinese Herbal - chemistry
Electronic tongues
Fingerprints
Forsythia - chemistry
Forsythia suspensa
Forsythia suspensa (Thunb.) Vahl
Fruit - chemistry
Fruits
Herbal medicine
High performance liquid chromatography
HPLC characteristic fingerprint
intelligent sensory technologies
Liquid chromatography
Markers
Medicinal plants
Medicine, Chinese Traditional
Parameters
Rutin
Sensory properties
Statistical analysis
Statistical methods
Traditional Chinese medicine
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Summary:Introduction Forsythia suspensa (Thunb.) Vahl (FS), the fruit of Oleaceae plants, as a large part of traditional Chinese medicine, is classified as “Qingqiao (Q)” and “Laoqiao (L)” based on the harvest time. Because the maturation of FS is a gradual process, its accurate identification based on different maturity levels is an important issue. Objectives We suggest colorimetric, electronic tongue, and high‐performance liquid chromatography (HPLC) characteristic fingerprints to discriminate FS in different harvest periods. Material and methods First, FS fruits from different harvest times were collected, and then, their colour parameters, E‐tongue sensory properties, HPLC characteristic fingerprints, and contents of nominal ingredients were determined. Finally, multivariate statistical analyses, including three‐dimensional scatter plots, hierarchical cluster, principal component, linear discriminant, similarity, and partial least squares discriminant analyses were performed. Results The results demonstrated that the three experimental techniques could effectively discriminate FS based on different harvest times with 100% accuracy. Under the qualitative conditions, nine common peaks were identified in the HPLC fingerprints of 60 samples, among which, six peaks [variable importance in projection (VIP) > 1] could be used as index peaks for qualitative identification. In fact, the contents of quality marker components, including forsythin, phillygenin, rutin and forsythoside A, were significant different (P 
ISSN:0958-0344
1099-1565
DOI:10.1002/pca.3104