Identification of <i>Forsythia suspensa</i> (Thunb.) Vahl in different harvest periods using intelligent sensory technologies, HPLC characteristic fingerprint coupled with chemometrics
Meng Pan, Ya Fei Su, Xue Jing Liu, Si Qi Xiang, Yan Ding, Qin Li
Abstract
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 < 0.001) at different harvest times. Interestingly, the quality markers not only accurately reflected the maturity of FS but also showed close correlations with the colour parameters and sensory E-tongue responses. CONCLUSION: In our present investigation, bionic technologies, including a colorimeter, E-tongue analysis, and HPLC characteristic fingerprints, combined with chemometrics, were employed to develop a novel and accurate method for discriminating FS based on different harvest times.