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 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.