CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China.
Molecules. 2022 Jun 30;27(13):4225. doi: 10.3390/molecules27134225.
Ginseng, which contains abundant ginsenosides, grows mainly in the Jilin, Liaoning, and Heilongjiang in China. It has been reported that the quality and traits of ginsengs from different origins were greatly different. To date, the accurate prediction of the origins of ginseng samples is still a challenge. Here, we integrated ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) with a support vector machine (SVM) for rapid discrimination and prediction of ginseng from the three main regions where it is cultivated in China. Firstly, we develop a stable and reliable UHPLC-Q-TOF-MS method to obtain robust information for 31 batches of ginseng samples after reasonable optimization. Subsequently, a rapid pre-processing method was established for the rapid screening and identification of 69 characteristic ginsenosides in 31 batches ginseng samples from three different origins. The SVM model successfully distinguished ginseng origin, and the accuracy of SVM model was improved from 83% to 100% by optimizing the normalization method. Six crucial quality markers for different origins of ginseng were screened using a permutation importance algorithm in the SVM model. In addition, in order to validate the method, eight batches of test samples were used to predict the regions of cultivation of ginseng using the SVM model based on the six selected quality markers. As a result, the proposed strategy was suitable for the discrimination and prediction of the origin of ginseng samples.
人参主要生长在中国的吉林、辽宁和黑龙江,其含有丰富的人参皂苷。已有报道称,不同产地的人参质量和特性存在显著差异。迄今为止,准确预测人参样品的产地仍然是一个挑战。在这里,我们整合了超高效液相色谱四极杆飞行时间质谱(UHPLC-Q-TOF-MS)和支持向量机(SVM),用于快速区分和预测中国三个主要种植区的人参。首先,我们开发了一种稳定可靠的 UHPLC-Q-TOF-MS 方法,经过合理优化,可获得 31 批人参样品的可靠信息。随后,建立了一种快速预处理方法,用于快速筛选和鉴定来自三个不同产地的 31 批人参样品中的 69 种特征性人参皂苷。SVM 模型成功区分了人参的产地,通过优化归一化方法,SVM 模型的准确性从 83%提高到 100%。利用 SVM 模型中的置换重要性算法筛选出 6 种不同产地人参的关键质量标志物。此外,为了验证该方法的适用性,我们使用 8 批测试样品,根据 6 种选定的质量标志物,使用 SVM 模型预测人参的种植区。结果表明,该策略适用于人参样品产地的鉴别和预测。