Materials and Reagents
A total of 359 main roots parts of P. notoginseng samples were collected from four different origins in Yunnan province, which include Honghe (HH), Kunming (KM), Qujing (QJ), and Wenshan (WS). Detailed information of P. notoginseng samples was shown in Fig. 1. All collected fresh samples were cleaned and dried at room temperature naturally, and the main roots, rhizome, and fibrous roots parts were separated and sealed for storage. Before the experiment, the main roots sample was grinded by Pulverizer (FW-100, Tianjin Huaxin Instrument Factory) and powder through 90 mesh sieves. Finally, dried in an oven at 50℃ and reserved under constant temperature situations for further analysis.
The standards were purchased through the Beijing Soledad Bao Technology Co (Beijing, P.R. China), including notoginsenoside R1 and ginsenosides Rg1, Rb1 (Batch number 1028C022, content ≥ 98%). Methanol and acetonitrile of chromatographically pure for HPLC were purchased from Sigma— Aldrich (Shanghai-China) and Merck & Co., Inc. (Kenilworth, NJ, USA), respectively. Analytic grade Ethanol reagent that was offered by Tianjin Zhiyuan Chemical Reagent Co., Ltd. Deionized (ultra-pure) water injected into the HPLC system was prepared by using a UPTL-II-40L system (Chengdu, China).
Phosphate-buffered saline (PBS) was purchased from Beijing Soledad Bao Technology Co. (Beijing, China). Rivaroxaban was purchased from Bayer Pharma AG (Berlin, Germany). The prothrombin time (PT) assay kit was purchased from Sysmex Corporation (Kobe, Japan).
Male rats (220-260g) were obtained from the Hunan Slake Jingda Experimental Animal Co., Ltd (License No. SCXK (Xiang)2019-0004). The animal research was approved through Yunnan University of Chinese Traditional Medicine, and housed at the experimental animal center of Yunnan University of Traditional Chinese Medicine. They were housed in a dark of air-conditioned room with controlled temperature is 23 ± 1℃, the humidity is 30–70%, and there is unlimited access to food and water. In addition, the animals were acclimated for no less than a week in the presence of any experiments.
Pharmacological Analysis
HPLC Determination and Saponin Content Analysis
All samples were analyzed by using an Agilent 1260 liquid chromatograph (Agilent Corporation, USA) coupled with Zorbax Eclipse Plus C18 column (4.6 mm × 250mm,5 µm, Agilent Corporation, USA). Mobile phase A was water, while mobile phase B was acetonitrile used to gradient elution. The gradient conditions were as follow: 0–12 min, 19% B, 12–60 min, 19%→36% B, 60–77 min, 36% B, 77–80 min, 36%-19%B, 80 min, 19% B. The flow rate was 0.6mL/min, with an injected volume of 10 µL, the column temperature is 30 ℃, and the samples were detected by absorption at 203 nm. The method validation according to ICH Harmonised Tripartite Guideline (2006) for measuring precision, repeatability and stability. Through Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004 A Chinese Pharmacopoeia Committee, Beijing, China) to analyze the similarity and fingerprint.
The sampling amount of each origin is based on the amount of crude drug 0.2 g was placed into a 10 mL centrifuge tube, to which 5 mL of methanol was added. After filtering through a nylon membrane filter (0.22 µm) as the test solution into 2 mL sample bottle. The mixed reference solution of notoginsenoside R1, ginsenoside Rg1 and ginsenoside Rb1 was prepared according to the standard of Chinese Pharmacopoeia 2020 Edition (Commission C.P., 2020), and identified through HPLC. Based on the standard curve of the control solution concentration, the test solution content was calculated.
Preparation Methods
The powder was obtained from 10 g of each sample, and 80 mL of 80% ethanol was added. Then, they were ultrasonic extraction for 30 min of each time, sucking filtration, repeated extraction, and the filtrates were combined. Reduced pressure distillation until solvent drying, we could obtain the total extract of P. notoginseng main roots. The PBS (pH = 7.4) was prepared with 0.01 M dibasic sodium phosphate, samples amount of each origin were converted at 0.2 g raw drug, and add 2 mL PBS was to total extract. The supernatant was isolated from 220–260 g of male rats which the strain was SD, anesthetized with intraperitoneal injection, 2 ml of blood was taken from the intraperitoneal vein (0.2 mL of sodium citrate + 1.8 mL of venous blood), gently reversed and separated within 1 h. It was prepared for plasma preparation of rats by centrifugation at 3000 r/min for 15 min. The positive control was prepared by diluting 10 mg/tablet of rivaroxaban to 0.001 mg·mL− 1.
Determination of the Prothrombin Time
Analysis was performed using the Sysmex CA-600 automated blood fluid analyzer (Sysmex Corporation, Kobe, Japan). The 200 µL of plasma was placed in 2 mL collection of blood vessels, the experimental group add 100 µL of samples, while the blank control group add 100 µL of PBS, and the positive control group add 100 µL of rivaroxaban dilution. Prothrombin time was determined by applying a fully automatic blood coagulation analyzer (CA-600, Sysmex, Japan). Determined three times in parallel per sample.
Correlation Coefficient Analysis
SPSS Statistic 21.0 was applied to process and analyze experimental data, whether saponin content was significantly different in P. notoginseng main roots of different origins. Data were examined for normality and homogeneity of variances, and a one-way variance comparison was conducted. Univariate homogeneity test in the observations, homogeneous variance if P > 0.05, and significant difference in univariate analysis if P < 0.05.
Spectral Analysis
ATR-FTIR Spectral Acquisition
FTIR spectra were collected with a Frontier FT spectrometer (PerkinElmer, USA) equipped with a deuterated triglycine sulfate (DTGS) detector, which coupled with ZnSe attenuated total reflectance accessory (Perkin Elmer, Norwalk, CT, USA). The instrument was preheated at 65% relative humidity for 30 min prior to the analysis. Furthermore, it was demanded that the indoor temperature at 25℃ and the relative humidity of under 45% be sustained throughout the whole experimentation of scanning. Reducing the absorption of CO2 and H2O when scanning the sample in order to remove the background. Scanned the spectral information in the absorbance of 4000 − 400 cm− 1. Each sample was scanned 16 times at a resolution of 4 cm− 1 and tested three times parallelly. Finally, the average spectrum was calculated to establish a model for the next analysis.
Data Preprocessing
The original spectrum has existed a lot of chemical information, but there were peak overlaps and interferences such as stray light, noise, and baseline drift (Y. Li et al, 2020). Therefore, the derivative (Arndt et al, 2020), multiplicative scatter correction (MSC) and standard normal variable (SNV) (Dhanoa et al, 1994), Savitzky-Golay (S-G) filtering(Savitzky and Golay 1964) and their combinations were applied for preprocessing to take the unnecessary signal variations away. Before preprocessing, applied MATLAB 2017a (the MathWorks) to divide the data into the test set and training set based on the classic Kernnard Stone (K-S) algorithm to eliminate human interference. Among them, 238 samples are used as the training set, and the other 121 samples are applied for external model verification. All pre-processing methods were carried out through SIMCA-P + 14.0 software (Umetrics, Umea, Sweden).
Selection of Feature Variable
The main purpose of variable selection is to select relevant and information-rich data to reduce the dimensionality and redundancy of the data. Besides, it could decrease irrelevant information's interference with the model, improve the efficiency of the model and the accuracy and reliability of the prediction consequences (Pei, Zhang and Wang 2020). The variable importance in the projection (VIP) method was the most commonly applied variable selection method in PLS models, and has been widely used to select important variables in PLS/PLS-DA models to reduce model dimensions and enhance interpretability (Galindo-Prieto, Trygg and Geladi 2017). The confidence interval of VIP was 95%. When the VIP value was > 1, indicating that the variable is important, it was from 0.5 to 1, the importance of the variable should be analyzed according to the specific problem. However, if the VIP value was < 0.5, it shows that the variable was not important (Liu et al, 2020). Therefore, the variables with VIP value > 1 in the 4000 − 400 bands were screened through SIMCA-P + 14.0 software (Umetrics, Umea, Sweden) for further modeling analysis.
The CARS algorithm is a newly established method for extracting wavenumbers of characteristic variables. Foremost, applied Monte Carlo sampling or random sampling to select a part of the samples in the calibration set, and then retain the maximum absolute value of the regression coefficient that is based on the PLS model through the adaptive weighted sampling method to evaluate each variable's importance. Finally, establishing models for each subset through the cross-validation, select the wavelength variable subset for the smallest value of root mean squares error of cross-validation (RMSECV) as the optimal subset (Li et al, 2009). The method was performed using MATLAB software (version R2017b, MathWorks, USA). In this study, the number of Monte Carlo Simulation was set to 500, the group number for cross-validation was set as 7-fold, and the pretreatment method was determined as center.
Pattern Recognition Technology
The PLS-DA as a method for linear multivariate data discrimination is widely used in chemometrics, with applications in food and herb areas, such as the authentication of origin, cultivation model, processing method, fraud, etc (Lu et al, 2020; Ballabio et al, 2018; Walkowiak et al, 2019; Górski, Kowalcze and Jakubowska 2019). It could calculate the probability of each class and select the class associated with the highest probability for sample classification, which is widely applied to cope with complicated data matrices through dimension reduction. Therefore, PLS-DA was performed to establish a discrimination model to identify the origin of P. notoginseng in this study. The evaluation of classification performance parameters was performed by confusion matrix. The total number of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) samples were summarized to calculate the sensitivity, specificity, accuracy, and performance of the model. A good model must have high sensitivity and specificity coefficients. The closer the value was to 1, showing that the better the model effect. PLS-DA models were established through SIMAC-P + 14.0 (Umetrics, Sweden) software. The related equations were shown behind:
$$Sensitivity=\frac{TP}{TP+FN}\times 100$$
1
$$Specificity=\frac{TN}{TN+FP}\times 100$$
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$$Accuracy=\frac{TP+TN}{Total samples}\times 100$$
3