Detection method for quality grade of traditional Chinese medicine

Abstract

Disclosed is a detection method for quality grade of traditional Chinese medicine (TCM), including: detecting the levels of quality control index components of TCM and efficacy-related in vitro activity by establishing a correlation between principal components of TCM and in vitro activity; determining the state of sample cluster by principal component analysis; constructing a logistic regression model of quality grade versus index components and bioactivity and establishing corresponding grade detection formulas of Chinese medicinal materials by fitting a large number of sample data for Chinese medicinal materials from different places of origin and batches. The method of the present invention realizes the mathematical expression of a standard for quality difference of TCM, and provides a feasible solution for the industrialized evaluation of quality grades of Chinese medicinal materials or herbal slices finally.

Claims

1. A detection method for quality grade of a traditional Chinese medicine (TCM), wherein the TCM includes Chinese medicinal materials or herbal slices; and the detection method is established by constructing a model of correlation between bioact and the comps of the TCM, the detection method comprising the following steps: (1) preparing a TCM test sample; (2) determining a comp assay that includes a. performing a bioact assay of the TCM test sample; and, b. performing a principal comp assay of the TCM test sample; and (3) establishing quality grade detection formulas for the TCM using a logistic regression model, the formulas classified into P.sub.Ex, P.sub.G, P.sub.F, and P.sub.p as follows, wherein P is in the range of 0≤P≤1, where Ex is Excellent, G is Good, F is Fair, P is Poor, act is activity, and comp is component: $P_{Ex}=\frac{\begin{array}{c}\exp ({\beta }_{Ex}+{\beta }_{\mathrm{Ex}\mathrm{act}1}{X}_{\mathrm{Ex}\mathrm{act}1}+{\beta }_{\mathrm{Ex}\mathrm{act}2}{X}_{\mathrm{Ex}\mathrm{act}2}+.Math.{\beta }_{\mathrm{Ex}\mathrm{act}n}{X}_{\mathrm{Ex}\mathrm{act}n}\\ +{\beta }_{\mathrm{Ex}\mathrm{comp}1}{X}_{\mathrm{Ex}\mathrm{comp}1}+{\beta }_{\mathrm{Ex}\mathrm{comp}2}{X}_{\mathrm{Ex}\mathrm{comp}2}+.Math.{\beta }_{\mathrm{Ex}\mathrm{comp}n}{X}_{\mathrm{Ex}\mathrm{comp}n})\end{array}}{\begin{array}{c}1+\exp ({\beta }_{Ex}+{\beta }_{\mathrm{Ex}\mathrm{act}1}{X}_{\mathrm{Ex}\mathrm{act}1}+{\beta }_{\mathrm{Ex}\mathrm{act}2}{X}_{\mathrm{Ex}\mathrm{act}2}+.Math.{\beta }_{\mathrm{Ex}\mathrm{act}n}{X}_{\mathrm{Ex}\mathrm{act}n}\\ +{\beta }_{\mathrm{Ex}\mathrm{comp}1}{X}_{\mathrm{Ex}\mathrm{comp}1}+{\beta }_{\mathrm{Ex}\mathrm{comp}2}{X}_{\mathrm{Ex}\mathrm{comp}2}+.Math.{\beta }_{\mathrm{Ex}\mathrm{comp}n}{X}_{\mathrm{Ex}\mathrm{comp}n})\end{array}};P_G=\frac{\begin{array}{c}\exp ({\beta }_G+{\beta }_{G\mathrm{act}1}{X}_{G\mathrm{act}1}+{\beta }_{G\mathrm{act}2}{X}_{G\mathrm{act}2}+.Math.{\beta }_{G\mathrm{act}n}{X}_{G\mathrm{act}n}\\ +{\beta }_{G\mathrm{comp}1}{X}_{G\mathrm{comp}1}+{\beta }_{G\mathrm{comp}2}{X}_{G\mathrm{comp}2}+.Math.{\beta }_{G\mathrm{comp}n}{X}_{G\mathrm{comp}n})\end{array}}{\begin{array}{c}1+\exp ({\beta }_G+{\beta }_{G\mathrm{act}1}{X}_{G\mathrm{act}1}+{\beta }_{G\mathrm{act}2}{X}_{G\mathrm{act}2}+.Math.{\beta }_{G\mathrm{act}n}{X}_{G\mathrm{act}n}\\ +{\beta }_{G\mathrm{comp}1}{X}_{G\mathrm{comp}1}+{\beta }_{G\mathrm{comp}2}{X}_{G\mathrm{comp}2}+.Math.{\beta }_{G\mathrm{comp}n}{X}_{G\mathrm{comp}n})\end{array}};P_F=\frac{\begin{array}{c}\exp ({\beta }_F+{\beta }_{F\mathrm{act}1}{X}_{F\mathrm{act}1}+{\beta }_{F\mathrm{act}2}{X}_{F\mathrm{act}2}+.Math.{\beta }_{F\mathrm{act}n}{X}_{F\mathrm{act}n}\\ +{\beta }_{F\mathrm{comp}1}{X}_{F\mathrm{comp}1}+{\beta }_{F\mathrm{comp}2}{X}_{F\mathrm{comp}2}+.Math.{\beta }_{F\mathrm{comp}n}{X}_{F\mathrm{comp}n})\end{array}}{\begin{array}{c}1+\exp ({\beta }_F+{\beta }_{F\mathrm{act}1}{X}_{F\mathrm{act}1}+{\beta }_{F\mathrm{act}2}{X}_{F\mathrm{act}2}+.Math.{\beta }_{F\mathrm{act}n}{X}_{F\mathrm{act}n}\\ +{\beta }_{F\mathrm{comp}1}{X}_{F\mathrm{comp}1}+{\beta }_{F\mathrm{comp}2}{X}_{F\mathrm{comp}2}+.Math.{\beta }_{F\mathrm{comp}n}{X}_{F\mathrm{comp}n})\end{array}};\mathrm{and},P_P=\frac{\begin{array}{c}\exp ({\beta }_P+{\beta }_{P\mathrm{act}1}{X}_{P\mathrm{act}1}+{\beta }_{P\mathrm{act}2}{X}_{P\mathrm{act}2}+.Math.{\beta }_{P\mathrm{act}n}{X}_{P\mathrm{act}n}\\ +{\beta }_{P\mathrm{comp}1}{X}_{P\mathrm{comp}1}+{\beta }_{P\mathrm{comp}2}{X}_{P\mathrm{comp}2}+.Math.{\beta }_{P\mathrm{comp}n}{X}_{P\mathrm{comp}n})\end{array}}{\begin{array}{c}1+\exp ({\beta }_P+{\beta }_{P\mathrm{act}1}{X}_{P\mathrm{act}1}+{\beta }_{P\mathrm{act}2}{X}_{P\mathrm{act}2}+.Math.{\beta }_{P\mathrm{act}n}{X}_{P\mathrm{act}n}\\ +{\beta }_{P\mathrm{comp}1}{X}_{P\mathrm{comp}1}+{\beta }_{P\mathrm{comp}2}{X}_{P\mathrm{comp}2}+.Math.{\beta }_{P\mathrm{comp}n}{X}_{P\mathrm{comp}n})\end{array}}.$

2. The detection method of claim 1, wherein the preparing of the TCM sample includes the following steps in the recited order: weighing approximately 0.3 g of Chinese medicinal materials or herbal slices in a conical flask with cover; adding 50 mL of methanol to the conical flask, sealing the conical flask tightly; weighing the conical flask; sonicating the conical flask for 30; cooling the conical flask; weighing the conical flask again; adding methanol to the conical flask to make-up for a weight loss; shaking the conical flask well; filtering the conical flask; and, collecting a subsequent filtrate from the conical flask as a sample solution; the determining of the comp assay includes conducting ultra performance liquid chromatography (UPLC) at a characteristic wavelength of the Chinese medicinal materials or herbal slices by using octadecyl silane (ODS) chemically bonded silica as packing the conducting including using 0.1% formic acid-water as mobile phase A and acetonitrile as mobile phase B, conducting gradient elution with a volume ratio of 2% for-the mobile phase B at 0-2 min; a volume ratio of 2% to 100% for the mobile phase B at 2-10 min; a volume ratio of 100% to 2% for the mobile phase B at 10-13 min at; a volume ratio of 2% for the mobile phase B at 13-20 min at 2%; a volume flow of 0.2 mL/min; a column temperature of 25° C.; and, an injection volume of 2 μL.

3. The detection method of claim 2, wherein the performing of the bioact assay includes detecting ABTS.sup.+ free radical scavenging act (%), DPPH radical scavenging act (%), and hydroxyl radical scavenging act.

4. The detection method of claim 3, wherein the performing of the principal comp assay includes a principal comp analysis (PCA) and a hierarchical clustering analysis (HCA).

5. The detection method of claim 4, wherein the TCM is any one of Cinnamomi Ramulus, Salviae Miltiorrhizae Radix et Rhizoma, or Achyranthis Bidentatae Radix.

6. The detection method of claim 5, wherein the quality grade detection formulas for Cinnamomi Ramulus are as follows, where Antiox is Antioxidant, Cin is Cinnamaldehyde, Cinac is Cinnamic acid, Cinalc is Cinnamyl alcohol, and Coum is Coumarin: $\begin{array}{c}{P}_{Ex}=\frac{\exp \left(-0.974{\mathrm{Antiox}}_{DPPH}+2.197{\mathrm{Antiox}}_{OH}-0\text{.96}{C}_{\mathrm{Cin}}+10.158C_{\mathrm{Cinac}}+44.7C_{\mathrm{Cinalc}}-43.222C_{\mathrm{Coum}}+32.543\right)}{1+\exp \left(-0.974{\mathrm{Antiox}}_{\mathrm{DPPH}}+2.197{\mathrm{Antiox}}_{OH}-0\text{.96}{C}_{\mathrm{Cin}}+10.158C_{\mathrm{Cinac}}+44.7C_{\mathrm{Cinalc}}-43.222C_{\mathrm{Coum}}+32.543\right)};\\ P_G=\frac{\exp \left(2.545{\mathrm{Antiox}}_{DPPH}-0.744{\mathrm{Antiox}}_{OH}-0.827C_{\mathrm{Cin}}-35.909C_{\mathrm{Cinac}}-91.4\text{.09}{C}_{\mathrm{Cinalc}}+117.883C_{\mathrm{Coum}}-449.205\right)}{1+\exp \left(2.545{\mathrm{Antiox}}_{DPPH}-0.744{\mathrm{Antiox}}_{OH}-0.827C_{\mathrm{Cin}}-35.909C_{\mathrm{Cinac}}-91.409C_{\mathrm{Cinalc}}+117.883C_{\mathrm{Coum}}-449.205\right)};\\ P_F=\frac{\exp \left(1.093{\mathrm{Antiox}}_{DPPH}-1.521{\mathrm{Antiox}}_{OH}+1.343C_{\mathrm{Cin}}+31.391C_{\mathrm{Cinac}}+22.043C_{\mathrm{Cinalc}}+6.076C_{\mathrm{Coum}}-196.728\right)}{1+\exp \left(1.093{\mathrm{Antiox}}_{DPPH}-1.521{\mathrm{Antiox}}_{OH}+1.343C_{\mathrm{Cin}}+31.391C_{\mathrm{Cinac}}+22.043C_{\mathrm{Cinalc}}+6.076C_{\mathrm{Coum}}-196.728\right)};\mathrm{and},\\ P_P=\frac{\exp \left(-0.875{\mathrm{Antiox}}_{DPPH}+0.892{\mathrm{Antiox}}_{OH}-0.952C_{\mathrm{Cin}}-33.664C_{\mathrm{Cinac}}-20.326C_{\mathrm{Cinalc}}-9.724C_{\mathrm{Coum}}+199.880\right)}{1+\exp \left(-0.875{\mathrm{Antiox}}_{DPPH}+0.892{\mathrm{Antiox}}_{OH}-0.952C_{\mathrm{Cin}}-33.664C_{\mathrm{Cinac}}-20.326C_{\mathrm{Cinalc}}-9.724C_{\mathrm{Coum}}+199.880\right)}.\end{array}$

7. The detection method of claim 5, wherein the quality grade detection formulas for Salviae Miltiorrhizae Radix et Rhizoma are as follows, where Tan is Tanshinone IIA, Sal is Salvianolic acid, and Antiox is Antioxidant: $\begin{array}{c}{P}_{\mathrm{Ex}}=\frac{\exp \left(-12.692+1.571C_{Tan}-1.056C_{\mathrm{Sal}}+1.272{\mathrm{Antiox}}_{DPPH}-0.152{\mathrm{Antiox}}_{OH}\right)}{1+\exp \left(-12.692+1.571C_{Tan}-1.056C_{\mathrm{Sal}}+1.272{\mathrm{Antiox}}_{DPPH}-0.152{\mathrm{Antiox}}_{OH}\right)};\\ P_G=\frac{\exp \left(118.369-3.453C_{Tan}+0.642C_{\mathrm{Sal}}-5.175{\mathrm{Antiox}}_{DPPH}+0.645{\mathrm{Antiox}}_{OH}\right)}{1+\exp \left(118.369-3.453C_{Tan}+0.642C_{\mathrm{Sal}}-5.175{\mathrm{Antiox}}_{DPPH}+0.645{\mathrm{Antiox}}_{OH}\right)};\\ P_F=\frac{\exp \left(69.307-12.857C_{Tan}+1.326C_{\mathrm{Sal}}-0.771{\mathrm{Antiox}}_{DPPH}-1.16{\mathrm{Antiox}}_{OH}\right)}{1+\exp \left(69.307-12.857C_{Tan}+1.326C_{\mathrm{Sal}}-0.771{\mathrm{Antiox}}_{DPPH}-1.16{\mathrm{Antiox}}_{OH}\right)};\mathrm{and}\\ P_P=\frac{\exp \left(-89.539+3.845C_{Tan}+1.166C_{\mathrm{Sal}}-0.24{\mathrm{Antiox}}_{DPPH}+0.119{\mathrm{Antiox}}_{OH}\right)}{1+\exp \left(-89.539+3.845C_{Tan}+1.166C_{\mathrm{Sal}}-0.24{\mathrm{Antiox}}_{DPPH}+0.119{\mathrm{Antiox}}_{OH}\right)}.\end{array}$

8. The detection method for quality grade of TCM according to claim 5, wherein quality grade detection formulas of the Achyranthis Bidentatae Radix are as follows, where Antiox is Antioxidant: $\begin{array}{c}{P}_{Ex}=\frac{\exp \left(-31.020+0.146{\mathrm{Antiox}}_{DPPH}+0.098{\mathrm{Antiox}}_{OH}+222.140C_{\mathrm{Ecdysone}}\right)}{1+\exp \left(-31.020+0.146{\mathrm{Antiox}}_{DPPH}+0.098{\mathrm{Antiox}}_{OH}+222.140C_{Ecdysone}\right)};\\ P_G=\frac{\exp \left(-5.713+0.068{\mathrm{Antiox}}_{DPPH}+0.023{\mathrm{Antiox}}_{OH}+17.3560C_{\mathrm{Ecdysone}}\right)}{1+\exp \left(-5.713+0.068{\mathrm{Antiox}}_{DPPH}+0.023{\mathrm{Antiox}}_{OH}+17.3560C_{\mathrm{Ecdysone}}\right)};\\ P_F=\frac{\exp \left(5.962-0.058{\mathrm{Antiox}}_{DPPH}-0.006{\mathrm{Antiox}}_{OH}-49.559C_{\mathrm{Ecdysone}}\right)}{1+\exp \left(5.962-0.058{\mathrm{Antiox}}_{DPPH}-0.006{\mathrm{Antiox}}_{OH}-49.559C_{\mathrm{Ecdysone}}\right)};\mathrm{and},\\ P_P=\frac{\exp \left(615.117-15.432{\mathrm{Antiox}}_{DPPH}+6.252{\mathrm{Antiox}}_{OH}-11574.00C_{\mathrm{Ecdysone}}\right)}{1+\exp \left(615.117-15.432{\mathrm{Antiox}}_{DPPH}+6.252{\mathrm{Antiox}}_{OH}-11574.00C_{\mathrm{Ecdysone}}\right)}.\end{array}$

Description

BRIEF DESCRIPTION OF DRAWINGS

(1) FIG. 1 is a scatter diagram of principal component classification of TCM in the solution of the present invention.

(2) FIG. 2 is a load diagram of principal component analysis of TCM in the solution of the present invention.

DETAILED DESCRIPTION

(3) To make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and examples. It should be understood that the examples described herein are merely used to explain the present invention, rather than to limit the present invention.

Example 1 Detection Method for Quality Grade of Cinnamomi Ramulus

(4) (1) Sample preparation: Approximately 0.3 g of Cinnamomi Ramulus powder (passed through #4 sieve) was weighed accurately in a conical flask with cover, mixed with 50 mL of methanol accurately, sealed tightly, weighed, sonicated (power 140 W, frequency 42 kHz) for 30 min, cooled, and weighed again; methanol was used to make up for a weight loss; the mixture was shaken well and filtered to collect a subsequent filtrate as a sample solution.

(5) (2) Acquisition of integral data for a fingerprint of Cinnamomi Ramulus by ultra performance liquid chromatography (UPLC): Using octadecyl silane (ODS) chemically bonded silica as packing, UPLC was conducted at a characteristic wavelength (254 nm) of principal components of Cinnamomi Ramulus; using 0.1% formic acid-water as mobile phase A and acetonitrile as mobile phase B, gradient elution was conducted according to the following conditions: volume ratio of the mobile phase B at 0-2 min: 2%; volume ratio of the mobile phase B at 2-10 min: 2% to 100%; volume ratio of the mobile phase B at 10-13 min: 100% to 2%; volume ratio of the mobile phase B at 13-20 min: 2%; volume flow: 0.2 mL/min; column temperature: 25° C.; injection volume: 2 μL. Chromatographic integration data were acquired.

(6) (3) Assay for in vitro antioxidant indexes: Results of Antioxidant.sub.ABTS+, Antioxidant.sub.DPPH, and Antioxidant.sub.OH. are listed in Table 1:

(7) TABLE-US-00001 TABLE 1 Results of in vitro antioxidant indexes of Cinnamomi Ramulus ABTS.sup.+ free radical DPPH radical Hydroxyl radical scavenging scavenging scavenging No. Place of origin activity (%) activity (%) activity (U/mL) S1 Cenxi City, Guangxi (Excellent) 10.61 64.47 87.09 S2 Cenxi City, Guangxi (Good) 10.26 20.82 36.09 S3 Cenxi City, Guangxi (Fair) 10.85 90.63 28.31 S4 Cenxi City, Guangxi (Poor) 9.25 59.61 51.43 S5 Heyuan County, Guangdong (tender Cinnamomi Ramulus) 12.16 61.40 65.48 S6 Guiping City, Guangxi (tender Cinnamomi Ramulus) 10.66 84.98 94.17 S7 Heyuan County, Guangdong (tender Cinnamomi Ramulus) 12.64 62.16 56.88 S8 Yulin City, Guangxi (tender Cinnamomi Ramulus) 8.98 70.95 80.09 S9 Lecheng Town, Guangdong 8.57 90.53 70.16 S10 Binheng Town, Guangdong 10.57 84.98 94.17 S11 Daquan Town, Guangxi 13.13 59.73 8.65 S12 Yunan County, Guangxi 23.78 70.29 16.31 S13 Wuhe Town, Guangdong 9.74 57.14 95.10 S14 Tongting Town, Guangxi 10.47 55.14 85.10 S15 Zhongsha Town, Guangxi 9.12 49.41 23.94 S16 Zhongsha Town, Guangxi 10.63 69.64 45.57 S17 Tongting Town, Guangxi 19.01 72.99 58.71 S18 Yulin City, Guangxi (inferior) 20.48 60.77 14.36 S19 Shaanxi Xingshengde Pharmaceutical Co., Ltd. 11.60 79.51 82.96 S20 Shaanxi Buchang Pharmaceutical Co., Ltd. 13.84 38.42 48.02
(4) Principal Component Analysis (PCA):

(8) After data alignment, integration, and standardization, the acquired data (n=6) were imported into Simca-p 14.1 software, and PCA was conducted with integral data and bioactivity data as observed values (X). Principal components with the most differential variables were extracted from the resulting data matrix. Eigenvalues and cumulative contribution were obtained from a correlation coefficient matrix R. A model fitted two principal components automatically. Model fitting degree was 95.9%. Contribution of principal component 1 (PC1) was 95.9%, with the most difference information, suggestive of a good fitting ability of the model. The first two principal components were projected, and respective run of data were analyzed by hierarchical clustering analysis (HCA). After preliminary judgment from components load results, chemical indexes with high contribution of sample difference included cinnamaldehyde, cinnamyl alcohol, cinnamic acid, and coumarin; in vitro activity indexes included DPPH radical scavenging activity (Antioxidant.sub.DPPH) and hydroxyl radical scavenging activity (Antioxidant.sub.OH). Appropriate amounts of reference substances (RS) of cinnamaldehyde, cinnamyl alcohol, cinnamic acid, and coumarin (principal components in Cinnamomi Ramulus) were weighed accurately in a brown volumetric flask, and prepared into a 0.5 mg/mL (mass concentration) solution with methanol; the solution was filtered through a 0.22 μm filter membrane to collect a subsequent filtrate as a reference solution. Content of each identified chemical component was calculated by external standard method of liquid chromatography. Variables were set as: C.sub.Cinnamaldehyde, C.sub.Cinnamyl alcohol, C.sub.Cinnamic acid, C.sub.Coumarin, Antioxidant.sub.DPPH, and Antioxidant.sub.OH.

(9) (5) Functional relationship among chemical component content, antioxidant activity, and experience level corresponding to samples of each training set was established by a binary logistic model:

(10) Model expressions are obtained as follows

(11) $P_{\mathrm{Excellent}}=\frac{\exp \left(\begin{array}{c}-0.974{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+2.197{\mathrm{Antioxidant}}_{\mathrm{OH}}-\\ \begin{array}{c}0.96C_{\mathrm{Cinnamaldehyde}}+10.158C_{\mathrm{Cinnamic}\mathrm{acid}}+\\ 44.7C_{\mathrm{Cinnamyl}\mathrm{alcohol}}-43.222C_{\mathrm{Coumarin}}+32.543\end{array}\end{array}\right)}{1+\exp \left(\begin{array}{c}-0.974{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+2.197{\mathrm{Antioxidant}}_{\mathrm{OH}}-\\ \begin{array}{c}0.96C_{\mathrm{Cinnamaldehyde}}+10.158C_{\mathrm{Cinnamic}\mathrm{acid}}+\\ 44.7C_{\mathrm{Cinnamyl}\mathrm{alcohol}}-43.222C_{\mathrm{Coumarin}}+32.543\end{array}\end{array}\right)}{P}_{\mathrm{Good}}=\frac{\exp \left(\begin{array}{c}2.545{\mathrm{Antioxidant}}_{\mathrm{DPPH}}-0.744{\mathrm{Antioxidant}}_{\mathrm{OH}}-\\ \begin{array}{c}0.827C_{\mathrm{Cinnamaldehyde}}-35.909C_{\mathrm{Cinnamic}\mathrm{acid}}-\\ 91.409C_{\mathrm{Cinnamyl}\mathrm{alcohol}}+117.883C_{\mathrm{Coumarin}}-449.205\end{array}\end{array}\right)}{1+\exp \left(\begin{array}{c}2.545{\mathrm{Antioxidant}}_{\mathrm{DPPH}}-0.744{\mathrm{Antioxidant}}_{\mathrm{OH}}-\\ \begin{array}{c}0.827C_{\mathrm{Cinnamaldehyde}}-35.909C_{\mathrm{Cinnamic}\mathrm{acid}}-\\ 91.409C_{\mathrm{Cinnamyl}\mathrm{alcohol}}+117.883C_{\mathrm{Coumarin}}-449.205\end{array}\end{array}\right)}{P}_{\mathrm{Fair}}=\frac{\exp \left(\begin{array}{c}1.093{\mathrm{Antioxidant}}_{\mathrm{DPPH}}-1.521{\mathrm{Antioxidant}}_{\mathrm{OH}}+\\ \begin{array}{c}1.343C_{\mathrm{Cinnamaldehyde}}+31.391C_{\mathrm{Cinnamic}\mathrm{acid}}+\\ 22.043C_{\mathrm{Cinnamyl}\mathrm{alcohol}}+6.076C_{\mathrm{Coumarin}}-196.728\end{array}\end{array}\right)}{1+\exp \left(\begin{array}{c}1.093{\mathrm{Antioxidant}}_{\mathrm{DPPH}}-1.521{\mathrm{Antioxidant}}_{\mathrm{OH}}+\\ \begin{array}{c}1.343C_{\mathrm{Cinnamaldehyde}}+31.391C_{\mathrm{Cinnamic}\mathrm{acid}}+\\ 22.043C_{\mathrm{Cinnamyl}\mathrm{alcohol}}+6.076C_{\mathrm{Coumarin}}-196.728\end{array}\end{array}\right)}{P}_{\mathrm{Poor}}=\frac{\exp \left(\begin{array}{c}-0.875{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+0.892{\mathrm{Antioxidant}}_{\mathrm{OH}}-\\ \begin{array}{c}0.952C_{\mathrm{Cinnamaldehyde}}-33.664C_{\mathrm{Cinnamic}\mathrm{acid}}-\\ 20.326C_{\mathrm{Cinnamyl}\mathrm{alcohol}}-9.724C_{\mathrm{Coumarin}}+199.88\end{array}\end{array}\right)}{1+\exp \left(\begin{array}{c}-0.875{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+0.892{\mathrm{Antioxidant}}_{\mathrm{OH}}-\\ \begin{array}{c}0.952C_{\mathrm{Cinnamaldehyde}}-33.664C_{\mathrm{Cinnamic}\mathrm{acid}}-\\ 20.326C_{\mathrm{Cinnamyl}\mathrm{alcohol}}-9.724C_{\mathrm{Coumarin}}+199.88\end{array}\end{array}\right)}$

(12) Measured value of each index was substituted in the above expressions to calculate a probability of which grade an influencing factor belongs to, thereby determining the grade of Cinnamomi Ramulus. Detailed grade calculation results of 20 batches of Cinnamomi Ramulus are listed in Table 2, and probabilities thereof are determined as >95%.

(13) TABLE-US-00002 TABLE 2 Grade classification results of Cinnamomi Ramulus Grade Source Probability 4 Wuhe Town, Guangdong/Ungraded 1.00 4 Binheng Town, Guangdong/Ungraded 1.00 4 Cenxi City, Guangxi/Superior 1.00 3 Yulin City, Guangxi/Ungraded 1.00 3 Heyuan County, Guangdong/Small 1.00 3 Shaanxi Xingshengde Pharmaceutical Co., Ltd./Ungraded 1.00 3 Cenxi City, Guangxi/Fair 1.00 2 Lecheng Town, Guangdong/Large 0.99 2 Heyuan County, Guangdong/Ungraded 0.99 2 Cenxi City, Guangxi/Good 1.00 2 Zhongsha Town, Guangxi/Ungraded 0.97 2 Yulin City, Guangxi/Inferior 1.00 2 Daquan Town, Guangxi/Ungraded 1.00 1 Tongting Town, Guangxi/Small 1.00 1 Tongting Town, Guangxi/Ungraded 1.00 1 Guiping City, Guangxi/Ungraded 1.00 1 Cenxi City, Guangxi/Poor 1.00 1 Shaanxi Buchang Pharmaceutical Co., Ltd./Ungraded 1.00 1 Zhongsha Town, Guangxi/Ungraded 1.00 1 Yunan County, Guangxi/Ungraded 1.00

Example 2 Detection Method for Quality Grade of Salviae Miltiorrhizae Radix Et Rhizoma

(14) (1) Sample preparation: Approximately 0.3 g of Salviae Miltiorrhizae Radix et Rhizoma powder (passed through #4 sieve) was weighed accurately in a conical flask with cover, mixed with 50 mL of methanol accurately, sealed tightly weighed, sonicated (power 140 W, frequency 42 kHz) for 30 min, cooled, and weighed again; methanol was used to make up for a weight loss; the mixture was shaken well and filtered to collect a subsequent filtrate as a sample solution.

(15) (2) Acquisition of integral data for a fingerprint of Salviae Miltiorrhizae Radix et Rhizoma by ultra performance liquid chromatography (UPLC): Using octadecyl silane (ODS) chemically bonded silica as packing, UPLC was conducted at a characteristic wavelength (270 nm) of principal components of Salviae Miltiorrhizae Radix et Rhizoma; using 0.1% formic acid-water as mobile phase A and acetonitrile as mobile phase B, gradient elution was conducted according to the following conditions: volume ratio of the mobile phase B at 0-2 min: 2%; volume ratio of the mobile phase B at 2-10 min: 2% to 100%; volume ratio of the mobile phase B at 10-13 min: 100% to 2%; volume ratio of the mobile phase B at 13-20 min: 2%; volume flow: 0.2 mL/min; column temperature: 25° C.; injection volume: 2 μL. Chromatographic integration data were acquired.

(16) (3) Assay for in vitro antioxidant indexes: Results of Antioxidant.sub.ABTS+, Antioxidant.sub.DPPH, and Antioxidant.sub.OH are listed in Table 3.

(17) TABLE-US-00003 TABLE 3 Results of in vitro antioxidant indexes of Salviae Miltiorrhizae Radix et Rhizoma ABTS.sup.+ free radical DPPH radical Hydroxyl radical scavenging scavenging scavenging Batch Place of origin activity (%) activity (%) activity (U/mL) S1 Fangcheng County, Henan 34.01 42.77 64.79 S2 Qingdao City, Shandong (Fair) 20.25 29.81 46.06 S3 Yantai City, Shandong 38.02 59.40 73.63 S4 Tai'an City, Shandong 34.25 42.49 53.71 S5 Zhenping County, Henan 36.75 54.17 66.93 S6 Zaozhuang City, Shandong 25.50 36.49 73.63 S7 Zibo City, Shandong 19.01 32.53 39.64 S8 Qingdao City, Shandong (Excellent) 20.75 53.32 55.90 S9 Mianchi County, Henan 34.75 50.50 68.32 S10 Shaanxi Xingshengde Pharmaceutical Co., Ltd. 36.50 43.24 63.48 S11 Shaanxi Buchang Pharmaceutical Co., Ltd. 39.25 56.72 76.50 S12 Wanrong County, Shanxi 21.25 34.24 53.55 S13 Xincai County, Henan (Excellent) 30.00 32.08 58.15 S14 Zhongjiang County, Sichuan (Excellent) 26.75 38.04 76.91 S15 Zhongjiang County, Sichuan (Poor) 19.10 30.27 26.84 S16 Danfeng County, Shangluo City 30.75 50.18 52.51 S17 Luonan County, Shangluo City 33.75 45.79 66.95 S18 Longkou Town, Henan 30.75 31.48 62.83 S19 Hanji Town, Henan 33.75 50.28 73.94 S20 Zhongjiang County, Sichuan (Good) 27.00 42.57 49.91 S21 Zhongjiang County, Sichuan (Fair) 21.25 32.97 63.96 S22 Zhongjiang County, Sichuan (Inferior) 38.50 48.41 51.74 S23 Huangshan County, Wannan City, Anhui 22.50 37.40 60.57 S24 Linyi City, Shandong 26.25 48.15 73.81 S25 Shaoguan City, Guangdong 20.50 45.25 69.74 S26 Jinan City, Shandong (Unassorted) 31.00 49.84 62.19 S27 Jinan City, Shandong (First Grade) 36.75 55.88 79.95 S28 Jinan City, Shandong (Second Grade) 29.75 54.67 73.11 S29 Lijiang City, Yunnan 20.75 36.55 36.23 S30 Counterfeit 16.00 35.37 53.15 S31 National Institutes for Food and Drug Control, China 29.75 31.72 68.70

(18) (4) Principal component analysis (PCA): Chromatographic integration data and antioxidant data were imported into multivariate statistical software for PCA. After data alignment, integration, and standardization, the acquired data (n=6) were imported into Simca-p 14.1 software, and PCA was conducted with integral data and bioactivity data as observed values (X). Principal components with the most differential variables were extracted from the resulting data matrix. Eigenvalues and cumulative contribution were obtained from a correlation coefficient matrix R. A model fitted two principal components automatically. Model fitting degree was 83.90%. Contribution of principal component 1 (PC1) was 66.1%, with the most difference information, suggestive of a good fitting ability of the model. The first two principal components were projected, and respective run of data were analyzed by hierarchical clustering analysis (HCA). After preliminary judgment from components load results, chemical indexes with high contribution of sample difference included tanshinone IIA and salvianolic acid B: in vitro activity indexes included DPPH radical scavenging activity (Antioxidant.sub.DPPH) and hydroxyl radical scavenging activity (Antioxidant.sub.OH). Appropriate amounts of reference substances (RS) of tanshinone IIA and salvianolic acid B (principal components in Salviae Miltiorrhizae Radix et Rhizoma) were weighed accurately in a brown volumetric flask, and prepared into 0.21 mg/mL and 0.22 mg/mL (mass concentration) solutions with methanol, respectively; the solutions were filtered through a 0.22 μm filter membrane to collect subsequent filtrates as reference solutions. Content of each identified chemical component was calculated by external standard method of liquid chromatography. Variables were set as: C.sub.Salvianolic acid B, C.sub.Tanshinone IIA, Antioxidant.sub.DPPH, and Antioxidant.sub.OH.

(19) (5) Functional relationship among chemical component content, antioxidant activity, and experience level corresponding to samples of each training set was established by a binary logistic model. Model expressions are obtained as follows:

(20) $P_{\mathrm{Excellent}}=\frac{\exp \left(\begin{array}{c}-12.692+1.571C_{\mathrm{Tanshinone}\mathrm{IIA}}-1.056C_{\mathrm{Salvianolic}\mathrm{acid}B}+\\ 1.272{\mathrm{Anitoxidant}}_{\mathrm{DPPH}}-0.152{\mathrm{Antioxidant}}_{\mathrm{OH}}\end{array}\right)}{1+\exp \left(\begin{array}{c}\begin{array}{c}-12.692+1.571C_{\mathrm{Tanshinone}\mathrm{IIA}}-\\ 1.056C_{\mathrm{Salvianolic}\mathrm{acid}B}+\end{array}\\ 1.272{\mathrm{Anitoxidant}}_{\mathrm{DPPH}}-0.152{\mathrm{Antioxidant}}_{\mathrm{OH}}\end{array}\right)}{P}_{\mathrm{Good}}=\frac{\exp \left(\begin{array}{c}118.369-3.453C_{\mathrm{Tanshinone}\mathrm{IIA}}+0.642C_{\mathrm{Salvianolic}\mathrm{acid}B}-\\ 5.175{\mathrm{Anitoxidant}}_{\mathrm{DPPH}}+0.645{\mathrm{Antioxidant}}_{\mathrm{OH}}\end{array}\right)}{1+\exp \left(\begin{array}{c}\begin{array}{c}118.369-3.453C_{\mathrm{Tanshinone}\mathrm{IIA}}+\\ 0.642C_{\mathrm{Salvianolic}\mathrm{acid}B}-\end{array}\\ 5.175{\mathrm{Anitoxidant}}_{\mathrm{DPPH}}+0.645{\mathrm{Antioxidant}}_{\mathrm{OH}}\end{array}\right)}{P}_{\mathrm{Fair}}=\frac{\exp \left(\begin{array}{c}69.307-12.857C_{\mathrm{Tanshinone}\mathrm{IIA}}+1.326C_{\mathrm{Salvianolic}\mathrm{acid}B}-\\ 0.771{\mathrm{Anitoxidant}}_{\mathrm{DPPH}}-1.16{\mathrm{Antioxidant}}_{\mathrm{OH}}\end{array}\right)}{1+\exp \left(\begin{array}{c}\begin{array}{c}69.307-12.857C_{\mathrm{Tanshinone}\mathrm{IIA}}+\\ 1.326C_{\mathrm{Salvianolic}\mathrm{acid}B}-\end{array}\\ 0.771{\mathrm{Anitoxidant}}_{\mathrm{DPPH}}-1.16{\mathrm{Antioxidant}}_{\mathrm{OH}}\end{array}\right)}{P}_{\mathrm{Poor}}=\frac{\exp \left(\begin{array}{c}-89.539+3.845C_{\mathrm{Tanshinone}\mathrm{IIA}}+1.166C_{\mathrm{Salvianolic}\mathrm{acid}B}-\\ 0.24{\mathrm{Anitoxidant}}_{\mathrm{DPPH}}+0.119{\mathrm{Antioxidant}}_{\mathrm{OH}}\end{array}\right)}{1+\exp \left(\begin{array}{c}\begin{array}{c}-89.539+3.845C_{\mathrm{Tanshinone}\mathrm{IIA}}+\\ 1.166C_{\mathrm{Salvianolic}\mathrm{acid}B}-\end{array}\\ 0.24{\mathrm{Anitoxidant}}_{\mathrm{DPPH}}+0.119{\mathrm{Antioxidant}}_{\mathrm{OH}}\end{array}\right)}$

(21) Measured value of each index was substituted in the above expressions to calculate a probability of which grade an influencing factor belongs to, thereby determining the grade of Salviae Miltiorrhizae Radix et Rhizoma. Detailed results are listed in Table 4, and probabilities thereof are determined as >87%.

(22) TABLE-US-00004 TABLE 4 Grade classification results of Salviae Miltiorrhizae Radix el Rhizoma Grade Place of origin/Original grade Probability 4 Shaanxi Buchang Pharmaceutical Co., Ltd. 0.99 4 Zhenping County, Henan 0.99 4 Yantai City, Shandong 0.99 4 Danfeng County, Shangluo City 0.99 4 Luonan County, Shangluo City 1.00 4 Shaanxi Xingshengde Pharmaceutical Co., Ltd. 0.99 3 Zhongjiang County, Sichuan (Good) 0.99 3 Mianchi County, Henan 0.91 3 Tai'an City, Shandong 0.99 3 Mianchi County, Henan 0.99 3 Zhongjiang County, Sichuan (Inferior) 0.99 3 Hanji Town, Henan 0.99 2 Zhongjiang County, Sichuan (Fair) 1.00 2 Zibo City, Shandong 0.99 2 Qingdao City, Shandong (Fair) 0.99 2 Wanrong County, Shanxi 0.93 2 Zaozhuang City, Shandong 0.87 2 Xincai County, Henan (Excellent) 0.99 2 Longkou Town, Henan 1.00 2 National Institutes for Food and Drug Control, China 0.99 2 Zhongjiang County, Sichuan (Excellent) 0.99 1 Linyi City, Shandong 0.99 1 Shaoguan City, Guangdong 1.00 1 Lijiang City, Yunnan 1.00 1 Counterfeit 0.99 1 Zhongjiang County, Sichuan (Poor) 0.99 1 Qingdao City, Shandong (Excellent) 0.99 1 Jinan City, Shandong (First Grade) 1.00 1 Jinan City, Shandong (Second Grade) 1.00 1 Huangshan County, Wannan City, Anhui 0.99 1 Jinan City, Shandong (Unassorted) 0.99

Example 3 Detection Method for Quality Grade of Achyranthis Bidentatae Radix

(23) (1) Sample preparation: Approximately 0.3 g of Achyranthis Bidentatae Radix powder (passed through #4 sieve) was weighed accurately in a conical flask with cover, mixed with 50 mL of methanol accurately, sealed tightly, weighed, sonicated (power 140 W, frequency 42 kHz) for 30 min, cooled, and weighed again; methanol was used to make up for a weight loss; the mixture was shaken well and filtered to collect a subsequent filtrate as a sample solution.

(24) (2) Acquisition of integral data for a fingerprint of Achyranthis Bidentatae Radix by ultra performance liquid chromatography (UPLC): Using octadecyl silane (ODS) chemically bonded silica as packing, UPLC was conducted at a characteristic wavelength (250 nm) of principal components of Achyranthis Bidentatae Radix; using 0.1% formic acid-water as mobile phase A and acetonitrile as mobile phase B, gradient elution was conducted according to the following conditions: volume ratio of the mobile phase B at 0-2 min: 2%; volume ratio of the mobile phase B at 2-10 min: 2% to 100%; volume ratio of the mobile phase B at 10-13 min: 100% to 2%; volume ratio of the mobile phase B at 13-20 min: 2%; volume flow: 0.2 mL/min; column temperature: 25° C.; injection volume: 2 μL. Chromatographic integration data were acquired.

(25) (3) Assay for in vitro antioxidant indexes: Results of Antioxidant.sub.ABTS+, Antioxidant.sub.DPPH, and Antioxidant.sub.OH are listed in Table 5.

(26) TABLE-US-00005 TABLE 5 Results of in vitro antioxidant indexes of Achyranthis Bidentatae Radix ABTS.sup.+ free DPPH radical Hydroxyl radical radical scavenging scavenging scavenging Place of origin activity (%) activity (%) activity (U/mL) Shaanxi Buchang Pharmaceutical Co., Ltd. 60.00 37.27 75.56 Shaanxi Xingshengde Pharmaceutical Co., Ltd. 91.05 52.19 44.29 Dingzhou City, Hebei 43.00 21.20 59.89 Jiabucun, Dafengxiang, Wuzhi County, Henan 96.50 84.03 49.06 Tuchengcun, Dahongqiaoxiang, 91.20 46.71 39.41 Wuzhi County, Jiaozuo City, Henan Wenxian County, Jiaozuo City, Henan 52.34 30.57 58.07 Wenxian County, Jiaozuo City, Henan 53.28 35.73 66.40 Wenxian County, Jiaozuo City, Henan 95.45 47.64 44.80 Wenxian County, Jiaozuo City, Henan 20.63 19.79 34.91 Anguo, Baoding City, Hebei 32.44 26.34 30.01 Anguo, Baoding City, Hebei 22.56 19.14 30.42 Anguo, Baoding City, Hebei 95.48 43.86 48.04 Anguo, Baoding City, Hebei 26.36 20.46 37.67 Jiaozuo City, Henan 94.86 52.76 47.22 Jiaozuo City, Henan 53.28 29.48 60.79 Jiaozuo City, Henan 32.76 22.00 77.05 Jiaozuo City, Henan 26.35 20.46 37.67 Niujiayingzi Town, Harqin Banner, 67.86 43.63 63.30 Chifeng City, Inner Mongolia Niujiayingzi Town, Harqin Banner, 41.56 30.96 28.26 Chifeng City, Inner Mongolia Niujiayingzi Town, Harqin Banner, 21.35 38.35 45.25 Chifeng City, Inner Mongolia Niujiayingzi Town, Harqin Banner, 65.38 32.34 50.87 Chifeng City, Inner Mongolia

(27) (4) Principal component analysis (PCA): Chromatographic integration data and antioxidant data were imported into multivariate statistical software for PCA. After data alignment, integration, and standardization, the acquired data (n=6) were imported into Simca-p 14.1 software, and PCA was conducted with integral data and bioactivity data as observed values (X). Principal components with the most differential variables were extracted from the resulting data matrix. Eigenvalues and cumulative contribution were obtained from a correlation coefficient matrix R. A model fitted two principal components automatically. Model fitting degree was 79.6%. Contribution of principal component 1 (PC1) was 63.9%, with the most difference information, suggestive of a good fitting ability of the model. The first two principal components were projected, and respective run of data were analyzed by hierarchical clustering analysis (HCA). After preliminary judgment from components load results, chemical index with high contribution of sample difference was β-ecdysterone; in vitro activity indexes included DPPH radical scavenging activity (Antioxidant.sub.DPPH) and hydroxyl radical scavenging activity (Antioxidant.sub.OH). Appropriate amount of reference substance (RS) of β-ecdysterone (a principal component in Achyranthis Bidentatae Radix) was weighed accurately in a brown volumetric flask, and prepared into a 1 mg/mL (mass concentration) solution with methanol, respectively; the solution was filtered through a 0.22 μm filter membrane to collect a subsequent filtrate as a reference solution. Content of the identified chemical component was calculated by external standard method of liquid chromatography. Variables were set as: C.sub.Ecdysterone, Antioxidant.sub.DPPH, and Antioxidant.sub.OH.

(28) (5) Functional relationship among chemical component content, antioxidant activity, and experience level corresponding to samples of each training set was established by a binary logistic model. Model expressions are obtained as follows:

(29) $P_{\mathrm{Excellent}}=\frac{\exp \left(\begin{array}{c}-31.02+0.146{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+\\ 0.098{\mathrm{Antioxidant}}_{\mathrm{OH}}+222.14C_{\mathrm{Ecdysone}}\end{array}\right)}{1+\exp \left(\begin{array}{c}-31.02+0.146{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+\\ 0.098{\mathrm{Antioxidant}}_{\mathrm{OH}}+222.14C_{\mathrm{Ecdysone}}\end{array}\right)}{P}_{\mathrm{Good}}=\frac{\exp \left(\begin{array}{c}-5.713+0.068{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+\\ 0.023{\mathrm{Antioxidant}}_{\mathrm{OH}}+17.356C_{\mathrm{Ecdysone}}\end{array}\right)}{1+\exp \left(\begin{array}{c}-5.713+0.068{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+\\ 0.023{\mathrm{Antioxidant}}_{\mathrm{OH}}+17.356C_{\mathrm{Ecdysone}}\end{array}\right)}{P}_{\mathrm{Fair}}=\frac{\exp \left(\begin{array}{c}5.962-0.058{\mathrm{Antioxidant}}_{\mathrm{DPPH}}-\\ 0.006{\mathrm{Antioxidant}}_{\mathrm{OH}}-49.559C_{\mathrm{Ecdysone}}\end{array}\right)}{1+\exp \left(\begin{array}{c}5.962-0.058{\mathrm{Antioxidant}}_{\mathrm{DPPH}}-\\ 0.006{\mathrm{Antioxidant}}_{\mathrm{OH}}-49.559C_{\mathrm{Ecdysone}}\end{array}\right)}{P}_{\mathrm{Poor}}=\frac{\exp \left(\begin{array}{c}615.117-15.432{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+\\ 6.252{\mathrm{Antioxidant}}_{\mathrm{OH}}-11574.C_{\mathrm{Ecdysone}}\end{array}\right)}{1+\exp \left(\begin{array}{c}615.117-15.432{\mathrm{Antioxidant}}_{\mathrm{DPPH}}+\\ 6.252{\mathrm{Antioxidant}}_{\mathrm{OH}}-11574.C_{\mathrm{Ecdysone}}\end{array}\right)}$

(30) Measured value of each index was substituted in the above expressions to calculate a probability of which grade an influencing factor belongs to, thereby determining the grade of Achyranthis Bidentatae Radix. Detailed results are listed in Table 6, and probabilities thereof are determined as >87%.

(31) TABLE-US-00006 TABLE 6 Grade classification results of Achyranthis Bidentatae Radix Grade Place of origin/Original grade Calculation result 4 Henan/Ungraded 1.00 4 Huaixian County/Ungraded 1.00 4 Unassorted goods/Fair 1.00 3 Inner Mongolia/First grade 0.99 3 Henan/First grade 1.00 3 Hebei/Third grade 1.00 3 Unassorted goods/Good 1.00 3 Shaanxi Xingshengde Pharmaceutical Co., Ltd./Ungraded 1.00 3 Shaanxi Buchang Pharmaceutical Co., Ltd./Ungraded 1.00 2 Inner Mongolia/Unassorted 0.96 2 Henan/Third grade 1.00 2 Henan/Second grade 0.97 2 Unassorted goods/Excellent 0.93 2 Hebei/Ungraded 1.00 2 Inner Mongolia/Unassorted 0.94 1 Unassorted goods/Poor 1.00 1 Hebei/First grade 0.99 1 Inner Mongolia/Second grade 0.99 1 Inner Mongolia/Third grade 1.00 1 Unassorted goods from Hebei/Ungraded 0.99 1 Unassorted goods/Poor 1.00