Method for quickly identifying core production area of broad pericarpium

Abstract

The application discloses a method for rapidly identifying core production area broad pericarps, and belongs to the technical field of traditional Chinese medicinal material identification, which comprises the following steps: obtaining sample libraries of core and non-core production area broad pericarps; directly sampling and analyzing by using an in-situ ion source mass spectrometry technology to obtain sample sets of the core and non-core production area broad pericarps under different ionization modes; fusing the mass spectrometry data of the broad pericarps under different ionization modes to obtain a comprehensive characteristic variable set; and modeling the mass spectrometry data of the broad pericarps of different production areas based on the comprehensive characteristic variable set, wherein the constructed model is used for identification and analysis of the core production area broad pericarp samples, and according to the contribution degree of the variable weight in the model, a key variable set is extracted as a multi-dimensional characteristic chemical fingerprint spectrum for identifying the core production area broad pericarps. The method of the application significantly improves the accuracy and efficiency of the core production area broad pericarp identification, and has the advantages of high efficiency, reliability and practicality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traditional Chinese medicine identification, in particular to a method for rapidly identifying core production areas of Pericarpium Citri Reticulatae Viride. BACKGROUND

[0002] Pericarpium Citri Reticulatae Viride is one of the ten authentic traditional Chinese medicines in Guangdong Province, and has very high edible and medicinal value. Influenced by factors such as variety, soil, climate, planting technology, etc., the quality, pharmacological effects, medicinal and edible values of Pericarpium Citri Reticulatae Viride in different production areas are significantly different. According to the production areas, it is divided into first-line production areas, second-line production areas and third-line production areas. Among them, the first-line production areas are core production areas, including Tianma, Meijiang, Chakeng, Dongjia and Xijia. The Pericarpium Citri Reticulatae Viride in the core production areas has unique aroma, high content of active substances such as citrus oil and hesperidin, and the highest quality and nutritional value. On the one hand, the Pericarpium Citri Reticulatae Viride in the core production areas and non-core production areas are very similar in appearance and sensory, and cannot be accurately distinguished by subjective experience; on the other hand, there is currently a lack of corresponding national detection standards and industry detection standards, and it is still not possible to provide objective and reliable modern identification methods. Therefore, the high added value and economic value of the Pericarpium Citri Reticulatae Viride in the core production areas cannot be guaranteed. At present, it is urgent to develop a reliable and practical rapid identification technology and method for the core production areas of Pericarpium Citri Reticulatae Viride, construct a multi-dimensional characteristic chemical fingerprint for identifying the Pericarpium Citri Reticulatae Viride in the core production areas, improve the accuracy of identification of the Pericarpium Citri Reticulatae Viride in the core production areas, and apply it to the rapid analysis of complex traditional Chinese medicine systems and the identification and quality evaluation of their authenticity, production areas, etc.

[0003] In summary, after the applicant's massive search, there are still the above-mentioned problems to be solved in the technical field of identifying the core production areas of Pericarpium Citri Reticulatae Viride. SUMMARY

[0004] Therefore, in order to solve the problem that the prior art cannot distinguish the core production areas and non-core production areas, and there are few identification methods, the present application provides a method for rapidly identifying the core production areas of Pericarpium Citri Reticulatae Viride, and the specific technical solutions are as follows:

[0005] A method for rapidly identifying the core production areas of Pericarpium Citri Reticulatae Viride, the method comprising the following steps:

[0006] Obtaining a core production area Pericarpium Citri Reticulatae Viride sample library and a non-core production area Pericarpium Citri Reticulatae Viride sample library;

[0007] Obtaining sample dataset A of in-situ mass spectrum analysis of the core production area Pericarpium Citri Reticulatae Viride under different ionization sources in positive and negative ion modes;

[0008] Obtaining sample dataset B of in-situ mass spectrum analysis of the non-core production area Pericarpium Citri Reticulatae Viride under different ionization sources in positive and negative ion modes;

[0009] constructing a comprehensive feature variable set of the core production area of broadleaf tree peels in-situ mass spectrum analysis data under different ionization source positive and negative ion modes based on the sample data set A and the sample data set B;

[0010] constructing a data model for identifying the core production area of broadleaf tree peels based on the comprehensive feature variable set;

[0011] applying the data model to identification of the core production area of broadleaf tree peels and non-core production area of broadleaf tree peels, and extracting a key variable set as a multi-dimensional characteristic chemical fingerprint spectrum of the core production area of broadleaf tree peels according to the contribution degree of variable weight in the data model;

[0012] quickly identifying the core production area of broadleaf tree peels according to the multi-dimensional characteristic chemical fingerprint spectrum of the core production area of broadleaf tree peels.

[0013] Further, the sample library of the core production area of broadleaf tree peels and the sample library of the non-core production area of broadleaf tree peels are obtained, and specifically include:

[0014] collecting a plurality of samples of the core production area of broadleaf tree peels to construct a sample library of the core production area of broadleaf tree peels;

[0015] collecting a plurality of samples of the non-core production area of broadleaf tree peels to construct a sample library of the non-core production area of broadleaf tree peels.

[0016] Further, the sample data set A is obtained as follows:

[0017] grinding the sample of the core production area of broadleaf tree peels into powder to obtain a sample powder of the core production area of broadleaf tree peels;

[0018] adding methanol to the sample powder of the core production area of broadleaf tree peels, and taking extraction liquid A after ultrasonic extraction;

[0019] performing mass spectrum analysis on the extraction liquid A to obtain a sample data set A under ionization source positive and negative ion modes.

[0020] Further, the sample data set B is obtained as follows:

[0021] grinding the sample of the non-core production area of broadleaf tree peels into powder to obtain a sample powder of the non-core production area of broadleaf tree peels;

[0022] adding methanol to the sample powder of the non-core production area of broadleaf tree peels, and taking extraction liquid B after ultrasonic extraction;

[0023] performing mass spectrum analysis on the extraction liquid B to obtain a sample data set B under ionization source positive and negative ion modes.

[0024] Further, the adding ratio of the sample powder of the core production area of broadleaf tree peels to methanol is 1g / L-10g / L.

[0025] Further, the non-core production area broad pericarpium citri ricti sample powder and the adding ratio of methanol are 1g / L-10g / L.

[0026] Further, the mass spectrum analysis is one of in-situ ionization mass spectrum technologies.

[0027] Further, the in-situ ionization mass spectrum technology is based on an electrospray ionization in-situ mass spectrum technology.

[0028] Further, the in-situ ionization mass spectrum technology is based on an atmospheric pressure chemical ionization in-situ mass spectrum technology.

[0029] Further, the conditions of the electrospray ionization in-situ mass spectrum technology are that the voltage is 3.5kV-4.5kV, the temperature of the ion transmission tube is 275℃, the sheath gas flow rate is 10arb-15arb, the auxiliary gas flow rate is 5arb-10arb, and the mass-to-charge ratio is 50-1000.

[0030] Further, the conditions of the atmospheric pressure chemical ionization in-situ mass spectrum technology are that the high-voltage corona discharge current is 34μA-5μA, the temperature of the ion transmission tube is 350℃, the sheath gas flow rate is 10arb-15arb, the auxiliary gas flow rate is 5arb-10arb, and the mass-to-charge ratio is 50-1000.

[0031] Further, the construction of the comprehensive characteristic variable set is as follows:

[0032] The sample data set A and the sample data set B are pre-processed and converted into dimensionless data, and then a series of characteristic variables with significant differences are screened out, which form the comprehensive characteristic variable set.

[0033] In the above scheme, the core production area broad pericarpium citri ricti and the non-core broad pericarpium citri ricti are analyzed by using the electrospray ionization and atmospheric pressure chemical ionization mass spectrum technologies, the sample data sets are obtained in different ionization source positive and negative ionization modes, the comprehensive characteristic variable set of the core production area broad pericarpium citri ricti mass spectrum analysis data is obtained, and then the data model is constructed, so that the core production area broad pericarpium citri ricti is more convenient and more accurately identified. In addition, the method of the present application obtains more comprehensive chemical component information, and significantly improves the accuracy and reliability of identification. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 It is a schematic diagram of the electrospray ionization source (ESI) in-situ mass spectrum technology;

[0035] Figure 2 It is a schematic diagram of the atmospheric pressure chemical ionization source (APCI) in-situ mass spectrum technology;

[0036] Figure 3Figure 1 is a schematic diagram of the positive and negative ion mode mass spectrum of the core area of Pericarpium Citri Reticulatae Viride obtained by ESI-MS technology, wherein, Figure 3 Figure 1a is a positive ion mode mass spectrum scan diagram of the core area of Pericarpium Citri Reticulatae Viride, Figure 3 Figure 1b is a negative ion mode mass spectrum diagram of the core area of Pericarpium Citri Reticulatae Viride;

[0037] Figure 4 Figure 2 is a schematic diagram of the positive and negative ion mode mass spectrum of the non-core area of Pericarpium Citri Reticulatae Viride obtained by ESI-MS technology, wherein, Figure 4 Figure 2a is a positive ion mode mass spectrum diagram of the non-core area of Pericarpium Citri Reticulatae Viride, Figure 4 Figure 2b is a negative ion mode mass spectrum diagram of the non-core area of Pericarpium Citri Reticulatae Viride;

[0038] Figure 5 Figure 3 is a schematic diagram of the positive and negative ion mode mass spectrum of the core area of Pericarpium Citri Reticulatae Viride obtained by APCI-MS technology, wherein, Figure 5 Figure 3a is a positive ion mode mass spectrum diagram of the core area of Pericarpium Citri Reticulatae Viride, Figure 5 Figure 3b is a negative ion mode mass spectrum diagram of the core area of Pericarpium Citri Reticulatae Viride;

[0039] Figure 6 Figure 4 is a schematic diagram of the positive and negative ion mode mass spectrum of the non-core area of Pericarpium Citri Reticulatae Viride obtained by APCI-MS technology, wherein, Figure 6 Figure 4a is a positive ion mode mass spectrum diagram of the non-core area of Pericarpium Citri Reticulatae Viride, Figure 6 Figure 4b is a negative ion mode mass spectrum diagram of the non-core area of Pericarpium Citri Reticulatae Viride;

[0040] Figure 7 Figure 5 is a confusion matrix diagram of the data model of the method for rapidly identifying the core area of Pericarpium Citri Reticulatae Viride according to the present application;

[0041] Figures 8-16 Figure 6 is a schematic diagram of the biomarker of the method for rapidly identifying the core area of Pericarpium Citri Reticulatae Viride according to the present application. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the embodiments thereof. It should be understood that the specific embodiments described herein are merely intended to explain the present application and not to limit the protection scope of the present application.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0044] The application discloses a method for rapidly identifying core production areas of broad pericarps.

[0045] S1. Obtain a core production area broad pericarp sample library and a non-core production area broad pericarp sample library, specifically:

[0046] 480 core production area broad pericarp samples in Xinhui District are collected as the core production area broad pericarp sample library; 320 non-core production area broad pericarp samples in Xinhui District are collected as the non-core production area broad pericarp sample library.

[0047] S2. Obtain a sample data set A of core production area broad pericarp mass spectrum analysis under different ionization source positive and negative ionization modes, specifically:

[0048] Grind the core production area broad pericarp samples into powders to obtain core production area broad pericarp sample powders passing through a No. 1 sieve;

[0049] Weigh 2 g of the core production area broad pericarp sample powders, add 25 ml of methanol, seal, ultrasonic extraction under the condition that the power is 300 W and the frequency is 40 KHz for 45 minutes, cool, shake uniformly, filter, filter again with a 0.22 mu m filter membrane, and obtain an extraction liquid A.

[0050] Adopt in-situ mass spectrum technology based on electrospray ionization (ESI) to perform mass spectrum analysis on the extraction liquid A, that is, obtain direct mass spectrum first scan data of the core production area broad pericarp under positive and negative ion modes in a normal temperature and atmospheric pressure environment, obtain a sample data set A and mark it as a first data set A, wherein the condition parameters of the electrospray ionization source (ESI) mass spectrum technology are as follows: the ionization voltage of the ESI ion source is 4.5 kV, the temperature of the ion transmission tube is 275 DEG C, the sheath gas flow rate is 10 arb, the auxiliary gas flow rate is 5 arb, the mass-to-charge ratio is m / z 50-1000, and other parameters adopt the default values of the supplier or the system automatic optimization.

[0051] Adopt in-situ mass spectrum technology based on atmospheric pressure chemical ionization (APCI) to obtain direct mass spectrum first scan data of the core production area broad pericarp under positive and negative ion modes in a normal temperature and atmospheric pressure environment, that is, obtain a sample data set A and mark it as a second data set A. The condition parameters of the atmospheric pressure chemical ionization source (APCI) mass spectrum technology are as follows: the high-voltage corona discharge current of the APCI ion source is 4.7 mu A, the temperature of the ion transmission tube is 350 DEG C, the sheath gas flow rate is 10 arb, the auxiliary gas flow rate is 5 arb, the mass-to-charge ratio range is m / z 50-1000, and other parameters adopt the default values of the supplier or the system automatic optimization.

[0052] S3. Obtain a sample data set B of non-core production area broad pericarp mass spectrum analysis under different ionization source positive and negative ion modes, specifically:

[0053] Gravel the non-core production area broad bark sample into powder to obtain the non-core production area broad bark sample powder passing the second sieve;

[0054] Take 2g of the non-core production area broad bark sample powder and add 25ml of methanol, seal, and ultrasonically extract under the condition of a power of 300W and a frequency of 40KHz for 45 minutes, cool, shake well, filter, and pass through a 0.22μm filter membrane again to obtain an extract B;

[0055] Mass spectrometry analysis is performed on the extract B based on an in-situ mass spectrometry technique based on an electrospray ionization source (ESI), that is, direct mass spectrometry first scan data of the non-core production area broad bark in positive and negative ion modes is obtained under the condition of room temperature and atmospheric pressure to obtain a sample data set B and mark it as a first sample data set B, wherein the condition parameters of the electrospray ionization source (ESI) mass spectrometry technique are as follows: the ionization voltage of the ESI ion source is 4.5kV, the temperature of the ion transmission tube is 275℃, the sheath gas flow rate is 10arb, the auxiliary gas flow rate is 5arb, the mass-to-charge ratio is m / z 50-1000, and other parameters use the default values of the supplier or are automatically optimized by the system.

[0056] Mass spectrometry analysis is performed on the extract B based on an in-situ mass spectrometry technique based on an atmospheric pressure chemical ionization source (APCI), that is, direct mass spectrometry first scan data of the core production area broad bark in positive and negative ion modes is obtained under the condition of room temperature and atmospheric pressure to obtain a sample data set B and mark it as a second sample data set B. Wherein the condition parameters of the mass spectrometry technique based on the atmospheric pressure chemical ionization source (APCI) are as follows: the high-voltage corona discharge current of the APCI ion source is 4.7μA, the temperature of the ion transmission tube is 350℃, the sheath gas flow rate is 10arb, the auxiliary gas flow rate is 5arb, the mass-to-charge ratio range is m / z 50-1000, and other parameters use the default values of the supplier or are automatically optimized by the system.

[0057] S4. Based on the sample data set A and the sample data set B, a comprehensive feature variable set of the core production area broad bark mass spectrometry analysis data in different ionization source positive and negative ion modes is constructed, specifically:

[0058] Feature variables containing more than 95% of the sample information are extracted from the four groups of mass spectrometry data of the first group data set A, the second group data set A, the first sample data set B, and the second sample data set B to obtain a comprehensive feature variable set of the core production area broad bark mass spectrometry analysis data in different ionization source positive and negative ion modes.

[0059] S5. Based on the comprehensive feature variable set, a data model for identifying the core production area broad bark is constructed, specifically:

[0060] A discriminant model is constructed using the bagging tree ensemble algorithm according to the results of the comprehensive feature vector set of the samples and the origin information of the samples, respectively. Meanwhile, the first group of data sets A, the second group of data sets A, the first sample data set B and the second sample data set B are modeled using more than 95% of the sample feature information, respectively. As shown in Figure 7 Figure 7 It can be seen from the confusion matrix diagram of the model in

[0061] S6. The data model is applied to the identification of the core area broad pericarpium and the non-core area broad pericarpium, and a key variable set is extracted as a multi-dimensional characteristic chemical fingerprint for identifying the core area broad pericarpium according to the contribution degree of the variable weight in the data model, specifically as follows:

[0062] According to the mass spectrum of the core area broad pericarpium and the non-core area broad pericarpium and the modeling results, the ion with a larger contribution rate in the model is determined to be analyzed by tandem mass spectrometry, and the secondary mass spectrum thereof is obtained, and qualitative analysis is performed in combination with the CID diagram of the secondary mass spectrum ion fragments and the standard product CID diagram, which can be regarded as an important biomarker for identifying the core broad pericarpium and the non-core area broad pericarpium, and the specific performance is shown in Figure 8 Figure 8 CID diagrams of these important biomarkers, wherein

[0063] Positive ion mode: m / z 127 (5-hydroxymethyl furfural), m / z 219 (sweet orange aldehyde), m / z 303 (hesperetin), m / z 343 (4', 5, 7, 8-tetramethoxy flavone), m / z 373 (tangeretin), m / z 389 (5-hydroxy-6-7-8-3-4'-pentamethoxy flavone), m / z 403 (chuanpeisin), m / z 433 (3, 5, 6, 7, 8, 3', 4'-heptamethoxy flavone);

[0064] Negative ion mode: m / z 301 (hesperetin).

[0065] According to the multi-dimensional characteristic chemical fingerprint of the core area broad pericarpium, the core area broad pericarpium can be quickly identified.

[0066] In addition, Figure 1 It is an ESI mass spectrometry technology schematic diagram; Figure 2 It is an APCI in-situ mass spectrometry technology schematic diagram; Figure 3 It is a first mass spectrum schematic diagram of the core area broad pericarpium obtained by an ESI in-situ mass spectrometry technology in Embodiment 1 of the present application.​​Figure 3 a is a positive ion mode primary mass spectrum scan graph, Figure 3 b is a negative ion mode primary mass spectrum graph; Figure 4 is a primary mass spectrum schematic diagram of non-core producing area broad pericarpium citri reticulatae obtained based on an electrospray ionization source (ESI) in-situ mass spectrometer technology in Embodiment 1 of the present application. Among them, Figure 4 c is a positive ion mode primary mass spectrum graph, Figure 4 d is a negative ion mode primary mass spectrum graph; Figure 5 is a positive and negative ion mode primary mass spectrum schematic diagram of core producing area broad pericarpium citri reticulatae obtained based on an atmospheric pressure chemical ionization source (APCI) in-situ mass spectrometer technology in Embodiment 1 of the present application. Among them, Figure 5 a is a positive ion mode primary mass spectrum graph, Figure 5 b is a negative ion mode primary mass spectrum graph; Figure 6 is a positive and negative ion mode primary mass spectrum schematic diagram of non-core producing area broad pericarpium citri reticulatae obtained based on an atmospheric pressure chemical ionization source (APCI) in-situ mass spectrometer technology in Embodiment 1 of the present application. Among them, Figure 6 c is a positive ion mode primary mass spectrum graph, Figure 6 d is a negative ion mode primary mass spectrum graph; Figure 7 is a confusion matrix diagram of a data model of a method for rapidly identifying core producing area broad pericarpium citri reticulatae in Embodiment 1 of the present application. From Figure 7 it can be seen that the distinguishing effect of the modeling using the comprehensive feature vector set is the best, and the core producing area broad pericarpium citri reticulatae and the non-core producing area broad pericarpium citri reticulatae can be identified by 100%, while the modeling effect using a single feature variable is not ideal, and there is a phenomenon of false judgment; Figures 8-16 is a collision-induced dissociation (CID) schematic diagram of a biomarker of a method for rapidly identifying core producing area broad pericarpium citri reticulatae in Embodiment 1 of the present application. Among them, Figure 8 is 5-hydroxymethylfurfural, Figure 9 is sweet orange aldehyde, Figure 10 is hesperetin 301 (negative ion mode), Figure 11 is hesperetin 303, Figure 12 is 4', 5, 7, 8-tetramethoxyflavone, Figure 13 is tangeretin, Figure 14 is 5-hydroxy-6-7-8-3-4'-pentamethoxyflavone, Figure 15 is nobiletin, Figure 16 is 3, 5, 6, 7, 8, 3', 4'-heptamethoxyflavone 433. Except that hesperetin 301 is a negative ion obtained in a negative ion mode, the others are protonated or quasi-molecular ions obtained in a positive ion mode.

[0067] Any combination of the technical features in the above-described embodiments can be made, and for the sake of brevity, not all possible combinations are described, however, as long as the combination of the technical features does not exist in contradiction, it shall be considered within the scope of the present disclosure.

[0068] The above-described embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it shall not be understood as a limitation on the patent scope of the present application. It shall be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these shall be within the protection scope of the present application. Therefore, the protection scope of the present application patent shall be subject to the appended claims.

Claims

1. A method for rapidly identifying the core producing area of Pericarpium Citri Reticulatae, characterized in that, The method comprises the following steps: Obtain a sample library of core production area broad bark of Pericampylus and a sample library of non-core production area broad bark of Pericampylus; Obtain sample dataset A of core production area broad bark of Pericampylus under different ionization source positive and negative ion modes; Obtain sample dataset B of non-core production area broad bark of Pericampylus under different ionization source positive and negative ion modes; Based on the sample dataset A and the sample dataset B, construct a comprehensive feature variable set of core production area broad bark of Pericampylus under different ionization source positive and negative ion modes, wherein the different ionization sources are electrospray ionization source and atmospheric pressure chemical ionization source; Based on the comprehensive feature variable set, construct a data model for identifying core production area broad bark of Pericampylus; Apply the data model to the identification of core production area broad bark of Pericampylus and non-core production area broad bark of Pericampylus, and extract a key variable set as a multi-dimensional characteristic chemical fingerprint spectrum of core production area broad bark of Pericampylus according to the contribution degree of variable weight in the data model; According to the multi-dimensional characteristic chemical fingerprint spectrum of core production area broad bark of Pericampylus, quickly identify the core production area broad bark of Pericampylus.

2. The method for rapidly identifying the core producing area of broad pericarpium according to claim 1, characterized in that, Obtain a sample library of core production area broad bark of Pericampylus and a sample library of non-core production area broad bark of Pericampylus, specifically comprising: Collect a plurality of core production area broad bark of Pericampylus samples to construct a core production area broad bark of Pericampylus sample library; Collect a plurality of non-core production area broad bark of Pericampylus samples to construct a non-core production area broad bark of Pericampylus sample library.

3. The method for rapidly identifying the core producing area of broad pericarpium according to claim 1, characterized in that, The sample dataset A is obtained by: Grinding the core production area broad bark of Pericampylus sample into powder to obtain core production area broad bark of Pericampylus sample powder; Adding methanol to the core production area broad bark of Pericampylus sample powder, and taking the extraction liquid A after ultrasonic extraction; Performing in-situ mass spectrometry analysis on the extraction liquid A under positive and negative ion modes of the ionization source to obtain sample dataset A.

4. The method for rapidly identifying the core producing area of broad pericarpium according to claim 3, characterized in that, The sample dataset B is obtained by: Grinding the non-core production area broad bark of Pericampylus sample into powder to obtain non-core production area broad bark of Pericampylus sample powder; Adding methanol to the non-core production area broad bark of Pericampylus sample powder, and taking the extraction liquid B after ultrasonic extraction; Performing mass spectrometry analysis on the extraction liquid B under positive and negative ion modes of the ionization source to obtain sample dataset B.

5. The method for rapidly identifying the core producing area of broad pericarpium according to claim 3, characterized in that, The addition ratio of the core production area broad bark of Pericampylus sample powder to methanol is 1g / L-10g / L.

6. The method for rapidly identifying the core producing area of broad pericarpium according to claim 4, characterized in that, The addition ratio of the non-core production area broad bark of Pericampylus sample powder to methanol is 1g / L-10g / L.

7. The method for rapidly identifying the core producing area of broad pericarpium according to claim 6, characterized in that, The construction of the comprehensive feature variable set is: Pretreat and transform the sample dataset A and the sample dataset B into dimensionless data, and then screen a series of feature variables with significant differences, which constitute the comprehensive feature variable set.

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