Method and system for identifying rhodiola rosea variety based on GC-IMS, storage medium and terminal

Abstract

The invention belongs to the technical field of traditional Chinese medicine identification and analysis, and particularly relates to a GC-IMS-based rhodiola rosea variety identification method and system, a storage medium and a terminal. The method comprises the following steps: firstly, detecting a standard sample set of known varieties through GC-IMS, and establishing a standardized model containing a fingerprint spectrum identification model, a principal component analysis identification model and a PLS-DA identification model; during application, GC-IMS data of a to-be-detected sample is matched with the model, so that rapid and accurate identification of rhodiola quadrifida, rhodiola tangutica and rhodiola crenulata can be realized. According to the method, the identification process is improved from empirical judgment to standardized and datamation comparison, the limitation of a traditional method is overcome, and a reliable technical means is provided for quality control of the rhodiola rosea medicinal material.

Description

Technical Field

[0001] This invention belongs to the field of traditional Chinese medicine identification and analysis technology, specifically involving a method, system, storage medium and terminal for identifying Rhodiola rosea varieties based on GC-IMS. Background Technology

[0002] Rhodiola is a perennial herbaceous plant belonging to the Crassulaceae family, possessing significant medicinal and health-promoting value. Different varieties of Rhodiola exhibit significant differences in chemical composition, pharmacological activity, and market price. Therefore, accurate identification of its varieties is crucial for ensuring the quality of medicinal materials, regulating market order, and promoting rational development and utilization.

[0003] Currently, the identification of Rhodiola rosea varieties mainly relies on morphological observation, microscopic structural analysis, and molecular biological methods. Morphological methods are easily affected by the growth environment and subjective judgment, resulting in limited accuracy; while molecular identification methods are highly accurate, they are complex to operate and costly, making them difficult to promote and use at the grassroots level.

[0004] Gas chromatography-ion mobility spectrometry (GC-IMS), as an emerging technique for detecting volatile organic compounds (VOCs), boasts advantages such as high sensitivity, rapid response, and ease of operation, and has been widely applied in food flavor analysis and environmental monitoring. However, its application in the systematic identification of Rhodiola rosea varieties, particularly in constructing standardized and quantifiable identification models, has not yet been publicly reported. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, storage medium, and terminal for identifying Rhodiola rosea varieties based on GC-IMS.

[0006] In one aspect, the present invention provides a method for identifying Rhodiola rosea varieties based on GC-IMS, characterized by comprising the following steps:

[0007] S1. Obtain standard sample sets of known varieties of Rhodiola four-lobed, Rhodiola tanguina, and Rhodiola grandiflora;

[0008] S2. Gas chromatography-ion mobility spectrometry was used to detect each standard sample and obtain the signal data of its volatile components;

[0009] S3. Based on the signal data, establish a discrimination model, wherein the model includes at least one of the following:

[0010] a) Fingerprint identification model: to identify one or more characteristic volatile substances for each Rhodiola rosea variety;

[0011] b) Principal component analysis identification model: Principal component analysis is performed on the peak volume data of volatile components in the standard sample set to determine the exclusive region boundary of each variety on the principal component score map;

[0012] c) PLS-DA identification model: Perform partial least squares discriminant analysis on the peak volume data of volatile components in the standard sample set to determine the exclusive region of each variety on the PLS-DA score map;

[0013] S4. Obtain GC-IMS volatile component signal data of the Rhodiola rosea sample to be tested;

[0014] S5. Match the data of the sample to be tested with the identification model to determine the variety; wherein the matching method includes:

[0015] The volatile component signals of the sample to be tested are matched with the characteristic volatile substances in the fingerprint identification model; and / or,

[0016] Principal component analysis was performed on the peak volume data of volatile components in the sample to be tested, and the PC1 and PC2 scores were matched with the boundaries of the specific regions in the principal component analysis identification model; and / or,

[0017] Partial least squares discriminant analysis was performed on the peak volume data of volatile components of the sample to be tested, and its position on the PLS-DA score map was matched with the specific region in the PLS-DA discrimination model.

[0018] In this invention, the characteristic substances of Rhodiola tetrafida include at least one of 2-hexenal, (E)-2-pentenal, isoamyl alcohol, and 1-pentanol; the characteristic substances of Rhodiola tangutica include at least one of n-valeric acid, benzaldehyde, isobutyraldehyde, methyl ethyl ketone, and isoamyl alcohol; and the characteristic substances of Rhodiola grandiflora include at least one of (E)-geraniol, octyl acetate, 4-terpenol, and geraniol acetate.

[0019] In this invention, Rhodiola rosea corresponds to the PC1 score range [-5.9, -5.5] and the PC2 score range [-5.5, -4.6]; Rhodiola tangutica corresponds to the PC1 score range [-5.4, -5.2] and the PC2 score range [4.9, 5.9]; Rhodiola grandiflora corresponds to the PC1 score range [10.6, 11.2] and the PC2 score range [-0.5, 0.1].

[0020] In this invention, in the PLS-DA score graph, Rhodiola rosea is in the first quadrant, Rhodiola tangutica is in the second quadrant, and Rhodiola grandiflora is in the third and fourth quadrants.

[0021] In some specific embodiments of the present invention, the fingerprint spectrum identification model, the principal component analysis identification model, and the PLS-DA identification model are used in combination. When the identification results of the three models are consistent, a final judgment result is given.

[0022] In this invention, the GC-IMS detection conditions include:

[0023] Headspace sampling conditions: incubation temperature 40~60℃, incubation time 15~25 min, injection volume 400~600 µL, incubation speed 400~600 r / min, injection needle temperature 70~100℃;

[0024] Chromatographic conditions: column temperature 55~65℃; carrier gas nitrogen, programmed pressure ramp mode;

[0025] IMS conditions: tritium source for ionization, migration tube temperature 40~50℃, electric field strength 500 V / cm, positive ion mode.

[0026] In some specific embodiments of the present invention, the programmed boosting mode is as follows: the initial flow rate is 1~3.0 mL / min and maintained for 1~3 min, then linearly increased to 8~12 mL / min within 7~9 min, then linearly increased to 90~110 mL / min within 9~11 min, then linearly increased to 140~160 mL / min within 9~11 min, and maintained for 8~12 min.

[0027] In some specific embodiments of the present invention, the programmed boost mode is as follows: the initial flow rate is 2.0 mL / min and held for 2 min, then linearly increased to 10.0 mL / min within 8 min, then linearly increased to 100.0 mL / min within 10 min, then linearly increased to 150.0 mL / min within 10 min, and held for 10 min.

[0028] In one aspect, the present invention provides a Rhodiola rosea variety identification system, the system comprising:

[0029] The data acquisition module is used to acquire GC-IMS data of Rhodiola rosea samples;

[0030] The model storage module is used to store the identification model established by the above identification methods;

[0031] The identification and analysis module is used to match the data acquired by the data acquisition module with the model in the model storage module, and to identify varieties using at least one identification model.

[0032] The results output module is used to output the identification results.

[0033] In one aspect, the present invention provides a storage medium having computer instructions stored thereon, which execute the authentication method when the computer instructions are run.

[0034] In one aspect, the present invention provides a terminal including a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the steps of the authentication method when executing the computer instructions.

[0035] The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0036] The embodiments of the subject matter and functional operation described in this specification can be implemented in: tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing device or for controlling the operation of a data processing device. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing device.

[0037] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by dedicated logic circuitry—such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as dedicated logic circuitry.

[0038] In this invention, processors suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, the computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, it is not mandatory for a computer to have such a device. Furthermore, the computer may be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name just a few.

[0039] The beneficial effects of this invention are:

[0040] (1) This invention constructs a triple identification model consisting of fingerprinting, principal component analysis, and partial least squares discriminant analysis, thereby realizing an identification mechanism that verifies the three methods. When the discrimination results of the three models are consistent, the system automatically outputs the final judgment conclusion, which significantly improves the scientific nature of the identification process and the reliability of the results, and provides a brand-new technical support system for the variety identification, origin traceability, and rational resource development of Rhodiola rosea medicinal materials.

[0041] (2) The identification scheme established in this invention is characterized by its speed and efficiency. Compared with molecular biology methods that are complex to operate and have a long cycle, this method has simple pretreatment, fast analysis speed, and can realize high-throughput detection of batch samples. Since the requirements for instruments, equipment and personnel professional background are relatively relaxed, this method is particularly suitable for rapid screening and quality monitoring in Chinese medicinal material production bases, circulation markets and regulatory sites, effectively improving identification efficiency and lowering the technical promotion threshold. Attached Figure Description

[0042] Figure 1 GC-IMS three-dimensional spectra of three Rhodiola rosea roots;

[0043] Figure 2 GC-IMS differential spectra of three Rhodiola rosea root varieties;

[0044] Figure 3 Qualitative analysis of volatile components in the roots of three Rhodiola rosea species;

[0045] Figure 4 Fingerprint spectra of volatile components from three Rhodiola rosea roots;

[0046] Figure 5 PCA score charts for three types of Rhodiola rosea root;

[0047] Figure 6 PLS-DA score graphs for three types of Rhodiola rosea root;

[0048] Figure 7 The permutation test diagram for the PLS-DA model of three types of Rhodiola rosea root is shown. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All features disclosed in this specification, or steps in all disclosed methods or processes, except for mutually exclusive features and / or steps, can be combined in any way.

[0050] 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 invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0051] Example 1

[0052] 1 Experimental Methods

[0053] 1.1 Materials

[0054] FlavourSpec® gas chromatography-ion mobility spectrometry system (GAS GmbH, Germany); CTC-PAL 3 static headspace autosampler (CTC Analytics AG, Zwingen GmbH, Switzerland); PL303 electronic balance (Mettler-Toledo Instruments Co., Ltd., Shanghai).

[0055] The three samples, Rhodiola quadrifida (Pall.) Fisch. & CA Mey., Rhodiola tangutica (Maxim.) SH Fu., and Rhodiola crenulata (Hook. f. & Thomson) H. Ohba., were identified by Researcher Gao Qingbo of the Northwest Plateau Institute of Biology, Chinese Academy of Sciences, as the whole plants of Rhodiola quadrifida (Pall.) Fisch. & CA Mey., Rhodiola tangutica (Maxim.) SH Fu., and Rhodiola crenulata (Hook. f. & Thomson) H. Ohba.

[0056] 1.2 Sample Preparation

[0057] The roots of Rhodiola four-lobed, Rhodiola tangutica and Rhodiola grandiflora (codes SL, TG and DH respectively) were used as experimental materials. The soil and sand on the surface of the roots were cleaned, dried with absorbent paper, and then cut into small pieces for later use.

[0058] 1.3 GC-IMS Analysis

[0059] Take 2.0g of each of the three samples and place them in a 20 mL headspace vial. Measure each sample in triplicate.

[0060] Headspace injection conditions: Incubation temperature: 50℃; incubation for 20 min; injection volume: 500 µL; splitless injection; incubation speed: 500 r / min; injection needle temperature: 85 ℃.

[0061] GC conditions: Column temperature: 60 ℃; Carrier gas: High-purity nitrogen (purity ≥ 99.999%); Programmed pressure increase: Initial flow rate 2.0 mL / min, held for 2 min, linearly increased to 10.0 mL / min within 8 min, linearly increased to 100.0 mL / min within 10 min, linearly increased to 150.0 mL / min within 10 min, held for 10 min; Run time: 40 min; Injector temperature: 80 ℃.

[0062] IMS conditions: Ionization source: Tritium source ( 3 H); migration tube length: 53 mm; electric field strength: 500 V / cm; migration tube temperature: 45℃; drift gas: high-purity nitrogen (purity ≥ 99.999%); flow rate: 75 mL / min; positive ion mode.

[0063] 1.4 Data Processing

[0064] A mixed standard of six ketones was tested, and calibration curves for retention time and retention index were established. Then, the retention index of the target substance was calculated based on its retention time, and qualitative analysis of the target substance was performed using database retrieval and comparison.

[0065] Data processing and analysis software were used to generate three-dimensional spectra, two-dimensional spectra, differential spectra, fingerprint spectra, PCA spectra, and PLS-DA spectra of volatile components for comparison of volatile organic compounds between samples.

[0066] 2 Results and Analysis

[0067] 2.1 GC-IMS Differential Spectrum Analysis

[0068] The volatile components of the roots of three Rhodiola rosea species—Rhodiola tetralobata, Rhodiola tangutica, and Rhodiola grandiflora—were determined using GC-IMS. The GC-IMS three-dimensional spectra of the three Rhodiola rosea species are shown below. Figure 1As shown in the figure, the three axes represent relative migration time (X-axis), retention time (Y-axis), and signal peak intensity (Z-axis), respectively. From the three-dimensional spectrum, it can be clearly seen that there are significant differences in the types and contents of volatile organic compounds in the three types of Rhodiola rosea roots.

[0069] To further compare the differences in volatile components among the three Rhodiola rosea samples, the spectrum of Rhodiola tetraloba was selected as a reference, and the spectra of the other samples were subtracted from the reference. The resulting GC-IMS difference graphs of the three Rhodiola rosea samples are shown below. Figure 2 As shown in the diagram. Identical substances cancel each other out in white, as the background color; blue areas in the reference sample indicate that the concentration of that substance is lower than that of the reference sample, with deeper blue indicating lower concentrations; red areas in the reference sample indicate that the concentration of that substance is higher than that of the reference sample, with deeper red indicating higher concentrations. Figure 2 It can be seen that the content of volatile compounds in the roots of the three Rhodiola species differs significantly, with the content of volatile components in the roots of Rhodiola grandiflora being significantly higher than that in the other two varieties.

[0070] 2.2 Qualitative Analysis of Volatile Substances

[0071] Based on the retention and migration times of each volatile substance, qualitative analysis was performed using the software's built-in NIST and IMS databases. The qualitative results are as follows: Figure 3 As shown in Table 1, a total of 71 volatile components were identified, including 26 alcohols, 17 aldehydes, 10 esters, 7 ketones, 4 acids, 3 alkenes, 2 furans, and 2 other compounds. In addition, 23 unidentified substances require further investigation. In the three Rhodiola rosea samples, alcohols, aldehydes, esters, and ketones were the main components of their volatile composition.

[0072] Table 1. Qualitative analysis results of volatile components in the roots of three types of Rhodiola rosea.

[0073]

[0074]

[0075] 2.3 Fingerprint Analysis

[0076] Fingerprint analysis of all volatile substances, such as Figure 4As shown, the differences in volatile substances among the three samples are visually compared. Each row in the figure represents all signal peaks in a single sample, and each column represents the signal peaks of the same volatile organic compound in different samples. The figure reveals significant differences in the fingerprint spectra of the three Rhodiola rosea samples. The main differences in the figure are marked as follows: Box A indicates the characteristic region where the signal intensity of the SL sample is significantly higher than that of other varieties, containing volatile substances such as 2-hexenal, (E)-2-pentenal, isoamyl alcohol, and 1-pentanol, with relatively high content; Box B indicates the characteristic region where the signal intensity of the TG sample is significantly higher than that of other varieties, containing volatile substances such as n-valeric acid, benzaldehyde, isobutyraldehyde, methyl ethyl ketone, and isoamyl, with higher content than the other two samples; Box C indicates the characteristic region of volatile substances contained in the DH sample, containing volatile substances such as (E)-geraniol, octyl acetate, 4-terpenol, geraniol acetate, 4-terpenol, 1-octanol, and linalool, with significantly higher content than the other two samples. The relative contents of volatile substances in the three Rhodiola rosea samples are shown in Table 2.

[0077] Table 2. Relative contents of volatile components in the roots of three Rhodiola rosea species

[0078]

[0079]

[0080]

[0081] 2.4 Principal Component Analysis

[0082] The peak volumes of volatile components were processed by PCA, and the results are as follows: Figure 5 As shown in the figure, PC1 contributed 73%, PC2 contributed 22%, and the cumulative contribution reached 95%, covering most of the effective information on volatile components in the roots of different Rhodiola rosea varieties. The three samples were distributed in different regions: Rhodiola tetralobata corresponded to the PC1 score range [-5.9, -5.5] and the PC2 score range [-5.5, -4.6]; Rhodiola tangutica corresponded to the PC1 score range [-5.4, -5.2] and the PC2 score range [4.9, 5.9]; Rhodiola grandiflora corresponded to the PC1 score range [10.6, 11.2] and the PC2 score range [-0.5, 0.1]. They also showed an aggregated state, indicating that the three Rhodiola rosea samples were well distinguished. The results show that GC-IMS can effectively identify and distinguish the roots of different Rhodiola rosea varieties through the analysis of volatile components.

[0083] 2.5 Partial Least Squares-Discriminant Analysis

[0084] To better verify the differences between samples, a supervised PLS-DA identification analysis was performed based on the PCA analysis. A PLS-DA model was established using the peak volume data of volatile components in three Rhodiola rosea samples detected by GC-IMS, as follows: Figure 6 As shown, the samples within each group are well clustered, and there is no overlap or intersection between groups, indicating that the three samples are well distinguished. In this model, R... 2 X=0.942, R 2 Y=0.997, Q 2 =0.994. R 2 The closer Q is to 1, the higher the goodness of fit. 2 A value closer to 1 indicates better predictive ability, suggesting a good fit and strong predictive power. The PLS-DA model was validated using a permutation test, and the results are as follows: Figure 7 As shown, R 2 = 0.206, less than 0.3, Q 2 = -0.302, less than 0.05, indicating that the model is reliable and there is no overfitting. PLS-DA results show that the three Rhodiola rosea samples are distributed in different quadrants: Rhodiola tetralobata SL in the first quadrant, Rhodiola tangutica TG in the second quadrant, and Rhodiola grandiflora DH in the third and fourth quadrants, indicating that there are significant differences among the three Rhodiola rosea samples. PLS-DA can effectively distinguish and differentiate the three Rhodiola rosea samples.

[0085] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying Rhodiola rosea varieties based on GC-IMS, characterized in that, Includes the following steps: S1. Obtain standard sample sets of known varieties of Rhodiola four-lobed, Rhodiola tanguina, and Rhodiola grandiflora; S2. Gas chromatography-ion mobility spectrometry was used to detect each standard sample and obtain the signal data of its volatile components; S3. Based on the signal data, establish a discrimination model, wherein the model includes at least one of the following: a) Fingerprint identification model: to identify one or more characteristic volatile substances for each Rhodiola rosea variety; b) Principal component analysis identification model: Principal component analysis is performed on the peak volume data of volatile components in the standard sample set to determine the exclusive region boundary of each variety on the principal component score map; c) PLS-DA identification model: Perform partial least squares discriminant analysis on the peak volume data of volatile components in the standard sample set to determine the exclusive region of each variety on the PLS-DA score map; S4. Obtain GC-IMS volatile component signal data of the Rhodiola rosea sample to be tested; S5. Match the data of the sample to be tested with the identification model to determine the variety; wherein the matching method includes: The volatile component signals of the sample to be tested are matched with the characteristic volatile substances in the fingerprint identification model; and / or, Principal component analysis was performed on the peak volume data of volatile components in the sample to be tested, and the PC1 and PC2 scores were matched with the boundaries of the specific regions in the principal component analysis identification model; and / or, Partial least squares discriminant analysis was performed on the peak volume data of volatile components of the sample to be tested, and its position on the PLS-DA score map was matched with the specific region in the PLS-DA discrimination model.

2. The identification method according to claim 1, characterized in that, The characteristic substances of Rhodiola tetraloba include at least one of 2-hexenal, (E)-2-pentenal, isoamyl alcohol, and 1-pentanol; the characteristic substances of Rhodiola tanguina include at least one of n-valeric acid, benzaldehyde, isobutyraldehyde, methyl ethyl ketone, and isoamyl alcohol; the characteristic substances of Rhodiola grandiflora include at least one of (E)-geraniol, octyl acetate, 4-terpenol, and geraniol acetate.

3. The identification method according to claim 1, characterized in that, Rhodiola rosea corresponds to the PC1 score range [-5.9, -5.5] and the PC2 score range [-5.5, -4.6]; Rhodiola tangutica corresponds to the PC1 score range [-5.4, -5.2] and the PC2 score range [4.9, 5.9]; Rhodiola grandiflora corresponds to the PC1 score range [10.6, 11.2] and the PC2 score range [-0.5, 0.1].

4. The identification method according to claim 1, characterized in that, In the PLS-DA score chart, Rhodiola rosea is in the first quadrant, Rhodiola tangutica is in the second quadrant, and Rhodiola grandiflora is in the third and fourth quadrants.

5. The identification method according to claim 1, characterized in that, The fingerprint spectrum identification model, principal component analysis identification model, and PLS-DA identification model are used in combination. When the identification results of the three models are consistent, a final judgment result is given.

6. The identification method according to claim 1, characterized in that, The GC-IMS detection conditions include: Headspace sampling conditions: incubation temperature 40~60℃, incubation time 15~25 min, injection volume 400~600 µL, incubation speed 400~600 r / min, injection needle temperature 70~100℃; Chromatographic conditions: column temperature 55~65℃; carrier gas nitrogen, programmed pressure ramp mode; IMS conditions: tritium source for ionization, migration tube temperature 40~50℃, electric field strength 500 V / cm, positive ion mode.

7. The identification method according to claim 6, characterized in that, The programmed pressure increase mode is as follows: the initial flow rate is 1~3.0 mL / min, maintained for 1~3 min, linearly increased to 8~12 mL / min within 7~9 min, linearly increased to 90~110 mL / min within 9~11 min, linearly increased to 140~160 mL / min within 9~11 min, and maintained for 8~12 min; further, the programmed pressure increase mode is as follows: the initial flow rate is 2.0 mL / min, maintained for 2 min, linearly increased to 10.0 mL / min within 8 min, linearly increased to 100.0 mL / min within 10 min, linearly increased to 150.0 mL / min within 10 min, and maintained for 10 min.

8. A Rhodiola rosea variety identification system, characterized in that, The system includes: The data acquisition module is used to acquire GC-IMS data of Rhodiola rosea samples; A model storage module is used to store the identification model established by the method of claim 1; The identification and analysis module is used to match the data acquired by the data acquisition module with the model in the model storage module, and to identify varieties using at least one identification model as described in claim 1. The results output module is used to output the identification results.

9. A storage medium storing computer instructions thereon, characterized in that, The computer instructions execute the identification method according to any one of claims 1-7.

10. A terminal, comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, characterized in that, When the processor executes computer instructions, it performs the steps of the identification method according to any one of claims 1-7.

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