Construction method and identification method of near-infrared identification model of Terminalia chebula medicinal material

Abstract

This invention discloses a method for constructing a near-infrared identification model for Terminalia chebula medicinal materials. The method includes: collecting a first predetermined number of Terminalia chebula medicinal material samples from the original Terminalia chebula bud, Terminalia chebula velutipes bud, and Terminalia chebula fruit, and performing near-infrared spectral measurements to obtain an initial spectral set; establishing a calibration set and a validation set; preprocessing the sample spectra within the calibration set to obtain a preprocessed spectral set; selecting wavelengths from the preprocessed spectral set to obtain a first wavelength group or a second wavelength group, wherein the first wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages, and the second wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages and from different buds; and establishing a model within the spectral range of the first wavelength group and / or the second wavelength group. This invention utilizes near-infrared spectroscopy technology, and the sample preparation process is simple, non-destructive, and free from chemical reagent contamination.

Description

Technical Field

[0001] This invention relates to the field of Chinese medicinal material analysis technology, and in particular to a method for constructing a near-infrared identification model and an identification method for Terminalia chebula. Background Technology

[0002] Terminalia chebula, Terminalia velutipes var. tomentella, and Terminalia chebula are all commonly used medicinal materials in Mongolian and Tibetan medicine. Terminalia chebula is the dried, mature fruit of Terminalia chebula Retz. or Terminalia velutipes Retz. var. tomentella Kurt., both belonging to the Combretaceae family. Terminalia chebula, also known as Tibetan Terminalia chebula, is the dried, immature fruit of Terminalia chebula Retz., also belonging to the Combretaceae family. The chemical components of these three medicinal materials are mainly classified into four categories: tannins, triterpenoids, phenolic acids, and aliphatic compounds.

[0003] Currently, the 2020 edition of the Chinese Pharmacopoeia includes standards for Terminalia chebula and Terminalia chebula, but these standards do not include quality research content. Current literature research on the identification methods for Terminalia chebula, Terminalia chebula vulgaris, and Terminalia chebula mainly includes morphological identification, thin-layer chromatography (TLC), and high-performance liquid chromatography (HPLC). Except for HPLC, the other two methods suffer from strong subjectivity and significant human error. While HPLC can detect microscopic changes in major components, it requires high-purity standards, has complex procedures, and a long detection cycle.

[0004] Research on near-infrared spectroscopy of Terminalia chebula is limited and mostly consists of quantitative studies on single varieties. While some literature includes infrared fingerprint identification studies of Terminalia chebula, Terminalia chebula var. chinensis, and Terminalia chebula var. hainanensis, near-infrared spectroscopy offers advantages over traditional infrared spectroscopy due to its wider electromagnetic spectral range and more comprehensive spectral information. Near-infrared spectroscopy, with wavelengths between visible and mid-infrared light (780–2526 nm), falls under the overtone and dominant frequency absorption spectra of molecular vibrations. It is primarily generated by the anharmonicity of molecular vibrations causing transitions from the ground state to higher energy levels, exhibiting strong penetrating power. Near-infrared spectroscopy (NIR) based on Fourier transform offers advantages such as macroscopic identification of complex systems and its non-destructive and rapid nature. However, it suffers from drawbacks including weak signal intensity, wide spectral bandwidth, and overlapping signals, making it impossible to directly extract qualitative / quantitative information about substances from the spectral signals. In fact, as an indirect analytical technique, successful application of near-infrared spectroscopy relies on qualitative and quantitative analytical models established using multivariate correction techniques. Therefore, near-infrared spectroscopy modeling methods have been a core component of near-infrared spectroscopy technology and a key research focus in this field in recent years. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for constructing a near-infrared identification model of Terminalia chebula medicinal material, which can obtain a near-infrared identification model with good selectivity and high recognition rate.

[0006] The technical problem to be solved by this invention is to provide a near-infrared identification method for Terminalia chebula medicinal material, which is simple to test, does not damage the sample, and is free from chemical reagent contamination.

[0007] To address the aforementioned technical problems, this invention provides a method for constructing a near-infrared identification model for Terminalia chebula medicinal material, comprising the following steps:

[0008] Near-infrared spectroscopy measurements were performed on a first predetermined number of samples of Terminalia chebula medicinal material, Terminalia chebula medicinal material, and Terminalia chebula medicinal material from the Combretaceae family to obtain the original spectral set.

[0009] A second preset number of sample spectra are randomly selected from the original spectral set as the calibration set, and the remaining sample spectra are used as the verification set.

[0010] The sample spectra within the calibration set are preprocessed to obtain a preprocessed spectral set.

[0011] The preprocessed spectral set is subjected to band selection to obtain a first wavelength group or a second wavelength group. The first wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages, and the second wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages and different origins.

[0012] A model is established within the spectral range of the first wavelength group and / or the second wavelength group.

[0013] In one embodiment, the wavelength range of the first wavelength group includes 11377.8 cm. -1 ~10202.5cm -1 9165.9cm -1 ~7149.9cm -1 and 5068.6cm -1 ~4154.5cm -1 .

[0014] In one embodiment, the wavelength range of the second wavelength group includes 9035.3 cm. -1 ~8504.8cm -1 7892.6cm -1 ~7451.9cm -1 5068.6cm -1 ~5052.27cm -1 4636cm -1 ~4489.1cm -1and 4440.1cm -1 ~4154.5cm -1 .

[0015] In one embodiment, when preprocessing the sample spectra within the calibration set, the preprocessing method is selected from one or a combination of spectral data normalization, spectral data de-interpolation, spectral data standardization, multivariate scattering correction, vector normalization, data smoothing, and differentiation.

[0016] In one embodiment, the sample spectra in the calibration set are preprocessed using vector normalization to obtain a preprocessed spectral set.

[0017] In one implementation, a model is established within the spectral range of the first wavelength group and / or the second wavelength group using a factorization method.

[0018] In one embodiment, the samples of Terminalia chebula medicinal material, Terminalia chebula medicinal material, and Terminalia chebula medicinal material of the Combretaceae family are processed before near-infrared spectroscopy measurement.

[0019] The sample processing includes: pulverizing the Terminalia chebula medicinal material samples, Terminalia velutipes medicinal material samples, and Terminalia chebula medicinal material samples, respectively, passing them through an 80-120 mesh sieve, and then sealing them in a vacuum bag for preservation.

[0020] In one embodiment, the number of Terminalia chebula medicinal material samples, Terminalia chebula medicinal material samples, and Terminalia chebula medicinal material samples of the first preset number of Combretaceae plants Terminalia chebula original, Terminalia chebula medicinal material samples, and Terminalia chebula medicinal material samples are the same.

[0021] In one implementation, the value of the second preset quantity is ≥ 2 / 3 times the value of the first preset quantity.

[0022] In one embodiment, the near-infrared spectroscopy measurement is performed under the following conditions: using the instrument's built-in background as a reference, the scanning range is 11500 cm⁻¹. -1 ~4000cm -1 The number of scans is 62–66, and the resolution is 14–18 cm. -1 .

[0023] In one embodiment, the near-infrared spectroscopy measurement is performed under the following conditions: using the instrument's built-in background as a reference, the scanning range is 11500 cm⁻¹. -1 ~4000cm -1 The number of scans was 64, and the resolution was 16cm. -1 .

[0024] In one implementation, it further includes:

[0025] The model was used to validate the spectra of samples in the validation set to evaluate whether the model could accurately identify Terminalia chebula medicinal materials at different maturity stages, or Terminalia chebula medicinal materials at different maturity stages and from different origins.

[0026] To address the aforementioned issues, a near-infrared identification method for Terminalia chebula medicinal materials from different maturity stages and origins is proposed, comprising the following steps:

[0027] Obtain the near-infrared identification model of the above-mentioned Terminalia chebula medicinal material;

[0028] Near-infrared spectra of the sample to be tested are collected, and the data of the near-infrared spectra of the sample to be tested are imported into the near-infrared identification model of the Terminalia chebula medicinal material to obtain the discrimination results.

[0029] Understandably, Terminalia chebula refers to the dried, mature fruit of the plant *Terminalia chebula* Retz. (family Combretaceae) and the dried, mature fruit of *Terminalia chebula* Retz. var. *tomentella* Kurt. (family Combretaceae). Western green fruit refers to the dried, immature fruit of the plant *Terminalia chebula* Retz. (family Combretaceae).

[0030] Understandably, the measuring instrument for acquiring near-infrared spectra may be, but is not limited to, a Fourier transform near-infrared spectrometer; the computer software used may be, but is not limited to, TANGO software or OPUS software; the spectral measurement mode may be, but is not limited to, diffuse reflectance mode; and the measurement parameters may be, but are not limited to, spectral scanning range, number of scans, resolution, and number of sample measurements.

[0031] Implementing this invention has the following beneficial effects:

[0032] This invention establishes a method for constructing a near-infrared identification model for Terminalia chebula medicinal materials, which can obtain a near-infrared identification model with good selectivity and high recognition rate. The method can construct a model applicable to the identification of mature and immature Terminalia chebula fruits, providing a basis for determining Terminalia chebula medicinal materials at different maturity stages. The method has low detection cost, no sample contamination, fast identification speed, and high recognition accuracy. Furthermore, the method can construct near-infrared identification models applicable to Terminalia chebula medicinal materials at different maturity stages and from different origins. It can accurately identify Terminalia chebula from the Combretaceae family, Terminalia chebula from the Trichosanthes kirilowii family, and immature Terminalia chebula. The sample preparation process is simple, does not damage the sample, has no chemical reagent contamination, and offers fast identification speed and high recognition accuracy. Attached Figure Description

[0033] Figure 1 These are the original spectra of 300 batches of medicinal material samples in Example 1 of this invention;

[0034] Figure 2 This is the original spectrum of the Terminalia chebula medicinal material sample, which is the origin of Terminalia chebula, a plant of the Combretaceae family, in Example 1 of this invention;

[0035] Figure 3 This is the original spectrum of the Terminalia chebula medicinal material sample from the Terminalia chebula var. velutina in Example 1 of this invention;

[0036] Figure 4 This is the original spectrum of the Terminalia chebula medicinal material sample in Example 1 of this invention;

[0037] Figure 5 This is the chromatogram of 186 batches of medicinal materials after vector normalization preprocessing in Example 1 of this invention;

[0038] Figure 6 This is a 2D distribution diagram of Terminalia chebula and Terminalia chebula in Embodiment 1 of the present invention;

[0039] Figure 7 This is the chromatogram of 280 batches of medicinal materials after vector normalization preprocessing in Example 2 of the present invention;

[0040] Figure 8 This is a 2D score diagram of Terminalia chebula, Terminalia velutipes, and Terminalia chebula from the Combretaceae family in Embodiment 2 of the present invention.

[0041] Figure 9 This is a 3D score image of Terminalia chebula, Terminalia velutipes, and Terminalia chebula from the Combretaceae family in Embodiment 2 of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Conversely, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosure of this invention.

[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 invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0044] the term

[0045] Unless otherwise stated or in case of contradiction, the terms or phrases used herein shall have the following meanings:

[0046] In this invention, the terms "combinations thereof", "any combination thereof", and "any combination thereof" include all suitable combinations of any two or more of the listed items.

[0047] In this invention, "preferred" is merely a description of a more effective implementation method or embodiment, and should be understood as not constituting a limitation on the scope of protection of this invention.

[0048] In this invention, the technical features described in an open-ended manner include both closed-ended technical solutions composed of the listed features and open-ended technical solutions that include the listed features.

[0049] In this invention, numerical ranges are involved, and unless otherwise specified, they include the two endpoints of the numerical range.

[0050] This invention provides a method for constructing a near-infrared identification model for Terminalia chebula medicinal material, comprising the following steps:

[0051] S1. Collect a first preset number of samples of Terminalia chebula medicinal material, Terminalia chebula medicinal material, and Terminalia chebula medicinal material from the Combretaceae family, and perform near-infrared spectral measurements to obtain the original spectral set.

[0052] Specifically, in order to ensure the representativeness and uniformity of the sample distribution, in one embodiment, the number of Terminalia chebula medicinal material samples, Terminalia chebula medicinal material samples, and Terminalia chebula medicinal material samples of the first preset number of Combretaceae plants Terminalia chebula origin, Terminalia chebula medicinal material samples, and Terminalia chebula medicinal material samples are the same.

[0053] Preferably, 50 batches of Terminalia chebula medicinal material samples, 50 batches of Terminalia chebula medicinal material samples, and 50 batches of Terminalia chebula medicinal material samples were collected. Each batch of samples was divided into two parts for near-infrared spectral measurement, resulting in an original spectral set consisting of 300 sample spectra.

[0054] More preferably, the collected samples of Terminalia chebula medicinal material from the Trichosanthes kirilowii plant originating from at least two different origins, the collected samples of Terminalia chebula medicinal material from the Trichosanthes kirilowii plant originating from at least two different origins, and the collected samples of Terminalia chebula medicinal material from at least two different origins.

[0055] Furthermore, the collected medicinal samples need to undergo sample preparation processing before near-infrared spectroscopy measurement. In one embodiment, the sample processing method involves pulverizing the medicinal samples of Terminalia chebula (from the Trichosanthes kirilowii plant), Terminalia chebula (from the Trichosanthes kirilowii plant), and Terminalia chebula (from Terminalia chebula plant), respectively, passing them through an 80-120 mesh sieve, and then sealing them in a vacuum bag for preservation.

[0056] Preferably, the sample processing method involves pulverizing the Terminalia chebula medicinal material samples from the Trichosanthes kirilowii basidiosus, the Terminalia chebula basidiosus basidiosus, and the Terminalia chebula medicinal material samples, respectively, passing them through a 100-mesh sieve, and then sealing them in a vacuum bag for preservation.

[0057] After obtaining the sample, near-infrared spectroscopy measurements were performed. In one embodiment, the instrument used was a TANGO-R Fourier transform near-infrared spectrometer (Bruker GmbH, Germany), the detector was an integrating sphere diffuse reflectance, and the analysis software was OPUS 7.5.

[0058] In one embodiment, the near-infrared spectroscopy measurement is performed under the following conditions: using the instrument's built-in background as a reference, the scanning range is 11500 cm⁻¹. -1 ~4000cm -1 The number of scans is 62–66, and the resolution is 14–18 cm. -1 .

[0059] Preferably, the near-infrared spectroscopy measurement conditions are: using the instrument's built-in background as a reference, the scanning range is 11500 cm⁻¹. -1 ~4000cm -1 The number of scans was 64, and the resolution was 16cm. -1 Each batch of samples was divided into two portions as parallel samples, and the measurements were repeated twice.

[0060] S2. Randomly select a second preset number of sample spectra from the original spectral set as the calibration set, and use the remaining sample spectra as the verification set;

[0061] Specifically, during the collection of the calibration set, it is necessary to ensure the representativeness and uniformity of the distribution of the calibration set. Therefore, this invention uses a random sampling method to select the calibration set from the original spectral set.

[0062] Furthermore, the number of calibration sets will affect the reliability of the model. In one embodiment, the value of the second preset number is ≥ 2 / 3 times the value of the first preset number, which helps to increase the robustness and reliability of the model.

[0063] S3. Preprocess the sample spectra in the calibration set to obtain the preprocessed spectrum set;

[0064] Specifically, during near-infrared spectroscopy measurements, factors unrelated to the target, such as temperature, stray light, and sample morphology, can affect the spectrum, introducing noise, background, and other interference signals. The presence of these interference signals not only complicates the near-infrared spectral range but also severely reduces the accuracy and stability of analytical models built upon near-infrared spectra. Therefore, appropriate preprocessing of the raw near-infrared spectra is necessary.

[0065] In one embodiment, when preprocessing the sample spectra within the calibration set, the preprocessing method may be one or a combination of spectral data normalization, spectral data de-interpolation, spectral data standardization, multivariate scattering correction, vector normalization, data smoothing, and differentiation. Preferably, vector normalization is used to preprocess the sample spectra within the calibration set to obtain a preprocessed spectral set. Preprocessing the original spectra using vector normalization can achieve better elimination of various noises and interferences, thereby improving the prediction accuracy of the model.

[0066] S4. Perform band selection on the preprocessed spectral set to obtain a first wavelength group or a second wavelength group. The first wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages, and the second wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages and different origins.

[0067] It should be noted that near-infrared spectroscopy is high-dimensional data, with each spectrum often containing hundreds or even thousands of wavelengths. The absorption characteristics of objects to near-infrared light mean that the correlation between different wavelengths in the near-infrared spectrum and the target being detected varies. Furthermore, the near-infrared spectrum itself is susceptible to noise and other interference, resulting in a large number of useless redundant wavelengths. If a separate model were built using all wavelengths, the presence of redundant wavelengths would degrade the model's performance or even prevent it from meeting the accuracy requirements of practical applications. Therefore, band selection is a crucial step in the model construction method. Based on extensive creative experiments, the inventors obtained a first wavelength group, whose wavelength range includes 11377.8 cm⁻¹. -1 ~10202.5cm -1 9165.9cm -1 ~7149.9cm -1 and 5068.6cm -1 ~4154.5cm -1 Modeling was performed within the first wavelength group to ultimately identify Terminalia chebula medicinal materials at different maturity stages, specifically identifying Terminalia chebula samples and Terminalia chebula fruit samples. Terminalia chebula refers to the dried, mature fruit of the Terminalia chebula var. chebula and the dried, mature fruit of Terminalia chebula var. chebula. The dried, immature fruit is the dried fruit of Terminalia chebula var. chebula. Simultaneously, band selection was performed on the preprocessed spectral set to obtain a second wavelength group, the wavelength range of which includes 9035.3 cm⁻¹. -1 ~8504.8cm -1 7892.6cm -1 ~7451.9cm -1 5068.6cm -1 ~5052.27cm -1 4636cm -1~4489.1cm -1 and 4440.1cm -1 ~4154.5cm -1 The second wavelength group is used to identify Terminalia chebula (the original form of Terminalia chebula), Terminalia velutipes (the original form of Terminalia chebula), and Terminalia chebula (the original form of Terminalia chebula) of the Combretaceae family. Understandably, the band selection can be, but is not limited to, automatic selection by software, manual selection, or a combination of automatic and manual selection; this invention does not limit this to any particular method.

[0068] S5. Establish a model within the spectral range of the first wavelength group and / or the second wavelength group.

[0069] Specifically, near-infrared spectroscopy is an indirect analytical technique whose successful application depends on a sound analytical model. Therefore, establishing an accurate analytical model is crucial for the application of near-infrared spectroscopy. In one embodiment, a first model is obtained by modeling within the spectral range of the first wavelength group using factorization. This first model is used to identify Terminalia chebula medicinal materials at different maturity stages. In another embodiment, a second model is obtained by modeling within the spectral range of the second wavelength group using factorization. This second model is used to identify Terminalia chebula medicinal materials at different maturity stages and from different origins.

[0070] S6. Use the model to validate the sample spectra in the validation set to evaluate whether the model can accurately identify Terminalia chebula medicinal materials at different maturity stages, or Terminalia chebula medicinal materials at different maturity stages and different origins.

[0071] In one implementation, a factorization method is used to model and calculate a threshold within the spectral range of the first or second wavelength group; the selectivity S-value is calculated to evaluate whether the model can achieve the preset purpose.

[0072] Selectivity (S-value) is an important indicator for evaluating the performance of qualitative models, reflecting the selectivity between different substances. The formula is: S = D / (T1 + T2), where D is the distance between the average spectra of the two classes of substances, and T1 and T2 are the threshold values ​​for the two classes. When S < 1, it indicates that the two classes of substances cannot be distinguished by the model; when S ≥ 1, it indicates that the two classes of substances can be distinguished.

[0073] Accordingly, a near-infrared identification method for Terminalia chebula medicinal material includes the following steps:

[0074] Obtain the near-infrared identification model of the above-mentioned Terminalia chebula medicinal material;

[0075] Near-infrared spectra of the sample to be tested are collected, and the data of the near-infrared spectra of the sample to be tested are imported into the near-infrared identification model of the Terminalia chebula medicinal material to obtain the discrimination results.

[0076] Specifically, the present invention provides a near-infrared identification method for Terminalia chebula medicinal materials at different maturity stages, comprising the following steps:

[0077] Obtain the first model;

[0078] The near-infrared spectrum of the sample to be tested is collected, and the data of the near-infrared spectrum of the sample to be tested is imported into the first model to obtain the discrimination result.

[0079] Meanwhile, this invention provides a near-infrared identification method for Terminalia chebula medicinal materials at different maturity stages and from different origins, comprising the following steps:

[0080] Obtain the second model;

[0081] The near-infrared spectrum of the sample to be tested is collected, and the data of the near-infrared spectrum of the sample to be tested is imported into the second model to obtain the discrimination result.

[0082] In addition, to improve the identification accuracy of the second model, the first and second models can be used in combination.

[0083] In one embodiment, the near-infrared identification method for Terminalia chebula medicinal materials at different maturity stages and from different origins includes the following steps:

[0084] Obtain the first model;

[0085] Near-infrared spectra of the sample to be tested are collected, and the data of the near-infrared spectra of the sample to be tested are imported into the first model to obtain the discrimination results, so as to distinguish between the mature fruit and the immature fruit of Terminalia chebula.

[0086] Obtain the second model;

[0087] The near-infrared spectrum of the sample to be tested is collected, and the data of the near-infrared spectrum of the sample to be tested is imported into the second model to obtain the discrimination result.

[0088] In this implementation, the first model is used to perform a discriminant analysis of the maturity stage, and then the second model is used to verify and further distinguish the origins, which helps to improve the reliability of identification.

[0089] As described above, this invention establishes a method for constructing a near-infrared identification model for Terminalia chebula medicinal materials, which can obtain a near-infrared identification model with good selectivity and high recognition rate. It also provides a near-infrared identification method for Terminalia chebula medicinal materials at different maturity stages, applicable to the identification of mature fruits and immature fruits, providing a basis for determining Terminalia chebula medicinal materials at different maturity stages. The method has low detection cost, no sample contamination during detection, fast identification speed, and high recognition accuracy.

[0090] Furthermore, the present invention also provides a near-infrared identification method for Terminalia chebula medicinal materials with different maturity stages and different origins. This method can be applied to the identification of Terminalia chebula medicinal materials with different maturity stages and different origins. It can accurately identify Terminalia chebula, Terminalia velutipes, and Terminalia chebula from the Combretaceae family. The sample preparation process is simple, does not damage the sample, has no chemical reagent contamination, and has a fast identification speed and high identification accuracy.

[0091] The present invention will be further described below with reference to specific embodiments:

[0092] Example 1

[0093] This embodiment provides a method for constructing a near-infrared identification model for Terminalia chebula medicinal material, including the following steps:

[0094] S1. Collect a first preset number of samples of Terminalia chebula medicinal material, Terminalia chebula medicinal material, and Terminalia chebula medicinal material from the Combretaceae family, and perform near-infrared spectral measurements to obtain the original spectral set.

[0095] 1) Instruments and Samples

[0096] Instrument: TANGO-R Fourier transform near-infrared spectrometer (Bruker GmbH, Germany), detector: integrating sphere diffuse reflectance, analysis software: OPUS 7.5.

[0097] Samples: A total of 150 batches of samples were collected, including samples of Terminalia chebula (the original plant of Terminalia chebula), samples of Terminalia velutina (the original plant of Terminalia chebula), and samples of Terminalia chebula. Sample information is shown in Table 1. Among them, Terminalia chebula refers to Terminalia chebula (the original plant of Terminalia chebula).

[0098] 2) Sample preparation

[0099] The samples of Terminalia chebula, Terminalia velutipes, and Terminalia chebula from the Combretaceae family were pulverized, passed through a 100-mesh sieve, and sealed in vacuum bags for later use.

[0100] 3) Near-infrared spectroscopy measurement conditions

[0101] Using the instrument's built-in background as a reference, the scanning range is 11500–4000 cm. -1 64 scans, 16cm resolution -1 Each batch of samples was divided into two parts as parallel samples, and the measurements were repeated twice. A total of 300 spectra from 150 batches of medicinal materials are shown below. Figure 1 50 batches of Terminalia chebula medicinal materials, 100 spectra (see...) Figure 2 100 images of 50 batches of Terminalia chebula medicinal materials can be found here. Figure 3 100 optical images of 50 batches of Terminalia chebula medicinal materials can be found Figure 4 .

[0102] Table 1 is an information table of medicinal material samples.

[0103]

[0104]

[0105]

[0106] S2. Randomly select a second preset number of sample spectra from the original spectral set as the calibration set, and use the remaining sample spectra as the verification set;

[0107] 280 sample spectra were randomly selected (92 batches of Terminalia chebula from the original Terminalia chebula plant of the Combretaceae family, 94 batches of Terminalia chebula vulgaris, and 94 batches of Terminalia chebula fruit) as the calibration set; and 20 batches of medicinal material samples (8 batches of Terminalia chebula from the original Terminalia chebula plant of the Combretaceae family, 6 batches of Terminalia chebula vulgaris, and 6 batches of Terminalia chebula fruit) as the validation set.

[0108] S3. The sample spectra in the calibration set are preprocessed using the vector normalization method to obtain the preprocessed spectrum set.

[0109] The spectra of 186 batches of Terminalia chebula and Terminalia chebula (both belonging to the Combretaceae family) were preprocessed using the vector normalization method. The results are shown in the figure. Figure 5 .

[0110] S4. Perform band selection on the preprocessed spectral set to obtain a first wavelength group, the wavelength range of the first wavelength group including 11377.8 cm⁻¹. -1 ~10202.5cm -1 9165.9cm -1 ~7149.9cm -1 and 5068.6cm -1 ~4154.5cm -1 The first wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages;

[0111] S5. Establish a first model within the spectral range of the first wavelength group;

[0112] The spectral range of the first wavelength group is modeled using the factorization method, and the threshold is automatically calculated.

[0113] S6. Use the first model to verify the sample spectra in the validation set to evaluate whether the first model can accurately identify Terminalia chebula and Terminalia chebula.

[0114] The selectivity S-value was calculated to evaluate whether the first model could accurately identify Terminalia chebula and Terminalia chebula.

[0115] Selectivity (S-value) is an important indicator for evaluating the performance of qualitative models, reflecting the selectivity between different substances. The formula is: S = D / (T1 + T2), where D is the distance between the average spectra of the two classes of substances, and T1 and T2 are the threshold values ​​for the two classes. When S < 1, it indicates that the two classes of substances cannot be distinguished by the model; when S ≥ 1, it indicates that the two classes of substances can be distinguished.

[0116] The results showed that the selectivity S values ​​between Terminalia chebula and Terminalia chebula were 1.034073 and 1.034066, respectively, both >1, indicating that they could be distinguished from each other, and that there were certain differences in the components between mature and immature fruits. This indicates that the first model constructed within the first wavelength group has good selectivity. The 2D score maps of Terminalia chebula and Terminalia chebula are shown below. Figure 6 The first model was used to verify the spectra of 14 samples to be tested, and the recognition rate was 100%, as shown in Table 2.

[0117] Table 2. First model and validation information constructed within the first wavelength group.

[0118]

[0119] Example 2

[0120] This embodiment provides a method for constructing a near-infrared identification model for Terminalia chebula medicinal material, including the following steps:

[0121] S1 to S2 are the same as in Example 1, except that:

[0122] S3. The sample spectra in the calibration set are preprocessed using the vector normalization method to obtain the preprocessed spectrum set.

[0123] The sample spectra of all calibration sets were preprocessed using vector normalization, and the results are shown in [Figure number missing]. Figure 7 .

[0124] S4. Perform band selection on the preprocessed spectral set to obtain a second wavelength group, the wavelength range of the second wavelength group including 9035.3 cm⁻¹. -1 ~8504.8cm -1 7892.6cm -1 ~7451.9cm -1 5068.6cm -1 ~5052.27cm -1 4636cm -1 ~4489.1cm -1 and 4440.1cm -1 ~4154.5cm -1The second wavelength group is used to distinguish Terminalia chebula, Terminalia velutipes, and Terminalia chebula from plants of the Combretaceae family;

[0125] S5. Establish a second model within the spectral range of the second wavelength group;

[0126] The threshold was calculated and modeled within the spectral range of the second wavelength group using a factorization method.

[0127] S6. Use the second model to validate the sample spectra in the validation set to evaluate whether the second model can accurately identify Terminalia chebula, Terminalia villosa, and Terminalia chebula of the Combretaceae family.

[0128] The selectivity S-value was calculated to evaluate that the first model can accurately identify Terminalia chebula, Terminalia villosa, and Terminalia chebula of the Combretaceae family.

[0129] The results showed that the selectivity S values ​​for Terminalia chebula (the original source of Terminalia chebula), Terminalia chebula (the original source of Terminalia velutina), and Terminalia chebula (the fruit of Terminalia chebula) were 1.034073, 1.353679, and 1.034066, respectively. All three S values ​​were greater than 1, indicating that they could be distinguished from each other. This suggests that there are differences in chemical composition between the two original sources of Terminalia chebula (the original source of Terminalia chebula) and Terminalia velutina, as well as between mature and immature fruits. The second model constructed within the second wavelength group had good selectivity.

[0130] The 2D score graph results for Terminalia chebula, Terminalia villosa, and Terminalia chebula of the Combretaceae family are shown below. Figure 8 The 3D scoring results for Terminalia chebula (origin of Terminalia chebula), Terminalia velutipes (origin of Terminalia chebula), and Terminalia chebula (origin of Terminalia chebula) are shown in the table below. Figure 9 .in Figure 8 and Figure 9 "Terminalia chebula" refers to the Terminalia chebula of the Terminalia chebula genus, while "Terminalia chebula" refers to the Terminalia chebula of the Combretaceae family. The second model was used to validate the spectra of 20 samples in the validation set, achieving a 100% recognition rate. The structure is shown in Table 3.

[0131] Table 3. Second model constructed within the second wavelength group and validation information.

[0132]

[0133] Example 3: A near-infrared identification method for Terminalia chebula medicinal materials at different maturity stages

[0134] Take a sample of Terminalia chebula medicinal material to be tested, and collect its near-infrared spectrum according to the method in Example 1. Import it into the first model. The model shows the results: the Terminalia chebula spectrum in the validation set is consistent with the Terminalia chebula spectrum constructed in the calibration set. The model judges and predicts that it is qualified and is a Terminalia chebula sample.

[0135] Alternatively, if the spectrum of Terminalia chebula in the validation set is consistent with the spectrum of Terminalia chebula constructed in the calibration set, and the model's discrimination and prediction are qualified, then it is a Terminalia chebula sample.

[0136] Example 4: A near-infrared identification method for Terminalia chebula medicinal materials with different maturity stages and different origins.

[0137] Take a sample of Terminalia chebula medicinal material to be tested, and collect its near-infrared spectrum according to the method in Example 2. Import it into the second model. The model shows the results: the Terminalia chebula spectrum in the validation set is consistent with the Terminalia chebula spectrum constructed in the calibration set. The model judges and predicts that it is qualified and is a Terminalia chebula sample.

[0138] Alternatively, if the spectrum of Terminalia chebula in the validation set is consistent with the spectrum of Terminalia chebula constructed in the calibration set, and the model's judgment and prediction are qualified, then it is a Terminalia chebula sample;

[0139] Alternatively, if the spectrum of Terminalia chebula in the validation set is consistent with the spectrum of Terminalia chebula constructed in the calibration set, and the model's discrimination prediction is qualified, then it is a Terminalia chebula sample.

[0140] In summary, this invention establishes a method for constructing a near-infrared identification model for Terminalia chebula medicinal materials, which can obtain a near-infrared identification model with good selectivity and high recognition rate. It also provides a near-infrared identification method for Terminalia chebula medicinal materials at different maturity stages, applicable to the identification of mature fruits and immature fruits, providing a basis for determining Terminalia chebula medicinal materials at different maturity stages. The method has low detection cost, no sample contamination during detection, fast identification speed, and high recognition accuracy.

[0141] Furthermore, the present invention also provides a near-infrared identification method for Terminalia chebula medicinal materials with different maturity stages and different origins. This method can be applied to the identification of Terminalia chebula medicinal materials with different maturity stages and different origins. It can accurately identify Terminalia chebula, Terminalia velutipes, and Terminalia chebula from the Combretaceae family. The sample preparation process is simple, does not damage the sample, has no chemical reagent contamination, and has a fast identification speed and high identification accuracy.

[0142] The above description is a preferred embodiment of the invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the invention, and these improvements and modifications are also considered to be within the scope of protection of the invention.

Claims

1. A method for constructing a near-infrared identification model of Terminalia chebula, characterized in that, It comprises the following steps: Collecting a first preset number of Terminalia chebula Rehder et Wils. samples, Terminalia chebula Rehder et Wils. samples and Terminalia billerica samples for near-infrared spectrum measurement to obtain an original spectrum set; Randomly selecting a second preset number of sample spectrums in the original spectrum set as a calibration set, and the remaining sample spectrums as a validation set; Pretreating the sample spectrums in the calibration set to obtain a pretreated spectrum set; Selecting a wave band for the pretreated spectrum set to obtain a first wavelength group or a second wavelength group, wherein the first wavelength group is used for identifying Terminalia chebula Rehder et Wils. at different maturity stages, and the second wavelength group is used for identifying Terminalia chebula Rehder et Wils. at different maturity stages and different origins; Establishing a model in the spectrum range of the first wavelength group and / or the second wavelength group; The wavelength range of the first wavelength group includes 11377.8 cm -1 10202.5 cm -1 , 9165.9 cm -1 7149.9 cm -1 and 5068.6 cm -1 4154.5 cm -1 ; The wavelength range of the second wavelength group includes 9035.3 cm -1 ~8504.8 cm -1 , 7892.6 cm -1 ~7451.9 cm -1 , 5068.6 cm -1 ~5052.27 cm -1 , 4636 cm -1 ~4489.1 cm -1 and 4440.1 cm -1 ~4154.5 cm -1 .

2. The method for constructing a near-infrared identification model of Terminalia chebula according to claim 1, wherein, When pretreating the sample spectrums in the calibration set, the pretreatment method is selected from one or a combination of spectrum data normalization, spectrum data decentering, spectrum data standardization, multivariate scatter correction, vector normalization, data smoothing and derivation.

3. The method for establishing the near-infrared identification model of Terminalia chebula drug material according to claim 2, characterized in that, The vector normalization method is used to pretreat the sample spectrums in the calibration set to obtain a pretreated spectrum set.

4. The method for constructing a near-infrared identification model of Terminalia chebula according to claim 1, wherein, The factorization method is used to establish a model in the spectrum range of the first wavelength group and / or the second wavelength group.

5. The method for establishing the near-infrared identification model of Terminalia chebula according to claim 1, characterized in that, The Terminalia chebula Rehder et Wils. samples, Terminalia chebula Rehder et Wils. samples and Terminalia billerica samples are subjected to sample treatment before near-infrared spectrum measurement; The sample treatment comprises: respectively crushing the Terminalia chebula Rehder et Wils. samples, Terminalia chebula Rehder et Wils. samples and Terminalia billerica samples, passing through an 80-120 mesh screen, and then sealing and storing in a vacuum bag.

6. The method for constructing a near-infrared identification model of Terminalia chebula according to claim 1, wherein, The number of Terminalia chebula Rehder et Wils. samples, Terminalia chebula Rehder et Wils. samples and Terminalia billerica samples in the first preset number of Terminalia chebula Rehder et Wils. samples, Terminalia chebula Rehder et Wils. samples and Terminalia billerica samples is the same.

7. The method for establishing the near-infrared identification model of Terminalia chebula according to claim 1, wherein, The value of the second preset number is greater than or equal to 2 / 3 times the value of the first preset number.

8. The method for constructing a near-infrared identification model of Terminalia chebula according to claim 1, wherein, The conditions of the near-infrared spectrum measurement are as follows: taking the built-in background of the instrument as a reference, the scanning range is 11500cm -1 4000cm -1 , the scanning times are 62-66, and the resolution is 14-18cm -1 .

9. The method for establishing the near-infrared identification model of Terminalia medicinal materials according to claim 8, characterized in that, The conditions for the near infrared spectrum measurement are as follows: taking the built-in background of the instrument as the reference, the scanning range is 11500cm -1 4000cm -1 , the scanning times are 64, and the resolution is 16cm -1 .

10. The method for constructing a near-infrared identification model of Terminalia chebula according to claim 1, wherein, It further comprises: Using the model to validate the sample spectrums in the validation set to evaluate whether the model can accurately identify Terminalia chebula Rehder et Wils. at different maturity stages or Terminalia chebula Rehder et Wils. at different maturity stages and different origins.

11. A near infrared method for identifying Terminalia chebula, characterized in that, It comprises the following steps: Obtaining a near-infrared identification model of Terminalia chebula Rehder et Wils. by using the method for constructing a near-infrared identification model of Terminalia chebula Rehder et Wils. according to any one of claims 1-10; Collecting the near-infrared spectrum of a to-be-tested sample, importing the data of the near-infrared spectrum of the to-be-tested sample into the near-infrared identification model of Terminalia chebula Rehder et Wils., and obtaining a discrimination result.

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