Near infrared spectrum identification method of polygonatum kingianum

Abstract

The invention discloses a near-infrared spectrum identification method of polygonatum kingianum, which comprises the following steps: (1) drying different types of polygonatum kingianum, taking the same amount of sample powder, adopting an integrating sphere diffuse reflection sampling system, using an Antaris II Fourier transform near-infrared spectrometer and TQ Analyst spectral analysis software to collect spectrums, and comparing the spectrums with the sample powder; obtaining near infrared spectrums of different types of rhizoma polygonati; (2) carrying out multiplicative scatter correction and second-order derivative preprocessing on the near infrared spectrums of different types of polygonatum sibiricum to obtain a preprocessed OPLS-DA score map; (3) according to the OPLS-DA scoring graph, different types of rhizoma polygonati are concentrated in different quadrants; polygonatum kingianum is located in the second quadrant. The invention establishes a method for quickly, accurately and nondestructively identifying the polygonatum kingianum, and solves the technical bottleneck of the high-value variety in the traditional identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traditional Chinese medicine research, and in particular to a near-infrared spectroscopy identification method for Polygonatum hookeri. BACKGROUND

[0002] As a traditional food and medicine homologous plant, Huangjing has the effects of moistening the lung, invigorating the spleen, and nourishing the kidney, and is included in the 2025 edition of Chinese Pharmacopoeia. The Huangjing medicinal materials circulating on the market have complex origins, and the original plants include species such as Polygonatum sibiricum Redoute, Polygonatum sibiricum Polygonatum cyathopetalum Y.S.Ling and P. hookeri (Andr.) Aitch. et J. Hook. f. var. latifolium Y.S. Ling. P. cyrtonema P. kingianum Polygonatum hookeri (Andr.) Aitch. et J. Hook. f. var. latifolium Y.S. Ling. P. kingianum grandifolium Polygonatum hookeri (Andr.) Aitch. et J. Hook. f. var. latifolium Y.S. Ling.

[0003] In recent years, near-infrared spectroscopy (NIRS) combined with multivariate statistical analysis has been widely used to trace the origin of food and herbs, and to determine the starch, fat, protein and saponin content in crops such as rice and soybeans for quality control. The development of NIRS applied to the identification and quantitative detection of food and medicine is very rapid. NIRS technology can be used to non-destructively identify species of lily plants and wild mushrooms, varieties of Salvia miltiorrhiza and Yunnan Salvia, and European rapeseed. Due to its rapid and non-destructive sampling, simple operation, good stability and high efficiency, NIRS has made progress in qualitative identification and quantitative analysis in the fields of food and medicine, thus showing a broad application prospect of NIRS. How to accurately identify the species of Huangjing using NIRS is also a research hotspot. SUMMARY

[0004] In view of the above problems existing in the prior art, the present application provides a near-infrared spectroscopy identification method for Polygonatum hookeri. The OPLS-DA model is used to correctly distinguish different species (varieties) of Huangjing, and the model is relatively stable and reliable. NIR spectroscopy combined with OPLS-DA can better identify Polygonatum hookeri medicinal material powder.

[0005] ​​The technical solutions of the present application are as follows: The first object of the present application is to provide a near-infrared spectroscopy identification method for large-leaf Yunnan rhizoma polygonati, comprising the following steps: (1) After drying different types of rhizoma polygonati, take equal amounts of sample powder using an integrating sphere diffuse reflection sampling system, use an Antaris II Fourier transform near-infrared spectrometer (Thermo Scientific Inc., Madison, WI, USA) and TQ Analyst spectral analysis software to collect spectra, and obtain near-infrared spectra of different types of rhizoma polygonati; the rhizoma polygonati includes large-leaf Yunnan rhizoma polygonati, Yunnan rhizoma polygonati, polyphyllous rhizoma polygonati, and rhizoma polygonati galli; (2) Perform multivariate scatter correction (MSC) and second derivative pretreatment on the near-infrared spectra of different types of rhizoma polygonati to obtain a pretreated OPLS-DA score plot; (3) According to the OPLS-DA score plot, different types of rhizoma polygonati are concentrated in different quadrants; the large-leaf Yunnan rhizoma polygonati is located in the second quadrant.

[0006] In an embodiment of the present application, in step (1), in order to obtain reproducible results, record all NIRS spectra 64 times with background air as a blank standard, the spectral range is from 10000 cm -1 to 4000 cm -1 , the spectral resolution is 8 cm -1 , and each sample is analyzed 3 times at room temperature, and the average spectrum is used for subsequent operation.

[0007] In an embodiment of the present application, in step (2), the pretreatment uses Unscrambler X 10.4 chemometrics software (Camo Process, Oslo, Norway), and data is plotted and analyzed using Origin 2021b.

[0008] In an embodiment of the present application, the near-infrared spectroscopy identification method can also identify Yunnan rhizoma polygonati, polyphyllous rhizoma polygonati, and rhizoma polygonati galli; the Yunnan rhizoma polygonati is located in the first quadrant, the polyphyllous rhizoma polygonati is located in the third quadrant, and the rhizoma polygonati galli is located in the fourth quadrant.

[0009] The OPLS-DA model established in the present application uses R2Y and Q2 to evaluate the fitting effect of the model.

[0010] In an embodiment of the present application, after MSC+SD pretreatment, the R2Y value of the OPLS-DA model established can reach 0.997, and the Q2 value can reach 0.472, indicating that the model not only has a very high fitting degree for training data, but also has good prediction performance, and can be stably and reliably used for identification of large-leaf Yunnan rhizoma polygonati.

[0011] R2Y represents the explanation rate of the established model to the Y matrix information, Q2 is a correlation coefficient of cross-validation, and indicates the prediction ability of the model.

[0012] R2Y is used for quantifying the explanation ability of the OPLS-DA model to the category variable matrix (Y matrix), that is, the goodness of the model fitting the known data. The calculation is based on the principle of square sum decomposition.

[0013] Total sum of squares (SST): first, the total variation of all response variables (that is, the numerical representation of the category label) in the Y matrix is calculated.

[0014]

[0015] Wherein, y i is the actual observation value of the i th sample, is the average value of all sample observation values.

[0016] Residual sum of squares (SSR): the difference between the model prediction value and the actual observation value is calculated.

[0017]

[0018] Wherein, is the prediction value of the i th sample of the model.

[0019] R2Y calculation: R2Y = 1-(SSR / SST) The value range of R2Y is between 0 and 1. The closer R2Y is to 1, the better the fitting degree of the model to the known data, that is, the model can explain most of the variation in the Y matrix.

[0020] Q2 is used to evaluate the prediction robustness of the OPLS-DA model, that is, the ability of the model to predict unknown samples. The calculation is carried out by cross-validation, and the leave-one-out cross-validation is preferred in the application.

[0021] Cross-validation process: 1. From the total of n samples, one sample is removed in turn.

[0022] 2. Reconstruct the OPLS-DA model using the remaining n-1 samples.

[0023] 3. Use the newly constructed model to predict the sample that is removed, and get its prediction value .

[0024] 4. Repeat the above steps until each sample is removed and predicted once.

[0025] Predicted Residual Sum of Squares (PRESS): the sum of the squared differences between the predicted and actual values for all samples in cross-validation.

[0026]

[0027] Calculation of Q2: Q2 = 1 - (PRESS / SST) Q2 is usually less than 1 and can be negative. The greater the Q2 value (closer to 1), the stronger the predictive ability and the higher the robustness of the model. Generally, Q2>0.5 indicates that the model has good predictive ability.

[0028] The beneficial technical effects of the present application are: The present application correctly distinguishes different species of Polygonatum using the OPLS-DA model, which is relatively stable and reliable. NIR spectroscopy combined with OPLS-DA can better identify Polygonatum filipes medicinal powder.

[0029] The present application creates a method that can quickly, accurately and non-destructively identify Polygonatum filipes, solving the technical bottleneck of this high-value variety in traditional identification.

[0030] The present application applies the pretreatment method of combining multiple scattering correction (MSC) and second derivative (SD) to near-infrared spectroscopy analysis of Polygonatum plants, effectively eliminating physical interference such as particle size and surface scattering, and amplifying subtle spectral characteristics related to functional groups. On this basis, the orthogonal partial least squares discriminant analysis (OPLS-DA) model is introduced, which can separate the systematic variation (orthogonal signal) unrelated to classification from the variation related to classification, thereby constructing a more powerful and more specific classification model.

[0031] Through the optimized OPLS-DA model, the present application clearly discovers and defines the specific distribution area (second quadrant) of Polygonatum filipes in the model score plot, thereby achieving rapid and accurate differentiation of Polygonatum filipes from P. yunnanense, P. cyathopetalum and P. kingianum. This provides a unique "spectral fingerprint" identification for this specific variety. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 Near-infrared spectrum of different types of Polygonatum in Example 1 of the present application; Figure 2 OPLS-DA result plot after pretreatment in Example 1 of the present application; Figure 3 OPLS-DA result plot after pretreatment in Comparative Example 1 of the present application. DETAILED DESCRIPTION

[0033] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0034] The experimental materials were dried rhizomes of Polygonatum sibiricum, obtained from the Polygonatum sibiricum germplasm resource nursery in Lin'an, Zhejiang Province. All were four-year-old cultivated Polygonatum sibiricum plants, harvested in October 2020. They were Polygonatum sibiricum (specifically, *Polygonatum sibiricum* species). P. sibiricum PS), Polygonatum multiflorum ( P. cyrtonema PC), Yunnan Polygonatum ( P. kingianum PK), Large-leaved Yunnan Polygonatum ( P. kingianum var. grandifolium The samples were 6 *Polygonatum cirrhifolium*, 52 *Polygonatum multiflorum*, 7 *Polygonatum yunnanense*, and 11 *Polygonatum yunnanense* (Table 1). The samples were pulverized and passed through a 60-mesh sieve. The resulting powder was stored in a drying oven as experimental raw material for later use.

[0035] Table 1

[0036] Note: PS. Polygonatum sibiricum; PC. Polygonatum multiflorum; PK. Polygonatum yunnanense; PKC. Polygonatum yunnanense.

[0037] Example 1 A near-infrared spectral identification method for Polygonatum yunnanensis includes the following steps: (1) After drying, equal amounts of sample powder from different species of Polygonatum were collected using an integrating sphere diffuse reflectance sampling system and an Antaris II Fourier transform near-infrared spectrometer (Thermo Scientific Inc., Madison, WI, USA) with TQ Analyst spectral analysis software to obtain the near-infrared spectra of different species of Polygonatum. Polygonatum included Polygonatum yunnanensis, Polygonatum yunnanensis, Polygonatum multiflorum, and Polygonatum sibiricum. To obtain reproducible results, background air was used as the blank standard, and all NIRS spectra were recorded 64 times, with the spectral range from 10000 cm⁻¹. -1 Up to 4000 cm -1 Spectral resolution of 8 cm -1 Each sample was analyzed three times at room temperature, and the average spectrum was used for subsequent operations; the results are as follows. Figure 1 As shown.

[0038] (2) The near-infrared spectra of different species of Polygonatum were preprocessed using multivariate scattering correction (MSC) and second derivative (SD). Preprocessing was performed using Unscrambler X 10.4 chemometrics software (Camo Process, Oslo, Norway), and Origin 2021b was used to plot and analyze the data to obtain the preprocessed OPLS-DA score map. The results are shown below.Figure 2 As shown.

[0039] (3) According to the OPLS-DA rating chart, different types of Polygonatum are concentrated in different quadrants; Polygonatum yunnanensis is located in the second quadrant, Polygonatum yunnanensis is located in the first quadrant, Polygonatum multiflorum is located in the third quadrant, and Polygonatum chrysanthemum is located in the fourth quadrant.

[0040] The R²Y value of the OPLS-DA model is 0.997, and the Q² value is 0.472.

[0041] Comparative Example 1 (1) Same as Example 1; (2) The near-infrared spectra of different species of Polygonatum were preprocessed using multivariate scattering correction (MSC) and first derivative (FD). The preprocessing was performed using Unscrambler X 10.4 chemometrics software (Camo Process, Oslo, Norway), and the data were plotted and analyzed using Origin 2021b to obtain the preprocessed OPLS-DA score map. The results are shown below. Figure 3 As shown.

[0042] (3) According to the OPLS-DA rating chart, there is no obvious distinction between different types of Polygonatum.

[0043] The embodiments provided above are not intended to limit the scope of the invention, nor are the described steps intended to limit the order of execution. Any obvious modifications made to the invention by those skilled in the art based on existing common knowledge also fall within the scope of protection defined by the claims.

Claims

1. A method for identifying Polygonum amplexicaule by near infrared spectroscopy, characterized in that, Comprising the following steps: (1) After drying different kinds of Rhizoma Polygonati, take equal amount of sample powder, use integral sphere diffuse reflection sampling system, use Antaris II Fourier transform near infrared spectrometer and TQ Analyst spectral analysis software to collect spectrum, obtain the near infrared spectrum of different kinds of Rhizoma Polygonati; Rhizoma Polygonati includes Rhizoma Polygonati Macrophyllum, Rhizoma Polygonati, Rhizoma Polygonati Multiflorum and Rhizoma Polygonati Galli; (2) The near infrared spectrum of different kinds of Rhizoma Polygonati is pretreated by multivariate scattering correction and second derivative, and the pretreated OPLS-DA score chart is obtained; (3) According to the OPLS-DA score chart, different kinds of Rhizoma Polygonati are concentrated in different quadrants; Rhizoma Polygonati Macrophyllum is located in the second quadrant.

2. The near infrared spectroscopic discrimination method according to claim 1, characterized by, In step (1), to obtain reproducible results, all NIRS spectra were recorded 64 times with background air as blank standard, in the spectral range from 10000 cm -1 to 4000 cm -1 with a spectral resolution of 8 cm -1 , each sample was analyzed 3 times at room temperature, and the average spectrum was used for further operations.

3. The near infrared spectroscopic discrimination method according to claim 1, characterized by, In step (2), the pretreatment uses Unscrambler X 10.4 chemometrics software, and the data is analyzed by drawing with Origin 2021b.

4. The near infrared spectroscopic discrimination method according to claim 1, characterized by, Rhizoma Polygonati, Rhizoma Polygonati Multiflorum and Rhizoma Polygonati Galli can also be identified; Rhizoma Polygonati is located in the first quadrant, Rhizoma Polygonati Multiflorum is located in the third quadrant, and Rhizoma Polygonati Galli is located in the fourth quadrant.

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