Method for quantitatively detecting content of amorphous state in atorvastatin calcium bulk drug

Abstract

The invention discloses a method for quantitatively detecting the content of an amorphous state in an atorvastatin calcium raw material medicine, and belongs to the technical field of detection and analysis, and the method comprises the following steps: S1, preparing amorphous atorvastatin calcium; s2, grinding, screening and mixing the atorvastatin calcium raw material medicine with the crystal form I and the amorphous atorvastatin calcium; s3, respectively obtaining standardized peak area data and near infrared spectrum data of the melting peak by using a differential scanning calorimetry method and a near infrared spectrum detection technology; s4, preprocessing the near infrared spectrum data, and performing feature extraction to obtain near infrared spectrum features; s5, fusing the near infrared spectrum characteristics and the standardized peak area data of the melting peak, and establishing a mathematical model based on the standardized fusion data and the amorphous atorvastatin calcium content; and S6, calculating by using the mathematical model obtained in S5 to obtain the amorphous state content in the atorvastatin calcium sample to be detected. The method is rapid and accurate, multi-dimensional data are fully utilized, and the quality of the raw material medicine can be effectively controlled.

Description

Technical Field

[0001] The present invention belongs to the technical field of detection and analysis, and particularly relates to a method for quantitatively detecting the content of amorphous form in an atorvastatin calcium raw material. Background Art

[0002] Atorvastatin calcium is a commonly used statin for regulating blood lipids. As a selective competitive inhibitor of 3-hydroxymethylglutaryl coenzyme A (HMG-CoA) reductase, atorvastatin can indirectly reduce cholesterol synthesis and increase low-density lipoprotein (LDL) synthesis. Furthermore, the drug also reduces VLDL concentration in the blood by inhibiting its synthesis and promoting its catabolism, thereby achieving the goal of lowering blood lipids.

[0003] Atorvastatin calcium can exist in different crystalline forms, and dozens of them have been discovered to date. The most common crystalline forms are Form I, Form II, Form IV, and amorphous. Amorphous atorvastatin calcium is susceptible to temperature, light, oxygen, and humidity, making it unstable and prone to transformation into other crystalline forms.

[0004] The Pharmacopoeia of the People's Republic of China (2020 edition) stipulates that when a solid drug has polymorphism and different crystal states affect the effectiveness, safety or quality of the drug, the state of the pharmaceutical crystal form in the raw material drug, solid preparation, semi-solid preparation, suspension, etc. should be qualitatively or quantitatively controlled. In the prior art, researchers have developed some drug crystal form detection methods. For example, the Chinese patent document with publication number CN107478634A discloses a method for determining amorphous rabeprazole sodium, which includes: providing a rabeprazole preparation; providing a Raman spectrum of the rabeprazole preparation; and determining the concentration of the 1164cm in the Raman spectrum. -1 -1364cm -1 The rabeprazole sodium in the preparation is determined to be an amorphous crystalline form by the characteristic peak at 1193 cm-1 in the Raman spectrum. -1 , 1273cm -1 and 1333cm -1 The amorphous form of rabeprazole sodium in the formulation is determined by detecting characteristic peaks at α and β. Some studies have also used powder X-ray diffraction to confirm the amorphous form (patent documents CN101597261A, CN103694165A, etc.). The above-mentioned detection methods using Raman spectroscopy or XRD only qualitatively identify a sample as amorphous and do not provide qualitative or further quantitative analysis of the presence of amorphous forms in crystalline drugs.

[0005] Currently, there is a lack of methods for determining the amorphous content of atorvastatin calcium API during the industrial production of atorvastatin calcium API. To improve API quality control and ensure patient safety, it is necessary to develop a method for quantitatively determining the amorphous content of atorvastatin calcium API for use in quality control during production. Summary of the Invention

[0006] The present invention provides a method for quantitatively detecting the amorphous content in an atorvastatin calcium raw material drug. The method has simple and efficient steps and can improve the ability to identify the quality of the raw material drug, thereby ensuring the quality and safety of the drug during the production process.

[0007] The specific technical solutions adopted are as follows:

[0008] A method for quantitatively detecting the amorphous content in atorvastatin calcium raw material comprises the following steps:

[0009] S1: Preparation of amorphous atorvastatin calcium;

[0010] S2: grinding, sieving and mixing the atorvastatin calcium crystalline form I and the amorphous atorvastatin calcium prepared in S1;

[0011] S3: Using differential scanning calorimetry to detect the mixed powder sample obtained in S2 to obtain normalized peak area data of the melting peak, pressing the mixed powder sample obtained in S2 into tablets, and detecting the tablets using near-infrared spectroscopy to obtain near-infrared spectral data;

[0012] S4: preprocessing near-infrared spectral data and extracting features to obtain near-infrared spectral features;

[0013] S5: Fusing near-infrared spectral features and melting peak normalized peak area data, and establishing a mathematical model based on the normalized fusion data and the content of amorphous atorvastatin calcium;

[0014] S6: The near-infrared spectrum characteristics and the melting peak normalized peak area data of the atorvastatin calcium sample to be tested are fused and normalized, and the data are input into the mathematical model of step S5 to calculate the amorphous content.

[0015] The present invention mixes amorphous atorvastatin calcium with a stable I-crystal atorvastatin calcium raw material, obtains multi-dimensional sample data (molecular vibration information and phase change enthalpy value) based on spectral analysis technology - near-infrared spectroscopy and thermal analysis technology - differential scanning calorimetry, and uses intermediate fusion (feature layer fusion) technology to fuse the two data and establish a quantitative calibration model. Compared with the use of near-infrared spectroscopy detection technology alone, the R 2 Higher, 0.9959 / 0.9803 (single near-infrared model R2 is 0.9597 / 0.9693), indicating that the fusion mathematical model explains more variations and has a better fitting effect.

[0016] Preferably, in step S1, the crystalline form I atorvastatin calcium is dissolved in a mixed solvent of acetone and ethyl acetate, stirred until completely dissolved, and then distilled under reduced pressure to reduce the volume of the solution, and then diethyl ether is added at room temperature, and distilled under reduced pressure again to obtain amorphous atorvastatin calcium.

[0017] Further preferably, the mass volume ratio of atorvastatin calcium to the mixed solvent is 1 g:10-20 mL; and the volume ratio of acetone to ethyl acetate in the mixed solvent is 1:1-5.

[0018] More preferably, the temperature of the first vacuum distillation is 30-50°C, and the temperature of the second vacuum distillation is 40-50°C.

[0019] Preferably, in step S2, the atorvastatin calcium API of crystal form I and amorphous atorvastatin calcium, both having a particle size in the range of 100 mesh-150 mesh, are mixed according to different preset mass fractions, and the mass ratio of the atorvastatin calcium API of crystal form I to amorphous atorvastatin calcium is in the range of 1:0.05-1.

[0020] Preferably, in step S3, when differential scanning calorimetry is used for detection, the sampling amount of the mixed powder sample is 1-5 mg, the crucible used is a non-punctured aluminum crucible, the heating rate is 1-10 K / min, the purge gas is N2, the flow rate is 50 mL / min, and the measurement range is 20-190°C.

[0021] Preferably, in step S3, when the near-infrared spectroscopy technique is used for detection, the near-infrared spectrometer used is equipped with a dual-polarization fiber optic reflection probe. During the detection process, the number of scans is 16-64 times; the resolution is 8 cm -1 ; spectral range 4000–12000 cm -1 .

[0022] Preferably, in step S4, the near-infrared spectral data are preprocessed using the Savitzky-Golay first-order derivative method or the standard normal variate transformation method, and the feature extraction is performed using the competitive adaptive reweighted sampling method.

[0023] Preferably, the method for establishing the mathematical model in step S5 is partial least squares method.

[0024] Specifically, in step S6, the atorvastatin calcium sample to be tested is ground and sieved, and a sample with a particle size in the range of 100 mesh to 150 mesh is taken. The melting peak normalized peak area data and near-infrared spectrum data are obtained under the same parameters as in step S3. At the same time, the near-infrared spectrum data is preprocessed according to the method of step S4, and feature extraction is performed to obtain near-infrared spectrum features.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention provides a method for quantitatively detecting the amorphous content in atorvastatin calcium raw materials, filling the gap in the current lack of methods for detecting the amorphous content of atorvastatin calcium in raw materials, providing a new idea for the quality control of atorvastatin calcium raw materials, improving the ability to identify the quality of raw materials, and thus helping to improve the quality of downstream preparations. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a differential scanning calorimetry curve diagram of the mixed powder samples h0-h5 in Example 1.

[0028] Figure 2 : This is the near-infrared spectra of the standard compressed samples h0-h5 in Example 1.

[0029] Figure 3 This is a graph showing how the cross-validated root mean square error changes with the number of Monte Carlo sampling times during the near-infrared spectral data feature extraction process in Example 1.

[0030] Figure 4 This is a graph showing how the number of features selected during the near-infrared spectral data feature extraction process in Example 1 changes with the number of Monte Carlo sampling times.

[0031] Figure 5 : is the performance evaluation diagram of the mathematical model M obtained in Example 1.

[0032] Figure 6 is a differential scanning calorimetry curve of the atorvastatin calcium sample to be tested in Example 1.

[0033] Figure 7 This is the near-infrared spectrum of the atorvastatin calcium sample to be tested in Example 1.

[0034] Figure 8 This is a performance evaluation diagram of the mathematical model obtained in Comparative Example 1.

[0035] Figure 9 3 is a graph showing the change of the cross-validated root mean square error with the number of Monte Carlo sampling during the near-infrared spectral feature extraction process in Example 2.

[0036] Figure 103 is a graph showing the change in the number of features selected during the near-infrared spectrum feature extraction process in Example 2 as a function of the number of Monte Carlo sampling times.

[0037] Figure 11 is a performance evaluation diagram of the mathematical model M obtained in Example 2.

[0038] Figure 12 This is a performance evaluation diagram of the mathematical model obtained in Comparative Example 2. DETAILED DESCRIPTION

[0039] The present invention will be further illustrated below in conjunction with the Examples and Comparative Examples. It should be understood that these Examples are intended only to illustrate the present invention and are not intended to limit the scope of the invention. The operating methods in the following examples where specific conditions are not specified are generally performed under conventional conditions or as recommended by the manufacturer. Contents not described in detail in this specification sheet belong to prior art known to those skilled in the art.

[0040] Example 1

[0041] In this embodiment, the method for quantitatively detecting the amorphous content in atorvastatin calcium raw material based on near infrared spectroscopy and differential scanning calorimetry specifically includes the following steps:

[0042] S1: Preparation of amorphous atorvastatin calcium;

[0043] Dissolve 20 g of Form I atorvastatin calcium in 200 mL of a mixed solvent of acetone and ethyl acetate (acetone:ethyl acetate = 1:1, v / v) and stir until completely dissolved to obtain a clear and transparent solution. Distill under reduced pressure at 50°C to a solution volume of approximately 60 mL. Cool the solution to room temperature, add 60 mL of anhydrous ether, and distill under reduced pressure at 50°C for 2 h to obtain amorphous atorvastatin calcium.

[0044] S2: grinding, sieving and mixing the atorvastatin calcium crystalline form I and the amorphous atorvastatin calcium prepared in S1;

[0045] The atorvastatin calcium bulk drug substance in form I and the amorphous atorvastatin calcium prepared in the above steps were ground using an agate mortar and sieved after appropriate grinding to obtain particles with a particle size range of 0.100 mm-0.154 mm (100 mesh-150 mesh).

[0046] Atorvastatin calcium Form I API and amorphous atorvastatin calcium were weighed at amorphous atorvastatin calcium content of 0%, 5%, 10%, 15%, 20%, and 30%, and placed in polytetrafluoroethylene tubes numbered h0, h1, h2, h3, h4, and h5, respectively. The tubes were placed in a homogenizer and removed every 30 minutes and placed on an oscillator for at least 3 hours. After mixing, a mixed powder sample was obtained.

[0047] S3: The mixed powder sample obtained in S2 was examined by differential scanning calorimetry to obtain normalized peak area data for the melting peak. The mixed powder sample obtained in S2 was tableted (approximately 53 mg of the sample was weighed, placed in a tableting mold, and pressed into a tablet with a diameter of 8 mm). The tableted sample was examined by near-infrared light detection technology to obtain near-infrared spectral data;

[0048] A differential scanning calorimeter (Mettler Toledo STARe System DSC3, Mettler Toledo International GmbH) was used to analyze approximately 2 mg of the mixed powder sample in an unpierced aluminum crucible at a heating rate of 10 K / min and a 50 mL / min N2 purge environment. The measurement range was 20-190°C. The differential scanning calorimetry curves were as follows: Figure 1 shown.

[0049] The compressed samples prepared in the above steps were tested using a near-infrared spectrometer with a dual-polarization fiber optic reflectance probe (ABB TALYS, ABB Beverage Co., Ltd.). During the test, the number of scans was 32 and the resolution was 8 cm. -1 ; Spectral range is 4000-12000cm -1 The obtained near-infrared spectral data are as follows: Figure 2 As shown, the near-infrared spectrum within a specific wavenumber range is selected for subsequent processing.

[0050] S4: preprocessing near-infrared spectral data and extracting features to obtain near-infrared spectral features;

[0051] The obtained near-infrared spectral data were preprocessed using the Savitzky-Golay first-order derivative method, and the features of the preprocessed near-infrared spectral data were extracted based on the competitive adaptive reweighted sampling method to obtain the near-infrared spectral features.

[0052] In the competitive adaptive reweighted sampling method, the number of Monte Carlo sampling is set to 50 times, the competitive adaptive reweighted sampling method is repeated 250 times, and the feature corresponding to the minimum root mean square error is selected as the target feature for extraction. Figure 3 and Figure 4 The root mean square error of cross-validation and the variation of the number of selected features with the number of Monte Carlo sampling in the feature extraction process are shown respectively.

[0053] S5: Fusing near-infrared spectral features and melting peak normalized peak area data, and establishing a mathematical model based on the normalized fusion data and the content of amorphous atorvastatin calcium;

[0054] After fusing the near-infrared spectral characteristics and the normalized peak area data of the melting peak (Table 1), the fused data were standardized and the mathematical model M of the fused data and the amorphous atorvastatin calcium content in the sample was established using the partial least squares method. The number of principal components of the model was 1, R 2 The value is 0.9959, indicating that the model fits well and can explain most of the variation. Figure 5 The performance evaluation graph of the above mathematical model M is shown.

[0055] Table 1 Normalized peak area data of melting peak of mixed powder samples

[0056]

[0057] S6: The near-infrared spectrum characteristics and the melting peak normalized peak area data of the atorvastatin calcium sample to be tested are merged and input into the mathematical model of step S5 to calculate the amorphous content.

[0058] According to the steps in S2, the atorvastatin calcium sample to be tested was ground and sieved, and a sample with a particle size of 100 mesh to 150 mesh was taken. The normalized peak area data of the melting peak was first measured under the same parameters as in step S3, as shown in Table 2. Then, the tablet was pressed and the near-infrared spectrum data was measured under the same parameters as in step S3. The corresponding differential scanning calorimetry curve and near-infrared spectrum are shown in Table 2. Figure 6 and Figure 7 As shown in Table 3, the near-infrared spectral data of the unknown sample were subjected to the same preprocessing and fusion processing as in the above steps. The fused data were normalized and then input into the established mathematical model M to calculate the amorphous content of the atorvastatin calcium sample to be tested. The results are shown in Table 3.

[0059] Table 2 Melting peak normalized peak area data of atorvastatin calcium samples to be tested

[0060]

[0061] Table 3 Amorphous content in the atorvastatin calcium samples to be tested

[0062]

[0063] Comparative Example 1

[0064] In this comparative example, the method for quantitatively detecting the amorphous content in atorvastatin calcium raw material based on near-infrared spectroscopy specifically comprises the following steps:

[0065] S1, S2: same as in Example 1;

[0066] S3: The mixed powder sample obtained in S2 was tableted (about 53 mg of the sample was weighed, placed in a tableting mold, and pressed into a tablet with a diameter of 8 mm). The tableted sample was detected using near-infrared light detection technology to obtain near-infrared spectral data. The near-infrared spectral detection conditions were the same as in Example 1.

[0067] S4: preprocessing the near-infrared spectral data and establishing a mathematical model between the preprocessed near-infrared spectral data and the content of amorphous atorvastatin calcium in the compressed tablet sample;

[0068] The Savitzky-Golay first-order derivative method was used to preprocess the near-infrared spectral data, and the partial least squares method was used to establish a mathematical model based on the preprocessed near-infrared spectral data and the amorphous atorvastatin calcium content in the sample. The number of principal components of the model was 1, R 2 The value is 0.9597, which is lower than 0.9959 in Example 1. Figure 8 The performance evaluation diagram of the obtained mathematical model is shown. The comparison results show that the fitting effect of the fusion model is better than that of the near-infrared model alone.

[0069] S5: Inputting the near-infrared spectrum data of the pretreated atorvastatin calcium sample to be tested into the mathematical model to calculate the amorphous content thereof.

[0070] The near-infrared spectral data of the unknown sample was tested under the same conditions as in step S3. After the near-infrared spectral data of the unknown sample underwent the same preprocessing as in step S4, the data was substituted into the established mathematical model to calculate the amorphous content of the atorvastatin calcium sample to be tested. The results are shown in Table 4.

[0071] Table 4 Amorphous content in the atorvastatin calcium samples to be tested

[0072]

[0073] Example 2

[0074] In this embodiment, the method for quantitatively detecting the amorphous content in atorvastatin calcium raw material based on near infrared spectroscopy and differential scanning calorimetry specifically includes the following steps:

[0075] S1, S2, S3: same as in Example 1;

[0076] S4: preprocessing near-infrared spectral data and extracting features to obtain near-infrared spectral features;

[0077] The obtained near-infrared spectral data were preprocessed using the standard normal variable transformation method, and the features of the preprocessed near-infrared spectral data were extracted based on the competitive adaptive reweighted sampling method to obtain the near-infrared spectral features.

[0078] In the competitive adaptive reweighted sampling method, the number of Monte Carlo sampling is set to 50 times, the competitive adaptive reweighted sampling method is repeated 250 times, and the feature corresponding to the minimum root mean square error is selected as the target feature for extraction. Figure 9 and Figure 10 The changes in the cross-validation root mean square error and the number of selected features during near-infrared spectral feature extraction are shown respectively.

[0079] S5: Fusing near-infrared spectral features and melting peak normalized peak area data, and establishing a mathematical model based on the normalized fusion data and the content of amorphous atorvastatin calcium;

[0080] After fusing the near-infrared spectral characteristics and the normalized peak area data of the melting peak (Table 5), the fused data were standardized and the mathematical model M of the fused data and the amorphous atorvastatin calcium content in the sample was established using the partial least squares method. The number of principal components of the model was 1, R 2 The value is 0.9803, indicating that the model fits well and can explain most of the variation. Figure 11 The performance evaluation graph of the above mathematical model M is shown.

[0081] Table 5 Standardized peak area data of melting peaks of mixed samples

[0082]

[0083] S6: The near-infrared spectrum characteristics and the melting peak normalized peak area data of the atorvastatin calcium sample to be tested are merged and input into the mathematical model of step S5 to calculate the amorphous content.

[0084] Following the steps in S2, the atorvastatin calcium sample to be tested was ground and sieved, and a sample with a particle size range of 100-150 mesh was obtained. The normalized peak area data for the melting peak was first measured under the same parameters as in step S3, as shown in Table 6. Next, tableting was performed, and near-infrared spectral data was measured under the same parameters as in step S3. The near-infrared spectral data of the unknown sample was subjected to the same preprocessing and fusion processing as in the above steps. The fused data was then standardized and incorporated into the established mathematical model M to calculate the amorphous content in the atorvastatin calcium sample to be tested. The results are shown in Table 7.

[0085] Table 6 Standardized peak area data of melting peaks of the tested samples

[0086]

[0087] Table 7 Amorphous content in the atorvastatin calcium samples to be tested

[0088]

[0089] Comparative Example 2

[0090] In this comparative example, the method for quantitatively detecting the amorphous content in atorvastatin calcium raw material based on near-infrared spectroscopy specifically comprises the following steps:

[0091] S1, S2: same as in Example 2;

[0092] S3: The mixed powder sample obtained in S2 was tableted (about 53 mg of the sample was weighed, placed in a tableting mold, and pressed into a tablet with a diameter of 8 mm). The tableted sample was detected using near-infrared light detection technology to obtain near-infrared spectral data. The near-infrared spectral detection conditions were the same as in Example 2.

[0093] S4: preprocessing the near-infrared spectral data and establishing a mathematical model between the preprocessed near-infrared spectral data and the content of amorphous atorvastatin calcium in the compressed tablet sample;

[0094] The near-infrared spectral data were preprocessed using the standard normal variable transformation method, and a mathematical model based on the preprocessed near-infrared spectral data and the amorphous atorvastatin calcium content in the sample was established using the partial least squares method. The number of principal components in the model was 1, R 2 The value is 0.9693, which is lower than 0.9803 in Example 2. Figure 12 The performance evaluation diagram of the obtained mathematical model is shown. The comparison results show that the fitting effect of the fusion model is better than that of the near-infrared model alone.

[0095] S5: Inputting the near-infrared spectrum data of the pretreated atorvastatin calcium sample to be tested into the mathematical model to calculate the amorphous content thereof.

[0096] The near-infrared spectral data of the unknown sample was tested under the same conditions as in step S3. After the near-infrared spectral data of the unknown sample underwent the same preprocessing as in step S4, the data was substituted into the established mathematical model to calculate the amorphous content of the atorvastatin calcium sample to be tested. The results are shown in Table 8.

[0097] Table 8 Amorphous content in the atorvastatin calcium sample to be tested

[0098]

[0099] The embodiments and comparative examples described above provide a detailed description of the technical solutions of the present invention. It should be understood that the above descriptions are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, supplements or similar substitutions made within the scope of the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for quantitatively detecting the amorphous content in atorvastatin calcium raw material, characterized in that: The following steps are involved: S1: Preparation of amorphous atorvastatin calcium; S2: grinding, sieving and mixing the atorvastatin calcium crystalline form I and the amorphous atorvastatin calcium prepared in S1; S3: Using differential scanning calorimetry to detect the mixed powder sample obtained in S2 to obtain normalized peak area data of the melting peak, pressing the mixed powder sample obtained in S2 into tablets, and detecting the tablets using near-infrared spectroscopy to obtain near-infrared spectral data; S4: preprocessing near-infrared spectral data and extracting features to obtain near-infrared spectral features; S5: Fusing near-infrared spectral features and melting peak normalized peak area data, and establishing a mathematical model based on the normalized fusion data and the content of amorphous atorvastatin calcium; S6: The near-infrared spectrum characteristics and the melting peak normalized peak area data of the atorvastatin calcium sample to be tested are fused and normalized, and the data are input into the mathematical model of step S5 to calculate the amorphous content.

2. The method for quantitatively detecting the amorphous content in atorvastatin calcium bulk drug according to claim 1, characterized in that: In step S1, the crystal form I atorvastatin calcium is dissolved in a mixed solvent of acetone and ethyl acetate, stirred until completely dissolved, and then distilled under reduced pressure to reduce the volume of the solution. Subsequently, ether is added at room temperature and distilled under reduced pressure again to obtain amorphous atorvastatin calcium.

3. The method for quantitatively detecting the amorphous content in atorvastatin calcium bulk drug according to claim 2, characterized in that: The mass volume ratio of atorvastatin calcium to the mixed solvent is 1 g:10-20 mL; the volume ratio of acetone to ethyl acetate in the mixed solvent is 1:1-5.

4. The method for quantitatively detecting the amorphous content in atorvastatin calcium bulk drug according to claim 1, characterized in that: In step S2, the atorvastatin calcium API of crystal form I with a particle size in the range of 100 mesh to 150 mesh and the amorphous atorvastatin calcium are mixed according to different preset mass fractions.

5. The method for quantitatively detecting the amorphous content in atorvastatin calcium bulk drug according to claim 1, characterized in that: In step S3, when differential scanning calorimetry is used for detection, the sampling amount of the mixed powder sample is 1-5 mg, the heating rate is 1-10 K / min, the purge gas is N2, the flow rate is 50 mL / min, and the measurement range is 20-190°C.

6. The method for quantitatively detecting the amorphous content in atorvastatin calcium bulk drug according to claim 1, characterized in that: In step S3, when using near-infrared spectroscopy technology for detection, the near-infrared spectrometer used is equipped with a dual-polarization fiber optic reflection probe. During the detection process, the number of scans is 16-64 times; the resolution is 8 cm -1 ; spectral range 4000–12000 cm -1 .

7. The method for quantitatively detecting the amorphous content in atorvastatin calcium bulk drug according to claim 1, characterized in that: In step S4, the near-infrared spectral data are preprocessed using the Savitzky-Golay first-order derivative method or the standard normal variate transformation method, and feature extraction is performed using the competitive adaptive reweighted sampling method.

8. The method for quantitatively detecting the amorphous content in atorvastatin calcium bulk drug according to claim 1, characterized in that: The method for establishing the mathematical model in step S5 is the partial least squares method.

9. The method for quantitatively detecting the amorphous content in atorvastatin calcium bulk drug according to claim 1, characterized in that: In step S6, the atorvastatin calcium sample to be tested is ground and sieved to obtain a sample with a particle size in the range of 100 mesh to 150 mesh. The melting peak normalized peak area data and near-infrared spectrum data are obtained under the same parameters as in step S3. At the same time, the near-infrared spectrum data is preprocessed according to the method of step S4, and feature extraction is performed to obtain near-infrared spectrum features.

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