CN113672869A - Method for rapidly judging content uniformity of medicine in granulation based on minimum excess spectrum calculation - Google Patents
Method for rapidly judging content uniformity of medicine in granulation based on minimum excess spectrum calculation Download PDFInfo
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Abstract
The invention provides a method for rapidly judging the uniformity of the content of a medicament in granulation based on minimum excess spectrum calculation by utilizing the excess spectrum idea. The actual proportion of the mixture is calculated by utilizing the minimum difference value between the theoretical spectrum and the actual spectrum through the minimum excess spectrum calculation, so that whether the API content is uniform or not is judged, the influence of external factors on the content change is removed through spectrum regression, the API content can be directly predicted without complex modeling optimization, and the method is innovatively applied to a granulation system. The establishment of the method is beneficial to monitoring and application of content uniformity in the granulation process, can judge the granulation end point, and provides technical support for subsequent mixing-granulation-tabletting continuous production, thereby improving the safety and effectiveness of the medicine.
Description
Technical Field
The invention belongs to the technical field of near infrared spectroscopy, and particularly relates to a method for rapidly judging the content uniformity of a medicine in granulation based on minimum excess spectrum calculation.
Background
In the production of the solid preparation, the granulation is positioned behind the mixing unit, and the prepared granules have good fluidity, uniform granularity and good compression formability, are easy for subsequent tabletting and are almost indispensable key process links in the production of the solid preparation. Critical Quality Attributes (CQAs) in granulation include moisture, density, particle size, etc., however little attention has been paid to Active Pharmaceutical Ingredient (API) content uniformity during granulation. In the pharmaceutical preparation, the particle size, moisture and density of the prepared granules do not reach the standard, which means that the uniformity of the extruded tablet is good, and the uniformity of the content of the medicine is very important for ensuring the treatment effect and reducing the side effect. The amount of binder used and the difference in the primary particle size of the drug substance and diluent during granulation can affect the uniformity of the API content obtained from granulation. Therefore, it is highly desirable to monitor the uniformity of the API content during granulation.
The traditional methods for detecting the content uniformity of the API comprise high performance liquid chromatography, gas chromatography, ultraviolet spectrophotometry and the like, and the methods all need offline sampling for detection, are time-consuming, labor-consuming and destroy samples. Near infrared spectroscopy (NIRS) is the most widely used Process Analysis Technology (PAT), and can rapidly and nondestructively detect a sample and simultaneously monitor the change values of multiple attributes (API content, moisture, particle size). The sample measured by the technology can be analyzed under the condition of not needing or only needing little sample preparation, and the increase of error sources caused by the increase of operation steps is avoided. Since this technique does not cause damage to the sample, the sample can be reused after the measurement of the sample, or the sample can be measured through the packaging material. However, the near infrared absorption bands are typically wider, overlapping, and 10-100 times weaker than the corresponding mid-infrared fundamental absorption band. These characteristics severely limit the sensitivity in the classical spectroscopic sense and require qualitative and quantitative analysis in combination with chemometric methods.
The method for judging the uniformity of the content of the granulation end point is a quantitative analysis method, mainly comprises a regression method, and a linear regression equation is constructed by utilizing the Lambert-beer law. The regression method includes Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), and the like. The traditional quantitative analysis method needs a large amount of training data for modeling analysis, and needs to consume a large amount of samples and time to establish a credible model. Meanwhile, the established prediction model is essentially to establish a corresponding relation equation of the spectrum data and the primary data, and the change process of the sample is not analyzed from the essence of the spectrum.
Disclosure of Invention
For the existing near infrared spectrum technology, the existing traditional quantitative analysis method needs a large amount of training data to perform modeling analysis, needs to consume a large amount of samples and time to establish a credible model, and meanwhile, the established prediction model is essentially a corresponding relation equation for establishing the spectral data and the primary data, and is lack of a change process for analyzing a sample from the essence of the spectrum.
Aiming at the problems in the prior art, the invention provides a method for rapidly judging the uniformity of the content of a drug in granulation based on a minimum excess spectrum, in order to effectively and accurately predict the content of API in the granulation process, greatly reduce samples consumed by a training set and improve the granulation production efficiency.
The minimum excess spectrum calculation is used as a more intuitive spectrum characterization method, the actual proportion of the mixture is calculated by using the minimum difference value of the theoretical spectrum and the actual spectrum, and then whether the API content is uniform or not is judged, and the proportion can be directly predicted without complex modeling optimization.
In order to achieve the above object, the present invention provides a method for rapidly determining the uniformity of drug content in granulation based on minimum excess spectrum calculation, comprising the steps of:
(1) collecting the stable near infrared spectrum of the granulation mixture with uniform mass fraction of the known raw material medicines in the granulation process as a training number set;
(2) calculating the near infrared spectrum of each component pure substance in the granulation mixture by using regression analysis;
(3) combining the pure substance spectrum calculated in the step (2) with the mass fractions of the raw materials and the auxiliary materials in each granulation mixture of the sample verification set, and calculating a theoretical granulation mixture spectrum;
(4) calculating a difference value of the actual measurement of the verification set and the spectrum of the theoretical granulation mixture calculated in the step (3), namely an excess spectrum;
(5) and solving a target function coefficient under the minimum excess spectrum by utilizing constrained linear least square programming, namely the mass fraction of each component of each granulation mixture, and further judging whether the drug content in the granulation is uniform.
Wherein, in step (1), the spectrum of the stabilization of the granulation mixture at the dry end of the granulation is collected as the training set.
In the step (2), the formula for calculating the pure substance spectrum of each component in the granulation mixture by using regression analysis is as follows:
DMfor the stable training of even drug content in the granulation, spectrum data is collected, C represents the mass fraction of the raw materials and the auxiliary materials in the previously set granulation mixture,
the pure substance spectrum is calculated according to the mass fraction and the stable training set spectrum.
In the step (3), the theoretical granulation mixed substance spectrum calculation formula is as follows:
In the step (4), the calculation formula of the excess spectrum is as follows:
dsamplerepresents the actually measured spectrum data of the granulation mixture consisting of the same raw material medicines and auxiliary materials as the theoretical granulation mixture, and epsilon is the difference value of the actually measured spectrum and the theoretical synthesis spectrum of the granulation mixture, namely the excess spectrum.
In the step (5), a target function coefficient under the minimum excess spectrum is solved by utilizing constrained linear least square programming, namely the mass fraction of each mixture, and the concrete formula is as follows:
s.t.
in case of using linear least square programming to find epsilon minimum
Is the predicted real-time mass fraction of each mixture, wherein the mass fraction is subject to two constraints: compared with the traditional PLS method, the method has the advantages that the variation range of the mass fraction is greatly reduced, and the prediction precision is improved. The spectrum is corrected by utilizing minimum excess spectrum calculation, and the pure substance spectrum is calculated by regression analysis, and the core is to solve the problem of constrained linear least square. The method carries out spectral regression by correlating mass fractions, considers factors which do not conform to the Lambert beer law, such as particle density and particle size of a granulating mixture, and expands the application range of the algorithm from a pure mixing system to a granulating system. Meanwhile, compared with the traditional PLS method, the method does not need to model through a large number of data sets to eliminate some factors which do not conform to the Lambert beer law, and the number of training sets is greatly reduced.
The invention has the beneficial effects that:
(1) the invention utilizes the idea of excess spectrum, and is based on an analysis technology for decomposing and synthesizing the spectrum by strictly following the Lambert beer law under the constraint environment. The minimum excess spectrum calculation is used as a more intuitive spectrum characterization method, the actual proportion of the mixture is calculated by utilizing the minimum difference value of the theoretical spectrum and the actual spectrum, whether the API content is uniform or not is judged, the influence of external factors on the content change is removed through spectrum regression, the API content can be directly predicted without complex modeling optimization, and the method is innovatively applied to a granulation system.
(2) Compared with the traditional PLS method, the method does not need to model through a large number of data sets to eliminate some factors which do not conform to the Lambert beer law, and the number of training sets is greatly reduced.
(3) The establishment of the method is beneficial to monitoring and application of content uniformity in the granulation process, can judge the granulation end point, and provides technical support for subsequent mixing-granulation-tabletting continuous production, thereby improving the safety and effectiveness of the medicine.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a diagram of a fluidized bed granulation experimental apparatus;
FIG. 2(a) is a PC1 score chart of the whole process of fluid bed granulation; (b) the PC1 load diagram of the whole process of fluid bed granulation;
FIG. 3 is a plot of the estimated API pure spectra versus the measured API pure spectra using the "minimum excess spectra calculation" method for the 5 lot calibration set (preprocessing method: SNV + CWT (sym2, 10));
FIG. 4 is a graph of the evaluation of the predicted performance of the minimum excess spectrum calculation: a, c and e are comparison of theoretical concentration and predicted concentration of API in the samples of the correction set, the verification set and the prediction set respectively; b, d, f are comparison of API of two different measuring methods in the correction set, the verification set and the test set respectively, minimum excess spectrum calculation prediction (square) and calculation (diamond) by using HPLC method, theoretical proportion (horizontal line); n-50 (near infrared); n-10 (HPLC);
FIG. 5 is a comparison graph of PLS and minimum excess spectrum calculation method prediction bias, the upper and lower border lines of the box corresponding to the 25 th and 75 th percentiles of the samples, respectively, and the line in the middle of the box being the median of the samples;
FIG. 6(a) (b) (c) are Raman images of 90%, 100%, 110% of the validation set respectively; (d) (e) (f) Raman image plots of 90%, 100%, 110% plot of the test set.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As introduced in the background art, the invention utilizes the minimum excess spectrum calculation as a more intuitive spectrum characterization method, and utilizes the minimum difference value of the theoretical spectrum and the actual spectrum to calculate the actual proportion of the mixture so as to judge whether the API content is uniform.
In an exemplary embodiment of the present invention, a method for rapidly determining the uniformity of drug content in granulation based on minimum excess spectrum calculation is provided, which comprises the following steps:
(1) collecting the stable near infrared spectrum of the granulating mixture with uniform mass fraction of the known raw material medicines in the granulating process as a training number set;
(2) calculating the near infrared spectrum of each component pure substance in the granulation mixture by using regression analysis;
(3) combining the pure substance spectrum calculated in the step (2) with the mass fractions of the raw materials and the auxiliary materials in each granulation mixture of the sample verification set, and calculating a theoretical granulation mixture spectrum;
(4) calculating a difference value of the spectrum of the granulation mixture calculated by the actual measurement of the verification set and the theoretical calculation calculated in the step (3), namely an excess spectrum;
(5) and solving a target function coefficient under the minimum excess spectrum by using constrained linear least square programming, namely obtaining the mass fraction of each mixture.
Preferably, in step (1), the stable near infrared spectrum of the granulation mixture at the end of the granulation drying is collected as a training set, at which time the moisture is substantially dry and the granulation is in a steady state, close to the starting material.
Preferably, the method for determining the stability of the near infrared spectrum is as follows: after Standard normal transformation (SNV) and wavelet transform (CWT) pretreatment are carried out on the whole granulation process spectrum, Principal Component Analysis (PCA) Analysis is carried out on a first frequency doubling absorption wave band of hydroxyl, and drying is finished and the spectral quality is stable when the terminal change trend of a first Principal Component score chart approaches to be stable; more preferably, the first double frequency absorption band of hydroxyl groups is 1300-1500 nm.
Preferably, the uniformity of the material is verified by off-line sampling using a third party testing method selected from the group consisting of high performance liquid chromatography, gas chromatography, ultraviolet-visible spectrophotometry, fluorescence detection, mass spectrometry, infrared spectroscopy.
Preferably, in step (2), the spectra of the pure substances of each component in the granulation mixture are calculated by regression analysis:
DMrepresents the stable training spectrum data of even drug content in the granulation, C represents the mass fraction of the raw materials and the auxiliary materials in the previously set granulation mixture,
the pure substance spectrum is calculated according to the mass fraction and the stable training set spectrum.
Preferably, in the step (3), the spectrum calculation formula of the theoretical granulating mixed substance is as follows:
Preferably, in step (4), the calculation formula of the excess spectrum is:
dsamplerepresents the actually measured spectrum data of the granulation mixture consisting of the same raw material medicines and auxiliary materials as the theoretical granulation mixture, and epsilon is the difference value of the actually measured spectrum and the theoretical synthesis spectrum of the granulation mixture, namely the excess spectrum.
Preferably, in the step (5), a constrained linear least square plan is used to calculate the objective function coefficient under the minimum excess spectrum, that is, the mass fraction of each mixture, and the specific formula is as follows:
s.t.
in the case of using linear minimum planning to find the minimum of epsilon
Is the predicted real-time mass fraction of each mixture, wherein the mass fraction is subject to two constraints: non-negative, the sum being 1.
Preferably, the method for rapidly judging the uniformity of the drug content in granulation based on the minimum excess spectrum calculation further comprises using a coefficient of determination (R)2) Predicting standard deviation (RMSEP), verifying the accuracy of the method by using a performance deviation Ratio (RPD), and calculating specific R2RMSEP, RPD value, usually RPD > 2, indicates that this method is feasible.
In another embodiment of the present invention, the granulation process of step (1) comprises: nifedipine as a medicine and auxiliary materials: lactose, microcrystalline cellulose, hydroxypropyl methylcellulose;
preferably, the collected near infrared spectrum has a wave band range of 1087-1211nm, and is subjected to pretreatment, wherein the pretreatment method is SNV + CWT.
In another embodiment of the present invention, the near infrared spectrum acquisition parameters during the granulation process are:
and (3) collecting the spectrum in a diffuse reflection mode, collecting the background spectrum of the polytetrafluoroethylene white board before placing the polytetrafluoroethylene white board in a near-infrared probe, adjusting the integration time to be 9.1ms, adjusting the full wavelength range to be 908.1-1676.0nm, and scanning each spectrum for 100 times. Spectra were collected throughout the granulation process, every 6 s. In order to prevent the near infrared probe from being contaminated, compressed air was blown every 10 seconds.
In order to make the technical solutions of the present application more clearly understood by those skilled in the art, the technical solutions of the present application will be described in detail below with reference to specific embodiments.
Example 1:
in the experiment, fluidized bed granulation is adopted as a research object, and the influence of variation sources (API concentration and excipient concentration) of raw and auxiliary materials on the on-line monitoring of the API content in the fluidized bed granulation process is mainly considered in the experimental design (see table 1 for details). The calibration set covers five concentration levels of drug substance (labeled concentrations of 75%, 90%, 100%, 110%, 125%) with the remaining two excipients in a ratio (lactose: microcrystalline cellulose ═ 2: 1). The validation set comprises bulk drugs (90%, 100%, 110% labeled concentration) and lactose (97%, 100%, 103% labeled concentration) at three concentration levels, the lactose concentration changes by 3%, and the total material mass of each batch does not change (the proportion of the converted bulk drugs to microcrystalline cellulose is shown in table 1), so as to challenge the robustness of the calibration model. The test set was independent of the calibration and validation sets, and included three concentration levels of drug substance (90%, 100%, 110% labeled concentration).
TABLE 1 design of the experiment
(1) The stable spectra of the granulation mixture with known uniformity of mass fractions of the components during the granulation process were collected as training set.
Experimental set-up as shown in figure 1, the experimental fluid bed granulation was used for this experiment, with a total weight of 1600g (API and excipient weight) per batch. The granulation formulation comprised nifedipine, lactose and microcrystalline cellulose with a binder of 10% hydroxypropylmethylcellulose (60 g solids content in the binder). In the mixing, granulating and drying processes of the fluidized bed, the air inlet temperature is set to be 70 ℃, the air exhaust proportion is set to be 20%, the atomizing pressure is set to be 1.0bar, and the flow of the peristaltic pump is set to be 14 mL/min.
The spectra of the whole granulation process were subjected to standard normal correction (SNV) and wavelet transform (CWT) pre-treatments, followed by PCA analysis. Fig. 2(a) is a first principal component score chart in which the curve trend completely coincides with the offline sampling moisture trend in the actual granulation process. Meanwhile, in combination with the load diagram of the first principal component (FIG. 2(b)), it can be seen that there is strong absorption in the region of 1300-1500nm, which is mainly the first double frequency absorption of hydroxyl, demonstrating that the change of the first principal component is mainly the change of water. The terminal change trend of the first principal component score chart is approximately stable, the moisture of the particles is approximately stable, the drying is basically finished, and the quality of the spectrum is stable, so that the spectrum at the moment is selected to be used for analyzing the API content.
When the spectrum is stabilized at the dry end of the pellet, samples are taken every 30s from the sampling port, about 10g each time, for a total of ten samples. The total of 11 experiments were performed in the calibration, validation and test sets, and 110 samples were obtained.
(2) The spectra of the pure substances of the components of the granulation mixture were calculated by regression analysis.
The comparison of the API pure spectrum calculated by combining the formula (1) with the 5 batches of calibration set terminal stable spectra and the API pure spectrum actually measured (the characteristic wave band range 1087 and 1211nm of the API, the preprocessing method SNV + CWT) is shown in FIG. 3, and the regression analysis calculates that the peak patterns of the obtained API pure spectrum and the API pure spectrum actually measured are basically coincident, thereby proving the effectiveness of the regression method.
(3) And (4) utilizing the spectral difference between the actual measurement and the theoretically calculated granulation mixture, namely obtaining an excess spectrum. The objective function coefficient under the minimum excess spectrum is obtained by combining with the constraint linear least square programming, namely the mass fraction of each mixture, the obtained result is shown in figure 4, and the comparison graphs (4(a), 4(c) and 4(e)) of the predicted API concentration and the theoretical measurement API concentration show that a good linear relation exists between the API concentration obtained by utilizing the calculation of the minimum excess spectrum and the theoretical API concentration (R is the weight fraction of each mixture)2 cal＝0.9684，R2 val＝0.8849，R2 test0.9354). The RMSEP values for the correction set, validation set, and test set were 3.0292%, 2.7684%, 2.0732%. The prediction results show that the prediction results of the correction set and the test set are ideal, but the R of the verification set2The method is somewhat low, probably because the proportion of auxiliary materials is changed by the verification set, and certain influence is generated on the robustness of the method.
(6) By means of R2And indexes such as RMSEP, RPD and the like verify the accuracy of the method. R calculated by the method of the invention2RMSEP, RPD values are compared to the classical method Partial Least Squares (PLS). The data set and the preprocessing method (SNV + CWT) used for PLS modeling are consistent with the method of the invention, and the comparison of the predicted performance indexes is shown in Table 2. R of correction set of 'minimum excess spectrum calculation' method2RMSEP, RPD are slightly inferior to the PLS method, but the related performance indicators of the test set and the validation set are superior to the PLS method, and "minimal excessThe RPD values of the frontal spectrum calculation method are all larger than 2, and the accuracy of the method is proved.
TABLE 2 comparison of "minimum excess Spectroscopy calculation" with PLS prediction results Performance indicators
It can also be seen by combining the deviation between the predicted value and the theoretical predicted value of the two methods and comparing fig. 5, that the predicted deviation of PLS is less than that predicted by the minimum excess spectrum calculation during the correction modeling process, because the spectrum matrix and the concentration matrix are corrected simultaneously during the PLS modeling, the prediction model is optimized. However, the deviation size and distribution of the minimum excess spectrum calculation is significantly smaller in the validation set and test set than in the PLS method. This accuracy is due to the constraint on the mixture ratio in equation (4). In the fluidized bed granulation process, the factors influencing the spectrum quality are more and more, and the spectrum difference is large. So if the concentration range is not constrained, it may happen that the predicted concentration difference is too large due to too large a spectral difference.
In order to further verify whether the API content predicted by the method of the present invention is uniform, a third party verification method was used to verify whether the granulation endpoint granules are uniform.
And carrying out online sampling and offline verification on the granulation end point particles. Third party validation methods, high performance liquid chromatography and raman spectroscopy imaging methods were used, wherein high performance liquid chromatography used pharmacopoeia specified methods for testing the relevant APIs. The raman spectroscopy is performed by using a confocal raman spectrometer (RA802, RENISHAW) with a laser power of 87.5 mW. Approximately 1g of the sample was spread on a glass slide and compacted with another slide, and a single spectrum of each component of the particle mixture was collected and then imaged using a Streamline image mode at a wave number ranging from 2501 to 95cm-1The area range is 200 mu m by 200 mu m, the acquisition step length is 5 mu m, 40000 points are acquired in all samples, live track automatic focusing is adopted in the whole acquisition process, and a wire5.1 is used for generating a Raman image.
Samples (a total of 110 samples) were taken for API content using HPLC off-line measurement of the dry end. The measured API concentrations for the two different analytical methods were plotted against the treatment time to compare the results (calibration, validation, and test sets 4(b), 4(d), and 4(f), respectively). As can be seen from the figure, the calculated predicted value of the minimum excess spectrum and the HPLC measured value are very close to the theoretical preset value, and the feasibility of the method is proved.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and the present invention is not limited thereto, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications and equivalents can be made in the technical solutions described in the foregoing embodiments, or equivalents thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A method for rapidly judging the content uniformity of a medicine in granulation based on minimum excess spectrum calculation is characterized by comprising the following steps:
(1) collecting the stable near infrared spectrum of the granulation mixture with uniform mass fraction of the known raw material medicines in the granulation process as a training number set;
(2) calculating the near infrared spectrum of each component pure substance in the granulation mixture by using regression analysis;
(3) combining the pure substance spectrum calculated in the step (2) with the verification set to collect the mass fractions of the raw materials and the auxiliary materials in each granulation mixture of the sample, and calculating the spectrum of the theoretical granulation mixture;
(4) calculating a difference value of the spectrum of the granulation mixture calculated by the actual measurement of the verification set and the theoretical calculation calculated in the step (3), namely an excess spectrum;
(5) and solving a target function coefficient under the minimum excess spectrum by utilizing constrained linear least square programming, namely the mass fraction of each component of each granulation mixture, and further judging whether the drug content in the granulation is uniform.
2. The method of claim 1, wherein in step (1), the stable near infrared spectrum of the granulated dry end granulation mixture is collected as a training set.
3. The method of claim 2, wherein the near infrared spectrum is determined to be stable by: after standard normal correction (SNV) and wavelet transform (CWT) pretreatment are carried out on the spectrum in the whole granulation process, PCA analysis is carried out on a first frequency doubling absorption waveband of hydroxyl, and when the variation trend of the tail end of a first principal component score chart approaches to stability, the drying is finished and the quality of the near infrared spectrum is stable;
preferably, the first double frequency absorption band of hydroxyl is 300-1500 nm.
4. The method of claim 1, wherein in step (2), the spectra of the pure substances of each component of the granulation mixture are calculated by regression analysis using the formula:
DMrepresents the stable training spectrum data of even drug content in the granulation, C represents the mass fraction of the raw materials and the auxiliary materials in the previously set granulation mixture,
the pure substance spectrum is calculated according to the mass fraction and the stable training set spectrum.
5. The method of claim 1, wherein in step (3), the theoretical granulation mixture spectra calculation formula is as follows:
6. The method of claim 1, wherein in step (4), the excess spectrum is calculated by the formula:
dsamplerepresents the actually measured spectrum data of the granulation mixture consisting of the same raw material medicines and auxiliary materials as the theoretical granulation mixture, and epsilon is the difference value of the actually measured spectrum and the theoretical synthesis spectrum of the granulation mixture, namely the excess spectrum.
7. The method of claim 1, wherein in step (5), the objective function coefficient under the minimum excess spectrum is obtained by using constrained linear least squares (nlms), which is the mass fraction of each mixture, and the specific formula is as follows:
s.t.
in case of using linear least square programming to find epsilon minimum
Is the predicted real-time mass fraction of each mixture, wherein the mass fraction is subject to two constraints: non-negative, the sum being 1.
8. The method of claim 1, further comprising using R2RMSEP, RPD, verifies the accuracy of the method.
9. The method of claim 1, wherein the granulating prescription comprises: nifedipine as a medicine and auxiliary materials:
lactose, microcrystalline cellulose, hydroxypropyl methylcellulose;
preferably, the collected near infrared spectrum has a wave band range of 1087-1211nm, and is subjected to pretreatment, wherein the pretreatment method is SNV + CWT.
10. The method of claim 1, wherein the granulation process nir spectra acquisition parameters are: and (3) collecting the spectrum in a diffuse reflection mode, collecting the background spectrum of the polytetrafluoroethylene white board before placing the polytetrafluoroethylene white board in a near-infrared probe, adjusting the integration time to be 9.1ms, adjusting the wavelength range to be 908.1-1676.0nm, and scanning each spectrum for 100 times.
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| CN1990831A (en) * | 2005-11-04 | 2007-07-04 | 法国石油公司 | Method for the determination of the conjugated diolefins content of a sample using near infrared spectrum and method for controlling a unit |
| CN107941744A (en) * | 2017-11-16 | 2018-04-20 | 广州白云山和记黄埔中药有限公司 | Quickly judge the method for borneol uniformity and application in Fufang Danshen Pian production process |
| CN108318442A (en) * | 2018-02-06 | 2018-07-24 | 江苏康缘药业股份有限公司 | A kind of detection method suitable for Chinese medicine suspending system |
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| CN1990831A (en) * | 2005-11-04 | 2007-07-04 | 法国石油公司 | Method for the determination of the conjugated diolefins content of a sample using near infrared spectrum and method for controlling a unit |
| CN107941744A (en) * | 2017-11-16 | 2018-04-20 | 广州白云山和记黄埔中药有限公司 | Quickly judge the method for borneol uniformity and application in Fufang Danshen Pian production process |
| CN108318442A (en) * | 2018-02-06 | 2018-07-24 | 江苏康缘药业股份有限公司 | A kind of detection method suitable for Chinese medicine suspending system |
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