CN115561223A - Method for detecting content of perhexiline aqueous extract based on Raman spectrum and application - Google Patents
Method for detecting content of perhexiline aqueous extract based on Raman spectrum and application Download PDFInfo
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Abstract
The invention provides a method for detecting the content of perhexiline aqueous extract based on Raman spectrum and application thereof, and relates to a content determination method of a traditional Chinese medicine compound preparation, which specifically comprises the following steps: detecting the content of the perhexiline aqueous extract by establishing a Raman spectrum and a quantitative correction model of the perhexiline aqueous extract content; also comprises the application of the method in detecting the content of the perhexiline aqueous extract. The method has the advantages of no need of using various chemical reagents and solvents, no need of pretreatment operation, short determination time and simple operation, greatly improves the information transparency of the extraction process, assists in reducing the production risk, and further improves the quality control level and the production quality of the perhexiline water extraction process.
Description
Technical Field
The invention relates to a content determination method of a traditional Chinese medicine compound preparation, in particular to a method for detecting the content of a perhexiline aqueous extract based on Raman spectrum and application thereof.
Background
At present, the quality inspection of Chinese medicine preparation still mostly adopts a post inspection mode, namely, the finished product is inspected. The quality attribute change of the medicine in the production process cannot be known in time by the inspection mode, and the quality control in the medicine production process is not facilitated. The process analysis technical guidelines published by the U.S. food and drug administration in 2004 emphasize the importance of quality control in the pharmaceutical manufacturing process. Therefore, there is a need for quality analysis of pharmaceutical process to ensure the stability and efficiency of pharmaceutical production, which means that rapid and effective pharmaceutical process analysis techniques are required.
The spectrum analysis technology has the advantages of rapidness, accuracy, no damage to a detection sample and the like, and is widely applied to the pharmaceutical industry, in particular to the near infrared spectrum technology. The near infrared spectrum is an absorption spectrum, is generated by frequency doubling and frequency synthesis absorption of hydrogen-containing atomic group stretching vibration, is mainly used for researching asymmetric vibration of polar groups, and is easily interfered by water peaks. Compared with the near infrared spectrum, the Raman spectrum is a scattering spectrum and is mainly used for researching the symmetrical vibration of the nonpolar group and the skeleton, and the Raman spectrum is less influenced by moisture and is more suitable for the analysis of a water phase system. However, the application of Raman spectroscopy in the detection of Chinese patent medicine production process is rarely reported, which is related to the lack of detection devices and the difficulty of modeling Raman spectroscopy quantitative correction models. The traditional quantitative correction model methods, such as Partial Least-Squares Regression (PLSR) and Support Vector machine Regression (SVR), require preprocessing of raman spectrum data and then modeling, otherwise the model prediction effect is poor, the preprocessing algorithms include competitive adaptive re-weighting algorithm (CARS), non-information variable elimination algorithm (UVE), continuous projection algorithm (SPA), and collaborative interval Partial Least Squares (sips), and there are few reports on the fusion processing of raman spectra by various preprocessing algorithms. With the rise of artificial intelligence, deep learning methods, particularly Convolutional Neural Networks (CNNs), can have better model performance. Patent application CN 112964690A provides a method for monitoring the extraction process of traditional Chinese medicine formula particles in real time based on Raman spectroscopy, various preprocessing methods and characteristic variable extraction methods are optimized for Raman spectroscopy, and a mathematical model between Raman spectrogram information and index component (liquiritin and glycyrrhizic acid) content is fitted through big data, so that an analysis method is provided for realizing the rapid real-time monitoring of the extraction process of traditional Chinese medicine formula particles, the method is favorable for improving the product quality of traditional Chinese medicine formula particles, the automation and the intellectualization of the production of traditional Chinese medicine formula particles are promoted, but various preprocessing algorithms are not fused with a convolutional neural network for Raman spectroscopy modeling, and the model performance is relatively poor.
The salvia miltiorrhiza and the ligusticum wallichii are used as monarch drugs and ministerial drugs of the perhexiline tablets, the water extraction process is the primary link of the production of the perhexiline tablets, and the quality of the water extraction liquid directly influences the quality of the final product. Danshensu, ferulic acid, rosmarinic acid and salvianolic acid B are the main components of the water extract of perhexiline and also the effective components of perhexiline tablets. At present, the determination of the active ingredients usually adopts high performance liquid chromatography, and patent application CN 108181389A discloses a method for simultaneously determining the contents of salvianolic acid B and ferulic acid in a perhexiline tablet, which specifically comprises the following steps: pretreating GUANXINNING tablet to obtain sample, injecting the sample into high performance liquid chromatograph, gradient eluting with mobile phase under the same chromatographic condition, and measuring the content of salvianolic acid B and ferulic acid. However, the pretreatment method is tedious and time-consuming, and cannot meet the requirement of rapid detection in the pharmaceutical production process.
In order to rapidly detect the content of the active ingredients and the soluble solids in the perhexiline aqueous extract and ensure the consistency of the quality of final products, a detection method which can be applied to rapidly detect the content of the active ingredients and the soluble solids in the perhexiline aqueous extract needs to be established urgently.
Disclosure of Invention
The invention provides a method for detecting the content of perhexiline aqueous extract based on Raman spectrum and application thereof, aiming at the problems in the prior art. Through the collection of the Raman spectrum of the perhexiline water extract, the content measurement, the abnormal spectrum removal and the establishment of a mathematical model between the Raman spectrum information fitted by big data and the perhexiline water extract content, the content of the danshensu, the ferulic acid, the rosmarinic acid, the salvianolic acid B and soluble solid matters in the perhexiline water extract can be detected in real time, the operation is simple and rapid, the pretreatment and additional reagents and solvents are not needed, and the matching device is expected to realize full-automatic online detection.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a method for detecting the content of a perhexiline aqueous extract based on a Raman spectrum detects the content of the perhexiline aqueous extract by establishing a Raman spectrum and a quantitative correction model of the perhexiline aqueous extract.
Preferably, the perhexiline aqueous extract comprises a drug effect substance and soluble solids.
Further preferably, the drug effect substances are danshensu, ferulic acid, rosmarinic acid and salvianolic acid B.
Preferably, the establishment of the quantitative calibration model comprises the following steps:
(1) Collecting perhexiline water extract;
(2) Collecting a Raman spectrum of the perhexiline aqueous extract;
(3) Determining the content of the drug effect substance by adopting a high performance liquid chromatography, and determining the content of the soluble solid by adopting an oven drying method;
(4) Identifying and removing the acquired abnormal Raman spectrum;
(5) Correcting the spectrum from which the abnormal Raman spectrum is removed;
(6) Screening out the characteristic wave band of the corrected Raman spectrum;
(7) And establishing a quantitative correction model of the Raman spectrum, the content of the drug effect substances and the content of soluble solids by using the screened characteristic wave bands and adopting a CNN algorithm.
Preferably, the collecting in step (1) is performed by: soaking Saviae Miltiorrhizae radix and rhizoma Ligustici Chuanxiong in purified water, and extracting with water.
Further preferably, the material-liquid ratio of the salvia miltiorrhiza medicinal material to the ligusticum wallichii medicinal material to the purified water is 1; the temperature of the water extraction is 70-100 ℃; the water extraction time is 1-2.5h, 1-2.5h and 1-2.5h respectively.
More preferably, the material-liquid ratio of the salvia miltiorrhiza medicinal material to the ligusticum wallichii medicinal material to the purified water is 1; the temperature of the water extraction is 84 ℃; the water extraction time is 2h, 1.5h and 1.5h respectively.
Preferably, the parameters of the acquisition in step (2) are: raman shift 176-3500cm -1 The integration time is 300-600ms, the integration power is 200-500mw, and the scanning times are 1-5.
Preferably, the chromatographic conditions of the high performance liquid chromatography in the step (3) are as follows: and (3) chromatographic column: hanbon Sci & Tech Hedera ODS-2 (4.6X 250mm,5 μm); mobile phase: 0.1% aqueous formic acid-acetonitrile; gradient elution: 0-12min, 5-38% acetonitrile; 12-20min, 38-48% acetonitrile; 20-35min, 48-100% acetonitrile; 35-39min, 100-5% acetonitrile; flow rate: 0.8mL/min; detection wavelength: 288nm; column temperature: 36 ℃; the method for drying the coating by the oven comprises the following steps: centrifuging the perhexiline water extract at 2500rpm for 10min, taking about 3mL of supernatant, placing in a constant-weight flat weighing bottle, drying in a water bath, placing in a 105 ℃ oven for drying for 6h, weighing, and calculating the content of soluble solids according to the following formula.
Wherein Sc is the soluble solid content of the extracting solution, W represents the quality of the extracting solution, and W is 2 Represents the total mass of the dried sample and the weighing bottle, W 1 The bottle mass was weighed.
Preferably, the method for identifying the abnormal raman spectrum collected in step (4) is principal component analysis-mahalanobis distance method.
Further preferably, the abnormal raman spectrum is an abnormal spectrum which may be generated in the acquisition process due to instrument, method, environment or manual operation errors.
Furthermore, the principal component analysis-mahalanobis distance method comprises the following specific steps:
obtaining a score matrix Si, j of a spectrum matrix Xi, j through principal component analysis, extracting the first n principal components, and enabling the cumulative principal component contribution rate to be larger than 85% to obtain a matrix Bi, nRn, j, wherein i is the number of samples, and j is the number of Raman spectrum variables;
the mahalanobis distance for each sample was calculated according to the following formula,
wherein μ is R n,j Array B i,n The mean value of the column vectors, Σ being R n,j Array B i,n R n,j The covariance matrix of (a);
calculating the standard deviation sigma and the mean value M of Di, and calculating the confidence limit according to the following formula:
Level＝M+A×σ
in the above formula, a is a constant, and is taken as a =3 herein (i.e., meets the 3sigma criterion);
and comparing the Di with the Level, and if the Di is more than or equal to the Level, considering the spectrum as an abnormal spectrum and discarding the abnormal spectrum.
Preferably, the correction method in step (5) is a convolution smoothing method (Savizkg-gold, SG) and a linear function normalization method (Min-Max Scaling).
Preferably, the screening method in step (6) is competitive adaptive re-weighting algorithm (CARS).
Preferably, the evaluation indexes of the quantitative correction model in step (7) are: training set correlation coefficient Rc 2 Test set correlation coefficient Rp 2 Training set root mean square error RMSEC and test set root mean square error RMSEP. Wherein,Rc 2 and Rp 2 The closer to 1,RMSEC and RMSEP are, the smaller and the closer the values are, the higher the model performance is.
The invention also provides application of the method for detecting the content of the perhexiline aqueous extract based on the Raman spectrum in detecting the content of the perhexiline aqueous extract.
Compared with the prior art, the invention has the following beneficial effects:
the detection method provided by the invention is simple and rapid to operate, does not need various chemical reagents, solvents and ovens, is environment-friendly, is expected to realize real-time full-automatic online detection of the content of the drug effect substances and the content of soluble solids in the water extraction process of perhexiline by matching devices, can increase the understanding of the water extraction process of perhexiline, increase the information transparency, assist in reducing the production risk and improve the stability of the production process, thereby guiding the production practice.
Drawings
FIG. 1 is a flow chart of a detection method according to an embodiment of the present invention.
FIG. 2 is a Mahalanobis distance distribution diagram in example 1 of the present invention, in which the horizontal line is the boundary between the normal spectrum and the abnormal spectrum.
Fig. 3 is a raman spectrum data preprocessing chart in example 1 of the present invention, in which a is an original raman spectrum, b is a raman spectrum after SG smoothing, and c is a raman spectrum after MinMax normalization.
Fig. 4 is a CARS-CNN model structural diagram in embodiment 1 of the present invention.
Fig. 5 is a graph comparing the predicted results and measured values of the CARS-CNN model training set and the test set model in example 1 of the present invention, in which a is the danshensu content, B is the ferulic acid content, c is the rosmarinic acid content, d is the salvianolic acid B content, and e is the soluble solid content.
FIG. 6 is a graph showing the comparison between the predicted results and the actual measured values of the CARS-PLSR model training set and the test set in example 1, wherein a represents the content of danshensu, B represents the content of ferulic acid, c represents the content of rosmarinic acid, d represents the content of salvianolic acid B, and e represents the soluble solid content.
FIG. 7 is a diagram showing the comparison between the prediction results and the actual measurement values of the SPA-SVR model training set and the test set in example 1 of the present invention, in which a represents the content of danshensu, B represents the content of ferulic acid, c represents the content of rosmarinic acid, d represents the content of salvianolic acid B, and e represents the content of soluble solid.
FIG. 8 is a graph showing the time-series variation of the prediction results of the CARS-PLSR, SPA-SVR and CARS-CNN models in example 1 of the present invention, in which a is the content of danshensu, B is the content of ferulic acid, c is the content of rosmarinic acid, d is the content of salvianolic acid B, and e is the soluble solid content.
Detailed Description
It is to be noted that the experimental methods described in the following examples are all conventional methods unless otherwise specified; the reagents and materials are commercially available, unless otherwise specified.
The present invention will be described below with reference to specific examples to make the technical aspects of the present invention easier to understand and grasp, but the present invention is not limited thereto.
Example 1
1. Collecting the perhexiline water extract:
soaking Saviae Miltiorrhizae radix and rhizoma Ligustici Chuanxiong in purified water, and extracting with water for three times. Wherein, the water extraction parameters are as follows: the ratio of the salvia miltiorrhiza medicinal material to the ligusticum wallichii medicinal material to the purified water is 1; the water extraction temperature is 84 ℃; the extraction is carried out for three times, and the extraction time for three times is 2h, 1.5h and 1.5h respectively.
When the temperature of the extraction liquid in the extraction tank rises to 84 ℃, carrying out first reflux extraction, wherein 10mL is sampled by a sampling valve every 5min for 1h in the extraction process, and 10mL is sampled by the sampling valve every 10min for 1h in the extraction process; adding purified water with the same amount as the first extraction amount after the liquid of the extracting solution in the extracting tank is discharged, continuously heating to 84 ℃, and then carrying out second reflux extraction, wherein 10mL of the extract is sampled through a sampling valve every 5min for the first 1h and 10mL of the extract is sampled through the sampling valve every 10min for the second 0.5h in the extraction process; adding purified water with the same amount as the first extraction amount after the liquid of the extraction liquid in the extraction tank is discharged, continuously heating to 84 ℃, and then carrying out reflux extraction for the third time, wherein 10mL is sampled by a sampling valve every 5min for the first 1h and 10mL is sampled by the sampling valve every 10min for the second 0.5h in the extraction process. A total of 7 batches of sample collection were carried out under the same conditions as described above, and 333 specimens were obtained.
2. The acquisition work of the raman spectrum of the perhexiline aqueous extract sample:
in general, analysis of an aqueous solution and a water-containing substance is not suitable for infrared spectroscopy, and the aqueous solution can be measured by raman spectroscopy because raman scattering of water is weak; meanwhile, when the content of the aqueous solution substance is measured by Raman spectroscopy, special sample preparation treatment is not needed, and the infrared spectroscopy needs sample preparation, so that the Raman spectroscopy is used for measuring the content of the perhexiline aqueous extract.
A Rapid OLRaman-2 portable Raman spectrometer is utilized, and the acquisition parameters are as follows: the Raman displacement is 176-3500cm < -1 >, the integration time is 500ms, the integration power is 400mw, and the acquisition work is carried out for 3 times of scanning times.
3. Identifying and removing abnormal spectra:
and (3) identifying and removing abnormal spectra possibly generated in the acquisition process due to instrument, method, environment or manual operation errors by adopting a principal component analysis-Mahalanobis distance method.
The algorithm is as follows:
obtaining a score matrix Si, j of a spectrum matrix Xi, j through principal component analysis, extracting the first n principal components to ensure that the cumulative principal component contribution rate is more than 85 percent, and obtaining a matrix Bi, nRn, j, wherein i is the number of samples and j is the number of Raman spectrum variables;
the mahalanobis distance for each sample was calculated according to the following formula,
mu is the mean value of Rn, j array Bi and n column vectors, and sigma is the covariance matrix of Rn, j array Bi, nRn, j;
calculating the standard deviation sigma and the mean value M of Di, and calculating the confidence limit according to the following formula:
Level＝M+A×σ
in the above formula, a is a constant, and is taken as a =3 herein (i.e., meets the 3sigma criterion);
and (3) comparing the Di with the Level, and if the Di is more than or equal to the Level, considering that the spectrum is an abnormal spectrum and discarding the abnormal spectrum.
As shown in fig. 2, the final anomalous spectral data is: 4-1-5, 4-3-1, 6-3-9, 7-2-3 and 7-2-8.
4. And (3) measuring the content of the perhexiline aqueous extract:
centrifuging the perhexiline water extract at 13000rpm for 10min, and taking the supernatant to perform content analysis of the drug effect substances according to the following chromatographic conditions:
analyzing the contents of four drug effect substances including danshensu, ferulic acid, rosmarinic acid and salvianolic acid B in the perhexiline water extract by an Agilent 1260-type high performance liquid chromatograph. Chromatographic conditions are as follows: a chromatographic column: hanbon Sci & Tech Hedera ODS-2 (4.6X 250mm,5 μm); mobile phase: 0.1% aqueous formic acid-acetonitrile; gradient elution: 0-12min, 5-38% acetonitrile; 12-20min, 38-48% acetonitrile; 20-35min, 48-100% acetonitrile; 35-39min, 100-5% acetonitrile; flow rate: 0.8mL/min; detection wavelength: 288nm; column temperature: at 36 ℃.
The results of the investigation of the liquid phase method before the actual sample experiment showed that the method was feasible.
The results of the investigation of the specific liquid phase methodology are shown in tables 1-3.
TABLE 1 Linear equation and Linear Range of four effective ingredients
TABLE 2 precision, repeatability and stability of liquid chromatography
TABLE 3 sample recovery for liquid chromatography
Centrifuging the perhexiline water extract at 2500rpm for 10min, taking about 3mL of supernatant, placing the supernatant into a constant-weight flat weighing bottle, drying the supernatant in a water bath to dryness, placing the dried supernatant into a 105 ℃ oven for drying for 6h, weighing, and calculating the content of soluble solids according to the following formula:
wherein Sc is the soluble solid content of the extracting solution, W represents the quality of the extracting solution, and W is 2 Represents the total mass of the dried sample and the weighing bottle, W 1 The bottle mass was weighed.
333 sample data obtained by seven batches of extraction are randomly divided into training set data (266 samples) and testing set data (67 samples) according to 4:1 by using Kennrd-Stone algorithm.
5. And (3) correcting the Raman spectrum:
SG smoothing and MinMax normalization algorithms were performed on the training and test set data to correct raman spectra, the results are shown in fig. 3.
6. Screening a Raman spectrum characteristic waveband:
respectively screening the optimal characteristic wave bands of the corrected training set Raman spectrum by using a competitive adaptive re-weighting algorithm (CARS), a non-information variable elimination algorithm (UVE), a continuous projection algorithm (SPA) and a collaborative interval partial least squares (siPLS), wherein the specific algorithm is as follows:
(1) The CARS method:
firstly, randomly selecting a part of proportion samples (generally 80-90% of total samples), and establishing a PLS model;
secondly, forcibly removing a variable with a smaller regression coefficient absolute value in the PLS model by adopting an Exponential Decay Function (EDF);
thirdly, adopting Adaptive Re-weighted Sampling (ARS) to further screen the variables;
and fourthly, selecting the subset with the minimum RMSECV value in the subsets by adopting a 10-fold cross validation method, wherein the subset contains the optimal characteristic spectrum band combination.
(2) UVE method:
firstly, adding a noise matrix N (N, p) into an original spectrum matrix A (N, p) to obtain a new matrix A (N, 2 p);
secondly, establishing a PLS model between A1 (n, 2 p) and concentration by a Leave-one-out method (Leave-one-out method), thereby obtaining a new matrix B (n, 2 p);
thirdly, calculating the standard deviation S (1, 2p) and the average value M (1, 2p) of each column vector of the matrix B (n, 2 p) and calculating the parameter C i ＝M i /S i And the interval [ p +1,2p]Maximum value C of inner Ci max ＝max(abs(c))
The fourth step is that [1,p]Intra-interval comparison C i And C max Size of (C), if i ＜C max Then, consider C i Is an information-free variable that should be eliminated.
(3) SPA method: the SPA algorithm is a forward wavelength selection algorithm that starts with a variable and adds a new variable every iteration until a certain number N of wavelength variables is met. The SPA algorithm can solve the collinearity problem of the spectrum.
(4) The siPLS method: the basic principle of the siPLS algorithm is to divide the whole spectrum into n subintervals, combine any possible different intervals, and calculate the RMSECV value of each combined interval by the leave-one-out method, wherein the combination with the smallest RMSECV value is considered as the optimal combination, and the variable contained in the optimal combination is the optimal characteristic waveband.
Taking PLSR algorithm as an example, model results based on different characteristic wave band extraction algorithms as shown in Table 4, CARS screening method is the best method for predicting the content of effective components and soluble solids in perhexiline water extract.
TABLE 4 PLSR model results based on different eigenband extraction algorithms
7. Establishing a quantitative correction model:
in the training set, the optimum characteristic wave band of the selected Raman spectrum is utilized, and a CNN method, a PLSR method and an SVR method are adopted to establish a quantitative correction model of the Raman spectrum of the perhexiline extracting solution and the content of the danshensu, the ferulic acid, the rosmarinic acid, the salvianolic acid B and the soluble solid matters. After the model is built, the performance of the model is verified by a test set. The model evaluation index comprises a training set correlation coefficient Rc 2 Test set correlation coefficient Rp 2 Training set root mean square error RMSEC and test set root mean square error RMSEP. Rp 2 The closer to 1, the stronger the prediction effect of the model is, the smaller the RMSEC and RMSEP and the closer the values are, the better the prediction performance and the robustness of the model are.
The Raman spectrum established by the CARS-CNN algorithm and the quantitative correction model of the content of the danshensu in the perhexiline water extract are as follows: calling a Sequential function to construct a model, and then creating a layer: creating a convolution 1 layer, wherein parameters of the convolution 1 layer are 32 filters, the size of a filter window is 3 multiplied by 3, the step length of each movement of a scanning window is 1, and a Rectified linear units (Relu) activating function is applied; creating a batch normalization layer; creating a maximum pooling layer, wherein the number of filters of the pooling layer is consistent with that of convolution 1 layers, the size of a filter window is 2 multiplied by 2, and the moving step length of a scanning window is 1 each time; creating a convolution 2 layer with the parameter of (16,3,1) and applying a Relu activation function; creating convolution 3 layers with the parameter of (16,3,1) and applying Relu activation function; creating convolution 4 layers with the parameter of (16,3,1) and applying Relu activation function; creating convolution 5 layers with the parameter (16,3,1) and applying the Relu activation function; creating convolution 6 layers with the parameter of (16,3,1) and applying Relu activation function; creating a convolution 7 layer with a parameter of 32,3,1), applying a Relu activation function; creating 8 layers of convolution, with the parameter of (64,3,1), and applying a Relu activation function; creating a flattening layer; creating a full connection layer 1, outputting the neuron number 64, and applying a Relu activation function; a fully connected 2 layer is created, the number of neurons is output 1, and the Linear activation function is applied. Then selecting a 'mean _ squared _ error' loss function, an Adam (lr = le-4) optimizer, finally calling a fit function to provide data to a model, designating the batch size to be 50, the iteration number to be 200, and setting Keras to stop training when the loss is not improved after 40 iterations.
The Raman spectrum established by the CARS-CNN algorithm and the quantitative correction model of the ferulic acid content in the perhexiline water extract are as follows: calling a Sequential function to construct a model, and then creating a layer: creating a convolution 1 layer, wherein the parameter of the convolution 1 layer is 64 filters, the size of a filter window is 3 multiplied by 3, the moving step length of a scanning window is 1 each time, and a Relu activation function is applied; creating a batch normalization layer; creating a convolution 2 layer with the parameter of (16,3,1) and applying a Relu activation function; creating convolution 3 layers with the parameter of (16,3,1) and applying Relu activation function; creating convolution 4 layers with the parameter of (16,3,1) and applying Relu activation function; creating convolution 5 layers with the parameter of (16,3,1) and applying Relu activation function; creating convolution 6 layers with the parameter of (16,3,1) and applying Relu activation function; creating a convolution 7 layer with the parameter of (32,3,1) and applying a Relu activation function; creating 8 layers of convolution with the parameter (64,3,1) and applying the Relu activation function; creating a convolution 9 layer with the parameter of (64,3,1) and applying a Relu activation function; creating a flattening layer; creating a full-connection layer 1, outputting the neuron number 64, and applying a Relu activation function; creating a fully-connected 2-layer, outputting the neuron number 16, and applying a Relu activation function; a fully connected 3 layer is created, the number of neurons is output 1, and the Linear activation function is applied. Then selecting a 'mean _ squared _ error' loss function, an Adam (lr = le-4) optimizer, finally calling a fit function to provide data to a model, designating the batch size to be 10 and the number of iterations to be 200, and setting Keras to stop training when the loss is not improved after 40 iterations.
The Raman spectrum established by the CARS-CNN algorithm and the quantitative correction model of the content of the rosmarinic acid in the perhexiline water extract are as follows: calling a Sequential function to construct a model, and then creating a layer: creating a convolution 1 layer, wherein the parameters of the convolution 1 layer are 16 filters, the size of a filter window is 3 multiplied by 3, the moving step length of a scanning window is 1 each time, and a Relu activation function is applied; creating a batch normalization layer; creating a maximum pooling layer, wherein the number of filters of the pooling layer is consistent with that of convolution 1 layers, the size of a filter window is 2 multiplied by 2, and the moving step length of a scanning window is 1 each time; creating a convolution 2 layer with the parameter of (16,3,1) and applying a Relu activation function; creating convolution 3 layers with the parameter of (16,3,1) and applying Relu activation function; creating convolution 4 layers with the parameter of (16,3,1) and applying Relu activation function; creating convolution 5 layers with the parameter of (16,3,1) and applying Relu activation function; creating convolution 6 layers with the parameter of (32,3,1) and applying Relu activation function; creating a convolution 7 layer with a parameter of 64,3,1), applying a Relu activation function; creating a flattening layer; creating a full-connection layer 1, outputting the neuron number 64, and applying a Relu activation function; creating a fully-connected 2-layer, outputting the neuron number 16, and applying a Relu activation function; a fully connected 3 layer is created, the number of neurons is output 1, and the Linear activation function is applied. Then selecting a 'mean _ squared _ error' loss function, an Adam (lr = le-4) optimizer, finally calling a fit function to provide data to a model, designating the batch size to be 50, the iteration number to be 200, and setting Keras to stop training when the loss is not improved after 40 iterations.
The Raman spectrum established by the CARS-CNN algorithm and the quantitative correction model of the content of the salvianolic acid B in the perhexiline water extract are as follows: calling a Sequential function to construct a model, and then creating a layer: creating a convolution 1 layer, wherein the parameters of the convolution 1 layer are 16 filters, the size of a filter window is 3 multiplied by 3, the moving step length of a scanning window is 1 each time, and a Relu activation function is applied; creating a batch normalization layer; creating a maximum pooling layer, wherein the number of filters of the pooling layer is consistent with that of convolution 1 layers, the size of a filter window is 2 multiplied by 2, and the moving step length of a scanning window is 1 each time; creating convolution 2 layers with parameters (16,3,1) and applying Relu activation function; creating convolution 3 layers with the parameter of (32,8,1) and applying Relu activation function; creating convolution 4 layers with the parameter of (64,8,1) and applying Relu activation function; creating a flattening layer; creating a full-connection layer 1, outputting the neuron number 64, and applying a Relu activation function; create fully connected 2 layers, output neuron number 1, apply Linear activation function. Then selecting a 'mean _ squared _ error' loss function, an Adam (lr = le-4) optimizer, finally calling a fit function to provide data to a model, designating the batch size to be 50, the iteration number to be 500, and setting Keras to stop training when the loss is not improved after 40 iterations.
The Raman spectrum established by the CARS-CNN algorithm and the quantitative correction model of the content of soluble solids in the perhexiline water extract are as follows: calling a Sequential function to construct a model, and then creating a layer: creating a convolution 1 layer, wherein the parameters of the convolution 1 layer are 16 filters, the size of a filter window is 3 multiplied by 3, the moving step length of a scanning window is 1 each time, and a Relu activation function is applied; creating a batch normalization layer; creating a maximum pooling layer, wherein the number of filters of the pooling layer is consistent with that of convolution 1 layers, the size of a filter window is 2 multiplied by 2, and the moving step length of a scanning window each time is 1; creating convolution 2 layers with parameters (16,8,1) and applying Relu activation function; creating convolution 3 layers with the parameter (32,8,1) and applying Relu activation function; creating convolution 4 layers with the parameter of (64,8,1) and applying Relu activation function; creating a flattening layer; creating a full connection layer 1, outputting the neuron number 64, and applying a Relu activation function; create fully connected 2 layers, output neuron number 1, apply Linear activation function. Then, selecting a 'mean _ squared _ error' loss function, an Adam (lr = le-4) optimizer, finally calling a fit function to provide data to a model, specifying a batch size of 50 and an iteration number of 500, and setting Keras to stop training when the loss is not improved after 40 iterations.
The structure diagram of the Raman spectrum and the model of the perhexiline water extract sample established by the CARS-CNN algorithm is shown in FIG. 4.
Table 5 shows CNN model structures and parameters, and Table 6 shows Rp of CARS-CNN, CARS-PLSR and SPA-SVR models 2 Values and RMSEP values results are summarized in the table. The comparison of the predicted values and the true values of the above three models is shown in fig. 5, 6 and 7, in which the predicted values and the true value points of the CARS-CNN are substantially on oblique lines, and the predicted values and the true value points of the CARS-PLSR and SPA-SVR models are relatively discrete. From Table 6, it can be seen that Rp of danshensu, ferulic acid, rosmarinic acid, salvianolic acid B and soluble solids in the CARS-CNN model 2 The values are 0.8458, 0.8667, 0.8491, 0.9246, 0.9415 respectively, which are obviously superior to the CARS-PLSR model and the SVR model. FIG. 8 is a time series fit graph of predicted values and true values, where the CARS-CNN model fits closer to the true values.
Therefore, the CARS-CNN model has the optimal performance, the prediction results of the content of the drug-effect substances and the content of soluble solids in the water extraction process of perhexiline are reliable, and the requirement of rapid detection is met.
TABLE 5 CNN model parameters
TABLE 6 summary of CARS-CNN, CARS-PLSR and SPA-SVR model results
Finally, it should be noted that the above-mentioned contents are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, and that the simple modifications or equivalent substitutions of the technical solutions of the present invention by those of ordinary skill in the art can be made without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for detecting the content of a perhexiline aqueous extract based on a Raman spectrum is characterized in that the content of the perhexiline aqueous extract is detected by establishing the Raman spectrum and a quantitative correction model of the content of the perhexiline aqueous extract.
2. The method of claim 1, wherein the aqueous perhexiline extract comprises a pharmaceutically effective substance and soluble solids; wherein the drug effective substances comprise danshensu, ferulic acid, rosmarinic acid and salvianolic acid B.
3. The method of claim 1, wherein the establishing of the quantitative calibration model comprises the steps of:
(1) Collecting perhexiline water extract;
(2) Collecting a Raman spectrum of the perhexiline aqueous extract;
(3) Determining the content of the drug effect substance by adopting a high performance liquid chromatography, and determining the content of the soluble solid by adopting an oven drying method;
(4) Identifying and removing the acquired abnormal Raman spectrum;
(5) Correcting the spectrum from which the abnormal Raman spectrum is removed;
(6) Screening out the characteristic wave band of the corrected Raman spectrum;
(7) And establishing a quantitative correction model of the Raman spectrum, the content of the drug effect substances and the content of soluble solids by using the screened characteristic wave bands and adopting a CNN algorithm.
4. The method of claim 3, wherein the collecting in step (1) is performed by: soaking Saviae Miltiorrhizae radix and rhizoma Ligustici Chuanxiong in purified water, and extracting with water.
5. The method according to claim 4, wherein the ratio of the red sage root, the chuanxiong rhizome and the purified water is 1; the temperature of the water extraction is 70-100 ℃; the water extraction time is 1-2.5h, 1-2.5h and 1-2.5h respectively.
6. The method of claim 3, wherein the parameters of the acquisition in step (2) are: raman shift 176-3500cm -1 The integration time is 300-600ms, the integration power is 200-500mw, and the scanning times are 1-5.
7. The method of claim 3, wherein the method of identifying abnormal Raman spectra collected in step (4) is principal component analysis-Mahalanobis distance method; the correction method in the step (5) is a convolution smoothing method and a linear function normalization method.
8. The method of claim 3, wherein the screening in step (6) is a competitive adaptive re-weighting algorithm.
9. The method according to claim 3, wherein the evaluation index of the quantitative calibration model in step (7) is: training set correlation coefficient Rc 2 Test set correlation coefficient Rp 2 Training set root mean square error RMSEC and test set root mean square error RMSEP.
10. Use of the method of any one of claims 1 to 9 for detecting the content of active substances and/or soluble solids in an aqueous perhexiline extract.
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| CN116794181A (en) * | 2023-06-21 | 2023-09-22 | 浙江大学 | Intelligent detection method for quality of traditional Chinese medicine pharmaceutical process based on spectrum transformation fusion |
| CN116794181B (en) * | 2023-06-21 | 2024-01-26 | 浙江大学 | Intelligent detection method for quality of traditional Chinese medicine pharmaceutical process based on spectrum transformation fusion |
| CN117368146A (en) * | 2023-12-08 | 2024-01-09 | 苏陀科技(北京)有限公司 | Rapid detection method for mycelium protein content |
| CN117368146B (en) * | 2023-12-08 | 2024-03-12 | 苏陀科技(北京)有限公司 | Rapid detection method for mycelium protein content |
| CN118090648A (en) * | 2024-04-02 | 2024-05-28 | 常州建昊建筑鉴定检测有限公司 | Method and system for detecting water content of engineering material |
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