CN113740291B - Online quality monitoring method for alcohol extraction process of Dizhen particles - Google Patents
Online quality monitoring method for alcohol extraction process of Dizhen particles Download PDFInfo
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- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 title claims abstract description 57
- 238000000605 extraction Methods 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000012544 monitoring process Methods 0.000 title claims abstract description 37
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- 239000008187 granular material Substances 0.000 claims abstract description 12
- 238000012360 testing method Methods 0.000 claims abstract description 11
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- 230000002159 abnormal effect Effects 0.000 claims description 22
- JEJFTTRHGBKKEI-UHFFFAOYSA-N deoxyschizandrin Natural products C1C(C)C(C)CC2=CC(OC)=C(OC)C(OC)=C2C2=C1C=C(OC)C(OC)=C2OC JEJFTTRHGBKKEI-UHFFFAOYSA-N 0.000 claims description 22
- 238000012937 correction Methods 0.000 claims description 19
- 238000012216 screening Methods 0.000 claims description 19
- 238000010586 diagram Methods 0.000 claims description 18
- FYSHYFPJBONYCQ-UHFFFAOYSA-N schisanhenol Natural products C1C(C)C(C)CC2=CC(OC)=C(OC)C(OC)=C2C2=C1C=C(OC)C(OC)=C2O FYSHYFPJBONYCQ-UHFFFAOYSA-N 0.000 claims description 16
- RTZKSTLPRTWFEV-OLZOCXBDSA-N Deoxygomisin A Chemical compound COC1=C2C=3C(OC)=C(OC)C(OC)=CC=3C[C@@H](C)[C@@H](C)CC2=CC2=C1OCO2 RTZKSTLPRTWFEV-OLZOCXBDSA-N 0.000 claims description 15
- RTZKSTLPRTWFEV-UHFFFAOYSA-N Isokadsuranin Natural products COC1=C2C=3C(OC)=C(OC)C(OC)=CC=3CC(C)C(C)CC2=CC2=C1OCO2 RTZKSTLPRTWFEV-UHFFFAOYSA-N 0.000 claims description 15
- 239000000463 material Substances 0.000 claims description 15
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- YBZZAVZIVCBPDJ-UHFFFAOYSA-N schizandrin B Natural products COC1=C2C=3C(OC)=C(OC)C(OC)=CC=3CC(C)C(C)(O)CC2=CC2=C1OCO2 YBZZAVZIVCBPDJ-UHFFFAOYSA-N 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 7
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- NBIIXXVUZAFLBC-UHFFFAOYSA-N Phosphoric acid Chemical compound OP(O)(O)=O NBIIXXVUZAFLBC-UHFFFAOYSA-N 0.000 claims description 6
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- 101150061025 rseP gene Proteins 0.000 claims description 5
- 238000010438 heat treatment Methods 0.000 claims description 4
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
- G01N30/14—Preparation by elimination of some components
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8696—Details of Software
Abstract
The invention discloses an online quality monitoring method for an alcohol extraction process of a dittany granule, which comprises the following steps: step one: collecting an extracting solution sample; step two: sampling test design; step three: collecting near infrared spectrum; step four: measuring the content of an alcohol extraction sample; step five: establishing a quantitative model; step six: establishing a Multivariate Statistical Process Control (MSPC) quality monitoring model; drawing a process track graph by adopting a PCA score graph, a Hotelling T2 and a DModX control graph, and a step seven: the invention provides an online quality monitoring method for an alcohol extraction process of a ground glossy privet fruit granule, which is used for realizing the online monitoring of the content of active ingredients in the alcohol extraction process of the ground glossy privet fruit granule; further realizing the rapid judgment and real-time release of the extraction end point; further improves the batch-to-batch stability of the quality of the ethanol extract of the ground glossy privet fruit particles and improves the quality of products.
Description
Technical Field
The invention relates to the field of traditional Chinese medicine pharmacy, in particular to an online quality monitoring method for an alcohol extraction process of a dittany granule.
Background
The Dizhen granule is prepared from eight medicines of cortex Lycii, fructus Ligustri Lucidi, ecliptae herba, fructus Schisandrae chinensis, semen astragali Complanati, cortex Albiziae, glycyrrhrizae radix and radix Curcumae, and has the main effects of clearing deficiency heat, nourishing liver and kidney and tranquilizing mind. Can be used for treating female climacteric syndrome with symptoms of yin deficiency and internal heat, such as dry heat and sweat, vexation and irritability, feverish sensation in palms and soles, insomnia and dreaminess, soreness of waist and knees, dry mouth, constipation, etc.
The production process flow of the dittany granules comprises alcohol extraction, water extraction, concentration, drying, granulation and the like, wherein the alcohol extraction process is to pulverize shizandra berry and glossy privet fruit into coarse powder and then extract the coarse powder with ethanol solution. The extraction process of the traditional Chinese medicine is a process of continuously dissolving out active ingredients in the traditional Chinese medicine, the extraction process is critical to the production and quality of the traditional Chinese medicine, and the quality of the traditional Chinese medicine extract affects the quality of the final traditional Chinese medicine product. If an effective quality detection means is lacking, the content change of the effective components in the extraction process cannot be monitored in real time, and the uniformity and stability of the product can be affected. At present, the quality detection of the alcohol extraction process of the fructus ligustri lucidi particles adopts a manual sampling-laboratory chromatographic detection method, the detection time is long, and the on-line and real-time monitoring and release control cannot be realized.
Therefore, the online quality monitoring method for the alcohol extraction process of the ground glossy privet fruit particles is researched, so that the online real-time monitoring of the effective components in the alcohol extraction process of the ground glossy privet fruit particles is realized, the quality stability of the extracting solution is ensured, and the uniformity and the stability of the quality of the final product are ensured.
Disclosure of Invention
The invention aims to provide an online quality monitoring method for an alcohol extraction process of a dittany granule, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an online quality monitoring method for an alcohol extraction process of a dittany granule comprises the following steps:
step one: collecting an extracting solution sample;
weighing 60g of each of the schisandra chinensis medicinal material and the glossy privet fruit medicinal material, pulverizing into coarse powder, sieving the coarse powder, placing the coarse powder into a 1000mL three-necked flask, placing the three-necked flask into a water bath kettle with the temperature of 85 ℃ for preheating, installing a reflux device, adding 95% ethanol with the volume of 840mL and the temperature of 75 ℃, taking 3mL of ethanol extract every 5min from the beginning of complete adding of the solvent, taking 18 ethanol extract samples in total, wherein the whole extraction time is 90 min;
step two: sampling test design;
designing 12 batches of tests, wherein the tests comprise A1-A8 and B1-B4, the A1-A8 are normal batches, the B1-B2 are process abnormal batches, wherein the B1 immediately removes a heating source after the extraction is started, the extraction temperature is kept at 40 ℃, abnormal conditions such as equipment damage or power failure in the production process of a factory are simulated, the abnormal feeding conditions are simulated by B3-B4, the feeding quantity of raw medicinal materials is reduced by B3, and the feeding quantity of the raw medicinal materials is increased by B4;
step three: collecting near infrared spectrum;
immediately scanning a near infrared transmission spectrum of the ethanol extraction sample after the ethanol extraction sample is taken out, setting the resolution as 8cm < -1 > and the optical path as 2mm by taking air as a background, collecting spectrum information of 10000-4000cm < -1 > for 32 times, scanning each sample for three times, and obtaining an average spectrum;
step four: measuring the content of an alcohol extraction sample;
step five: establishing a quantitative model;
the establishment method comprises the following steps:
s1, eliminating abnormal points and dividing a sample set;
the Monte Carlo cross-validation algorithm is realized through MATLAB R2018b software, so that abnormal samples are removed, and the sample set is divided into a correction set and a validation set according to the ratio of 3:1 by adopting an SPXY algorithm;
s2, screening characteristic wavelengths;
the data of the effective components of terprivet glycoside, schisandrin A and schisandrin B are screened by adopting siPLS, CARS, RF, MC-UVE to select characteristic wavelengths. The screening and combining interval of the terprivet glycoside is 5403.5-5600.2, 6005.2-6201.9, 8010.8-8207.5 and 8211.4-8408.1 cm < -1 >; the screening and combining interval of the schisandrin A is 5457.5-5696.7, 6417.9-6653.2, 7135.3-7370.6 and 8809.2-9044.5 cm < -1 >; the screening and combining interval of the schizandrin A is 6005.2-6201.9, 7609.7-7806.4, 7810.3-8007.0 and 8612.5-8809.2 cm < -1 >; the screening and combining interval of the schisandrin B is 5403.5-5600.2, 5604.1-5800.8, 6005.2-6201.9 and 6406.3-6603.0 cm < -1 >;
s3, establishing and evaluating a quantitative model
After the abnormal samples are removed and the characteristic wavelength is screened, respectively establishing PLSR quantitative analysis models of all index components;
step six: establishing a Multivariate Statistical Process Control (MSPC) quality monitoring model;
and drawing a process track graph by adopting a PCA score graph, a Hotelling T2 and a DModX control graph.
Step seven: verifying a Multivariate Statistical Process Control (MSPC) quality monitoring model;
and selecting two normal batches A4 and A5 to verify the established statistical monitoring model.
As a further scheme of the invention: the specific measurement steps in the fourth step are as follows:
s1: the chromatographic conditions were as follows:
the column was Xselect HSS T3 (4.6X105 mm,5 μm); the detection wavelength is 230nm, the flow rate is 1 mL.min < -1 >, the sample injection amount is 10 mu L, 0.1% phosphoric acid is taken as a mobile phase A, acetonitrile is taken as a mobile phase B, and gradient elution is carried out according to the following procedures: 0-10 min: 18-20% of B, 10-20 min: 20-25% of B, 20-35 min: 25-70% of B, 35-50 min: 70-75% of B, 50-60 min:75% B;
s2: preparing a reference substance solution;
taking a proper amount of schizandrin A, schizandrin B and terprivet glycoside reference substances, precisely weighing, adding a proper amount of 95% ethanol to prepare a mixed reference substance solution containing 1099 mug terprivet glycoside, 504.0 mug schizandrin A, 113 mug schizandrin A and 302 mug schizandrin B in each 1 mL;
s3: and (5) preparing a test solution.
Taking a proper amount of alcohol extract sample, filtering, and taking subsequent filtrate to obtain the sample solution.
As still further aspects of the invention: the model evaluation index in the fifth step comprises correction set error Root Mean Square (RMSECV), correction set correlation coefficient (Rc), correction set error Root Mean Square (RMSEC), correction set relative deviation (RSEC), verification set correlation coefficient (Rp), verification set error Root Mean Square (RMSEP) and verification set relative deviation (RSEP).
As still further aspects of the invention: the PCA score chart in the sixth step shows the trend of the PC1 score of a certain batch of samples in the alcohol extraction process, and since the PC1 explains 94.46%, the trend of the PC1 basically represents the overall trend of the samples. Hotelling T 2 The control diagram shows the distance from the sample to the main component model, the abnormality is prompted according to the deviation degree, the DModX control diagram is an external residual matrix of the main component model, the external data change of the model is reflected, the two control diagrams are usually combined and mutually supplemented, and the process judgment is more accurate; calculation of PC1 score, hotelling T, of training set data by SIMCA 14.1 data analysis software 2 And simultaneously calculating the average value (Avg) and standard deviation (sigma) of the PC1 score of the training data at each alcohol extraction time point, setting the upper limit and the lower limit as the average value +/-3 sigma, setting 95% of Hotelling T2 as the upper control limit, and setting the average value +3sigma as the upper control limit by DModX.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides an online quality monitoring method for an alcohol extraction process of a ground glossy privet fruit granule, which realizes online monitoring of the content of active ingredients in the alcohol extraction process of the ground glossy privet fruit granule;
2. further realizing the rapid judgment and real-time release of the extraction end point;
3. further improves the batch-to-batch stability of the quality of the ethanol extract of the ground glossy privet fruit particles and improves the quality of products.
Drawings
FIG. 1 is a correlation chart of predicted and measured values of NIRs of index components in a correction set and a verification set (A. Terprivet glycoside, B. Schizandrin A, C. Schizandrin A, D. Schizandrin B).
Fig. 2 is a graph of a PCA monitoring model of an alcohol extraction process.
FIG. 3 is a graph of a monitoring model of Hotelling T2 during the alcohol extraction process.
Fig. 4 is a diagram of a DModX monitoring model of an alcohol extraction process.
FIG. 5 is a graph of an alcohol extraction process monitoring model-A4, A5.
FIG. 6 is a diagram of an alcohol extraction process monitoring model-B1, B2.
FIG. 7 is a diagram of an alcohol extraction process monitoring model-B3, B4.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 to 7, in an embodiment of the present invention, an online quality monitoring method for an alcohol extraction process of a fructus ligustri lucidi granule includes the following steps:
step one: collecting an extracting solution sample;
weighing 60g of each of the schisandra chinensis medicinal material and the glossy privet fruit medicinal material, pulverizing into coarse powder, sieving the coarse powder, placing the coarse powder into a 1000mL three-necked flask, placing the three-necked flask into a water bath kettle with the temperature of 85 ℃ for preheating, installing a reflux device, adding 95% ethanol with the volume of 840mL and the temperature of 75 ℃, taking 3mL of ethanol extract every 5min from the beginning of complete adding of the solvent, taking 18 ethanol extract samples in total, wherein the whole extraction time is 90 min;
step two: sampling test design;
designing 12 batches of tests, wherein the test conditions are shown in table 1, the test conditions comprise A1-A8 and B1-B4, A1-A8 are normal batches, B1-B2 are process abnormal batches, wherein B1 immediately removes a heating source after extraction begins, B2 keeps the extraction temperature at 40 ℃, abnormal conditions such as equipment damage or power failure in the production process of a factory are simulated, B3-B4 simulate abnormal feeding conditions, B3 reduces the feeding amount of raw medicinal materials, and B4 increases the feeding amount of the raw medicinal materials;
TABLE 1 Experimental design for alcohol extraction process
Step three: collecting near infrared spectrum;
immediately scanning a near infrared transmission spectrum of the ethanol extraction sample after the ethanol extraction sample is taken out, setting the resolution as 8cm < -1 > and the optical path as 2mm by taking air as a background, acquiring spectrum information of 10000-4000cm < -1 > for 32 times, scanning each sample for three times, and obtaining an average spectrum;
step four: measuring the content of an alcohol extraction sample;
the specific measurement steps are as follows:
s1: the chromatographic conditions were as follows:
the column was Xselect HSS T3 (4.6X105 mm,5 μm); the detection wavelength is 230nm, the flow rate is 1 mL.min < -1 >, the sample injection amount is 10 mu L, 0.1% phosphoric acid is taken as a mobile phase A, acetonitrile is taken as a mobile phase B, and gradient elution is carried out according to the following procedures: 0-10 min: 18-20% of B, 10-20 min: 20-25% of B, 20-35 min: 25-70% of B, 35-50 min: 70-75% of B, 50-60 min:75% B;
s2: preparing a reference substance solution;
taking a proper amount of schizandrin A, schizandrin B and terprivet glycoside reference substances, precisely weighing, adding a proper amount of 95% ethanol to prepare a mixed reference substance solution containing 1099 mug terprivet glycoside, 504.0 mug schizandrin A, 113 mug schizandrin A and 302 mug schizandrin B in each 1 mL;
s3: preparation of test solution
Taking a proper amount of alcohol extract sample, filtering, and taking subsequent filtrate to obtain the sample solution.
Step five: establishing a quantitative model;
the model evaluation indexes comprise correction set error Root Mean Square (RMSECV), correction set correlation coefficient (Rc), correction set error Root Mean Square (RMSEC), correction set relative deviation (RSEC), verification set correlation coefficient (Rp), verification set error Root Mean Square (RMSEP) and verification set relative deviation (RSEP); it is generally considered that the closer Rc and Rp are to 1, the stronger the correlation between the fitting value of the correction set and the predicted value of the verification set and the measured value of the sample is, and when RMSECV, RMSEC, RMSEP three indexes are smaller and mutually close, the smaller the RSEC and the RSEP are, the higher the precision and the stability of the established quantitative analysis model are, and the better the performance is;
the establishment method comprises the following steps:
s1, eliminating abnormal points and dividing a sample set;
the Monte Carlo cross-validation algorithm is realized through MATLAB R2018b software, so that abnormal samples are removed, and the sample set is divided into a correction set and a validation set according to the ratio of 3:1 by adopting an SPXY algorithm;
s2, screening characteristic wavelengths;
the data of the effective components of terprivet glycoside, schisandrin A and schisandrin B are screened by adopting siPLS, CARS, RF, MC-UVE to select characteristic wavelengths. The screening and combining interval of the terprivet glycoside is 5403.5-5600.2, 6005.2-6201.9, 8010.8-8207.5 and 8211.4-8408.1 cm < -1 >; the screening and combining interval of the schisandrin A is 5457.5-5696.7, 6417.9-6653.2, 7135.3-7370.6 and 8809.2-9044.5 cm < -1 >; the screening and combining interval of the schizandrin A is 6005.2-6201.9, 7609.7-7806.4, 7810.3-8007.0 and 8612.5-8809.2 cm < -1 >; the screening and combining interval of the schisandrin B is 5403.5-5600.2, 5604.1-5800.8, 6005.2-6201.9 and 6406.3-6603.0 cm < -1 >;
s3, establishing and evaluating a quantitative model
After the abnormal samples are removed and the characteristic wavelength is screened, each index component PLSR quantitative analysis model is respectively built, parameter summary of the PLSR models built by different wavelength screening methods of each index component is shown in a table 2, and as can be seen in the table 2, key variables obtained by the four wavelength screening methods are improved to a certain extent compared with the PLSR quantitative model built by a full spectrum, and meanwhile, the variable number required by modeling is greatly reduced. In the four wavelength screening methods, RF shows excellent feature extraction capability in 4 index models, and the method can remove most of irrelevant variables in near infrared spectrums while ensuring excellent model precision and stability. In the PLSR quantitative model with the optimal performance, RMSEC values of terligustrin, schizandrin A and schizandrin B are 0.0570, 0.0060, 0.0018 and 0.0031 respectively, RMSEP values are 0.0613, 0.0054, 0.0034 and 0.0037 respectively, the RMSEC and RMSEP values are relatively close and smaller, rc and Rp of the four index models are close to or higher than 0.9, RSEP in verification sets is less than 5%, and RPD values are more than 2, so that the requirements of process analysis are basically met. The verification set of terligustrum lucidum glycosides, schizandrin A and schizandrin B and the correlation graph of the actual measurement value and the predicted value in the correction set are shown in figure 1, wherein the actual measurement value and the predicted value point in the correction set are uniformly distributed on two sides of a straight line y=x, the actual measurement value and the predicted value of the verification set are relatively close, and the requirement of the unknown sample content prediction can be met;
TABLE 2 alcohol extraction of sample index componentsNIRsModel parameters
Step six: establishing a Multivariate Statistical Process Control (MSPC) quality monitoring model;
and drawing a process track diagram by adopting a PCA score diagram, a Hotelling T2 and a DModX control diagram, wherein the PCA score diagram shows the change trend of the PC1 score of a batch of samples in the alcohol extraction process, and the change trend of the PC1 basically represents the overall change trend of the samples because the PC1 accounts for 94.46%. Hotelling T 2 The control diagram shows the distance from the sample to the main component model, the abnormality is prompted according to the deviation degree, the DModX control diagram is an external residual matrix of the main component model, the external data change of the model is reflected, the two control diagrams are usually combined and mutually supplemented, and the process judgment is more accurate.
Calculation of PC1 score, hotelling T, of training set data by SIMCA 14.1 data analysis software 2 The value and DModX value are calculated, the average value (Avg) and standard deviation (sigma) of the PC1 score of the training data at each alcohol extraction time point are calculated, the upper limit and the lower limit are set to be the average value + -3 sigma, 95% of Hotelling T2 is set to be the upper control limit, and DModX is set to be the average value +3 sigma to be the controlAn upper limit.
Step seven: verifying a Multivariate Statistical Process Control (MSPC) quality monitoring model;
and (3) selecting two normal batches A4 and A5 to verify the established statistical monitoring model, wherein the trend track diagram of the alcohol extraction process is shown in fig. 5, the batches A4 and A5 in the three monitoring models do not exceed the set control limit, the trend is basically consistent with the training samples of each batch 6 of the modeling, and the condition that the established model has no fault false alarm is indicated.
Notice that:
1. equipment faults such as process parameter setting errors, power failure and the like are frequently encountered in the production process, if the faults can be found and the waste of raw materials can be eliminated in time in the production process, the batches B1 and B2 respectively simulate abnormal process conditions, the heating of the batch B1 is stopped immediately after the extraction is started, the temperature of the batch B2 is lower than the extraction temperature, and the extraction process is completed at 40 ℃. FIG. 6 shows the case of batches B1 and B2 in the MSPC model, where the principal components of B1 and B2 were below the lower score limit throughout the alcohol extraction process in the PCA score plot. Hotelling T 2 The control graph does not exceed the statistic T 2 95% control limit of (c), but far above the T of normal batch samples 2 Statistics, in the DModX control chart, B1 and B2 are higher than the DModX upper control limit;
2. besides the process abnormality, the abnormal feeding solid-liquid ratio also causes larger quality fluctuation in the production process, B3 and B4 respectively simulate the situation of too little or too much feeding of medicinal materials, as shown in fig. 7, the PCA score graph shows that the PC1 score of B3 runs near the lower control limit in the whole process because the feeding of medicinal materials is lower than the normal condition, and the PC1 score of B4 is higher than the normal condition because the feeding of medicinal materials is higher than the normal condition in the initial stage, and gradually rises to exceed the upper control limit after 20min, which indicates that the internal components are higher than the normal value. Hotelling T 2 The control diagram shows that B3 and B4 operate near the 95% control limit, and eventually B4 is abnormal beyond the control limit. In the DModX control chart, B3 and B4 are higher than the control upper limit, and the two abnormal conditions prove that the established monitoring model can be used as a tool for judging process abnormality and can play a role in timing errors in the production process.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.
Claims (4)
1. An online quality monitoring method for an alcohol extraction process of a dittany granule is characterized by comprising the following steps of: the monitoring method comprises the following steps:
step one: collecting an extracting solution sample;
weighing 60g of each of the schisandra chinensis medicinal material and the glossy privet fruit medicinal material, pulverizing into coarse powder, sieving the coarse powder, placing the coarse powder into a 1000mL three-necked flask, placing the three-necked flask into a water bath kettle with the temperature of 85 ℃ for preheating, installing a reflux device, adding 95% ethanol with the volume of 840mL and the temperature of 75 ℃, taking 3mL of ethanol extract every 5min from the beginning of complete adding of the solvent, taking 18 ethanol extract samples in total, wherein the whole extraction time is 90 min;
step two: sampling test design;
designing 12 batches of tests, wherein the tests comprise A1-A8 and B1-B4, the A1-A8 are normal batches, the B1-B2 are process abnormal batches, wherein the B1 immediately removes a heating source after the extraction is started, the extraction temperature is kept at 40 ℃ by the B2, the equipment damage or power failure abnormal conditions in the production process of a factory are simulated, the feeding abnormal conditions are simulated by the B3-B4, the feeding quantity of raw medicinal materials is reduced by the B3, and the feeding quantity of the raw medicinal materials is increased by the B4;
step three: collecting near infrared spectrum;
immediately after the ethanol extraction sample is taken out, the near infrared transmission spectrum is scanned, and the resolution is set to be 8cm by taking air as a background -1 The optical path is 2mm, the scanning times are 32 times, and 10000-4000cm of the scanning times are collected -1 Each sample is scanned three times, and an average spectrum is obtained;
step four: measuring the content of an alcohol extraction sample;
step five: establishing a quantitative model;
the establishment method comprises the following steps:
s1, eliminating abnormal points and dividing a sample set;
the Monte Carlo cross-validation algorithm is realized through MATLAB R2018b software, so that abnormal samples are removed, and the sample set is divided into a correction set and a validation set according to the ratio of 3:1 by adopting an SPXY algorithm;
s2, screening characteristic wavelengths;
the effective components of terligustrazine, schisandrin A and schisandrin B are respectively subjected to siPLS, CARS, RF, MC-UVE screening with characteristic wavelength, wherein the screening combination interval of terligustrazine is 5403.5-5600.2, 6005.2-6201.9, 8010.8-8207.5 and 8211.4-8408.1 cm < -1 >; the screening and combining interval of the schisandrin A is 5457.5-5696.7, 6417.9-6653.2, 7135.3-7370.6 and 8809.2-9044.5 cm < -1 >; the screening and combining interval of the schizandrin A is 6005.2-6201.9, 7609.7-7806.4, 7810.3-8007.0 and 8612.5-8809.2 cm < -1 >; the screening and combining interval of the schisandrin B is 5403.5-5600.2, 5604.1-5800.8, 6005.2-6201.9 and 6406.3-6603.0 cm < -1 >;
s3, establishing and evaluating a quantitative model
After the abnormal samples are removed and the characteristic wavelength is screened, respectively establishing PLSR quantitative analysis models of all index components;
step six: establishing a Multivariate Statistical Process Control (MSPC) quality monitoring model;
drawing a process track graph by adopting a PCA score graph, a Hotelling T2 and a DModX control graph;
step seven: verifying a Multivariate Statistical Process Control (MSPC) quality monitoring model;
and selecting two normal batches A4 and A5 to verify the established statistical monitoring model.
2. The online quality monitoring method for the alcohol extraction process of the ground glossy privet fruit particles according to claim 1, which is characterized in that: the specific measurement steps in the fourth step are as follows:
s1: the chromatographic conditions were as follows:
the column was Xselect HSS T3.6X105 mm,5 μm; detection wavelength of 230nm and flow rate of 1 mL.min -1 Sample injection amount10. Mu.L of the solution was eluted with a gradient of 0.1% phosphoric acid as mobile phase A and acetonitrile as mobile phase B according to the following procedure: 0-10 min: 18-20% of B, 10-20 min: 20-25% of B, 20-35 min: 25-70% of B, 35-50 min: 70-75% of B, 50-60 min:75% B;
s2: preparing a reference substance solution;
taking a proper amount of schizandrin A, schizandrin B and terprivet glycoside reference substances, precisely weighing, adding a proper amount of 95% ethanol to prepare a mixed reference substance solution containing 1099 mug terprivet glycoside, 504.0 mug schizandrin A, 113 mug schizandrin A and 302 mug schizandrin B in each 1 mL;
s3: preparing a sample solution;
taking a proper amount of alcohol extract sample, filtering, and taking subsequent filtrate to obtain the sample solution.
3. The online quality monitoring method for the alcohol extraction process of the ground glossy privet fruit particles according to claim 1, which is characterized in that: the model evaluation index in the fifth step comprises correction set error Root Mean Square (RMSECV), correction set correlation coefficient (Rc), correction set error Root Mean Square (RMSEC), correction set relative deviation (RSEC), verification set correlation coefficient (Rp), verification set error Root Mean Square (RMSEP) and verification set relative deviation (RSEP).
4. The online quality monitoring method for the alcohol extraction process of the ground glossy privet fruit particles according to claim 1, which is characterized in that: the PCA score chart in the step six shows the change trend of the PC1 score of a certain batch of samples in the alcohol extraction process, and the change trend of the PC1 basically represents the overall change trend of the samples because the PC1 accounts for 94.46 percent, hotelling T 2 The control diagram shows the distance from the sample to the main component model, the abnormality is prompted according to the deviation degree, the DModX control diagram is an external residual matrix of the main component model, the external data change of the model is reflected, the two control diagrams are combined and mutually supplemented, and the process judgment is more accurate; calculation of PC1 score, hotelling T, of training set data by SIMCA 14.1 data analysis software 2 Values and DModX values, while calculating the average value (Avg) and standard square of the PC1 score of the training data at each time point of alcohol extractionThe difference (σ), upper and lower limits are set to mean ± 3σ, 95% of Hotelling T2 is set to upper control limit, and DModX sets to mean +3σ as upper control limit.
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