CN117169167A - Production method and system of traditional Chinese medicine type probiotic preparation based on hyperspectral image - Google Patents
Production method and system of traditional Chinese medicine type probiotic preparation based on hyperspectral image Download PDFInfo
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Abstract
The application belongs to the technical field of hyperspectral analysis and traditional Chinese medicine type probiotic production, and provides a method and a system for producing a traditional Chinese medicine type probiotic preparation based on hyperspectral images, wherein the method comprises the following steps: the method comprises the steps of adding a mixed solution consisting of traditional Chinese medicine powder and strain stock solution into a fermentation tank for fermentation, collecting spectrum data of the mixed solution through an imaging spectrometer, preprocessing the spectrum data to obtain a treatment chart, carrying out real-time fermentation stage analysis according to the treatment chart, calculating the fermentation stage level, and dynamically judging and controlling the fermentation process through the fermentation stage level obtained in real time and historical data of the fermentation stage level. The fermentation of the traditional Chinese medicine type probiotics in the fermentation environment is quantized in a time-varying stage, so that the utilization rate of raw materials is improved, and the quality and the efficacy of the product are effectively ensured. Especially in mass production, the problem of low fermentation level of a large amount of effective components of the traditional Chinese medicinal materials caused by subjective experience judgment can be effectively avoided, so that the waste of the traditional Chinese medicinal materials and production resources is effectively avoided.
Description
Technical Field
The application belongs to the technical field of hyperspectral analysis and traditional Chinese medicine type probiotic production, and particularly relates to a production method and a production system of a traditional Chinese medicine type probiotic preparation based on hyperspectral images.
Background
Chinese herbal medicines gradually draw attention of the breeding industry because of their various effects of promoting animal growth and development, improving immunity, preventing diseases and the like. In order to exert the pharmacological actions of the Chinese herbal medicines to the maximum extent, a bioconversion technology is introduced, the Chinese herbal medicines can generate new active substances through microbial fermentation, the drug effect is enhanced, the toxic and side effects are reduced, and the aims of synergism and toxicity reduction are achieved. Currently, such traditional Chinese medicine type probiotic products have been considered as one of the best alternatives to antibiotics.
However, current fermentation processes generally rely on subjective experience, and it is generally considered that the process is completed by artificially prescribing that the Chinese herbal medicine is fermented for several hours under the action of a specific strain in a specific environment. In fact, the components of the Chinese herbal medicines are complex, and in the production process of the traditional Chinese medicine probiotics, various factors such as the types, the quality and the quantity of the probiotics, the temperature and the pH value of the environment and the like can influence the fermentation process, so that the fermentation time required by the Chinese herbal medicines in each batch is different. On one hand, depending on an empirical production method, incomplete fermentation or excessive fermentation is often caused, so that the effective fermentation level of the Chinese herbal medicine is low, and the quality and the drug effect of the product are affected; on the other hand, the biochemical experiment can only be carried out after the process is finished, so that the product quality evaluation can be carried out, if the quality is unqualified, medicinal materials and production resources are wasted, the production cost is improved and the production efficiency is reduced by phase change. Therefore, there is a need to develop a production method that can monitor the progress of fermentation in real time.
Disclosure of Invention
The application aims to provide a method and a system for producing a traditional Chinese medicine type probiotic preparation based on hyperspectral images, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In order to achieve the above object, according to an aspect of the present application, there is provided a method for producing a probiotic preparation of traditional Chinese medicine based on hyperspectral images, the method comprising the steps of:
s100, adding a mixed solution consisting of traditional Chinese medicine powder and strain stock solution into a fermentation tank for fermentation;
s200, collecting spectrum data of the mixed solution through an imaging spectrometer;
s300, preprocessing the optical data to obtain a processing diagram;
s400, carrying out real-time fermentation stage analysis according to the processing diagram and calculating the fermentation stage level;
s500, dynamically judging and controlling the fermentation process through the fermentation stage level obtained in real time and the historical data thereof.
Further, in step S100, the method for adding the mixed solution of the traditional Chinese medicine powder and the strain stock solution into the fermentation tank for fermentation is as follows: pretreating Chinese medicinal materials including cortex Phellodendri, radix Ginseng Rubra, radix Isatidis, herba Taraxaci, etc., cleaning, cutting, and grinding; the strain stock solution comprises one or more strain stock solutions of lactobacillus, enterococcus, saccharomycetes, bifidobacterium and bacillus, wherein the strain stock solution can be replaced by a probiotic culture solution; fully mixing the traditional Chinese medicine powder with the strain stock solution, and putting the formed mixed solution into a fermentation tank for fermentation.
Further, in step S200, the method for collecting the spectrum data of the mixed solution by the imaging spectrometer is: a transparent collecting window is arranged on the side wall of the fermentation tank, and the collecting window is made of one of quartz plates or fused silica; fixing the hyperspectral camera outside the acquisition window; setting a time period as a measurement interval Tgap, tgap E [10,20] minutes, measuring in real time every Tgap by a hyperspectral camera, obtaining spectrum data of the mixed liquid, and uploading the obtained spectrum data to a server.
Further, in step S300, the method for preprocessing the spectrum data to obtain the processing chart is: in the server, preprocessing is carried out on the optical data, the preprocessing step comprises normalization processing, intensity correction and offset correction, finally, image smoothing filtering processing is carried out through a moving average method to obtain a processing diagram, and pixel values of the processing diagram are recorded as gray values. The mixed liquid spectrum data at different moments is more comparable through pretreatment, and then scattering correction is carried out, so that interference caused by scattering of fine traditional Chinese medicine particles is avoided.
Further, in step S400, the method of performing real-time fermentation step analysis and calculating the fermentation step level according to the processing chart is that the spectrum data preprocessing image is divided into a plurality of areas by the edge recognition algorithm, each area is used as a analysis area, and the number of the analysis areas is recorded as an n cat; respectively marking a pixel point with the maximum gray value and a pixel point with the minimum gray value in a dividing region as gray electrode pixels and gray lean pixels, marking a straight line connecting the gray electrode pixels and the gray lean pixels as gray gradual lines, marking the distance of the gray gradual lines as gray gradual distances, and marking a pixel point corresponding to the middle point of the gray gradual lines as the center of the dividing region;
forming a circular area serving as a reference area by taking the center of the separation area as a center point and the gray gradual distance as a side length; the arithmetic average value of the gray values of all the pixel points in the reference position is recorded as reference position gray analysis level Cgpl, and the ratio of the average value of the gray values of all the pixel points in one analysis area to the reference position gray analysis level is recorded as node ratio PAPV; constructing a sequence of reference gray levels of each of the analysis regions to be referred to as reference gray sequences Cp_Ls; the ratio of the maximum element to the minimum element in the reference greyscale sequence is denoted as greyscale offset CPVT; calculating a fermentation level CVR according to the reference gray level and the pitch ratio:
；
wherein j1 is an accumulated variable, cp_Ls (j 1) represents the j1 st element of the reference gray scale sequence, ECgpl is the median of the reference gray scale sequence, exp () is an exponential function with a natural constant e as a base, and hs < > is a harmonic mean function.
The above-mentioned fermentation level is obtained by combining with reference gray level calculation, effectively acquires gray value information with high-efficiency characteristics in the preprocessed image, and performs hierarchical quantization analysis on each gray value data acquired, however, the information collecting mode can appear the phenomenon that part of effective information is not involved in quantization, especially when gray value levels of all pixels in a region are similar, which can lead to the fact that the calculated fermentation level is not accurate enough in judging the fermentation process, but the prior art can not solve the problems of delay and inaccurate identification of the fermentation level in judging, so that the reference gray level information is more comprehensive, and the application adaptability to the fermentation level is stronger, so the application provides a more preferable scheme.
Preferably, in step S400, the method of performing real-time fermentation step analysis and calculating the fermentation step level according to the processing chart is that the processing chart is divided into areas by a super-pixel segmentation algorithm, each obtained area is respectively marked as a analysis area, and the average value of the gray values of each pixel point in any analysis area is calculated and marked as the gray value DAPL of the corresponding analysis area; constructing the gray values of each analysis area into a sequence from small to large to be marked as a gray value sequence, calculating to obtain the average value of each element in the gray value sequence to be marked as E_DAPL;
the first sensitive element of any element in the gray value sequence is the previous element of the element in the gray value sequence; the second sensitive element of any element in the gray value sequence is the latter element of the element in the gray value sequence; the sensitive analysis area corresponding to any analysis area is the analysis area corresponding to the sensitive analysis element of the analysis area; if the gray value of one analysis area is smaller than E_DAPL, the sensitive analysis area corresponding to the analysis area is the analysis area corresponding to the first sensitive analysis element of the analysis area, otherwise, the sensitive analysis area corresponding to the analysis area is the analysis area corresponding to the second sensitive analysis element of the analysis area; the ratio of the gray value of the analysis area to the gray value of the sensitive analysis area is marked as the sensitive calculation level SAPL;
wherein the first or last element in the sequence of gray values is in a condition that the formation of the sensitive analysis area cannot be identified, and the subsequent operation ignores the analysis area.
The pixel point with the maximum gray value in the analysis area is marked as a sensitive analysis site of the analysis area; the line between the sensitive analysis site of the analysis area and the sensitive analysis site of the analysis area is marked as a sensitive analysis compact line, and the length of the sensitive analysis compact line is used as the sensitive analysis compact distance DSA of the analysis area; the sensitivity and resistance index SRI of any analysis area is obtained through gray value, sensitivity calculation level and sensitivity analysis compact distance calculation, and the calculation method comprises the following steps:
；
wherein exp () is an exponential function with a natural constant e as a base;
each other analysis area with overlapping pixels corresponding to the sensitive analysis compact line of the analysis area is used as a sensitive analysis channel of the analysis area;
wherein each other of the analysis areas where there are coincident pixels with the sensitive analysis compact line corresponding to the analysis area means that if any one element in one of the analysis areas is on a straight line where the sensitive analysis compact line is located, the analysis area is defined to be coincident with the sensitive analysis compact line.
Taking the minimum value corresponding to the gray value of each sensitive analysis site in the sensitive analysis channel of one analysis area as the gray sign lower limit FPRZ of the analysis area, acquiring the gray values of all pixel points which are larger than the FPRZ and do not belong to the analysis area in the sensitive analysis channel to form a sequence and recording the sequence as a sub-analysis sequence ARV_Ls; the fermentation stage level CVR is obtained through calculation of allergy resistance indexes, ash sign lower limits and sub-analysis sequences, and the calculation method comprises the following steps:
；
wherein i1 is an accumulation variable, NEA is the total number of the analysis areas, and FPRZ i1 ARV_ls is the lower gray scale limit of the ith 1 st region, ARV_ls is the subsequence of the ith 1 st region, rds<>And forming ratios of the elements in the calling sequence and the minimum value in the calling sequence through the polar difference function polar difference proportional function, and returning to the average value of the ratios.
The beneficial effects are that: the fermentation process is calculated according to the fermentation level of the Chinese herbal medicine in real time and judged according to the quantized result, and the process or the time-varying stage of fermentation in the fermentation environment of the Chinese herbal medicine type probiotics is quantized, so that the effective components in the Chinese herbal medicine can be converted into active substances to the greatest extent in the fermentation process, the risk of inactivation of the active substances due to excessive fermentation is effectively reduced, the utilization rate of raw materials is greatly improved, and the quality and the efficacy of products are effectively ensured. Particularly, in mass production, the problem of low fermentation level of a large amount of effective components of traditional Chinese medicinal materials caused by subjective experience judgment can be effectively avoided, so that waste of traditional Chinese medicinal materials and production resources is avoided to a great extent, the production cost is greatly reduced, and a powerful technical support is provided for mass production of traditional Chinese medicine type probiotic preparations.
Further, in step S500, the method for dynamically determining and controlling the fermentation process by the fermentation level obtained in real time and the history data thereof is to preset a fermentation level threshold, calculate the fermentation level in real time, set a time period as a monitoring window TRef, TRef e [20, 100] min, and use the median of the fermentation level in the latest TRef period as the first fermentation level; and when the first fermentation stage level is greater than the fermentation stage level threshold, judging that the reaction is terminated, and stopping the fermentation process.
Because the method has certain dependence on the preset threshold value, the dynamic identification or strain capacity of the actual operation process is insufficient, and the personnel requirements of the personnel to be tested are high, the application provides a more preferable scheme as follows:
preferably, in step S500, an alternative method for dynamically discriminating and controlling the fermentation process by the fermentation stage level obtained in real time and its history data is to obtain and record the fermentation stage level in real time; taking the difference value of the fermentation stage level obtained at one moment and the moment before the moment as the fermentation stage overflow value at the moment; the minimum length of reaction time for the reverse fermentation process was noted as DurBs; the fermentation process starts until the fermentation step overflow value at each moment in the DurBs period after the fermentation process forms an overflow value reference sequence; the lower quartile value of the overflow value reference sequence is marked as a fermentation stage overflow threshold value; if the fermentation stage overflow value at the current moment is smaller than the fermentation stage overflow threshold value, defining that the current moment meets the overflow condition; setting a time period as a monitoring window TRef, TRef epsilon [20, 100] minutes, TRef < 0.5DurBs; the frequency duty ratio with the maximum value or the minimum value in each ferment stage level in the latest TRef is the oscillation ratio ExRt; a ratio threshold is set at QRt, QRt ε [0.4,0.6], and a default value of QRt is set at 0.4. If the oscillation ratio at the current moment is larger than the ratio threshold, defining that the oscillation condition is met at the current moment; if the reaction time length of the reverse fermentation process at the current moment exceeds DurBs and the overflow condition and the oscillation condition are met, judging that the reaction is terminated and stopping the fermentation process.
The concrete calculation method of the oscillation ratio is as follows: and forming a fermentation level sequence by each fermentation level in the TRef recently according to the sequence obtained by calculation, and defining the elements as extreme value elements if any element in the fermentation level sequence is larger than the previous element and the next element or smaller than the previous element and the next element, wherein the ratio of the number of the extreme value elements in the fermentation level sequence to the total number of the elements in the fermentation level sequence is recorded as ExRt.
Preferably, all undefined variables in the present application, if not explicitly defined, may be thresholds set manually.
The application also provides a production system of the traditional Chinese medicine type probiotic preparation based on the hyperspectral image, which comprises the following steps: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements steps in the method for producing a traditional Chinese medicine type probiotic preparation based on hyperspectral images when the processor executes the computer program, the system for producing the traditional Chinese medicine type probiotic preparation based on hyperspectral images can be operated in a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud data center, and the like, and the executable system can include, but is not limited to, a processor, a memory, and a server cluster, and the processor executes the computer program to be operated in units of the following systems:
the fermentation initial triggering unit is used for adding a mixed solution consisting of traditional Chinese medicine powder and strain stock solution into a fermentation tank for fermentation;
the data acquisition unit is used for acquiring spectrum data of the mixed liquid through the imaging spectrometer;
the graphic preprocessing unit is used for preprocessing the spectrum data to obtain a processing diagram;
the fermentation stage analysis unit is used for carrying out real-time fermentation stage analysis according to the processing diagram and calculating the fermentation stage level;
and the dynamic discrimination control unit is used for dynamically discriminating and controlling the fermentation process through the fermentation stage level obtained in real time and the historical data thereof.
The beneficial effects of the application are as follows: the application provides a method and a system for producing a traditional Chinese medicine type probiotic preparation based on hyperspectral images, which acquire spectral data through a near infrared spectrum analysis technology, and quantify the progress or the stepwise fermentation change along with time in the fermentation environment of traditional Chinese medicine type probiotics, so that the effective components in the traditional Chinese medicine can be converted into active substances to a greater extent in the fermentation process, and meanwhile, the risk of inactivation of the active substances due to excessive fermentation is effectively reduced, thereby greatly improving the utilization rate of raw materials and efficiently ensuring the quality and the efficacy of products. Particularly, in mass production, the problem of low fermentation level of a large amount of effective components of traditional Chinese medicinal materials caused by subjective experience judgment can be effectively avoided, so that waste of traditional Chinese medicinal materials and production resources is effectively avoided, the production cost is greatly reduced, and a powerful technical support is provided for mass production of traditional Chinese medicine probiotics.
Drawings
The above and other features of the present application will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present application, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
FIG. 1 is a flow chart showing a method for producing a traditional Chinese medicine type probiotic preparation based on hyperspectral images;
fig. 2 is a diagram showing a structure of a production system of a traditional Chinese medicine type probiotic preparation based on hyperspectral images.
Detailed Description
The conception, specific structure, and technical effects produced by the present application will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
Referring to fig. 1, which is a flowchart illustrating a method for producing a traditional Chinese medicine type probiotic preparation based on hyperspectral images, a method for producing a traditional Chinese medicine type probiotic preparation based on hyperspectral images according to an embodiment of the present application is described below with reference to fig. 1, and the method includes the following steps:
in the embodiment 1, S100, adding a mixed solution consisting of traditional Chinese medicine powder and strain stock solution into a fermentation tank for fermentation;
s200, collecting spectrum data of the mixed solution through an imaging spectrometer;
s300, preprocessing the optical data to obtain a processing diagram;
s400, carrying out real-time fermentation stage analysis according to the processing diagram and calculating the fermentation stage level;
s500, dynamically judging and controlling the fermentation process through the fermentation stage level obtained in real time and the historical data thereof.
Further, in step S100, the method for adding the mixed solution of the traditional Chinese medicine powder and the strain stock solution into the fermentation tank for fermentation is as follows: pretreating Chinese medicinal materials including cortex Phellodendri, radix Ginseng Rubra, radix Isatidis and herba Taraxaci to obtain Chinese medicinal powder, wherein the pretreatment comprises cleaning, cutting and grinding; the strain stock solution comprises lactobacillus, enterococcus, saccharomycetes, bifidobacterium and bacillus; fully mixing the traditional Chinese medicine powder with the strain stock solution, and putting the formed mixed solution into a fermentation tank for fermentation.
Further, in step S200, the method for collecting the spectrum data of the mixed solution by the imaging spectrometer is: a transparent collecting window is arranged on the side wall of the fermentation tank, and the collecting window is made of quartz plates; fixing the hyperspectral camera outside the acquisition window; setting a time period as a measurement interval Tgap, wherein the measurement interval Tgap is 10 minutes, measuring the hyperspectral camera in real time every Tgap, obtaining spectrum data of the mixed liquid, and uploading the obtained spectrum data to a server.
Further, in step S300, the method for preprocessing the spectrum data to obtain the processing chart is: in the server, preprocessing is carried out on the optical data, the preprocessing step comprises normalization processing, intensity correction and offset correction, finally, image smoothing filtering processing is carried out through a moving average method to obtain a processing diagram, and pixel values of the processing diagram are recorded as gray values.
Further, in step S400, the method of performing real-time fermentation step analysis and calculating the fermentation step level according to the processing chart is that the spectrum data preprocessing image is divided into a plurality of areas by the edge recognition algorithm, each area is used as a analysis area, and the number of the analysis areas is recorded as an n cat; respectively marking a pixel point with the maximum gray value and a pixel point with the minimum gray value in a dividing region as gray electrode pixels and gray lean pixels, marking a straight line connecting the gray electrode pixels and the gray lean pixels as gray gradual lines, marking the distance of the gray gradual lines as gray gradual distances, and marking a pixel point corresponding to the middle point of the gray gradual lines as the center of the dividing region;
forming a circular area serving as a reference area by taking the center of the separation area as a center point and the gray gradual distance as a side length; the arithmetic average value of the gray values of all the pixel points in the reference position is recorded as reference position gray analysis level Cgpl, and the ratio of the average value of the gray values of all the pixel points in one analysis area to the reference position gray analysis level is recorded as node ratio PAPV; constructing a sequence of reference gray levels of each of the analysis regions to be referred to as reference gray sequences Cp_Ls; the ratio of the maximum element to the minimum element in the reference greyscale sequence is denoted as greyscale offset CPVT; calculating a fermentation level CVR according to the reference gray level and the pitch ratio:
；
wherein j1 is an accumulated variable, cp_Ls (j 1) represents the j1 st element of the reference gray scale sequence, ECgpl is the median of the reference gray scale sequence, exp () is an exponential function with a natural constant e as a base, and hs < > is a harmonic mean function.
Preferably, in step S500, an alternative method for dynamically discriminating and controlling the fermentation process by the fermentation stage level obtained in real time and its history data is to obtain and record the fermentation stage level in real time; taking the difference value of the fermentation stage level obtained at one moment and the moment before the moment as the fermentation stage overflow value at the moment; the minimum length of reaction time for the reverse fermentation process was noted as DurBs; the fermentation process starts until the fermentation step overflow value at each moment in the DurBs period after the fermentation process forms an overflow value reference sequence; the lower quartile value of the overflow value reference sequence is marked as a fermentation stage overflow threshold value; if the fermentation stage overflow value at the current moment is smaller than the fermentation stage overflow threshold value, defining that the current moment meets the overflow condition; setting a time period as 30 minutes for the monitoring window TRef; the frequency duty ratio with the maximum value or the minimum value in each ferment stage level in the latest TRef is the oscillation ratio ExRt; a ratio threshold is set at QRt, and QRt is set at 0.4. If the oscillation ratio at the current moment is larger than the ratio threshold, defining that the oscillation condition is met at the current moment; if the reaction time length of the reverse fermentation process at the current moment exceeds DurBs and the overflow condition and the oscillation condition are met, judging that the reaction is terminated and stopping the fermentation process.
Example 2, in which the method of example 1 was used to prepare a traditional Chinese medicine type probiotic, the difference between example 2 and example 1 is that in step S400, a real-time fermentation step analysis is performed according to a processing chart, and a fermentation step level is calculated by dividing the processing chart into regions by a super-pixel segmentation algorithm, marking each obtained region as a region of analysis, and calculating an average value of gray values of each pixel point in any region of analysis, and marking the average value of gray values of each pixel point as a gray value DAPL of the corresponding region of analysis; constructing the gray values of each analysis area into a sequence from small to large to be marked as a gray value sequence, calculating to obtain the average value of each element in the gray value sequence to be marked as E_DAPL;
the first sensitive element of any element in the gray value sequence is the previous element of the element in the gray value sequence; the second sensitive element of any element in the gray value sequence is the latter element of the element in the gray value sequence; the sensitive analysis area corresponding to any analysis area is the analysis area corresponding to the sensitive analysis element of the analysis area; if the gray value of one analysis area is smaller than E_DAPL, the sensitive analysis area corresponding to the analysis area is the analysis area corresponding to the first sensitive analysis element of the analysis area, otherwise, the sensitive analysis area corresponding to the analysis area is the analysis area corresponding to the second sensitive analysis element of the analysis area; the ratio of the gray value of the analysis area to the gray value of the sensitive analysis area is marked as the sensitive calculation level SAPL;
the pixel point with the maximum gray value in the analysis area is marked as a sensitive analysis site of the analysis area; the line between the sensitive analysis site of the analysis area and the sensitive analysis site of the analysis area is marked as a sensitive analysis compact line, and the length of the sensitive analysis compact line is used as the sensitive analysis compact distance DSA of the analysis area; the sensitivity and resistance index SRI of any analysis area is obtained through gray value, sensitivity calculation level and sensitivity analysis compact distance calculation, and the calculation method comprises the following steps:
；
wherein exp () is an exponential function with a natural constant e as a base; each other analysis area with overlapping pixels corresponding to the sensitive analysis compact line of the analysis area is used as a sensitive analysis channel of the analysis area;
taking the minimum value corresponding to the gray value of each sensitive analysis site in the sensitive analysis channel of one analysis area as the gray sign lower limit FPRZ of the analysis area, acquiring the gray values of all pixel points which are larger than the FPRZ and do not belong to the analysis area in the sensitive analysis channel to form a sequence and recording the sequence as a sub-analysis sequence ARV_Ls; the fermentation stage level CVR is obtained through calculation of allergy resistance indexes, ash sign lower limits and sub-analysis sequences, and the calculation method comprises the following steps:
；
wherein i1 is an accumulation variable, NEA is the total number of the analysis areas, and FPRZ i1 ARV_ls is the lower gray scale limit of the ith 1 st region, ARV_ls is the subsequence of the ith 1 st region, rds<>And forming ratios of each element in the calling sequence and the minimum value in the calling sequence through the polar difference function polar difference proportional function, and returning to the average value of the ratios, wherein ln () is a logarithmic function taking a natural constant e as a base.
The comparative example differs from example 1 in that the fermentation period was set to 5X 24 hours.
Table 1 active ingredient acquisition comparison
* Total polysaccharides, alkaloids, flavonoids and total saponins are the main active ingredients of Chinese herbal medicines.
Table 2 comparison of the effects of the traditional Chinese medicine type probiotic preparations as the auxiliary materials of the chicken feed respectively
* Immune organ index= (immune organ weight/empty fresh weight of live chicken) ×1000.
As shown above, table 1 shows the preparation effects of the main active ingredients of the Chinese herbal medicines in different fermentation termination processes, and it can be seen that the effects of obtaining active ingredients of the example 1 and the example 2 of the Chinese herbal medicine type probiotic preparation produced by the method are improved to different degrees, and better effects on productivity and preparation effect are achieved. Table 2 shows the effect comparison of the traditional Chinese medicine type probiotic preparation formed by different fermentation termination methods as auxiliary materials of chicken feed, and further shows that the intelligent judgment promotion effect of the method is realized in the fermentation process.
The system for producing a traditional Chinese medicine type probiotic preparation based on hyperspectral images provided by the embodiment of the application is shown in fig. 2, which is a structural diagram of the system for producing the traditional Chinese medicine type probiotic preparation based on hyperspectral images, and the system for producing the traditional Chinese medicine type probiotic preparation based on hyperspectral images comprises: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in an embodiment of a production system for a hyperspectral image based traditional Chinese medicine type probiotic preparation as described above when executing the computer program.
The system comprises: a memory, a processor in a feed bar and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in units of the system:
the fermentation initial triggering unit is used for adding a mixed solution consisting of traditional Chinese medicine powder and strain stock solution into a fermentation tank for fermentation;
the data acquisition unit is used for acquiring spectrum data of the mixed liquid through the imaging spectrometer;
the graphic preprocessing unit is used for preprocessing the spectrum data to obtain a processing diagram;
the fermentation stage analysis unit is used for carrying out real-time fermentation stage analysis according to the processing diagram and calculating the fermentation stage level;
and the dynamic discrimination control unit is used for dynamically discriminating and controlling the fermentation process through the fermentation stage level obtained in real time and the historical data thereof.
The production system of the traditional Chinese medicine type probiotic preparation based on the hyperspectral image can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The production system of the traditional Chinese medicine type probiotic preparation based on hyperspectral images can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the example is merely an example of a hyperspectral image based production system for a traditional Chinese medicine type probiotic preparation, and is not limited to a hyperspectral image based production system for a traditional Chinese medicine type probiotic preparation, and may include more or fewer components than the example, or may combine some components, or different components, for example, the hyperspectral image based production system for a traditional Chinese medicine type probiotic preparation may further include an input/output device, a network access device, a bus, and the like.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the production system operation system of the traditional Chinese medicine type probiotic preparation based on hyperspectral images, and various interfaces and lines are used to connect various parts of the whole production system operation system of the traditional Chinese medicine type probiotic preparation based on hyperspectral images.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the production system of the traditional Chinese medicine type probiotic preparation based on hyperspectral image by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Although the present application has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the application. Furthermore, the foregoing description of the application has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the application that may not be presently contemplated, may represent an equivalent modification of the application.
Claims (9)
1. The production method of the traditional Chinese medicine type probiotic preparation based on hyperspectral images is characterized by comprising the following steps of: s100, adding a mixed solution consisting of traditional Chinese medicine powder and strain stock solution into a fermentation tank for fermentation;
s200, collecting spectrum data of the mixed solution through an imaging spectrometer;
s300, preprocessing the optical data to obtain a processing diagram;
s400, carrying out real-time fermentation stage analysis according to the processing diagram and calculating the fermentation stage level;
s500, dynamically judging and controlling the fermentation process through the fermentation level obtained in real time and the historical data thereof;
in step S400, the method of performing real-time fermentation stage analysis and calculating the fermentation stage level according to the processing diagram includes dividing the processing diagram into a plurality of analysis areas, defining gray pole pixels and gray lean pixels according to gray values of pixels in the analysis areas, positioning the center of the analysis areas through connecting straight lines of the gray pole pixels and the gray lean pixels, selecting a reference area according to the center of the analysis areas, and taking an average value of gray values in the reference area as a reference gray analysis level; combining the reference ash analysis levels of the reference domains to form a reference ash analysis sequence, calculating ash analysis offset through the reference ash analysis sequence, and finally calculating the fermentation stage level according to the reference ash analysis levels and the ash analysis offset.
2. The method for producing a hyperspectral image-based traditional Chinese medicine type probiotic preparation according to claim 1, wherein in step S100, the method for adding the mixed solution of the traditional Chinese medicine powder and the strain stock solution into a fermentation tank for fermentation is as follows: pretreating Chinese medicinal materials including one or more of cortex Phellodendri, radix Ginseng Rubra, radix Isatidis, and herba Taraxaci to obtain Chinese medicinal powder, wherein the pretreatment comprises cleaning, cutting, and grinding; the strain stock solution comprises one or more strain stock solutions of lactobacillus, enterococcus, saccharomycetes, bifidobacterium and bacillus; fully mixing the traditional Chinese medicine powder with the strain stock solution, and putting the formed mixed solution into a fermentation tank for fermentation.
3. The method for producing a hyperspectral image-based traditional Chinese medicine type probiotic preparation according to claim 1, wherein in step S200, the method for collecting the spectrum data of the mixed solution by an imaging spectrometer is as follows: a transparent collecting window is arranged on the side wall of the fermentation tank, and the collecting window is made of one of quartz plates or fused silica; fixing the hyperspectral camera outside the acquisition window; setting a time period as a measurement interval Tgap, tgap E [10,20] minutes, measuring in real time every Tgap by a hyperspectral camera, obtaining spectrum data of the mixed liquid, and uploading the obtained spectrum data to a server.
4. The method for producing a hyperspectral image-based traditional Chinese medicine type probiotic preparation according to claim 1, wherein in step S300, the method for preprocessing the spectrum data to obtain a processing chart is as follows: in the server, preprocessing is carried out on the optical data, the preprocessing step comprises normalization processing, intensity correction and offset correction, and finally, image smoothing filtering processing is carried out through a moving average method to obtain a processing diagram.
5. The method for producing a hyperspectral image-based traditional Chinese medicine type probiotic preparation according to claim 1, wherein in step S400, the method for performing real-time fermentation step analysis and calculating the fermentation step level according to the processing chart is that the spectral data preprocessing image is divided into a plurality of areas by an edge recognition algorithm, each area is used as a dividing area, and the number of dividing areas is recorded as Ncat; respectively marking a pixel point with the maximum gray value and a pixel point with the minimum gray value in a dividing region as gray electrode pixels and gray lean pixels, marking a straight line connecting the gray electrode pixels and the gray lean pixels as gray gradual lines, marking the distance of the gray gradual lines as gray gradual distances, and marking a pixel point corresponding to the middle point of the gray gradual lines as the center of the dividing region;
forming a circular area serving as a reference area by taking the center of the separation area as a center point and the gray gradual distance as a side length; the arithmetic average value of the gray values of all the pixel points in the reference position is recorded as reference position gray analysis level Cgpl, and the ratio of the average value of the gray values of all the pixel points in one analysis area to the reference position gray analysis level is recorded as node ratio PAPV; constructing a sequence of reference gray levels of each of the analysis regions to be referred to as reference gray sequences Cp_Ls; the ratio of the maximum element to the minimum element in the reference greyscale sequence is denoted as greyscale offset CPVT; and calculating the fermentation level according to the reference gray level and the pitch ratio.
6. The method for producing a hyperspectral image-based traditional Chinese medicine type probiotic preparation according to claim 1, wherein in step S400, a method for performing real-time fermentation step analysis and calculating the fermentation step level according to a processing chart is that the processing chart is divided into areas by a super-pixel segmentation algorithm, each obtained area is respectively marked as a dividing area, and an average value of gray values of each pixel point in any dividing area is marked as a gray value of a corresponding dividing area; constructing the gray values of each analysis area into a sequence from small to large to be marked as a gray value sequence, calculating to obtain the average value of each element in the gray value sequence to be marked as E_DAPL;
the first sensitive element of any element in the gray value sequence is the previous element of the element in the gray value sequence; the second sensitive element of any element in the gray value sequence is the latter element of the element in the gray value sequence; the sensitive analysis area corresponding to any analysis area is the analysis area corresponding to the sensitive analysis element of the analysis area; if the gray value of one analysis area is smaller than E_DAPL, the sensitive analysis area corresponding to the analysis area is the analysis area corresponding to the first sensitive analysis element of the analysis area, otherwise, the sensitive analysis area corresponding to the analysis area is the analysis area corresponding to the second sensitive analysis element of the analysis area; the ratio of the gray value of the analysis area to the gray value of the sensitive analysis area is recorded as the sensitive calculation level;
the pixel point with the maximum gray value in the analysis area is marked as a sensitive analysis site of the analysis area; the line between the sensitive analysis site of the analysis area and the sensitive analysis site of the analysis area is marked as a sensitive analysis compact line, and the length of the sensitive analysis compact line is used as the sensitive analysis compact distance DSA of the analysis area; obtaining sensitivity resistance indexes of any analysis area through gray value, sensitivity level and sensitivity compact distance calculation, and taking each other analysis area with coincident pixels on the sensitivity compact line corresponding to the analysis area as a sensitivity analysis channel of the analysis area;
taking the minimum value corresponding to the gray value of each sensitive analysis site in the sensitive analysis channel of one analysis area as the gray sign lower limit FPRZ of the analysis area, acquiring the gray values of all pixel points which are larger than the FPRZ and do not belong to the analysis area in the sensitive analysis channel to form a sequence and recording the sequence as a sub-analysis sequence ARV_Ls; and calculating through allergy resistance indexes and sub-analysis sequences to obtain the fermentation stage level.
7. The method for producing a hyperspectral image-based traditional Chinese medicine type probiotic preparation according to claim 1, wherein in step S500, the method for dynamically discriminating and controlling the fermentation process by the fermentation level obtained in real time and its history data is to preset a fermentation level threshold, calculate the fermentation level in real time, set a time period as a monitoring window TRef, TRef e [20, 100] min, and use the median value of the fermentation level in the latest TRef period as the first fermentation level; and when the first fermentation stage level is greater than the fermentation stage level threshold, judging that the reaction is terminated, and stopping the fermentation process.
8. The method for producing a hyperspectral image-based traditional Chinese medicine type probiotic preparation according to claim 7, wherein in step S500, an alternative method for dynamically discriminating and controlling the fermentation process by the fermentation level obtained in real time and its history data is to obtain and record the fermentation level in real time; taking the difference value of the fermentation stage level obtained at one moment and the moment before the moment as the fermentation stage overflow value at the moment; the minimum length of reaction time for the reverse fermentation process was noted as DurBs; the fermentation process starts until the fermentation step overflow value at each moment in the DurBs period after the fermentation process forms an overflow value reference sequence; the lower quartile value of the overflow value reference sequence is marked as a fermentation stage overflow threshold value; if the fermentation stage overflow value at the current moment is smaller than the fermentation stage overflow threshold value, defining that the current moment meets the overflow condition; setting a time period as a monitoring window TRef, TRef epsilon [20, 100] minutes, TRef < 0.5DurBs; the frequency duty ratio with the maximum value or the minimum value in each ferment stage level in the latest TRef is the oscillation ratio ExRt; setting a ratio threshold as QRt, QRt epsilon [0.4,0.6]; if the oscillation ratio at the current moment is larger than the ratio threshold, defining that the oscillation condition is met at the current moment; if the reaction time length of the reverse fermentation process at the current moment exceeds DurBs and the overflow condition and the oscillation condition are met, judging that the reaction is terminated and stopping the fermentation process.
9. The production system of the traditional Chinese medicine type probiotic preparation based on the hyperspectral image is characterized by comprising the following components: a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the method for producing a hyperspectral image based traditional Chinese medicine type probiotic preparation according to any one of claims 1 to 8 when the computer program is executed, and the system for producing the hyperspectral image based traditional Chinese medicine type probiotic preparation is operated in a computing device of a desktop computer, a notebook computer, a palm computer and a cloud data center.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105241824A (en) * | 2015-09-30 | 2016-01-13 | 江苏大学 | Method for quantitatively detecting solid fermentation index distribution difference through hyperspectral image technology |
| US20160011103A1 (en) * | 2014-07-08 | 2016-01-14 | Sumitomo Electric Industries, Ltd. | Optical measuring method and manufacturing method of the alcohol |
| CN114075577A (en) * | 2021-06-21 | 2022-02-22 | 四川生力源生物工程有限公司 | Method for controlling fermentation procedure in traditional Chinese medicine probiotic composite fermentation process |
| CN115304410A (en) * | 2022-10-12 | 2022-11-08 | 广东省农业科学院动物科学研究所 | Method for treating Chinese herbal medicine solid waste |
| CN115624090A (en) * | 2022-11-10 | 2023-01-20 | 三株福尔制药有限公司 | Probiotic traditional Chinese medicine feed fermentation production method capable of extracting sample for detection |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160011103A1 (en) * | 2014-07-08 | 2016-01-14 | Sumitomo Electric Industries, Ltd. | Optical measuring method and manufacturing method of the alcohol |
| CN105241824A (en) * | 2015-09-30 | 2016-01-13 | 江苏大学 | Method for quantitatively detecting solid fermentation index distribution difference through hyperspectral image technology |
| CN114075577A (en) * | 2021-06-21 | 2022-02-22 | 四川生力源生物工程有限公司 | Method for controlling fermentation procedure in traditional Chinese medicine probiotic composite fermentation process |
| CN115304410A (en) * | 2022-10-12 | 2022-11-08 | 广东省农业科学院动物科学研究所 | Method for treating Chinese herbal medicine solid waste |
| CN115624090A (en) * | 2022-11-10 | 2023-01-20 | 三株福尔制药有限公司 | Probiotic traditional Chinese medicine feed fermentation production method capable of extracting sample for detection |
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