Microbial pollution detection method and system based on poria cocos jelly production process

Abstract

The invention discloses a microbial contamination detection method and system based on a poria cocos jelly production process, and relates to the technical field of food safety detection. The microbial contamination detection method based on the poria cocos jelly production process comprises the following steps: S1, a heterogeneity evaluation module; s2, matrix complexity evaluation; and S3, processing complexity evaluation. Whether heterogeneity optimization is carried out or not is judged through the heterogeneity evaluation index obtained through the heterogeneity evaluation parameters, and then whether matrix processing is carried out or not is judged based on the matrix complexity evaluation index obtained through the matrix complexity evaluation parameters. Finally, whether treatment step optimization is carried out or not is judged based on the treatment complexity evaluation index obtained based on the treatment complexity evaluation parameters, the effect of improving the microbiological pollution detection accuracy of the poria cocos jelly production process is achieved, and the problem that in the prior art, the microbiological pollution detection accuracy of the poria cocos jelly production process is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of food safety detection, and in particular to a microbial contamination detection method and system based on a production process of Poria cocos jelly. Background Art

[0002] As a food that combines traditional ingredients with modern technology, it is crucial to ensure the quality and safety of Poria jelly during its production process. Microbial contamination is one of the most common and serious problems in food safety, and may come from the raw materials themselves, the production environment, processing equipment, worker operations, and other aspects. Microbial contamination not only affects the taste, appearance, and shelf life of food, but can also lead to foodborne diseases in severe cases. Therefore, microbial contamination detection is of great significance for ensuring the quality and safety of Poria jelly, improving consumer trust, and meeting food hygiene standards.

[0003] The existing technology collects samples from different stages of the Poria jelly production line, pre-treats the collected samples and detects the test data, and finally processes and analyzes the test data to obtain information such as the degree and type of microbial contamination, thereby improving the sanitary safety of the Poria jelly production process.

[0004] For example, the patent application with announcement number: CN118777545A discloses a rapid food detection system and method, which includes: a sample collection unit, a data collection module, a data analysis module and a result output module; the reaction index of the experimental data is calculated according to an algorithm, and the obtained reaction index is formed into a set corresponding to the number of the food sample. The reaction index in the set is compared with the index threshold. When any abnormal value appears, the data analysis module sends an abnormal signal. After receiving the judgment signal, the result output module converts it into a judgment result and displays the judgment result through the display unit of the result output module.

[0005] For example, the invention patent with announcement number CN103091261B announces a digital food safety rapid detection system, including: a sample processing module, a sample detection module, an information management module, a wireless communication module, and a power module; the detection system includes: a sample processing module for weighing, crushing, centrifuging, separating, enriching and extracting samples to be detected; a sample detection module connected to the sample processing module, for performing physical and chemical testing, pathogenic foodborne bacteria testing, and biological toxin testing on samples processed by the sample processing module, obtaining sample detection information, and outputting the sample detection information; an information management module connected to the sample detection module, for receiving the sample detection information output by the sample detection module, managing samples, instruments, and systems, and outputting the sample detection information; and a connected to the information management module, for receiving the sample detection information output by the information management module.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the existing technology, the detection of different microorganisms may be interfered by other components in Poria jelly (such as sugars, gums, spices, etc.), which may cause the detection method to be interfered with by the matrix effect, and there is a problem of low accuracy in the detection of microbial contamination in the Poria jelly production process. Summary of the invention

[0007] The embodiments of the present application solve the problem of low accuracy of microbial contamination detection in the production process of Poria cocos jelly in the prior art by providing a microbial contamination detection method and system based on the production process of Poria cocos jelly, thereby improving the accuracy of microbial contamination detection in the production process of Poria cocos jelly.

[0008] The embodiment of the present application provides a microbial contamination detection method based on the production process of Poria jelly, comprising the following steps: S1, evaluating according to the obtained heterogeneity evaluation parameters to obtain a heterogeneity evaluation index, judging whether to perform heterogeneity optimization based on the obtained heterogeneity evaluation index, and the heterogeneity evaluation index is used to quantitatively evaluate the heterogeneity degree of the Poria jelly sample; S2, if heterogeneity optimization is performed, evaluating according to the obtained matrix complexity evaluation parameters to obtain a matrix complexity evaluation index, judging whether to perform matrix treatment based on the obtained matrix complexity evaluation index, and the matrix complexity evaluation index is used to quantitatively evaluate the complexity of the matrix of the Poria jelly sample; S3, if matrix treatment is performed, evaluating according to the obtained processing complexity evaluation parameters to obtain a processing complexity evaluation index, judging whether to perform processing step optimization based on the obtained processing complexity evaluation index, and the processing complexity evaluation index is used to quantitatively evaluate the complexity of the processing steps of the Poria jelly sample.

[0009] Furthermore, the specific method for obtaining the heterogeneity evaluation parameter is as follows: obtaining the microbial concentration of the Poria jelly sample of each Poria jelly sample, and performing statistics to obtain the average microbial concentration of the Poria jelly sample and the variance of the microbial concentration of the Poria jelly sample; performing microbial detection on different regions in the Poria jelly sample to obtain the regional microbial concentration of each region, and performing statistics to obtain the average regional microbial concentration and the standard deviation of the regional microbial concentration; the specific method for obtaining the matrix complexity evaluation parameter is as follows: performing microbial detection on the Poria jelly sample to obtain the initial number of microorganisms; performing microbial extraction on the Poria jelly sample to obtain an extract, and using a fluorescent staining method to count the microorganisms in the extract to obtain the number of microorganisms after extraction; separating the extract to obtain separated microorganisms, and counting them to obtain the number of separated microorganisms; detecting the microbial solution not containing the Poria jelly sample by a fluorescence photometer to obtain a first signal intensity, and the first signal intensity is A signal intensity represents the signal intensity without matrix interference; a second signal intensity is obtained by detecting the microbial solution after the Poria jelly sample is mixed with the microbial solution through a fluorescence photometer, and the second signal intensity represents the signal intensity with matrix interference; the matrix complexity evaluation parameters include the initial microbial number, the microbial number after extraction, the microbial number after separation, the first signal intensity and the second signal intensity; the specific method of obtaining the processing complexity evaluation parameters is as follows: performing microbial detection on the treated Poria jelly sample to obtain the post-processed microbial concentration; performing extraction processing on the treated Poria jelly sample to obtain the post-processed extracted microbial concentration; performing microbial detection on the microbial standard sample with a preset microbial concentration range, until the corresponding microbial concentration when the microorganisms can no longer be detected is recorded as the minimum detection concentration; the processing complexity evaluation parameters include the post-processed microbial concentration, the post-processed extracted microbial concentration and the minimum detection concentration.

[0010] Furthermore, the specific method for evaluating and obtaining the heterogeneity evaluation index based on the obtained heterogeneity evaluation parameters is as follows: obtaining the heterogeneity index based on the relative relationship between the microbial concentration, the average microbial concentration and the microbial concentration variance of the Poria jelly sample; obtaining the heterogeneity correction index based on the relative relationship between the heterogeneity index and the total number of Poria jelly samples; obtaining the microbial distribution variation coefficient based on the relative relationship between the regional microbial concentration standard deviation of the Poria jelly sample and the regional microbial concentration average of the Poria jelly sample, and the microbial distribution variation coefficient is obtained by performing a ratio operation on the regional microbial concentration standard deviation of the Poria jelly sample and the regional microbial concentration average of the Poria jelly sample; and processing the heterogeneity index, the heterogeneity correction index and the microbial distribution variation coefficient to obtain the heterogeneity evaluation index.

[0011] Furthermore, the specific method for judging whether to perform heterogeneity optimization based on the obtained heterogeneity evaluation index is as follows: judging whether the heterogeneity evaluation index is less than a preset heterogeneity threshold: if the heterogeneity evaluation index is less than the preset heterogeneity threshold, heterogeneity optimization is not performed; if the heterogeneity evaluation index is not less than the preset heterogeneity threshold, a preset person is prompted to perform heterogeneity optimization; the heterogeneity optimization includes adjusting sample selection and optimizing the mixing process.

[0012] Furthermore, the specific method for evaluating the matrix complexity assessment index based on the acquired matrix complexity assessment parameters is as follows: the extraction efficiency is obtained based on the relative relationship between the number of microorganisms after extraction and the initial number of microorganisms, and the extraction efficiency is obtained by performing a ratio operation on the number of microorganisms after extraction and the initial number of microorganisms; the separation efficiency is obtained based on the relative relationship between the number of microorganisms after separation and the number of microorganisms after extraction, and the separation efficiency is obtained by performing a ratio operation on the number of microorganisms after separation and the number of microorganisms after extraction; the matrix interference coefficient is obtained based on the relative relationship between the second signal intensity and the first signal intensity, and the matrix interference coefficient is obtained by performing a ratio operation on the difference between the first signal intensity and the second signal intensity and the first signal intensity; the extraction efficiency, separation efficiency and matrix interference coefficient are processed to obtain the matrix complexity assessment index.

[0013] Furthermore, the specific method for judging whether to perform matrix treatment based on the obtained matrix complexity assessment index is as follows: judging whether the matrix complexity assessment index is less than a preset matrix complexity threshold: if the matrix complexity assessment index is less than the preset matrix complexity threshold, matrix treatment is not performed; if the matrix complexity assessment index is not less than the preset matrix complexity threshold, a preset person is prompted to perform matrix treatment; the matrix treatment includes removing impurities, diluting the matrix, balancing the pH of the matrix, and removing and separating interfering substances in the matrix.

[0014] Furthermore, the specific method for evaluating the processing complexity assessment parameters obtained to obtain the processing complexity assessment index is as follows: the microbial loss rate is obtained according to the relative relationship between the initial microbial number and the microbial concentration after treatment, and the microbial loss rate is obtained by performing a ratio operation on the difference between the initial microbial number and the microbial concentration after treatment and the microbial concentration after treatment; the processing recovery rate is obtained according to the relative relationship between the microbial concentration extracted after treatment and the microbial concentration after treatment, and the processing recovery rate is obtained by performing a ratio operation on the microbial concentration extracted after treatment and the microbial concentration after treatment; the minimum concentration deviation is obtained according to the relative relationship between the minimum detection concentration and the preset minimum detection concentration in the database, and the minimum concentration deviation is obtained by performing a ratio operation on the minimum detection concentration and the preset minimum detection concentration in the database; the microbial loss rate, the processing recovery rate and the minimum concentration deviation are processed to obtain the processing complexity assessment index.

[0015] Furthermore, the specific method for obtaining the processing complexity evaluation index is: In the formula, n represents the number of the Poria jelly sample in the batch to be tested, , N represents the total number of Poria jelly samples, CLF represents the processing complexity evaluation index of the batch of Poria jelly samples to be tested, It represents the matrix complexity evaluation index of the batch of Poria jelly samples to be tested after optimization. represents the initial microbial count of the nth Poria jelly sample in the batch to be tested, represents the microbial concentration of the nth Poria jelly sample after treatment in the batch to be tested, represents the microbial concentration extracted from the nth Poria jelly sample in the batch to be tested after processing, DJC represents the minimum detection concentration, Indicates the preset minimum detection concentration.

[0016] Furthermore, the specific process of judging whether to optimize the processing steps based on the acquired processing complexity assessment index is as follows: judging whether the processing complexity assessment index is less than a preset processing complexity threshold: if the processing complexity assessment index is less than the preset processing complexity threshold, the processing steps will not be optimized; if the processing complexity assessment index is not less than the preset processing complexity threshold, the preset personnel will be prompted to optimize the processing steps; the processing step optimization includes simplifying the processing steps and optimizing the extraction conditions.

[0017] The embodiment of the present application provides a microbial contamination detection system based on the production process of Poria jelly, including: a heterogeneity assessment module, a matrix complexity assessment module and a processing complexity assessment module; wherein the heterogeneity assessment module is used to evaluate according to the obtained heterogeneity assessment parameters to obtain a heterogeneity assessment index, and judge whether to perform heterogeneity optimization based on the obtained heterogeneity assessment index, and the heterogeneity assessment index is used to quantitatively evaluate the heterogeneity degree of the Poria jelly sample; the matrix complexity assessment module is used to evaluate according to the obtained matrix complexity assessment parameters to obtain a matrix complexity assessment index if heterogeneity optimization is performed, and judge whether to perform matrix treatment based on the obtained matrix complexity assessment index, and the matrix complexity assessment index is used to quantitatively evaluate the complexity of the matrix of the Poria jelly sample; the processing complexity assessment module is used to evaluate according to the obtained processing complexity assessment parameters to obtain a processing complexity assessment index if matrix treatment is performed, and judge whether to perform processing step optimization based on the obtained processing complexity assessment index, and the processing complexity assessment index is used to quantitatively evaluate the complexity of the processing steps of the Poria jelly sample.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The heterogeneity assessment index obtained by the heterogeneity assessment parameters is used to determine whether to perform heterogeneity optimization, and then the matrix complexity assessment index obtained based on the matrix complexity assessment parameters is used to determine whether to perform matrix treatment. Finally, the processing complexity assessment index obtained based on the processing complexity assessment parameters is used to determine whether to perform processing step optimization, thereby improving the accuracy of microbial contamination detection data of Poria jelly, thereby achieving an improvement in the low accuracy of microbial contamination detection in the Poria jelly production process, and effectively solving the problem of low accuracy of microbial contamination detection in the Poria jelly production process in the prior art.

[0019] 2. The heterogeneity index was obtained through the microbial concentration, the average microbial concentration and the variance of the microbial concentration of Poria jelly samples, and then the heterogeneity correction index was obtained according to the heterogeneity index and the total number of Poria jelly samples. Then, the microbial distribution variation coefficient was obtained according to the regional microbial concentration standard deviation and the regional microbial concentration average of Poria jelly samples. Finally, the heterogeneity index, the heterogeneity correction index and the microbial distribution variation coefficient were processed to obtain the heterogeneity evaluation index, thereby quantitatively evaluating the degree of heterogeneity of Poria jelly samples, and thus providing a reliable basis for the subsequent optimization of the heterogeneity of Poria jelly samples.

[0020] 3. The extraction efficiency was obtained by the relative relationship between the number of microorganisms after extraction and the initial number of microorganisms, and then the separation efficiency was obtained according to the relative relationship between the number of microorganisms after separation and the number of microorganisms after extraction. Then the matrix interference coefficient was obtained according to the relative relationship between the second signal intensity and the first signal intensity. Finally, the extraction efficiency, separation efficiency and matrix interference coefficient were processed to obtain the matrix complexity evaluation index, thereby quantitatively evaluating the complexity of the Poria jelly sample matrix, and then realizing the subsequent processing of the Poria jelly sample matrix. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for detecting microbial contamination based on the production process of Poria jelly provided in an embodiment of the present application; Figure 2 A schematic diagram showing the change of the processing complexity evaluation index provided in the embodiment of the present application as the microbial loss rate changes; Figure 3 A schematic diagram showing the change of the processing complexity evaluation index as the processing recovery rate changes provided in the embodiment of the present application; Figure 4 A schematic diagram showing changes in the processing complexity evaluation index provided in an embodiment of the present application as the minimum concentration deviation changes; Figure 5 This is a schematic diagram of the structure of a microbial contamination detection system based on the Poria jelly production process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application solve the problem of low accuracy of microbial contamination detection in the production process of Poria cocos jelly in the prior art by providing a microbial contamination detection method and system based on the production process of Poria cocos jelly. The heterogeneity evaluation parameters are obtained for evaluation to obtain a heterogeneity evaluation index. Based on the obtained heterogeneity evaluation index, it is determined whether to perform heterogeneity optimization. Then, the matrix complexity evaluation parameters are obtained for evaluation to obtain a matrix complexity evaluation index. Based on the obtained matrix complexity evaluation index, it is determined whether to perform matrix treatment. Finally, the processing complexity evaluation parameters are obtained for evaluation to obtain a processing complexity evaluation index. Based on the obtained processing complexity evaluation index, it is determined whether to perform processing step optimization. This improves the accuracy of microbial contamination detection in the production process of Poria cocos jelly.

[0023] The technical solution in the embodiments of the present application is to solve the problem of low accuracy of microbial contamination detection in the above-mentioned Poria jelly production process, and the overall idea is as follows: determine whether to perform heterogeneity optimization based on the obtained heterogeneity evaluation index, then determine whether to perform matrix treatment based on the obtained matrix complexity evaluation index, and finally determine whether to perform processing step optimization based on the obtained processing complexity evaluation index, thereby achieving the effect of improving the accuracy of microbial contamination detection in the Poria jelly production process.

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1 As shown, it is a flow chart of a microbial contamination detection method based on a Poria jelly production process provided in an embodiment of the present application, and the method comprises the following steps: S1, heterogeneity assessment: evaluating according to the obtained heterogeneity assessment parameters to obtain a heterogeneity assessment index, judging whether to perform heterogeneity optimization based on the obtained heterogeneity assessment index, and the heterogeneity assessment index is used to quantitatively evaluate the heterogeneity degree of the Poria jelly sample; S2, matrix complexity assessment: if heterogeneity optimization is performed, evaluating according to the obtained matrix complexity assessment parameters to obtain a matrix complexity assessment index, judging whether to perform matrix treatment based on the obtained matrix complexity assessment index, and the matrix complexity assessment index is used to quantitatively evaluate the complexity of the matrix of the Poria jelly sample; S3, processing complexity assessment: if matrix treatment is performed, evaluating according to the obtained processing complexity assessment parameters to obtain a processing complexity assessment index, judging whether to perform processing step optimization based on the obtained processing complexity assessment index, and the processing complexity assessment index is used to quantitatively evaluate the complexity of the processing steps of the Poria jelly sample.

[0026] It should be added that the specific method for obtaining the heterogeneity assessment parameters is as follows: in the production of Poria jelly, heterogeneity mainly refers to the differences in Poria jelly in different batches or under different production conditions, as well as the differences in different areas of the same Poria jelly; the Poria jelly sample microbial concentration of each Poria jelly sample is obtained by rapid polymerase chain reaction method, and the average microbial concentration of the Poria jelly sample and the variance of the microbial concentration of the Poria jelly sample are obtained by statistics; microbial detection is performed on different areas of the Poria jelly sample to obtain the regional microbial concentration of each area, and the average microbial concentration and the standard deviation of the regional microbial concentration are obtained by statistics; the Poria jelly sample is divided into several small square areas according to an equidistant grid, and the specific grid size is selected according to the size of the Poria jelly sample. For example, if the length of the sample is 30 cm and the width is 20 cm, a grid with a side length of 5 cm is selected, then the number of rows is 6 and the number of columns is 4, and a total of 24 small squares are divided.

[0027] The specific method for obtaining the matrix complexity assessment parameters is as follows: performing microbial detection on the Poria jelly sample to obtain the initial microbial count; performing microbial extraction on the Poria jelly sample to obtain an extract, and using the fluorescent staining method to count the microorganisms in the extract to obtain the number of microorganisms after extraction; separating the extract to obtain separated microorganisms, and counting them to obtain the number of separated microorganisms; detecting the microbial solution not containing the Poria jelly sample by a fluorescence photometer to obtain a first signal intensity, and the first signal intensity represents the signal intensity without matrix interference; detecting the microbial solution after the Poria jelly sample and the microbial solution are mixed by a fluorescence photometer to obtain a second signal intensity, and the second signal intensity represents the signal intensity with matrix interference; the matrix complexity assessment parameters include the initial microbial count, the microbial count after extraction, the microbial count after separation, the first signal intensity and the second signal intensity.

[0028] The specific method of obtaining the processing complexity assessment parameters is as follows: performing microbial detection on the treated Poria jelly sample to obtain the post-processing microbial concentration; performing extraction processing on the treated Poria jelly sample to obtain the post-processing extracted microbial concentration; performing microbial detection on the microbial standard sample within the configured preset microbial concentration range until the microorganisms in the microbial standard sample can no longer be detected, and the corresponding microbial concentration in the microbial standard sample is recorded as the minimum detection concentration; the processing complexity assessment parameters include the post-processing microbial concentration, the post-processing extracted microbial concentration and the minimum detection concentration; the preset microbial concentration range is represented by the range corresponding to the minimum and maximum values ​​of the microbial concentration in the accurate microbial detection data within the historical time period.

[0029] In this embodiment, by optimizing heterogeneity, the consistency of the microbial concentration and other indicators of the Poria jelly samples can be ensured, and the batch differences can be reduced, thereby improving the uniformity of the samples; by evaluating and optimizing the complexity of the matrix, the interference of the matrix on the microbial count can be reduced, and the accuracy of the microbial detection results can be ensured; by evaluating the processing complexity, the processing steps in the production process can be ensured to be efficient and simple, and unnecessary complex operations can be reduced, thereby improving production efficiency; thereby achieving an improvement in the accuracy of microbial contamination detection in the Poria jelly production process.

[0030] Furthermore, the specific method for evaluating the heterogeneity evaluation parameters obtained is as follows: the heterogeneity index (i.e., QT) is obtained according to the relative relationship between the microbial concentration of Poria jelly samples, the average microbial concentration and the variance of the microbial concentration, and the heterogeneity index is obtained by ratioing the absolute value of the difference between the microbial concentration of Poria jelly samples and the average microbial concentration to the square root of the variance of the microbial concentration; the heterogeneity correction index (i.e., IT) is obtained according to the relative relationship between the heterogeneity index and the total number of Poria jelly samples, and the heterogeneity correction index is obtained by ratioing the difference between the heterogeneity index and the total number of Poria jelly samples to the heterogeneity index; the microbial distribution variation coefficient (i.e., IT) is obtained according to the relative relationship between the standard deviation of the regional microbial concentration of Poria jelly samples and the average regional microbial concentration of Poria jelly samples. ), the coefficient of variation of microbial distribution was obtained by ratio calculation between the standard deviation of regional microbial concentration of Poria jelly samples and the average value of regional microbial concentration of Poria jelly samples; the heterogeneity index, heterogeneity correction index and coefficient of variation of microbial distribution were processed to obtain the heterogeneity evaluation index.

[0031] Among them, the specific method for obtaining the heterogeneity assessment index is: In the formula, n represents the number of the Poria jelly sample in the batch to be tested, , N represents the total number of Poria cocos jelly samples in the batch to be tested (the total number of Poria cocos jelly samples in the batch to be tested is greater than 1), YZP represents the heterogeneity assessment index of the Poria cocos jelly samples in the batch to be tested, QT represents the heterogeneity index, and IT represents the heterogeneity correction index. represents the coefficient of variation of the microbial distribution of the nth Poria jelly sample in the batch to be tested, represents the microbial concentration of the nth Poria jelly sample in the batch to be tested, NDy represents the average microbial concentration of the Poria jelly samples, represents the variance of the microbial concentration of the nth Poria jelly sample in the batch to be tested, represents the standard deviation of the regional microbial concentration of the nth Poria jelly sample in the batch to be tested, It represents the average regional microbial concentration of the nth Poria jelly sample in the batch to be tested.

[0032] In this embodiment, the heterogeneity evaluation index in this algorithm involves processing multiple independent variables (heterogeneity index, heterogeneity correction index and microbial distribution variation coefficient), and there is a mutual influence relationship between these independent variables; the heterogeneity index is used to quantitatively evaluate the heterogeneity between samples; if the heterogeneity index is higher, it means that the difference in microbial concentration between samples is larger, that is, the heterogeneity is higher; when the heterogeneity index increases, if the total number of Poria jelly samples remains unchanged, the heterogeneity correction index will also increase accordingly, indicating that the proportion of observed heterogeneity in the total variation increases; the heterogeneity correction index reflects the proportion of observed heterogeneity in the total variation; the microbial distribution variation coefficient quantifies the uniformity of microbial distribution in Poria jelly samples by comparing the standard deviation of regional microbial concentration with the average value; if the microorganisms are very evenly distributed in the sample, then the heterogeneity between samples may be relatively lower, therefore, the larger the microbial distribution variation coefficient, the smaller the heterogeneity index and heterogeneity correction index may be; in summary, the heterogeneity evaluation index is positively correlated with the heterogeneity index and the heterogeneity correction index; the heterogeneity evaluation index is negatively correlated with the microbial distribution variation coefficient.

[0033] Through the above steps, the heterogeneity of Poria jelly samples was quantitatively evaluated, thereby providing a reliable basis for the subsequent heterogeneity optimization of Poria jelly samples.

[0034] Furthermore, the specific method for determining whether to perform heterogeneity optimization based on the obtained heterogeneity evaluation index is as follows: determining whether the heterogeneity evaluation index is less than a preset heterogeneity threshold: if the heterogeneity evaluation index is less than the preset heterogeneity threshold, heterogeneity optimization is not performed; if the heterogeneity evaluation index is not less than the preset heterogeneity threshold, the preset personnel are prompted to perform heterogeneity optimization; heterogeneity optimization includes adjusting sample selection and optimizing the mixing process.

[0035] In this embodiment, the preset heterogeneity threshold is represented by the average value of the heterogeneity assessment index of the qualified heterogeneity of the Poria jelly samples in the historical time period; in the production of Poria jelly, heterogeneity mainly refers to the differences shown by Poria jelly in different batches or under different production conditions and the differences in different regions of the same Poria jelly.

[0036] Adjust sample selection: select Poria jelly samples from different batches and production lines of Poria jelly to ensure that the Poria jelly samples can truly reflect the overall microbial contamination situation; for each batch or production line of Poria jelly, multiple packages or products from different regions should be selected as Poria jelly samples to reduce the impact of heterogeneity on the test results.

[0037] Optimize the mixing process: put the collected Poria jelly samples into a sterile container and use a high-speed homogenizer to mix them evenly; if the jelly contains larger particles (such as fruit pieces) or ingredients that are difficult to disperse (such as pectin or thickeners), the mixing time can be appropriately extended or a stronger mixing force can be used; for microorganisms that are difficult to disperse (such as fungal hyphae, bacterial clumps, etc.) or jelly containing larger particles, surfactants (such as polyoxyethylene sorbitan monooleate Tween-80) can be added to destroy the aggregation state of the microorganisms and improve the extraction efficiency.

[0038] Through the above steps, the impact of heterogeneity on the microbial contamination detection results is reduced, thereby improving the accuracy of microbial contamination detection data.

[0039] Furthermore, the specific method for evaluating the matrix complexity evaluation index based on the obtained matrix complexity evaluation parameters is as follows: the extraction efficiency (i.e. ), the extraction efficiency is obtained by calculating the ratio of the number of microorganisms after extraction to the initial number of microorganisms; the separation efficiency (i.e. ), the separation efficiency is obtained by calculating the ratio of the number of microorganisms after separation to the number of microorganisms after extraction; the matrix interference coefficient (i.e. ), the matrix interference coefficient is obtained by performing a ratio operation on the difference between the first signal intensity and the second signal intensity and the first signal intensity; the extraction efficiency, separation efficiency and matrix interference coefficient are processed to obtain a matrix complexity evaluation index.

[0040] Among them, the specific method for obtaining the matrix complexity evaluation index is: In the formula, n represents the number of the Poria jelly sample in the batch to be tested, , N represents the total number of Poria jelly samples, JZF represents the matrix complexity evaluation index of the batch of Poria jelly samples to be tested, It represents the heterogeneity evaluation index of the optimized batch of Poria jelly samples to be tested. represents the number of microorganisms after extraction of the nth Poria jelly sample in the batch to be tested, represents the initial microbial count of the nth Poria jelly sample in the batch to be tested, represents the number of separated microorganisms of the nth Poria jelly sample in the batch to be tested, CXQ represents the second signal intensity, BXQ represents the first signal intensity, and BX represents the interference coefficient.

[0041] In this embodiment, the initial number of microorganisms and the number of microorganisms after extraction are not zero; the matrix complexity evaluation index in this algorithm involves processing multiple independent variables (extraction efficiency, separation efficiency and matrix interference coefficient), and there is a mutual influence relationship between these independent variables; the extraction efficiency directly affects the starting number of microorganisms in the subsequent separation step. If the extraction efficiency is lower, the number of microorganisms obtained after extraction will be smaller, which in turn affects the lower separation efficiency, because the separation process is based on the sample obtained after extraction; the larger the matrix interference coefficient, the more likely it is that the accuracy of the analysis results will decrease, thereby affecting the accurate evaluation of the extraction efficiency and separation efficiency; if the extraction efficiency and separation efficiency are lower, the sample may still contain more matrix components, resulting in an increase in the matrix interference coefficient; in summary, the matrix complexity evaluation index is negatively correlated with the extraction efficiency and separation efficiency, and the matrix complexity evaluation index is positively correlated with the matrix interference coefficient.

[0042] definition ,in, is the extraction efficiency of the nth Poria jelly sample in the batch to be tested, and is defined as ,in, is the separation efficiency of the nth Poria jelly sample in the batch to be tested, and is defined as ,in, is the matrix interference coefficient. Taking the heterogeneity evaluation index of the optimized batch of Poria jelly samples to be tested as 1 and the total number of Poria jelly samples as 1 as an example, the statistical table of the change of the matrix complexity evaluation index is shown in Table 1: Table 1 Statistics of changes in matrix complexity evaluation index

[0043] It can be seen from the first and second groups of data in the table that when the extraction efficiency increases, the matrix complexity evaluation index decreases; it can be seen from the second and third groups of data that when the separation efficiency increases, the matrix complexity evaluation index decreases; it can be seen from the third and fourth groups of data that when the matrix interference coefficient increases, the matrix complexity evaluation index increases.

[0044] Through the above steps, the complexity of the Poria jelly sample matrix was quantitatively evaluated, and the subsequent processing of the Poria jelly sample matrix was achieved.

[0045] Furthermore, the specific method for judging whether to perform matrix treatment based on the obtained matrix complexity assessment index is as follows: judging whether the matrix complexity assessment index is less than the preset matrix complexity threshold: if the matrix complexity assessment index is less than the preset matrix complexity threshold, matrix treatment is not performed; if the matrix complexity assessment index is not less than the preset matrix complexity threshold, the preset personnel are prompted to perform matrix treatment; matrix treatment includes removing impurities, diluting the matrix, balancing the pH of the matrix, and removing and separating interfering substances in the matrix.

[0046] In this embodiment, the preset matrix complexity threshold is represented by the maximum value of the matrix complexity evaluation index of the Poria jelly sample matrix that meets the requirements in the historical time period.

[0047] Removing impurities: The Poria jelly sample is homogenized to make the microorganisms and impurities therein more evenly distributed. A filter is used to filter out large particles of impurities (such as fibers, particulate matter, etc.). Centrifugal technology is used to separate the suspended matter (including some microorganisms and impurities) in the Poria jelly sample from the liquid matrix to further remove impurities.

[0048] Dilution matrix: Use sterile phosphate buffer or sterile saline as the diluent to gradually dilute the Poria jelly sample to the preset microbial concentration range so as to obtain more accurate microbial counts in subsequent tests; the preset microbial concentration range is represented by the range corresponding to the minimum and maximum microbial concentrations in the accurate microbial detection data within a historical time period.

[0049] Balance the pH of the matrix: According to the growth characteristics of the microorganisms and the needs of the detection method, use acid or alkaline solutions (such as hydrochloric acid, sulfuric acid, sodium hydroxide, etc.) to adjust the pH of the Poria jelly sample.

[0050] Removal and separation of interfering substances in the matrix: Use centrifugation technology to separate the suspended matter in the Poria jelly sample from the liquid matrix, filter the small molecule interfering substances in the sample through a filter, and use extraction technology to extract the specific components (including interfering substances) in the Poria jelly sample, thereby achieving the removal or separation of interfering substances; centrifugation technology places the Poria jelly sample in a centrifuge tube, selects the appropriate centrifugal speed and time, and starts the centrifuge, so that the sample is separated into suspended matter and liquid matrix under the action of centrifugal force; extraction technology uses the different solubility of different substances in two immiscible solvents to achieve the separation of substances.

[0051] Through the above steps, impurities and interfering substances are removed, reducing interference with subsequent test results, thereby achieving improved accuracy of microbial detection.

[0052] Furthermore, the specific method for evaluating the treatment complexity evaluation index based on the obtained treatment complexity evaluation parameters is as follows: the microbial loss rate (i.e. The microbial loss rate is obtained by calculating the ratio of the difference between the initial microbial count and the microbial concentration after treatment to the microbial concentration after treatment; the treatment recovery rate (i.e. The treatment recovery rate is obtained by calculating the ratio of the microbial concentration extracted after treatment to the microbial concentration after treatment; the minimum concentration deviation (i.e. ), the minimum concentration deviation is obtained by calculating the ratio of the minimum detection concentration to the preset minimum detection concentration in the database; the microbial loss rate, treatment recovery rate and minimum concentration deviation are processed to obtain the treatment complexity evaluation index.

[0053] It should be added that the specific method for obtaining the processing complexity evaluation index is: In the formula, n represents the number of the Poria jelly sample in the batch to be tested, , N represents the total number of Poria jelly samples, CLF represents the processing complexity evaluation index of the batch of Poria jelly samples to be tested, It represents the matrix complexity evaluation index of the batch of Poria jelly samples to be tested after optimization. represents the initial microbial count of the nth Poria jelly sample in the batch to be tested, represents the microbial concentration of the nth Poria jelly sample after treatment in the batch to be tested, represents the microbial concentration extracted from the nth Poria jelly sample in the batch to be tested after processing, DJC represents the minimum detection concentration, Indicates the preset minimum detection concentration.

[0054] In this embodiment, the preset minimum detection concentration is represented by the minimum value of the detection concentration in the historical time period; the processing complexity evaluation index in this algorithm involves processing multiple independent variables (microorganism loss rate, processing recovery rate and minimum concentration deviation), and there is a mutual influence relationship between these independent variables; the level of microorganism loss rate directly affects the processing recovery rate. When the microorganism loss rate is higher, the number of recovered microorganisms will naturally decrease, resulting in a lower processing recovery rate and may also increase the minimum concentration deviation; when the processing recovery rate is higher, it means that the processing process has a better capture and retention effect on microorganisms, which helps to reduce the minimum concentration deviation; when the processing efficiency is lower, the microorganism loss rate will increase; in summary, the processing complexity evaluation index is negatively correlated with the processing recovery rate, and the processing complexity evaluation index is positively correlated with the microorganism loss rate and the minimum concentration deviation.

[0055] The matrix complexity assessment index may be affected by the heterogeneity assessment index. If the heterogeneity assessment index is higher, it may mean that the matrix itself has obvious complexity or diversity, so the matrix complexity assessment index is larger; the processing complexity assessment index may be affected by the matrix complexity assessment index. The larger the matrix complexity assessment index is, the more complex the processing process may be, and the larger the processing complexity assessment index may be.

[0056] Take the optimized matrix complexity evaluation index as 1, the preset minimum detection concentration as 0.5 CFU / g, and the total number of Poria jelly samples as 1 as an example. Figure 2 As shown, it is a schematic diagram of the change of the processing complexity evaluation index provided in the embodiment of the present application with the change of microbial loss rate. When the processing recovery rate and the minimum concentration deviation are 1, the processing complexity evaluation index increases with the increase of the microbial loss rate.

[0057] like Figure 3 As shown, it is a schematic diagram of the change of the processing complexity evaluation index provided in the embodiment of the present application with the change of the processing recovery rate. When the microbial loss rate and the minimum concentration deviation are 1, the processing complexity evaluation index decreases with the increase of the processing recovery rate.

[0058] like Figure 4 As shown, it is a schematic diagram of the change of the processing complexity evaluation index provided in the embodiment of the present application with the change of the minimum concentration deviation. When the microbial loss rate and the processing recovery rate are 1, the processing complexity evaluation index increases with the increase of the minimum concentration deviation.

[0059] Through the above steps, the complexity of the processing steps of the Poria jelly sample was quantitatively evaluated, and the subsequent processing steps of the Poria jelly sample were optimized.

[0060] Furthermore, the specific process of determining whether to optimize the processing steps based on the acquired processing complexity assessment index is as follows: Determine whether the processing complexity assessment index is less than a preset processing complexity threshold: if the processing complexity assessment index is less than the preset processing complexity threshold, the processing steps will not be optimized; if the processing complexity assessment index is not less than the preset processing complexity threshold, the preset personnel will be prompted to optimize the processing steps; the processing step optimization includes simplifying the processing steps and optimizing the extraction conditions.

[0061] In this embodiment, the preset processing complexity threshold is represented by the maximum value of the processing complexity evaluation index of the processing steps of the Poria cocos jelly samples in the historical time period.

[0062] Simplify the processing steps: For example, during the processing of the Poria jelly sample, the dilution and homogenization steps of the Poria jelly sample can be combined to reduce the number of times the Poria jelly sample is transferred and processed.

[0063] Optimize extraction conditions: select the extraction method (such as oscillation extraction, ultrasonic extraction, enzymatic extraction, etc.) according to the characteristics of the microorganisms and the conditions of the matrix. For example, enzymatic extraction is suitable for the situation where the microorganisms in Poria jelly are tightly wrapped by pectin, cellulose and other components, making it difficult to extract directly. For enzymatic extraction, the extraction time may need to be adjusted according to the activity and concentration of the enzyme, the extraction temperature needs to be set according to the optimal temperature of the enzyme used, and the extraction pH value needs to be adjusted according to the optimal pH value of the enzyme used.

[0064] like Figure 5 As shown, it is a structural schematic diagram of a microbial contamination detection system based on the production process of Poria jelly provided in an embodiment of the present application. The microbial contamination detection system based on the production process of Poria jelly provided in an embodiment of the present application includes: a heterogeneity assessment module, a matrix complexity assessment module and a processing complexity assessment module; wherein the heterogeneity assessment module is used to evaluate according to the obtained heterogeneity assessment parameters to obtain a heterogeneity assessment index, and judge whether to perform heterogeneity optimization based on the obtained heterogeneity assessment index, and the heterogeneity assessment index is used to quantitatively evaluate the heterogeneity degree of the Poria jelly sample; the matrix complexity assessment module is used to evaluate according to the obtained matrix complexity assessment parameters to obtain a matrix complexity assessment index if heterogeneity optimization is performed, and judge whether to perform matrix treatment based on the obtained matrix complexity assessment index, and the matrix complexity assessment index is used to quantitatively evaluate the complexity of the matrix of the Poria jelly sample; the processing complexity assessment module is used to evaluate according to the obtained processing complexity assessment parameters to obtain a processing complexity assessment index if matrix treatment is performed, and judge whether to perform processing step optimization based on the obtained processing complexity assessment index, and the processing complexity assessment index is used to quantitatively evaluate the complexity of the processing steps of the Poria jelly sample.

[0065] In this embodiment, the processing steps of microbial contamination detection in the production process of Poria jelly involve multiple links such as sample preparation, matrix processing, and extraction. During the processing, it is necessary to strictly abide by the aseptic operation procedures (such as wearing sterile gloves and using sterile instruments) to prevent contamination by external microorganisms; select a suitable matrix according to the detection requirements, such as culture medium, physiological saline, etc.; disinfect the matrix to kill the microorganisms therein, and the commonly used disinfection methods include: thermal disinfection (such as steam disinfection), chemical disinfection (such as using alcohol, chloride and other disinfectants) and ultraviolet disinfection; dissolve the collected Poria jelly sample in an appropriate solvent, such as physiological saline or culture medium; separate the microorganisms from impurities in the Poria jelly sample by filtration or centrifugation; enrich and culture the separated microorganisms for subsequent microbial detection and identification.

[0066] Through the heterogeneity assessment module, matrix complexity assessment module and processing complexity assessment module, a quantitative assessment of the heterogeneity, matrix complexity and processing complexity of Poria jelly samples was achieved, which helps to identify potential problem points in the process of microbial contamination detection and take targeted optimization measures, thereby improving the accuracy of microbial contamination detection in the Poria jelly production process.

[0067] In summary, the embodiment of the present application determines whether to perform heterogeneity optimization based on the heterogeneity assessment index obtained by the heterogeneity assessment parameters, then determines whether to perform matrix treatment based on the matrix complexity assessment index obtained based on the matrix complexity assessment parameters, and finally determines whether to perform processing step optimization based on the processing complexity assessment index obtained based on the processing complexity assessment parameters, thereby improving the accuracy of the microbial contamination detection data of Poria jelly, thereby achieving an improvement in the low accuracy of microbial contamination detection in the Poria jelly production process, and effectively solving the problem of low accuracy of microbial contamination detection in the Poria jelly production process in the prior art.

[0068] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0073] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for detecting microbial contamination based on the production process of Poria jelly, characterized in that: The following steps are involved: S1, evaluating the obtained heterogeneity evaluation parameters to obtain a heterogeneity evaluation index, and judging whether to perform heterogeneity optimization based on the obtained heterogeneity evaluation index, wherein the heterogeneity evaluation index is used to quantitatively evaluate the degree of heterogeneity of the Poria jelly samples; S2, if heterogeneity optimization is performed, the matrix complexity evaluation index is obtained according to the matrix complexity evaluation parameter, and whether matrix treatment is performed is determined based on the obtained matrix complexity evaluation index, wherein the matrix complexity evaluation index is used to quantitatively evaluate the complexity of the matrix of the Poria jelly sample; S3, if matrix treatment is performed, the obtained processing complexity assessment parameters are used to evaluate the processing complexity assessment index, and based on the obtained processing complexity assessment index, it is determined whether to optimize the processing steps. The processing complexity assessment index is used to quantitatively evaluate the complexity of the processing steps of the Poria jelly sample.

2. The method for detecting microbial contamination based on the production process of Poria jelly according to claim 1, characterized in that: The specific method for obtaining the heterogeneity assessment parameters is as follows: The microbial concentration of each Poria jelly sample is obtained, and the mean value and variance of the microbial concentration of the Poria jelly sample are obtained by statistics; Microbial detection was performed on different areas of the Poria jelly sample to obtain the regional microbial concentration of each area, and the mean value and standard deviation of the regional microbial concentration were obtained by statistical analysis. The specific method for obtaining the matrix complexity evaluation parameter is as follows: Microbiological testing of Poria jelly samples was performed to obtain the initial microbial count; The microorganisms of the Poria jelly samples were extracted to obtain the extract, and the microorganism counts of the extract were performed using the fluorescent staining method to obtain the number of microorganisms after extraction; The extract is subjected to separation treatment to obtain separated microorganisms, and the separated microorganisms are counted to obtain the number of separated microorganisms; Detecting the microbial solution without the Poria jelly sample by a fluorescence photometer to obtain a first signal intensity, wherein the first signal intensity represents the signal intensity without matrix interference; The microbial solution after the Poria jelly sample and the microbial solution are mixed is detected by a fluorescence photometer to obtain a second signal intensity, wherein the second signal intensity indicates the signal intensity with matrix interference; The matrix complexity assessment parameters include initial microbial count, post-extraction microbial count, post-separation microbial count, first signal intensity, and second signal intensity; The specific method of obtaining the processing complexity evaluation parameter is as follows: The treated Poria jelly samples were tested for microorganisms to obtain the microbial concentration after treatment; The treated Poria jelly samples were subjected to extraction treatment to obtain the microbial concentration extracted after treatment; By performing microbial detection on the microbial standard samples within the preset microbial concentration range, the microbial concentration of the corresponding microbial standard samples when the microorganisms in the microbial standard samples can no longer be detected is recorded as the lowest detection concentration; The processing complexity assessment parameters include post-processing microbial concentration, post-processing extracted microbial concentration and minimum detection concentration.

3. The method for detecting microbial contamination based on the production process of Poria jelly as claimed in claim 2, characterized in that: The specific method for evaluating the heterogeneity evaluation index according to the obtained heterogeneity evaluation parameters is as follows: The heterogeneity index was obtained based on the relative relationship between the microbial concentration, the mean microbial concentration, and the variance of the microbial concentration of the Poria jelly samples; The heterogeneity correction index was obtained based on the relative relationship between the heterogeneity index and the total number of Poria jelly samples; The microbial distribution variation coefficient is obtained according to the relative relationship between the regional microbial concentration standard deviation of the Poria jelly samples and the regional microbial concentration average value of the Poria jelly samples, and the microbial distribution variation coefficient is obtained by performing a ratio operation on the regional microbial concentration standard deviation of the Poria jelly samples and the regional microbial concentration average value of the Poria jelly samples; The heterogeneity index, heterogeneity correction index and coefficient of variation of microbial distribution were processed to obtain the heterogeneity assessment index.

4. The method for detecting microbial contamination based on the production process of Poria jelly according to claim 1, characterized in that: The specific method for judging whether to perform heterogeneity optimization based on the obtained heterogeneity evaluation index is as follows: Determine whether the heterogeneity assessment index is less than the preset heterogeneity threshold: If the heterogeneity assessment index is less than the preset heterogeneity threshold, heterogeneity optimization will not be performed; If the heterogeneity evaluation index is not less than the preset heterogeneity threshold, the preset personnel will be prompted to perform heterogeneity optimization; The heterogeneity optimization includes adjusting sample selection and optimizing the mixing process.

5. The method for detecting microbial contamination based on the production process of Poria jelly as claimed in claim 2, characterized in that: The specific method for evaluating the matrix complexity evaluation index according to the obtained matrix complexity evaluation parameters is as follows: The extraction efficiency is obtained according to the relative relationship between the number of microorganisms after extraction and the initial number of microorganisms, and the extraction efficiency is obtained by performing a ratio operation on the number of microorganisms after extraction and the initial number of microorganisms; The separation efficiency is obtained according to the relative relationship between the number of microorganisms after separation and the number of microorganisms after extraction, wherein the separation efficiency is obtained by performing a ratio operation on the number of microorganisms after separation and the number of microorganisms after extraction; Obtaining a matrix interference coefficient according to a relative relationship between the second signal intensity and the first signal intensity, wherein the matrix interference coefficient is obtained by performing a ratio operation on a difference between the first signal intensity and the second signal intensity and the first signal intensity; The extraction efficiency, separation efficiency and matrix interference coefficient were processed to obtain the matrix complexity evaluation index.

6. The method for detecting microbial contamination based on the production process of Poria jelly according to claim 1, characterized in that: The specific method for judging whether to perform substrate processing based on the obtained substrate complexity evaluation index is as follows: Determine whether the matrix complexity evaluation index is less than the preset matrix complexity threshold: If the matrix complexity assessment index is less than the preset matrix complexity threshold, no matrix processing is performed; If the matrix complexity assessment index is not less than the preset matrix complexity threshold, the preset personnel will be prompted to perform matrix processing; The matrix treatment includes removing impurities, diluting the matrix, balancing the pH value of the matrix, and removing and separating interfering substances in the matrix.

7. The method for detecting microbial contamination based on the production process of Poria jelly according to claim 2, characterized in that: The specific method of evaluating the obtained processing complexity evaluation parameters to obtain the processing complexity evaluation index is as follows: The microbial loss rate is obtained according to the relative relationship between the initial microbial number and the microbial concentration after treatment, and the microbial loss rate is obtained by performing a ratio operation on the difference between the initial microbial number and the microbial concentration after treatment and the microbial concentration after treatment; The treatment recovery rate is obtained according to the relative relationship between the microbial concentration extracted after treatment and the microbial concentration after treatment, and the treatment recovery rate is obtained by performing a ratio operation on the microbial concentration extracted after treatment and the microbial concentration after treatment; Obtaining a minimum concentration deviation according to a relative relationship between the minimum detection concentration and a preset minimum detection concentration in a database, wherein the minimum concentration deviation is obtained by performing a ratio operation on the minimum detection concentration and the preset minimum detection concentration in the database; The microbial loss rate, treatment recovery rate and minimum concentration deviation were processed to obtain the treatment complexity assessment index.

8. The method for detecting microbial contamination based on the production process of Poria jelly according to claim 7, characterized in that: The specific method for obtaining the processing complexity evaluation index is: In the formula, n represents the number of the Poria jelly sample in the batch to be tested, , N represents the total number of Poria jelly samples, CLF represents the processing complexity evaluation index of the batch of Poria jelly samples to be tested, It represents the matrix complexity evaluation index of the batch of Poria jelly samples to be tested after optimization. represents the initial microbial count of the nth Poria jelly sample in the batch to be tested, represents the microbial concentration of the nth Poria jelly sample after treatment in the batch to be tested, represents the microbial concentration extracted from the nth Poria jelly sample in the batch to be tested after processing, DJC represents the minimum detection concentration, Indicates the preset minimum detection concentration.

9. The method for detecting microbial contamination based on the production process of Poria jelly according to claim 1, characterized in that: The specific process of determining whether to optimize the processing steps based on the obtained processing complexity evaluation index is as follows: Determine whether the processing complexity evaluation index is less than the preset processing complexity threshold: If the processing complexity evaluation index is less than the preset processing complexity threshold, the processing step optimization is not performed; If the processing complexity evaluation index is not less than the preset processing complexity threshold, the preset personnel will be prompted to optimize the processing steps; The process step optimization includes simplifying the process steps and optimizing the extraction conditions.

10. A microbial contamination detection system based on the production process of Poria jelly, characterized in that: include: Heterogeneity Assessment Module, Matrix Complexity Assessment Module, and Processing Complexity Assessment Module; The heterogeneity assessment module is used to evaluate the obtained heterogeneity assessment parameters to obtain a heterogeneity assessment index, and to determine whether to perform heterogeneity optimization based on the obtained heterogeneity assessment index. The heterogeneity assessment index is used to quantitatively evaluate the degree of heterogeneity of the Poria jelly samples. The matrix complexity evaluation module is used to evaluate the matrix complexity evaluation index according to the obtained matrix complexity evaluation parameters if heterogeneity optimization is performed, and judge whether to perform matrix processing based on the obtained matrix complexity evaluation index, and the matrix complexity evaluation index is used to quantitatively evaluate the complexity of the matrix of the Poria jelly sample; The processing complexity assessment module is used to obtain a processing complexity assessment index based on the acquired processing complexity assessment parameters if matrix processing is performed, and to determine whether to perform processing step optimization based on the acquired processing complexity assessment index. The processing complexity assessment index is used to quantitatively evaluate the complexity of the processing steps of the Poria jelly sample.

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