Quantitative bacteria quality control analysis method and device and readable storage medium thereof

Abstract

The invention provides a quantitative bacterial strain quality control analysis method and device and a readable storage medium thereof, and solves the existing quantitative bacterial strain quality control problem. The method comprises the following steps: firstly, establishing an automatic strain culture operation instruction, then establishing standard substance data by utilizing an automatic culture counting system, detecting a sample culture process, carrying out correlation analysis on a sample and a standard substance by analyzing parameters such as growth progress, strain purity, characteristic comparison and bacterial colony quantity, and finally obtaining a conclusion. According to the method, the microorganism growth process is introduced into a quality control system for the first time, automation and standardization of quality control are realized, a laboratory standard product database can be established for direct comparison quality control, the consistency of microorganism laboratory experiments is improved, a new way is provided for quantitative bacterium quality control, and the reliability of a drug microorganism detection result is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of colony counting, and in particular to a quantitative bacteria quality control analysis method, a device and a readable storage medium thereof. Background Art

[0002] Microbial testing plays a key role in drug quality control, and the accuracy and reliability of its results are of great significance to ensuring drug quality and safety. Commercial quantitative strains are widely used in the field of drug microbial control due to their advantages such as clear bacterial content range and convenient use (no need for pre-culture and easy dilution), such as culture medium growth ability verification, microbial limit test, antibacterial efficacy test, sterility control, positive control, method verification and capability verification.

[0003] However, there are many problems in the current quantitative strain market. On the one hand, there is a lack of clear and unified production specifications and reference indicators, and there is no standard to follow for the qualification and capability assessment of manufacturers, resulting in different factory standards for quantitative strains from different manufacturers, and it is difficult to ensure quality consistency. On the other hand, in the quality control link, there is a shortage of suitable quantitative strain standard substances. Most laboratories have a single quality control method for quality control bacteria, mainly relying on manual counting for simple quantitative assessment. This method is greatly affected by the experience of the operator, has poor stability and consistency, and lacks effective monitoring of the microbial growth process. It is difficult to comprehensively and accurately evaluate the quality of quantitative bacteria and cannot meet the strict requirements of drug microbial testing for accuracy and reliability.

[0004] The current fully automatic colony culture counting system, such as CN217499272U, can automatically count microorganisms in batches, improve the accuracy of colony counting, save the entire process data of colony growth, facilitate traceability and supervision, and save manpower. However, there is still a lack of a scientific, systematic, automated and standardized quantitative bacterial quality control analysis method, which can fill the gap in existing technology, ensure the accuracy and reliability of drug microbial detection results, and provide strong protection for drug quality and safety. Summary of the invention

[0005] The embodiments of the present invention provide a quantitative bacteria quality control analysis method, device and readable storage medium thereof, which are aimed at solving the problems that the existing quantitative strain quality control technology lacks unified standards and specifications, mainly relies on manual operation, has poor stability and consistency, ignores growth process monitoring, and is difficult to meet the accuracy and reliability requirements of drug microbial detection.

[0006] The core technology of the present invention is mainly based on the automatic culture counting system. By establishing standard data and detecting the sample culture process, it analyzes and compares the bacterial content, bacterial consistency, bacterial characteristics, growth progress and other aspects to achieve comprehensive quality control of quantitative bacteria.

[0007] In a first aspect, the present invention provides a quantitative bacterial quality control analysis method, the method comprising the following steps: S00. Establish the operating procedures for the automatic strain culture technology, including strain preservation, use, dilution, inoculation, machine loading, culture, result observation and data analysis; S10. With the help of the automatic culture counting system, the strains that have met the quality standards are cultured on the machine, and multi-dimensional colony data are collected to build a standard database; Cultivate and test samples to obtain comprehensive multi-dimensional colony data, including growth progress, strain purity, feature comparison, and colony count; S20. Qualitatively compare the degree of fit between the sample and the standard in multiple different aspects, including one or more combinations of growth progress, strain purity, color consistency and colony count, deeply mine the connotation of colony data association, and determine the quality status of the sample; S30, integrating the analysis data and results of the above steps, determining whether the sample meets the quality requirements, and obtaining the analysis results; Among them, the automatic culture counting system applies automatic culture counting technology.

[0008] Furthermore, in step S20, the growth progress is obtained based on the growth and size data of the colonies during the culture period; the purity of the strain is obtained by the similarity of the colony colors.

[0009] Furthermore, in step S20, the specific steps for qualitatively comparing the degree of fit between the sample and the standard in terms of growth progress are as follows: During the entire culture cycle, the detection sensitivity of the automatic culture counting system is kept fixed, and the culture dishes are scanned and monitored at set time intervals to obtain dynamic information on the growth status of each colony; According to the preset colony growth count requirements, the colonies with growth progress above the preset threshold are accurately selected from the plate as the key inspection objects, aiming to focus on the actively growing and representative colony groups; Quantify growth progress based on established growth rate models; The calculated growth rate data will be analyzed for correlation with the standard growth progress data already in the database.

[0010] Furthermore, in step S20, the specific steps for qualitatively comparing the compatibility of the sample with the standard in terms of bacterial purity are as follows: The colony color was selected as the key feature for evaluating the purity of bacterial strains; After the cultivation is completed, the first model and the second model are respectively established according to the read or measured colony color values; Through the first model and the second model, the colony color data is converted into quantifiable Euclidean distance data and similarity values, and then the purity of the strain is inferred; The first model is used to calculate the distance between each colony and the standard point; the second model is used to calculate the similarity.

[0011] Furthermore, the growth rate model is:

[0012] Among them, R represents the growth rate, t1 represents the initial culture time, t2 represents the detection time, n represents the number of colonies before the preset threshold, N represents the plate count result, and N d Represents the standard plate count result. The larger the growth rate R, the higher the activity of the strain.

[0013] Furthermore, the first model is:

[0014] Among them, d i represents the distance between the ith colony and the standard point (R0, G0, B0), (R i ,G i ,B i ) represents the color data of the ith colony; The second model is:

[0015] Among them, s represents the similarity, n represents the number of colonies, Represents the mean distance between all colonies and the standard point (R0, G0, B0). The smaller s is, the higher the colony similarity and the higher the purity of the strain.

[0016] Furthermore, in step S20, the sample and standard data are analyzed by Bland-Altman to determine the color consistency of the two colonies.

[0017] In a second aspect, the present invention provides a quantitative bacteria quality control analysis device based on automatic culture counting technology, comprising: The operating procedure establishment module is used to establish the operating procedures for the automatic strain culture technology, including strain preservation, use, dilution, inoculation, machine loading, culture, result observation and data analysis; Automatic culture counting system, which cultures strains that meet quality standards, collects colony data and builds a standard database; At the same time, the samples and standards are cultured and tested simultaneously to obtain comprehensive multi-dimensional data, including growth progress, strain purity, feature comparison and colony count; The analysis module qualitatively compares the compatibility of samples with standards in multiple aspects, including growth progress, strain purity, color consistency, and colony count, deeply mines the data association connotation, and determines the quality status of the samples; The output module integrates the analysis data and results of the above steps, determines whether the sample meets the quality requirements, and obtains the analysis results; Among them, the automatic culture counting system applies automatic culture counting technology.

[0018] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned quantitative bacteria quality control analysis method.

[0019] In a fourth aspect, the present invention provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute the process, and the process includes the quantitative bacteria quality control analysis method described above.

[0020] The main contributions and innovations of the present invention are as follows: 1. Comprehensive and systematic quality control method The microbial growth process is included in the evaluation system: Existing technologies are mostly limited to simple quantitative evaluation of quantitative bacteria, while the present invention pioneered a comprehensive investigation from the initial stage to the final stage of microbial growth, and obtained multiple parameters such as growth progress, strain purity, feature comparison analysis and colony count through automatic culture counting technology, and for the first time included the microbial growth process as a whole in the quality control evaluation system. This makes the evaluation of the quality of quantitative bacteria more comprehensive and in-depth, not only focusing on the final colony count, but also considering the dynamic changes during the growth process, effectively avoiding misjudgment caused by relying only on a single indicator or ignoring the growth process, greatly improving the accuracy and reliability of quality control results, and providing a more solid data foundation for drug microbial testing.

[0021] 2. Automated and standardized quality control process Reduce human errors and improve work efficiency: The traditional quality control operation process relies heavily on manual counting, and its results are greatly affected by the operator's experience. Stability and consistency are difficult to guarantee, and manual operation is cumbersome, time-consuming and laborious. The present invention realizes the automation of the quality control process with the help of an automatic culture counting system. From strain culture to data collection and analysis, the entire process is highly standardized, effectively reducing the interference of human factors, greatly improving work efficiency, and ensuring the stability and repeatability of each experimental result. It provides an efficient and reliable quality control solution for microbial testing laboratories, which helps to improve the quality control level of the entire industry.

[0022] Provide a reference example for the establishment of standards: Due to the lack of clear production specifications and reference indicators, the current quantitative strain market is chaotic, and the quality of products from different manufacturers varies. The automated and standardized quality control process of the present invention provides a valuable reference method for establishing relevant standards for quantitative bacteria in the field of microbial detection, which helps to regulate the market order, promote the development of the entire industry in a more scientific and standardized direction, promote the standardization process of quantitative bacteria production and quality control, and lay a solid technical support foundation for ensuring the quality and safety of drugs.

[0023] 3. Establishment and application of laboratory standard database Realize direct automated comparison quality control: The laboratory standard product database established by the present invention stores the strain culture process data that has been strictly confirmed to meet the quality standards. When performing quality control, the quantitative bacteria that need to be controlled can be directly and automatically compared with the standard product data in the database, without the need to perform the tedious preparation of reference products every time as in the prior art. This not only saves time and resources, but also avoids the errors that may be introduced due to differences in the preparation process of reference products, significantly improves the convenience and accuracy of quality control work, and brings great convenience to the daily quality control work of the laboratory.

[0024] Ensure experimental consistency and improve data comparability: Through this database, laboratories can implement unified requirements for quality control strains and ensure that each quality control experiment is conducted under the same standards and conditions, thereby greatly improving the experimental consistency of microbiology laboratories. The experimental results are more comparable, whether it is the comparison of results between different batches in the same laboratory or the sharing and communication of data between different laboratories, they can be carried out under a unified standard framework, which is conducive to promoting academic research and technical exchanges in the field of microbial testing and promoting the overall development of the industry.

[0025] The details of one or more embodiments of the invention are set forth in the following drawings and description so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flow chart of a data analysis process in a quantitative bacteria quality control analysis method according to an embodiment of the present invention; Figure 2 is a diagram of the culture process of a Staphylococcus aureus standard product according to an embodiment of the present invention; Figure 3 is a diagram of a Staphylococcus aureus quality control sample culture process according to an embodiment of the present invention; Figure 4is a diagram of the culture process of a quality control sample of simulated contamination of Staphylococcus aureus according to an embodiment of the present invention; Figure 5 is a diagram of a culture process of an Escherichia coli quality control sample according to an embodiment of the present invention; Figure 6 is a diagram of a culture process of a Bacillus subtilis standard product according to an embodiment of the present invention; Figure 7 is a diagram of a first Bacillus subtilis quality control sample cultivation process according to an embodiment of the present invention; Figure 8 is a diagram of a second Bacillus subtilis quality control sample cultivation process according to an embodiment of the present invention; Fig. 9 is a scatter plot of Staphylococcus aureus standard substance-quality control sample according to an embodiment of the present invention; Fig.10 is a scatter plot of Staphylococcus aureus standard substance-simulated contaminated quality control sample according to an embodiment of the present invention; Fig.11 is a scatter plot of Staphylococcus aureus standard substance-Escherichia coli quality control sample according to an embodiment of the present invention; Fig.12 The first Bacillus subtilis standard-quality control sample scatter plot according to the embodiment of the present invention Figure 1 ; Fig.13 The second Bacillus subtilis standard-quality control sample scatter plot according to the embodiment of the present invention Figure 2 ; Fig.14 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0028] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0029] Embodiment 1 The present invention aims to propose a quantitative bacteria quality control analysis method. Specifically, the specific steps of the method are as follows: S00. Establish the operating procedures for the automatic strain culture technology, including strain preservation, use, dilution, inoculation, machine loading, culture, result observation and data analysis; S10. With the help of the automatic culture counting system, the strains that have met the quality standards are cultured on the machine, and the colony data are collected to build a standard database; At the same time, the samples and standards are cultured and tested simultaneously to obtain comprehensive multi-dimensional data, including growth progress, strain purity, feature comparison and colony count; S20, qualitatively compare the fit between the sample and the standard in multiple aspects, including growth progress, strain purity, color consistency and colony count, deeply mine the data association connotation and determine the quality status of the sample; S30, integrating the analysis data and results of the above steps, determining whether the sample meets the quality requirements, and obtaining the analysis results; Among them, the automatic culture counting system applies automatic culture counting technology. Preferably, the automatic culture counting system of the present invention belongs to the CN217499272U disclosed product, and the specific structure and principle are not repeated here.

[0030] In this embodiment, the data information collected by the automated counting system includes multiple parameters such as growth progress, strain purity, feature comparison analysis, and colony count. The order of the analysis methods can be changed, and the parameters are but not limited to growth progress, strain purity, feature comparison analysis, and colony count; ① Qualitatively compare the growth progress of samples and standards, and collect data on colony growth and colony size at data collection nodes during the culture cycle; ② Qualitatively compare the purity of the strains in the sample, and determine the purity consistency of the strains by comparing the color similarity between each colony; ③ Qualitatively compare the similarity of the average color data of the sample and the standard to determine the consistency of the color of the sample and the standard; ④ Determine the consistency between the number of colonies in the sample and the quality control standard or product test report data.

[0031] In this embodiment, ① Growth progress: the time when each colony in the culture dish is detected and identified is counted (the detection sensitivity is fixed), and the detection data is analyzed for correlation with the database. According to the requirements of the Chinese Pharmacopoeia for colony growth counting, the colonies in the top 50% of the growth progress in the plate are taken as the objects of investigation, and the growth rate model is established , where R represents the growth rate, t1 represents the initial culture time, t2 represents the detection time, n represents the 50% colony number, and N represents the plate count result, where Nd Represents the standard plate count result. The greater the growth rate, the higher the vitality of the strain. With this model, the time course of colony growth and the change in quantity can be organically combined to accurately calculate the growth rate value, thereby intuitively reflecting the vitality of the strain. The greater the growth rate, the higher the vitality of the strain. Under the same culture conditions, its growth and reproduction speed is faster, and its adaptability to the environment and metabolic activity are stronger.

[0032] Among them, during the detection process, the detection sensitivity remains fixed, which is an important prerequisite for ensuring data accuracy and comparability. During the entire culture cycle, the automatic culture counting system scans and monitors the culture dish at set time intervals (daily, hourly, etc., set according to actual needs) to obtain dynamic information on the growth status of each colony, providing detailed original data for subsequent precise analysis. The top 50% of colonies are the focus of investigation. This screening principle aims to focus on actively growing and representative colony groups to more accurately reflect the growth characteristics and vitality level of the strain, and avoid deviations in results due to interference from individual abnormally growing colonies.

[0033] The calculated growth rate data is analyzed for correlation with the growth progress data of the standard samples already in the database. By comparing the growth rates of the sample and the standard, it is possible to determine whether the growth characteristics of the sample strain meet the expected standards, thereby providing a key basis for qualitative confirmation of the sample quality. For example, if the growth rate of the sample is similar to or not inferior to the standard, it indicates that the sample is normal in terms of growth progress; conversely, if the growth rate difference is significant, it indicates that there may be problems with the strain quality or abnormal culture conditions, which require further in-depth analysis and investigation. This data-driven correlation analysis method can effectively improve the scientificity and accuracy of quality control analysis and provide strong support for ensuring the quality of quantitative bacteria.

[0034] ② Strain purity: The purity or similarity of the strain can be evaluated by analyzing the similarity of colony characteristics. Here, the colony color is selected as the feature for evaluating the strain purity. The first model and the second model are established based on the read or measured colony color value:

[0035] s=

[0036] Where s represents the similarity, n represents the number of colonies, Represents the distance between the ith colony and the standard point (R0, G0, B0), Represents the mean distance between all colonies and the standard point (R0, G0, B0), (R i ,G i ,B i) represents the color data of the ith colony, where the smaller s is, the higher the colony similarity and the higher the purity of the strain.

[0037] The purity of the strain is determined based on the calculated similarity value. In general, the smaller the s value, the higher the color similarity between the colonies, that is, the higher the purity of the strain. For example, when the s value is less than or equal to the set threshold (such as 10), the sample can be considered to meet the requirements in terms of strain purity; conversely, if the s value exceeds the threshold range, it indicates that there may be problems such as strain contamination or mutation, which require further analysis and confirmation. At the same time, through the accumulation and analysis of experimental data of different strains, it can be found that the s values ​​of different strains have a certain characteristic range. Users can verify the data based on actual standards and determine a more accurate s value range to better limit and judge the strain purity information and ensure the accuracy and reliability of quality control results.

[0038] ③ Read the colony color characteristic data of the sample, and convert the colony color data into Euclidean distance data according to the first model, and determine the similarity and consistency of the two colonies through Bland-Altman descriptive statistics sample and standard data analysis.

[0039] Among them, Bland-Altman analysis is a statistical analysis method used to evaluate the consistency between two measurement methods or data. For example, after converting the sample colony color data into Euclidean distance data, the Bland-Altman analysis method is used to perform descriptive statistics on these data of the sample and the standard, so as to judge whether the difference between the two is within an acceptable range, and then determine whether the two colonies are consistent, providing an important basis for evaluating the quality of quantitative bacteria.

[0040] ④Confirmation of colony count: the number of colonies on the counting plate is cultured through an automated counting system, and is evaluated and calculated with reference to the quantitative bacteria standard or test report data. The acceptable standard is the requirements of the Chinese Pharmacopoeia.

[0041] Embodiment 2 Based on the same concept, this embodiment is based on the first embodiment and uses a Staphylococcus aureus standard (current effective quality control bacteria that have been quality controlled) for detection. The specific steps are as follows: Step 1: Establish operating procedures For strain preservation, use, dilution, etc., refer to the general requirements and instructions for use of quantitative strains; Inoculation: Take 0.1 ml of the diluted quantitative strain and inoculate it into TSA medium for coating operation according to relevant requirements; Complete the automatic culture counting equipment operation and set the constant temperature culture and culture time according to the instrument instructions; Results observation and data analysis: After the cultivation, the results were observed and the data were analyzed. The data analysis process was as follows Figure 1 As shown. Figure 2 This is a diagram of the culture process of Staphylococcus aureus standard.

[0042] Step 2: Preparation Experimental equipment: Fully automatic colony counting workstation SCW-160 (Zhejiang Tailin Medical Engineering Co., Ltd.), or the CN217499272U public product; Biosafety Cabinet 1389 (Thermo Fisher Scientific).

[0043] Reagents and consumables are as shown in Table 1: Table 1

[0044] Step 3: Sample preparation ① Preparation of bacterial suspension: In a biological safety cabinet, prepare Staphylococcus aureus quantitative bacteria into working bacterial suspension of appropriate dilution level according to the operating procedures (the concentration of bacterial suspension is not more than 100 cfu / ml); ② Plate coating: Take 0.1 ml of bacterial suspension in the biosafety cabinet and transfer it into TSA medium. Use a coating rod to evenly coat the bacterial suspension. ③ Complete the information entry and operation of the automatic culture counting device according to the instrument instructions, set the culture temperature to 33.5℃, and culture for 5 days.

[0045] ④Cultivation result confirmation data statistics: Growth rate R = 3.1; Colony similarity S = 4.8; The feature distance data matrix is ​​as follows in Table 2: Table 2

[0046] Among them, the number of colonies N=70.

[0047] Embodiment 3 Based on the same concept, the same steps and equipment as in Example 2, for the quality control sample of Staphylococcus aureus, the difference from Example 2 is that the reagents and consumables are as shown in Table 3: Table 3

[0048] ①-③ are the same as in Example 2; ④Cultivation result confirmation data statistics: Growth rate R = 3.1; Colony similarity S = 4.3; The feature distance data matrix is ​​shown in Table 4: Table 4

[0049] Among them, the number of colonies N = 30. Figure 3 This is a diagram of the culture process of Staphylococcus aureus quality control samples.

[0050] Embodiment 4 Based on the same concept, the same steps and equipment as in Example 2, for the simulated contaminated quality control sample of Staphylococcus aureus, the difference from Example 2 is that the reagents and consumables are as shown in Table 5: Table 5

[0051] ① Preparation of bacterial solution: dilute the homemade bacterial suspension of Staphylococcus aureus and Escherichia coli to an appropriate concentration (the concentration of the bacterial suspension is not more than 100 cfu / ml) in a biological safety cabinet, and mix the two bacterial suspensions in equal proportions to obtain a mixed suspension of the two bacteria, which is used to simulate contaminated quality control bacteria; ② Plate coating: Take 0.1 ml of bacterial suspension in the biosafety cabinet and transfer it into TSA medium. Use a coating rod to evenly coat the bacterial suspension. ③ Complete the automatic culture counting equipment information entry and machine operation according to the instrument instructions, set the culture temperature to 33.5℃, and culture for 5 days; ④Cultivation result confirmation data statistics: Growth rate R = 2.5; Colony similarity S = 15.9; The feature distance data matrix is ​​shown in Table 6: Table 6

[0052] Among them, the number of colonies N = 82. Figure 4 This is a diagram of the culture process of quality control samples simulated with contamination of Staphylococcus aureus.

[0053] Embodiment 5 Based on the same concept, the same steps and equipment as in Example 2, for the Escherichia coli quality control sample (simulating the wrong strain quality control sample), the difference from Example 2 is that the reagents and consumables are as shown in Table 7: Table 7

[0054] ①-③ are the same as in Example 2; ④Cultivation result confirmation data statistics: Growth rate R = 2.5; Colony similarity S=7; The feature distance data matrix is ​​as follows in Table 8: Table 8

[0055] Among them, the number of colonies N = 73. Figure 5 This is a diagram of the culture process of Escherichia coli quality control samples.

[0056] By analyzing Examples 2 to 5 and comparing the correlation between the quality control group and the control group, it can be concluded that: Step 1. Comparing the growth rates of quality control products and standard products, it can be found that the growth rate of Staphylococcus aureus quality control products is not much different from that of the standard products, and the growth rate is not inferior to that of the standard products, while the growth rates of simulated contamination quality control products and Escherichia coli quality control products are faster than those of the standard products. The reason for this is the influence of bacterial species factors. Here, the growth rate range of bacterial species can be accurately determined by increasing the amount of data of standard products and optimizing the growth rate comparison parameters.

[0057] Step 2, the purity values ​​of the colonies in Examples 2, 3, and 5 are all less than or equal to 10, where the smaller the similarity, the higher the purity consistency of the strain. In Example 4, the S=15.9 of the contaminated sample is much greater than 10, which means that the purity consistency of the strain is poor. According to the experimental data, it can be found that the S values ​​of different strains are somewhat different. The user can verify the data based on the actual standard product and determine the S value range to limit the strain data information.

[0058] Step 3: Analyze the characteristics of the quality control products and standard products of Examples 3 to 5 and Example 2. According to Tables 9-11 and Figure 9-11 , you can see: Table 9 Staphylococcus aureus quality control sample statistics

[0059] Table 10 Statistics of Staphylococcus aureus simulated contamination quality control samples

[0060] Table 11 Statistics of Escherichia coli quality control products

[0061] Among them, the first method is the standard product, and the second method is the quality control product, specifically the statistical analysis method, which is to compare the differences between the two methods.

[0062] ① In the comparison of Staphylococcus aureus standard and quality control, the data of p=0.081>0.05 did not show significance, and the difference between Staphylococcus aureus standard and quality control did not deviate significantly from the number 0. According to the scatter plot, the 95% confidence interval of the difference was -1.055 -1.481, and the ordinates of the scatter points were all within the 95% confidence interval, indicating that the data had a good level of consistency, which also indicated that the Staphylococcus aureus standard and control were consistent.

[0063] ② In the comparison of Staphylococcus aureus standard and simulated contamination quality control, the data showed significance at p=0.00<0.05. The difference between Staphylococcus aureus standard and simulated contamination quality control was significantly different from 0. According to the scatter plot, the 95% confidence interval of the difference was -51.515-16.920, with a large range. In addition, some data points on the ordinate of the scatter plot did not fall within the 95% confidence interval. The consistency level of the data was poor, which proved that the Staphylococcus aureus standard and simulated contamination quality control were not consistent.

[0064] ③ In the comparison of Staphylococcus aureus standard and Escherichia coli quality control, the data showed significance at P=0<0.05. The difference between Staphylococcus aureus standard and Escherichia coli quality control was obviously different from 0. According to the scatter plot, the 95% confidence interval of the difference was -9.971-25.297, which had a large range. In addition, there were multiple data points on the ordinate of the scatter plot that did not fall within the 95% confidence interval. The data did not have a good consistency level, which meant that Staphylococcus aureus standard and Escherichia coli were not consistent.

[0065] Embodiment 6 Based on the same concept, this embodiment is based on the first embodiment and uses a Bacillus subtilis standard (current effective quality control bacteria that have been quality controlled) for detection. The specific steps are as follows: Step 1: Same as Example 2 Results observation and data analysis: After the cultivation, the results were observed and the data were analyzed. The data analysis process was as follows Figure 1 As shown. Figure 6 This is a diagram of the culture process of Bacillus subtilis standard.

[0066] Step 2: Preparation Experimental equipment: Fully automatic colony counting workstation SCW-160 (Zhejiang Tailin Medical Engineering Co., Ltd.) Biosafety Cabinet 1389 (Thermo Fisher Scientific).

[0067] Reagents and consumables are as shown in Table 12: Table 12

[0068] Step 3: Sample preparation ① Preparation of bacterial suspension: In a biological safety cabinet, prepare the Bacillus subtilis quantitative bacteria into working bacterial suspension of appropriate dilution level according to the operating procedures (the concentration of bacterial suspension is not more than 100 cfu / ml); ② Plate coating: Take 0.1 ml of bacterial suspension in the biosafety cabinet and transfer it into TSA medium. Use a coating rod to evenly coat the bacterial suspension. ③ Complete the information entry and operation of the automatic culture counting device according to the instrument instructions, set the culture temperature to 33.5℃, and culture for 5 days.

[0069] ④Cultivation result confirmation data statistics: Growth rate R = 1.25; Colony similarity S = 6.25; The feature distance data matrix is ​​shown in Table 13: Table 13

[0070] Among them, the number of colonies N=42.

[0071] Embodiment 7 Based on the same concept, the same steps and equipment as in Example 2, for the first Bacillus subtilis quality control sample, the difference from Example 2 is that the reagents and consumables are as shown in Table 14: Table 14

[0072] ①-③ are the same as in Example 2; ④Cultivation result confirmation data statistics: Growth rate R = 1.75; Colony similarity S = 4.16; The feature distance data matrix is ​​shown in Table 15: Table 15

[0073] Among them, the number of colonies N = 30. Figure 7 This is a diagram of the culture process of Bacillus subtilis quality control samples.

[0074] Embodiment 8 Based on the same concept, the same steps and equipment as in Example 2, for the second Bacillus subtilis quality control sample, the difference from Example 2 is that the reagents and consumables are as shown in Table 16: Table 16

[0075] ①-③ are the same as in Example 2; ④Cultivation result confirmation data statistics: Growth rate R = 1.33; Colony similarity S = 4.09; The feature distance data matrix is ​​shown in Table 17: Table 17

[0076] Among them, the number of colonies N = 36. Figure 8 This is a diagram of the culture process of the second Bacillus subtilis quality control sample.

[0077] By analyzing Examples 6-8 and comparing the correlation between the quality control group and the control group, it can be concluded that: Step 1: By comparing the growth rates of the quality control and the standard, it can be found that the growth rates of the first and second Bacillus subtilis quality control are faster than those of the standard. This may not necessarily be the case, but should be different depending on the bacterial growth conditions.

[0078] In step 2, the purity values ​​of the colonies in Examples 6, 7, and 8 are all less than or equal to 10, wherein the smaller the similarity, the higher the purity consistency of the representative strain. The purity of the Bacillus subtilis standard and the Bacillus subtilis quality control product meets the requirements, and the quality control product is better than the standard product.

[0079] Step 3: Analyze the characteristics of the quality control products and standard products of Examples 7 and 8 and Example 6. Fig.12 and 13 , you can see: Table 18 Statistics of the first Bacillus subtilis quality control samples

[0080] Table 19 Second Bacillus subtilis quality control sample statistics

[0081] (1) In the first Bacillus subtilis standard and quality control, p=0.718>0.05 data did not show significance, and the difference between the Bacillus subtilis standard and the quality control did not deviate significantly from the number 0. According to the scatter plot, the 95% confidence interval of the difference was -16.927-17.952, and the ordinates of the scatter points were all within the 95% confidence interval, indicating that the data had a good level of consistency, which also means that the Bacillus subtilis standard and the control were consistent.

[0082] (2) In the second Bacillus subtilis standard and simulated contamination quality control, p=0.298>0.055 data did not show significance. The difference between the Bacillus subtilis standard and the quality control did not deviate significantly from the number 0, and the scatter plot showed that the 95% confidence interval of the difference was -20.395 - 24.423. Although there was a group of data points on the ordinate of the scatter plot that did not fall within the 95% confidence interval, the comprehensive T test results showed that the two groups of data had good data consistency, which means that the Bacillus subtilis standard and the control were consistent.

[0083] Embodiment 9 Based on the same concept, the present invention also proposes a quantitative bacteria quality control analysis device based on automatic culture counting technology, comprising: The operating procedure establishment module is used to establish the operating procedures for the automatic strain culture technology, including strain preservation, use, dilution, inoculation, machine loading, culture, result observation and data analysis; Automatic culture counting system, which cultures strains that meet quality standards, collects colony data and builds a standard database; Cultivate and test samples to obtain comprehensive multi-dimensional colony data, including growth progress, strain purity, feature comparison, and colony count; The analysis module qualitatively compares the compatibility of samples with standards in multiple aspects, including one or more combinations of growth progress, strain purity, color consistency, and colony count, deeply mines the connotation of colony data association, and determines the quality status of samples; The output module integrates the analysis data and results of the above steps, determines whether the sample meets the quality requirements, and obtains the analysis results; Among them, the automatic culture counting system applies automatic culture counting technology.

[0084] Embodiment 10 This embodiment also provides an electronic device, referring to Fig.14 , comprises a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any of the above method embodiments.

[0085] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0086] Among them, the memory 404 may include a large capacity memory 404 for data or instructions. For example, but not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 404 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 404 may be inside or outside the data processing device. In a specific embodiment, the memory 404 is a non-volatile memory. In a specific embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0087] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0088] The processor 402 implements any quantitative bacteria quality control analysis method in the above embodiments by reading and executing computer program instructions stored in the memory 404 .

[0089] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0090] The transmission device 406 can be used to receive or send data via a network. The specific examples of the above-mentioned network may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0091] Input / output devices 408 are used to input or output information.

[0092] Embodiment 11 This embodiment also provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute the process. The process includes the quantitative bacteria quality control analysis method according to the first embodiment.

[0093] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0094] In general, various embodiments may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0095] Embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, at this point, it should be noted that, for example, Figure 1 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or storage blocks implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0096] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A quantitative bacterial quality control analysis method, characterized in that: The following steps are involved: S00. Establish the operating procedures for the automatic strain culture technology, including strain preservation, use, dilution, inoculation, machine loading, culture, result observation and data analysis; S10. With the help of the automatic culture counting system, the strains that have met the quality standards are cultured on the machine, and multi-dimensional colony data are collected to build a standard database; Cultivate and test the samples to obtain comprehensive multi-dimensional colony data, including one or more combinations of growth progress, strain purity, feature comparison, and colony count; S20. Qualitatively compare the compatibility of samples with standards in multiple aspects, including one or more combinations of growth progress, strain purity, color consistency, and colony count, deeply explore the connotation of colony data association, and determine the quality status of samples; S30, integrating the analysis data and results of the above steps, determining whether the sample meets the quality requirements, and obtaining the analysis results; Among them, the automatic culture counting system applies automatic culture counting technology.

2. The quantitative bacteria quality control analysis method according to claim 1, characterized in that: In step S20, the growth progress is obtained based on the growth and size data of the colonies during the culture period; the purity of the strain is obtained by the similarity of the colony colors.

3. The quantitative bacteria quality control analysis method according to claim 1, characterized in that: In step S20, the specific steps for qualitatively comparing the growth progress of the sample with that of the standard are as follows: During the entire culture cycle, the detection sensitivity of the automatic culture counting system is kept fixed, and the culture dishes are scanned and monitored at set time intervals to obtain dynamic information on the growth status of each colony; According to the preset colony growth count requirements, the colonies with growth progress above the preset threshold are accurately selected from the plate as the key inspection objects, aiming to focus on the actively growing and representative colony groups; Quantify growth progress based on established growth rate models; The calculated growth rate data will be analyzed for correlation with the standard growth progress data already in the database.

4. The quantitative bacteria quality control analysis method according to claim 1, characterized in that: In step S20, the specific steps for qualitatively comparing the compatibility of the sample with the standard in terms of bacterial purity are as follows: Colony color was selected as the key feature for evaluating the purity of bacterial strains; After the cultivation is completed, the first model and the second model are respectively established according to the read or measured colony color values; Through the first model and the second model, the colony color data is converted into quantifiable Euclidean distance data and similarity values, and then the purity of the strain is inferred; The first model is used to calculate the distance between each colony and the standard point; the second model is used to calculate the similarity.

5. The quantitative bacteria quality control analysis method according to claim 3, characterized in that: The growth rate model is: ； Among them, R represents the growth rate, t1 represents the initial culture time, t2 represents the detection time, n represents the number of colonies before the preset threshold, N represents the plate count result, and N d Represents the standard plate count result. The larger the growth rate R, the higher the activity of the strain.

6. The quantitative bacteria quality control analysis method according to claim 4, characterized in that: The first model is: ； Among them, d i represents the distance between the ith colony and the standard point (R0, G0, B0), (R i ,G i ,B i ) represents the color data of the ith colony; The second model is: ； Among them, s represents the similarity, n represents the number of colonies, Represents the mean distance between all colonies and the standard point (R0, G0, B0). The smaller s is, the higher the colony similarity and the higher the purity of the strain.

7. The quantitative bacteria quality control analysis method according to claim 6, characterized in that: In step S20, the sample and standard data were analyzed by Bland-Altman to determine the color consistency of the two colonies.

8. A quantitative bacteria quality control analysis device based on automatic culture counting technology, characterized in that: include: The operating procedure establishment module is used to establish the operating procedures for the automatic strain culture technology, including strain preservation, use, dilution, inoculation, machine loading, culture, result observation and data analysis; Automatic culture counting system, which cultures strains that meet quality standards, collects colony data and builds a standard database; Cultivate and test samples to obtain comprehensive multi-dimensional colony data, including growth progress, strain purity, feature comparison, and colony count; The analysis module qualitatively compares the compatibility of samples with standards in multiple aspects, including one or more combinations of growth progress, strain purity, color consistency, and colony count, deeply mines the connotation of colony data association, and determines the quality status of samples; The output module integrates the analysis data and results of the above steps, determines whether the sample meets the quality requirements, and obtains the analysis results; Among them, the automatic culture counting system applies automatic culture counting technology.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the quantitative bacteria quality control analysis method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes the quantitative bacteria quality control analysis method according to any one of claims 1 to 7.

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