CN112800126B - Processing method and system of fluorescence photoelectric detection instrument for predicting detection time - Google Patents
Processing method and system of fluorescence photoelectric detection instrument for predicting detection time Download PDFInfo
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Abstract
A processing method of a fluorescence photoelectric detection instrument about prediction detection time comprises the following steps: s100, acquiring multiple groups of source data in real time by an android main program; s200, calling a python subprogram by the android main program; s300, respectively carrying out data processing on multiple groups of source data by a python subprogram to obtain multiple groups of fitting data, respectively judging whether inflection points appear on the multiple groups of fitting data, if the inflection points appear on the multiple groups of fitting data, executing the next step, and if not, returning to the step S100; s400, the android main program stores the fitting data into a database; and S500, the android main program displays the fitting data and the inflection point data in a visual mode. According to the invention, through mixed compilation of andorid and python, the data processing and analyzing capability is improved, and the detection time is greatly shortened.
Description
Technical Field
The invention relates to the technical field of microbial detection, in particular to a processing method and a processing system of a fluorescence photoelectric detection instrument for predicting detection time.
Background
A large amount of bacterial microorganisms exist in the nature, and the number of bacteria can be a mark for judging whether the cleaning is carried out. Such as the number of coliform bacteria in drinking water or in food. The total number of bacterial colonies of the food seriously exceeds the standard, which indicates that the sanitary condition of the food can not meet the basic sanitary requirement, the nutritional ingredients of the food can be damaged, the food is accelerated to be rotten and deteriorated, and the food loses the edible value. Consumers eat food with serious overproof microorganisms, so that the intestinal diseases such as dysentery and the like are easy to cause symptoms such as vomit, diarrhea and the like, and the health and the safety of human bodies are harmed.
There are probably three methods available in the prior art for detecting microorganisms: plate culture method, MPN method and photoelectric detection method.
In bacteriological studies, the shape and type of a colony are usually observed by using a flat plate culture method, in which a gel-like solid medium such as agar or gelatin is made flat, and then cells, tissues or organs of a microorganism or a multicellular organism are inoculated onto the flat plate culture medium. The disadvantages of the plate culture method are: the detection needs 2 to 7 days, the required time is long, and the operation is complex.
The MPN method (most recent similar number method), also called maximum possible number method, is a common method in food inspection, the biochemical reaction is based on lactose fermentation, and gram-negative bacteria, such as bacillus coli, generally ferment lactose and produce acid and gas at 37 ℃ for 24 h; if gas is produced, streaking is needed to inoculate on a flat plate, and after culturing for 24h at 37 ℃, colony morphology, gram staining and re-fermentation verification are observed. The MPN method has the following defects: it is desirable that the sample be homogeneous, suitable for testing liquid samples, and can only be used to test gas producing species in the fermentation medium.
The specific protease contained in the microorganism can carry out enzyme digestion on a fluorescent substrate in a culture medium reagent so as to release molecules with strong fluorescence, the molecules are excited to emit fluorescence under the irradiation of a light source with a specific wavelength, a fluorescent photoelectric detection instrument can emit the light with the specific wavelength, collect a fluorescent signal and convert the optical signal into an electric signal for quantitative analysis and the like, the higher the concentration of a sample is, the less the time required for detection is, and the photoelectric detection method has wide attention of researchers due to high sensitivity, low cost and strong specificity. When the microorganism is quantitatively detected (PCR detection), an experimenter can estimate the detection time length of a strain to be detected according to own experience, and input the estimated time length into program software of the fluorescence photoelectric detection instrument, the fluorescence photoelectric detection instrument can detect according to the estimated time length and output detection data, analyze the data after the detection is finished, calibrate a peak value, wherein the peak value (or called as an inflection point) is target data corresponding to a quantitative value, however, the estimated time length of the experimenter is not accurate enough, the peak value possibly appears in advance or lags behind in the estimated time length, if the peak value appears in advance, the detection time after the peak value appears is unnecessary time waste, and if the peak value appears in lag, the detection is invalid.
Disclosure of Invention
In order to solve the above-mentioned deficiencies in the prior art, one of the main objects of the present invention is to provide a processing method for predicting the detection time of a fluorescence photoelectric detection instrument, and the technical solution to the problem is realized as follows:
a processing method of a fluorescence photoelectric detection instrument for predicting detection time comprises the following steps:
s100, acquiring multiple groups of source data in real time by an android main program;
s200, calling a python subprogram by the android main program;
s300, the python subprogram respectively carries out data processing on the multiple groups of source data to obtain multiple groups of fitting data, whether inflection points appear on the multiple groups of fitting data is respectively judged, if the inflection points appear on the multiple groups of fitting data, the next step is executed, and if not, the step S100 is returned;
s400, the android main program stores the fitting data into a database;
and S500, the android main program displays the fitting data and the inflection point data in a visual mode.
The data processing and inflection point determination in step S300 specifically includes the following sub-steps:
s310, importing source data in real time;
s320, fitting a curve;
s330, eliminating an initial fluctuation numerical value;
s340, smoothing the curve;
and S350, judging whether an inflection point appears.
In step S320, the fitting function is f (x) = a/(1 + np.exp (b-c × x)), the output value of the fitting function corresponds to the corresponding concentration value, where x is the data length of the source data, and a, b, and c are fitting coefficients calculated by a python iteration method according to the source data value and the source data size.
Wherein, step S330 specifically includes the following substeps:
step S330 specifically includes the following substeps:
s331, finding the minimum value of the source data through the fitting function by an np.min function;
s332, searching subscript of the minimum value through circular traversal;
and S333, assigning all data before the subscript corresponding to the minimum value in the source data to be 0 to obtain intermediate processing data.
Wherein, step S340 specifically includes the following substeps:
s341, creating a first difference array;
s342, forward differentiating the intermediate processing data twice through np.diff function and assigning the result to a first difference array;
s343, returning the average value of the first differential array through an np.mean function;
s344, setting a first threshold, determining whether an average value of the first difference array is smaller than the first threshold, if so, assigning all the first difference arrays to 0, otherwise, returning the first difference array to the first difference array, and performing inverse calculation on the data in the first difference array to obtain fitting data, where the first threshold is a parameter reasonably selected in consideration of data accuracy and data smoothness, and may determine how fast the data changes, and when smaller than the first threshold, it is proved that the data changes less, and the changes are negligible, where the first threshold is 5 to 10, and preferably, the first threshold is 7.5.
Wherein, step S350 specifically includes the following sub-steps:
s351, creating a second difference array;
s352, forward difference is carried out on the fitting data for two times through an np.diff function, and the result is assigned to a second difference array;
s353, products between adjacent data in the second difference array are judged through traversal loop, if one of the adjacent products is larger than 0 and the other product is smaller than 0, an inflection point is judged to appear, and data corresponding to the inflection point are called as inflection point data (time, fluorescence intensity corresponding to the time or od value), the next step is executed, otherwise, the step S310 is executed;
and S354, substituting the corresponding inflection point data into a fitting function to calculate a corresponding concentration value, and simultaneously outputting the fitting data and the inflection point data to an android main program.
Before step S310, step S301 is further included for roughly predicting whether an inflection point trend appears, and specifically includes the following steps:
s3011, importing source data at a fixed time interval delta t, wherein delta t is more than or equal to 5 and less than or equal to 10min;
s3012, creating a third difference array;
s3013, carrying out forward difference on the source data for the second time through an np.diff function, and assigning a result to a third difference array; s3014, setting a second threshold, if the difference between the two forward differences is larger than the second threshold, executing the step S310, otherwise, returning to execute the step S3011.
Another object of the present invention is to provide a processing system for predicting detection time of a fluorescence photoelectric detection instrument, and the technical solution to the problem is realized by:
a processing system of a fluorescence photoelectric detection instrument for predicting detection time comprises a data acquisition module, a subprogram calling module, a data processing module, a data storage module and a data display module;
a data acquisition module, configured to acquire source data generated by the detection instrument, corresponding to step S100;
a subprogram calling module for calling the python subprogram, corresponding to the step S200;
a data processing module, configured to process the source data and identify an inflection point, corresponding to step S300;
a data storage module, configured to store the fitted data, corresponding to step S400;
and a data display module, configured to fit the data for visual display, corresponding to step S500.
Wherein, the data processing module still includes:
an inflection point rough pre-judging module, configured to roughly pre-judge whether an inflection point trend occurs, corresponding to step S301;
a fitting curve module for fitting the source data, corresponding to step S320;
a fluctuation value elimination module for eliminating the initial fluctuation value, corresponding to step S330;
a smooth curve module, configured to smooth the final curve, corresponding to step S340;
an inflection point determining module, configured to determine an identified inflection point, corresponding to step S350.
The beneficial effects of the invention are:
(1) According to the method, through mixed editing of andorid and python, the inflection point is identified and the detection is stopped at the first time by the python subprogram, so that the detection time is greatly shortened;
(2) In step S301, whether a knee trend occurs is roughly pre-determined, so that the operation frequency can be reduced to further increase the operation speed.
(3) The inflection point of the detection curve is easier to identify and judge through the operation of smoothing the curve;
(4) And the inflection point is judged after forward difference of the source data is performed twice by the diff function, so that the running speed is increased.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1: an android main program flow chart;
FIG. 2 is a schematic diagram: the embodiment is a python subroutine flowchart;
FIG. 3: example two python subroutines flow chart;
FIG. 4: fitting a detection curve;
FIG. 5: the system of the invention is a block diagram.
Detailed Description
The technical solution in the embodiments of the present invention is clearly and completely described below with reference to the drawings in the embodiments of the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
In practice, in order to improve the working efficiency, a plurality of samples of the same (single bacteria) or different strains (multiple bacteria) can be put in at one time, and the fluorescence photoelectric detection instrument generates a plurality of groups of source data. A software system for processing and analyzing source data is installed on a mobile terminal (such as a tablet computer and a mobile phone), the source data is transmitted to the mobile terminal through a serial port, the software system comprises an android main program and a python subprogram, the python is an interpreted, object-oriented and dynamic data type high-level programming language, the compatibility is high, and the software system is good at processing and analyzing a large amount of data.
The first embodiment is as follows:
the processing method of the fluorescence photoelectric detection instrument of the invention about the predicted detection time comprises the following steps as shown in figure 1:
s100, acquiring multiple groups of source data in real time by an android main program, wherein in the embodiment, the android main program stores the multiple groups of source data into a temporary txt file in sequence, and the source data are fluorescence intensity values or od values when the number of colonies is detected;
s200, calling a python subprogram by the android main program;
s300, the python subprogram respectively carries out data processing on the multiple groups of source data to obtain multiple groups of fitting data, whether inflection points appear on the multiple groups of fitting data is respectively judged, if the inflection points appear on the multiple groups of fitting data, the next step is executed, and if not, the step S100 is returned;
the data processing and inflection point determination in step S300 specifically includes the following sub-steps, as shown in fig. 2:
s310, importing source data in real time, and importing the source data in the local temporary txt file by using an np.
S320, fitting a curve, wherein the fitting function is f (x) = a/(1 + np.exp (b-c x)), the output value of the fitting function corresponds to a corresponding concentration value, wherein x is the data length, and a, b and c are fitting coefficients obtained by calculation by a python iterative method according to the source data value and the source data quantity;
s330, eliminating initial fluctuation values, wherein in a stage (short time) of initial detection, the fluorescence photoelectric detection instrument is still in an unstable state, the initial fluctuation values with small values are arranged at the most front section of source data, belong to invalid interference data, have no reference value, influence the display effect of a fitted curve graph and need to be eliminated; the middle and rear sections of the source data are stable effective data, and the starting point value of the effective data is the lowest value of all the source data, and according to the characteristics of the source data, the specific eliminating method can comprise the following substeps: s331, finding the minimum value of the source data through the fitting function through an np.min function; s332, searching a subscript of the minimum value through circular traversal; s333, assigning all data (namely initial fluctuation values) in the source data before the subscript corresponding to the minimum value to be 0 to obtain intermediate processing data;
s340, smoothing the curve, specifically comprising the following substeps of S341, creating a first difference array; s342, forward differentiating the intermediate processing data twice through np.diff function and assigning the result to a first difference value array; s343, returning the average value of the first differential array through np.mean function; s344, setting a first threshold, judging whether the average value of the first difference array is smaller than the first threshold, if so, assigning all the first difference array to be 0, otherwise, returning the first difference array to the first difference array, and performing reverse calculation on data in the first difference array to obtain fitting data, wherein the first threshold is a parameter reasonably selected by considering both data precision and data smoothness, and can judge the speed of data change, and when the first threshold is smaller, the data change is proved to be smaller, the change can be ignored, the first threshold is 5-10, and the optimal value is 7.5;
s350, judging whether an inflection point appears, and specifically comprising the following substeps: s351, creating a second difference array; s352, forward differentiating the fitting data twice through an np.diff function and assigning a result to a second differential array; s353, traversing the loop to judge the product between the adjacent data in the second difference array, if one of the adjacent products is larger than 0 and the other is smaller than 0, judging that an inflection point appears, and if the data corresponding to the inflection point is called as inflection point data (time, fluorescence intensity corresponding to the time or od value), executing the next step, otherwise, returning to execute the step S310; s354, substituting the corresponding inflection point data into a fitting function to calculate a corresponding concentration value, and simultaneously outputting the fitting data and the inflection point data to an android main program.
In addition, it should be noted that, compared with the prior art, the method has great advantages, in the prior art, when the inflection point is calculated, an equation is generally formed between adjacent fitting data, a second-order derivative equation is calculated, then the fitting data are substituted into the second-order derivative equation to calculate the second-order derivative of each data, because the detection instrument generates more data and needs to acquire and calculate the data in real time, the operation amount is large, the operation speed can be greatly reduced and the instrument performance can be reduced by using the prior art, but the method only needs to judge the product between the adjacent data in the second difference array, the algorithm is relatively simple, the operation speed is high, and the judgment precision is not lost.
S400, the android main program stores the fitting data into a database;
s500, the android main program displays the fitting data and the inflection point data in a visual mode, for example, a detection curve (shown in figure 4) is displayed, inflection point data are labeled and reminded, if single-bacterium detection is carried out, detection can be stopped immediately when an experimenter sees that the inflection point appears on the detection curve or the inflection point is reminded by a testing system, and if multi-bacterium detection is carried out, detection is stopped after all inflection points appear. The android main program can identify the inflection point at the first time and prompt an experimenter, so that the detection speed is increased. In other embodiments, step S600 may be added after step S500, and the android main program automatically stops the detection of the fluorescence photoelectric detection instrument after the inflection point appears, without the operation of a laboratory technician.
Example two:
the difference between the second embodiment and the first embodiment is as follows: step S301 is further included before step S310, as shown in fig. 3, for roughly pre-determining whether an inflection point trend occurs, specifically including the following steps: s3011, importing source data at a fixed time interval delta t, wherein delta t is more than or equal to 5 and less than or equal to 10min;
s3012, creating a third difference array;
s3013, carrying out forward difference on the source data for the second time through an np.diff function, and assigning a result to a third difference array;
s3014, setting a second threshold, if the second forward difference is larger than the previous difference (for roughly pre-judging whether the trend of the inflection point appears), executing the step S310, otherwise, returning to the step S3011, wherein the second threshold is a numerical value which is determined according to multiple tests and can identify the trend of the inflection point. The reason for adding the above steps is that: because the fluorescence photoelectric detector is sent once every 50 milliseconds, each strain has a growth period, the detection duration of the fluorescence photoelectric detector generally has an inflection point after several hours, if data are read and operated in real time, the source data and the operation amount are very large, the source data are introduced at a time interval delta t and operated in consideration of the hardware memory and the operation speed, and when the inflection point trend occurs, the source data are continuously read and operated in real time to accurately capture the inflection point, so the operation times can be reduced, and the operation speed is further improved. The processing system of the fluorescence photoelectric detection instrument for predicting the detection time, as shown in fig. 4, comprises a data acquisition module, a subprogram calling module, a data processing module, a data storage module and a data display module.
A data acquisition module, configured to acquire source data generated by the detection instrument, corresponding to step S100;
wherein the data processing module further comprises:
an inflection point rough pre-judging module, configured to roughly pre-judge whether an inflection point trend occurs, corresponding to step S301;
a fitting curve module for fitting the source data, corresponding to step S320;
a fluctuation value elimination module for eliminating the initial fluctuation value, corresponding to step S330;
a smooth curve module, configured to smooth the final curve, corresponding to step S340;
and an inflection point determining module, configured to determine an identified inflection point, corresponding to step S350.
A subprogram calling module for calling the python subprogram, corresponding to the step S200;
a data processing module, configured to process the source data and identify an inflection point, corresponding to step S300;
a data storage module, configured to store the fitted data, corresponding to step S400;
and a data display module, configured to perform visual display on the fitting data, corresponding to step S500.
Comparative test
The method of the invention is used for respectively testing the concentration of escherichia coli, the concentration of pseudomonas aeruginosa and the total number of colonies of mixed strains and comparing with the prior art:
comparison test one: detection of Escherichia coli
Injecting escherichia coli samples with different concentrations into a reagent bottle ready for detection, properly shaking and uniformly mixing, and putting the reagent bottle into a detector; and selecting strains, marking samples and other information on the Android control panel, inputting the information, starting detection after the setting is finished, and obtaining the detection result shown in table 1.
TABLE 1
Comparative experiment two: pseudomonas aeruginosa detection
Injecting the pseudomonas aeruginosa samples with different concentrations into a reagent bottle ready for detection, properly shaking and uniformly mixing, and putting the reagent bottle into a detector; and selecting strains, marking samples and other information on the Android control panel for inputting, starting detection after the setting is finished, and obtaining detection results shown in table 2.
TABLE 2
And (4) a third comparative test: total colony count assay
Injecting any bacteria sample (only adding escherichia coli, or mixing multiple bacteria) with different concentrations into a reagent bottle for immediate examination, shaking properly and mixing uniformly, and putting the reagent bottle into a detector; and selecting strains, marking samples and other information on the Android control panel for inputting, starting detection after the setting is finished, and obtaining detection results shown in table 3.
TABLE 3
And (4) test conclusion: after the method is applied, an experimenter does not need to estimate time, the experiment can be stopped immediately after the inflection point appears, the actual detection time is the time when the inflection point appears, in the prior art, the actual detection time is the estimated time of the experimenter, and the comparison data in the tables 1, 2 and 3 show that the time estimated by the experimenter for each concentration is 48 hours before the method is applied and when the total number of escherichia coli, pseudomonas aeruginosa and bacterial colonies is detected, but the detection time of each strain and each concentration is obviously reduced by applying the method, particularly the detection time is obviously reduced under the condition of higher concentration.
The foregoing description is only of the preferred embodiments of the present invention, and it should be understood that the described embodiments are only a few, and not all, of the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Claims (7)
1. A processing method of a fluorescence photoelectric detection instrument about prediction detection time is characterized by comprising the following steps:
s100, acquiring multiple groups of source data in real time by an android main program;
s200, calling a python subprogram by the android main program;
s300, the python subprogram respectively carries out data processing on the multiple groups of source data to obtain multiple groups of fitting data, whether inflection points appear on the multiple groups of fitting data is respectively judged, if the inflection points appear on the multiple groups of fitting data, the next step is executed, and if not, the step S100 is returned;
s400, the android main program stores the fitting data into a database;
s500, the android main program displays the fitting data and the inflection point data in a visual mode;
the data processing and inflection point determination in step S300 specifically includes the following sub-steps:
s310, importing source data in real time;
s320, fitting a curve;
s330, eliminating an initial fluctuation numerical value;
s340, smoothing a curve;
s350, judging whether an inflection point appears;
in step S320, the fitting function is f (x) = a/(1 + np.exp (b-c × x)), the output value of the fitting function corresponds to the corresponding concentration value, where x is the data length of the source data, and a, b, and c are fitting coefficients calculated by a python iteration method according to the source data value and the source data amount.
2. The method for processing a fluorescence photoelectric detection instrument according to claim 1, wherein step S330 specifically includes the following sub-steps:
s331, finding the minimum value of the source data through the fitting function through an np.min function;
s332, searching a subscript of the minimum value through circular traversal;
and S333, assigning all data before the subscript corresponding to the minimum value in the source data to be 0 to obtain intermediate processing data.
3. The method for processing the predicted detection time of the fluorescence photoelectric detection instrument according to claim 2, wherein the step S340 specifically comprises the following sub-steps:
s341, creating a first difference array;
s342, forward differentiating the intermediate processing data twice through np.diff function and assigning the result to a first difference array;
s343, returning the average value of the first differential array through an np.mean function;
and S344, setting a first threshold, judging whether the average value of the first difference array is smaller than the first threshold, if so, assigning all the first difference array to be 0, otherwise, returning the first difference array to the first difference array, and performing reverse calculation on the data in the first difference array to obtain fitting data, wherein the first threshold is a parameter reasonably selected by considering both data precision and data smoothness, and can judge the speed of data change, and the first threshold is 5-10.
4. The method for processing a fluorescence photoelectric detection instrument according to claim 3, wherein the first threshold of step S344 is 7.5.
5. The method for processing the predicted detection time of the fluorescence photoelectric detection instrument according to claim 4, wherein the step S350 comprises the following sub-steps:
s351, creating a second difference array;
s352, forward differentiating the fitting data twice through an np.diff function and assigning a result to a second differential array;
s353, products between adjacent data in the second difference array are judged through traversal loop, if one of the adjacent products is larger than 0 and the other one is smaller than 0, an inflection point is judged to appear, the data corresponding to the inflection point is called as inflection point data, the next step is executed, and if not, the step S310 is executed;
s354, substituting the corresponding inflection point data into a fitting function to calculate a corresponding concentration value, and simultaneously outputting the fitting data and the inflection point data to an android main program.
6. The method for processing the fluorescence photoelectric detection instrument according to claim 1, further comprising a step S301 for roughly predicting whether an inflection point trend appears before step S310, and specifically comprising the steps of:
s3011, importing source data at a fixed time interval delta t, wherein delta t is more than or equal to 5 and less than or equal to 10min;
s3012, creating a third difference array;
s3013, carrying out forward difference on the source data for the second time through an np.diff function, and assigning a result to a third difference array;
s3014, setting a second threshold, if the difference between the two forward differences is larger than the second threshold, executing the step S310, otherwise, returning to execute the step S3011.
7. A processing system of a fluorescence photoelectric detection instrument for predicting detection time is used for realizing the method of claim 6, and is characterized by comprising a data acquisition module, a subprogram calling module, a data processing module, a data storage module and a data display module;
a data acquisition module, configured to acquire source data generated by the detection instrument, corresponding to step S100;
a subprogram calling module for calling the python subprogram, corresponding to the step S200;
a data processing module, configured to process the source data and identify an inflection point, corresponding to step S300;
a data storage module, configured to store the fitted data, corresponding to step S400;
a data display module, configured to fit data for visual display, corresponding to step S500;
the data processing module further comprises:
an inflection point rough pre-judging module, configured to roughly pre-judge whether an inflection point trend occurs, corresponding to step S310;
a fitting curve module for fitting the source data, corresponding to step S320;
a fluctuation value elimination module for eliminating the initial fluctuation value, corresponding to step S330;
a smooth curve module, configured to smooth the final curve, corresponding to step S340;
an inflection point determining module, configured to determine an identified inflection point, corresponding to step S350.
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