CN115728286A - Multi-dimensional bacteria spectrum acquisition method, bacteria identification method and application equipment - Google Patents
Multi-dimensional bacteria spectrum acquisition method, bacteria identification method and application equipment Download PDFInfo
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Abstract
The invention belongs to the technical field of bacteria identification, and discloses a multi-dimensional bacteria spectrum acquisition method, a bacteria identification method and application equipment, wherein the multi-dimensional bacteria spectrum acquisition method comprises the following steps: the method comprises the steps of cracking a bacterial suspension by using ultrasonic waves to obtain a bacterial lysate, mixing a plurality of surface enhanced Raman scattering concentrated suspension substrates with the bacterial lysate to obtain a plurality of mixed solutions, forming a liquid film by using the plurality of mixed solutions, and performing surface enhanced Raman scattering analysis on the liquid film to obtain multi-dimensional surface enhanced Raman scattering bacterial spectrum data. The invention can obtain the multi-dimensional spectral information of bacteria, thereby realizing the rapid and accurate detection and identification of the bacteria sample.
Description
Technical Field
The invention belongs to the technical field of bacteria identification, and particularly relates to a multi-dimensional bacteria spectrum acquisition method, a bacteria identification method and application equipment.
Background
In the life health field, the method has important significance in quickly and accurately detecting the information of different types of bacteria. The existing microorganism detection technologies, such as polymerase chain reaction, enzyme-linked immunosorbent assay, mass spectrometry and the like, can realize accurate identification of microorganisms, but the identification process is time-consuming and labor-consuming, and for the detection of a composite sample containing multiple bacteria, the existing technologies can only detect a few bacterial species of the composite sample.
Disclosure of Invention
The embodiment of the application provides a multi-dimensional bacteria spectrum acquisition method, a bacteria identification method and application equipment, which can acquire multi-dimensional spectrum information of bacteria and realize quick and accurate detection and identification of bacteria samples.
In a first aspect, an embodiment of the present application provides a method for obtaining a multidimensional bacterial spectrum, including:
using ultrasonic waves to crack the bacterial suspension to obtain bacterial lysate;
respectively mixing a plurality of surface enhanced Raman scattering concentrated suspension liquid substrates and the bacterial lysate to obtain a plurality of mixed liquids;
forming a liquid film by using the plurality of mixed liquids;
and carrying out surface enhanced Raman scattering analysis on the liquid film to obtain multi-dimensional surface enhanced Raman scattering bacteria spectral data.
In a second aspect, an embodiment of the present application provides a bacteria identification method implemented based on the multidimensional bacteria spectrum acquisition method described in the first aspect, including:
constructing a multidimensional surface enhanced Raman scattering bacteria spectrum database by utilizing the multidimensional surface enhanced Raman scattering bacteria spectrum data;
building a deep learning model;
training the deep learning model by utilizing the multidimensional bacteria surface enhanced scattering spectrum database to obtain a trained deep learning model;
acquiring a single-dimensional Surface Enhanced Raman Scattering (SERS) bacterial spectrum of bacteria to be detected;
and inputting the single-dimensional surface enhanced Raman scattering data of the bacteria to be detected into a trained deep learning model, and outputting the types and subtypes of the bacteria to be detected by the trained deep learning model.
In a third aspect, an embodiment of the present application provides a bacteria identification apparatus, including:
the database construction module is used for constructing a multi-dimensional surface enhanced Raman scattering bacteria spectrum database by utilizing the multi-dimensional surface enhanced Raman scattering bacteria spectrum data;
the model building module is used for building a deep learning model;
the model training module is used for training the deep learning model by utilizing the multidimensional bacteria surface enhanced scattering spectrum database to obtain a trained deep learning model;
the data acquisition module is used for acquiring a single-dimensional surface enhanced Raman scattering bacterial spectrum of the bacteria to be detected;
and the bacteria identification module is used for inputting the single-dimensional surface enhanced Raman scattering data of the bacteria to be detected into the trained deep learning model, and the trained deep learning model outputs the type and subtype of the bacteria to be detected.
In a fourth aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the steps of the bacteria identification method according to the second aspect of the embodiment of the present invention.
In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the bacteria identification method according to the second aspect of the present invention.
In a sixth aspect, the present application provides a computer program product, which when run on a terminal device, causes the terminal device to perform the steps of the bacteria identification method according to the second aspect.
According to the method for acquiring the multi-dimensional bacteria spectrum provided by the first aspect of the invention, the bacteria lysate is obtained by cracking the bacteria suspension liquid with ultrasonic waves, a plurality of surface enhanced Raman scattering concentrated suspension liquid substrates and the bacteria lysate are respectively mixed to obtain a plurality of mixed liquids, the plurality of mixed liquids are utilized to form a liquid film, and the liquid film is subjected to surface enhanced Raman scattering analysis to obtain multi-dimensional surface enhanced Raman scattering bacteria spectrum data. The invention can obtain the multi-dimensional spectral information of bacteria and realize the rapid and accurate detection and identification of bacteria samples.
It is to be understood that, for the beneficial effects of the second aspect to the sixth aspect, reference may be made to the relevant description in the first aspect, and details are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart of a multi-dimensional bacterial spectrum acquisition method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a purchase list of 17 bacteria including bacterial subtypes, provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of an SERS spectrum of Shigella shigella 1 under different substrates according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of SERS spectra of Shigella dysenteriae type 2 under different substrates according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of SERS spectra of Escherichia coli under different substrates according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of SERS spectra of Bacillus faecalis under different substrates according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of the principal component analysis of Shigella shigella 1 under different substrates according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of the principal component analysis of Shigella shigella type 2 dysentery bacterium under different substrates according to the present invention;
FIG. 9 is a schematic diagram of principal component analysis of Escherichia coli on different substrates according to the present invention;
FIG. 10 is a schematic diagram showing the analysis of the principal components of the Bacillus faecalis on different substrates according to the present invention;
FIG. 11 is a schematic diagram of a first process for preparing a plurality of substrates for surface enhanced Raman scattering concentrated suspensions according to an embodiment of the present invention;
FIG. 12 is a schematic view of a first flowchart of a bacteria identification method according to an embodiment of the present invention;
FIG. 13 is a schematic diagram of model identification accuracy of 2-dimensional data provided by an embodiment of the present invention;
FIG. 14 is a schematic diagram of model identification accuracy of 5-dimensional data provided by an embodiment of the invention;
FIG. 15 is a schematic diagram of model identification accuracy of 6-dimensional data provided by an embodiment of the invention;
FIG. 16 is a schematic diagram of a first process for obtaining a single-dimensional Surface Enhanced Raman Scattering (SERS) spectrum of a bacterium to be detected according to an embodiment of the present invention;
FIG. 17 is a schematic structural view of a bacteria recognition apparatus according to an embodiment of the present invention;
fig. 18 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The bacteria identification method implemented based on the multi-dimensional bacteria spectrum acquisition method in the first aspect is executed by a processor of a terminal device when a computer program with corresponding functions is run, a multi-dimensional surface enhanced raman scattering bacteria spectrum database is constructed by using the multi-dimensional surface enhanced raman scattering bacteria spectrum data, a deep learning model is built, the multi-dimensional surface enhanced raman scattering bacteria spectrum database is used for training the deep learning model to obtain a trained deep learning model, a single-dimensional surface enhanced raman scattering bacteria spectrum of a to-be-detected bacteria is acquired, the single-dimensional surface enhanced raman scattering data of the to-be-detected bacteria is input into the trained deep learning model, the type and subtype of the to-be-detected bacteria are output by the trained deep learning model, and rapid and accurate detection and identification of a bacteria sample can be achieved.
In application, the terminal device may be a computing device capable of implementing a data processing function, such as a Tablet Personal Computer (Tablet PC), a notebook Computer (Laptop), a Personal Computer (PC), or a cloud Server (Server), and the specific type of the terminal device is not limited in this embodiment of the application.
As shown in fig. 1, in an embodiment, the method for obtaining a multidimensional bacterial spectrum provided in the embodiment of the present application includes the following steps S101 to S104:
step S101, using ultrasonic wave to crack the bacterial suspension to obtain bacterial cracking liquid, and entering step S102.
In application, an ultrasonic cell disruption instrument can be utilized to carry out ultrasonic lysis on bacteria to obtain a bacterial lysate.
In one embodiment, the bacterial suspension comprises a suspension of at least one of shigella flexneri, shigella dysenteriae, staphylococcus aureus, bacillus cereus, bacillus thuringiensis, escherichia coli, and enterococcus faecalis.
In application, the bacteria of the bacterial suspension are purchased from China center for Industrial culture Collection of microorganisms, and total 17 bacteria including bacterial subtypes are shown in FIG. 2, which shows a schematic purchase list of 17 bacteria including bacterial subtypes.
Step S102, mixing a plurality of surface enhanced Raman scattering concentrated suspension liquid substrates and the bacterial lysate respectively to obtain a plurality of mixed liquids, and entering step S103.
In application, a certain number of surface enhanced raman scattering concentrated suspensions (e.g., 2, 3,6, etc., and the specific number can be adjusted according to actual needs) can be selected optionally, and the certain number of surface enhanced raman scattering concentrated suspensions and the bacterial lysate are poured into a container (e.g., a centrifuge tube, etc.) respectively and mixed to obtain a plurality of mixed solutions.
In step S103, a liquid film is formed using the plurality of mixed liquids, and the process proceeds to step S104.
In application, the multiple mixed solutions can be respectively dripped on the hydrophobic surface of a hydrophobic carrier (such as teflon tape) to obtain liquid droplets of the mixed solutions, and then the liquid droplets are adsorbed on the surface of a liquid film preparation plate (such as steel plate surface with circular through holes) to form a liquid film.
And S104, performing surface enhanced Raman scattering analysis on the liquid film to obtain multi-dimensional surface enhanced Raman scattering bacteria spectral data.
In application, a Raman spectrometer can be used for selecting a certain range area of a liquid film, setting a test point and automatically collecting all surface enhanced Raman scattering data of a sample in the selected area. For example, a 5 x 5 micron region can be selected on the formed liquid film, a certain step size (e.g., 0.1um, 0.2um, etc.) is set, the raman spectrometer is started, and the raman spectrometer automatically acquires all the surface enhanced raman scattering data of the sample points in the selected region, so that the rapid acquisition of a large amount of surface enhanced raman scattering data is realized.
FIG. 3, FIG. 4, FIG. 5, and FIG. 6 are respectively the SERS spectra of Shigella dysenteriae type 1, shigella dysenteriae type 2, escherichia coli, and enterococcus faecalis on different substrates, wherein the horizontal axis represents the Raman shift in cm -1 The ordinate represents normalized intensity, and the ordinate represents dimensionless, and the corresponding substrates of the 4 spectral curves are, from top to bottom, a silver nano-substrate modified based on 11-Mercapto-undecanoic Acid (MUA), a silver nano-substrate modified based on 11-Mercapto-1-Undecanol (11-Mercapto-1-undeanol, MUO), a silver nano-substrate modified by 4-Mercaptophenylboronic Acid (4-Mercaptophenylboronic Acid, 4B), and a silver nano-substrate modified by 3-Mercaptopropionic Acid (3 COOH), respectively.
FIG. 7, FIG. 8, FIG. 9, and FIG. 10 are schematic diagrams showing the analysis of the main components of Shigella dysenteriae type 1, shigella dysenteriae type 2, escherichia coli, and enterococcus faecalis on different substrates, respectively, and the analysis results on the different substrates are marked with MUA, MUO, 4B, and 3COOH, respectively.
In one embodiment, step S102 is preceded by:
various surface enhanced raman scattering concentrated suspension substrates were prepared.
In application, a plurality of different types or structures of noble metal nano materials and a plurality of different modifiers are utilized to prepare a plurality of functionalized modified surface enhanced Raman scattering concentrated suspension liquid substrates.
In one embodiment, as shown in fig. 11, the preparing the plurality of surface enhanced raman scattering concentrated suspension substrates includes the following steps S201 to S202:
step S201, precious metal nanomaterials of different types or structures are obtained, and the process proceeds to step S102.
In application, the types of the noble metal nano material can comprise silver nano, gold nano and the like, and the structure of the noble metal nano material can comprise gold-silver alloy, a silver-core gold shell and the like.
And S202, modifying the noble metal nano materials with different types or structures by utilizing a plurality of self-assembled monomolecular layers to obtain a plurality of surface enhanced Raman scattering concentrated suspension liquid substrates.
In applications, the self-assembled monolayer may be 11-Mercapto-1-Undecanol (11-Mercapto-1-Undecanol, MUO), 11-Mercapto-undecanoic Acid (11-Mercapto-undecanoic Acid, MUA), 1-Dodecanethiol (1-Dodecanethiol, DT), 4-Mercaptophenylboronic Acid (4-mercaptylphenylboronic Acid, 4B), 3-Mercaptopropionic Acid (3-Mercaptopropionic Acid,3 COOH), etc.
In the application, taking the modification of the silver nanoparticles by using various self-assembled monolayers as an example, the following processes can be included:
preparing a certain amount of 1mM Ag NPs suspension;
1mL of 1mM Ag NPs suspension is added into the centrifuge tubes respectively, and then a proper amount of various modifier solutions are added into the centrifuge tubes respectively;
rotating the centrifugal tube for 20s, and then standing for 30-40 min;
after standing, centrifuging the centrifuge tube at 10000rpm for 10min;
after the centrifugation is finished, transferring 980uL of supernatant in the centrifuge tube by using a pipette gun, and adding 1mL of ethanol into the centrifuge tube for washing the residual modifier;
centrifuging the centrifuge tube at 10000rpm for 10min;
after the centrifugation is finished, transferring 980uL of supernatant in the centrifugal tube by using a liquid transfer gun, and then adding 1mL of deionized water for resuspension and precipitation;
centrifuging the centrifuge tube at 10000rpm for 10min;
after centrifugation is finished, 960uL of supernatant in the centrifuge tube is removed by a pipette, and a plurality of surface enhanced Raman scattering concentrated suspension substrates are obtained.
For example, modifying silver nanoparticles with 4-mercaptophenylboronic acid may include the following process:
preparing a certain amount of 1mM silver nano colloidal solution reduced by sodium citrate, and preparing 0.1% of 4-mercaptophenylboronic acid ethanol solution;
1mL of 1mM silver nano colloidal solution and 10uL of 0.1% 4-mercaptophenylboronic acid ethanol solution are added into a 1.5mL centrifuge tube, and after the addition is finished, the centrifuge tube is immediately placed into a vortex mixer for 10s to 15s;
after the vortex is finished, standing the centrifugal tube for 10min;
after standing, putting the centrifuge tube into a high-speed freezing centrifuge, setting the rotating speed of the high-speed freezing centrifuge to 10000rpm, and setting the centrifugation time to 10min, finishing the modification and concentration of the silver nanoparticles by 4-mercaptophenylboronic acid, and obtaining modified silver nanoparticles;
after the centrifugation is finished, transferring the supernatant in the centrifugal tube by using a liquid transfer gun, then adding 1mL of ultrapure water into the centrifugal tube, putting the centrifugal tube into an ultrasonic cleaner, and uniformly dispersing the modified silver nanoparticles in the ultrapure water through ultrasonic treatment;
after the ultrasonic treatment is finished, putting the centrifugal tube into a high-speed freezing centrifuge again, setting the rotating speed of the high-speed freezing centrifuge to 10000rpm, and setting the centrifugation time to 10min to finish washing the unreacted 4-mercaptophenylboronic acid modifier in the centrifugal tube;
after the centrifugation is finished, transferring the supernatant in the centrifugal tube by using the liquid transferring gun again, then adding 1mL of ultrapure water into the centrifugal tube again, putting the centrifugal tube into a high-speed freezing centrifuge, setting the rotating speed of the high-speed freezing centrifuge to 10000rpm, and setting the centrifugation time to 10min, thereby finishing the washing of the absolute ethyl alcohol which cannot form a liquid film in the centrifugal tube through the step S103;
after the centrifugation is finished, the supernatant in the centrifugal tube is removed by using the pipette again to obtain the silver nano particles modified by the 4-mercaptophenylboronic acid.
In one embodiment, before step S102, the method further includes:
the bacteria are cultured to obtain a bacterial suspension.
In application, culturing the bacteria to obtain a bacterial suspension may comprise the following processes:
culturing the bacteria in 100mL sterile nutrient broth for 24h at 37 ℃ to obtain cultured bacteria;
after the culture is finished, mixing the cultured bacteria with a certain amount of 0.9% NaCl solution by using vortex, and then centrifuging the cultured bacteria, wherein the centrifugal speed is 4000rpm, and the centrifugal time is 5min;
after centrifugation is finished, washing the bacteria for 2 times by using 10mL of 0.9% NaCl aqueous solution, and removing a growth medium;
after washing, the bacteria were stored in 0.9% NaCl solution, in which the cell Density of the cells was measured by Optical Density (OD) 600, the OD600 value was 1.0, and the storage temperature was 4 ℃ to obtain a bacterial suspension.
In application, the preparation of the various surface enhanced raman scattering concentrated suspension substrates and the cultivation of the bacteria and the obtaining of the bacterial suspension are not limited in the implementation sequence. For example, the preparing of the plurality of surface enhanced raman scattering concentrated suspension substrates and the culturing of the bacteria to obtain the bacterial suspension may be performed simultaneously, or the preparing of the plurality of surface enhanced raman scattering concentrated suspension substrates and the culturing of the bacteria to obtain the bacterial suspension may be performed first, or the culturing of the bacteria to obtain the bacterial suspension may be performed first, and then the preparing of the plurality of surface enhanced raman scattering concentrated suspension substrates may be performed.
As shown in fig. 12, in an embodiment, the bacteria identification method implemented based on the multi-dimensional bacteria spectrum obtaining method provided in the embodiment of the present application includes the following steps S301 to S305:
s301, establishing a multidimensional surface enhanced Raman scattering bacteria spectrum database by using the multidimensional surface enhanced Raman scattering bacteria spectrum data, and entering S302.
In application, each substrate can acquire surface enhanced Raman scattering data of one dimension, and multiple substrates can acquire surface enhanced Raman scattering data of multiple dimensions, so that each bacterium and multiple substrates are combined to acquire the surface enhanced Raman scattering data of the multiple dimensions of the bacterium, and the surface enhanced Raman scattering spectrum database of the multiple dimensions of the bacterium is constructed by summarizing the surface enhanced Raman scattering data of the multiple dimensions of the bacterium.
Step S302, building a deep learning model, and entering step S303.
In one embodiment, the deep learning model includes a one-dimensional convolution structure and a multi-layered perceptron structure;
the one-dimensional convolution structure is used for extracting bacterial spectral features;
the multilayer perceptron structure is used for classifying bacteria.
In application, the deep learning model may include a main framework of a one-dimensional Convolution (Convolution) structure for extracting high-dimensional spatial features from input spectral data and a multi-layered Perceptron (Perceptron) structure for nonlinear transformation of features and outputting classification results of bacteria.
In application, the deep learning model may include a 29-layer network structure, wherein the one-dimensional convolution structure may include a one-dimensional convolution layer, an Activation (Activation) layer, a one-dimensional Pooling (Pooling) layer, a drop (Dropout) layer, a flattening (Flatten) layer, and the like, and the multi-layer perceptron may include a 2-layer perceptron, a 2-layer Activation layer, and the like.
In application, the number of convolution kernels of 2 layers of convolution layers at the input end of the one-dimensional convolution structure is set to be the same as the dimensionality number of input spectrum data, the convolution kernels are used for learning the characteristics of a multi-dimensional spectrum, meanwhile, the size of the convolution kernels is set to be larger, and an expansion convolution technology is adopted, so that the mutual relation of adjacent spectrum signals in a larger range can be represented conveniently; the convolution kernels of the other convolution layers of the one-dimensional convolution structure are set to be 3 x 3 in small size, so that the network model can be conveniently integrated into more nonlinear characterization processes, the high-dimensional feature extraction capability of the network model is further improved, and the parameter number and the calculation complexity of the network model are greatly reduced, and the continuous deepening of the network is facilitated.
In application, a Flatten layer is arranged at the last layer of the one-dimensional convolution structure and is used for expanding high-dimensional features along one dimension; and an activation layer adopting a Softmax activation function is arranged at the last layer of the multilayer perceptron and is used for finishing bacteria classification.
In application, dropout layers are respectively added in the deeper layer of the one-dimensional convolution structure and the multilayer perceptron structure, and are used for improving the effectiveness of gradient back propagation during network model training and enabling the optimization process of network model parameters to be more easily converged.
Step S303, training the deep learning model by using the multidimensional bacterial surface enhanced scattering spectrum database to obtain a trained deep learning model, and entering step S304.
In application, the spectral data composed of at least two or more different substrates can be obtained from the multidimensional bacterial surface enhanced scattering spectral database and used as a data set of a deep learning model, and the deep learning model is trained to obtain the trained deep learning model. For example, taking 6-dimensional spectral data as an example, optionally 6 different substrates form a 6-dimensional spectral data set, 150 pieces of spectral data can be acquired for each 1 substrate material, 15300 pieces of spectral data in 6 dimensions, 15300 pieces of data are divided into a training set and a verification set, and the ratio can be set to be 7.
In application, as shown in fig. 13, 14, and 15, schematic diagrams of model identification accuracy of 2-dimensional, 5-dimensional, and 6-dimensional data are respectively exemplarily shown, where 2D, 5D, and 6D in row 1 of the table in the diagrams respectively represent 2-dimensional, 5-dimensional, and 6-dimensional data, where the numerical values in row 2 of the table respectively represent the model accuracy of a single substrate, the chemical formulas in row 3 respectively represent different substrates, and Ag NP respectively represents different substrates S Is silver nano-particle reduced by sodium citrate, 11COOH is silver nano-particle modified by 11-Mercapto undecanoic Acid (11 COOH), 11OH is silver nano-particle modified by 11-Mercapto-1-Undecanol (11-Mercapto-1-Undecanol, 11 OH), 12CH3 is silver nano-particle modified by 1-Dodecanethiol (1-Dodecanethiol, 12CH 3), 3COOH is silver nano-particle modified by 3-mercaptopropionic Acid, and 3OH is silver nano-particle modified by 3-Mercapto-1-propanol (3-Mercapto-1-Propanol,3 OH), 4B is a 4-mercaptophenylboronic acid modified silver nanoparticle, the values in the last column of the table respectively represent the model accuracy when multiple substrates are used, the multiple substrates used fill the corresponding multiple substrates with the shadow of each row.
Step S304, acquiring a single-dimensional surface enhanced Raman scattering bacterial spectrum of the bacteria to be detected, and entering step S305.
As shown in fig. 16, in one embodiment, the acquiring a single-dimensional surface enhanced raman scattering bacterial spectrum of a bacterium to be detected includes the following steps S401 to S404:
step S401, obtaining the suspension of bacteria to be detected, and entering step S402.
In application, the bacteria to be detected can be collected on the site where the bacteria are required to be detected, and then the bacteria suspension to be detected is prepared.
S402, using ultrasonic waves to crack the bacterial suspension to be detected to obtain a bacterial lysate to be detected, and entering S403.
In application, an ultrasonic cell disruptor can be used for carrying out ultrasonic disruption on bacteria to obtain a bacteria lysate.
Step S403, mixing the bacterial lysate to be detected and any one of the surface enhanced Raman scattering concentrated suspension bases prepared in advance to form a liquid film, and entering step S404.
And S404, carrying out surface enhanced Raman scattering analysis on the liquid film to obtain a single-dimensional surface enhanced Raman scattering bacterial spectrum.
In application, the specific implementation steps of step S403 and step S404 may refer to the related descriptions of step S102, step S103, and step S104, and are not described herein again.
Step S305, inputting the single-dimensional surface enhanced Raman scattering data of the bacteria to be detected into a trained deep learning model, and outputting the types and subtypes of the bacteria to be detected by the trained deep learning model.
In application, the single-dimensional surface enhanced Raman scattering data of the bacteria to be detected are input into the trained deep learning model, the trained deep learning model can output specific types of the bacteria to be detected, and the accuracy degree can reach the subtype level.
The embodiment of the application also provides a bacteria identification device which is used for executing the steps in the bacteria identification method embodiment. The device may be a Virtual Appliance (Virtual Appliance) in the bacteria recognition apparatus, which is run by a processor of the bacteria recognition apparatus, or may be the bacteria recognition apparatus itself.
As shown in fig. 17, a bacteria recognition apparatus 100 according to an embodiment of the present application includes:
the database construction module 101 is used for constructing a multidimensional surface enhanced Raman scattering bacteria spectrum database by using the multidimensional surface enhanced Raman scattering bacteria spectrum data, and the multidimensional surface enhanced Raman scattering bacteria spectrum database enters the model construction module 102;
the model building module 102 is used for building a deep learning model and entering the model training module 103;
the model training module 103 is used for training the deep learning model by using the multidimensional bacterial surface enhanced scattering spectrum database to obtain a trained deep learning model, and the trained deep learning model enters the data acquisition module 104;
the data acquisition module 104 is used for acquiring a single-dimensional surface enhanced raman scattering bacterial spectrum of the bacteria to be detected and entering the bacteria identification module 105;
and the bacteria identification module 105 is used for inputting the single-dimensional surface enhanced Raman scattering data of the bacteria to be detected into the trained deep learning model, and the trained deep learning model outputs the types and subtypes of the bacteria to be detected.
In application, each unit in the above apparatus may be a software program module, may be implemented by different logic circuits integrated in a processor or by a separate physical component connected to the processor, and may also be implemented by a plurality of distributed processors.
As shown in fig. 18, an embodiment of the present application further provides a terminal device 200, including: at least one processor 201 (only one processor is labeled in the figure), a memory 202, and a computer program 203 stored in the memory 202 and operable on the at least one processor 201, wherein the processor 201 implements the steps of any of the above embodiments of the bacteria identification method when executing the computer program 203.
In an application, the bacteria identification device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that fig. 18 is merely an example of a bacteria identification device, and does not constitute a limitation of the bacteria identification device, and may include more or less components than those shown, or some components may be combined, or different components may be included, for example, an input/output device, a network access device, and the like.
In an Application, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In some embodiments, the memory may be an internal storage module of the bacteria identification device, for example, a hard disk or a memory of the bacteria identification device. The memory may also be an external storage device of the bacteria recognition device in other embodiments, such as a plug-in hard disk provided on the bacteria recognition device, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and so on. Further, the memory may also include both an internal memory module of the bacteria identification device and an external memory device. The memory is used for storing an operating system, application programs, a Boot Loader (Boot Loader), data, and other programs, such as program codes of the computer programs. The memory may also be used to temporarily store data that has been output or is to be output.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus may be divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned embodiments of the bacteria identification method.
The embodiment of the present application provides a computer program product, which when running on a terminal device, enables the terminal device to implement the steps in the above-mentioned bacteria identification method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/testing device, a recording medium, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A multidimensional bacterial spectrum acquisition method is characterized by comprising the following steps:
using ultrasonic waves to crack the bacterial suspension to obtain bacterial lysate;
respectively mixing a plurality of surface enhanced Raman scattering concentrated suspension liquid substrates and the bacterial lysate to obtain a plurality of mixed liquids;
forming a liquid film by using the plurality of mixed liquids;
and carrying out surface enhanced Raman scattering analysis on the liquid film to obtain multi-dimensional surface enhanced Raman scattering bacteria spectral data.
2. The method for obtaining multidimensional bacterial spectra according to claim 1, wherein the bacterial suspension comprises a suspension of at least one of shigella flexneri, shigella dysenteriae, staphylococcus aureus, bacillus cereus, bacillus thuringiensis, escherichia coli, and enterococcus faecalis.
3. The method for acquiring a multidimensional bacterial spectrum according to claim 1 or 2, wherein before the step of mixing the plurality of surface enhanced raman scattering concentrated suspension substrates and the bacterial lysate respectively to obtain a plurality of mixed solutions, the method comprises:
various surface enhanced raman scattering concentrated suspension substrates were prepared.
4. The method for obtaining multidimensional bacterial spectra according to claim 3, wherein the preparing a plurality of surface enhanced Raman scattering concentrated suspension substrates comprises:
obtaining noble metal nano materials of different types or structures;
and modifying the noble metal nano materials with different types or structures by utilizing various self-assembled monomolecular layers to obtain various surface enhanced Raman scattering concentrated suspension liquid substrates.
5. A bacteria identification method realized based on the multi-dimensional bacteria spectrum acquisition method of any one of claims 1 to 4, which is characterized by comprising the following steps:
constructing a multidimensional surface enhanced Raman scattering bacteria spectrum database by utilizing the multidimensional surface enhanced Raman scattering bacteria spectrum data;
building a deep learning model;
training the deep learning model by utilizing the multidimensional bacteria surface enhanced scattering spectrum database to obtain a trained deep learning model;
acquiring a single-dimensional Surface Enhanced Raman Scattering (SERS) bacterial spectrum of bacteria to be detected;
and inputting the single-dimensional surface enhanced Raman scattering data of the bacteria to be detected into a trained deep learning model, and outputting the types and subtypes of the bacteria to be detected by the trained deep learning model.
6. The bacteria identification method according to claim 5, wherein the deep learning model includes a one-dimensional convolution structure and a multilayer perceptron structure;
the one-dimensional convolution structure is used for extracting the spectral characteristics of bacteria;
the multilayer perceptron structure is used for classifying bacteria.
7. The bacteria identification method of claim 5, wherein the obtaining of the single-dimensional surface enhanced Raman scattering bacteria spectrum of the bacteria to be detected comprises:
obtaining a bacterial suspension to be detected;
using ultrasonic waves to crack the bacterial suspension to be detected to obtain a bacterial lysate to be detected;
mixing the lysate of the bacteria to be detected and any surface enhanced Raman scattering concentrated suspension liquid substrate prepared in advance to form a liquid film;
and carrying out surface enhanced Raman scattering analysis on the liquid film to obtain a single-dimensional surface enhanced Raman scattering bacterial spectrum.
8. A bacteria identification device, comprising:
the database construction module is used for constructing a multi-dimensional surface enhanced Raman scattering bacteria spectrum database by utilizing the multi-dimensional surface enhanced Raman scattering bacteria spectrum data;
the model building module is used for building a deep learning model;
the model training module is used for training the deep learning model by utilizing the multidimensional bacterial surface enhanced scattering spectrum database to obtain a trained deep learning model;
the data acquisition module is used for acquiring a single-dimensional surface enhanced Raman scattering bacterial spectrum of the bacteria to be detected;
and the bacteria identification module is used for inputting the single-dimensional surface enhanced Raman scattering data of the bacteria to be detected into the trained deep learning model, and the trained deep learning model outputs the type and subtype of the bacteria to be detected.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the bacteria identification method according to any one of claims 5 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the bacteria identification method according to any one of claims 5 to 7.
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| CN117697788A (en) * | 2024-01-16 | 2024-03-15 | 浙江大学 | Traditional Chinese medicine production environment inspection robot and microorganism content determination method |
| CN117697788B (en) * | 2024-01-16 | 2024-06-11 | 浙江大学 | Traditional Chinese medicine production environment inspection robot and microorganism content determination method |
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