CN113505816A - Microorganism identification method based on gold nanoparticles and machine learning algorithm - Google Patents
Microorganism identification method based on gold nanoparticles and machine learning algorithm Download PDFInfo
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- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 title claims abstract description 44
- 238000010801 machine learning Methods 0.000 title claims abstract description 42
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- 238000004458 analytical method Methods 0.000 claims abstract description 73
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- 238000012512 characterization method Methods 0.000 claims abstract description 11
- 238000001514 detection method Methods 0.000 claims abstract description 11
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Abstract
The invention discloses a microorganism identification method based on gold nanoparticles and a machine learning algorithm, which comprises the following steps: the method comprises the following steps: synthesizing gold nanoparticles; step two: performing characterization detection on the gold nanoparticles; step three: constructing a machine learning analysis data set and an analysis model by using the characterization data; step four: analyzing and processing the data set to obtain a machine learning analysis result; step five: visually plotting the analysis result; step six: and (4) constructing a microorganism clustering tree and evaluating the accuracy of a model. According to the method, based on the capability difference of different microorganisms for synthesizing the gold nanoparticles, the characterization detection results are summarized and analyzed to form a data set, a model is constructed, the data set is analyzed and processed by using a machine learning algorithm and visually mapped, and the types of microorganisms are accurately judged through a graphical analysis report. The method can quickly and accurately identify the microorganisms, is simple to operate, does not need large-scale instruments for assistance, and is suitable for popularization and promotion.
Description
Technical Field
The invention relates to the technical field of microorganism identification, in particular to a microorganism identification method based on gold nanoparticles and a machine learning algorithm.
Background
The microorganisms can utilize self-enzymes, proteins, lipids, saccharides and other biomolecules to precisely regulate inorganic ions, and inorganic nanoparticles are generated. Microorganisms can synthesize one hundred more single element nanoparticles including gold (Au). Microbiologically synthesized nanoparticles have wide applications in medicine, energy, electronics, and biology.
Accurate identification of microorganisms is of great significance in the fields of clinical diagnosis, food processing, environmental pollution, livestock and poultry breeding and the like. Conventional methods for identifying microorganisms include phenotypic identification and genetic identification. Both methods have the defects of complicated operation procedures, large-scale instrument assistance, long time consumption and the like. Therefore, the development of a novel microorganism identification method is of great significance in the field of microorganism detection and identification.
In recent years, with the development of big data analysis technology, machine learning is used for data processing, and information hidden in huge data can be acquired and utilized more efficiently. Machine learning studies have been performed by automatically generalizing logic or rules from data by selecting appropriate algorithms and making predictions based on the results of this generalization and new data. At present, the application range of machine learning methods is continuously expanding, and an integrated research system using cross fusion of machine learning and multiple disciplines is emerging.
Disclosure of Invention
In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a microorganism identification method based on gold nanoparticles and a machine learning algorithm. The technical problem to be solved by the invention is as follows: the identification of the microorganism is simpler and more convenient.
In order to achieve the purpose, the invention provides the following technical scheme: a microorganism identification method based on gold nanoparticles and a machine learning algorithm comprises the following steps:
the method comprises the following steps: synthesis of gold nanoparticles
Sucking 0.5mL of 10mM chloroauric acid solution by a liquid transfer machine, putting the chloroauric acid solution into a 1.5mL reaction tube, sucking 0.5mL of microorganism suspension with OD600 of 1 and 0.05mL of 1mM sodium hydroxide solution by the liquid transfer machine, adding the microorganism suspension and the sodium hydroxide solution into the reaction tube, and putting the reaction tube into a constant-temperature metal bath for reaction for 12 hours to obtain gold nanoparticles;
step two: characterization detection of gold nanoparticles
Measuring the ultraviolet-visible spectrum of the gold nanoparticle solution by using a spectrometer, and determining the wavelength corresponding to the maximum absorption peak of the spectrum;
secondly, measuring the particle size and the surface potential of the gold nano-gold particles by utilizing a nano-particle size and potentiometer;
step three: construction of machine learning analysis data set and analysis model using characterization data
Summarizing the detection results of the gold nanoparticle characterization in the step two to construct a machine learning analysis data set, wherein the data set comprises data 612, 504 are used for establishing a machine learning analysis model, and 108 are used for verifying the machine learning analysis model;
step four: analyzing and processing the data set in the third step to obtain the machine learning analysis result
Performing principal component analysis, linear discriminant analysis and random forest analysis processing on the data set in the third step by using R software to obtain a machine learning analysis result;
step five: visualizing the analysis results
Performing visual mapping on the analysis result in the fourth step by using R software;
step six: constructing a microorganism clustering tree according to the analysis result and evaluating the model accuracy
And (4) constructing a microorganism clustering tree by using the R software and the analysis result in the step four, and comparing the obtained microorganism clustering tree with the microorganism evolutionary tree to realize the evaluation of the model accuracy.
In a preferred embodiment, the temperature of the isothermal metal bath in the first step is 37 ℃ and the reaction process is oscillated at a frequency of 500 rpm.
In a preferred embodiment, in the fourth step, when the R software is used to perform principal component analysis, linear discriminant analysis, and random forest analysis, the steps include:
and (3) main component analysis: the R-packs "vegan" and "rgl" are called to perform the analysis,
linear discriminant analysis: calling the R packet 'MSAA' to realize analysis,
random forest analysis: and calling the R packet 'random forest' to realize analysis.
In a preferred embodiment, when the analysis result is used for visualization drawing in the step five, the R package "ggplot 2" is called to implement drawing.
In a preferred embodiment, when the analysis result is used for constructing the microbial clustering tree in the sixth step, the R package "cluster" and "ape" are called to realize the analysis, and the microbial evolutionary tree in the sixth step is constructed by collecting microbial gene sequences from the NCBI and using MEGA7.0 software.
Compared with the prior art, the invention has the technical effects and advantages that:
the method adopts the microorganism synthesized gold nanoparticles, detects the microorganism synthesized gold nanoparticles by using a spectrometer, a nanometer granularity and a potentiometer based on the capability difference of different microorganisms synthesized gold nanoparticles, then assembles the detection result into a data set construction model, analyzes and processes the data set by using a machine learning algorithm and visually maps, and accurately judges the types of microorganisms through a graphical analysis report. The accuracy of the model can be evaluated by utilizing a machine learning algorithm to construct a microorganism clustering tree on the data set analysis processing result and comparing the microorganism clustering tree with a known microorganism evolutionary tree. The invention can quickly and accurately identify the microorganism, can realize the identification of the microorganism at the phylum, the order, the genus and the species levels, has the accuracy rate of 100 percent, is simple to operate, does not need large-scale instruments for assistance, and is suitable for popularization and promotion.
Drawings
FIG. 1 is a flow chart of microorganism identification based on gold nanoparticles and a machine learning algorithm according to the present invention;
FIG. 2 is a schematic diagram of the result of microorganism identification based on gold nanoparticles and machine learning algorithm according to the present invention;
FIG. 3 is a graph comparing the results of the identification of the present invention with those of the gene-based identification.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a microorganism identification method based on gold nanoparticles and a machine learning algorithm comprises the following steps:
the method comprises the following steps: synthesis of gold nanoparticles
Sucking 0.5mL of 10mM chloroauric acid solution by a liquid transfer machine, putting the chloroauric acid solution into a 1.5mL reaction tube, sucking 0.5mL of microorganism suspension with OD600 of 1 and 0.05mL of 1mM sodium hydroxide solution by the liquid transfer machine, adding the microorganism suspension and the sodium hydroxide solution into the reaction tube, and putting the reaction tube into a constant-temperature metal bath for reaction for 12 hours to obtain gold nanoparticles;
step two: characterization detection of gold nanoparticles
Measuring the ultraviolet-visible spectrum of the gold nanoparticle solution by using a spectrometer, and determining the wavelength corresponding to the maximum absorption peak of the spectrum;
secondly, measuring the particle size and the surface potential of the gold nano-gold particles by utilizing a nano-particle size and potentiometer;
step three: construction of machine learning analysis data set and analysis model using characterization data
Summarizing the detection results of the gold nanoparticle characterization in the step two to construct a machine learning analysis data set, wherein the data set comprises data 612, 504 are used for establishing a machine learning analysis model, and 108 are used for verifying the machine learning analysis model;
step four: analyzing and processing the data set in the third step to obtain the machine learning analysis result
Performing principal component analysis, linear discriminant analysis and random forest analysis processing on the data set in the third step by using R software to obtain a machine learning analysis result;
step five: visualizing the analysis results
Performing visual mapping and biological clustering tree construction on the analysis result in the fourth step by using R software;
step six: constructing a microorganism clustering tree according to the analysis result and evaluating the model accuracy
And (4) constructing a microorganism clustering tree by using the R software and the analysis result in the step four, and comparing the obtained microorganism clustering tree with the microorganism evolutionary tree to realize the evaluation of the model accuracy.
In a preferred embodiment, the temperature of the isothermal metal bath in the first step is 37 ℃ and the reaction process is oscillated at a frequency of 500 rpm.
In a preferred embodiment, in the fourth step, when the R software is used to perform principal component analysis, linear discriminant analysis, and random forest analysis, the steps include:
and (3) main component analysis: the R-packs "vegan" and "rgl" are called to perform the analysis,
linear discriminant analysis: calling the R packet 'MSAA' to realize analysis,
random forest analysis: and calling the R packet 'random forest' to realize analysis.
FIG. 2 shows the results of microorganism identification using the microorganism-synthesized gold nanoparticles and machine learning algorithm of the present invention, wherein A is a summary table of all analysis results; b is the identification of the microorganism at the phylum level; c is the identification of bacteria at the target level; d is the identification of the fungus at the level of the eye; e is the identification of staphylococci at the genus level; f is the identification of the mould at the genus level.
In a preferred embodiment, when the analysis result is used for visualization drawing in the step five, the R package "ggplot 2" is called to implement drawing.
In a preferred embodiment, when the analysis result is used for constructing the microorganism clustering tree in the sixth step, the R package "cluster" and "ape" are called to realize analysis; and in the sixth step, the microbial evolutionary tree is constructed by collecting microbial gene sequences from NCBI and utilizing MEGA7.0 software.
FIG. 3 shows the comparison of the identification results of the present invention with those of the gene-based identification, wherein A is the established gold nanoparticle-based microorganism identification system of the present invention, and B is the conventional genotype-based identification system.
The method adopts the microorganism synthesized gold nanoparticles, detects the microorganism synthesized gold nanoparticles by using a spectrometer, a nanometer granularity and a potentiometer based on the capability difference of different microorganisms synthesized gold nanoparticles, then assembles the detection result into a data set construction model, analyzes and processes the data set by using a machine learning algorithm and visually maps, and accurately judges the types of microorganisms through a graphical analysis report. The accuracy of the model can be evaluated by utilizing a machine learning algorithm to construct a microorganism clustering tree on the data set analysis processing result and comparing the microorganism clustering tree with a known microorganism evolutionary tree. The invention can quickly and accurately identify the microorganism, can realize the identification of the microorganism at the phylum, the order, the genus and the species levels, has the accuracy rate of 100 percent, is simple to operate, does not need large-scale instruments for assistance, and is suitable for popularization and promotion.
The points to be finally explained are: first, in the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" should be understood broadly, and may be a mechanical connection or an electrical connection, or a communication between two elements, and may be a direct connection, and "upper," "lower," "left," and "right" are only used to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed;
secondly, the method comprises the following steps: in the drawings of the disclosed embodiments of the invention, only the structures related to the disclosed embodiments are referred to, other structures can refer to common designs, and the same embodiment and different embodiments of the invention can be combined with each other without conflict;
and finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (5)
1. A microorganism identification method based on gold nanoparticles and a machine learning algorithm is characterized by comprising the following steps:
the method comprises the following steps: synthesis of gold nanoparticles
Sucking 0.5mL of 10mM chloroauric acid solution by a liquid transfer machine, putting the chloroauric acid solution into a 1.5mL reaction tube, sucking 0.5mL of microorganism suspension with OD600 of 1 and 0.05mL of 1mM sodium hydroxide solution by the liquid transfer machine, adding the microorganism suspension and the sodium hydroxide solution into the reaction tube, and putting the reaction tube into a constant-temperature metal bath for reaction for 12 hours to obtain gold nanoparticles;
step two: characterization detection of gold nanoparticles
Measuring the ultraviolet-visible spectrum of the gold nanoparticle solution by using a spectrometer, and determining the wavelength corresponding to the maximum absorption peak of the spectrum;
secondly, measuring the particle size and the surface potential of the gold nano-gold particles by utilizing a nano-particle size and potentiometer;
step three: construction of machine learning analysis data set and analysis model using characterization data
Summarizing the detection data characterized by the gold nanoparticles in the step two to construct a machine analysis data set, wherein the data set comprises data 612 groups, 504 groups are used for establishing a machine learning analysis model, and 108 groups are used for verifying the learning analysis model;
step four: analyzing and processing the data set in the third step to obtain the machine learning analysis result
Performing principal component analysis, linear discriminant analysis and random forest analysis processing on 108 groups of data in the third step by using R software to obtain a machine learning analysis result;
step five: visualizing the analysis results
Performing visual mapping on the analysis result in the fourth step by using R software;
step six: constructing a microorganism clustering tree according to the analysis result and evaluating the model accuracy
And (4) constructing a microorganism clustering tree by using the R software and the analysis result in the step four, and comparing the obtained microorganism clustering tree with the microorganism evolutionary tree to realize the evaluation of the model accuracy.
2. The microorganism identification method based on gold nanoparticles and machine learning algorithm as claimed in claim 1, characterized in that: in the first step, the temperature of the constant-temperature metal bath is 37 ℃, and the reaction process is oscillated under the condition of 500 rpm.
3. The microorganism identification method based on gold nanoparticles and machine learning algorithm as claimed in claim 1, characterized in that: in the fourth step, the steps of performing principal component analysis, linear discriminant analysis and random forest analysis processing by using R software are as follows:
and (3) main component analysis: the R-packs "vegan" and "rgl" are called to perform the analysis,
linear discriminant analysis: calling the R packet 'MSAA' to realize analysis,
random forest analysis: and calling the R packet 'random forest' to realize analysis.
4. The microorganism identification method based on gold nanoparticles and machine learning algorithm as claimed in claim 1, characterized in that: and in the fifth step, when the analysis result is utilized to carry out visual mapping, calling the R package 'ggplot 2' to realize mapping.
5. The microorganism identification method based on gold nanoparticles and machine learning algorithm as claimed in claim 1, characterized in that: and in the sixth step, when the analysis result is used for constructing the microbial clustering tree, calling R package 'cluster' and 'ape' to realize analysis, wherein the microbial evolutionary tree in the sixth step is constructed by collecting a microbial gene sequence from NCBI and using MEGA7.0 software.
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