Evaluation method and system for rapidly detecting microbial pollution in cereal food

Abstract

The invention relates to an evaluation method and system for rapidly detecting microbial contamination in cereal food, and the method comprises the following steps: obtaining a cereal food sample to be detected, pretreating the cereal food sample to be detected, generating a nucleic acid sample, then carrying out infrared spectroscopic analysis, and generating microorganism hydrogen-containing group characteristic information; the method comprises the following steps: preparing specific nanoparticles according to hydrogen-containing group characteristic information of microorganisms, combining the specific nanoparticles with a specific microorganism sample, and carrying out spectral analysis by adopting a laser-induced breakdown spectroscopy technology to generate cereal food spectral data; and inputting cereal food spectrum data generated by mutual complementation of the infrared spectroscopic analysis and the laser-induced breakdown spectroscopy into the trained machine learning model, so that the accuracy and the sensitivity of evaluating the microbial contamination of the cereal food sample to be detected can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial detection, and in particular to an evaluation method and system for rapidly detecting microbial contamination in cereal foods. Background Art

[0002] Since microbial contamination accounts for a large proportion of food, and the contamination is wide-ranging and harmful, rapid detection of microorganisms is an important part of food safety monitoring.

[0003] At present, the safety of grain food is mainly tested by microbial technology, which mainly relies on advanced detection methods and new technologies. In terms of demand, the requirements for its accuracy and technicality are also increasing. Moreover, with the development of science and technology, the trend of standardization, specialization and low energy of food safety is becoming increasingly obvious, which has laid a solid foundation for the development of the food safety detection industry. In addition, with the continuous development of food safety technology, more and more technicians are combining it with biotechnology, such as PCR, nucleic acid probes and other technologies, as well as new detection technologies, such as biosensors and biochips, which are new technologies that need to be studied in depth.

[0004] The existing methods for detecting microbial contamination in cereal foods are usually only used for qualitative methods, but not for quantitative testing, due to the lack of accurate testing of samples. Before using rapid testing technology for different cereal foods, it is necessary to have a comprehensive understanding of the characteristics, application scope and matters that need to be paid attention to for each cereal food. When using rapid testing technology to detect microorganisms in food, it is impossible to quickly detect certain specific microorganisms in cereal foods, which leads to the problem of low accuracy of the detection method for microbial contamination in cereal foods. Summary of the invention

[0005] In order to improve the accuracy of detecting microbial contamination in cereal foods, the present application provides an evaluation method and system for rapidly detecting microbial contamination in cereal foods.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides an evaluation method for rapid detection of microbial contamination in cereal foods, the method comprising the following steps: Obtain samples of cereal foods to be tested; Pre-treating the grain food samples to be tested to generate nucleic acid samples; Infrared spectroscopy analysis of nucleic acid samples was performed to generate characteristic information of hydrogen-containing groups in microorganisms; Prepare specific nanoparticles based on the characteristic information of hydrogen-containing groups of microorganisms; After combining specific nanoparticles with specific microorganism samples to generate nanomicroorganism samples, spectral analysis is performed on the nanomicroorganism samples according to laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism samples are prepared according to the grain food samples to be tested; The cereal food spectral data is input into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

[0007] As a preferred solution, the step of inputting the cereal food spectral data into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested includes the following steps: Get the cereal food spectral dataset; The spectral features of the cereal food spectral dataset were extracted based on the principal component analysis technique; Inputting the spectral features into a preset machine learning model, training the preset machine learning model, and performing cross-validation to generate a trained machine learning model; wherein the preset machine learning model is any one of a convolutional neural network, a support vector machine, and a random forest; The cereal food spectral data is input into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

[0008] As a preferred solution, in the step of performing spectral analysis on nano-microorganism samples according to the laser induced breakdown spectroscopy technique to generate spectral data of cereal food, the steps include the following: Applying laser pulses to the nano-microorganism sample according to the laser induced breakdown spectroscopy technique to generate plasma in the nano-microorganism sample; Spectral data of plasma emission is collected to generate spectral data of cereal foods.

[0009] As a preferred embodiment, the step of pre-treating the cereal food sample to be tested to generate the nucleic acid sample includes the following steps: Performing microbial separation operations on the grain food samples to be tested according to the microfluidic chip technology and optical tweezers technology to generate microbial samples; Performing nucleic acid extraction operations on microbial samples to generate nucleic acid extracts; The nucleic acid extracts are amplified by specific sequences according to the multiplex PCR technique to generate nucleic acid samples.

[0010] As a preferred embodiment, the step of performing infrared spectroscopy analysis on nucleic acid samples to generate characteristic information of hydrogen-containing groups of microorganisms includes the following steps: Prepare according to the nucleic acid sample to generate a liquid to be tested; Perform spectral scanning on the liquid to be detected according to the near-infrared spectroscopy technology to generate spectral data of nucleic acid samples; After preprocessing the spectral data of nucleic acid samples, characteristic information of hydrogen-containing groups of microorganisms was generated based on principal component analysis technology.

[0011] As a preferred embodiment, in the step of preparing specific nanoparticles according to the characteristic information of hydrogen-containing groups of microorganisms, the following steps are included: According to the characteristic information of hydrogen-containing groups of microorganisms, the microorganism samples are screened to obtain specific microorganism samples; According to the specific microbial samples, corresponding specific nanoparticles are prepared; wherein the specific microbial samples include but are not limited to: aflatoxin samples, Escherichia coli samples, Listeria monocytogenes samples, Salmonella samples and Staphylococcus aureus samples.

[0012] As a preferred solution, in the step of performing microbial separation operation on the cereal food sample to be tested according to the microfluidic chip technology and the optical tweezers technology to generate the microbial sample, the steps include the following: The grain food samples to be tested are filtered, centrifuged and chemically treated according to the microfluidic chip technology to generate purified grain food samples; Multiple microbial cells were obtained from purified cereal food samples according to the optical tweezers technique to generate microbial samples.

[0013] Accordingly, the present invention also provides an evaluation system for rapid detection of microbial contamination in cereal foods, comprising: a sample acquisition module, a pretreatment module, a first spectral analysis module, a preparation module, a second spectral analysis module and a contamination evaluation module; A sample acquisition module, used for acquiring cereal food samples to be tested; A pretreatment module, used for pretreatment of the grain food sample to be tested to generate a nucleic acid sample; The first spectrum analysis module is used to perform infrared spectrum analysis on nucleic acid samples to generate characteristic information of hydrogen-containing groups of microorganisms; A preparation module is used to prepare specific nanoparticles based on the characteristic information of hydrogen-containing groups of microorganisms; The second spectral analysis module is used to combine specific nanoparticles with specific microorganism samples to generate nanomicroorganism samples, and then perform spectral analysis on the nanomicroorganism samples according to laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism samples are prepared according to the grain food samples to be tested; The contamination assessment module is used to input the grain food spectral data into the trained machine learning model so that the trained machine learning model can identify the grain food spectral data and generate the assessment results of the microbial contamination of the grain food samples to be tested. Accordingly, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned evaluation methods for rapidly detecting microbial contamination in cereal foods are implemented.

[0014] Accordingly, the present invention also provides a storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of any of the above-mentioned evaluation methods for rapidly detecting microbial contamination in cereal foods.

[0015] The technical solution of the present invention is to obtain a grain food sample to be detected, and pre-treat the grain food sample to be detected to generate a nucleic acid sample; perform infrared spectroscopy analysis on the nucleic acid sample to generate characteristic information of hydrogen-containing groups of microorganisms; prepare specific nanoparticles according to the characteristic information of hydrogen-containing groups of microorganisms; combine the specific nanoparticles with a specific microorganism sample to generate a nanomicroorganism sample, and then perform spectral analysis on the nanomicroorganism sample according to laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism sample is prepared according to the grain food sample to be detected; input the grain food spectral data into a trained machine learning model so that the trained machine learning model can identify the grain food spectral data and generate an evaluation result of the microbial contamination of the grain food sample to be detected. In the technical scheme used in the present invention, infrared spectroscopy technology provides chemical information of microorganisms, while nanotechnology and laser induced breakdown spectroscopy technology can enhance the signals of specific microorganisms. The technical scheme of the present invention combines infrared spectroscopy analysis and laser induced breakdown spectroscopy technology, and inputs the cereal food spectral data generated by the two technologies complementing each other into the trained machine learning model, which can effectively improve the accuracy and sensitivity of the assessment of microbial contamination of the cereal food samples to be tested; further, laser induced breakdown spectroscopy technology performs spectral analysis on nanomicrobial samples to achieve quantitative analysis of microorganisms, so as to solve the technical problem that microorganisms cannot be quantitatively detected in the prior art; at the same time, specific microbial samples can be screened out according to the characteristic information of hydrogen-containing groups of microorganisms, and specific nanoparticles can be combined with specific microbial samples to detect specific microbial contamination in cereal foods, thereby further improving the accuracy of microbial contamination in cereal foods. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1is a flowchart of the steps of an evaluation method for rapid detection of microbial contamination in cereal foods in an embodiment of the present application; Figure 2 is a structural diagram of an evaluation system for rapid detection of microbial contamination in cereal foods in an embodiment of the present application; Figure 3 It is a schematic diagram of the hardware structure of the electronic device in the embodiment of the present application.

[0017] Figure Number: A sample acquisition module 201 , a pre-processing module 202 , a first spectral analysis module 203 , a preparation module 204 , a second spectral analysis module 205 and a pollution assessment module 206 . DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application, they are only examples of devices and methods consistent with some aspects of the embodiments of the present application.

[0019] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0020] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0022] The existing methods for detecting microbial contamination in cereal foods are usually only used for qualitative methods, but not for quantitative testing, due to the lack of accurate testing of samples. Before using rapid testing technology for different cereal foods, it is necessary to have a comprehensive understanding of the characteristics, application scope and matters that need to be paid attention to for each cereal food. When using rapid testing technology to detect microorganisms in food, it is impossible to quickly detect certain specific microorganisms in cereal foods, which leads to the problem of low accuracy of the detection method for microbial contamination in cereal foods.

[0023] In view of this, an embodiment of the present application provides an evaluation method and system for rapid detection of microbial contamination in cereal foods. The scheme obtains a cereal food sample to be detected, and pre-treats the cereal food sample to be detected to generate a nucleic acid sample; performs infrared spectroscopy analysis on the nucleic acid sample to generate characteristic information of microbial hydrogen-containing groups; prepares specific nanoparticles based on the characteristic information of microbial hydrogen-containing groups; combines the specific nanoparticles with a specific microbial sample to generate a nanomicrobial sample, and then performs spectral analysis on the nanomicrobial sample based on laser induced breakdown spectroscopy technology to generate cereal food spectral data; wherein the specific microbial sample is prepared based on the cereal food sample to be detected; and inputs the cereal food spectral data into a trained machine learning model so that the trained machine learning model recognizes the cereal food spectral data to generate an evaluation result of the microbial contamination of the cereal food sample to be detected. In the technical scheme used in the present invention, infrared spectroscopy technology provides chemical information of microorganisms, while nanotechnology and laser induced breakdown spectroscopy technology can enhance the signals of specific microorganisms. The technical scheme of the present invention combines infrared spectroscopy analysis and laser induced breakdown spectroscopy technology, and inputs the cereal food spectral data generated by the two technologies complementing each other into the trained machine learning model, which can effectively improve the accuracy and sensitivity of the assessment of microbial contamination of the cereal food samples to be tested; further, laser induced breakdown spectroscopy technology performs spectral analysis on nanomicrobial samples to achieve quantitative analysis of microorganisms, so as to solve the technical problem that microorganisms cannot be quantitatively detected in the prior art; at the same time, specific microbial samples can be screened out according to the characteristic information of hydrogen-containing groups of microorganisms, and specific nanoparticles can be combined with specific microbial samples to detect specific microbial contamination in cereal foods, thereby further improving the accuracy of microbial contamination in cereal foods.

[0024] The evaluation method for rapid detection of microbial contamination in cereal foods provided in the embodiments of the present application relates to the field of microbial detection technology. The evaluation method for rapid detection of microbial contamination in cereal foods provided in the embodiments of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle-mounted terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a method for calculating the sideslip angle of the center of mass of a vehicle, etc., but is not limited to the above forms.

[0025] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the first context of computer-executable instructions executed by a computer, such as program modules. First, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0026] The present application is further described in detail below in conjunction with the accompanying drawings.

[0027] In one embodiment, if Figure 1 As shown, the present application discloses an evaluation method for rapid detection of microbial contamination in cereal foods, which specifically comprises the following steps: S101: Obtain a cereal food sample to be tested.

[0028] In one embodiment, the step of performing a microbial separation operation on the cereal food sample to be tested according to the microfluidic chip technology and the optical tweezers technology to generate a microbial sample includes the following steps: Filtering, centrifuging and chemically treating the grain food samples to be tested according to the microfluidic chip technology to generate purified grain food samples; Multiple microbial cells were obtained from purified cereal food samples according to the optical tweezers technique to generate microbial samples.

[0029] S102: Pre-treat the cereal food sample to be tested to generate a nucleic acid sample.

[0030] In one embodiment, the step of pre-treating the cereal food sample to be tested to generate the nucleic acid sample includes the following steps: Performing microbial separation operations on the grain food samples to be tested according to the microfluidic chip technology and optical tweezers technology to generate microbial samples; Performing nucleic acid extraction operations on microbial samples to generate nucleic acid extracts; The nucleic acid extracts are amplified by specific sequences according to the multiplex PCR technique to generate nucleic acid samples.

[0031] Preferably, the present scheme uses microfluidic chip technology to physically or chemically treat cereal food samples to extract microbial cells, which can produce the following technical benefits: microfluidic chips can accurately control the capture, separation and detection processes of cells through microchannel design, significantly improving the accuracy of single-cell analysis. Using the principle of microfluidic dynamics, precise manipulation of cells can be achieved, reducing sample loss and external interference. By constructing multiple micro-reaction chambers or microchannel networks on a microfluidic chip, a large number of cell samples can be processed in parallel to achieve high-throughput screening. This not only improves experimental efficiency, but also reduces costs, making large-scale single-cell analysis possible. Microfluidic chip technology can integrate multiple experimental steps such as sample processing, cell culture, and reaction monitoring on a single chip to form an integrated experimental platform. This integrated design simplifies experimental operations, reduces human errors, and improves the reliability and repeatability of experiments. Select materials with excellent biocompatibility and chemical stability, such as polydimethylsiloxane (PDMS), glass, etc., to make microfluidic chips. These materials can not only effectively reduce cell damage, but also maintain the long-term activity of cells. Microfluidic chip analysis technology can integrate multiple functional modules such as sample separation, enrichment, mixing, derivatization, reaction and detection on a microchip, operate fluids in micron-level pipes, and has the advantages of low sample consumption and short reaction time. Microfluidic chips can highly simulate the culture conditions and living environment of microorganisms, while implementing pre-treatment operations such as sorting, cultivation and separation of microorganisms, and combined with SERS detection technology to achieve high-sensitivity non-destructive detection of microorganisms, and obtain more Raman spectral information of microorganisms.

[0032] From the above, it can be concluded that the use of microfluidic chip technology to process cereal food samples can not only improve the accuracy and efficiency of microbial detection, but also reduce costs and reduce sample consumption.

[0033] Furthermore, multiplex PCR technology can simultaneously detect the nucleic acid sequences of multiple microorganisms, thereby effectively improving the efficiency of assessing microbial contamination in cereal foods.

[0034] S103: Perform infrared spectroscopy analysis on the nucleic acid sample to generate characteristic information of hydrogen-containing groups of the microorganism.

[0035] In one embodiment, the step of performing infrared spectroscopy analysis on nucleic acid samples to generate characteristic information of hydrogen-containing groups of microorganisms includes the following steps: Prepare according to the nucleic acid sample to generate a liquid to be tested; Perform spectral scanning on the liquid to be detected according to the near-infrared spectroscopy technology to generate spectral data of nucleic acid samples; After preprocessing the spectral data of nucleic acid samples, characteristic information of hydrogen-containing groups of microorganisms was generated based on principal component analysis technology.

[0036] In a specific embodiment, the liquid to be detected is spectrally scanned according to the near infrared spectroscopy technology to generate the nucleic acid sample spectral data specifically as follows: The near infrared spectrometer is used to perform spectral scanning on the liquid to be detected, and the spectral data after the interaction between light of different wavelengths and the liquid to be detected is collected to generate the spectral data of the nucleic acid sample. The liquid to be detected can be a common liquid or a uniform suspension.

[0037] In a specific embodiment, preprocessing the nucleic acid sample spectral data includes: performing operations such as denoising, normalization, and baseline correction on the nucleic acid sample spectral data to improve data quality.

[0038] Furthermore, principal component analysis (PCA) is a commonly used dimensionality reduction technique that can be used to extract the most important features from spectral data. PCA can be used to convert high-dimensional spectral data into low-dimensional data while retaining most of the variation information. In microbial detection, PCA can help identify the spectral features that best represent the characteristics of microorganisms, thereby performing effective classification and identification.

[0039] Preferably, the spectral range used by infrared spectroscopy is 700-2500nm. Infrared spectroscopy can provide information about the vibration of organic molecules in microbial cells, which can be used to identify and classify different microorganisms. In this step, by analyzing the spectral data, the "fingerprints" of the microorganisms can be obtained, which are a direct reflection of the type and state of the microorganisms.

[0040] S104: Prepare specific nanoparticles based on the characteristic information of hydrogen-containing groups of microorganisms.

[0041] In one embodiment, the step of preparing specific nanoparticles according to the characteristic information of hydrogen-containing groups of microorganisms includes the following steps: According to the characteristic information of hydrogen-containing groups of microorganisms, the microorganism samples are screened to obtain specific microorganism samples; According to the specific microbial samples, corresponding specific nanoparticles are prepared; wherein the specific microbial samples include but are not limited to: aflatoxin samples, Escherichia coli samples, Listeria monocytogenes samples, Salmonella samples and Staphylococcus aureus samples.

[0042] The embodiments of the present invention can detect a variety of foodborne pathogens and contaminants including aflatoxin, Escherichia coli, Listeria monocytogenes, Salmonella, Staphylococcus aureus, etc. The application of these technologies improves the sensitivity and accuracy of microbial contamination detection in cereal foods and meets the needs of rapid food safety detection.

[0043] S105: After combining specific nanoparticles with specific microorganism samples to generate nanomicroorganism samples, spectral analysis is performed on the nanomicroorganism samples according to laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism samples are prepared according to the grain food samples to be tested.

[0044] In one embodiment, the step of performing spectral analysis on nano-microorganism samples according to laser induced breakdown spectroscopy technology to generate spectral data of cereal food includes the following steps: Applying laser pulses to the nano-microorganism sample according to the laser induced breakdown spectroscopy technique to generate plasma in the nano-microorganism sample; Spectral data of plasma emission is collected to generate spectral data of cereal foods.

[0045] Preferably, the size of the specific nanoparticles is 1-100 nanometers, and the lateral flow band detection platform can achieve rapid identification of microorganisms.

[0046] Nanotechnology and laser-induced breakdown spectroscopy (LIBS) are used to further detect and confirm the presence of microorganisms. Nanotechnology is used here to enhance the LIBS signal, making the detection of microorganisms more sensitive and accurate. Nanoparticles can specifically bind to microorganisms or enhance the spectral characteristics of microorganisms, thereby producing a more obvious signal in LIBS analysis.

[0047] During LIBS analysis, a focused pulsed laser is used to excite the sample and remove a very small amount of material from its surface. The material is heated to 10,000 degrees Celsius or even higher, and when the high-energy pulsed laser excites the sample, the electrons in the outer shells of the outer atoms are excited, forming electron vacancies, making the atoms unstable. These excited electrons and atomic nuclei form a plasma. After the pulsed laser stops, the plasma begins to cool, and the electrons in the outer electron shells gradually fill the vacancies. In this process, a large amount of energy released when the electrons move between two energy levels or shells is emitted as light depending on the element, forming a spectrum containing thousands of peaks.

[0048] Infrared spectroscopy provides chemical information of microorganisms, while nanotechnology and laser-induced breakdown spectroscopy can enhance the signals of specific microorganisms. The technical solution of the present invention combines infrared spectroscopy analysis and laser-induced breakdown spectroscopy technology, and inputs the grain food spectral data generated by the two complementary technologies into the trained machine learning model, which can effectively improve the accuracy and sensitivity of the assessment of microbial contamination of the grain food samples to be tested. At the same time, this dual detection mechanism of infrared spectroscopy combined with laser-induced breakdown spectroscopy technology can reduce false positives and false negatives, and improve the reliability of detection.

[0049] S106: Inputting the cereal food spectral data into the trained machine learning model, so that the trained machine learning model can identify the cereal food spectral data and generate an evaluation result of the microbial contamination of the cereal food sample to be tested.

[0050] In one embodiment, the step of inputting the cereal food spectral data into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of microbial contamination of the cereal food sample to be tested includes the following steps: Get the cereal food spectral dataset; The spectral features of the cereal food spectral dataset were extracted based on the principal component analysis technique; Inputting the spectral features into a preset machine learning model, training the preset machine learning model, and performing cross-validation to generate a trained machine learning model; wherein the preset machine learning model is any one of a convolutional neural network, a support vector machine, and a random forest; The cereal food spectral data is input into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

[0051] Furthermore, after obtaining the cereal food spectral dataset, the method further includes: performing data preprocessing operations on the cereal food spectral dataset, including denoising, normalization, deconvolution, etc., to improve data quality.

[0052] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0053] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments. Figure 2 , Figure 2 An evaluation system for rapid detection of microbial contamination in cereal foods is illustrated, characterized in that the system comprises: a sample acquisition module, a pretreatment module, a first spectral analysis module, a preparation module, a second spectral analysis module and a contamination evaluation module; A sample acquisition module, used for acquiring cereal food samples to be tested; A pretreatment module, used for pretreatment of the grain food sample to be tested to generate a nucleic acid sample; The first spectrum analysis module is used to perform infrared spectrum analysis on nucleic acid samples to generate characteristic information of hydrogen-containing groups of microorganisms; A preparation module is used to prepare specific nanoparticles based on the characteristic information of hydrogen-containing groups of microorganisms; The second spectral analysis module is used to combine specific nanoparticles with specific microorganism samples to generate nanomicroorganism samples, and then perform spectral analysis on the nanomicroorganism samples according to laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism samples are prepared according to the grain food samples to be tested; The contamination assessment module is used to input the cereal food spectral data into the trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

[0054] For the specific definition of an evaluation system for rapid detection of microbial contamination in cereal foods, please refer to the definition of an evaluation method for rapid detection of microbial contamination in cereal foods above, which will not be repeated here. Each module in the above-mentioned evaluation system for rapid detection of microbial contamination in cereal foods can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0055] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0056] In one embodiment, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the electronic equipment includes: The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the vehicle center of mass sideslip angle calculation method of the embodiment of this application; Input / output interface 803, used to implement information input and output; The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); A bus 805 that transmits information between various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804); The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0057] The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store a database. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an evaluation method for rapid detection of microbial contamination in cereal food is implemented.

[0058] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain samples of cereal foods to be tested; Pre-treating the grain food samples to be tested to generate nucleic acid samples; Infrared spectroscopy analysis of nucleic acid samples was performed to generate characteristic information of hydrogen-containing groups in microorganisms; Prepare specific nanoparticles based on the characteristic information of hydrogen-containing groups of microorganisms; After combining specific nanoparticles with specific microorganism samples to generate nanomicroorganism samples, spectral analysis is performed on the nanomicroorganism samples according to laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism samples are prepared according to the grain food samples to be tested; The cereal food spectral data is input into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

[0059] In one embodiment, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtain samples of cereal foods to be tested; Pre-treating the grain food samples to be tested to generate nucleic acid samples; Infrared spectroscopy analysis of nucleic acid samples was performed to generate characteristic information of hydrogen-containing groups in microorganisms; Prepare specific nanoparticles based on the characteristic information of hydrogen-containing groups of microorganisms; After combining specific nanoparticles with specific microorganism samples to generate nanomicroorganism samples, spectral analysis is performed on the nanomicroorganism samples according to laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism samples are prepared according to the grain food samples to be tested; The cereal food spectral data is input into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

[0060] The evaluation method and system for rapid detection of microbial contamination in cereal foods provided in the embodiments of the present application obtain a cereal food sample to be detected, and pre-treat the cereal food sample to be detected to generate a nucleic acid sample; perform infrared spectroscopy analysis on the nucleic acid sample to generate characteristic information of hydrogen-containing groups of the microorganism; prepare specific nanoparticles based on the characteristic information of hydrogen-containing groups of the microorganism; combine the specific nanoparticles with a specific microbial sample to generate a nanomicrobial sample, and then perform spectral analysis on the nanomicrobial sample based on laser induced breakdown spectroscopy technology to generate cereal food spectral data; wherein the specific microbial sample is prepared based on the cereal food sample to be detected; input the cereal food spectral data into a trained machine learning model so that the trained machine learning model recognizes the cereal food spectral data and generates an evaluation result of the microbial contamination of the cereal food sample to be detected. In the technical scheme used in the present invention, infrared spectroscopy technology provides chemical information of microorganisms, while nanotechnology and laser induced breakdown spectroscopy technology can enhance the signals of specific microorganisms. The technical scheme of the present invention combines infrared spectroscopy analysis and laser induced breakdown spectroscopy technology, and inputs the cereal food spectral data generated by the two technologies complementing each other into the trained machine learning model, which can effectively improve the accuracy and sensitivity of the assessment of microbial contamination of the cereal food samples to be tested; further, laser induced breakdown spectroscopy technology performs spectral analysis on nanomicrobial samples to achieve quantitative analysis of microorganisms, so as to solve the technical problem that microorganisms cannot be quantitatively detected in the prior art; at the same time, specific microbial samples can be screened out according to the characteristic information of hydrogen-containing groups of microorganisms, and specific nanoparticles can be combined with specific microbial samples to detect specific microbial contamination in cereal foods, thereby further improving the accuracy of microbial contamination in cereal foods.

[0061] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0062] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0063] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for rapid detection of microbial contamination in cereal foods, characterized in that: The method comprises: Obtain samples of cereal foods to be tested; Pre-treating the cereal food sample to be tested to generate a nucleic acid sample; Performing infrared spectroscopy analysis on the nucleic acid sample to generate characteristic information of hydrogen-containing groups of microorganisms; preparing specific nanoparticles according to the characteristic information of hydrogen-containing groups of the microorganism; After combining the specific nanoparticles with a specific microorganism sample to generate a nanomicroorganism sample, the nanomicroorganism sample is subjected to spectral analysis according to laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism sample is prepared according to the grain food sample to be tested; The cereal food spectral data is input into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

2. The method for rapid detection of microbial contamination in cereal foods according to claim 1, characterized in that: The step of inputting the cereal food spectral data into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an evaluation result of the microbial contamination of the cereal food sample to be tested includes the following steps: Get the cereal food spectral dataset; Extracting spectral features of the cereal food spectral dataset according to principal component analysis technology; Inputting the spectral features into a preset machine learning model, training the preset machine learning model, and performing cross-validation to generate the trained machine learning model; wherein the preset machine learning model is any one of a convolutional neural network, a support vector machine, and a random forest; The cereal food spectral data is input into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

3. The method for rapid detection of microbial contamination in cereal foods according to claim 2, characterized in that: The step of performing spectral analysis on the nano-microorganism sample according to the laser induced breakdown spectroscopy technique to generate spectral data of cereal food includes the following steps: Applying laser pulses to the nano-microorganism sample according to the laser induced breakdown spectroscopy technique to generate plasma from the nano-microorganism sample; Spectral data of the plasma emission is collected to generate spectral data of the cereal food.

4. The method for rapid detection of microbial contamination in cereal foods according to claim 3, characterized in that: The step of pre-treating the cereal food sample to be tested to generate a nucleic acid sample includes the following steps: Performing a microbial separation operation on the cereal food sample to be tested according to the microfluidic chip technology and the optical tweezers technology to generate a microbial sample; performing a nucleic acid extraction operation on the microbial sample to generate a nucleic acid extract; The nucleic acid extract is subjected to specific sequence amplification according to the multiplex PCR technique to generate the nucleic acid sample.

5. The method for rapid detection of microbial contamination in cereal foods according to claim 4, characterized in that: The step of performing infrared spectroscopy analysis on the nucleic acid sample to generate characteristic information of hydrogen-containing groups of microorganisms includes the following steps: Prepare according to the nucleic acid sample to generate a liquid to be detected; Performing spectral scanning on the liquid to be detected according to near-infrared spectroscopy technology to generate spectral data of nucleic acid samples; After preprocessing the nucleic acid sample spectral data, the characteristic information of the hydrogen-containing groups of the microorganism is generated according to the principal component analysis technology.

6. The method for rapid detection of microbial contamination in cereal foods according to claim 5, characterized in that: In the step of preparing specific nanoparticles according to the characteristic information of hydrogen-containing groups of the microorganism, the steps include: Screening the microbial samples according to the characteristic information of hydrogen-containing groups of the microorganisms to obtain specific microbial samples; According to the specific microbial sample, the corresponding specific nanoparticles are prepared; wherein the specific microbial sample includes but is not limited to: aflatoxin sample, Escherichia coli sample, Listeria monocytogenes sample, Salmonella sample and Staphylococcus aureus sample.

7. The method for rapid detection of microbial contamination in cereal foods according to claim 6, characterized in that: In the step of performing a microbial separation operation on the cereal food sample to be detected according to the microfluidic chip technology and the optical tweezers technology to generate a microbial sample, the following steps are included: Filtering, centrifuging and chemically treating the cereal food sample to be tested according to the microfluidic chip technology to generate a purified cereal food sample; A plurality of microbial cells are obtained from the purified cereal food sample according to the optical tweezers technique to generate the microbial sample.

8. An evaluation system for rapid detection of microbial contamination in cereal foods, characterized in that: The system comprises: a sample acquisition module, a pretreatment module, a first spectral analysis module, a preparation module, a second spectral analysis module and a pollution assessment module; The sample acquisition module is used to acquire the cereal food sample to be tested; The pre-processing module is used to pre-process the cereal food sample to be tested to generate a nucleic acid sample; The first spectral analysis module is used to perform infrared spectral analysis on the nucleic acid sample to generate characteristic information of hydrogen-containing groups of microorganisms; The preparation module is used to prepare specific nanoparticles according to the characteristic information of hydrogen-containing groups of the microorganisms; The second spectral analysis module is used to combine the specific nanoparticles with the specific microorganism sample to generate a nanomicroorganism sample, and then perform spectral analysis on the nanomicroorganism sample according to the laser induced breakdown spectroscopy technology to generate grain food spectral data; wherein the specific microorganism sample is prepared according to the grain food sample to be tested; The contamination assessment module is used to input the cereal food spectral data into a trained machine learning model so that the trained machine learning model can identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the evaluation method for rapid detection of microbial contamination in cereal foods as claimed in any one of claims 1 to 7 are implemented.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of an evaluation method for rapid detection of microbial contamination in cereal foods as claimed in any one of claims 1 to 7 are implemented.

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