An assessment method and system for rapid detection of microbial contamination in cereal foods.

Abstract

This invention relates to an assessment method and system for rapid detection of microbial contamination in cereal foods. The method includes: acquiring a cereal food sample to be tested, preprocessing the sample to generate a nucleic acid sample, and then performing infrared spectroscopy analysis to generate microbial hydrogen-containing group characteristic information; based on the microbial hydrogen-containing group characteristic information, preparing specific nanoparticles and binding them with specific microbial samples, and then performing spectral analysis using laser-induced breakdown spectroscopy to generate cereal food spectral data; inputting the cereal food spectral data generated by the complementary infrared spectral analysis and laser-induced breakdown spectroscopy into a trained machine learning model, which can effectively improve the accuracy and sensitivity of the assessment of microbial contamination in the cereal food sample to be tested.

Description

Technical Field

[0001] This invention relates to the field of microbial detection technology, and in particular to an assessment method and system for the rapid detection of microbial contamination in cereal foods. Background Technology

[0002] Since microbial contamination accounts for a large proportion of food, and its scope and harm are wide, rapid detection of microorganisms is an important part of food safety monitoring.

[0003] Currently, the safety testing of grain products using microbial technology mainly relies on advanced detection methods and new technologies. From a demand perspective, the requirements for accuracy and technical sophistication are constantly increasing. Furthermore, with the development of science and technology, the standardization, specialization, and energy efficiency of food safety are becoming increasingly apparent, laying a solid foundation for the development of the food safety testing industry. In addition, with the continuous development of food safety technologies, more and more technicians are combining them with biotechnology, such as PCR and nucleic acid probes, as well as new detection technologies like biosensors and biochips—all of which are new technologies that require further in-depth research.

[0004] Existing methods for detecting microbial contamination in cereal foods often lack precision testing for samples, limiting their application to qualitative methods rather than quantitative detection. Before employing rapid detection technologies for different cereal foods, a comprehensive understanding of each food's characteristics, application scope, and necessary precautions is crucial. Furthermore, rapid detection technologies cannot quickly detect certain specific microorganisms in cereal foods, leading to low accuracy in microbial contamination detection methods. Summary of the Invention

[0005] To improve the accuracy of detecting microbial contamination in cereal foods, this application provides an assessment method and system for the rapid detection of microbial contamination in cereal foods.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide an assessment method for the rapid detection of microbial contamination in cereal foods, the method comprising the following steps:

[0007] Obtain samples of grain-based foods to be tested;

[0008] Preprocess the cereal food samples to be tested to generate nucleic acid samples;

[0009] Infrared spectroscopy analysis of nucleic acid samples generates characteristic information of hydrogen-containing groups in microorganisms;

[0010] Specific nanoparticles were prepared based on the characteristics of hydrogen-containing groups in microorganisms.

[0011] After combining specific nanoparticles with specific microbial samples to generate nanomicrobial samples, the nanomicrobial samples are subjected to spectral analysis using laser-induced breakdown spectroscopy to generate spectral data of cereal foods; wherein, the specific microbial samples are prepared based on the cereal food samples to be tested.

[0012] The spectral data of cereal foods are input into a trained machine learning model so that the trained machine learning model can identify the spectral data of cereal foods and generate an assessment result of the microbial contamination of the cereal food samples to be tested.

[0013] As a preferred embodiment, the step of inputting the spectral data of cereal foods into a trained machine learning model, so that the trained machine learning model can identify the spectral data of cereal foods and generate an assessment result of the microbial contamination of the cereal food sample to be tested, includes the following steps:

[0014] Obtain a spectral dataset of cereal foods;

[0015] Spectral features of cereal food spectral datasets were extracted using principal component analysis.

[0016] The spectral features are input into a preset machine learning model, the preset machine learning model is trained, and cross-validation is performed to generate a trained machine learning model; wherein, the preset machine learning model can be any one of convolutional neural network, support vector machine, and random forest;

[0017] The spectral data of cereal foods are input into a trained machine learning model so that the trained machine learning model can identify the spectral data of cereal foods and generate an assessment result of the microbial contamination of the cereal food samples to be tested.

[0018] As a preferred embodiment, the step of performing spectral analysis on nanomicrobial samples based on laser-induced breakdown spectroscopy to generate spectral data for cereal foods includes the following steps:

[0019] Laser pulses are applied to nanomicrobial samples using laser-induced breakdown spectroscopy to induce plasma generation in the nanomicrobial samples.

[0020] Collect spectral data of plasma emission to generate spectral data of cereal foods.

[0021] As a preferred approach, the pretreatment of the cereal food sample to be tested to generate a nucleic acid sample includes the following steps:

[0022] Microbial samples are generated by separating microorganisms from cereal food samples using microfluidic chip technology and optical tweezers technology.

[0023] Nucleic acid extraction is performed on microbial samples to generate nucleic acid extracts;

[0024] Nucleic acid samples are generated by amplifying specific sequences from nucleic acid extracts using multiplex PCR technology.

[0025] As a preferred approach, the step of performing infrared spectroscopy analysis on nucleic acid samples to generate microbial hydrogen-containing group characteristic information includes the following steps:

[0026] The nucleic acid sample is used to prepare a liquid for testing.

[0027] Near-infrared spectroscopy is used to perform spectral scanning on the liquid to be tested, generating spectral data for nucleic acid samples.

[0028] After preprocessing the spectral data of nucleic acid samples, principal component analysis was used to generate characteristic information of hydrogen-containing groups of microorganisms.

[0029] As a preferred embodiment, the step of preparing specific nanoparticles based on the characteristic information of hydrogen-containing groups of microorganisms includes the following steps:

[0030] Based on the hydrogen-containing group characteristics of microorganisms, specific microbial samples are obtained by screening from microbial samples;

[0031] Based on specific microbial samples, corresponding specific nanoparticles are prepared; wherein, specific microbial samples include, but are not limited to: aflatoxin samples, Escherichia coli samples, Listeria monocytogenes samples, Salmonella samples, and Staphylococcus aureus samples.

[0032] As a preferred embodiment, the step of separating microorganisms from the cereal food sample to be tested using microfluidic chip technology and optical tweezers technology to generate a microbial sample includes the following steps:

[0033] Microfluidic chip technology is used to filter, centrifuge, and chemically process the cereal food samples to be tested, thereby generating purified cereal food samples.

[0034] Multiple microbial cells were obtained from purified cereal food samples using optical tweezers technology to generate microbial samples.

[0035] Accordingly, the present invention also provides an assessment 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 assessment module;

[0036] The sample acquisition module is used to acquire samples of grain foods to be tested.

[0037] The preprocessing module is used to preprocess the cereal food samples to be tested to generate nucleic acid samples;

[0038] The first spectral analysis module is used to perform infrared spectral analysis on nucleic acid samples to generate characteristic information of hydrogen-containing groups of microorganisms;

[0039] The preparation module is used to prepare specific nanoparticles based on the hydrogen-containing group characteristics of microorganisms;

[0040] The second spectral analysis module is used to combine specific nanoparticles with specific microbial samples to generate nanomicrobial samples. Then, the nanomicrobial samples are subjected to spectral analysis using laser-induced breakdown spectroscopy to generate spectral data of cereal foods. The specific microbial samples are prepared based on the cereal food samples to be tested.

[0041] The contamination assessment module is used to input spectral data of cereal foods into a trained machine learning model, enabling the model to identify the spectral data and generate assessment results of microbial contamination in the cereal food samples to be tested.

[0042] Accordingly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of any of the above-mentioned assessment methods for rapid detection of microbial contamination in cereal foods.

[0043] Accordingly, the present invention also provides a storage medium storing a computer program, characterized in that, when executed by a processor, the computer program implements the steps of any of the above-mentioned assessment methods for rapid detection of microbial contamination in cereal foods.

[0044] The technical solution of this invention involves obtaining a sample of cereal food to be tested, preprocessing the sample to generate a nucleic acid sample, performing infrared spectroscopy analysis on the nucleic acid sample to generate microbial hydrogen-containing group characteristic information, preparing specific nanoparticles based on the microbial hydrogen-containing group characteristic information, combining the specific nanoparticles with a specific microbial sample to generate a nanomicrobial sample, and then performing spectral analysis on the nanomicrobial sample using laser-induced breakdown spectroscopy to generate cereal food spectral data. The specific microbial sample is prepared based on the cereal food sample to be tested. The cereal food spectral data is input into a trained machine learning model to enable the trained machine learning model to identify the cereal food spectral data and generate an assessment result of microbial contamination of the cereal food sample to be tested. In the technical solution used in this invention, infrared spectroscopy provides chemical information about microorganisms, while nanotechnology and laser-induced breakdown spectroscopy can enhance the signals of specific microorganisms. This invention combines infrared spectroscopy and laser-induced breakdown spectroscopy, inputting the resulting spectral data of grain foods into a trained machine learning model. This effectively improves the accuracy and sensitivity of assessing microbial contamination in the grain food samples to be tested. Furthermore, laser-induced breakdown spectroscopy enables quantitative analysis of microorganisms by performing spectral analysis on nano-microbial samples, solving the technical problem of the inability to quantitatively detect microorganisms in existing technologies. Simultaneously, based on the hydrogen-containing group characteristics of microorganisms, specific microbial samples can be screened. By combining specific nanoparticles with specific microbial samples, it is possible to detect specific microbial contamination in grain foods, thereby further improving the accuracy of microbial contamination detection in grain foods. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the steps of an assessment method for rapidly detecting microbial contamination in cereal foods, as described in this application.

[0046] Figure 2 This is a structural diagram of an assessment system for rapid detection of microbial contamination in cereal foods, as described in an embodiment of this application.

[0047] Figure 3 This is a schematic diagram of the hardware structure of the electronic device in the embodiments of this application.

[0048] Icon labels:

[0049] The sample acquisition module 201, the pretreatment module 202, the first spectral analysis module 203, the preparation module 204, the second spectral analysis module 205, and the pollution assessment module 206 are included. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit the scope of this application. In the following description, when referring to the accompanying 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 those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application.

[0051] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0052] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0054] Existing methods for detecting microbial contamination in cereal foods often lack precision testing for samples, limiting their application to qualitative methods rather than quantitative detection. Before employing rapid detection technologies for different cereal foods, a comprehensive understanding of each food's characteristics, application scope, and necessary precautions is crucial. Furthermore, rapid detection technologies cannot quickly detect certain specific microorganisms in cereal foods, leading to low accuracy in microbial contamination detection methods.

[0055] In view of this, this application provides an assessment method and system for rapid detection of microbial contamination in cereal foods. This method involves acquiring a cereal food sample to be tested and preprocessing it to generate a nucleic acid sample; performing infrared spectroscopy analysis on the nucleic acid sample to generate microbial hydrogen-containing group characteristic information; preparing specific nanoparticles based on the microbial hydrogen-containing group characteristic information; combining the specific nanoparticles with the specific microbial sample to generate a nanomicrobial sample; and then performing spectral analysis on the nanomicrobial sample using laser-induced breakdown spectroscopy to generate cereal food spectral data. The specific microbial sample is prepared based on the cereal food sample to be tested. The cereal food spectral data is input into a trained machine learning model to enable the model to identify the cereal food spectral data and generate an assessment result of the microbial contamination of the cereal food sample to be tested. In the technical solution used in this invention, infrared spectroscopy provides chemical information about microorganisms, while nanotechnology and laser-induced breakdown spectroscopy can enhance the signals of specific microorganisms. This invention combines infrared spectroscopy and laser-induced breakdown spectroscopy, inputting the resulting spectral data of grain foods into a trained machine learning model. This effectively improves the accuracy and sensitivity of assessing microbial contamination in the grain food samples to be tested. Furthermore, laser-induced breakdown spectroscopy enables quantitative analysis of microorganisms by performing spectral analysis on nano-microbial samples, solving the technical problem of the inability to quantitatively detect microorganisms in existing technologies. Simultaneously, based on the hydrogen-containing group characteristics of microorganisms, specific microbial samples can be screened. By combining specific nanoparticles with specific microbial samples, it is possible to detect specific microbial contamination in grain foods, thereby further improving the accuracy of microbial contamination detection in grain foods.

[0056] The rapid detection method for assessing microbial contamination in cereal foods provided in this application relates to the field of microbial detection technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing a method for calculating the vehicle's center of gravity sideslip angle, but is not limited to the above forms.

[0057] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in a first context of computer-executable instructions, such as program modules, executed by a computer. Firstly, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0058] The present application will be further described in detail below with reference to the accompanying drawings.

[0059] In one embodiment, such as Figure 1 As shown, this application discloses an assessment method for the rapid detection of microbial contamination in cereal foods, specifically including the following steps:

[0060] S101: Obtain the grain food sample to be tested.

[0061] In one embodiment, the step of generating a microbial sample by separating microorganisms from a cereal food sample to be tested using microfluidic chip technology and optical tweezers technology includes the following steps:

[0062] Microfluidic chip technology is used to filter, centrifuge, and chemically process the cereal food samples to be tested, thereby generating purified cereal food samples.

[0063] Multiple microbial cells were obtained from purified cereal food samples using optical tweezers technology to generate microbial samples.

[0064] S102: Preprocess the cereal food samples to be tested to generate nucleic acid samples.

[0065] In one embodiment, the step of preprocessing the cereal food sample to be tested to generate a nucleic acid sample includes the following steps:

[0066] Microbial samples are generated by separating microorganisms from cereal food samples using microfluidic chip technology and optical tweezers technology.

[0067] Nucleic acid extraction is performed on microbial samples to generate nucleic acid extracts;

[0068] Nucleic acid samples are generated by amplifying specific sequences from nucleic acid extracts using multiplex PCR technology.

[0069] Preferably, this scheme employs microfluidic chip technology to physically or chemically process cereal food samples to extract microbial cells, achieving the following beneficial technical effects: Microfluidic chips, through their microchannel design, can precisely control the capture, separation, and detection processes of cells, significantly improving the accuracy of single-cell analysis. Utilizing microfluidic dynamics principles, precise cell manipulation can be achieved, reducing sample loss and external interference. By constructing multiple microreaction chambers or microchannel networks on the microfluidic chip, large numbers of cell samples can be processed in parallel, achieving 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 onto a single chip, forming an integrated experimental platform. This integrated design simplifies experimental operations, reduces human error, and improves experimental reliability and repeatability. Materials with excellent biocompatibility and chemical stability, such as polydimethylsiloxane (PDMS) and glass, are selected to fabricate the microfluidic chip. These materials not only effectively reduce cell damage but also maintain long-term cell viability. Microfluidic chip analysis technology integrates multiple functional modules such as sample separation, enrichment, mixing, derivatization, reaction, and detection onto a single microchip. It manipulates fluids within micrometer-scale channels, offering advantages such as low sample consumption and short reaction times. Microfluidic chips can highly simulate the culture conditions and living environment of microorganisms, simultaneously performing pretreatment operations such as microbial sorting, cultivation, and isolation. Combined with SERS detection technology, it achieves highly sensitive and non-destructive detection of microorganisms, acquiring more Raman spectral information.

[0070] As can be seen from the above, using microfluidic chip technology to process cereal food samples can not only improve the accuracy and efficiency of microbial detection, but also reduce costs and sample consumption.

[0071] Furthermore, multiplex PCR technology can simultaneously detect the nucleic acid sequences of multiple microorganisms, thereby effectively improving the assessment efficiency of microbial contamination in cereal foods.

[0072] S103: Perform infrared spectroscopy analysis on nucleic acid samples to generate characteristic information of hydrogen-containing groups of microorganisms.

[0073] In one embodiment, the step of performing infrared spectroscopy analysis on nucleic acid samples to generate microbial hydrogen-containing group characteristic information includes the following steps:

[0074] The nucleic acid sample is used to prepare a liquid for testing.

[0075] Near-infrared spectroscopy is used to perform spectral scanning on the liquid to be tested, generating spectral data for nucleic acid samples.

[0076] After preprocessing the spectral data of nucleic acid samples, principal component analysis was used to generate characteristic information of hydrogen-containing groups of microorganisms.

[0077] In one specific embodiment, the spectral scanning of the liquid to be tested using near-infrared spectroscopy to generate nucleic acid sample spectral data is as follows:

[0078] Near-infrared spectrometers are used to perform spectral scanning on the liquid to be tested, collecting spectral data after the interaction of light of different wavelengths with the liquid to be tested, thus generating spectral data for nucleic acid samples. The liquid to be tested can be a common liquid or a homogeneous suspension.

[0079] In one specific embodiment, preprocessing of nucleic acid sample spectral data includes operations such as denoising, normalization, and baseline correction to improve data quality.

[0080] 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 transforms 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 enabling effective classification and identification.

[0081] Preferably, the infrared spectroscopy technique uses a spectral range of 700-2500 nm. Infrared spectroscopy can provide information about the vibrations 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 microorganisms can be obtained, which are direct reflections of the species and state of the microorganisms.

[0082] S104: Specific nanoparticles are prepared based on the characteristics of hydrogen-containing groups in microorganisms.

[0083] In one embodiment, the step of preparing specific nanoparticles based on the hydrogen-containing group characteristics of microorganisms includes the following steps:

[0084] Based on the hydrogen-containing group characteristics of microorganisms, specific microbial samples are obtained by screening from microbial samples;

[0085] Based on specific microbial samples, corresponding specific nanoparticles are prepared; wherein, specific microbial samples include, but are not limited to: aflatoxin samples, Escherichia coli samples, Listeria monocytogenes samples, Salmonella samples, and Staphylococcus aureus samples.

[0086] The embodiments of this invention can detect a variety of foodborne pathogens and contaminants, including aflatoxin, Escherichia coli, Listeria monocytogenes, Salmonella, and Staphylococcus aureus. The application of these technologies improves the sensitivity and accuracy of microbial contamination detection in cereal foods, meeting the need for rapid food safety testing.

[0087] S105: After combining specific nanoparticles with specific microbial samples to generate nanomicrobial samples, the nanomicrobial samples are subjected to spectral analysis using laser-induced breakdown spectroscopy to generate spectral data of cereal foods; wherein, the specific microbial samples are prepared based on the cereal food samples to be tested.

[0088] In one embodiment, the step of performing spectral analysis on nanomicrobial samples based on laser-induced breakdown spectroscopy to generate spectral data for cereal foods includes the following steps:

[0089] Laser pulses are applied to nanomicrobial samples using laser-induced breakdown spectroscopy to induce plasma generation in the nanomicrobial samples.

[0090] Collect spectral data of plasma emission to generate spectral data of cereal foods.

[0091] Preferably, the size of the specific nanoparticles is 1-100 nanometers, and the transverse flow detection platform can achieve rapid identification of microorganisms.

[0092] Nanotechnology and laser-induced breakdown spectroscopy (LIBS) are used to further detect and confirm the presence of microorganisms. Nanotechnology is applied here to enhance the LIBS signal, making the detection of microorganisms more sensitive and accurate. Nanoparticles can specifically bind to microorganisms or enhance their spectral characteristics, thereby producing a more pronounced signal in LIBS analysis.

[0093] In LIBS analysis, a focused pulsed laser is used to excite the sample, and a small amount of material is removed from its surface. The material is heated to 10,000 degrees Celsius or even higher. When the sample is excited by the high-energy pulsed laser, electrons in the outer electron shells are excited, creating electron vacancies and making the atoms unstable. These excited electrons and atomic nuclei form a plasma. After the pulsed laser stops, the plasma begins to cool, and electrons in the outer electron shells fill the vacancies step by step. During this process, the large amount of energy released by electrons moving between two energy levels or shells is emitted as light, depending on the element, forming a spectrum containing thousands of peaks.

[0094] Infrared spectroscopy provides chemical information about microorganisms, while nanotechnology and laser-induced breakdown spectroscopy can enhance the signals of specific microorganisms. The technical solution of this invention combines infrared spectroscopy and laser-induced breakdown spectroscopy, using the complementary spectral data of grain foods generated by these two technologies. This data is then input into a trained machine learning model, effectively improving the accuracy and sensitivity of assessing microbial contamination in the grain food samples being tested. Simultaneously, this dual detection mechanism combining infrared spectroscopy and laser-induced breakdown spectroscopy reduces false alarms and false negatives, improving the reliability of the detection.

[0095] S106: Input the spectral data of cereal foods into the trained machine learning model so that the trained machine learning model can identify the spectral data of cereal foods and generate an assessment result of the microbial contamination of the cereal food sample to be tested.

[0096] In one embodiment, the step of inputting spectral data of cereal foods into a trained machine learning model, so that the trained machine learning model can identify the spectral data of cereal foods and generate an assessment result of microbial contamination of the cereal food sample to be tested, includes the following steps:

[0097] Obtain a spectral dataset of cereal foods;

[0098] Spectral features of cereal food spectral datasets were extracted using principal component analysis.

[0099] The spectral features are input into a preset machine learning model, the preset machine learning model is trained, and cross-validation is performed to generate a trained machine learning model; wherein, the preset machine learning model can be any one of convolutional neural network, support vector machine, and random forest;

[0100] The spectral data of cereal foods are input into a trained machine learning model so that the trained machine learning model can identify the spectral data of cereal foods and generate an assessment result of the microbial contamination of the cereal food samples to be tested.

[0101] Furthermore, after obtaining the cereal food spectral dataset, the process also includes: performing data preprocessing operations on the cereal food spectral dataset, including denoising, normalization, deconvolution, etc., to improve data quality.

[0102] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this application.

[0103] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. Please refer to... Figure 2 , Figure 2 An assessment system for rapid detection of microbial contamination in cereal foods is illustrated, characterized in that the system includes: a sample acquisition module, a pretreatment module, a first spectral analysis module, a preparation module, a second spectral analysis module, and a contamination assessment module;

[0104] The sample acquisition module is used to acquire samples of grain foods to be tested.

[0105] The preprocessing module is used to preprocess the cereal food samples to be tested to generate nucleic acid samples;

[0106] The first spectral analysis module is used to perform infrared spectral analysis on nucleic acid samples to generate characteristic information of hydrogen-containing groups of microorganisms;

[0107] The preparation module is used to prepare specific nanoparticles based on the hydrogen-containing group characteristics of microorganisms;

[0108] The second spectral analysis module is used to combine specific nanoparticles with specific microbial samples to generate nanomicrobial samples. Then, the nanomicrobial samples are subjected to spectral analysis using laser-induced breakdown spectroscopy to generate spectral data of cereal foods. The specific microbial samples are prepared based on the cereal food samples to be tested.

[0109] The contamination assessment module is used to input spectral data of cereal foods into a trained machine learning model, so that the trained machine learning model can identify the spectral data of cereal foods and generate assessment results of microbial contamination of the cereal food samples to be tested.

[0110] Specific limitations regarding an assessment system for the rapid detection of microbial contamination in cereal foods can be found in the above-described limitations regarding an assessment method for the rapid detection of microbial contamination in cereal foods, and will not be repeated here. The modules in the aforementioned assessment system for the rapid detection of microbial contamination in cereal foods can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0111] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment 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.

[0112] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the electronic device includes:

[0113] The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, 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 this application.

[0114] The memory 802 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and called by the processor 801 to execute the vehicle center of gravity sideslip angle calculation method of the embodiments of this application.

[0115] The 803 input / output interface is used to implement information input and output.

[0116] The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0117] Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804);

[0118] The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0119] The electronic device's processor provides computational and control capabilities. Its memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The electronic device's database stores the database. Its network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements an assessment method for the rapid detection of microbial contamination in cereal foods.

[0120] 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 executes the computer program to perform the following steps:

[0121] Obtain samples of grain-based foods to be tested;

[0122] Preprocess the cereal food samples to be tested to generate nucleic acid samples;

[0123] Infrared spectroscopy analysis of nucleic acid samples generates characteristic information of hydrogen-containing groups in microorganisms;

[0124] Specific nanoparticles were prepared based on the characteristics of hydrogen-containing groups in microorganisms.

[0125] After combining specific nanoparticles with specific microbial samples to generate nanomicrobial samples, the nanomicrobial samples are subjected to spectral analysis using laser-induced breakdown spectroscopy to generate spectral data of cereal foods; wherein, the specific microbial samples are prepared based on the cereal food samples to be tested.

[0126] The spectral data of cereal foods are input into a trained machine learning model so that the trained machine learning model can identify the spectral data of cereal foods and generate an assessment result of the microbial contamination of the cereal food samples to be tested.

[0127] In one embodiment, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0128] Obtain samples of grain-based foods to be tested;

[0129] Preprocess the cereal food samples to be tested to generate nucleic acid samples;

[0130] Infrared spectroscopy analysis of nucleic acid samples generates characteristic information of hydrogen-containing groups in microorganisms;

[0131] Specific nanoparticles were prepared based on the characteristics of hydrogen-containing groups in microorganisms.

[0132] After combining specific nanoparticles with specific microbial samples to generate nanomicrobial samples, the nanomicrobial samples are subjected to spectral analysis using laser-induced breakdown spectroscopy to generate spectral data of cereal foods; wherein, the specific microbial samples are prepared based on the cereal food samples to be tested.

[0133] The spectral data of cereal foods are input into a trained machine learning model so that the trained machine learning model can identify the spectral data of cereal foods and generate an assessment result of the microbial contamination of the cereal food samples to be tested.

[0134] The assessment method and system for rapid detection of microbial contamination in cereal foods provided in this application embodiment involves: acquiring a cereal food sample to be tested and preprocessing it to generate a nucleic acid sample; performing infrared spectroscopy analysis on the nucleic acid sample to generate microbial hydrogen-containing group characteristic information; preparing specific nanoparticles based on the microbial hydrogen-containing group characteristic information; combining the specific nanoparticles with the specific microbial sample to generate a nanomicrobial sample; and performing spectral analysis on the nanomicrobial sample using laser-induced breakdown spectroscopy to generate cereal food spectral data; wherein the specific microbial sample is prepared based on the cereal food sample to be tested; and 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 in the cereal food sample to be tested. In the technical solution used in this invention, infrared spectroscopy provides chemical information about microorganisms, while nanotechnology and laser-induced breakdown spectroscopy can enhance the signals of specific microorganisms. This invention combines infrared spectroscopy and laser-induced breakdown spectroscopy, inputting the resulting spectral data of grain foods into a trained machine learning model. This effectively improves the accuracy and sensitivity of assessing microbial contamination in the grain food samples to be tested. Furthermore, laser-induced breakdown spectroscopy enables quantitative analysis of microorganisms by performing spectral analysis on nano-microbial samples, solving the technical problem of the inability to quantitatively detect microorganisms in existing technologies. Simultaneously, based on the hydrogen-containing group characteristics of microorganisms, specific microbial samples can be screened. By combining specific nanoparticles with specific microbial samples, it is possible to detect specific microbial contamination in grain foods, thereby further improving the accuracy of microbial contamination detection in grain foods.

[0135] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can 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 a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0136] Those skilled in the art will clearly understand that for the sake of convenience and brevity 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.

[0137] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An assessment method for rapid detection of microbial contamination in cereal foods, characterized in that, The method includes: Obtain samples of grain-based foods to be tested; The grain food sample to be tested is pretreated to generate a nucleic acid sample; Infrared spectroscopy analysis was performed on the nucleic acid sample to generate characteristic information of hydrogen-containing groups of microorganisms; Based on the hydrogen-containing group characteristics of the microorganisms, specific nanoparticles were prepared. After combining the specific nanoparticles with a specific microbial sample to generate a nanomicrobial sample, the nanomicrobial sample is subjected to spectral analysis using laser-induced breakdown spectroscopy to generate spectral data of the cereal food; wherein, the specific microbial sample is prepared based on the cereal food sample to be tested; The spectral data of the cereal food is input into a trained machine learning model so that the trained machine learning model can identify the spectral data of the cereal food and generate an assessment result of the microbial contamination of the cereal food sample to be tested. The step of performing spectral analysis on the nanomicrobial sample using laser-induced breakdown spectroscopy to generate spectral data for cereal foods includes the following steps: A laser pulse is applied to the nanomicrobial sample according to the laser-induced breakdown spectroscopy technique to generate plasma in the nanomicrobial sample. Collect the spectral data of the plasma emission to generate the spectral data of the grain food; Specifically, the step of preprocessing the grain food sample to be tested to generate a nucleic acid sample includes the following steps: Microbial separation is performed on the grain food sample to be tested using microfluidic chip technology and optical tweezers technology to generate a microbial sample; Nucleic acid extraction was performed on the microbial sample to generate a nucleic acid extract; The nucleic acid extract is amplified with a specific sequence using multiplex PCR technology to generate the nucleic acid sample.

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

3. The assessment method for rapid detection of microbial contamination in cereal foods according to claim 2, characterized in that, The step of performing infrared spectroscopy analysis on the nucleic acid sample to generate microbial hydrogen-containing group characteristic information includes the following steps: The nucleic acid sample is used to prepare a liquid for testing. The liquid to be tested is spectrally scanned using near-infrared spectroscopy to generate spectral data for the nucleic acid sample. After preprocessing the spectral data of the nucleic acid sample, the characteristic information of the hydrogen-containing groups of the microorganism is generated according to the principal component analysis technique.

4. The assessment method for rapid detection of microbial contamination in cereal foods according to claim 3, characterized in that, The step of preparing specific nanoparticles based on the hydrogen-containing group characteristics of the microorganisms includes the following steps: Based on the hydrogen-containing group characteristics of the microorganisms, specific microbial samples are obtained by screening from the microbial samples; Based on the specific microbial samples, the corresponding specific nanoparticles are prepared; wherein the specific microbial samples include: aflatoxin samples, Escherichia coli samples, Listeria monocytogenes samples, Salmonella samples, and Staphylococcus aureus samples.

5. The assessment method for rapid detection of microbial contamination in cereal foods according to claim 4, characterized in that, The step of separating microorganisms from the grain food sample to be tested using microfluidic chip technology and optical tweezers technology to generate a microbial sample includes the following steps: The microfluidic chip technology is used to filter, centrifuge, and chemically process the grain food sample to be tested to generate a purified grain food sample. Multiple microbial cells are obtained from the purified cereal food sample using the optical tweezers technique to generate the microbial sample.

6. An assessment system for rapid detection of microbial contamination in cereal foods, applied to the assessment method for rapid detection of microbial contamination in cereal foods as described in any one of claims 1-5, characterized in that, The system includes: a sample acquisition module, a pretreatment module, a first spectral analysis module, a preparation module, a second spectral analysis module, and a contamination assessment module; The sample acquisition module is used to acquire the grain food sample to be tested; The preprocessing module is used to preprocess the grain 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 microbial hydrogen-containing group characteristic information; The preparation module is used to prepare specific nanoparticles based on the hydrogen-containing group characteristic information of the microorganisms; The second spectral analysis module is used to combine the specific nanoparticles with a specific microbial sample to generate a nanomicrobial sample, and then perform spectral analysis on the nanomicrobial sample using laser-induced breakdown spectroscopy to generate spectral data of the cereal food; wherein, the specific microbial sample is prepared based on the cereal food sample to be tested; The contamination assessment module is used to input the spectral data of the grain food into a trained machine learning model, so that the trained machine learning model can identify the spectral data of the grain food and generate an assessment result of the microbial contamination of the grain food sample to be tested.

7. 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, it implements the steps of an assessment method for rapid detection of microbial contamination in cereal foods as described in any one of claims 1 to 5.

8. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of an assessment method for rapid detection of microbial contamination in cereal foods as described in any one of claims 1 to 5.

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