A method for rapid detection of eight marine Bacillus species and its application in fermented foods

Abstract

The present invention discloses a method for rapid detection of eight types of marine Bacillus and its application in fermented foods, belonging to the field of microbial detection and ecological applications. The method of the present invention is used to perform Raman detection on eight strains of marine Bacillus, and a training model is established using the characteristic bands of marine Bacillus to identify unknown strains. The method is applied to Daqu to prepare a Daqu suspension sample by gradient centrifugation, homogenization or vortexing, and Raman detection is performed on single cells. The established marine Bacillus training model is used to perform machine learning on the Raman spectra of unknown bacteria to predict the type of strain, thereby achieving real-time, rapid, and label-free detection of marine Bacillus in Daqu and determining its marine Bacillus type. The detection and analysis time can be shortened to a few minutes.

Description

Technical Field

[0001] The invention relates to a method for rapidly detecting eight types of marine bacilli and application thereof in fermented foods, belonging to the field of microbial detection and ecological application. Background Art

[0002] Marine Bacillus, belonging to the phylum Firmicutes, class Bacillus, order Nucleocapsidales, and family Bacillaceae, is a Gram-positive bacterium that is aerobic or facultatively anaerobic. It produces spores, most of which are non-capsulated and highly stable to environmental conditions such as heat, gastric acid, and humidity. Marine Bacillus has been isolated from various environmental samples, including deep-sea sediment cores, salt pans, fermented shrimp paste, soy sauce, and traditional fermented foods such as kimchi and pickled cucumbers. Marine Bacillus produces numerous metabolites with broad antimicrobial spectrum activity, low pH sensitivity, and excellent thermal stability. Furthermore, Bacillus produces metabolites with strong protease, lipase, and amylase activities. These metabolites can be used as feed additives to improve feed conversion and effectively enhance the intestinal ecology of farmed animals, thus possessing extensive applications in food science and other fields. The brewing process is an extremely complex and natural multi-microbial fermentation process, and the composition of the environmental microbial community during the brewing process is often a key factor in determining the yield and quality of fermented foods. Literature has reported that marine Bacillus is a key strain that influences and determines the production of flavor compounds in baijiu (white liquor). It facilitates the saccharification of starch and the breakdown of proteins in the Daqu (daqu) raw material. Marine Bacillus has also been found in fermented vegetables like kimchi and pickled cucumbers, and is believed to metabolize complex flavor compounds and secrete a rich enzyme system. Therefore, rapid and reliable identification of marine Bacillus species is essential during the fermentation process of fermented foods. By identifying marine Bacillus species and changes in their content, fermentation progress or abnormalities can be monitored, ultimately ensuring product quality during food production.

[0003] At present, the rapid identification of marine Bacillus mainly includes standard plate colony counting, polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). Colony counting is a standard method for bacterial detection, but it takes 24 to 72 hours, is labor-intensive and time-consuming. ELISA and PCR have high sensitivity, shortening the detection time to a few hours, but cross-reactions may occur between antibodies in ELISA reactions, and protein samples have a short retention time. PCR is prone to false positives for bacteria, and both require multiple pretreatment steps, precision instruments and professional operations. Marine Bacillus mostly produces bacteria with a size of 0.6 to 0.9 × 1.0 -1.5Microspores are small, and extracting their genomes is particularly challenging for spores that contain heat-resistant enzymes. Consequently, detection of marine Bacillus species based on plate counts, ELISA, and PCR techniques is often slow and difficult. To overcome these limitations, there is a need for methods that can detect marine Bacillus species and their spores with shorter analysis times and higher sensitivity.

[0004] Raman spectroscopy is a type of scattering spectrum. Based on the Raman scattering effect discovered by Indian scientist C.V. Raman, Raman spectroscopy analyzes scattered light at frequencies different from the incident light to obtain information about molecular vibrations and rotations, which is then applied to molecular structure research. Generally speaking, a Raman spectrum is a unique chemical fingerprint for a specific molecule or material. In recent years, Raman spectroscopy has been found to have applications in biology, including analysis of single cells. The Raman spectrum of a single cell is a superposition of the vibrational modes of its intracellular components, composed of Raman peaks corresponding to each type of chemical bond. This provides multidimensional information about the composition and relative abundance of metabolites within a single cell. Raman spectroscopy can be used to differentiate between bacteria and fungi and identify storage compounds and cytochromes within individual microbial cells. Raman spectroscopy enables label-free, rapid, and in situ detection of microorganisms at the species level. This approach identifies strain types with high sensitivity, specificity, and accuracy. Compared to plate culture methods, it can also be used to analyze uncultured microorganisms. In the publicly authorized patents CN104136908B and CN102272585B, Raman spectroscopy technology processes culture and blood samples through a sealed separation device, thereby enabling effective Raman analysis. However, for special food fermentation process samples, such as the solid raw materials of barley, wheat and other raw materials attached to the bran shells of Daqu microorganisms, and the vegetable leaf raw materials attached to kimchi microorganisms, it is necessary to optimize the sample processing to facilitate the rapid Raman detection of marine Bacillus and its spores. The advantage of traditional Raman spectroscopy machine learning methods for strain identification is that they can handle binary classification problems, but for the multi-classification problem of marine Bacillus at the species level, it is necessary to design corresponding strategies and increase the modeling complexity to achieve accurate identification of key microorganisms marine Bacillus, and then determine the types and proportions of marine Bacillus in the fermentation process. This is of great significance for providing food brewing quality assurance measures and reducing pollution in the brewing industry. Summary of the Invention

[0005] Since conventional marine Bacillus identification takes a long time and has poor specificity, it is not suitable for brewing companies to quickly identify the types and contents of marine Bacillus during the fermentation process to guide production. It is necessary to develop a method for rapid identification and proportion determination of marine Bacillus in fermented food samples rich in marine Bacillus.

[0006] The purpose of the present invention is to provide a method for detecting marine Bacillus based on gold nanoparticle Raman spectroscopy. The method detects eight marine Bacillus species with high abundance in traditional brewing process after pretreatment. The characteristic peaks of marine Bacillus include 744cm -1 、746cm -1 、748cm -1 , 750cm -1 、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm -1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1 , these characteristic bands of marine Bacillus were selected, and a supervised machine learning algorithm was used to train 8 types of marine Bacillus. The trained supervised algorithm can give accurate classification standards. Compared with other strain identification models, the strain identification time is shorter and the accuracy is higher.

[0007] Fermented food samples, such as daqu (Chinese koji), fermented mash, and kimchi, require the identification of marine Bacillus species and content to guide production. This invention utilizes specialized pretreatment for complex, solid-state fermentation samples rich in marine Bacillus. Unlike conventional sample processing, this method utilizes gradient centrifugation, homogenization, and vortexing to prepare bacterial suspensions. This treatment maximizes sample biomass, resulting in extremely low background Raman noise and a high single-cell spectral signal-to-noise ratio. This invention enables real-time, rapid, and label-free detection of marine Bacillus in fermentation samples, offering the advantages of high timeliness, high sensitivity, strong specificity, and the ability to perform large-scale online testing.

[0008] The present invention provides a neural network discriminant analysis classification model for identifying the species of marine Bacillus strains. The neural network model is established according to the following steps:

[0009] (1) Preparation of marine Bacillus suspension:

[0010] Oceanobacillus caeni, Oceanobacillus kimchii, Oceanobacillus siheyensis, Oceanobacillus sojae, Oceanobacillus oncorhynchi, Oceanobacillus profundus, Oceanobacillus jeddahense, and Oceanobacillus kapialis strains were inoculated into a culture medium for fermentation, and the culture fluids obtained at different culture times were obtained to obtain marine Bacillus suspensions.

[0011] (2) Pretreatment of marine Bacillus suspension:

[0012] After centrifuging the bacterial suspension prepared in step (1), the precipitate was collected, sterile water was added to the precipitate, and then centrifuged. This was repeated 2 to 3 times to obtain a pretreated marine Bacillus suspension.

[0013] (3) Preparation of gold nanoparticles:

[0014] Using the trisodium citrate heating reduction method, ultrapure water and a 10 mg / mL potassium chloroaurate solution were added to a flask, mixed evenly, and heated to boiling. 0.1% trisodium citrate solution was then quickly added and the boiling was continued. When the solution turned purple-red, heating was stopped to obtain gold nanoparticles with a diameter of 10 to 50 nm.

[0015] (4) Raman spectroscopy detection:

[0016] The marine Bacillus suspension obtained in step (2) and the gold nanoparticles obtained in step (3) were mixed and then single-cell Raman spectrum was collected, wherein the spectrum collection conditions were: using a 532 nm laser and scanning the spectrum range from 500 to 3750 cm -1 , the laser intensity was 1-300 mW, the acquisition time was 1-20 s / time, the cumulative number of times was 1, and 50-2000 cells were collected from the marine Bacillus suspensions with different culture times;

[0017] (5) Processing of single-cell Raman spectroscopy data:

[0018] The single-cell Raman spectroscopy data obtained in step (4) are subjected to cosmic ray elimination, background noise removal, baseline correction, Savitzky-Golay smoothing, and all data are normalized;

[0019] (6) Model building:

[0020] Use machine learning to establish a neural network discriminant analysis classification model for marine Bacillus strains: Use the neural network machine learning algorithm to select the 744 cm -1 、746cm -1 、748cm -1 , 750cm -1 、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm -1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1 The characteristic bands are used for machine learning, and a training data set and a test data set are set. The training data set is 70% of the collected data, and the test data set is 30% of the collected data.

[0021] The parameters of the neural network machine learning algorithm are: 100 hidden layers, ReLu activation function, Adam optimizer, and learning rate 0.0001.

[0022] In one embodiment of the present invention, gold nanoparticles are prepared by using a trisodium citrate heating reduction method. 47 mL of ultrapure water and 3 mL of a 10 mg / mL potassium chloroaurate solution are added to a flask. After mixing evenly, the mixture is heated to boiling at 120°C. 2 mL of a 0.1% trisodium citrate solution is quickly added and the mixture is kept boiling. When the solution turns purple-red, heating is stopped. The resulting gold nanoparticles have a diameter of 10 to 50 nm. After cooling to room temperature, the mixture is stored at 4°C in the dark.

[0023] In one embodiment of the present invention, the marine Bacillus includes but is not limited to: Oceanobacilluscaeni CGMCC1.10860, Oceanobacillus kimchii JCM16803, Oceanobacillus iheyensisCGMCC1.8643, Oceanobacillus sojae NBRC105379, Oceanobacillus oncorhynchiCGMCC1.8877, Oceanobacillus profundus CGMCC1.15219, Oceanobacillus jeddahenseDSM28586, and Oceanobacillus kapialis DSM 23158.

[0024] In one embodiment of the present invention, the different culture times in step (1) are culture solutions obtained by culturing for 6 hours, 12 hours, and 24 hours.

[0025] In one embodiment of the present invention, in the Raman spectroscopy detection in step (4), the laser intensity is 3 mW, the acquisition time is 2 s / time, the cumulative number of times is 1, and 100 cells are collected from the marine Bacillus suspensions with different culture times.

[0026] In one embodiment of the present invention, the bacterial suspension in step (4) is mixed with gold nanoparticles and placed on a Raman chip, and then dried for Raman spectrum detection; the volume ratio of the gold nanoparticles to the bacterial suspension is (1:1) to (1:1×10 5 ).

[0027] In one embodiment of the present invention, the volume ratio of the gold nanoparticles to the bacterial suspension is 1:1.

[0028] In one embodiment of the present invention, the Raman chip is an aluminum-plated Raman chip.

[0029] In one embodiment of the present invention, in the Raman spectroscopy detection in step (4), the single cell includes a Bacillus with a size of 1 to 2 μm and a size of 0.6 to 0.9×1.0 -1.5 μm spores.

[0030] The present invention also provides a method for rapidly identifying and detecting marine Bacillus in a sample, the method comprising the following steps:

[0031] (1) Collect samples;

[0032] (2) Sample pretreatment:

[0033] Add the solution to the sample obtained in step (1), stir, shake with a homogenizer or a vortex instrument, let it stand, centrifuge and take the supernatant, centrifuge the obtained supernatant and take the precipitate;

[0034] The precipitate was added with sterile water and centrifuged, and this was repeated 2 to 3 times to obtain the pretreated sample;

[0035] (3) Preparation of gold nanoparticles:

[0036] Using the trisodium citrate heating reduction method, ultrapure water and a 10 mg / mL potassium chloroaurate solution were added to a flask, mixed evenly, and heated to boiling. 0.1% trisodium citrate solution was then quickly added and the boiling was continued. When the solution turned purple-red, heating was stopped to obtain gold nanoparticles with a diameter of 10 to 50 nm.

[0037] (4) Raman spectroscopy detection:

[0038] The sample obtained in step (2) and the gold nanoparticles obtained in step (3) were mixed and then single-cell Raman spectrum was collected. The spectrum collection conditions were as follows: using a 532 nm laser and scanning the spectrum range from 500 to 3750 cm -1 , the laser intensity was 1-300 mW, the acquisition time was 1-20 s / time, the cumulative number of times was 1, and 50-2000 cells were collected from the marine Bacillus suspensions with different culture times;

[0039] (5) Processing of single-cell Raman spectroscopy data:

[0040] The single-cell Raman spectroscopy data obtained in step (4) are subjected to cosmic ray elimination, background noise removal, baseline correction, Savitzky-Golay smoothing, and all data are normalized;

[0041] (6) Identification of marine Bacillus

[0042] The above neural network discriminant analysis classification model was used to identify the unknown strains, and the characteristic band 744 cm of the single cell Raman spectroscopy data of the sample obtained in step (5) was -1 、746cm -1 、748cm -1 , 750cm -1 、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm-1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1 By inputting the data into the neural network discriminant analysis classification model, the eight types of marine Bacillus in the sample can be identified. According to the model strain classification standard, if the score is greater than or equal to 0.9, the strain with the highest score is identified as this strain; if the score is less than 0.9, it is identified as not belonging to marine Bacillus, but to other types of strains.

[0043] In one embodiment of the present invention, the marine Bacillus is: Oceanobacillus caeni, Oceanobacillus kimchii, Oceanobacillus iheyensis, Oceanobacillus sojae, Oceanobacillus oncorhynchi, Oceanobacillus profundus, Oceanobacillusjeddahense, Oceanobacillus kapialis.

[0044] In one embodiment of the present invention, step (2) is as follows: 5 g of the sample obtained in step (1) is added to 20 mL of the solution, mixed evenly by homogenizer or vortexer, allowed to stand, centrifuged at a low speed of 500 rpm, and the supernatant is collected;

[0045] The supernatant was centrifuged at 7000 rpm to collect the precipitate; the precipitate was repeatedly rinsed with sterile water and centrifuged three times.

[0046] In one embodiment of the present invention, the sample in step (1) refers to a solid or liquid raw material mixture containing multiple microorganisms, the block or powdered fermentation sample is crushed into powder using a mortar or grinder, and the liquid fermentation sample is filtered using gauze.

[0047] In one embodiment of the present invention, the sample obtained in step (1) is added to the solution, stirred, and rotated using a homogenizer for 10 to 60 minutes at a speed of 3 to 12 times / second, or vortexed using a vortexer for 15 to 90 minutes, then allowed to stand for 2 to 5 minutes, the solution is aspirated, and the solution is centrifuged at 500 rpm for 2 minutes, and the supernatant is retained;

[0048] Remove the supernatant and centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Rinse with sterile water and centrifuge three times.

[0049] In one embodiment of the present invention, the sample includes but is not limited to Daqu, fermented grains, brewed food, and fermented food.

[0050] The present invention also provides application of the above method in detecting the relative content of marine bacillus in fermentation sample microorganisms.

[0051] In one embodiment of the present invention, the marine Bacillus includes but is not limited to: Oceanobacillus caeni, Oceanobacillus kimchii, Oceanobacillus iheyensis, Oceanobacillus sojae, Oceanobacillus oncorhynchi, Oceanobacillus profundus, Oceanobacillus jeddahense, and Oceanobacillus kapialis.

[0052] In one embodiment of the present invention, the application is:

[0053] According to the above method, 300 to 10,000 single-cell Raman spectra were collected for microorganisms in the fermentation sample community;

[0054] The above input single cell Raman spectrum was selected at 744 cm -1 、746cm -1 、748cm -1 , 750cm -1 、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm -1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1The characteristic bands were input into the above-mentioned marine Bacillus neural network discriminant analysis classification model to output the unknown single-cell types in the fermentation sample. The total number of single-cell categories was counted using the output 300 to 10,000 single-cell prediction results, and the ratio of the number of each marine Bacillus to the total number of single cells was calculated to obtain the relative content of marine Bacillus in the microorganisms of the fermentation sample.

[0055] In one embodiment of the present invention, the percentage of marine Bacillus in the fermentation sample microorganisms is calculated. The total number of output single cell types is counted, and the ratio of the number of each marine Bacillus to the total number of single cells is calculated to obtain the relative content of marine Bacillus in the fermentation sample microorganisms.

[0056] The calculation formula is:

[0057]

[0058] In one embodiment of the present invention, the percentage of marine Bacillus in the fermentation sample microorganisms requires that 300 to 10,000 single-cell Raman spectra be collected to calculate the proportion of marine Bacillus in the fermentation sample microbial community. A larger number of single-cell Raman spectra collected can better represent the fermentation sample microbial community. In this embodiment, the collection amount is 800 to 1,000 single-cell Raman spectra.

[0059] Beneficial effects

[0060] (1) The present invention uses gold nanoparticles to establish a Raman spectrum standard library of marine Bacillus with different growth cycles. Marine Bacillus (including spores) in different fermentation cycles are included in the marine Bacillus Raman spectrum standard library, which has strong adaptability. The prediction model of marine Bacillus can be reused. After it is established once, as long as the same Raman spectrum measurement conditions are maintained, the single cell to be identified can be directly input into the model for identification.

[0061] (2) Complex substances in fermentation samples are prone to produce fluorescence Raman interference. The direct separation of cereals, vegetable substances and microorganisms can be achieved by combining sample homogenization and gradient centrifugation. The present invention is easier to use in actual production in fermentation food factories, has low cost and is simple to operate.

[0062] (3) The present invention uses a neural network based on the characteristic bands of the Raman spectrum of marine Bacillus to predict strains. Compared with other strain identification models, the sample detection time can be shortened to about 1 hour, and the accuracy rate can reach more than 90%. The strain identification time is shorter and the accuracy rate is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 : Raman spectra of 8 marine Bacillus in Example 1 of the present invention.

[0064] Figure 2 : The model confusion matrix obtained by neural network training in Example 1 of the present invention. DETAILED DESCRIPTION

[0065] The present invention will be further described below with reference to specific embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0066] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a" does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0067] The microbial community structure described in the following examples refers to the collection of different single cells in a fermentation sample using single-cell Raman spectroscopy within a certain space.

[0068] The Daqu, fermented grains, shrimp paste, and kimchi involved in the following examples were sampled from: Maotai Town Distillery, Dalian shrimp paste, and Korean kimchi fermentation process.

[0069] The Oceanobacillus caeni CGMCC1.10860, Oceanobacillus siheyensis CGMCC1.8643, Oceanobacillus oncorhynchi CGMCC1.8877, and Oceanobacillus profundus CGMCC1.15219 involved in the following examples were purchased from China General Microbiological Culture Collection Center.

[0070] The Oceanobacillus jeddahense DSM28586 and Oceanobacillus skapialis DSM 23158 involved in the following examples were purchased from the DSMZ strain bank in Germany.

[0071] The Oceanobacillus kimchii JCM16803 involved in the following examples was purchased from the Japan JCM Collection.

[0072] The Oceanobacillus sojae NBRC105379 involved in the following examples was purchased from the NBRC Type Culture Collection in Japan.

[0073] The Oceanobacillus caeni, Oceanobacillus kimchii, Oceanobacillus iheyensis, Oceanobacillus sojae, Oceanobacillus oncorhynchi, Oceanobacillus profundus, Oceanobacillus jeddahense, and Oceanobacillus kapialis involved in the following examples are numbered OB1, OB2, OB3, OB4, OB5, OB6, OB7, and OB8, respectively.

[0074] The culture medium involved in the following examples is as follows:

[0075] Beef extract peptone medium: weigh 10.0 g / L peptone, 3.0 g / L beef extract, 5.0 g / L NaCl, and 22 g / L agar, pH 7.0-7.2, 121°C, 0.1 MPa, and sterilize for 30 min.

[0076] The calculation of the percentage of marine Bacillus in the fermentation sample microorganisms involved in the following examples is:

[0077] The total number of output single cell categories is counted, and the ratio of the number of each marine Bacillus to the total number of single cells is calculated to obtain the relative content of marine Bacillus in the fermentation sample microorganisms. The calculation formula is:

[0078]

[0079] Example 1: Construction of a neural network discriminant analysis classification model

[0080] (1) Preparation of marine Bacillus suspension

[0081] Oceanobacillus caeni CGMCC1.10860, Oceanobacillus kimchiiJCM16803, Oceanobacillus iheyensis CGMCC1.8643, Oceanobacillus sojaeNBRC105379, Oceanobacillus oncorhynchi CGMCC1.8877, Oceanobacillus profundusCGMCC1.15219, Oceanobacillus jeddahense DSM28586, and Oceanobacillus kapialis DSM23158 were inoculated into beef extract peptone medium and cultured at 37°C for 24 hours. The culture fluids of the marine Bacillus after 6 hours, 12 hours, and 24 hours of culture were collected to obtain marine Bacillus suspensions.

[0082] (2) Pretreatment of marine Bacillus suspension:

[0083] 1 mL of culture solution cultured for 6 h, 12 h, and 24 h was respectively aspirated into a 1.5 mL centrifuge tube, centrifuged at 7000 rpm for 2 min, the supernatant was discarded, 500 μL of sterile water was added to the precipitate, and the mixture was mixed by pipetting. The supernatant was discarded, 500 μL of sterile water was added to the precipitate, and the mixture was mixed by pipetting. The above steps were repeated 3 times to prepare marine Bacillus suspensions cultured for 6 h, 12 h, and 24 h, respectively.

[0084] (3) Preparation of gold nanoparticles:

[0085] Using the trisodium citrate heating reduction method, add 47mL of ultrapure water and 3mL of 10mg / mL potassium chloroaurate solution into a flask, mix well and heat to boiling, then quickly add 2mL of 0.1% trisodium citrate solution and continue boiling. When the solution turns purple-red, stop heating. The resulting gold nanoparticles have a diameter of 10-50nm. After cooling to room temperature, store at 4℃ in the dark.

[0086] (4) Raman spectroscopy detection

[0087] The bacterial suspension obtained in step (2) after culturing for 6 h, 12 h, and 24 h was mixed with gold nanoparticles in a volume ratio of 1:1, and 2.5 μL was applied to an aluminum-coated Raman chip and allowed to stand for 10 min to air-dry.

[0088] Single-cell Raman spectra were collected using a confocal Raman spectrometer with an excitation wavelength of 532 nm and a scanning spectral range of 500–3750 cm -1 , the power under the microscope is 3mW, and the scanning time is 5s.

[0089] 100 samples of each marine Bacillus were collected from the bacterial suspension cultured for 6h, 12h, and 24h. The Raman spectra were subjected to cosmic ray removal, baseline correction (iterative adaptive weighted penalized least squares method, airPLS), Savitzky-Golay smoothing, and normalization to obtain the Raman spectral standard database ( Figure 1 ).

[0090] (5) Using machine learning to establish a neural network discriminant analysis classification model for marine Bacillus strains: Using a neural network machine learning algorithm, the single-cell Raman spectrum data of marine Bacillus obtained in step (4) was selected at 744 cm -1 、746cm -1 、748cm -1 , 750cm -1 、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm -1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1 The characteristic bands are used for machine learning, and a training data set and a test data set are set. The training data set is 70% of the collected data, and the test data set is 30% of the collected data.

[0091] The parameters of the neural network machine learning algorithm are: 100 hidden layers, ReLu activation function, Adam optimizer, and learning rate 0.0001.

[0092] Obtain the neural network model evaluation parameters and confusion matrix of 8 bacteria ( Figure 2 ), the obtained model has the best effect.

[0093] Example 2: Identification of 8 marine Bacillus species in Daqu

[0094] Daqu, the fermentation base for baijiu, is mostly made from wheat. This wheat is ground with water, mixed with the mother koji, and pressed into briquettes. This briquettes, accumulated in a storage tank, form the daqu, which contains various bacteria and enzymes. Daqu plays a crucial role in baijiu brewing, including inoculating microorganisms, feeding grains, saccharifying and fermenting, and producing aroma. Researching marine spore species can help better control baijiu flavor compounds, improve traditional craftsmanship, and produce higher-quality baijiu, ultimately enhancing the state-of-the-art in baijiu brewing.

[0095] (1) Sample processing

[0096] Three kinds of Daqu samples were collected from different qu rooms, and the qu blocks were crushed in a mortar to a size of 1-10 mm. After mixing the powders, 5 g was weighed, added with 20 mL of sterile water, and vibrated in a homogenizer for 10 minutes at a speed of 10 times / second, working for 5 seconds, and resting for 5 seconds to ensure that the microorganisms are fully dispersed in the sterile water solvent.

[0097] Then, the solution was allowed to stand for 5 minutes, 6 mL of the solution was aspirated, and the solution was centrifuged at 500 rpm for 2 minutes, and the supernatant was retained;

[0098] Remove the supernatant and centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Rinse with sterile water and centrifuge three times.

[0099] Pretreated Daqu samples 1 to 3 were prepared respectively.

[0100] Preparation of gold nanoparticles:

[0101] Using the trisodium citrate heating reduction method, add 47mL of ultrapure water and 3mL of 10mg / mL potassium chloroaurate solution into a flask, mix well and heat to boiling, then quickly add 2mL of 0.1% trisodium citrate solution and continue boiling. When the solution turns purple-red, stop heating. The resulting gold nanoparticles have a diameter of 10-50nm. After cooling to room temperature, store at 4℃ in the dark.

[0102] (2) The pretreated Daqu samples 1 to 3 were mixed with gold nanoparticles in a volume ratio of 1:1, and 2.5 μL was placed on an aluminum-coated Raman chip. The mixture was air-dried for 10 min and single-cell Raman spectra were collected using a confocal Raman spectrometer. The excitation wavelength was 532 nm and the scanning spectral range was 500–3750 cm -1 ; The power under the microscope was 3 mw, and the scanning time was 2 s; the number of accumulations was: 1, followed by cosmic ray removal, baseline correction (iterative adaptive weighted penalized least squares method, airPLS), Savitzky-Golay smoothing, and normalized Raman data processing to obtain the Raman spectrum of Daqu microorganisms. 1,000 single-cell Raman spectra were collected for each type of Daqu.

[0103] (3) The above neural network discriminant analysis classification model was used to identify the unknown strains, and the single-cell Raman spectroscopy data of the sample obtained in step (2) was selected at 744 cm -1 、746cm -1 、748cm -1 , 750cm -1 、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm -1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1 The characteristic bands are input into the neural network discriminant analysis classification model obtained in Example 1, and the eight types of marine Bacillus in the sample can be identified; according to the model strain classification standard, if the score is greater than or equal to 0.9, the strain with the highest score is identified as this strain; if the score is less than 0.9, it is identified as not belonging to marine Bacillus, but to other types of strains.

[0104] Based on the model strain identification and classification criteria, a neural network discriminant model was used to identify 3,000 marine Bacillus cells collected. The results are shown in Table 1.

[0105] Table 1: Proportions of marine Bacillus in different types of Daqu

[0106]

[0107] The results showed that by counting the identified species of 1,000 single cells in each Daqu, the proportion of marine Bacillus in the Daqu microbial community could be obtained.

[0108] Furthermore, using the method of the present invention, the detection and analysis time for a single cell is 2 s, while the detection and analysis time for 1000 single cells in each Daqu is 2000 s, i.e., 0.33 h.

[0109] Example 3: Identification of 8 marine Bacillus species in fermented grains

[0110] The brewing process of baijiu (white liquor) involves a process called heap fermentation. This involves the addition of high-temperature koji (dàqu), which is then piled on the ground for a period of time before being fermented in a cellar. This heaping process enriches the functional microorganisms. The identification of the types and concentrations of marine Bacillus species in this process allows for better monitoring of the dynamic microbial changes during heap fermentation.

[0111] (1) Sample processing

[0112] Two samples of mash from the first and second rounds of Maotai-flavor liquor were collected respectively. The mash was crushed in a mortar to a size of 1-10 mm. After mixing the powders, 5 g was weighed and added with 20 mL of sterile water. The mixture was shaken in a homogenizer for 10 minutes at a speed of 10 times / second, working for 5 seconds and resting for 5 seconds to ensure that the microorganisms were fully dispersed in the sterile water solvent.

[0113] Then, the solution was allowed to stand for 5 minutes, 6 mL of the solution was aspirated, and the solution was centrifuged at 500 rpm for 2 minutes, and the supernatant was retained;

[0114] Remove the supernatant and centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Rinse with sterile water and centrifuge three times.

[0115] Pretreated mash samples 1 to 2 were prepared respectively.

[0116] Preparation of gold nanoparticles:

[0117] Using the trisodium citrate heating reduction method, add 47mL of ultrapure water and 3mL of 10mg / mL potassium chloroaurate solution into a flask, mix well and heat to boiling, then quickly add 2mL of 0.1% trisodium citrate solution and continue boiling. When the solution turns purple-red, stop heating. The resulting gold nanoparticles have a diameter of 10-50nm. After cooling to room temperature, store at 4℃ in the dark.

[0118] (2) The pretreated mash samples 1-2 were mixed with gold nanoparticles in a volume ratio of 1:1, and 2.5 μL was taken onto an aluminum-coated Raman chip. The mixture was allowed to stand for 10 min and air-dried. Single-cell Raman spectra were collected using a confocal Raman spectrometer with an excitation wavelength of 532 nm and a scanning spectral range of 500-3750 cm -1 ; The power under the microscope was 3 mw, and the scanning time was 2 s; the number of accumulations was: 1, followed by cosmic ray removal, baseline correction (iterative adaptive weighted penalized least squares method, airPLS), Savitzky-Golay smoothing, and normalized Raman data processing to obtain the Raman spectrum of Daqu microorganisms. 1000 single-cell Raman spectra were collected for each mash.

[0119] (3) The above neural network discriminant analysis classification model was used to identify the unknown strains, and the single-cell Raman spectroscopy data of the sample obtained in step (2) was selected at 744 cm -1 、746cm -1 、748cm -1 , 750cm -1 、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm -1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1 The characteristic bands are input into the neural network discriminant analysis classification model obtained in Example 1, and the eight types of marine Bacillus in the sample can be identified; according to the model strain classification standard, if the score is greater than or equal to 0.9, the strain with the highest score is identified as this strain; if the score is less than 0.9, it is identified as not belonging to marine Bacillus, but to other types of strains.

[0120] Based on the model strain identification and classification criteria, a neural network discriminant model was used to identify 2,000 marine Bacillus cells collected. The results are shown in Table 2.

[0121] Table 2: Proportions of marine Bacillus in different types of mash

[0122]

[0123]

[0124] The results showed that the proportion of marine Bacillus in the microbial community of each mash can be obtained by counting the identified species of 1000 single cells in each mash.

[0125] Moreover, using the method of the present invention, the detection and analysis time of a single cell is 2 s, while the detection and analysis time of 1000 single cells in each mash is 2000 s, that is, 0.33 h.

[0126] Example 4: Identification of 8 marine Bacillus species in shrimp paste

[0127] Shrimp paste is a viscous paste-like food made by fermenting small shrimps or shrimp processing by-products. The microorganisms on the surface and inside the shrimp are the main microorganisms in shrimp paste fermentation. Studies have shown that Bacillus is an important component of the shrimp paste microbial community, among which marine Bacillus strains have been detected in the Bacillus genus, so marine Bacillus in shrimp paste can be quickly identified.

[0128] (1) Sample processing

[0129] Four shrimp paste samples were collected and filtered through three layers of sterile gauze. 5 g of the filtered liquid was mixed and weighed, 20 mL of sterile water was added, and the mixture was shaken in a homogenizer for 10 minutes at a speed of 10 times / second, working for 5 seconds, and resting for 5 seconds to fully disperse the microorganisms in the sterile water solvent.

[0130] Then, the solution was allowed to stand for 5 minutes, 6 mL of the solution was aspirated, and the solution was centrifuged at 500 rpm for 2 minutes, and the supernatant was retained;

[0131] Remove the supernatant and centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Rinse with sterile water and centrifuge three times.

[0132] Pretreated shrimp paste samples 1 to 4 were prepared respectively.

[0133] Preparation of gold nanoparticles:

[0134] Using the trisodium citrate heating reduction method, add 47mL of ultrapure water and 3mL of 10mg / mL potassium chloroaurate solution into a flask, mix well and heat to boiling, then quickly add 2mL of 0.1% trisodium citrate solution and continue boiling. When the solution turns purple-red, stop heating. The resulting gold nanoparticles have a diameter of 10-50nm. After cooling to room temperature, store at 4℃ in the dark.

[0135] (2) The pretreated shrimp paste samples 1 to 4 were mixed with gold nanoparticles in a volume ratio of 1:1, and 2.5 μL was placed on an aluminum-coated Raman chip. The mixture was air-dried for 10 min and single-cell Raman spectra were collected using confocal Raman spectroscopy. The excitation wavelength was 532 nm and the scanning spectral range was 500–3750 cm -1 ; The power under the microscope was 3 mw, the scanning time was 2 s; the number of accumulations was: 1, and then cosmic ray removal, baseline correction (iterative adaptive weighted penalized least squares method, airPLS), Savitzky-Golay smoothing, and normalized Raman data processing were performed to obtain the Raman spectrum of Daqu microorganisms. 800 single-cell Raman spectra were collected for each shrimp paste.

[0136] (3) The above neural network discriminant analysis classification model was used to identify the unknown strains, and the single-cell Raman spectroscopy data of the sample obtained in step (2) was selected at 744 cm -1 、746cm -1 、748cm -1 , 750cm -1 、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm -1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1 The characteristic bands are input into the neural network discriminant analysis classification model obtained in Example 1, and the eight types of marine Bacillus in the sample can be identified; according to the model strain classification standard, if the score is greater than or equal to 0.9, the strain with the highest score is identified as this strain; if the score is less than 0.9, it is identified as not belonging to marine Bacillus, but to other types of strains.

[0137] Based on the model strain identification and classification criteria, a neural network discriminant model was used to identify 3200 marine Bacillus cells collected. The results are shown in Table 3.

[0138] Table 3: Proportion of marine Bacillus in different types of shrimp paste

[0139]

[0140] The results showed that the proportion of marine Bacillus in the shrimp paste microbial community could be obtained by counting the identified species of 800 single cells in each shrimp paste.

[0141] Moreover, using the method of the present invention, the detection and analysis time of a single cell is 2 seconds, while the detection and analysis time of 800 single cells of each shrimp paste is 1600 seconds, that is, 0.26 hours.

[0142] Example 5: Identification of 8 marine Bacillus species in kimchi

[0143] Kimchi is made from fresh fruits and vegetables, supplemented with spices, with or without fermentation agents, and is made by anaerobic fermentation through salt water immersion. Among them, marine Bacillus is a functional microorganism among kimchi bacteria.

[0144] (1) Sample processing

[0145] Four samples of Korean kimchi fermentation process were collected, and the pickled kimchi was crushed in a mortar to a size of 1-10 mm. After mixing the powder, 5 g was weighed, added with 20 mL of sterile water, and vibrated in a homogenizer for 10 minutes at a speed of 10 times / second, working for 5 seconds, and resting for 5 seconds to ensure that the microorganisms are fully dispersed in the sterile water solvent.

[0146] Then, the solution was allowed to stand for 5 minutes, 6 mL of the solution was aspirated, and the solution was centrifuged at 500 rpm for 2 minutes, and the supernatant was retained;

[0147] Remove the supernatant and centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Centrifuge at 7000 rpm for 2 minutes. Discard the supernatant and add 6 mL of sterile water to the pellet. Mix thoroughly by pipetting. Rinse with sterile water and centrifuge three times.

[0148] Pretreated kimchi samples 1 to 4 were prepared respectively.

[0149] Preparation of gold nanoparticles:

[0150] Using the trisodium citrate heating reduction method, add 47mL of ultrapure water and 3mL of 10mg / mL potassium chloroaurate solution into a flask, mix well and heat to boiling, then quickly add 2mL of 0.1% trisodium citrate solution and continue boiling. When the solution turns purple-red, stop heating. The resulting gold nanoparticles have a diameter of 10-50nm. After cooling to room temperature, store at 4℃ in the dark.

[0151] (2) The pretreated kimchi samples 1 to 4 were mixed with gold nanoparticles in a volume ratio of 1:1, and 2.5 μL was placed on an aluminum-coated Raman chip. The mixture was air-dried for 10 min and single-cell Raman spectra were collected using a confocal Raman spectrometer. The excitation wavelength was 532 nm and the scanning spectral range was 500–3750 cm -1 ; The power under the microscope was 3 mw, the scanning time was 2 s; the number of accumulations was: 1, and then cosmic ray removal, baseline correction (iterative adaptive weighted penalized least squares method, airPLS), Savitzky-Golay smoothing, and normalized Raman data processing were performed to obtain the Raman spectrum of Daqu microorganisms. 1000 single-cell Raman spectra were collected for each kimchi.

[0152] (3) The above neural network discriminant analysis classification model was used to identify the unknown strains, and the single-cell Raman spectroscopy data of the sample obtained in step (2) was selected at 744 cm -1 、746cm -1 、748cm -1 , 750cm -1、1128cm -1 、1130cm -1 、1304cm -1 、1314cm -1 、1316cm -1 、1480cm -1 、1482cm -1 、1580cm -1 、1582cm -1 、1586cm -1 、1588cm -1 、1670cm -1 、1678cm -1 、1680cm -1 The characteristic bands are input into the neural network discriminant analysis classification model obtained in Example 1, and the eight types of marine Bacillus in the sample can be identified; according to the model strain classification standard, if the score is greater than or equal to 0.9, the strain with the highest score is identified as this strain; if the score is less than 0.9, it is identified as not belonging to marine Bacillus, but to other types of strains.

[0153] Based on the model strain identification and classification criteria, a neural network discriminant model was used to identify 4,000 marine Bacillus cells collected. The results are shown in Table 4.

[0154] Table 4: Proportions of marine Bacillus in different types of kimchi

[0155]

[0156]

[0157] The results showed that the proportion of marine Bacillus in the kimchi microbial community could be obtained by counting the identified species of 1,000 single cells in each kimchi.

[0158] Moreover, using the method of the present invention, the detection and analysis time of a single cell is 2 s, while the detection and analysis time of 1000 single cells in each kimchi is 2000 s, that is, 0.33 h.

[0159] Although the present invention has been disclosed above in terms of preferred embodiments, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the definition of the claims.

Claims

1. A neural network discriminant analysis classification model for identifying marine Bacillus strains, characterized in that: The neural network model is established according to the following steps: (1) Preparation of marine Bacillus suspension: Respectively Oceanobacillus caeni 、 Oceanobacillus kimchii 、 Oceanobacillus iheyensis 、 Oceanobacillus sojae 、 Oceanobacillus oncorhynchi 、 Oceanobacillus profundus 、 Oceanobacillus jeddahense 、 Oceanobacillus kapialis The strain is inoculated into a culture medium for fermentation culture, and the culture fluids obtained at different culture times are respectively obtained as marine Bacillus suspensions; (2) Pretreatment of marine Bacillus suspension: After centrifuging the bacterial suspension prepared in step (1), the precipitate was collected, sterile water was added to the precipitate, and the precipitate was centrifuged. This was repeated 2 to 3 times to obtain a pretreated marine Bacillus suspension. (3) Preparation of gold nanoparticles: Using the trisodium citrate heating reduction method, ultrapure water and a 10 mg / mL potassium chloroaurate solution were added to a flask. After mixing evenly and heating to boiling, 0.1% trisodium citrate solution was quickly added and the boiling was continued. When the solution turned purple-red, heating was stopped to obtain gold nanoparticles with a diameter of 10-50 nm. (4) Raman spectroscopy detection: The marine Bacillus suspension obtained in step (2) was mixed with the gold nanoparticles obtained in step (3) and then single-cell Raman spectra were collected. The spectrum collection conditions were as follows: using a 532 nm laser and scanning the spectrum range from 500 to 3750 cm -1 , laser intensity was 1-300 mW, acquisition time was 1-20 s / time, cumulative number was 1, and 50-2000 cells were collected from the marine Bacillus suspensions with different culture times; (5) Processing of single-cell Raman spectroscopy data: The single-cell Raman spectroscopy data obtained in step (4) are subjected to cosmic ray elimination, background noise removal, baseline correction, Savitzky-Golay smoothing, and all data are normalized; (6) Model building: Use machine learning to establish a neural network discriminant analysis classification model for marine Bacillus strains: Use the neural network machine learning algorithm to select the 744 cm -1 、746 cm -1 、748 cm -1 , 750 cm -1 、1128 cm -1 、1130 cm -1 、1304 cm -1 、1314 cm -1 、1316 cm -1 、1480 cm -1 、1482 cm -1 、1580 cm -1 、1582 cm -1 、1586 cm -1 、1588 cm -1 、1670 cm -1 、1678 cm -1 、1680 cm -1 Machine learning is performed on the characteristic bands, and a training data set and a test data set are set. The training data set is 70% of the collected data, and the test data set is 30% of the collected data. The parameters of the neural network machine learning algorithm are: 100 hidden layers, ReLu activation function, Adam optimizer, and learning rate 0.0001; The marine Bacillus is Oceanobacillus caeni CGMCC 1.10860, Oceanobacillus kimchii JCM 16803, Oceanobacillus iheyensis CGMCC 1.8643, Oceanobacillus sojae NBRC 105379, Oceanobacillus oncorhynchi CGMCC 1.8877, Oceanobacillus profundus CGMCC 1.15219, Oceanobacillus jeddahense DSM 28586, Oceanobacillus kapialis DSM23158; The different culture times in step (1) are culture solutions obtained by culturing for 6 h, 12 h, and 24 h; The bacterial suspension in step (4) was mixed with gold nanoparticles and placed on a Raman chip. After drying, Raman spectroscopy was performed. The volume ratio of gold nanoparticles to bacterial suspension was (1:1)~(1:1×10 5 ).

2. A method for rapid identification and detection of marine Bacillus in fermented food samples, characterized in that: The method comprises the following steps: (1) Collect samples; (2) Sample pretreatment: The sample obtained in step (1) was crushed to a size of 1-10 mm, the powder was mixed and 5 g was weighed, 20 mL of sterile water was added and the mixture was shaken using a homogenizer for 10 min at a speed of 10 times / s, 5 s on, and 5 s off to fully disperse the microorganisms in the sterile water solvent; Then, the mixture was allowed to stand for 5 min, 6 mL of the solution was aspirated, and the solution was centrifuged at 500 rpm for 2 min, and the supernatant was retained; The supernatant was centrifuged at 7000 rpm for 2 min, the supernatant was discarded, the precipitate was added with 6 mL of sterile water, pipetted and mixed, and then centrifuged at 7000 rpm for 2 min. The supernatant was discarded, the precipitate was added with 6 mL of sterile water, pipetted and mixed, and repeatedly rinsed with sterile water and centrifuged 3 times to obtain the pretreated sample; (3) Preparation of gold nanoparticles: Using the trisodium citrate heating reduction method, ultrapure water and a 10 mg / mL potassium chloroaurate solution were added to a flask. After mixing evenly and heating to boiling, 0.1% trisodium citrate solution was quickly added and the boiling was continued. When the solution turned purple-red, heating was stopped to obtain gold nanoparticles with a diameter of 10-50 nm. (4) Raman spectroscopy detection: The sample obtained in step (2) and the gold nanoparticles obtained in step (3) were mixed and then single-cell Raman spectra were collected. The spectrum collection conditions were as follows: using a 532 nm laser and scanning the spectrum range from 500 to 3750 cm -1 , laser intensity was 1~300mW, acquisition time was 1~20s / time, cumulative number was 1, 50~2000 cells were collected from the marine Bacillus suspensions with different culture times; (5) Processing of single-cell Raman spectroscopy data: The single-cell Raman spectroscopy data obtained in step (4) are subjected to cosmic ray elimination, background noise removal, baseline correction, Savitzky-Golay smoothing, and all data are normalized; (6) Identification of marine Bacillus The neural network discriminant analysis classification model of claim 1 is used to identify the unknown strain, and the characteristic band 744 cm of the single cell Raman spectroscopy data of the sample obtained in step (5) is -1 、746 cm -1 、748 cm -1 , 750 cm -1 、1128 cm -1 、1130 cm -1 、1304 cm -1 、1314 cm -1 、1316 cm -1 、1480 cm -1 、1482 cm -1 、1580 cm -1 、1582 cm -1 、1586 cm -1 、1588 cm -1 、1670 cm -1 、1678 cm -1 、1680 cm -1 The results were input into the neural network discriminant analysis classification model to identify the eight types of marine Bacillus in the sample. According to the model strain classification standard, if the score was greater than or equal to 0.9, the strain with the highest score was identified as that strain; if the score was less than 0.9, it was identified as not belonging to marine Bacillus but to other types of strains. The marine bacillus is: Oceanobacillus caeni CGMCC 1.10860, Oceanobacillus kimchii JCM 16803, Oceanobacillus iheyensis CGMCC 1.8643, Oceanobacillus sojae NBRC 105379, Oceanobacillus oncorhynchi CGMCC 1.8877, Oceanobacillus profundus CGMCC 1.15219, Oceanobacillus jeddahense DSM 28586, Oceanobacillus kapialis DSM23158; The samples include but are not limited to Daqu, fermented mash, and shrimp paste.

3. Use of the method according to claim 2 in detecting the relative content of marine Bacillus in fermentation sample microorganisms.

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