Single bacterium identification method and system based on terahertz near-field microscope

Abstract

The single bacterium identification method and system based on a terahertz near-field microscope comprises the following steps: a metal probe of the terahertz near-field microscope collects the topography height and near-field intensity of several points on the surface of a bacterium to be identified; the topography height and near-field intensity of the several points are compared with a pre-stored topography height-near-field intensity relationship curve; and the type of the bacterium to be identified is determined based on the comparison result. The present application can quickly and accurately identify the type of the bacterium by collecting the topography height and near-field intensity of the surface of the bacterium and comparing the topography height and near-field intensity with the pre-stored topography height-near-field intensity relationship, and the identification steps are simple and do not require too many bacterium processing steps.

Description

Technical Field

[0001] This invention relates to the field of terahertz biological imaging, specifically to a method and system for identifying single bacteria based on terahertz near-field microscopy. Background Technology

[0002] Bacterial infections threaten human health. To rapidly identify bacterial species, polymerase chain reaction (PCR) and high-throughput sequencing (HTS) based on nucleic acid amplification are used to determine bacterial load and species. However, PCR and HTS require large sample volumes, and cross-contamination or false positives induced by low-value nucleic acid fragments can lead to inaccurate identification results. Single-bacterial identification methods require smaller sample volumes and offer high accuracy and speed, which is beneficial for promoting the shift from traditional empirical treatment to personalized anti-infective therapy.

[0003] Terahertz (THz) radiation, through photomatter interaction, can be used to extract label-free and accurate optical parameters from biological samples. Terahertz scattering scanning near-field optical microscopy (THz s-SNOM), based on atomic force microscopy (AFM), can obtain the morphology and physical properties of samples by measuring the interaction between terahertz radiation and the sample. Furthermore, unlike traditional optical microscopes, terahertz near-field microscopy can achieve sub-micron resolution, detecting sample structures and properties that are invisible to traditional optical microscopes, ultimately achieving nanoscale imaging and spectroscopy. Summary of the Invention

[0004] One objective of this invention is to provide a method for identifying single bacteria based on terahertz near-field microscopy. This method utilizes a probe of a terahertz near-field microscope to detect the surface of a single bacterium to be identified, collects the morphological height and near-field intensity corresponding to one or more points on the surface of the single bacterium, and determines the species of the single bacterium based on the relationship between the morphological height and the near-field intensity. This method can significantly shorten the identification time of single bacteria and improve the accuracy of identification. In addition, this identification method can also detect whether a single bacterium has undergone changes under the action of antibiotics, realizing rapid drug sensitivity testing.

[0005] This invention is achieved through the following technical solution:

[0006] A method for identifying single bacteria based on terahertz near-field microscopy includes the following steps:

[0007] The probe of the terahertz near-field microscope collects the morphological height and near-field intensity of several points on the surface of the bacteria to be identified;

[0008] The topography height and near-field intensity of the aforementioned points are compared with the pre-stored topography height-near-field intensity relationship curve;

[0009] The type of bacteria to be identified is determined based on the comparison results.

[0010] In this technical solution, a terahertz scattering scanning near-field optical microscope (AFM) is used to acquire characteristic information of bacterial cell walls. Terahertz near-field microscopy is an existing technology that utilizes a terahertz radiation source, such as a diode-based solid-state terahertz source, to emit continuous terahertz radiation. This terahertz radiation is collimated by a parabolic mirror and irradiates the tip of an AFM probe, exciting a terahertz nano-localized field at the probe tip. After the bacteria to be identified are placed on a substrate, the terahertz nano-localized field is scattered by the near-field interaction between the probe and the bacteria when the probe approaches the bacteria. By processing the scattered field signal, characteristic information of the bacterial cell wall is obtained, resulting in nano-imaging of a single bacterium.

[0011] The bacterial cell wall features that can be acquired using the probe of a terahertz near-field microscope mainly include bacterial morphological height and near-field intensity. However, for the differentiation of individual bacteria, comparing their morphological height can only identify bacteria with large differences, while bacteria with small differences are difficult to distinguish. Furthermore, the terahertz nanofield exhibits a decay effect; the intensity of the local field decreases as the distance between the probe tip and the substrate increases. For the scattered field carrying the dielectric properties of bacteria, the intensity of the scattered field is strongly modulated by the dielectric height (mainly referring to the bacterial cell wall height). Therefore, it is inaccurate to compare the dielectric properties of different bacteria and thus identify different bacterial species solely by using changes in near-field intensity at various points on the bacterial surface.

[0012] The inventors discovered a relationship between the morphological height and near-field intensity at various points on bacteria. The near-field intensity at any point decreases non-linearly with increasing morphological height, and different bacteria exhibit varying near-field intensities at the same morphological height due to their different dielectric properties. Therefore, in this technical solution, by comparing the morphological height and corresponding near-field intensity of one or more collected points with pre-stored morphological height-near-field intensity relationship curves for various bacteria, the bacterial species can be identified based on the comparison results.

[0013] In this technical solution, the comparison between the collected morphological height and near-field intensity and the relationship curve can be achieved either by manually judging whether the difference between the morphological height and near-field intensity and a certain relationship curve is less than a threshold; or by checking whether the difference between the curve obtained by fitting the morphological height and near-field intensity of multiple collected points and the relationship curve is less than a threshold; or by inputting the morphological height and near-field intensity of multiple points into a trained support vector machine (SVM), and using the trained vector machine to identify bacteria based on the difference between the morphological height and near-field intensity and the relationship curve, and outputting the bacterial identification result.

[0014] During identification, one point or multiple points on the bacterial surface can be collected.

[0015] During identification, one type of bacteria can be identified at a time, or multiple bacteria can be placed on a substrate and the probe can be moved to collect the morphology height and near-field intensity of the surface of multiple bacteria.

[0016] In one or more embodiments, when collecting samples from several points on the surface of the bacteria to be identified, the morphological height of the collected samples is 0–200 nm.

[0017] This technical solution utilizes the different dielectric properties of bacteria and the differences in the relationship curves between morphological height and near-field intensity. It can not only quickly and accurately identify different types of bacteria, but also identify drug-resistant and non-drug-resistant bacteria within the same species, thus achieving rapid drug sensitivity testing. Compared with traditional drug sensitivity testing (AST), it can significantly reduce the identification time from 3-5 days to 1-2 hours.

[0018] In this technical solution, by acquiring the morphological height and near-field intensity of the bacterial surface and comparing the morphological height and near-field intensity with the pre-stored morphological height-near-field intensity relationship, the bacterial species can be quickly and accurately identified based on the different dielectric properties of the bacteria, without requiring excessive bacterial processing steps and with simple operation; in addition, it can also identify drug-resistant and drug-free bacteria within the same bacterial species, thereby achieving rapid drug sensitivity testing.

[0019] Furthermore, the morphological height and near-field intensity of several points on the surface of known bacterial species are collected, and a pre-stored morphological height-near-field intensity relationship curve is established based on the morphological height and near-field intensity.

[0020] In this technical solution, before bacterial identification, the morphology height-near-field intensity relationship curves for multiple known species can be obtained. Specifically, the morphology height and near-field intensity of a known bacterial species are scanned using a terahertz near-field microscope probe. Alternatively, multiple bacteria of the same species can be scanned to obtain a dataset of morphology height and near-field intensity. Based on the near-field intensity and morphology height data in the dataset, the morphology height-near-field intensity relationship curve for that bacterial species is fitted.

[0021] In this invention, the topography height-near-field intensity relationship curve can be expressed by various formulas. To simplify the near-field model of complex multilayer media, preferably, the topography height-near-field intensity relationship curve is as follows:

[0022] E = A × e Bd +C

[0023] In the formula, E is the near-field intensity of any point i on the bacterial surface, d is the morphological height of any point i on the bacterial surface, and A, B, and C are constants, among which the constant B is different for different types of bacteria.

[0024] In this technical solution, the near-field intensity decreases exponentially with bacterial morphology height, consistent with the theoretical calculations of the finite dipole model. In the morphology height-near-field intensity relationship curve, constants A and C are mainly affected by factors such as the signal-to-noise ratio of the terahertz near-field microscope. When the identification experimental conditions, such as the probe, substrate, radiation source, and optical path, are exactly the same, the A and C values ​​of the relationship curves for different bacterial species are identical. The difference lies in the value of constant B, which is mainly affected by the dielectric properties of the bacteria. The larger the dielectric constant, the larger the value of constant B, thus allowing different bacteria to correspond to a specific relationship curve.

[0025] Furthermore, when collecting data from several points on the surface of the bacteria to be identified, the morphological height of these points, i.e., the height of the bacterial cell wall being measured, is 40–100 nm. When the morphological height is too low, for example, less than 40 nm, the near-field intensity of each bacterium approaches 1, while when the morphological height is greater than a certain value, such as 200 nm, the near-field intensity of each bacterium approaches 0. Therefore, identifying bacteria within this morphological height range becomes very difficult. By comparing multiple morphological height-near-field intensity relationship curves, it was found that the near-field intensity differences between different bacteria are significant when the morphological height is 40–100 nm. Therefore, collecting the near-field intensity of points on the bacterial surface with morphological heights within the range of 40–100 nm is beneficial to improving the accuracy of identification. At the same time, collecting only points within this range helps to shorten the scanning time, and it is even possible to collect only points on a single straight line, thus improving the efficiency of identification.

[0026] Furthermore, the probe of the terahertz near-field microscope presses against several points on the surface of the bacteria to be identified to obtain the elastic modulus of the bacteria to be identified. Based on the elastic modulus and the comparison results of the morphology height and near-field intensity with the morphology height-near-field intensity relationship curve, the species of bacteria to be identified is determined together.

[0027] In this technical solution, a near-field probe can be used not only to collect the surface morphology height and near-field intensity of bacteria, but also to perform high-precision detection of the mechanical properties of living cells in their natural state through probe compression. Different bacteria, due to differences in their cell wall physiological structure and chemical composition, exhibit different elastic moduli when compressed by the probe. These moduli can be calculated based on information such as the rebound distance, time, and velocity of the cantilever connecting the probe. For example, the elastic modulus of *E. coli* is typically 1–10 MPa, while that of yeast is typically 0.1–1 MPa. Therefore, combining the results of comparing the elastic modulus with the morphology height-near-field intensity relationship curve can further improve the accuracy of identification.

[0028] In one or more embodiments, the elastic modulus at various points on the bacterial surface can also be used to train the support vector machine and serve as one of the input data for the support vector machine during identification.

[0029] Furthermore, when indenting several points on the surface of the bacteria to be identified, the morphological height of the indented points is 20–200 nm. In this technical solution, when using a probe to perform an indentation experiment on several points on the bacterial surface, it is preferable to indent a region with a certain thickness and a morphological height greater than 20 nm to obtain a more accurate elastic modulus. More preferably, the morphological height of the indented points is 50–80 nm. In one or more embodiments, the elastic modulus of multiple points on the bacterial surface can be collected. In some preferred embodiments, since the difference in elastic modulus is not significant at different locations on the bacterial surface, the elastic modulus of only one point on the bacterial surface, such as the center, can be collected to improve identification efficiency.

[0030] Furthermore, the extrusion force applied by the probe to the bacteria to be identified is 100–300 pN. During the extrusion process, the metal probe applies a small extrusion force to the bacteria so that the bacterial surface undergoes only minor, easily recoverable deformation, avoiding damage to the bacteria by the probe. Preferably, the extrusion force is no greater than 200 pN.

[0031] Furthermore, the bacteria to be identified are placed on a monolayer graphene substrate for identification. Since the scattering field of bacteria is relatively weak, in this technical solution, a monolayer of graphene is deposited on the surface of a conventional silicon substrate via chemical vapor deposition. The bacteria are then placed on the monolayer graphene for identification, utilizing the high terahertz reflection characteristic of graphene to increase the intensity of the bacterial scattering field.

[0032] Another object of the present invention is to provide a single-bacterial identification system based on terahertz near-field microscopy, based on any of the aforementioned single-bacterial identification methods. Specifically, the system includes:

[0033] Terahertz near-field microscopy is used to collect the morphology height and near-field intensity of several points on the surface of bacteria to be identified.

[0034] A relation curve database is used to store pre-stored topography height-near-field intensity relation curves;

[0035] The analysis unit is used to compare the collected morphology height and near-field intensity with the pre-stored morphology height-near-field intensity relationship curve, and to determine the type of bacteria to be identified based on the comparison results.

[0036] In one or more embodiments, the analysis unit may employ artificial intelligence recognition algorithms such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), ensemble learning methods, and Random Forests. Preferably, the analysis unit employs a Support Vector Machine. When training the Support Vector Machine, the near-field intensity and morphological height of various bacteria, such as Staphylococcus aureus and Escherichia coli, can be mixed, with 75% of the data used as the training set and the remaining 25% used as the test set. During identification, the morphological height and near-field intensity of several points on the bacterial surface are input into the trained Support Vector Machine, which outputs the species of bacteria to be identified.

[0037] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0038] 1. This invention obtains the morphological height and near-field intensity of the bacterial surface, and compares the morphological height and near-field intensity with the pre-stored morphological height-near-field intensity relationship, which can quickly and accurately identify the types of bacteria. Moreover, the identification steps are simple and do not require too many bacterial processing steps.

[0039] 2. This invention can identify drug-resistant and non-drug-resistant bacteria within the same species, thereby enabling rapid drug susceptibility testing;

[0040] 3. The morphology height-near field intensity relationship curve designed in this invention can simplify the near field model of complex multilayer media, and the simulated near field intensity decays exponentially with the bacterial morphology height, which is consistent with the theoretical calculation results of the finite dipole model.

[0041] 4. This invention improves the accuracy of identification and shortens the scanning time by collecting the correspondence between the morphological height and near-field intensity of several points of different cell walls with morphological heights of 40-100 nm.

[0042] 5. This invention collects the elastic modulus at several points with a morphological height of 20-200 nm, and combines the comparison results of the elastic modulus with the morphological height-near field intensity relationship curve, which can further improve the accuracy of identification. Attached Figure Description

[0043] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0044] Figure 1 This is a flowchart of a single-bacterial identification method according to a specific embodiment of the present invention;

[0045] Figure 2 This is a flowchart of another single-bacterial identification method in a specific embodiment of the present invention;

[0046] Figure 3 The normalized near-field intensity of Staphylococcus aureus S(a), Escherichia coli (c) and Staphylococcus aureus-A(e) in a specific embodiment of the present invention, as well as the morphological height and near-field intensity spectra of the three bacteria (b, d, f) along the dashed line;

[0047] Figure 4 The morphological height-near-field intensity relationship curves of three bacteria, Staphylococcus aureus S, Escherichia coli, and Staphylococcus aureus-A, are shown in a specific embodiment of the present invention.

[0048] Figure 5 This is a schematic diagram of the single-bacterial identification system in a specific embodiment of the present invention;

[0049] Figure 6 The normalized near-field strengths of Staphylococcus aureus-A1(a), Staphylococcus aureus-A2(b), Staphylococcus aureus-R(c) and Staphylococcus aureus-RA(d) in specific embodiments of the present invention;

[0050] Figure 7 The image represents the AST of a single bacterium based on terahertz nanoimaging technology. The horizontal axis represents the near-field intensity, and the vertical axis represents the probability density distribution of the near-field intensity. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0052] In the description of this invention, it should be understood that the terms "front", "rear", "left", "right", "up", "down", "vertical", "horizontal", "high", "low", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this invention.

[0053] Example 1:

[0054] like Figure 1 The single-bacterial identification method based on terahertz near-field microscopy shown includes the following steps:

[0055] The probe of the terahertz near-field microscope collects the morphological height and near-field intensity of several points on the surface of the bacteria to be identified;

[0056] The topography height and near-field intensity of the aforementioned points are compared with the pre-stored topography height-near-field intensity relationship curve;

[0057] The type of bacteria to be identified is determined based on the comparison results.

[0058] In some embodiments, to effectively suppress background noise signals, high-order harmonic signal demodulation can be used to record the scattered field generated by the near-field interaction between the probe and bacteria. In one or more embodiments, the terahertz radiation emitted by the terahertz solid-state source is 80–120 GHz, preferably about 100 GHz.

[0059] During identification, either a single point on the bacterial surface or multiple points on the bacterial surface can be collected. In some embodiments, increasing the number of collected points will improve the accuracy of identification; for example, a probe can be used to scan along the bacterial surface to collect the morphological height and near-field intensity of all points on the bacterial surface. In some embodiments, to save collection time, only points along one or more straight lines with significant differences in morphological height and near-field intensity can be collected.

[0060] During identification, one type of bacteria can be identified at a time, or multiple bacteria can be placed on a substrate and the morphology height and near-field intensity of the surface of multiple bacteria can be collected by moving the metal probe.

[0061] In this embodiment, by utilizing the different dielectric properties of bacteria and the differences in the relationship curves between morphological height and near-field intensity, it is possible not only to quickly and accurately identify different types of bacteria, but also to identify drug-resistant and non-drug-resistant bacteria within the same species, thereby achieving rapid drug sensitivity testing. Compared with traditional drug sensitivity testing, the identification time can be significantly reduced from 3 to 5 days to 1 to 2 hours.

[0062] In some embodiments, the morphological height and near-field intensity of several points on the surface of known bacterial species are collected, and a pre-stored morphological height-near-field intensity relationship curve is established based on the morphological height and near-field intensity. In one or more embodiments, commonly used data fitting software, such as Origin or Excel, can be used to fit the relationship curve. Alternatively, the collected data can be divided into a training set and a test set according to a certain ratio, and the support vector machine can be trained using the training set to obtain the relationship curve.

[0063] In some preferred embodiments, the topography height-near-field intensity relationship curve is as follows:

[0064] E = A × e Bd +C

[0065] In the formula, E represents the near-field intensity of any point i on the bacterial surface, d represents the morphological height of any point i on the bacterial surface, and A, B, and C are constants, where the constant B differs for different types of bacteria. In some embodiments, when collecting data from several points on the surface of the bacteria to be identified, the morphological height of the collected points is 40–100 nm. Collecting the near-field intensity of points with morphological heights within the range of 40–100 nm on the bacterial surface helps improve the accuracy of identification. Simultaneously, collecting only points within this range helps shorten the scanning time; it is even possible to collect only points along a straight line, thus improving the efficiency of identification. Preferably, the morphological height of the collected points is 40–80 nm.

[0066] In some preferred embodiments, the bacteria to be identified are placed on a monolayer graphene substrate for identification. Placing the bacteria on a monolayer graphene substrate for identification utilizes the high terahertz reflection characteristic of graphene to increase the intensity of the scattered field of the bacteria.

[0067] Example 2:

[0068] like Figure 2 The single-bacterial identification method based on terahertz near-field microscopy shown includes the following steps:

[0069] The probe of the terahertz near-field microscope collects the morphology height and near-field intensity of several first collection points on the surface of the bacteria to be identified;

[0070] The topography height and near-field intensity of the several first acquisition points are compared with the pre-stored topography height-near-field intensity relationship curve to obtain the comparison results;

[0071] The probe of the terahertz near-field microscope is pressed against several second sampling points on the surface of the bacteria to be identified to obtain the elastic modulus of the bacteria to be identified.

[0072] The species of bacteria to be identified is determined based on the comparison results of the elastic modulus, morphological height and near-field intensity with the morphological height-near-field intensity relationship curve.

[0073] In some embodiments, an elastic modulus database is constructed by collecting the elastic modulus of the surfaces of known bacterial species. The elastic modulus of several second collection points is then compared with the data in the elastic modulus database to identify the bacterial species.

[0074] In some embodiments, when several points on the surface of the bacteria to be identified are compressed, the morphological height of the compressed points is 20–200 nm. In one or more preferred embodiments, since the elastic modulus varies little across the bacterial surface, the elastic modulus of only one point on the bacterial surface, such as the center, can be collected to improve identification efficiency.

[0075] In some embodiments, to avoid damage or irreversible deformation to the bacteria by the probe, the extrusion force applied by the metal probe to the bacteria to be identified is 100 to 300 pN, preferably not greater than 200 pN.

[0076] Example 3:

[0077] like Figure 5 The single-bacterial identification system based on terahertz near-field microscopy shown employs the bacterial identification method described in any of the foregoing embodiments. The system includes:

[0078] Terahertz near-field microscopy is used to collect the morphology height and near-field intensity of several points on the surface of bacteria to be identified.

[0079] A relation curve database is used to store pre-stored topography height-near-field intensity relation curves;

[0080] The analysis unit is used to compare the collected morphology height and near-field intensity with the pre-stored morphology height-near-field intensity relationship curve, and to determine the type of bacteria to be identified based on the comparison results.

[0081] In some embodiments, the analysis unit employs a support vector machine.

[0082] Example 4:

[0083] This example is used for the identification of different types of bacteria.

[0084] The surfaces of Staphylococcus aureus S, Escherichia coli, and Staphylococcus aureus A were scanned separately. Staphylococcus aureus A was a Staphylococcus aureus S treated with ampicillin, exhibiting a fine but irregular morphology and a significantly decreased AFM height, with near-field intensity favoring that of Escherichia coli. After scanning, the following results were obtained: Figure 3 The morphological height and near-field intensity of several points on the surface of the three types of bacteria are shown. A support vector machine is trained using the morphological height and near-field intensity of the three bacteria to construct a model as follows: Figure 4 The three curves showing the relationship between morphology height and near-field intensity exhibit significant differences in near-field intensity within the morphology height range of 40–80 nm due to the different dielectric properties of the bacteria.

[0085] The probe of the terahertz near-field microscope along Figure 3 (a) to Figure 3 (c) The dashed line scans the bacteria to be identified, and the morphological height and near-field intensity of several points on the dashed line are obtained. The collected morphological height and near-field intensity are input into the trained support vector machine. Based on the comparison results between the input data and the morphological height-near-field intensity relationship curve, the support vector machine outputs the type of bacteria to be identified.

[0086] Example 5:

[0087] This embodiment is used for the identification of antibiotic-sensitive Staphylococcus aureus and MRSA (drug-resistant Staphylococcus aureus).

[0088] Specifically, after Staphylococcus aureus was exposed to antibiotics, the three-dimensional morphology and terahertz nanoimaging of some Staphylococcus aureus strains showed significant differences, such as... Figure 6 (a) shows Staphylococcus aureus-A1, while other Staphylococcus aureus strains show no significant changes in three-dimensional morphology after exposure to antibiotics, only changes in near-field intensity, such as... Figure 6 (b) shows Staphylococcus aureus-A2. Therefore, it is impossible to determine whether Staphylococcus aureus has been treated with antibiotics based solely on its three-dimensional morphology. Figure 6 (c) and Figure 6 (d) represent drug-resistant Staphylococcus aureus-R before antibiotic treatment and drug-resistant Staphylococcus aureus-RA after antibiotic treatment, respectively.

[0089] like Figure 7 As shown, there are significant differences in the near-field intensity changes between antibiotic-susceptible Staphylococcus aureus A1 and A2 and drug-resistant Staphylococcus aureus R and RA in the morphological height range of 40–80 nm. Therefore, after scanning the surface of the four bacteria, the morphological height and near-field intensity of the collected points can be compared with the pre-stored morphological height-near-field intensity curves to identify whether Staphylococcus aureus is antibiotic-susceptible or drug-resistant.

[0090] The terms "first," "second," etc., used in this invention (e.g., first acquisition point, second acquisition point) are merely for clarity of description and are not intended to restrict any order or emphasize importance. Furthermore, the term "connection" used in this invention, unless otherwise specified, can refer to a direct connection or an indirect connection via other components.

[0091] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying single bacteria based on terahertz near-field microscopy, characterized in that, Includes the following steps: The probe of the terahertz near-field microscope collects the morphological height and near-field intensity of several points on the surface of the bacteria to be identified; The topography height and near-field intensity of the aforementioned points are compared with the pre-stored topography height-near-field intensity relationship curve; The type of bacteria to be identified is determined based on the comparison results; The topography height-near-field intensity relationship curve is as follows: In the formula, E Let be the near-field intensity at any point i on the bacterial surface. d Let i be the morphological height of any point i on the bacterial surface. A, B, C The constant is denoted by , where the constants for different types of bacteria are denoted by . B different; When collecting samples from several points on the surface of the bacteria to be identified, the morphological height of the collected points is 40~100 nm.

2. The method for single-bacterial identification based on terahertz near-field microscopy according to claim 1, characterized in that, The morphological height and near-field intensity of several points on the surface of known bacterial species are collected, and a pre-stored morphological height-near-field intensity relationship curve is established based on the morphological height and near-field intensity.

3. The method for single-bacterial identification based on terahertz near-field microscopy according to claim 1, characterized in that, The terahertz near-field microscope probe is pressed against several points on the surface of the bacteria to be identified to obtain the elastic modulus of the bacteria. Based on the elastic modulus and the comparison results of the morphology height and near-field intensity with the morphology height-near-field intensity relationship curve, the species of bacteria to be identified is determined.

4. The method for single-bacterial identification based on terahertz near-field microscopy according to claim 3, characterized in that, When several points on the surface of the bacteria to be identified are squeezed, the morphological height of the squeezed points is 20~200 nm.

5. The method for single-bacterial identification based on terahertz near-field microscopy according to claim 3, characterized in that, The pressure applied by the probe to the bacteria to be identified is 100~300 pN.

6. The method for single-bacterial identification based on terahertz near-field microscopy according to claim 1, characterized in that, The bacteria to be identified were placed on a single-layer graphene substrate for identification.

7. A single-bacterial identification system based on terahertz near-field microscopy, characterized in that, The bacterial identification method according to any one of claims 1 to 6, wherein the system comprises: Terahertz near-field microscopy is used to collect the morphology height and near-field intensity of several points on the surface of bacteria to be identified. A relation curve database is used to store pre-stored topography height-near-field intensity relation curves; The analysis unit is used to compare the collected morphology height and near-field intensity with the pre-stored morphology height-near-field intensity relationship curve, and to determine the type of bacteria to be identified based on the comparison results.

8. The single-bacterial identification system based on terahertz near-field microscopy according to claim 7, characterized in that, The analysis unit employs a support vector machine.

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