Sample Measurement Device

Abstract

To provide a sample measurement apparatus capable of quickly and accurately identifying a minute object to be measured.SOLUTION: A sample measurement apparatus 1 pertaining to one embodiment of the present invention comprises a flow path device 10 and a measurement unit 30. The flow path device 10 includes a minute flow path 14 in which a sample liquid 40 containing an object 42 to be measured that is dispersed in a solvent 41, an introduction part 13 for introducing the sample liquid 40 into the minute flow path 14 and located at one end of the minute flow path 14, a discharge part 15 located at the other end of the minute flow path 14 from which the sample liquid 40 is discharged from the minute flow path 14, a first electrode 23 located at a position corresponding to the introduction part 13 when seen in a plan view, and a second electrode 24 located at a position corresponding to the discharge part 15 when seen in a plan view. The measurement unit 30 applies an AC voltage to the first electrode 23 and second electrode 24 and identifies the object 42 to be measured on the basis of the AC characteristic of the object 42 to be measured at the time the object 42 to be measured has passed through the minute flow path 14.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a sample measurement device, and more particularly to a sample measurement device capable of measuring a minute measurement object. [Background technology]

[0002] Known methods for detecting viruses, bacteria, microorganisms, or viroids (hereinafter simply referred to as viruses, etc.) include the polymerase chain reaction (PCR) method and the immunochromatography method. Meanwhile, as another detection method, a method for sensing viruses, etc. using electrical measurement is being considered. One example of the electrical measurement method is a technique in which the virus, etc. to be detected is dispersed in water and the virus, etc. is electrically detected as particles. From the viewpoint of preventing infectious diseases and the like and preventing their spread, there is a demand for detection of viruses, etc. in environments such as indoors, livestock barns, and outdoors. The above-mentioned electrical measurement method is suitable for sensing in such environments (for example, Patent Documents 1-2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-202824 A [Patent Document 2] JP 2020-098211 A Summary of the Invention [Problem to be solved by the invention]

[0004] As described above, there is a need for a technique for quickly and accurately identifying minute measurement objects such as viruses, bacteria, microorganisms, or viroids in the environment from the viewpoint of preventing infections and the like and preventing their spread. In view of such problems, an object of the present invention is to provide a sample measurement device capable of quickly and accurately identifying minute measurement objects. [Means for solving the problem]

[0005] A sample measurement device according to one embodiment of the present invention comprises a flow path device having a microchannel through which a sample solution containing a measurement object dispersed in a solvent flows, an inlet portion disposed on one side of the microchannel for introducing the sample solution into the microchannel, an outlet portion disposed on the other side of the microchannel for discharging the sample solution from the microchannel, a first electrode disposed at a position corresponding to the inlet portion when viewed in a plane, and a second electrode disposed at a position corresponding to the outlet portion when viewed in a plane, and a measurement portion that applies an AC voltage to the first electrode and the second electrode and identifies the measurement object based on the AC characteristics of the measurement object when the measurement object passes through the microchannel. Effect of the Invention

[0006] According to the present invention, it is possible to provide a sample measurement device capable of quickly and accurately identifying a minute measurement object. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a cross-sectional view for explaining a sample measurement device according to an embodiment. [Diagram 2] FIG. 1 is a cross-sectional view for explaining a sample measurement device according to an embodiment. [Diagram 3] FIG. 2 is an exploded perspective view for explaining a flow path device of the sample measurement device according to the embodiment. [Figure 4] 1 is a graph showing AC characteristics measured by a sample measuring device according to an embodiment. [Diagram 5] 4 is a graph showing an example of a measurement result of the sample measurement device according to the embodiment. [Figure 6] FIG. 1 is a diagram for explaining the effect of the present invention. [Figure 7] FIG. 4 is a diagram for explaining the state of an electric field in a microchannel. [Figure 8] FIG. 1 is a diagram for explaining the effect of the present invention. [Figure 9]FIG. 13 is a diagram for explaining machine learning in the sample measurement device according to the embodiment. [Figure 10] FIG. 13 is a diagram for explaining machine learning in the sample measurement device according to the embodiment. [Figure 11] FIG. 13 is a diagram showing the measurement results of a sample measuring device. [Figure 12] FIG. 13 is a diagram showing the measurement results of a sample measuring device. [Figure 13] FIG. 13 is a diagram showing the measurement results of a sample measuring device. [Figure 14] FIG. 13 is a diagram showing the measurement results of a sample measuring device. [Figure 15] FIG. 13 is a diagram showing the measurement results of a sample measuring device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. 1 and 2 are cross-sectional views for explaining a sample measurement device according to an embodiment. As shown in Fig. 1 and Fig. 2, the sample measurement device 1 according to the present embodiment includes a flow path device 10 and a measurement section 30. The flow path device 10 includes an inlet section 13, a minute flow path 14, an outlet section 15, a first electrode 23, and a second electrode 24. Note that Fig. 2 shows a state in which a measurement target 42 has been introduced into the sample measurement device 1 shown in Fig. 1.

[0009] As shown in FIG. 2, the flow channel device 10 includes a microchannel 14 through which a sample solution 40 containing a measurement target 42 dispersed in a solvent 41 flows, an inlet 13 disposed on one side (upstream side) of the microchannel 14 and introducing the sample solution 40 into the microchannel 14, and an outlet 15 disposed on the other side (downstream side) of the microchannel 14 and discharging the sample solution 40 from the microchannel 14. An inlet 16 is provided on the upper surface of the inlet 13, and the sample solution 40 is introduced into the inlet 13 from the inlet 16. An outlet 17 is provided on the upper surface of the outlet 15, and the sample solution 40 is discharged to the outside from the outlet 17. A first electrode 23 is disposed at a position corresponding to the inlet 13 when viewed from above. A second electrode 24 is disposed at a position corresponding to the outlet 15 when viewed from above.

[0010] The sample measurement device 1 according to the present embodiment is a device used for identifying a measurement target 42 such as a virus, bacteria, or microorganism. The sample measurement device 1 according to the present embodiment can also identify targets other than viruses, bacteria, and microorganisms, so long as the objects are dispersible in a solvent 41. In the present embodiment, an ionic liquid can be used as the solvent 41. Also, in the present embodiment, a liquid other than an ionic liquid (for example, an aqueous solution) can be used as the solvent 41.

[0011] Fig. 3 is an exploded perspective view for explaining a flow path device of a sample measurement apparatus according to this embodiment. As shown in Fig. 3, a flow path device 10 includes a flow path chip 11 in which an inlet portion 13, a minute flow path 14, and an outlet portion 15 are formed, and an electrode chip 21 in which a first electrode 23 and a second electrode 24 are formed on a substrate 22. In this embodiment, the flow path device 10 is configured by bonding the electrode chip 21 and the flow path chip 11 together. Note that Fig. 3 shows a state in which the flow path chip 11 is turned upside down.

[0012] The flow channel chip 11 can be formed using silicone rubber 12. Specifically, the flow channel chip 11 can be produced by micromolding the silicone rubber 12. In producing a mold for micromolding, the micro flow channel 14 may be produced by using a focused-ion-beam chemical-vapor-deposition (FIB-CVD) method, and the inlet portion 13 and the outlet portion 15 may be produced by using a photoresist. In addition, the mold for micromolding may be produced by using a back exposure method in which a mother mold pattern made of photoresist is directly formed on a photomask.

[0013] The electrode chip 21 can be produced by forming a first electrode 23 and a second electrode 24 on a substrate 22. For example, a PCB substrate can be used for the substrate 22. Furthermore, the first electrode 23 and the second electrode 24 can be made of Au, Cu, or the like.

[0014] The flow channel chip 11 and the electrode chip 21 may be bonded together using an adhesive. In addition, since the flow channel chip 11 is formed using silicone rubber 12, the adhesiveness of the silicone rubber 12 may be utilized to bond the flow channel chip 11 and the electrode chip 21 together.

[0015] For example, the area of ​​a cross section perpendicular to the direction in which the sample solution 40 flows in the microchannel 14 shown in FIG. 2 More than 100μm 2 The length of the microchannel 14 is not less than 0.1 μm and not more than 100 μm. These dimensions can be changed appropriately depending on the size of the object 42 to be measured.

[0016] The measurement unit 30 is connected to the first electrode 23 and the second electrode 24. The measurement unit 30 applies an AC voltage to the first electrode 23 and the second electrode 24, and identifies the object to be measured 42 based on the AC characteristics of the object to be measured 42 when the object to be measured 42 passes through the microchannel 14.

[0017] The measurement unit 30 may use any circuit as long as it is a circuit capable of measuring the AC characteristics of the measurement object 42. The measurement unit 30 is configured to be capable of applying an AC voltage having a frequency of several kHz to several GHz, preferably 1 kHz to 100 MHz, and more preferably 1 MHz to 10 MHz, to the first electrode 23 and the second electrode 24. For example, the measurement unit 30 may be a lock-in amplifier.

[0018] The measurement unit 30 is configured to obtain a composite impedance and a phase using the measured AC characteristics, and to identify the object using the obtained composite impedance and phase. In this embodiment, the composite impedance and the phase may be used to obtain parameters corresponding to the resistance component, zeta potential, and dielectric constant of the object 42, and the object 42 may be identified using the parameters corresponding to the obtained resistance component, zeta potential, and dielectric constant.

[0019] Next, an operation for identifying the measurement target 42 using the sample measurement device 1 according to the present embodiment will be described.

[0020] When identifying the measurement object 42, first, the measurement object 42 dispersed in the solvent 41 is caused to flow through the microchannel 14. Next, an AC voltage is applied to the first electrode 23 and the second electrode 24, and the AC characteristics of the measurement object 42 are measured when the measurement object 42 passes through the microchannel 14. For example, the measurement unit 30 applies an AC voltage having a frequency of 1 kHz or more and 100 MHz or less to the first electrode 23 and the second electrode 24. Note that in this embodiment, an AC voltage may be applied to the first electrode 23 and the second electrode 24 in advance, and then the measurement object 42 dispersed in the solvent 41 may be caused to flow through the microchannel 14.

[0021] In this embodiment, the measurement unit 30 may apply a DC voltage between the first electrode 23 and the second electrode 24 to introduce the positively or negatively charged measurement object 42 from the introduction unit 13 into the microchannel 14. Specifically, when the measurement object 42 is positively charged, the first electrode 23 is set to a positive electrode, the second electrode 24 is set to a negative electrode, and a DC voltage for electrophoresis is applied between the first electrode 23 and the second electrode 24, so that the positively charged measurement object 42 can be introduced from the introduction unit 13 into the microchannel 14. When the measurement object 42 is negatively charged, the first electrode 23 is set to a negative electrode, the second electrode 24 is set to a positive electrode, and a DC voltage for electrophoresis is applied between the first electrode 23 and the second electrode 24, so that the negatively charged measurement object 42 can be introduced from the introduction unit 13 into the microchannel 14. At this time, the measurement unit 30 may apply an AC voltage for measurement and a DC voltage for electrophoresis to the first electrode 23 and the second electrode 24 in a superimposed manner.

[0022] At this time, the speed at which the object to be measured 42 passes through the microchannel 14 can be adjusted by adjusting the magnitude and time of the DC voltage applied to the first electrode 23 and the second electrode 24. In this embodiment, the number of objects to be measured 42 that pass through the microchannel 14 at the same time is, in principle, one. When the object to be measured 42 is introduced into the microchannel 14 using the DC voltage applied to the first electrode 23 and the second electrode 24 in this manner, the accuracy at which the object to be measured 42 is introduced into the microchannel 14 can be improved. Therefore, the detection accuracy of the object to be measured 42 can be improved.

[0023] That is, when the introduction part 13 is pressurized to move the sample solution 40 into the microchannel 14 and move the object to be measured 42 into the microchannel 14, if the number of the object to be measured 42 passing through the microchannel 14 is small, such as when the concentration of the object to be measured 42 in the sample solution 40 is low, the moving speed cannot be controlled, and there is a problem that the measurement accuracy does not improve. In contrast, in the present embodiment, the object to be measured 42 is introduced into the microchannel 14 using a DC voltage applied to the first electrode 23 and the second electrode 24, so that the accuracy when the object to be measured 42 is introduced into the microchannel 14 can be improved.

[0024] In addition, in this embodiment, the measurement object 42 flowing through the microchannel 14 can be made to move back and forth by switching the polarity of the DC voltage applied to the first electrode 23 and the second electrode 24. This can further improve the detection accuracy of the measurement object 42.

[0025] Moreover, in this embodiment, since there is no need for equipment (such as an external pump) for introducing the measurement object 42 into the microchannel 14, the device configuration can be made smaller. Furthermore, in this embodiment, since the concentration polarization can be induced by a DC voltage and the state can be measured by an AC voltage, the surface potential of the measurement object 42 can be measured with higher accuracy.

[0026] After measuring the AC characteristics of the object 42 to be measured as described above, the measured AC characteristics are used to identify the object 42 to be measured. Specifically, the measured AC characteristics are used to find a composite impedance and a phase, and the object 42 to be measured is identified using the found composite impedance and phase.

[0027] In this embodiment, the parameters corresponding to the resistance component, zeta potential, and dielectric constant of the measurement object 42 may be obtained using the synthetic impedance and the phase, and the measurement object 42 may be identified using the obtained parameters corresponding to the resistance component, zeta potential, and dielectric constant. For example, the measurement object 42 may be identified by mapping the parameters corresponding to the resistance component, zeta potential, and dielectric constant of the measurement object 42 on a three-dimensional coordinate system with the resistance component, zeta potential, and dielectric constant as axes. Here, the parameter corresponding to the resistance component is a parameter corresponding to the size of the measurement object 42. The parameter corresponding to the zeta potential is a parameter corresponding to the surface potential of the measurement object 42. The parameter corresponding to the dielectric constant is a parameter corresponding to the structure of the measurement object 42, such as a spherical shell structure or a membrane capacitance, or a parameter corresponding to the material of the measurement object 42.

[0028] Since the size, surface potential, and structure differ depending on the measurement object 42, the resistance component, zeta potential, and dielectric constant, which are parameters corresponding to these, are obtained, and the parameters corresponding to the resistance component, zeta potential, and dielectric constant of the measurement object 42 are mapped on a three-dimensional coordinate system, thereby making it possible to classify the measurement object 42. For example, by mapping the measurement results of a plurality of types of measurement objects 42 on a three-dimensional coordinate system and accumulating the data, it is possible to improve the accuracy in identifying the measurement object 42.

[0029] Furthermore, in this embodiment, a lock-in amplifier may be used to extract an in-phase component, which is an AC characteristic, and a phase component that is out of phase with the in-phase component, and the extracted in-phase component and phase component may be used to determine a composite impedance and a phase, and the target object may be identified using a time change in the composite impedance and a time change in the phase.

[0030] Fig. 4 is a graph showing AC characteristics measured by the sample measuring device according to the present embodiment, and shows AC characteristics measured using a lock-in amplifier. That is, Fig. 4 shows time response waveforms of current and phase change. Looking at the enlarged view in Fig. 4, it can be seen that the current and phase changes are synchronized and are waveforms originating from the same measurement object 42.

[0031] Fig. 5 is a graph showing an example of the measurement results of the sample measuring device according to the present embodiment. Fig. 5 shows histograms of current change values ​​for polystyrene beads with a diameter of 1 µm, polystyrene beads with a diameter of 2 µm, and Escherichia coli. In the measurement of Fig. 5, the width, height, and length of the microchannel 14 of the flow channel device 10 were set to 3 µm, 4 µm, and 30 µm, respectively. Au plating was used for the first electrode 23 and the second electrode 24. A lock-in amplifier was used for the measurement.

[0032] As shown in Fig. 5, in this embodiment, a unimodal distribution according to the particle size was obtained. Here, the horizontal axis in Fig. 5 is the current value, and the larger the particle diameter of the object to be measured 42, the larger the amount of change in current becomes, suggesting that a change in current value according to the particle diameter of the object to be measured 42 is obtained.

[0033] As described above, in the invention according to this embodiment, a measurement object 42 is measured using a flow path device 10 including a microchannel 14, an inlet portion 13 that introduces a sample solution 40 into the microchannel 14, an outlet portion 15 that discharges the sample solution 40 from the microchannel 14, a first electrode 23 arranged at a position corresponding to the inlet portion 13, and a second electrode 24 arranged at a position corresponding to the outlet portion 15. Thus, the present invention can provide a sample measurement device that can quickly and accurately identify a minute measurement object.

[0034] FIG. 6 is a diagram for explaining the effect of the present invention. As shown in the left diagram of FIG. 6, in the configuration of a conventional nanopore, that is, in the configuration in which electrodes are provided on each of the upper and lower surfaces of a substrate having a hole through which a measurement target passes, the substrate, which is an insulator, is sandwiched between two electrodes, so that the capacitance of the flow path device tends to be large. In other words, if the capacitance of the flow path device is C, the dielectric constant of the substrate is ε, the thickness of the substrate is d, and the area of ​​the substrate is S, the capacitance of the device is expressed as C=εS / d. Therefore, in the configuration of a conventional nanopore, as shown in the left diagram of FIG. 6, d is small and S is large, so that the capacitance of the flow path device tends to be large. In contrast, in the flow path device (straw-type nanopore) according to this embodiment, as shown in the right diagram of FIG. 6, d can be made large while S can be made small, so that the capacitance of the flow path device can be reduced.

[0035] FIG. 7 is a top view for explaining the state of the electric field in the microchannel. The left diagram of FIG. 7 shows the state of the electric field in a conventional nanopore, and the right diagram shows the state of the electric field in the straw-shaped nanopore of this embodiment. As shown in the left diagram of FIG. 7, in the conventional nanopore, the electric field was concentrated only in the hole through which the object to be measured passes. In contrast, in this embodiment, when viewed in a plane, the first electrode 23 and the second electrode 24 sandwich the microchannel 14. Therefore, as shown in FIG. 7, when an AC voltage is applied to the first electrode 23 and the second electrode 24, the electric field can be concentrated near the microchannel 14, so that the detection accuracy of the object to be measured 42 can be improved.

[0036] In this embodiment, since the capacitance of the flow channel device can be reduced as described above, the cutoff frequency can be increased to 400 times (2 MHz) as compared to the conventional frequency, as shown in Fig. 8. This improves the detection accuracy of the object to be measured.

[0037] In addition, in this embodiment, the measurement unit 30 may further include a predictive model generation unit that uses the measured AC characteristics and information on the object to be measured 42 as teacher data and generates, by machine learning, a predictive model that predicts the object to be measured 42 based on the measured AC characteristics.

[0038] The prediction model generation unit may extract an in-phase component, which is an AC characteristic, and a phase component that is out of phase with the in-phase component, calculate a composite impedance and a phase using the extracted in-phase component and phase component, and generate a prediction model using a feature of a time change in the composite impedance and a feature of a time change in the phase.

[0039] The prediction model generation unit may generate the prediction model by machine learning using a neural network. For example, the prediction model generation unit may generate the prediction model by machine learning using a convolution neural network (CNN) or a long short term memory (LSTM).

[0040] Machine learning will be explained in detail below. The inventors of the present application have been working on the development of a measurement unit 30 (classifier) ​​that utilizes a neural network to obtain higher accuracy in classifying bacterial species from individual measurement waveforms derived from bacteria. As a result, they have extracted the feature quantities shown in Fig. 9 and constructed a classification algorithm using a neural network shown in Fig. 10. That is, in the spikes (waveforms) shown in Fig. 9, the height h of the spikes and the width t of the spikes are extracted as feature quantities. n (n=0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90) were extracted, and a total of 12 features were extracted per spike. Here, the value of n corresponds to the depth (%) when the baseline is 0% and the bottom of the spike is 100%, and the width of the spike at each depth t n is extracted as a feature.

[0041] A prediction model was then generated using the extracted features. At this time, a neural network as shown in Fig. 10 was used. By combining the sample measurement device according to the present embodiment described above with machine learning, a sample measurement device with a maximum accuracy of 97.6% was constructed. EXAMPLES

[0042] Examples will be described below. <Experimental Method> The following bacterial samples were prepared: Phosphate-buffered saline (PBS; 17-516Q, Lonza, Morrisville, NC, USA) was used as the measurement solvent, and 0.01% artificial phospholipid surfactant (Lipidoid BL206, NOF Corporation) was used as the anti-adsorption agent. The following six human infectious bacteria were used: (1) S. enterica (Se, NBRC 3313) (2)V. parahaemolyticus (Vp, NBRC12711) (3)P. vulgaris (Pv, NBRC 3045) (4) K. pneumoniae (Kp,NBRC 13277) (5)M. intracellulare (Mi, distributed by Kobe City Institute of Environmental Health) (6)M. mesophilicum (Mm, NBRC 15688)

[0043] M. intracellulare was cultured on "7H11 agar medium," and the rest were cultured on "standard agar medium." One colony of each bacterial culture was dispersed in 300 pL of the measurement solution and used for the measurement immediately after vortexing for about 10 seconds.

[0044] (AC nanopore measurement method) The microchannel in the conventional nanopore in the left diagram of Figure 6 and the microchannel in the present invention are also referred to as nanopores below. In this experiment, measurements were performed using these nanopores. Here, the method of measuring the AC characteristics of a sample using the nanopores described above is referred to as AC nanopore measurement. A lock-in amplifier was used for the measurements. The lock-in amplifier extracts only the signal components with the same frequency as the reference signal from the noise background and measures the magnitude and phase of the current. In the AC nanopore measurement method, a spike-like descending current and an ascending phase change are observed synchronously for a single particle. In other words, a waveform similar to that in Figure 4 is obtained. The magnitude of the current is proportional to the volume of the particle and was used to determine the size of the bacteria. Furthermore, a set of these current and phase spike waveforms was used as a machine learning feature to classify bacteria.

[0045] (Nanopores) In the AC nanopore measurement method of this experiment, a commercially available nanopore (NP2000, Izon Science Ltd., hereinafter referred to as Izon nanopore) and a straw-shaped nanopore of the present invention (see FIG. 1) were used. The cross-sectional shape of the microchannel of the straw-shaped nanopore was 3×3 μm, and the length was 30 μm.

[0046] (Measurement method) For the Izon nanopore, the bacterial suspension was added to the upper cell with a pressure head of 3 mm (30 Pa) in all experiments, and the nanopore was driven by hydrostatic pressure. For the straw-shaped nanopore, 50 pl was injected into the inlet and 20 pl into the outlet, and the nanopore was driven by the pressure head generated by the difference between the two. The applied voltage was 0.1 V for the Izon nanopore and 0.7 V for the straw-shaped nanopore. A minimum of 500 counts were measured for all measurements.

[0047] <Experimental Results> The change in the magnitude of the current accompanying the passage of a particle (measurement object) is proportional to the volume of the particle. Figure 11 is a histogram of the size distribution of bacterial particles measured by AC nanopore measurement using a straw-shaped nanopore. In Figure 11, the horizontal axis indicates the magnitude of the current, and the vertical axis indicates the frequency. The histogram was created by counting at least 500 particles for each particle size, but due to variation in counts, the vertical axis was normalized so that the sum of the counts for each particle was 100%. In the histogram shown in Figure 11, each bacterial size (current value) is distributed overlappingly, making it difficult to classify the bacteria when evaluated based on size alone.

[0048] Therefore, the inventors of the present application classified bacteria using machine learning. First, pulse waveforms of current magnitude and phase obtained by AC nanopore measurement were used for learning. In machine learning, the number of waveforms in the waveform set consisting of current magnitude and phase was first uniformed. Next, the 12 combinations with the highest learning effect for the peak height of each waveform when the peak height was set to 100% and the waveform width for each 10% increase were used as training data (see Figure 9). For CNN-based classification, the model was built with three layers (one input layer, one hidden layer, and one output layer).

[0049] Figures 12 and 13 show matrix evaluations of particle classification accuracy when the Random Forest and CNN methods were applied to the measurement results using the Izon nanopore. In Figures 12 and 13, (a) shows classification results for current magnitude only, (b) shows classification results for phase only, and (c) shows classification results for current magnitude and phase. In Figures 12 and 13, the darker the color of the squares on the diagonal line sloping downward to the right, the higher the classification accuracy. The numbers in the matrix indicate the number of classified particles.

[0050] Comparing the random forest method and the CNN method, as shown in (a), when only the magnitude of the current was used, the classification accuracy was 61.4% for the random forest method and 66.6% for the CNN method. As shown in (b), when only the phase was used, the classification accuracy was 57.2% for the random forest method and 70.7% for the CNN method. On the other hand, when both the magnitude and phase of the current were used, as shown in (c), the classification accuracy was 66.5% for the random forest method and 78.3% for the CNN method, showing that the CNN method had a higher classification accuracy.

[0051] Next, we will consider the classification accuracy of particles when the CNN method is applied to the measurement results using the straw-shaped nanopore. Figure 14 shows a matrix evaluation of the classification accuracy of particles when the CNN method is applied to the measurement results using the straw-shaped nanopore. As shown in Figure 14, the measurement using the straw-shaped nanopore showed higher classification accuracy in all sample sets compared to the measurement using the Izon nanopore (Figure 13). When comparing the case (c) using both the magnitude and phase of the current, the classification accuracy of the Izon nanopore (Figure 13) was 78.3%, while the classification accuracy of the straw-shaped nanopore (Figure 14) was 97.8%, showing a high classification accuracy of more than 19% for the straw-shaped nanopore. From these results, it was found that the combination of the CNN method and the straw-shaped nanopore can significantly improve the classification accuracy of bacteria.

[0052] In the above, we have explained an example in which bacteria were used as the particles to be measured, but in order to confirm the classification accuracy for smaller particles, we will explain the experimental results when viruses were used as the measurement target in the AC nanopore measurement method using a straw-shaped nanopore. Here, the following four viruses were measured. (A) Influenza Virus Type A (H1N1) (B) Influenza Virus Type A (H3N2) (C) Influenza Virus type B (Tokio / 53 / 99 strain) (D)Respiratory Syncytial Virus

[0053] The classification accuracy of these viruses was evaluated when the CNN method was applied to the measurement results of the magnitude and phase of the current to classify the particles. Figure 15 is a diagram showing a matrix evaluation of the classification accuracy of particles when the CNN method was applied to the measurement results of the magnitude and phase of the current for viruses in a straw-shaped nanopore. The numbers in the matrix in Figure 15 are the accuracy rates of particle classification. As shown in Figure 15, high classification accuracy was shown, with 0.94 for Influenza Virus A (H1N1), 1.00 for Influenza Virus A (H3N2), 0.95 for Influenza Virus B (Tokio / 53 / 99 lineage), and 0.86 for Respiratory Syncytial Virus. The overall classification accuracy was 96.52%, and it was confirmed that the classification accuracy could be roughly equivalent to that of bacteria (97.8%, Figure 14).

[0054] Therefore, by performing measurements by the AC nanopore measurement method using the straw-shaped nanopore of the present invention, it is possible to classify microscopic particles such as bacteria and viruses with high accuracy.

[0055] While the present invention has been described above in accordance with the above-described embodiment, the present invention is not limited to the configuration of the above-described embodiment, and naturally includes various modifications, alterations, and combinations that may be made by a person skilled in the art within the scope of the invention as defined in the claims of the present application. [Explanation of symbols]

[0056] 1 Sample measurement device 10 Flow path device 11 Flow Chip 12 Silicone rubber 13 Introduction 14 Microchannel 15 Discharge section 16 Introduction 17 Outlet 21 Electrode tip 22 Substrate 23 1st electrode 24 2nd electrode 30 Measuring part 40 Sample Solution 41 Solvents 42 Measurement Object

Claims

1. A flow path device, A measuring unit, the flow channel device includes a flow channel chip in which an inlet portion, a microflow channel, and a discharge portion are formed, and an electrode chip in which a first electrode and a second electrode are formed on a substrate; the microchannel is formed in the channel chip so as to extend in a direction parallel to a main surface of the channel chip, and a sample solution containing a measurement target dispersed in a solvent flows through the microchannel; The introduction unit is disposed on one side of the microchannel, and introduces the sample solution into the microchannel; the discharge unit is disposed on the other side of the microchannel, and the sample solution is discharged from the microchannel; the first electrode is disposed at a position corresponding to the introduction portion when viewed in a plan view from a direction perpendicular to a main surface of the electrode tip, the second electrode is disposed at a position corresponding to the discharge portion when viewed in a plan view from the direction perpendicular to the main surface of the electrode tip, the measurement unit applies an AC voltage to the first electrode and the second electrode, and identifies the object to be measured based on AC characteristics of the object to be measured when the object to be measured passes through the microchannel; The flow channel device is configured by bonding the electrode chip and the flow channel chip together. Sample measurement device.

2. The sample solution, the first electrode is introduced into the introduction portion through an introduction port that is an opening provided in the channel chip at a position corresponding to the first electrode, The sample measurement device according to claim 1 , wherein the sample is discharged from the discharge section through a discharge port that is an opening provided in the channel chip at a position corresponding to the second electrode.

3. A sample measurement device as described in claim 1 or 2, wherein the microchannel is a channel whose longitudinal direction is parallel to the main surface of the channel chip.

4. 4. The sample measurement device according to claim 3, wherein the area of ​​a cross section of the microchannel perpendicular to a direction in which the sample solution flows is 0.05 μm 2 or more and 100 μm 2 or less.

5. 4. The sample measurement device according to claim 3, wherein the length of the microchannel is 0.1 μm or more and 100 μm or less.

6. The sample measurement device according to claim 1 or 2, wherein the measurement unit is configured to be capable of introducing the positively or negatively charged object to be measured from the introduction unit into the microchannel by applying a DC voltage between the first electrode and the second electrode.

7. 7. The sample measurement device according to claim 6, wherein the measurement section is configured to be able to apply the AC voltage and the DC voltage in a superimposed manner to the first electrode and the second electrode.

8. The sample measurement device according to claim 1 , wherein the measurement unit includes a lock-in amplifier.

9. 3. The sample measurement device according to claim 1, wherein the measurement unit is configured to be able to apply an AC voltage having a frequency of 1 kHz to 100 MHz to the first electrode and the second electrode.

10. 3. The sample measurement device according to claim 1, wherein the measurement section determines a composite impedance and a phase using AC characteristics of the object to be measured, and identifies the object to be measured using the determined composite impedance and phase.

11. Using the synthetic impedance and the phase, parameters corresponding to a resistance component, a zeta potential, and a dielectric constant of the object to be measured are obtained, respectively; The sample measurement device according to claim 10 , wherein the object to be measured is identified using parameters corresponding to the obtained resistance component, zeta potential, and dielectric constant.

12. 3. The sample measurement device according to claim 1, wherein the measurement unit includes a prediction model generation unit that uses the AC characteristics of the object to be measured and information about the object to be measured as teacher data and generates, by machine learning, a prediction model that predicts the object to be measured based on the AC characteristics of the object to be measured.

13. The prediction model generation unit, extracting an in-phase component and a phase component out of phase with the in-phase component, which are the AC characteristics; determining a synthetic impedance and a phase using the extracted in-phase component and the extracted phase component; The sample measurement device according to claim 12 , wherein the prediction model is generated using a feature amount of the time change of the synthetic impedance and a feature amount of the time change of the phase.

14. The sample measurement device according to claim 12 , wherein the prediction model generation unit generates the prediction model by machine learning using a neural network.

15. The sample measurement device according to claim 1 , wherein the measurement object is a bacterium or a virus.

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