Measurement method and measurement device

Abstract

To output an index about the activity of microorganisms on the basis of a new parameter unknown in the conventional art.SOLUTION: A measurement device 1000 includes an AE sensor 100 including a sensitive part 102 to come into direct contact with a culture solution or medium of microorganisms, and a signal processor 400 that outputs an index about the activity of the microorganisms on the basis of an AE signal detected by the sensitive part 102 of the AE sensor 100.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a measurement method and a measurement device. [Background technology]

[0002] Conventionally, it has been known to measure various parameters as indicators of the activity (respiration, photosynthesis, fermentation, and other chemical reactions) of microorganisms, including algae, protists, and fungi such as yeast.

[0003] For example, in the alcoholic beverage manufacturing process, the activity of yeast can be determined by measuring the temperature of the mash, since there is a correlation between the degree of yeast fermentation and the temperature of the mash. Also, in the cultivation of cyanobacteria, oxygen is produced when the cyanobacteria photosynthesize, so it is known that the dissolved oxygen concentration in the culture solution can be measured. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-219412 Summary of the Invention [Problem to be solved by the invention]

[0005] Although conventional techniques are capable of grasping the activity status of microorganisms, there is a need to grasp the activity status of microorganisms from more diverse perspectives, i.e., to output an index of microbial activity based on new parameters not known in conventional techniques.

[0006] Therefore, an object of one embodiment of the present invention is to output an index relating to microbial activity based on a new parameter not known in the prior art. [Means for solving the problem]

[0007] A measurement method according to one embodiment of the present invention includes an installation step of installing an AE sensor so that a sensitive part of the AE sensor is in direct contact with a culture solution or a medium for the microorganisms, and a signal processing step of outputting an index relating to the activity of the microorganisms based on the AE signal detected by the sensitive part of the AE sensor.

[0008] Furthermore, the measurement method of one embodiment of the present invention further includes a determination step of determining whether or not microbial AE has occurred based on the AE signal detected by the sensing part of the AE sensor, and the signal processing step is configured to output an indicator of microbial activity based on the determination made by the determination step.

[0009] In addition, in the measurement method according to one embodiment of the present invention, the determining step is configured to obtain a frequency spectrum of the AE signal detected by the sensitive part of the AE sensor, determine a peak frequency with the strongest spectral component, and determine that a microbial AE has occurred if the peak frequency exceeds a predetermined lower limit frequency.

[0010] In addition, in the measurement method of one embodiment of the present invention, the determining step is configured to determine the frequency spectrum of background noise of the AE signal, determine the frequency spectrum of a signal waveform of the AE signal that exceeds a threshold value, and determine, for each frequency component, the ratio of the frequency spectrum of the signal waveform to the frequency spectrum of the background noise; and determine that a microbial AE has occurred when the ratio is equal to or greater than a predetermined value within a preset frequency range and when the ratio is less than the predetermined value outside the frequency range.

[0011] In addition, in the measurement method according to one embodiment of the present invention, the determining step is configured to count the number of peaks in the AE signal that exceed a predetermined peak threshold value and whose interval between adjacent peaks is smaller than a predetermined upper peak interval value, and to determine that a microbial AE has occurred if the number of counted peaks is equal to or greater than a predetermined lower peak limit number.

[0012] In addition, in the measurement method according to one embodiment of the present invention, the signal processing step is configured to output the frequency of occurrence of microbial AE as an index relating to the activity of the microorganisms.

[0013] In addition, in the measurement method according to one embodiment of the present invention, the signal processing step is configured to output the location of occurrence of microbial AE as an index relating to the activity of the microorganisms.

[0014] In addition, in a measurement method according to one embodiment of the present invention, the installation step is configured to install a first AE sensor and a second AE sensor, and the signal processing step is configured to output the location of microbial AE occurrence based on the arrival time difference between a first AE signal detected by the first AE sensor and a second AE signal detected by the second AE sensor, or the signal intensity ratio between the first AE signal and the second AE signal.

[0015] In addition, in the measurement method according to one embodiment of the present invention, the signal processing step is configured to output a peak frequency distribution of microbial AE as an index relating to the activity of the microorganisms.

[0016] Furthermore, the measurement method according to one embodiment of the present invention further includes an adjusting step of adjusting the culture environment of the microorganisms based on the index relating to the activity of the microorganisms obtained by the signal processing step.

[0017] A measuring device according to one embodiment of the present invention includes an AE sensor having a sensing part configured to come into direct contact with a culture solution or medium for a microorganism, and a signal processing part configured to output an index relating to the activity of the microorganism based on an AE signal detected by the sensing part of the AE sensor.

[0018] In addition, in one embodiment of the measuring device of the present invention, the signal processing unit is configured to determine whether or not a microbial AE has occurred based on the AE signal detected by the sensing unit of the AE sensor, and to output an index regarding microbial activity based on the determination of whether or not a microbial AE has occurred.

[0019] In addition, in the measuring device of one embodiment of the present invention, the signal processing unit is configured to obtain the frequency spectrum of the AE signal detected by the sensing unit of the AE sensor, obtain the peak frequency with the strongest spectral component, and determine that microbial AE has occurred if the peak frequency exceeds a predetermined lower limit frequency.

[0020] In addition, in one embodiment of the measuring device of the present invention, the signal processing unit is configured to calculate the frequency spectrum of background noise of the AE signal, calculate the frequency spectrum of a signal waveform of the AE signal that exceeds a threshold value, and calculate, for each frequency component, a ratio of the frequency spectrum of the signal waveform to the frequency spectrum of the background noise, and determine that a microbial AE has occurred when the ratio is equal to or greater than a predetermined value within a preset frequency range and when the ratio is less than the predetermined value outside the frequency range.

[0021] Furthermore, in the measuring device of one embodiment of the present invention, in addition to the above determination, the signal processing unit is further configured to count the number of peaks in the AE signal that exceed a predetermined peak threshold value and whose interval between adjacent peaks is smaller than a predetermined upper peak interval limit, and to determine that a microbial AE has occurred when the number of counted peaks is equal to or greater than a predetermined lower peak limit number.

[0022] In addition, in the measurement device according to one embodiment of the present invention, the signal processing unit is configured to output an occurrence frequency of microbial AE as an index relating to the activity of the microorganisms.

[0023] In addition, in the measuring device according to one embodiment of the present invention, the signal processing unit is configured to output a position where microbial AE occurs as an index relating to the activity of the microorganisms.

[0024] In addition, in a measuring device according to an embodiment of the present invention, the AE sensor includes a first AE sensor and a second AE sensor, and the signal processing unit is configured to output the position where the AE has occurred based on the arrival time difference between a first AE signal detected by the first AE sensor and a second AE signal detected by the second AE sensor, or the signal intensity ratio between the first AE signal and the second AE signal.

[0025] In addition, in the measurement device according to one embodiment of the present invention, the signal processing unit is configured to output a peak frequency distribution of microbial AE as an index relating to the activity of the microorganisms. [Effects of the Invention]

[0026] According to one embodiment of the present invention, an index relating to microbial activity can be output based on a new parameter not known in the prior art. [Brief explanation of the drawings]

[0027] [Figure 1] FIG. 1 is a diagram schematically showing the overall configuration of the measurement device of this embodiment. [Figure 2A] FIG. 2A is a diagram showing a schematic diagram of an AE sensor installed in a culture solution. [Figure 2B] FIG. 2B is a diagram showing a schematic diagram of the AE sensor being installed in the culture medium. [Figure 3A] FIG. 3A is a diagram schematically illustrating the structure of a piezoelectric AE sensor. [Figure 3B] FIG. 3B is a diagram showing a schematic structure of a capacitor-type AE sensor. [Figure 3C] FIG. 3C is a diagram schematically showing the structure of a voice coil type AE sensor. [Figure 3D]FIG. 3D is a diagram showing a schematic structure of an ECM type AE sensor. [Figure 3E] FIG. 3E is a diagram showing a schematic structure of an ECS type AE sensor. [Figure 4] FIG. 4 is a diagram for explaining the improvement of the S / N ratio by frequency filtering. [Figure 5A] FIG. 5A is a diagram showing an example of the second AE determination method. [Figure 5B] FIG. 5B is a diagram showing an example of the second AE determination method. [Figure 6A] FIG. 6A is a diagram showing an example of the third AE determination method. [Figure 6B] FIG. 6B is a diagram showing an example of the third AE determination method. [Figure 7] FIG. 7 is a diagram showing an example of an additional AE determination method. [Figure 8A] FIG. 8A is a diagram schematically showing AE measurement of sake mash in the first example. [Figure 8B] FIG. 8B is a diagram schematically showing AE measurement of sake mash in the first example. [Figure 9A] FIG. 9A shows the results of AE measurement of sake mash. [Figure 9B] FIG. 9B shows the results of AE measurement of sake mash. [Figure 10] FIG. 10 is a diagram showing AE measurement of cyanobacteria in the second example. [Figure 11] FIG. 11 shows the results of AE measurement of cyanobacteria. [Figure 12] FIG. 12 is a diagram showing a comparison between the distribution of AE generation positions of cyanobacteria and illuminance. [Figure 13] FIG. 13 is a diagram for explaining estimation of the AE generation position of cyanobacteria. [Figure 14] FIG. 14 is a diagram showing a schematic diagram of AE measurement of a Shiitake mushroom bed in the third example. [Figure 15A] FIG. 15A shows the results of AE measurement of Shiitake mushroom bed A. [Figure 15B] FIG. 15B shows the results of AE measurement of Shiitake mushroom bed B. [Figure 16] FIG. 16 is a diagram schematically showing AE measurement of a mixture of sawdust, rice bran, and water in the fourth example. [Figure 17] FIG. 17 shows the results of AE measurement of a mixture of sawdust, rice bran, and water. [Figure 18] FIG. 18 shows the peak frequency distribution of AE for a mixture of sawdust, rice bran, and water. [Figure 19] FIG. 19 is a diagram showing a first example of production control using microbial AE measurement. [Figure 20] FIG. 20 is a diagram showing a second example of production control using microbial AE measurement. [Figure 21] FIG. 21 is a flowchart of the measurement method of this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0028] The measurement method and measurement device of this embodiment will be described below with reference to the drawings. FIG. 1 is a diagram schematically illustrating the overall configuration of the measurement device of this embodiment. The measurement device 1000 of this embodiment is configured to measure elastic waves (acoustic emission, AE) generated by microbial activity. Specifically, microorganisms, including algae, protists, and fungi such as yeast, generate gases through their activity (chemical reactions such as respiration, photosynthesis, and fermentation), resulting in rapid gas diffusion, such as bubbling. AE generated by microbial activity (hereinafter referred to as "microbial AE" or simply "AE") refers to elastic waves generated in liquid culture media when bubbles nucleate in the liquid, coalesce, detach from the solid phase, or disappear at the liquid surface. Furthermore, when the culture media is solid (e.g., a mixture of culture media and culture solution), gas pockets trapped within the culture media are considered equivalent to bubbles, and microbial AE refers to elastic waves generated when gas pockets emerge, coalesce, or disappear due to gas escape to the outside.

[0029] The measuring device 1000 is equipped with an AE sensor 100 having a sensing part 102 configured to sense microbial AE. FIG. 2A is a diagram showing a schematic diagram of the installation of the AE sensor in a culture solution. As shown in FIG. 2A, the AE sensor 100 is configured so that the sensing part 102 comes into direct contact with a culture solution 602 for the microorganisms. For example, when culturing yeast or cyanobacteria in a liquid, the AE sensor 100 is installed inside a culture tank 600. The AE sensor 100 may be attached to the wall or bottom of the culture tank 600, or may be suspended in the liquid or floated on the liquid surface. In either case, since the sensing part 102 of the AE sensor 100 is in direct contact with the culture solution 602, even weak, high-frequency AE can be detected.

[0030] Figure 2B is a diagram that shows a schematic diagram of the installation of an AE sensor relative to a culture medium. As shown in Figure 2B, the AE sensor 100 is configured so that the sensitive part 102 comes into direct contact with the culture medium 604 of the microorganisms. For example, when culturing in a solid culture medium 604 such as a bacteria bed, the AE sensor 100 is attached to the surface of the culture medium 604 with a band or the like, or a hole is drilled in the culture medium 604 and the AE sensor 100 is inserted inside. In either case, the sensitive part 102 of the AE sensor 100 is in direct contact with the culture medium 604, making it possible to detect weak, high-frequency AE.

[0031] The AE sensor 100 can be, for example, a piezoelectric element, a microphone (including MEMS), or an electret element. The following describes the types of AE sensors. FIG. 3A is a diagram schematically illustrating the structure of a piezoelectric AE sensor. The AE sensor 100 can be a piezoelectric type (excellent in detecting ultrasonic AE signals at 100 kHz or higher) commonly used as an AE sensor. Specifically, as shown in FIG. 3A, the AE sensor 100 can be configured by sandwiching a piezoelectric ceramic 110 or a piezoelectric polymer 110 between two electrodes 112 and attaching a matching layer 114 to one of the electrodes 112. Because the piezoelectric AE sensor 100 has a significantly higher acoustic impedance than the culture solution or medium, it is configured so that the sensing part 102 comes into contact with the culture solution or medium via the matching layer 114, such as silicone resin.

[0032] 3B is a diagram showing a schematic diagram of the structure of a capacitor-type AE sensor. The AE sensor 100 can be a capacitor-type sensor used as a microphone. Specifically, the AE sensor 100 is configured to include two opposing electrodes 122 and a protective layer 124 attached to one of the electrodes 122 (diaphragm). Because the capacitor-type AE sensor 100 has low pressure resistance, it is configured so that the sensitive part 102 comes into contact with the culture solution or medium via the protective layer 124 to prevent the diaphragm from bending excessively and breaking.

[0033] 3C is a diagram showing a schematic structure of a voice coil type AE sensor. The AE sensor 100 can be a voice coil type used as a microphone. Specifically, the AE sensor 100 is configured to include a module 130 including a voice coil, a magnet, and a diaphragm, and a protective layer 134 attached to the module 130. Because the voice coil type AE sensor 100 has low pressure resistance, the sensor 102 is configured to contact the culture solution or medium via the protective layer 134 to prevent the diaphragm from being damaged by excessive bending.

[0034] 3D is a diagram showing a schematic diagram of the structure of an ECM-type AE sensor. The AE sensor 100 can be an ECM (electret condenser microphone) type used as a microphone. Specifically, the AE sensor 100 can be configured by sandwiching an electret 140 between two electrodes 142 and attaching a protective layer 144 to one of the electrodes (diaphragm) 142. Because the ECM-type AE sensor 100 has low pressure resistance, it is configured so that the sensitive part 102 comes into contact with the culture solution or medium via the protective layer 144 to prevent the diaphragm from bending excessively and breaking.

[0035] Figure 3E is a schematic diagram showing the structure of an ECS-type AE sensor. The AE sensor 100 can be constructed by placing a spacer 156 between electrets 150, sandwiching the electrets 150 between two electrodes 152, and attaching a protective layer 154 to one of the electrodes (diaphragm) 152. The ECS (electret capacitor sensor)-type AE sensor 100 has excellent pressure resistance, allowing the element to be directly in contact with the culture medium. However, to prevent damage due to scratches, the sensor element 102 is configured to be in contact with the culture medium or medium via a protective layer 154 made of silicone resin or other material. A thinner protective layer 154 improves sensitivity but reduces durability. The protective layer 154 also functions as a matching layer. Because the ECS converts mechanical vibrations into electrical signals through deformation of the microscopic air gap (microgap, the space between the electrets 150 and the electrodes 152) formed by the spacer 156, its acoustic impedance is significantly lower than that of a piezoelectric sensor and higher than that of a microphone. When the ECS is placed in the culture medium, the acoustic impedance of the liquid is higher than that of the ECS. Therefore, the protective layer 154 can also function as a matching layer by using a material such as silicone resin with an acoustic impedance equal to or lower than that of the liquid. Furthermore, in this case, a protective layer thickness of up to approximately 10 mm has little effect on detection sensitivity (if too thick, AE attenuation in the protective layer cannot be ignored). When the ECS is placed on the surface of or in the culture medium, a medium with high acoustic impedance can be considered the same as the culture medium (for example, the medium for shiitake mushrooms is made of wood, which has a significantly high acoustic impedance). On the other hand, if the medium has low acoustic impedance due to its high air content (for example, the medium for shiitake mushrooms is a porous material made of compressed sawdust, which has a low acoustic impedance), the protective layer of the ECS is ineffective as a matching layer, so it is best to keep it as thin as possible. In this case, a thickness of 0.01 to 1 mm is desirable for durability. The ECS-type AE sensor 100 is capable of AE measurement over a wide frequency range, from 0.1 to 200 kHz, making it applicable to all AE determination methods. It should be noted that with regard to ECS type AE sensors, the disclosures of Japanese Patent Nos. 5305304 and 6214054 can be incorporated by reference in their entirety into this specification.

[0036] As described above, the microbial AE can be detected by placing an electret sensor, piezoelectric sensor, or microphone in the culture solution or on the surface or inside the culture medium as the AE sensor 100. By bringing the sensitive part 102 of the AE sensor 100 into direct contact with the culture solution or culture medium, it becomes possible to detect weak microbial AE containing high-frequency components.

[0037] Returning to the description of FIG. 1 , the measuring device 1000 includes a preamplifier 200 for amplifying the signal output from the AE sensor 100 and an A / D converter 300 for converting the analog signal output from the preamplifier 200 into a digital signal. That is, the signal output from the AE sensor 100 passes through the preamplifier 200 (an analog amplification circuit (including a frequency filter)) and then undergoes A / D conversion by the A / D converter 300. The A / D conversion converts the signal data into digital data that is discretized at a sampling time ts. The sampling time ts is 100 ns to 10 ms, but is preferably 100 ns to 10 μs to measure a wide frequency band and avoid the effects of external disturbances such as noise. Note that with regard to the processing of the signal output from the AE sensor 100, the disclosure of JP 2018-54502 A is incorporated herein by reference in its entirety.

[0038] The measuring device 1000 includes a signal processing unit 400 configured to determine whether or not a microbial AE has occurred based on a signal detected by the sensing unit 102 of the AE sensor 100 (specifically, a signal output from the A / D converter 300 in this embodiment), and to output an index related to microbial activity based on the determination. The signal processing unit 400 can be implemented as part of a computer including a computing device such as a CPU (central processing unit), a memory, and the like. The measuring device 1000 also includes an input / output interface 500, including a mouse, keyboard, and display, connected to the signal processing unit 400. The AE determination method performed by the signal processing unit 400 to determine whether or not a microbial AE has occurred will be described below.

[0039] First AE determination method (1) When the digital data output from the A / D converter 300 exceeds a preset threshold value Vth, the signal processing unit 400 takes in the digital data for a recording length of Tw to obtain a signal waveform. (2) The signal processing unit 400 performs frequency filtering on the signal waveform as needed. (3) The signal processing unit 400 calculates the amplitude Vpp of the signal waveform as the AE signal strength, and calculates the S / N ratio Rsn using the noise level Vnz according to the following formula: Rsn=Vpp / Vnz

[0040] Vnz may be a fixed value, a background noise level measured at intervals of 1 min to 1 hour, or the signal level immediately before the signal waveform exceeds the threshold. When there is only one AE sensor 100, the signal processor 400 determines AE if the S / N ratio Rsn exceeds a predetermined value. When there are multiple AE sensors 100, the signal processor 400 can improve the accuracy of the AE determination by adding a condition for AE determination that all S / N ratios Rsn of sensors other than the sensor with the maximum S / N ratio Rsn do not exceed a predetermined noise level. Regarding the first AE determination method, the disclosures of Japanese Patent Laid-Open Nos. 2015-87214 and 2018-54502 are incorporated herein by reference in their entirety.

[0041] FIG. 4 is a diagram illustrating the improvement of the S / N ratio by frequency filtering. The vertical axis of the graph in FIG. 4 represents the signal strength of the signal waveform, and the horizontal axis represents the passage of time. The upper part of FIG. 4 shows the signal strength over time when frequency filtering is not performed. In this case, the S / N ratio Rsn is calculated using the above formula, and Rsn is 8.8 dB. On the other hand, the lower part of FIG. 4 shows the signal strength over time when 50 kHz high-pass filtering is performed. In this case, Rsn is 11 dB, and the S / N ratio is improved. As described above, the signal processing unit 400 can detect AE signals buried in low-frequency noise by removing frequency components lower than a predetermined lower limit frequency.

[0042] Second AE determination method (1) The signal processing unit 400 obtains a signal waveform in the same manner as in the first AE determination method. (2) The signal processing unit 400 calculates the S / N ratio Rsn in the same manner as in the first AE determination method. (3) The signal processing unit 400 obtains the frequency spectrum of the AE signal detected by the sensing unit 102 of the AE sensor 100, finds the peak frequency with the strongest spectral component, and can determine that AE has occurred if the peak frequency exceeds a predetermined lower limit frequency. That is, if the S / N ratio Rsn exceeds a predetermined value, the signal processing unit 400 obtains the frequency spectrum of the signal waveform using a Fourier transform, a wavelet transform, or the like, and finds the frequency (peak frequency) fp with the strongest spectral component. The signal processing unit 400 determines that AE has occurred if fp exceeds a predetermined lower limit frequency. (4) If the S / N ratio Rsn does not exceed a predetermined value, the signal processing unit 400 performs high-pass filtering (removes frequency components lower than a predetermined lower limit frequency). (5) The signal processing unit 400 performs AE determination on the obtained signal waveform using a method similar to the first AE determination method (assuming that the AE signal is buried in low-frequency noise).

[0043] In view of the following points, the signal processing unit 400 can measure microbial AE over a wide frequency range of 0.1 kHz to 1000 kHz (preferably 10 to 100 kHz) by using, for example, the ECS type AE sensor 100 described above. (1) When measuring AE in a culture medium, the distribution of bubble sizes can be determined. (The smaller the bubble size, the higher the AE frequency.) The size of the events increases and the frequency decreases in the following order: bubble nucleation and disappearance → detachment from the interface of grown bubbles → coalescence of bubbles. Therefore, the current foaming state can be determined from the frequency distribution. (2) In the case of microbial AE, which is dominated by high-frequency components above 10 kHz, it is easy to separate it from noise. By determining signal waveforms with strong high-frequency components and weak low-frequency components from the frequency spectrum of the signal waveform as AE, it is possible to prevent sudden noise from being mistakenly identified as AE.

[0044] 5A and 5B are diagrams illustrating an example of the second AE determination method. The vertical axis of the upper graphs in FIGS. 5A and 5B represents the signal strength of the signal waveform, and the horizontal axis represents the passage of time. The lower graphs in FIGS. 5A and 5B show the results of wavelet transforming the signal waveform, with the vertical axis representing frequency and the horizontal axis representing the passage of time. The left side of each of FIGS. 5A and 5B shows the graphs obtained without filtering, while the right side shows the graphs obtained with 50 kHz high-pass filtering.

[0045] In the example of FIG. 5A, both with and without filtering, the peak frequency fp (near 70 kHz) is higher than the lower limit frequency fl (20 kHz), and therefore it is determined that AE has occurred. In contrast, the example of FIG. 5B shows a signal waveform when electromagnetic noise has occurred, rather than AE. In this case, if a sensor that has difficulty detecting signals in the low frequency range, such as a piezoelectric element, is used, only peak frequencies higher than the lower limit frequency fl (20 kHz) may be detected, as shown on the right side of FIG. 5B, which could result in an erroneous determination that AE has occurred. On the other hand, by using a sensor that can detect signals in a wide band, such as 0.1-200 kHz, such as the ECS-type AE sensor 100, it is possible to detect peak frequencies lower than the lower limit frequency fl (20 kHz), thereby preventing erroneous AE detection.

[0046] Third AE judgment method (1) The signal processing unit 400 sets the frequency spectrum components of noise in advance. Alternatively, the signal processing unit 400 periodically measures background noise every minute to every day, obtains its frequency spectrum, and treats it as a noise component. (2) The signal processing unit 400 obtains a signal waveform in the same manner as in the first AE determination method, and determines a frequency spectrum. (3) The signal processing unit 400 calculates the ratio rf of the frequency component of the signal waveform to the frequency component of the noise for each frequency component. (4) The signal processing unit 400 determines that AE has occurred if the rf exceeds a predetermined value within a preset frequency range and the rf at frequencies outside the range does not exceed the predetermined value.

[0047] An example of the third AE determination method will be described. Figures 6A and 6B are diagrams showing an example of the third AE determination method. The upper graph in Figure 6A shows the spectral intensity of 0.0 to 1.0 ms in Figure 5A as background noise and 1.8 to 2.8 ms as AE, respectively, and the lower graph in Figure 6A shows the S / N ratio rf for the upper graph (measured in a fermentation bath). The upper graph in Figure 6B shows the spectral intensity of 0.0 to 1.0 ms in Figure 5B as background noise and 1.8 to 2.8 ms as AE, respectively, and the lower graph in Figure 6B shows the S / N ratio rf for the upper graph (measured in a fermentation bath).

[0048] In the graph at the bottom of Figure 6A, rf is equal to or greater than a predetermined value in the frequency range equal to or greater than the lower limit frequency (20 kHz), and is equal to or less than 2, less than the predetermined value, in the frequency range below the lower limit frequency (20 kHz). Therefore, signal processing unit 400 can determine that microbial AE has occurred for the signal waveform of Figure 5A. On the other hand, in the graph at the bottom of Figure 6B, rf is equal to or greater than a predetermined value in the frequency range equal to or greater than the lower limit frequency (20 kHz), but there is a peak exceeding 100 in the frequency range below the lower limit frequency (20 kHz), and is not less than the predetermined value. Therefore, signal processing unit 400 can determine that microbial AE has not occurred for the signal waveform of Figure 5B. In this way, signal processing unit 400 can determine AE by calculating the spectral intensity without performing frequency filtering.

[0049] Additional AE determination method (peak count) Any of the first to third AE determination methods can further include a peak count determination. The signal processing unit 400 checks the number of peaks in the signal waveform using the following method. (1) Peaks exceeding a predetermined peak threshold (2) The interval between adjacent peaks is smaller than the upper limit of the specified peak interval. If the number of peaks in the peak group that satisfies the above is equal to or greater than a predetermined lower limit number of peaks, the signal processing unit 400 determines that the signal is AE. This determination makes it possible to remove sudden electromagnetic noise.

[0050] An example of an additional AE determination method will be described. FIG. 7 is a diagram showing an example of the additional AE determination method. The vertical axis of the upper and lower graphs in FIG. 7 represents the signal strength of the signal waveform, and the horizontal axis represents the passage of time. The signal processing unit 400 can determine that a microbial AE has occurred when it is determined that a microbial AE has occurred by any of the above-described first to third AE determination methods and the following condition is further satisfied. For any of the signal waveforms in FIG. 7, the signal processing unit 400 performs determination by setting the peak threshold to twice the background noise, the upper peak interval upper limit to 50 ms, and the lower peak limit number to 2.

[0051] In the upper graph of FIG. 7, the signal processing unit 400 counts nine peaks (triangles in the graph) that exceed a predetermined peak threshold, and the intervals between adjacent peaks are all smaller than the upper peak interval limit. Because the number of counted peaks (9) is equal to or greater than the lower peak interval limit (2), the signal processing unit 400 can determine that a microbial AE has occurred for the waveform in the upper graph of FIG. 7. Meanwhile, in the lower graph of FIG. 7, the signal processing unit 400 does not count two peaks (triangles in the graph) that exceed the peak threshold, because the interval between these adjacent peaks exceeds the upper peak interval limit. Because the number of counted peaks (0) is less than the lower peak interval limit (2), the signal processing unit 400 can determine that the waveform in the lower graph of FIG. 7 is noise.

[0052] Next, the output of indices related to microbial activity will be described. In consideration of the following items, the signal processing unit 400 can use the AE occurrence frequency, the amplitude and number of peaks of the AE waveform, the frequency spectrum of the AE waveform, and the arrival time of the AE as indices of the amount of microbial activity. (1) A high frequency of AE occurrence indicates a high level of microbial activity (gas production through respiration and photosynthesis). (2) Higher AE frequencies are more directly related to microbial activity (AEs resulting from small events such as the nucleation and annihilation of dissolved gases have higher frequencies). (3) The location of the AE (activity location) can be obtained from the difference in arrival time of the AE. Alternatively, the signal intensity ratio can be calculated from the amplitude and number of peaks to obtain a rough location of the AE (activity location).

[0053] <First Example> As a first example, AE measurement was performed on mash stored in a wooden barrel at a sake brewing facility. Figures 8A and 8B are schematic diagrams showing AE measurement of mash in the first example. Figure 8A shows AE measurement during the brewing period in November, and Figure 8B shows AE measurement during the brewing period in January. During the brewing period in November, the AE sensor 100 was suspended from mash 700 contained in a wooden barrel 702, and during the brewing period in January, the AE sensor 100 was inserted into a stainless steel pipe 704 and inserted into mash 700. At this time, a temperature sensor 706 was also installed. In this example, an ECS-type AE sensor 100 equipped with a 0.1 mm thick silicone resin protective layer 154 was used. Background noise was measured every minute, and AE measurement was performed using four times this as the threshold (sampling frequency: 500 kHz, signal waveform length: 1 kwords).

[0054] Figures 9A and 9B show the results of AE measurements on sake mash. Figure 9A shows the results of AE measurements on sake mash brewed in November, and Figure 9B shows the results of AE measurements on sake mash brewed in January. In the graphs on the left of Figures 9A and 9B, the vertical axis shows the number of AE events detected (occurrences) per hour, and the horizontal axis shows the passage of time. In the graphs on the right of Figures 9A and 9B, the vertical axis shows temperature, and the horizontal axis shows the passage of time.

[0055] As shown in Figures 9A and 9B, the signal processing unit 400 can output the frequency of AE occurrence as an index related to microbial activity. Furthermore, as shown in Figure 9A, when the mash was brewed in November, the temperature rapidly increased to nearly 30°C and then gradually decreased. Meanwhile, the frequency of AE occurrence (the number of AE events detected per hour) fluctuated significantly up until one week after brewing and then decreased significantly. As shown in Figure 9B, when the mash was brewed in January, the temperature gradually increased to 22°C and then began to decrease. Meanwhile, the number of AE events fluctuated significantly during the measurement period, with the largest fluctuations occurring up until 12 days after brewing.

[0056] Although the behavior of AE generation varied greatly depending on the brewing time, the alcohol concentrations after measurement were nearly identical, at 19.6% and 19.5%, respectively. In this way, AE measurement makes it possible to visualize the behavior of yeast, which cannot be determined from temperature or alcohol concentration. Furthermore, even if the alcohol concentration is the same, the taste of the finished sake varies greatly depending on the brewing time. In other words, by monitoring the behavior of AE generation, it is possible to predict differences in the characteristics of sake depending on the brewing time.

[0057] <Second Example> In the second example, cyanobacteria were cultured in a 100 x 100 x 100 mm acrylic culture tank and AE measurements were performed. Figure 10 shows the AE measurement of cyanobacteria in the second example. Cyanobacteria were collected from the Arakawa River, and only the finest cyanobacteria were extracted using a syringe filter. After culturing for one month, AE measurements were performed. In this example, an ECS-type AE sensor 100 equipped with a 0.1 mm thick silicone resin protective layer 154 was used. In this example, two AE sensors 100 (a first AE sensor 100-1 and a second AE sensor 100-2) were attached to the inner wall surface of the culture tank 802 so that the sensing parts 102 of each sensor faced the center of the culture tank 802, and the distance between the sensors in the depth direction was 50 mm (center-to-center distance). Culture solution 800 and cyanobacteria were placed inside culture tank 802, which was then placed in a greenhouse at Saitama University. The background noise was measured every minute, and AE measurements were performed using four times that noise as the threshold (sampling frequency: 500 kHz, signal waveform length: 10 kwords). Illuminance measurements were also performed at the same time. Of the five days of measurements, days 0, 3, and 4 were sunny.

[0058] In this embodiment, the signal processing unit 400 performed the AE determination in the following manner. (1) Method A: The first AE determination method was used. The signal processing unit 400 did not use frequency filtering, and determined that an AE occurred when the S / N ratio Rsn exceeded 4. (2) Method B: A second AE determination method was used. The signal processing unit 400 set 60 kHz as the predetermined lower limit frequency, and determined that an AE occurred when the S / N ratio Rsn exceeded 4. Both methods added AE detection by peak counting (peak threshold: 6 dB S / N ratio, upper limit of peak interval: 10 ms).

[0059] Figure 11 shows the results of AE measurement of cyanobacteria. The top graph in Figure 11 shows the results of AE measurement using Method A above, with the vertical axis showing the number of AEs detected (occurrences) per hour and the horizontal axis showing the passage of time. The middle graph in Figure 11 shows the results of AE measurement using Method B above, with the vertical axis showing the number of AEs detected (occurrences) per hour and the horizontal axis showing the passage of time. The bottom graph in Figure 11 shows the vertical axis showing illuminance and the horizontal axis showing the passage of time.

[0060] The top graph in Figure 11 shows that the number of AEs generated increased on clear days in both cases, which is thought to be due to active cyanobacteria. However, the middle graph in Figure 11 shows that the number of AEs generated on days 0 and 3 was significantly lower with Method B than with Method A. This is because the AEs detected with Method A contain many AEs with strong low-frequency components. When cyanobacteria photosynthesize, oxygen is produced, which increases the frequency of dissolved oxygen nucleation and disappearance. For this reason, it is thought that the behavior of AEs with strong high-frequency components is strongly related to the photosynthetic activity of cyanobacteria. In other words, it is thought that the AE generation behavior with Method B more accurately represents the photosynthetic activity of cyanobacteria, and AE measurements can provide information about the photosynthetic activity of cyanobacteria that cannot be obtained by illuminance measurements.

[0061] Furthermore, the signal processing unit 400 can output the location of AE occurrence as an index related to microbial activity. Fig. 12 is a diagram showing a comparison between the distribution of AE occurrence locations in cyanobacteria and illuminance. In the upper graph of Fig. 12, the vertical axis represents illuminance, and the horizontal axis represents the passage of time. The middle graph of Fig. 12 shows the distribution of AE occurrence locations in cyanobacteria, with the vertical axis representing the depth position of AE occurrence in cyanobacteria and the horizontal axis representing the passage of time. The lower graph of Fig. 12 shows the results of AE measurement, with the vertical axis representing the number of AE detections (occurrences) per hour, and the horizontal axis representing the passage of time.

[0062] The signal processing unit 400 can output the AE occurrence position based on the signal intensity ratio between the first AE signal detected by the first AE sensor 100-1 and the second AE signal detected by the second AE sensor 100-2. That is, in Method B, the signal processing unit 400 estimated the AE occurrence position from the intensity of the signal waveform. As shown in FIG. 10, the first AE sensor 100-1 and the second AE sensor 100-2 installed at the top and bottom are designated as CH1 and CH2, respectively, and the signal ratio R12 was calculated using the amplitudes Vpp1 and Vpp2 of the respective signal waveforms from the following formula: R12=Vpp2 / (Vpp1+Vpp2)

[0063] The signal processing unit 400 then defined the depthwise position of the culture tank 802 as z, and multiplied R12 by 50 mm to determine the AE occurrence position in the depthwise direction. As shown in the middle graph of FIG. 12, the estimated AE occurrence position distribution shows that the AE occurrence position in the depthwise direction is not constant but fluctuates greatly from day to day, but on sunny days, it can be seen that AE occurs more frequently at shallow depths. This is thought to be because CO2 becomes insufficient when the cyanobacteria actively photosynthesize. In this case, CO2 diffuses more quickly from the outside near the surface of the culture solution, so the cyanobacteria tend to be active at shallow depths. In this way, estimating the AE occurrence position distribution makes it possible to determine the position at which the cyanobacteria are actively photosynthesizing, thereby enabling efficient agitation of the culture tank 802.

[0064] The signal processing unit 400 can also output the AE occurrence position based on the arrival time difference between the first AE signal detected by the first AE sensor 100-1 and the second AE signal detected by the second AE sensor 100-2. Fig. 13 is a diagram for explaining the estimation of the AE occurrence position of cyanobacteria. In the upper graph of Fig. 13, the vertical axis represents the signal strength of the first AE signal detected by the first AE sensor 100-1 (CH1) and the second AE signal detected by the second AE sensor 100-2 (CH2), and the horizontal axis represents the passage of time. The lower graph of Fig. 13 is an enlarged portion of the upper graph of Fig. 13.

[0065] The signal processing unit 400 can also estimate the AE occurrence position from the difference in the arrival time of AE from multiple AE sensors. As shown in Fig. 13, the arrival time of AE at two AE sensors Ch1 and Ch2 is the time of the first peak that exceeds the threshold, and the difference in the arrival time of Ch1 from Ch2 is the arrival time difference Δt. In this case, the AE occurrence position z shown in Fig. 13 is expressed by the following equation: z=v Δt / 2+h / 2

[0066] Here, h is the distance between the centers of the Ch1 and Ch2 sensors, and v is the speed of sound in the culture medium. For example, if h = 50 mm, the speed of sound in the culture medium is 1500 m / s, and Δt is 8 μs, then z is 31 mm, and the AE occurrence position is 31 mm from the Ch1 AE sensor 100-1 toward Ch2. With two sensors, only one-dimensional position estimation is possible, but increasing the number of sensors not only improves accuracy but also enables three-dimensional position estimation. When estimating the AE occurrence position based on the arrival time difference, the time resolution has a significant impact on accuracy. Therefore, a higher sampling frequency for AE measurement is better, preferably 100 kHz to 10 MHz.

[0067] <Third Example> As a third example, AE measurement was performed on a shiitake mushroom bed. FIG. 14 is a diagram schematically illustrating AE measurement of a shiitake mushroom bed in the third example. As shown in FIG. 14, when performing AE measurement on a mushroom bed (culture medium), the AE sensor 100 was attached to the mushroom bed 900 with a rubber band 902 so that the sensing part 102 was in direct contact with the mushroom bed 900. In this example, an ECS-type AE sensor 100 with a 0.1 mm thick silicone resin protective layer 154 was used. The culture tank was placed in a greenhouse at Saitama University, and AE measurement was performed by measuring background noise every minute and setting twice that value as the threshold (sampling frequency: 96 kHz, signal waveform length: 10 kwords).

[0068] In this example, two fully matured mushroom beds were prepared. One of them (bush bed A) was subjected to AE measurement as is, and the formation of numerous fruiting bodies was observed two days after the start of measurement. The other (bush bed B) was sterilized by autoclaving and then subjected to AE measurement, but no fruiting body formation was observed. In this example, the signal processing unit 400 performed 500 Hz high-pass filtering using the first AE determination method to determine AE.

[0069] FIG. 15A shows the results of AE measurement of Shiitake mushroom bed A. FIG. 15B shows the results of AE measurement of Shiitake mushroom bed B. In the upper graphs of FIGS. 15A and 15B, the vertical axis indicates the number of AEs detected (occurrences) per hour, and the horizontal axis indicates the passage of time. In the lower graphs of FIGS. 15A and 15B, the vertical axis indicates the peak frequency of AE, and the horizontal axis indicates the passage of time. As shown in the lower graphs of FIGS. 15A and 15B, the signal processing unit 400 can output the peak frequency distribution of microbial AE as an index related to microbial activity.

[0070] As shown in Figure 15B, almost no AE was detected in fungal bed B, where no fruiting bodies formed. However, as shown in Figure 15A, numerous AE were detected in fungal bed A. The majority of the detected AE had frequency peaks in the 1-5 kHz range, suggesting that the AE was generated by the sudden diffusion of gases accumulated inside the fungal bed as mycelium grew and fruiting bodies formed. Shiitake mushroom fruiting is known to be promoted by external stimuli such as impact or electric shock. Measuring AE before and after the application of external stimuli can determine whether fungal activity has increased, allowing for efficient fruiting body formation. Microbial activity can be more easily understood by outputting the peak frequency distribution of microbial AE.

[0071] <Fourth Example> Wood chips (including sawdust and sawdust) are mixed with bran and fermented by bacteria to produce fertilizer, or the resulting heat can be used for hot baths. In these cases, the fermentation activity can be visualized by measuring microbial AE. As a fourth example, AE measurements were performed on a mixture of sawdust, rice bran, and water. Figure 16 is a diagram showing a schematic diagram of AE measurements on a mixture of sawdust, rice bran, and water in the fourth example.

[0072] As shown in Figure 16, a mixture of sawdust, rice bran, and water can be placed in bathtub 910, where the heat from fermentation makes it possible to enjoy a warm bath. Microbial AE measurements were performed in this bathtub 910. Specifically, ECS-type first AE sensor 100-1, second AE sensor 100-2, first temperature sensor 706-1, and second temperature sensor 706-2 were attached to stainless steel pipe 912, which was then placed in bathtub 910. The signals output from AE sensors 100-1 (Ch1) and 100-2 (Ch2) were amplified by preamplifier 200 and then recorded for 12 hours using a PCM recorder (sampling frequency: 48 kHz). AE was then detected from the recorded data using the third AE determination method, with a lower limit frequency of 2 kHz.

[0073] FIG. 17 shows the results of AE measurement of a mixture of sawdust, rice bran, and water. The vertical axis of the upper graph in FIG. 17 indicates the average temperature per hour, and the horizontal axis indicates the passage of time. In the upper graph in FIG. 15, T1 is the temperature measured by the first temperature sensor 706-1, and T2 is the temperature measured by the second temperature sensor 706-2. The vertical axis of the lower graph in FIG. 17 indicates the number of AE events detected (occurrences) per hour, and the horizontal axis indicates the passage of time. In the lower graph in FIG. 17, Ch1 is the number of AE events detected by the first AE sensor 100-1, and Ch2 is the number of AE events detected by the second AE sensor 100-2.

[0074] Because the bath was thoroughly stirred once before measurement began, the bath 910 was low temperature at the start of measurement, and the temperature inside the bath rose as fermentation progressed. Meanwhile, the number of AEs peaked, with the first AE sensor 100-1 showing the maximum number of AEs 5 hours after measurement began, and the second AE sensor 100-2 showing the maximum number of AEs 7 hours after measurement began. From these results, it can be seen that moisture was lost as the temperature rose, preventing fermentation from progressing, and therefore the fermentation activity reached its maximum 5-7 hours later. In other words, by measuring microbial AE, it is possible to grasp the fermentation activity in real time, which cannot be determined by temperature measurement.

[0075] It is believed that the microbial AE in this example is generated by multiple events. Fig. 18 is a diagram showing the peak frequency distribution of AE of a mixture of sawdust, rice bran, and water. The upper graph in Fig. 18 shows the peak frequency distribution of AE detected by the first AE sensor 100-1, and the lower graph in Fig. 18 shows the peak frequency distribution of AE detected by the second AE sensor 100-2. The vertical axis of the upper and lower graphs in Fig. 18 indicates the peak frequency of AE, and the horizontal axis indicates the passage of time.

[0076] As shown in Figure 18, the signal processing unit 400 can output the peak frequency distribution of microbial AE as an indicator of microbial activity. As a result, as shown in Figure 18, it can be seen that AE is generated over a wide frequency range of 2-16 kHz. Possible sources of AE include evaporation of water (high frequency), sudden diffusion of water vapor or CO2 gas (low frequency), destruction of wood chips due to decomposition of cellulose in the wood chips (high frequency), and falling of destroyed wood chips (low frequency), and the peak frequencies of AE generated by these events vary greatly. However, all of these events occur during fermentation and reflect the activity of bacteria. By outputting the peak frequency distribution of microbial AE as an indicator of microbial activity, it becomes easier to understand the state of microbial activity.

[0077] In this example, the measurement environment reached 70°C. The measurement method and device of this embodiment are useful in that they can easily measure microbial activity in real time even in such a high-temperature environment. As described above, the first to fourth examples described above enable the output of an index of microbial activity based on microbial AE, a new parameter not known in the prior art, thereby enabling a more comprehensive understanding of the microbial activity state. Furthermore, the first to fourth examples described above output an index of microbial activity based on microbial AE, enabling the understanding of the microbial activity state that cannot be obtained using parameters used in the prior art. For example, temperature measurement used in the prior art detects temperature changes as heat is generated by microbial activity and then diffuses into the culture medium. Therefore, it is not possible to identify the location where the microorganisms are actively active, and due to its low responsiveness, it is difficult to determine the duration of active activity (as is evident from the results in Figure 17). Furthermore, the dissolved oxygen measurement used in the prior art measures changes in dissolved oxygen concentration due to microbial photosynthesis and respiration. Therefore, multi-point measurement may enable the identification of the location where the microorganisms are actively active. However, multi-point measurement is not practical due to the high cost of the sensors used for measurement. Furthermore, once the dissolved oxygen reaches saturation due to photosynthesis, the dissolved oxygen concentration no longer changes, making it impossible to determine microbial activity. In contrast, this embodiment allows for low-cost multipoint measurements using an ECS or microphone, making it possible to identify locations where microbial activity is active. Furthermore, since the AE occurrence frequency is related to the gas bubble nucleation frequency, and the bubble nucleation frequency also changes when the dissolved gas concentration changes due to microbial activity, the AE occurrence frequency can be used to measure the microbial activity state with excellent time response. Furthermore, even when the dissolved gas reaches saturation, bubbles are generated in an amount equal to the amount of gas released by the microorganisms, causing AE, so the AE occurrence frequency can be used to determine microbial activity.

[0078] <Production Management> Once the signal processing unit 400 outputs indicators related to microbial activity, these parameters can be used to adjust the microbial culture environment for production control (such as the temperature of the culture medium or culture medium, moisture content, electrical conductivity, pH, temperature and humidity around the culture medium, light intensity, CO2 or CO2 concentration, etc.). Here, an example of production control using the first embodiment will be described. When brewing mash, the mash is periodically stirred to activate the yeast, and the timing of this is determined empirically. Figure 19 shows a first example of production control using microbial AE measurement. The graph in Figure 19 shows the AE measurement results when stirring was performed, with the vertical axis indicating the number of AE events detected (occurrences) per hour and the horizontal axis indicating the passage of time.

[0079] As a result of stirring the mash at the times indicated by arrows A, B, and C in Figure 19, AE increased after stirring, indicating that the yeast was activated. It also shows that stirring when the frequency of AE occurrence has dropped significantly is more effective.

[0080] In other words, yeast can be activated efficiently by stirring the mash when the frequency of AE generation falls below a certain level. Temperature control is used in stainless steel tank brewing, and yeast activity can be controlled by raising the temperature when the frequency of AE generation falls below a certain level and lowering the temperature when it exceeds that level.

[0081] Next, we will explain an example of production management for other microorganisms. In this example, a solution containing yeast was placed in a 100 x 100 x 100 mm acrylic culture tank and AE measurements were performed. First, 400 mL of distilled water was mixed with 0.25 mass% dry yeast, and this was poured into a culture tank equipped with an ECS-type AE sensor 100 to begin AE measurements. After 30 minutes, 10 mass% sugar water was added. Furthermore, at the start of measurements, the temperature was maintained at 22°C by air conditioning, but after 450 minutes, the air conditioning was turned off and the temperature was raised to 27°C.

[0082] Figure 20 shows a second example of production control using microbial AE measurement. In the graph in Figure 20, the vertical axis shows the number of AEs detected (occurrences) per minute, and the horizontal axis shows the passage of time. As shown in Figure 20, almost no AEs were detected until 30 minutes after the start of AE measurement, indicating that the yeast was not active. Then, when sugar water was added (arrow a in Figure 20), the number of AEs increased, indicating that yeast activity had begun. Furthermore, when the temperature began to rise (arrow b in Figure 20), the number of AEs increased significantly, indicating that yeast activity had been promoted.

[0083] In this way, by adjusting the sugar and temperature so that the AE generation frequency falls within a certain range, yeast activity can be maintained at a constant level. For example, if an AE sensor is attached to bread dough during fermentation to prevent yeast activity from becoming too high, it becomes easy to prevent a decline in bread quality due to over-fermentation.

[0084] Next, the measurement method of this embodiment will be described. Fig. 21 is a flowchart of the measurement method of this embodiment. As shown in Fig. 21, in the measurement method of this embodiment, first, the AE sensor 100 is installed so that the sensitive part 102 of the AE sensor 100 is in direct contact with the culture solution or culture medium of the microorganisms (installation step 102). In installation step 102, one AE sensor 100 may be installed, or multiple AE sensors (for example, a first AE sensor 100-1 and a second AE sensor 100-2) may be installed.

[0085] Next, the measurement method determines whether or not a microbial AE has occurred based on the AE signal detected by the sensing unit 102 of the AE sensor 100 (determination step 104). Specifically, the determination step 104 determines whether or not a microbial AE has occurred using the above-described first to third AE determination methods and an additional determination method.

[0086] Next, the measurement method outputs an index related to microbial activity based on the determination made in the determination step 104 (signal processing step 106). Specifically, the signal processing step 106 can output, as an index related to microbial activity, the frequency of occurrence of microbial AE, the location of occurrence of microbial AE, the peak frequency distribution of microbial AE, or the like, as shown in the first to fourth embodiments above. For example, when outputting the location of occurrence of microbial AE, the signal processing step 106 can output the location of occurrence of AE based on the arrival time difference between the first AE signal detected by the first AE sensor 100-1 and the second AE signal detected by the second AE sensor 100-2, or the signal intensity ratio between the first AE signal and the second AE signal, as described above. The signal processing step 106 can present the index related to microbial activity to the user via the input / output interface 500.

[0087] Next, the measurement method adjusts the culture environment for the microorganisms based on the indicators related to the activity of the microorganisms obtained in the signal processing step 106 (adjustment step 108). Specifically, as in the first and second production control examples above, adjustment step 108 can adjust the temperature, moisture content, electrical conductivity, pH, temperature and humidity around the culture medium, amount of light, CO2 or CO2 concentration, etc., of the culture solution or medium so that the activity of the microorganisms is in a desired state.

[0088] The measuring device 1000 and measuring method of the present invention have been described above. Hereinafter, possible uses of the present invention will be described. Expected uses (products that can be cultured using this method) (1) Brewing alcohol (a) Visualization of yeast fermentation (detection of AE associated with gas release during fermentation and bubble nucleation and disappearance) (b) Quantifying the differences in fermentation depending on the type of raw material and yeast used (c) Improving quality and production efficiency by controlling the amount of fermentation activity (temperature, moisture, addition of raw materials and yeast, etc.) (d) Energy and labor savings through minimal brewing management (minimum necessary management is possible by monitoring yeast activity)

[0089] (2) Fermented foods other than brewed alcohol (a) Visualization of yeast fermentation (detection of AE associated with gas release during fermentation and bubble nucleation and disappearance) (b) Yogurt, kimchi, natto, vinegar (bacteria such as lactic acid bacteria, natto bacteria, and acetic acid bacteria), sake, bread, soy sauce, miso, cheese, and tea (yeast and mold) (c) Quantifying the differences in fermentation depending on the type of dough and yeast (d) Understanding the optimal environment for fermentation (preventing excessive fermentation) (e) Baking each batch under optimal fermentation conditions (improving quality)

[0090] (3) Cultivation of cyanobacteria and euglena related to biomass production including biofuel (a) Cultivation of cyanobacteria and euglena (b) Bioethanol, biogas, and compost production (c) Simple measurement of photosynthetic activity of algae and protists (detection of AE associated with the nucleation and disappearance of dissolved oxygen) (d) Quantifying differences in photosynthesis among different types of algae and protists (e) Improving quality and production efficiency by controlling photosynthetic activity (temperature, light intensity, agitation, CO2 concentration) (f) Energy and labor savings through minimal management during cultivation (minimum necessary management is possible by monitoring photosynthetic activity)

[0091] (4) Cultivation of mushrooms such as shiitake mushrooms (mushroom bed cultivation and log cultivation) (a) Measurement of fungal activity (detection of AE associated with gas release during mycelial growth and fruiting body formation) (b) Quantifying the differences in activity depending on the type of Shiitake fungus and medium (c) Improving quality and production efficiency by controlling the activity of Shiitake mushroom fungi (temperature, light intensity, moisture, mechanical and electrical stimulation) (d) Energy and labor savings through minimal management during cultivation (visualization of the effect of external stimuli on promoting fruiting body formation)

[0092] (5) Fermented baths, fermented extracts, and other beauty products that utilize fermentation [Explanation of symbols]

[0093] 100 AE sensors 100-1 First AE sensor 100-2 Second AE sensor 102 Sensing part 102 Installation Steps 104 Judgment Step 106 Signal Processing Steps 108 Adjustment Steps 400 signal processing section 1000 Measuring Devices fl lower limit frequency fp Peak frequency z AE occurrence position Δt arrival time difference

Claims

1. An installation step of installing an ECS type AE sensor so that the sensing part of the ECS type AE sensor is in direct contact with a culture medium or a culture medium of a microorganism that generates gas through its activity; a signal processing step of outputting an indicator of microbial activity based on the AE signal detected by the sensing part of the AE sensor; Including, The method further includes a determination step of determining whether or not a microorganism AE has occurred based on the AE signal detected by the sensing part of the AE sensor, the signal processing step is configured to output an index regarding microbial activity based on the determination made in the determination step; The determination step is configured to obtain a frequency spectrum of the AE signal detected by the sensing unit of the AE sensor, obtain a peak frequency at which a spectral component is strongest, and determine that a microbial AE has occurred when the peak frequency exceeds a predetermined lower limit frequency. Measurement method.

2. An installation step of installing an ECS type AE sensor so that the sensing part of the ECS type AE sensor is in direct contact with a culture medium or a culture medium of a microorganism that generates gas through its activity; a signal processing step of outputting an indicator of microbial activity based on the AE signal detected by the sensing part of the AE sensor; Including, The method further includes a determination step of determining whether or not a microorganism AE has occurred based on the AE signal detected by the sensing part of the AE sensor, the signal processing step is configured to output an index regarding microbial activity based on the determination made in the determination step; The determination step is configured to determine a frequency spectrum of background noise of the AE signal, determine a frequency spectrum of a signal waveform of the AE signal that exceeds a threshold, determine a ratio of the frequency spectrum of the signal waveform to the frequency spectrum of the background noise for each frequency component, and determine that a microbial AE has occurred when the ratio is equal to or greater than a predetermined value in a frequency range equal to or greater than a lower limit frequency and is less than the predetermined value in a frequency range less than the lower limit frequency. Measurement method.

3. In addition to the determination, the determination step is further configured to count the number of peaks in the AE signal that exceed a predetermined peak threshold value and have an interval between adjacent peaks that is smaller than a predetermined upper peak interval value, and to determine that a microbial AE has occurred when the number of counted peaks is equal to or greater than a predetermined lower peak limit number. The measurement method according to claim 1 or 2.

4. The signal processing step is configured to output the occurrence frequency of a microbial AE as an indicator of the activity of the microbial organism. The measurement method according to any one of claims 1 to 3.

5. The signal processing step is configured to output the occurrence location of the microorganism AE as an indicator of the activity of the microorganism. The measurement method according to any one of claims 1 to 4.

6. the installing step is configured to install a first AE sensor and a second AE sensor; the signal processing step is configured to output a location of occurrence of a microorganism AE based on an arrival time difference between a first AE signal detected by the first AE sensor and a second AE signal detected by the second AE sensor, or a signal intensity ratio between the first AE signal and the second AE signal. The measurement method according to claim 5.

7. The signal processing step is configured to output a peak frequency distribution of the microbial AE as an indicator of the activity of the microbial organism. The measurement method according to any one of claims 1 to 6.

8. The method further comprises adjusting the culture environment of the microorganism based on the indicator regarding the activity of the microorganism obtained by the signal processing step. The measurement method according to any one of claims 1 to 7.

9. An ECS type AE sensor having a sensing part configured to be in direct contact with a culture medium or culture medium of a microorganism that generates gas through its activity; a signal processing unit configured to output an indicator regarding the activity of the microorganisms based on the AE signal detected by the sensing unit of the AE sensor; Including, the signal processing unit is configured to determine whether or not a microorganism AE has occurred based on the AE signal detected by the sensing unit of the AE sensor, and to output an index regarding microbial activity based on the determination of whether or not a microorganism AE has occurred; The signal processing unit is configured to obtain a frequency spectrum of the AE signal detected by the sensing unit of the AE sensor, obtain a peak frequency at which a spectral component is strongest, and determine that a microbial AE has occurred when the peak frequency exceeds a predetermined lower limit frequency. Measuring equipment.

10. An ECS type AE sensor having a sensing part configured to be in direct contact with a culture medium or a culture medium of a microorganism that generates gas through its activity; a signal processing unit configured to output an indicator regarding the activity of the microorganisms based on the AE signal detected by the sensing unit of the AE sensor; Including, the signal processing unit is configured to determine whether or not a microorganism AE has occurred based on the AE signal detected by the sensing unit of the AE sensor, and to output an index regarding microbial activity based on the determination of whether or not a microorganism AE has occurred; The signal processing unit is configured to obtain a frequency spectrum of background noise of the AE signal, obtain a frequency spectrum of a signal waveform of the AE signal that exceeds a threshold value, obtain a ratio of the frequency spectrum of the signal waveform to the frequency spectrum of the background noise for each frequency component, and determine that a microbial AE has occurred when the ratio is equal to or greater than a predetermined value in a frequency range equal to or greater than a lower limit frequency and is less than the predetermined value in a frequency range less than the lower limit frequency. Measuring equipment.

11. In addition to the determination, the signal processing unit is further configured to count the number of peaks of the AE signal that exceed a predetermined peak threshold value and have an interval between adjacent peaks that is smaller than a predetermined upper peak interval value, and to determine that a microbial AE has occurred when the number of counted peaks is equal to or greater than a predetermined lower peak limit number.

11. The measuring device according to claim 9 or 10.

12. The signal processing unit is configured to output an occurrence frequency of a microbial AE as an index regarding the activity of the microbial organism.

12. The measuring device according to any one of claims 9 to 11.

13. The signal processing unit is configured to output the occurrence position of the microorganism AE as an indicator regarding the activity of the microorganism.

13. The measuring device according to any one of claims 9 to 12.

14. the AE sensor includes a first AE sensor and a second AE sensor; the signal processing unit is configured to output an AE generation position based on an arrival time difference between a first AE signal detected by the first AE sensor and a second AE signal detected by the second AE sensor, or a signal intensity ratio between the first AE signal and the second AE signal.

14. The measuring device of claim 13.

15. The signal processing unit is configured to output a peak frequency distribution of a microbial AE as an index regarding the activity of the microbial organism.

15. The measuring device according to any one of claims 9 to 14.

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