Analysis method for fecal gas component ratios for Bristol type classification

Abstract

This application relates to a fecal gas analysis method for Bristol type classification and physical condition analysis, and more specifically, to a fecal gas component ratio analysis method that can classify the Bristol type of feces according to the gas component ratio of each fecal sample and analyze the physical condition. By using the fecal gas component ratio analysis method for Bristol type classification and physical condition analysis according to one embodiment of this application, it is possible to classify the Bristol type of feces regardless of measurement time, person, meal menu, age, and fecal weight, and also to understand the individual's eating habits. The above analysis method can be applied to personal health monitoring through fecal Bristol type classification.

Description

Technical Field

[0001] This application relates to a fecal gas analysis method for Bristol type classification and physical condition analysis. More specifically, it relates to a fecal gas component ratio analysis method that can classify the Bristol type of feces based on the component ratio of gases for each feces and analyze the physical condition. This application claims priority based on Korean Patent Application No. 10-2023-0073105 filed on June 7, 2023, and all the contents disclosed in the specification and drawings of that application are incorporated herein by reference.

Background Art

[0002] Due to changes in eating habits and various stresses on modern people, digestive system diseases are increasing. In order to grasp the progress and causes of such digestive system diseases, it is important to check the condition of defecation, which is the digestive result of the food ingested daily.

[0003] Although it is desirable for an individual to directly check the condition of feces after defecation, there are also people who dislike it, and it is difficult for an individual to continuously grasp information such as defecation time, frequency, and type of odor.

[0004] As another method of checking the condition of defecation, there is a method of using defecation gas. In this case, the absolute value of the gas concentration directly generated from defecation can be measured by one or more gas sensors to check the condition of defecation. However, since the absolute value of the gas concentration changes depending on the measurement conditions and situations, there is a limit in that the accuracy decreases.

[0005] Examples of technologies that have developed fecal gas analysis include an analysis system, a fecal odor gas analysis system, and an exhaled gas analysis system (Japanese Patent Publication No. 2020-187074). However, regarding the analysis of fecal gas, there is still a situation where in-depth development and various studies on more accurate and effective analysis methods are necessary.

[0006] On the other hand, fecal gas concentration is highly sensitive to time; the amount of gas decreases over time and is also sensitive to the amount and type of feces and the measurement environment. However, conventional fecal gas analysis methods involve measuring feces collected in a toilet using a toilet bowl installed in a laboratory, and do not measure the gas concentration immediately after defecation. Therefore, there is a disadvantage in that accurate analysis is difficult based solely on absolute measurements.

[0007] Therefore, the inventors diligently worked to develop a fecal gas analysis method that complements the shortcomings of the prior art. As a result, they developed a fecal gas component ratio analysis method in which, when the measured values ​​[ppm] from the sensors used for analysis of each fecal gas are converted to component ratios [%], the change over time is not large and remains constant. This method is hardly affected by other environmental factors such as time and the weight of the feces during fecal gas analysis, and enables more accurate Bristol type classification of feces and assessment of health status. Thus, the present invention was completed. [Overview of the project] [Problems that the invention aims to solve]

[0008] This application relates to a fecal gas analysis method for Bristol type classification and physical condition analysis, and more specifically, aims to provide a fecal gas component ratio analysis method that can classify the Bristol type of feces according to the gas component ratio of each fecal sample and analyze the physical condition.

[0009] However, the problems that this application seeks to solve are not limited to those mentioned above, and other problems not mentioned should be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0010] The first aspect of this application provides a fecal gas component ratio analysis method for Bristol type classification. [Effects of the Invention]

[0011] By utilizing the fecal gas component ratio analysis method for Bristol type classification and physical condition analysis according to one embodiment of the present invention, it is possible to classify the Bristol type of feces regardless of measurement time, person, meal menu, age, and fecal weight, as well as to understand an individual's eating habits. Therefore, the above analysis method can be applied to personal health monitoring through fecal Bristol type classification. [Brief explanation of the drawing]

[0012] [Figure 1] This is a diagram showing the Bristol Fecal Scale. [Figure 2a] This figure shows the gas concentration graphs corresponding to the time output from 11 sensors and the total gas concentration measurement graph (Figure 2d), which is the real-time sum of the measurements from the 11 sensors, when the gas from one stool sample (78g of human stool of type 6) is measured. [Figure 2b] This figure shows the gas concentration graphs corresponding to the time output from 11 sensors and the total gas concentration measurement graph (Figure 2d), which is the real-time sum of the measurements from the 11 sensors, when the gas from one stool sample (78g of human stool of type 6) is measured. [Figure 2c] This figure shows the gas concentration graphs corresponding to the time output from 11 sensors and the total gas concentration measurement graph (Figure 2d), which is the real-time sum of the measurements from the 11 sensors, when the gas from one stool sample (78g of human stool of type 6) is measured. [Figure 2d] This figure shows the gas concentration graphs corresponding to the time output from 11 sensors and the total gas concentration measurement graph (Figure 2d), which is the real-time sum of the measurements from the 11 sensors, when the gas from one stool sample (78g of human stool of type 6) is measured. [Figure 2e] This figure shows the time-dependent component ratio graphs of 11 sensors when measuring the gas in one stool sample (78g of human stool of type 6). [Figure 2f] This figure shows the time-dependent component ratio graphs of 11 sensors when measuring the gas in one stool sample (78g of human stool of type 6). [Figure 2g] This figure shows the time-dependent component ratio graphs of 11 sensors when measuring the gas in one stool sample (78g of human stool of type 6). [Figure 3a] This figure (Figure 3c) shows the gas concentration graphs corresponding to the time output from seven sensors and the total gas concentration measurement graph (real-time sum of the measurements from the seven sensors) when the gas from one stool sample (22.4g of 4-type stool from person A) is measured. [Figure 3b] This figure (Figure 3c) shows the gas concentration graphs corresponding to the time output from seven sensors and the total gas concentration measurement graph (real-time sum of the measurements from the seven sensors) when the gas from one stool sample (22.4g of 4-type stool from person A) is measured. [Figure 3c] This figure (Figure 3c) shows the gas concentration graphs corresponding to the time output from seven sensors and the total gas concentration measurement graph (real-time sum of the measurements from the seven sensors) when the gas from one stool sample (22.4g of 4-type stool from person A) is measured. [Figure 3d] This figure shows the time-dependent component ratio graphs of seven sensors when measuring the gas in one stool sample (22.7g of 4-type stool from person A). [Figure 3e] This figure shows the time-dependent component ratio graphs of seven sensors when measuring the gas in one stool sample (22.7g of 4-type stool from person A). [Figure 4a] This figure shows the fecal gas concentration measurement graph [ppm] from the H2S sensor and the total measurement graph [ppm] from the sensor set. [Figure 4b] This figure shows the real-time component ratio graph [%] of the H2S sensor. [Figure 5a] This figure shows the fecal gas concentration measurement graph [ppm] from the C2H4 sensor and the total measurement graph [ppm] from the sensor set. [Figure 5b] This figure shows the real-time component ratio graph [%] of the C2H4 sensor. [Figure 6a]It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types). [Figure 6b] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types). [Figure 6c] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types). [Figure 6d] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types). [Figure 6e] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types). [Figure 6f] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types). [Figure 6g] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types) with the component ratios re-analyzed excluding NH3. [Figure 6h] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types) with the component ratios re-analyzed excluding NH3. [Figure 6i] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types) with the component ratios re-analyzed excluding NH3. [Figure 6j] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types) with the component ratios re-analyzed excluding NH3. [Figure 6k] It is a figure comparing real-time component ratio graphs by fecal type (2 types, 4 types, and 6 types) with the component ratios re-analyzed excluding NH3. [Figure 6l]This figure compares real-time component ratio graphs for different fecal types (2-type, 4-type, and 6-type) after re-analyzing the component ratios excluding NH3. [Figure 7] This figure shows a bar graph of the component ratios from a CH2O sensor (data from person A). [Figure 8] This figure shows a bar graph of the component ratios from an NO2 sensor (data from individuals A, B, C, and D). [Figure 9] This figure shows a bar graph of the component ratios of the NH3 sensor (data from person A). [Figure 10] This figure shows a bar graph of the component ratios from an H2S sensor (data for person A). [Modes for carrying out the invention]

[0013] In the following, embodiments of the present application will be described in detail with reference to the attached drawings, so that they can be easily implemented by a person with ordinary skill in the art to which the present application pertains. However, the present application can be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly illustrate the present application, parts unrelated to the description have been omitted from the drawings, and similar parts throughout the specification are denoted by similar reference numerals.

[0014] Throughout the specification of this application, when a member is described as being "on top of" another member, this includes not only cases where the member is in contact with another member, but also cases where there is yet another member between the two members.

[0015] Throughout this specification, when a part “includes” a component, this means, unless otherwise stated, that it may include other components rather than excluding them. Throughout this specification, terms of degree such as “about” and “substantially” are used in the sense of, or close to, the numerical values ​​of the manufacturing and material tolerances inherent to the meaning referred to, and are used to prevent unscrupulous infringers from unfairly exploiting disclosures that refer to precise or absolute numerical values ​​to aid in understanding this application. Throughout this specification, terms of degree such as “~step” or “~step” do not mean “~step for.”

[0016] Throughout the specification of this application, the term “these combinations” as used in the Markush expression means one or more mixtures or combinations selected from the group of components described in the Markush expression, and means including one or more selected from the group of components.

[0017] Throughout the specification of this application, the phrase "A and / or B" means "A or B, or A and B."

[0018] The following will provide a detailed explanation of the embodiment and examples of this application with reference to the attached drawings. However, this application is not limited to these embodiment and examples and drawings.

[0019] The first aspect of this application provides a fecal gas component ratio analysis method for Bristol type classification.

[0020] In one embodiment of the present invention, the present invention provides a fecal gas component ratio analysis method for Bristol type classification, comprising the steps of: (a) measuring the gas concentration value (ppm) of feces using a gas sensor; (b) deriving a real-time component ratio graph for each gas using the gas concentration value measured above; (c) determining component ratio values ​​from the real-time component ratio graph derived above; (d) deriving a graph using the component ratio values ​​obtained above; and (e) classifying the Bristol type of feces by comparing the graph derived above with the component ratio graph for each Bristol type of feces.

[0021] According to one embodiment of the present invention, the component ratio analysis method of the present invention is a method for monitoring human health through fecal gas data, and is a method for determining the subject's constitution, health status, presence or absence of disease, etc., by analyzing the gas measurement value [ppm] from each sensor as a component ratio in a total gas concentration measurement graph measured by 11 gas sensors constituting a Sensor Array.

[0022] Fecal gas concentration is highly sensitive to time, decreasing over time. It also changes sensitively depending on the amount and type of feces and the measurement environment. Therefore, analysis for health monitoring is difficult using only absolute measurement values ​​[ppm]. However, as with the component ratio analysis method of this application, when the time-dependent gas measurement graph [ppm] output by each sensor is converted into a time-dependent component ratio graph [%] (real-time component ratio graph), it has been confirmed that, regardless of the measurement time after defecation, it tends to output a fairly constant value over time compared to the gas concentration measurement value [ppm].

[0023] According to one embodiment of the present application, the gas sensor in step (a) above may be one or more selected from the group consisting of NH3, CO, H2, VOC, NO, NO2, H2S, SO2, C2H4, CH2O, and ETO gas sensors, but is not limited thereto. In addition to the above sensor set, any combination of multiple sensors (including partial combinations of the 11 sensors and all other combinations of sensors not mentioned in the present application) can be effectively applied to monitor and determine a person's physical condition.

[0024] In one embodiment of the present application, step (b) above may be carried out in accordance with the following [Formula 1] and [Formula 2].

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[0025] According to one embodiment of the present application, the component ratio value in step (c) of the present application may be obtained by taking a single data value from the real-time component ratio graph for each gas derived in step (b), or by taking values ​​within a predetermined range and calculating the average value, but is not limited to these. The range for calculating the average value may change depending on the data analysis.

[0026] According to one embodiment of the present application, the graph in step (d) above means all graphs that can be represented in graphic form, such as bar graphs, line graphs, pie charts, and band graphs, and is not limited to any particular graph.

[0027] The present invention will be described in more detail below using embodiments of the present application, but the embodiments described below are for illustrative purposes only to aid in understanding the present application, and the content of the present application is not limited to the embodiments described below.

[0028] [Examples] Example 1. Setting up the gas sensor and placing the sample. (1) Preparation of the gas sensor After connecting the power supply and stabilizing the gas sensors, the gas sensor array was configured to measure human exhaled breath to confirm that the gas sensors were properly connected and functioning.

[0029] (2) Preparation and setup of the sample After placing the stool collection paper in the toilet, the system was switched on to begin receiving data from the gas sensors. After 2 minutes, the fan was activated to draw in and circulate outside air to the gas sensor array. After another 2 minutes, the prepared stool was placed on the collection paper, and the toilet lid was closed to create a sealed environment. By attaching a tube to the toilet lid, the toilet and the gas sensor array were connected, allowing the fecal gas from the toilet to be drawn into the sensor array. The CO, H2, NH3, VOC, NO, NO2, H2S, SO2, C2H4, CH2O, and ETO sensors were used for component ratio analysis. After measuring fecal gas for approximately 3 to 30 minutes, the tube was disconnected. After 2 minutes, the gas sensor switch was turned off, and data transmission was interrupted.

[0030] (3) Determining whether each sensor is reacting or not. When measuring fecal gas, the presence or absence of a reaction was checked for each sensor, and the measured values ​​of sensors that were not considered to be reacting were all corrected to 0 ppm over time. This is because if the measured values ​​were not corrected to 0 ppm, the sensor noise would be read as a measured value, affecting the analysis of component ratios. In this case, the criterion for determining whether or not there was a reaction was that if the signal that reacted at the time of fecal gas measurement was greater than the maximum signal noise value before fecal gas measurement multiplied by 0.8, then a reaction was considered to be present.

[0031] (Maximum signal noise value for each sensor before fecal gas measurement) × 0.8 < (Magnitude of the signal that was detected during fecal gas measurement)

[0032] Example 2. Analysis of Real-Time Component Ratio Graph (1) Output of real-time component ratio graph After determining whether each sensor reacted to fecal gas, a real-time component ratio graph was output as shown below. In the following experiment, the NH3 sensor was used as the reference (i.e., the experiment was conducted with ex_sensor set to NH3).

[0033] When calculating the component ratio of the NH3 sensor (ex_sensor in Equation 1 below), the real-time component ratio graph of the NH3 sensor was output using the sum of the measured values ​​of all sensors (including NH3) that make up the sensor set, as shown in [Equation 1] below. In order to output the real-time component ratio of the NH3 sensor (sensors that did not react were set to 0 ppm), the calculation was based on the total gas measurement concentration (Total_ppm) obtained by adding up the measured values ​​of all 11 sensors in real time (see Equation 1).

[0034]

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[0035] In contrast, when calculating the component ratios of the other sensors excluding NH3, in order to prevent the other sensors from being significantly affected by the NH3 sensor, the component ratio graph of the remaining sensors was output using the "NH3 excluded sensor set measurement sum graph [ppm]", which is the sum of the measurements of the other sensors excluding the NH3 sensor. The remaining sensors were calculated based on no_ex_sensor_Total ppm (in this case, no_NH3_Total ppm), which is the real-time sum of the measured values ​​of the 10 sensors excluding the NH3 sensor (sensors that did not react were 0 ppm) (see Equation 2).

[0036]

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[0037] In the gas concentration measurement graphs shown in Figures 2a to 2d and 3a to 3c below, the sensor readings continuously decreased over time throughout the entire fecal gas measurement interval. However, in Figures 2e to 2g and 3d, and in Figure 3e, the component ratio graphs for each sensor showed almost no decrease over time from a predetermined point in time (around 10 minutes in Figures 2e to 2g), and were confirmed to output fairly constant values. Furthermore, in Figures 2a to 2d and 2e to 2g, it was confirmed that even when the sensor set used for component ratio analysis changed, a constant component ratio value was output over time.

[0038] *In a real-time component ratio graph, the meaning of outputting a constant value for the component ratio over time. " Before the removal of feces (fecal measurement interval), the level does not continuously and consistently decrease or increase over time. In the component ratio graphs in [Figure 3d] and [Figure 3e], although there is a slight fluctuation in the component ratio signal of the NH3 sensor in particular, it does not show a continuous decrease or increase over time, indicating that this is not due to the influence of time. The reason why the sensor outputs a slight fluctuation signal is thought to be due to the influence of ambient airflow and other factors during the relatively long measurement period of 30 minutes.

[0039] In Figures 2e through 2g, the component ratios of several sensors decreased during the first 5 minutes of measurement. This decrease did not occur continuously throughout the entire 30-minute measurement period of fecal gas in the experiment, but only during the initial period when the gas concentration [ppm] changed rapidly. This was found to be due to differences in the rate and slope of decrease between the "single sensor measurement graph [ppm]" and the "sum of measurement graph [ppm]" which is the sum of all measurement values ​​from the sensor set. For example, as shown in Figure 4a, the "H2S sensor measurement concentration [ppm]" decreased more rapidly than the "sum of measurement graph [ppm]" for the sensor set, so the component ratio graph in Figure 4b appears to decrease over time in the initial period. In contrast, the "C2H4 sensor measurement concentration [ppm]" decreased at almost the same rate as the "sum of measurement graph [ppm]" for the sensor set, so there was no change in the component ratio value in the real-time component ratio graph in Figure 5b.

[0040] As shown in Figures 6a to 6f, it was confirmed that the component ratio of the sensor, particularly the NH3 sensor, changed significantly as the fecal type approached from Type 2 to Type 6. This confirmed that it is possible to understand a person's health status using component ratio analysis. In the real-time component ratio graphs of fecal gas for Type 4 and Type 6, the sections where the sensor signal jumps are signals caused by repeatedly measuring and not measuring fecal gas during the experiment, and these sections were not considered in the component ratio analysis.

[0041] [Table 1]

[0042] As shown in [Table 1] above, the relative positions of the component ratios of the other sensors, excluding the NH3 sensor, remained unchanged in all measured feces. However, the relative position of the NH3 sensor relative to the other sensors changed for each feces sample. In [Figures 6a] to [Figures 6f], the NH3 sensor outputted component ratio values ​​of approximately 90% or more as the feces type approached 1-2 types, and component ratio values ​​of 10% or less as it approached 6 types. Thus, because the NH3 sensor changed significantly depending on the feces type, it was found that the component ratios of the other sensors were more influenced by the NH3 component ratio than by the feces type. Therefore, in this study, the NH3 sensor was classified as a specific sensor among the 11 sensor sets analyzed, and the component ratios of the remaining sensors were re-analyzed excluding the specific sensor (NH3 sensor) to analyze the tendencies and characteristics according to feces type and the health status of the sample collector. In addition to the sensor set described above, when analyzing component ratios in any combination of multiple sensors (including partial combinations of the 11 sensors and all other sensor combinations not mentioned in this application), it is possible to select and classify specific sensors to analyze the component ratios.

[0043] On the other hand, as shown in Figures 6g to 6l, with the exception of the NH3 sensor, it was difficult to analyze the trends and characteristics of each stool type through the real-time component ratio graph. Therefore, the inventors quantified the real-time component ratio graph analyzed above, summarized the component ratio values ​​accounted for by each sensor constituting the sensor set in a single stool gas, and analyzed them as a bar graph.

[0044] Example 3. Analysis of Real-Time Component Ratio Bar Graph The analysis of the real-time component ratio bar graph was performed in the following steps.

[0045] Step 1. Collect stool sample data. To analyze the ratio of fecal gas components, we collected data from 18 stool samples taken on different days from three individuals, as shown in [Table 2] below. The collected stool samples differed in Bristol type, diet, individual, sex, age, and stool weight.

[0046] [Table 2]

[0047] Step 2. Derive a real-time component ratio graph for each stool sample, quantify the component ratios, and convert them into bar graphs.

[0048] Step 3. Analyzing the bar graph When 18 stool samples collected from the three individuals mentioned above were analyzed using 11 sensor sets to obtain a bar graph of component ratios, the NO2 and NH3 sensors showed a tendency for component ratio values ​​to gradually decrease as the stool approached diarrheal stool, i.e., Type 6-7. Furthermore, when analyzing only the stool data from person A, the CH2O sensor was found to be able to distinguish between Type 6-7 and Type 3-4 to some extent.

[0049] When analyzing only the data from person A, it was easier to understand the Bristol type and the subject's health status. In the CH2O sensor bar graph, it was possible to distinguish between Bristol Type 3, 4, and 6-7 types (see Figure 7), and in the H2S sensor, although it was a normal healthy stool type 4, it was pale in color and it was possible to obtain more detailed information, such as the fact that the subject had an upset stomach (see Figure 10). In addition, in the NH3 sensor bar graph, although it was a 4 type, it was pale in color and it was possible to obtain information about an upset stomach, as well as information about stools from type 6 that occurred after consuming spicy food (see Figure 9). Thus, it was found that it is possible to distinguish and understand a person's health status and Bristol Stool type through the analysis of the ratio of 11 sensor components.

[0050] In conclusion, it was confirmed that the fecal gas component ratio analysis method of this application can be used to classify fecal types and assess a person's health status, regardless of the measurement time of fecal gas or the weight of the feces.

[0051] The above description of the present application is illustrative, and a person with ordinary skill in the art to which the present application pertains should understand that it can be easily modified into other specific forms without altering the technical idea or essential features of the present application. Therefore, the above-described embodiments should be understood to be illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a dispersed manner, and similarly, components described as dispersed may be implemented in a combined form.

[0052] The scope of this application is defined by the claims, which are set forth below rather than by the detailed description above, and all modified or altered forms derived from the meaning and scope of the claims, as well as the concept of equivalents thereof, should be interpreted as being included within the scope of this application.

Claims

1. In the fecal gas component ratio analysis method for Bristol type classification, (a) A step of measuring the gas concentration value (ppm) of feces using a gas sensor, (b) A step of deriving a real-time component ratio graph for each gas using the gas concentration values ​​measured above, (c) The step of obtaining the component ratio values ​​from the real-time component ratio graph derived above, (d) The step of deriving a graph using the component ratio values ​​obtained above, (e) A fecal gas component ratio analysis method for Bristol type classification and physical condition analysis, comprising the step of classifying the fecal Bristol type by comparing the graph derived above with a graph of component ratios for each Bristol type of feces.

2. The gas sensor in step (a) above is NH ３ CO, H ２ ,VOC,NO,NO ２ H ２ S, SO ２ , C ２ H ４ ,CH ２ The fecal gas component ratio analysis method for Bristol type classification and physical condition analysis according to claim 1, wherein one or more are selected from the group consisting of O and ETO.

3. Step (b) is performed according to the following formulas [Formula 1] and [Formula 2], the fecal gas component ratio analysis method for Bristol type classification and physical condition analysis according to claim 1. [Math 1]

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