KR20200128829A - Detection method for target substances in the balance - Google Patents

Abstract

According to the present invention, a detection method comprises: a step (a) of capturing a first reference color area displayed in a first reference color, a second reference color area, and a detection area of which a color is changed by reacting with a target; a step (b) of converting a captured result into a grayscale tone; a step (c) of extracting a gradation value from the converted captured result; a step (d) of converting the extracted gradation value into a standard gradation value; and a step (e) of detecting the target concentration from the standard gradation value.

Description

Method for detecting a target substance in the collection solution and detection pad {DETECTION METHOD FOR TARGET SUBSTANCES IN THE BALANCE}

The present technology relates to a method of detecting a target substance in a liquid collection solution, and more particularly, to a color and a detection pad in advance for forming a plurality of patches whose color tone changes in response to the target substance.

Body abnormalities of humans and animals can be identified with high accuracy by detecting the concentration of substances contained in body fluids in the living body. An example of detecting body fluid components is a urine test. When the subject provides urine to the patch containing the indicator, the color changes due to the reaction between the components in the urine and the indicator. The changed color is compared with the reference color in the colorimetric table to detect the presence and/or concentration of the component. Another example is the commonly used pregnancy test. During pregnancy, human chorionic gonadotropin (hCG) is excreted in the urine, and the pregnancy tester has a receptor that directly binds to the chorionic gonadotropin, so that it combines with the chorionic gonadotropin to display a predetermined shape and color. do.

Since the color displayed by the colorimetric table changes according to the concentration of the target, the colorimetric table displays various colors to indicate the target density. However, there are many cases in which the color formed by providing the body fluid to the indicator and the color of the colorimetric table do not correspond accurately, and the color may be misrecognized according to the surrounding environment, so accurate detection may be difficult. Furthermore, it is difficult for the subject to immediately grasp the past history and changes in the present compared to the past because the examination is only one-time.

The present embodiments are one of the problems to be solved to provide a device capable of detecting the presence and/or concentration of a target material easily.

The detection method according to the present embodiment includes the steps of: (a) photographing a first reference color region marked with a first reference color, a second reference color region, and a detection region whose color changes in response to the target, and (b) photographing. Converting the result to grayscale, (c) extracting a gradation value from the converted photographing result, (d) converting the extracted gradation value to a standard gradation value, and (e) a standard gradation value And performing target concentration detection from

The detection pad for inspecting a target in a fluid according to the present embodiment includes: a first reference color, a machine readable code, and a machine readable code used to remove the effect of discoloration by the fluid. The second reference color region and the reagent whose color reacts with the target to change include the detection region.

Meanwhile, the method according to an embodiment of the present invention for solving the above-described problems may be implemented with a program for executing the method on a computer and a recording medium in which the program is recorded.

According to the present embodiment, it is possible to quantitatively detect a target in a bodily fluid without using a colorimetric table, and it is possible to convert this into data to store a past history or the like.

1 is an example of a detection pad according to this embodiment.
2 is a flow chart showing an outline of a detection method according to the present embodiment.
3 is a diagram showing a state in which a detection pad according to the present embodiment is photographed by a portable terminal.
4 is a diagram illustrating a photographing result converted to grayscale.
5 is a diagram illustrating a method of detecting a first reagent patch and a fourth reagent patch.
6(A) and 6(B) are diagrams schematically illustrating a part of a detection pad in order to explain a process of obtaining a gray level value by an application.
Figure 7(a) is a result of measuring the concentration of protein (mg/ml) and standard gradation value in urine, and Fig. 7(b) is the concentration of protein (mg/ml) and standard gradation value in urine It is a diagram of the result of curve fitting for the relationship with
Figure 8(a) is a result of measuring the concentration (pH) of hydrogen ions in urine and the standard gradation value, and Fig. 8(b) shows the relationship between the concentration (pH) of hydrogen ions in urine and the standard gradation value. It is a diagram of the result of curve fitting for the relationship.
Fig. 9(a) is a result of measuring the concentration (mg/ml) of glucose contained in urine and a standard gradation value, and Fig. 9(b) is a result of measuring the concentration (mg/ml) of glucose contained in urine and a standard gradation value It is a diagram of the result of curve fitting for the relationship with
Fig. 10(a) is a result of measuring the occult blood (RBC/ul) and standard grayscale values contained in urine, and Fig. 10(b) shows the relationship between the occult blood (RBC/ul) and standard grayscale values. This is a diagram of the result of curve fitting.
11 is a diagram illustrating a deviation according to the type of a portable terminal that performs photographing.

Hereinafter, an embodiment of a detection pad according to the present embodiment will be described with reference to the accompanying drawings. 1 is an embodiment of a detection pad 10 according to this embodiment.

2 is a flow chart showing an outline of a detection method according to the present embodiment.

In the embodiment illustrated in FIG. 1, the first reference color region displayed as the first reference color may be any one or more of the QR code 110 displayed as the first reference color and the predetermined region 120 displayed as the first reference color. have. In one embodiment, the QR code 110 may be displayed in a square shape. The QR code 110 may include direction markers 112, 114, and 116 positioned at three vertices of a square. The QR code 110 may store the classification of the detection pad 10, the detection target of the detection pad, and the number of reagent patches included in the detection area 300.

The area 120 indicated by the first reference color may be an area in a predetermined area indicated by the first reference color on the substrate sub, and according to another embodiment not shown, the first reference color area is a first reference color. It may include letters, numbers, etc. indicated by, and may further include a pattern. In one embodiment, the first reference color may be black.

In one embodiment, the detection pad 10 may be formed on the substrate sub. For example, the substrate may be a white paper, and the QR code 110 and/or the first reference color region 120 may be formed by printing on the substrate sub.

As will be described later, the second reference color region 200 is used to remove coloration by a fluid including an environment, lighting, and a target during shooting, and may be formed as a second reference color. have. In an embodiment, when the substrate sub is white, a predetermined area of the substrate sub may be the second reference color area 200. In another embodiment, the second reference color region 200 may be attached to a predetermined region of the substrate sub as a white patch through which the color of the fluid may permeate. In yet another embodiment, the second reference color region 200 may be printed on a predetermined region of the substrate sub with white ink that may permeate the color of the fluid. In an embodiment, the second reference color may be white.

The detection area 300 may include one or more reagent patches, and the reagent patches include reagents whose color changes by reacting with a target contained in a fluid. In an embodiment, the reagent reacts with the target in the fluid to vary the degree of color development according to the concentration of the target. For example, the reagent is any one of glucose in the fluid, protein in the fluid, hydrogen ions in the fluid, occult blood in the fluid, bilirubin in the fluid, urobilinogen in the fluid, ketone bodies in the fluid, nitrite in the fluid, specific gravity of the fluid, and white blood cells in the fluid. It may be a reagent that reacts with and changes color.

For example, the fluid is a human or animal body fluid, and may be any one of blood, urine, saliva, and sweat.

In the embodiment illustrated in FIG. 1, the detection region 300 may include four reagent patches 312, 314, 316, 318, which are glucose in urine, protein in urine, hydrogen ions in urine, and urine. It reacts with the occult blood inside, and the degree of discoloration can vary depending on their concentration.

In an embodiment not shown, the detection region may include ten reagent patches, each of which is glucose in urine, protein in urine, hydrogen ions in urine, occult blood in urine, bilirubin in urine, urobilinogen in urine, in urine. It reacts with any one of ketone bodies, nitrite in urine, specific gravity of urine, and white blood cells in urine, and the degree of discoloration can be varied according to their concentration.

Hereinafter, a detection method using the detection pad 10 according to the present embodiment will be described. 3 is a diagram showing a state in which the detection pad 10 according to the present embodiment is photographed by a portable terminal. Referring to FIG. 3, a user photographs a first reference color area displayed as a first reference color using the mobile terminal 20, a second reference color patch, and a detection area whose color changes in response to the target (S100). do. In one embodiment, the user's portable terminal 20 may be a smart phone as shown in FIG. 3 having a camera module, and in another embodiment not shown, the portable terminal is any one of a mobile phone, a tablet, and a laptop. Can be

The user may take a picture of the detection pad 10 using a camera provided in the portable terminal 20 and an application stored in the portable terminal 20. As an example, when taking a picture, the application may show a frame F corresponding to a square of the QR code 110 and a guide mark N that helps the user to take a picture.

The application converts the photographing result into gray scale (S200). 4 is a diagram illustrating a photographing result converted to grayscale. However, FIG. 4 is only a diagram illustrated for explanation, and the state converted to grayscale may not be displayed to the user. As illustrated in FIG. 2, images of the first to fourth reagent patches 312, 314, 316, and 318 included in the detection area 300 are also converted to grayscale.

A gradation value is extracted from the photographing result converted to grayscale (S300). In an embodiment, the application may detect the location of the detection area 300 before extracting the grayscale value. Referring to Figure 5, the application forms a center point (O) between the two direction markers (112, 116) located in the diagonal direction of the QR code 110 square. Subsequently, a reference point P is formed at a distance of a predetermined ratio r with respect to the length between the center point O and the direction marker. The position (a) of the first reagent patch 312 included in the detection area 300 may be determined by rotating the reference point based on the center point O by a predetermined first angle θ1.

Subsequently, by rotating the reference point P based on the center point O by a predetermined second angle θ2, the position (b) of the fourth reagent patch 318 included in the detection area 300 may be determined. In addition, the application divides the distance between the position (a) of the first reagent patch 312 and the position (b) of the fourth reagent patch 318 in three equal portions, and the position of the second reagent patch 314 and the third reagent patch ( 316) can be identified.

In one embodiment, the application may extract grayscale values from a plurality of different points in the first reagent patch 312, the second reagent patch 314, the third reagent patch 316, and the fourth reagent patch 318. have. The application extracts gradation values for a plurality of points in the second reference color region 200, the first reference color region and the first to fourth reagent patches 312, 314, 316, and 318. 6(A) and 6(B) show that the application can calculate the gradation values in the second reference color area 200, the first reference color area, and the first to fourth reagent patches 312, 314, 316, and 318. It is a diagram schematically showing a part of the detection pad in order to explain the obtaining process. In the embodiment shown in FIG. 6(A), the application includes the QR code 110 displayed in the first reference color, the first to fourth reagent patches 312, 314, 316, 318, and the second reference color area ( The gray scale value can be extracted by obtaining a gray scale value from a plurality of different points of 200), and obtaining an average value of them. In the embodiment shown in FIG. 6(B), the application includes the QR code 110 displayed in the first reference color, the first to fourth reagent patches 312, 314, 316, 318, and the second reference color area ( The gray scale value may be extracted by obtaining a gray scale value from a plurality of different points of 200), and calculating an average value from values excluding the maximum value and the minimum value among them.

The application corrects the extracted grayscale value to a standard grayscale value (S400). When the detection area 300 is photographed, light reflected from the floor, ceiling, and wall may be color cast to the first to fourth reagent patches 312, 314, 316, 318, and fluorescent lighting and incandescent lighting There may be color casting by artificial lighting of the back.

In addition, when a fluid including a target is applied to the detection region 300, reagent patches may be discolored due to the color of the fluid. For example, when the fluid is urine, the reagent patches may be colored yellow, and when the fluid is blood, the reagent patches may be colored red.

The application photographs the second reference color region and sets the extracted gradation value to 255, and sets the extracted gradation value to 0 by photographing the first reference color region. The extracted grayscale values are scaled in a total of 256 steps to convert the grayscale values photographed from the first to fourth reagent patches 312, 314, 316, and 318 into a standard grayscale value. In an embodiment, the application may perform standard grayscale value conversion using Equation 1.

(I _cal : calculated standard gradation value, I _i: gradation value extracted from the previous step, G ₁ : gradation value extracted from the first reference color region, G ₂ : gradation value extracted from the second reference color region)

Table 2 shows the first to fourth reagent patches 312 and 314 when the gradation value G1 extracted from the first reference color region is 15 and the gradation value G2 extracted from the second reference color region is 231. The gradation values (I1, I2, I3, I4) extracted from 316 and 318 and the gradation values (G1, G2) extracted from the first and second reference color regions are used as standard gradation values (Ical1, Ical1, Ical1). ,Ical1, Gcal1, Gcal2) is an example of conversion. As illustrated in Table 2, it can be seen that the extracted grayscale values are converted into standard grayscale values of 0 to 255 in total 256 levels.

Extracted gradation value Standard gradation value G1 15 Gcal1 0 G2 231 Gcal2 255 I1 175 Ical1 183 I2 195 Ical2 212 I3 134 Ical3 152 I4 125 Ical4 135

The above example is an example for displaying the standard grayscale value as 8-bit digital data, and the grayscale value extracted by calculating Equation (1) of Equation 2 below is divided into 1024 steps of 0 to 1023. You can get the value. Alternatively, a standard grayscale value obtained by dividing the extracted grayscale value into 64 steps of 0 to 63 by calculating Equation (2) of Equation 2 below may be obtained. If the standard grayscale value divided into higher steps is used, a standard grayscale value having a high resolution can be obtained, and if the standard grayscale value divided into lower steps is used, the calculation speed can be improved.

By scaling the photographed gradation value into a plurality of gradation values, it is possible to remove the effect of change in gradation value due to color casting by the environment and light source during the photographing process, and discoloration due to color of the fluid. The difference according to the difference between the mobile terminal and the camera module performing the photographing can be similarly removed.

The application detects the target density from the standard grayscale value (S500). In an embodiment, the application detects the target density by calculating a density equation for a standard grayscale value. Figure 7(a) is a result of measuring the concentration of protein (mg/ml) and standard gradation value in urine, and Fig. 7(b) is the concentration of protein (mg/ml) and standard gradation value in urine It is a diagram of the result of curve fitting for the relationship with In FIG. 7, the horizontal axis represents the standard gray scale indicated by 256 levels, and the vertical axis represents the concentration of protein (mg/ml) in urine. The relationship between the target concentration obtained through the experiment and the standard grayscale value can be obtained through the curve fitting process, and a curve fitting for the relationship between the protein concentration (mg/ml) in urine and the standard grayscale value The resulting equation is shown in Equation 3 below.

(protein: protein concentration, Ical: standard grayscale value)

Figure 8(a) is a result of measuring the concentration (pH) of hydrogen ions in urine and the standard gradation value, and Fig. 8(b) shows the relationship between the concentration (pH) of hydrogen ions in urine and the standard gradation value. It is a diagram of the result of curve fitting for the relationship. In Fig. 8, the horizontal axis represents the standard gray scale expressed in 256 steps, and the vertical axis represents the hydrogen ion concentration (pH). The relationship between the target concentration obtained through the experiment and the standard grayscale value can be obtained through the curve fitting process, and the curve fitting result for the relationship between the concentration (pH) of hydrogen ions in urine and the standard grayscale value The equation is shown in Equation 4 below.

Fig. 9(a) is a result of measuring the concentration (mg/ml) of glucose contained in urine and a standard gradation value, and Fig. 9(b) is a result of measuring the concentration (mg/ml) of glucose contained in urine and a standard gradation value It is a diagram of the result of curve fitting for the relationship with In FIG. 9, the horizontal axis represents the standard gray scale indicated by 256 steps, and the vertical axis represents the concentration of glucose (mg/ml). The relationship between the target concentration obtained through the experiment and the standard grayscale value can be obtained through the curve fitting process, and a curve fitting for the relationship between the concentration of glucose in urine (mg/ml) and the standard grayscale value The resulting equation is shown in Equation 5 below.

Fig. 10(a) is a result of measuring the occult blood (RBC/ul) and standard grayscale values contained in urine, and Fig. 10(b) shows the relationship between the occult blood (RBC/ul) and standard grayscale values. This is a diagram of the result of curve fitting. In FIG. 10, the horizontal axis represents the standard gray scale indicated by 256 steps, and the vertical axis represents occult blood (RBC/ul). Through the curve fitting process, the relationship between the target concentration obtained through the experiment and the standard grayscale value can be obtained, and the curve fitting result equation for the relationship between the occult blood (RBC/ul) contained in urine and the standard grayscale value is It is as shown in Equation 6 below.

In another embodiment of detecting the concentration of the target, the application may store the concentration value of the target with respect to the standard grayscale values. The application can detect the density value of the target stored as the calculated standard gray scale value. In an embodiment, the application may interpolate or extrapolate a concentration value of a target stored as a standard grayscale value, and calculate the result of the operation as the concentration value of the target.

11 is a diagram showing a deviation according to the type of a portable terminal performing photographing.

The drawing shows that the deviation between the measuring models is reduced. The removal of deviations between models is carried out in the process of deriving the equations for the above four types of targets. There were variations depending on the camera's performance and manufacturer, and the color intensity values for each detection concentration were measured in the same sample using three smartphones from various manufacturers (Samsung Galaxy s7, lg g6, Huawei y6), and the average of the measured values was calculated. The median value (average value) was derived. Using the average value of the measured intensity obtained here, the formulas (ln(protein), ln(pH), ln(glucose), ln(blood)) of the four detection targets were derived and applied.

As a result, by applying the formula using the average value, it was possible to obtain the effect of reducing the deviation between the measuring models.

Meanwhile, the above-described method according to various embodiments of the present invention may be implemented as a program and provided to each server or devices while being stored in various non-transitory computer readable media. Accordingly, the user terminal 100 can access the server or device and download the program.

The non-transitory readable medium refers to a medium that stores data semi-permanently and can be read by a device, not a medium that stores data for a short moment, such as a register, cache, or memory. Specifically, the above-described various applications or programs may be provided by being stored in a non-transitory readable medium such as a CD, DVD, hard disk, Blu-ray disk, USB, memory card, and ROM.

Although it has been described with reference to the embodiment shown in the drawings in order to help understand the present invention, this is an embodiment for implementation, is only illustrative, and a person having ordinary knowledge in the art therefrom various modifications and equivalent It will be appreciated that other embodiments are possible. Accordingly, the true technical scope of the present invention should be determined by the appended claims.

10: detection pad sub: substrate
20: mobile terminal 110: QR code
112, 114, 116: direction marker 120: area marked with the first reference color
200: second reference color area 300: detection area
312, 314, 316, 318: first, second, third, fourth reagent patch
S100~S500: Each step of the detection method

Claims (13)

A reagent detection area in which a code that can be read by a machine is displayed as a first reference color, a second reference color area used to remove the effect of discoloration by fluid, and a plurality of reagent patches that change color in response to a target A method of reacting a detection pad comprising a target with a fluid containing a target, photographing the detection pad to detect a target included in the fluid by image analysis,
(a) photographing a first reference color region displayed as a first reference color, a second reference color region, and a detection region whose color changes in response to the target;
(b) converting the photographing result to grayscale;
(c) extracting a gradation value from the converted photographing result;
(d) converting the extracted grayscale value to a standard grayscale value; and
(e) A detection method comprising the step of performing the target density detection from the standard grayscale value. The method of claim 1,
Step (c)
A detection method that is performed by calculating a gray scale average value for a plurality of different points in each of the first reference color region, the second reference color region, and the detection region. The method of claim 1,
Step (d)
(d1) scaling a gradation value of the first reference color to 0 and a gradation value of the second reference color region to 255; and
(d2) adjusting the gradation value extracted from the detection area according to the result of the step (d1). The method of claim 1,
The step (e),
A detection method for detecting the target density by calculating a density equation for a standard gray level value using the standard gray level value obtained in step (d). The method of claim 4,
The density formula for the standard grayscale value is a detection method in which a formula corresponding to a model for photographing is used in step (a). The method of claim 1,
The step (e),
A detection method for detecting the target density for the standard gray level value by searching a memory in which the target density is stored compared to a standard gray level value as the standard gray level value obtained in step (d). The method of claim 6,
The detection method in which the lookup table in which the target density compared to the standard grayscale value is stored is a lookup table corresponding to a model that is photographed in step (a). The method of claim 1,
The step (d),
The detection method further comprising a step of correcting the model for performing the photographing in step (a). The method of claim 1,
The first reference color area is a square QR code with direction markers displayed at three vertices,
The step (c),
(c1) forming a center point between the two direction markers located in the diagonal direction of the rectangle,
(c2) forming a reference point at a distance between the center point and a predetermined ratio with respect to the length of the direction marker;
(c3) detecting the detection area by rotating the reference point by a first predetermined angle based on the center point. The method of claim 9,
The detection region includes a first reagent patch, a second reagent patch, a third reagent patch, and a fourth reagent patch,
In the detection method, step (c3) is performed to detect the first reagent patch,
(c4) detecting a fourth reagent patch by rotating the reference point by a second predetermined angle based on the center point, and
(c5) detecting a second reagent patch and a third reagent patch by dividing between the detected first reagent patch and the second reagent patch by three. A portable terminal in which an application that performs the method of any one of claims 1 to 10 is stored. A detection program capable of executing the method of any one of claims 1 to 10 on a computer. A non-volatile recording medium on which a detection program capable of executing the method of any one of claims 1 to 10 on a computer is recorded.

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