Multi-dimensional data analysis model construction and multi-target quinolone colorimetric detection method

Abstract

The invention belongs to the technical field of antibiotic colorimetric detection, and discloses a multidimensional data analysis model construction and multi-target quinolone colorimetric detection method. The method specifically comprises the steps of multi-dimensional sensing system construction, target action, colorimetric signal acquisition, data processing, model construction and result analysis and judgment. According to the multi-dimensional system data analysis method provided by the invention, the accuracy and reliability of target sensing detection are improved, the reproducibility between sensor detection batches is improved, and meanwhile, the detection difficulty is reduced. The invention provides a universal multi-target molecule rapid detection and identification method, and provides a technical support for constructing a multi-target specific detection sensor and the like and a sensing technology.

Description

Technical Field

[0001] The invention relates to the technical field of antibiotic colorimetric detection, and in particular to a multidimensional data analysis model construction and a multi-target quinolone colorimetric detection method. Background Art

[0002] Antibiotic aptamer sensor is a sensor that uses aptamers as recognition elements to detect and measure the presence and concentration of specific antibiotics. Colorimetric antibiotic aptamer sensor is a sensor based on colorimetric signal molecules and aptamer technology, which uses colorimetric changes to detect the presence and concentration of specific antibiotics. Among them, nanogold colorimetric antibiotic aptamer sensor is the most commonly used colorimetric detection technology, which combines the advantages of nanomaterials and biosensor technology to provide an effective, rapid and sensitive means for antibiotic detection. At present, this sensor has been widely used in environmental monitoring, food safety and medical testing. With the deepening of research and the advancement of technology, it will show broad application potential in more fields.

[0003] Pattern recognition algorithm is a classical mathematical method to study the automatic processing and interpretation of patterns. It can analyze the internal statistical laws when multiple objects and multiple indicators are interrelated, build analysis models and classify samples according to their characteristics. It is suitable for multidimensional data science research. Based on different pattern recognition algorithms such as principal component analysis, linear discriminant analysis, convolutional neural network and fusion algorithm, multidimensional data can be reduced in dimension to achieve classification and identification research of multiple samples. At present, aptamer sensing technology analysis mainly uses one-dimensional data results. This result can intuitively reflect the selectivity, quantitative or qualitative analysis ability of aptamer sensors for targets. However, the emergence of broad-spectrum aptamers leads to non-specific sensing signals when multiple antibiotics coexist in the detection system. Therefore, the one-dimensional data analysis method is not only difficult to quantify the concentration of a single target, but also cannot confirm the antibiotic attribution.

[0004] Multimodal sensors are sensor assemblies composed of multiple sensors of the same or different types arranged according to functional requirements. They can realize synchronous data collection, data processing and data analysis of multi-indicators and multi-dimensional information. Multimodal sensors can be simply understood as array sensors fused with pattern recognition algorithms. Pattern recognition algorithms can analyze multidimensional data information, realize deep data mining, recognition model construction, and realize differential identification of multiple factors in complex systems. Therefore, it is of great significance to develop a multidimensional data analysis model construction and multi-target quinolone colorimetric detection method. Summary of the invention

[0005] In view of this, the present invention provides a multidimensional data analysis model construction and a multi-target quinolone colorimetric detection method to solve the problem that the current detection methods have low accuracy in quantitative analysis and qualitative identification of target molecules.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] The present invention provides a multidimensional data analysis model construction and a multi-target quinolone colorimetric detection method, comprising the following steps:

[0008] (1) Nucleic acid aptamer APT, wherein APT 1 、APT 2 、APT 3 …APT N , a multidimensional sensing system is constructed using nucleic acid aptamers APT and colorimetric signal molecules, where N rows × M columns;

[0009] (2) The target molecule T, where T 1 、T 2 、T 3 、T 4 ……T Y , respectively added into the multi-dimensional sensing system and placed for a certain time t;

[0010] (3) The multi-dimensional sensing system collects images, converts them into grayscale values, reads the grayscale value signals of each colorimetric system, and obtains a grayscale value data matrix; wherein, N rows × M columns;

[0011] (4) Conduct pattern recognition analysis on the above data and build a model;

[0012] (5) Result analysis and judgment.

[0013] Furthermore, in step (1), the nucleic acid aptamer APT should differentially recognize the target molecule T 1 、T 2 、T 3 、T 4 ……T Y , the difference is ≥10%.

[0014] Furthermore, in step (1), M≥Y.

[0015] Furthermore, in step (1), when N≥Y and Y=1, N≥2; when N≥Y and Y≥2, N≥4.

[0016] Furthermore, in step (1), the colorimetric signal molecule includes nanogold, nanosilver, 3,3',5,5'-tetramethylbenzidine or 2,2'-hydrazine-bis-3-ethylbenzothiazoline-6-sulfonic acid.

[0017] Furthermore, in step (2), each target molecule T can only be added to one column of the multidimensional sensing system.

[0018] Furthermore, in step (2), time t≥ system stabilization time t 稳 .

[0019] Furthermore, in step (3), the gray value signal of each colorimetric system should be read at no less than 3 points A. 1 , A 2 , A 3 ……A Z , and take the average value A.

[0020] Furthermore, in step (4), the method used for pattern recognition analysis and model construction includes principal component analysis PCA, linear discriminant analysis LDA or independent component analysis ICA.

[0021] It can be seen from the above technical solution that compared with the prior art, the present invention has the following beneficial effects:

[0022] (1) The multidimensional sensing system provided by the present invention has the advantages of simple preparation, high stability, rapid response, and high-throughput detection.

[0023] (2) The multi-dimensional sensing system data model analysis provided by the present invention has simple steps, reliable results, strong recognition, and intuitive and easy-to-read analysis results.

[0024] (3) The antibiotic colorimetric detection method provided by the present invention is simple, rapid, and sensitive, and can realize the qualitative and quantitative detection of small molecules such as antibiotics, which is of great significance for environmental risk assessment and occupational exposure risk analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0026] Figure 1 Schematic diagram of the construction of multidimensional data analysis model and multi-target quinolone colorimetric detection method;

[0027] Figure 2 The result diagram of the multi-dimensional nanogold colorimetric system and data acquisition analysis described in Example 1;

[0028] Figure 3 The result diagram of the multi-dimensional nano-gold colorimetric system and data acquisition analysis described in Example 2;

[0029] Figure 4 The multi-dimensional array sensing image and gray value image described in Example 3;

[0030] Figure 5 The discriminant model diagram was constructed for different target molecules using the PCA method;

[0031] Figure 6 This is the result of analyzing multidimensional colorimetric data using the PCA method, where A is tetracycline, B is oxytetracycline, and C is chloramphenicol. DETAILED DESCRIPTION

[0032] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0033] Example 1

[0034] Multidimensional nanogold colorimetric system and data acquisition analysis:

[0035] 200 μL of nanogold solution was placed in a microplate (4 rows × 8 columns) and the smartphone captured images (e.g. Figure 2 a), and converted into a grayscale image with the help of PS (as shown in Figure 2 b), read the gray value of each micropore 6 times and take the average value and record it in the table (as shown in Figure 2 c).

[0036] Example 2

[0037] Multidimensional nanogold colorimetric system and data acquisition analysis:

[0038] 140 μL of nanogold solution was placed in a microplate (4 rows × 8 columns), 20 μL of APT solution of different concentrations (concentrations were 0.1, 0.5, 1, and 2 μmol / L) was added to each horizontal row, and incubated at room temperature for 30 min. 40 μL of NaCl solution of different concentrations (concentrations were 150, 200, 250, 300, 350, and 400 mmol / L) was added to each vertical column, and images were collected by smartphone (e.g. Figure 3 a), and converted into a grayscale image with the help of PS (as shown in Figure 3 b), read the gray value of each micropore 6 times and take the average value and record it in the table (as shown in Figure 3 c).

[0039] Example 3

[0040] Analytical model building:

[0041] by Figure 3 Take the model analysis as an example. Figure 3 Each column is a different colorimetric system, namely APT 1 Colorimetric system, APT 2 Colorimetric system, APT 3 Colorimetric system, APT4 Colorimetric system, APT 5 Colorimetric system, APT 6 Colorimetric system, APT 7 Colorimetric system, APT 8 Colorimetric system. Each row represents a different target T Y Add colorimetric signals in different systems, a total of six target molecules, namely T 1 、T 2 、T 3 、T 4 、T 5 、T 6 .

[0042] First, the multi-dimensional sensing colorimetric system image is converted into grayscale values ​​(such as Figure 4 shown).

[0043] Secondly, use PS software to read and record the grayscale value of the multi-dimensional sensing system (as shown in Table 1).

[0044] Table 1 Figure 3 Corresponding to the gray value of each colorimetric system

[0045]

[0046]

[0047] Again, the PCA method is used to construct the analysis model diagram (such as Figure 5 a).

[0048] Finally, the PCA model is constructed for analysis and discrimination.

[0049] from Figure 5 As can be seen in a, the PCA method is used to analyze the multidimensional colorimetric data. 1 、T 2 、T 3 、T 4 、T 5 、T 6 It shows significant dispersion, indicating that the target molecules can be identified based on the multidimensional colorimetric sensing system data and the PCA model.

[0050] To better illustrate the above method, two sets of data are analyzed again below (as shown in Table 2 and Figure 5 b).

[0051] Table 2 Gray values ​​of different colorimetric systems

[0052]

[0053]

[0054] from Figure 5 As can be seen in b, the PCA method is used to analyze the multidimensional colorimetric data. 1 、T 2 、T 3 、T 4 、T 5 、T 6 It also showed significant dispersion, which once again proved that the identification of target molecules can be achieved based on multidimensional colorimetric sensing system data and using PCA to build a model.

[0055] Example 4

[0056] Result determination method:

[0057] 4 target detections (target molecule T 1 、T 2 、T 3 、T 4 ) is used as an example to illustrate the discrimination method and standard (as shown in Table 3).

[0058] Table 3 Results judgment criteria

[0059]

[0060]

[0061] / means the ellipse has no intersection; √ means the ellipse has no intersection

[0062] Example 5

[0063] Antibiotic testing:

[0064] 140 μL of nanogold solution was placed in a microplate (3 rows × 4 columns);

[0065] 20 μL of tetracycline, ofloxacin, enrofloxacin, and any sequence aptamer (APT 1 、APT 2 、APT 3 , APT control group) solution (final concentration of aptamer system was 4 μmol / L) and incubated at room temperature for 30 min; tetracycline, ofloxacin, and enrofloxacin (final concentration was 30 μmol / L) were added to rows 1 to 3 and incubated at room temperature for 10 min; 40 μL of NaCl solution of different concentrations was added to each microwell in columns 1 to 4 (final concentration was 50 mmol / L). The image was collected by a smartphone and converted into a grayscale image with the help of PS. The grayscale value of each microwell was read 4 times and the average value was recorded.

[0066] The above experiment was repeated 3 times.

[0067] The acquired data is used to construct an analysis model using the PCA method (such as Figure 6 shown).

[0068] from Figure 6 It can be seen that the three antibiotic molecules also showed significant dispersion when the multidimensional colorimetric data were analyzed by the PCA method, proving that the target antibiotics can be identified based on the multidimensional colorimetric sensing system data and using PCA to build a model.

[0069] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A multidimensional data analysis model construction and a multi-target quinolone colorimetric detection method, characterized in that: The steps include: (1) Nucleic acid aptamers APT, including APT1, APT2, APT3, ... APT N , a multidimensional sensing system is constructed using nucleic acid aptamers APT and colorimetric signal molecules, where N rows × M columns; (2) The target molecule T, where T1, T2, T3, T4, ..., T Y , respectively added into the multi-dimensional sensing system and placed for a certain time t; (3) The multi-dimensional sensing system collects images, converts them into grayscale values, reads the grayscale value signals of each colorimetric system, and obtains a grayscale value data matrix; wherein, N rows × M columns; (4) Conduct pattern recognition analysis on the above data and build a model; (5) Result analysis and judgment.

2. The multidimensional data analysis model construction and multi-target quinolone colorimetric detection method according to claim 1, characterized in that: In step (1), the nucleic acid aptamer APT should differentially recognize target molecules T1, T2, T3, T4...T Y , the difference is ≥10%.

3. The multidimensional data analysis model construction and multi-target quinolone colorimetric detection method according to claim 1 or 2, characterized in that: In step (1), M≥Y.

4. The multidimensional data analysis model construction and multi-target quinolone colorimetric detection method according to claim 3, characterized in that: In step (1), when N≥Y and Y=1, N≥2; when N≥Y and Y≥2, N≥4.

5. The multidimensional data analysis model construction and multi-target quinolone colorimetric detection method according to claim 1, 2 or 4, characterized in that: In step (1), the colorimetric signal molecule includes nanogold, nanosilver, 3,3',5,5'-tetramethylbenzidine or 2,2'-hydrazine-bis-3-ethylbenzothiazoline-6-sulfonic acid.

6. The multidimensional data analysis model construction and multi-target quinolone colorimetric detection method according to claim 5, characterized in that: In step (2), each target molecule T can only be added to one multidimensional sensing system.

7. The multidimensional data analysis model construction and multi-target quinolone colorimetric detection method according to claim 6, characterized in that: In step (2), time t≥ system stability time t 稳 .

8. The multidimensional data analysis model construction and multi-target quinolone colorimetric detection method according to claim 6 or 7, characterized in that: In step (3), the gray value signal of each colorimetric system should be read at no less than 3 points A1, A2, A3...A Z , and take the average value A.

9. The multidimensional data analysis model construction and multi-target quinolone colorimetric detection method according to claim 8, characterized in that: In step (4), the method used for pattern recognition analysis and model construction includes principal component analysis PCA, linear discriminant analysis LDA or independent component analysis ICA.

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