Difunctional fluorescent array sensor, preparation method thereof and application of difunctional fluorescent array sensor in bacterial detection and fermentative carbohydrate detection

Abstract

The invention relates to a fluorescent array sensor, in particular to a difunctional fluorescent array sensor containing a modified high-molecular polymer, a preparation method of the difunctional fluorescent array sensor and application of the difunctional fluorescent array sensor in bacterial detection and fermentation carbohydrate detection. The array sensor comprises at least two different fluorescent sensing units, each fluorescent sensing unit is a PN-coumarin dye compound formed by covalent interaction of PN and coumarin dye, PN is a nitrophenylboronic acid modified high-molecular polymer PEI, the coumarin dye is 6, 7-diphenyl-1, 3, 4-triazole-3-carboxylic acid, and the coumarin dye is a coumarin dye compound. The compound is selected from the group consisting of 2, 7-dihydroxy-2H-benzopyran-2-one ES or 4-methylesculetin MS. The compound fluorescent array sensor disclosed by the invention can simultaneously realize distinguishing detection of thirteen types of bacteria related to IBS and fourteen FODMAPs, and has the advantages of high sensitivity, short detection time, low cost, good repeatability, high accuracy, no need of professional technicians and the like.

Description

Technical Field

[0001] The invention relates to a fluorescence array sensor, in particular to a dual-function fluorescence array sensor containing a modified high molecular polymer, a preparation method thereof and application thereof in bacteria detection and fermentable carbohydrate detection. Background Art

[0002] Irritable bowel syndrome (IBS) is a common functional intestinal disorder characterized by abdominal pain, bloating, and gastrointestinal dysfunction, affecting a significant proportion of the population worldwide (5% to 20%) and often associated with psychological conditions such as anxiety and depression. Because the symptoms of IBS are very similar to those of other intestinal diseases, clinical misdiagnosis is common. Currently, the diagnosis of IBS mainly relies on the use of medical methods to exclude other diseases and then evaluate according to the Rome criteria, which is a time-consuming and difficult to obtain accurate results. In addition, different degrees of IBS require different treatments, which highlights the urgency of developing new diagnostic technologies to monitor the progression of IBS. Recent studies have shown that IBS may originate from an imbalance in the intestinal flora. Therefore, the development of new methods that can monitor the complex changes in the intestinal flora in real time has great potential for accurately diagnosing IBS.

[0003] Currently, there are no effective drugs for the treatment of IBS, and dietary intervention (especially low FODMAP diet) has become one of the main strategies to alleviate its symptoms. FODMAPs (fermentable oligosaccharides, disaccharides, monosaccharides and polyols) are known triggers for gastrointestinal symptoms in functional bowel disorders (such as IBS). These compounds are ubiquitous and have been widely studied in various foods, including beans, honey and pasta, but they are difficult to identify in processed products due to changes in certain components caused by food processing. Currently, the detection of FODMAPs mainly relies on chromatographic methods such as high-performance liquid chromatography (HPLC), gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). However, these methods are costly, time-consuming and require large equipment support, which limits their application in real-time detection. In addition, the development of biosensors and surface-enhanced Raman scattering (SERS) technologies has promoted the identification of FODMAPs, but their complex components and cumbersome pretreatment limit their widespread use. Therefore, there is an urgent need for tools that can effectively, accurately and quickly identify FODMAP diets.

[0004] Inspired by the mammalian taste system, the array-based "chemical tongue" technology can better distinguish similar substances and complex mixtures through its unique parallel detection mechanism and the cross-reactions from nonspecific interactions. However, this nonspecific design faces the disadvantages of severe interference and poor selectivity when analyzing complex samples such as feces.

[0005] Therefore, there is still a need to develop a detection method with strong specificity and high selectivity in this field. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a composite fluorescence array sensor with simple operation, high sensitivity and wide detection range. Another purpose of the present invention is to provide the application of the above-mentioned fluorescence array sensor.

[0007] Technical solution:

[0008] In a first aspect of the present invention, a dual-function fluorescence array sensor is provided.

[0009] The dual-function fluorescence array sensor of the present invention contains at least two different fluorescence sensing units, wherein the fluorescence sensing unit is a PN-coumarin dye complex formed by covalent interaction between PN and coumarin dye, wherein the PN is a high molecular polymer PEI modified by nitrobenzene boronic acid, and the coumarin dye is 6,7-dihydroxy-2H-benzopyran-2-one ES or 4-methylesculetin MS;

[0010] The structural formula of the nitrobenzene boronic acid modified polymer PEI, i.e. PN, is as shown in Formula I:

[0011]

[0012] In formula I, n=10-15.

[0013] The structural formulas of the coumarin dyes 6,7-dihydroxy-2H-benzopyran-2-one (ES) and 4-methylesculetin (MS) are as follows:

[0014]

[0015] Preferably, the dual-function fluorescence array sensor comprises a plurality of different fluorescence sensing units, and the fluorescence sensing unit is a PN-coumarin dye complex formed by mixing a PN solution with a concentration of 0.1 mM-2 mM and an ES or MS solution with a concentration of 0.01 mM.

[0016] In a preferred embodiment of the present invention, the dual-function fluorescence array sensor contains 6 different fluorescence sensing units C1-C6, corresponding to 6 PN-coumarin dye complexes, and the pH value, components and concentrations of each component before mixing of each complex are shown in the following table:

[0017]

[0018] In the fluorescent sensing units C1, C3 and C5, the molar ratio of PN to ES is 1:1.

[0019] In the fluorescence sensing units C2, C4, and C6, the molar ratio of PN to MS is 1:1.

[0020] In a second aspect of the present invention, a method for preparing the dual-function fluorescence array sensor as described in the first aspect is provided.

[0021] The array sensor comprises 6 different fluorescence sensing units C1-C6, and the method comprises the following steps:

[0022] (1) Synthesizing PEI modified with nitrobenzene boronic acid by condensation reaction between branched PEI with a molecular weight of 10,000 and 3-boric acid-5-nitrobenzoic acid, followed by purification and impurity removal by dialysis, and obtaining PN after freeze-drying;

[0023] (2) Dissolve ES and MS in methanol and dilute them with three pH buffer solutions to a final concentration of 0.01 mM. Dilute the PN synthesized in step 1) with three pH buffer solutions to a final concentration of 0.35 mM, 0.2 mM, 1.5 mM, 0.25 mM, 0.15 mM, and 1.2 mM. Mix PN solutions with concentrations of 0.35 mM, 0.2 mM, and 1.5 mM with ES solutions. Mix PN solutions with concentrations of 0.25 mM, 0.15 mM, and 1.2 mM with MS solutions to obtain 6 fluorescent sensing units C1-C6; wherein the pH values ​​of the three buffer solutions are 7.4, 8.5, and 9.5, respectively; the molar ratio of PN to ES is 1:1, and the molar ratio of PN to MS is 1:1.

[0024] Furthermore, in step (1), the specific preparation method of PN is as follows:

[0025] 1) 3-boric acid-5-nitrobenzoic acid, 1-ethyl-3-(3-dimethylaminopropyl)carbonyl diimide hydrochloride and 1-hydroxypyrrolidine-2,5-dione were dissolved in a buffer solution and stirred at room temperature for 1 hour;

[0026] 2) Dissolve the branched PEI with a molecular weight of 10,000 in a buffer solution, slowly drip it into the reaction solution of step 1), and continue to stir the reaction at room temperature overnight. After the reaction is completed, dialysis is performed to purify and remove impurities, and PN is obtained after freeze-drying. In the reaction, 1-ethyl-3-(3-dimethylaminopropyl) carbonyldiimide hydrochloride and 1-hydroxypyrrolidine-2,5-dione are used as condensation agents.

[0027] More preferably, the molar ratio of 3-boric acid-5-nitrobenzoic acid, 1-ethyl-3-(3-dimethylaminopropyl)carbonyl diimide hydrochloride, 1-hydroxypyrrolidine-2,5-dione and PEI is: 0.9:1.4:1.4:1.9.

[0028] The reaction route for preparing PN is as follows:

[0029]

[0030] In formula I, n=10-15.

[0031] Furthermore, in step (2), the three pH buffer solutions are PBS with a pH of 7.4 and a concentration of 0.01 M; glycine-NaOH with a pH of 8.5 and a concentration of 0.05 M; and glycine-NaOH with a pH of 9.5 and a concentration of 0.05 M.

[0032] In the third aspect of the present invention, there is also provided application of the dual-function fluorescence array sensor as described in the first aspect in bacteria detection.

[0033] Furthermore, the bacteria are thirteen types of bacteria associated with irritable bowel syndrome (IBS), including Staphylococcus aureus (S. aureus), Bacillus cereus (B. cereus), Klebsiella pneumoniae (K. pneumoniae), Enterococcus faecalis (E. faecalis), Lactobacillus rhamnosus (L. rhamnosus GG), Lactobacillus plantarum (L. plantarum), Escherichia coli (E. coli), Serratia marcescens (S. marcescens), Escherichia cloacae (E. cloacae), Klebsiella pneumoniae (K. pneumoniae), Enterobacter aerogenes (E. aerogenes), Acinetobacter baumannii (A. baumannii) and Salmonella. One or more combinations thereof.

[0034] In a fourth aspect of the present invention, a method for detecting bacteria using the dual-function fluorescence array sensor as described in the first aspect is also provided. Different sensing units of the dual-function fluorescence array sensor are respectively mixed with bacteria, the fluorescence intensity of each sensing unit is measured, the fluorescence data is processed, and the bacteria are classified through a data matrix to achieve differentiation and detection of multiple bacteria.

[0035] Furthermore, the fluorescence data is processed using multiple algorithms such as linear discriminant analysis, and the bacteria are classified through the data matrix to obtain the two-dimensional array discrimination fingerprint of the bacteria, realizing visual identification. The obtained two-dimensional array discrimination fingerprint is used as a model to detect unknown samples, and the accuracy of the prediction of unknown samples is calculated to realize the differentiation and detection of bacteria.

[0036] Furthermore, the detection method specifically comprises the following steps:

[0037] (1) After fully mixing different sensing units of the fluorescence array sensor and the sample to be tested, the fluorescence intensity data is detected, and the above detection steps are repeated for different sensing units;

[0038] (2) using statistical analysis software to process and analyze the fluorescence data, using linear discriminant analysis to convert the fluorescence response pattern into a standard pattern, classifying the data matrix, and obtaining a two-dimensional array fingerprint of the bacteria contained in the test solution to achieve visual identification;

[0039] (3) The obtained two-dimensional array distinguishing fingerprint map is used as a model to detect unknown samples, and the accuracy of the unknown sample prediction is calculated to achieve the distinction and detection of unknown bacteria.

[0040] In the fifth aspect of the present invention, there is also provided the use of the dual-function fluorescence array sensor as described in the first aspect in the detection of FODMAPs.

[0041] Furthermore, the FODMAPs include one or more combinations of D(+)-galactose (DGal), D-fructose (DFru), glucose (Glu), maltose (Mal), lactose (Lac), sucrose (Sur), raffinose (Raf), stachyose (Sta), galacto-oligosaccharides GOS (Gal), fructo-oligosaccharides FOS (Fru), D(+)-xylose (xylo-oligosaccharides) (Dxyl), D-mannose (Man), D-sorbitol (Sor), and xylitol (Xyl).

[0042] In the sixth aspect of the present invention, a method for detecting FODMAPs using the dual-function fluorescence array sensor as described in the first aspect is also provided. Different sensing units of the fluorescence array sensor are used to mix with FODMAPs, the fluorescence intensity of each sensing unit is measured, the fluorescence data is processed, and the bacteria are classified through the data matrix to achieve the distinction and detection of FODMAPs.

[0043] Furthermore, the fluorescence data was processed using a variety of algorithms such as linear discriminant analysis, and the bacteria were classified through the data matrix to obtain the two-dimensional array discrimination fingerprint of FODMAPs for visual identification. The obtained two-dimensional array discrimination fingerprint was used as a model to detect unknown samples, and the accuracy of the prediction of unknown samples was calculated to achieve the distinction and detection of FODMAPs.

[0044] Furthermore, the method specifically includes the following steps:

[0045] (1) After fully mixing different sensor units and FODMAPs samples, the fluorescence intensity data was detected, and the above detection steps were repeated for different sensor units;

[0046] (2) Use statistical analysis software to process and analyze fluorescence data, use linear discriminant analysis to convert fluorescence response patterns into canonical patterns, classify FODMAPs through data matrix, and obtain a two-dimensional array fingerprint of FODMAPs in the test product to achieve visual identification;

[0047] (3) The obtained two-dimensional array distinguishing fingerprint map is used as a model to detect unknown samples, and the accuracy of the unknown sample prediction is calculated to achieve the distinction and detection of unknown FODMAPs.

[0048] Working principle: The array sensor constructed in the present invention is mainly constructed based on the specific recognition of polyphenol compounds by boric acid and the principle of display agent displacement (IDA).

[0049] Beneficial effects:

[0050] Compared with the prior art, the present invention has the following outstanding and significant advantages: the fluorescent array sensor based on modified high molecular polymers of the present invention utilizes dynamic covalent interactions to form a polymer-fluorescent substance complex, and based on the surface of IBS-related intestinal bacteria and similar vicinal diol structures in FODMAPs, it can simultaneously realize the differentiated detection of thirteen bacteria and fourteen FODMAPs, with high accuracy and sensitivity, short detection time, low cost, high repeatability, and no need for professional technicians. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1AF in are the quenching curves of the selected sensor elements PN (C1-C6) for ES and MS of coumarin derivatives, respectively;

[0052] Figure 2 It is the fluorescence response signal of 6 sensor units to 13 bacteria;

[0053] Figure 3 It is the LDA discrimination diagram of the rapid identification of bacteria by 6 sensor units;

[0054] Figure 4 It is the fluorescence response signal of 6 sensor units to the intestinal flora in the feces of 31 mice;

[0055] Figure 5 This is the LDA discrimination diagram of the six sensor units for rapid identification of intestinal flora in mice with different degrees of IBS;

[0056] Figure 6 It is the fluorescence response signal of 6 sensor units to 14 FODMAPs;

[0057] Figure 7 This is the LDA discrimination diagram of the six sensor units for rapid identification of FODMAPs;

[0058] Figure 8 It is the fluorescence response signal of 6 sensor units to 10 commercially purchased soy milks;

[0059] Fig. 9 This is the LDA discrimination diagram of 6 sensor units for rapid identification of 10 commercially purchased soy milks. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0061] The purchase sources of raw materials involved in the present invention are as follows:

[0062] 3-Boric acid-5-nitrobenzoic acid (purchased from Anaiji Chemical)

[0063] 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (purchased from Anaiji Chemical)

[0064] 1-Hydroxypyrrolidine-2,5-dione (purchased from Bidex Pharmaceuticals)

[0065] Branched PEI with a molecular weight of 10,000 (purchased from Sigma-Aldrich)

[0066] Example 1 Synthesis of high molecular weight polymer PN

[0067] The synthetic route is:

[0068]

[0069] Where n = 10-15

[0070] The following steps are involved:

[0071] 3-boronic acid-5-nitrobenzoic acid (197.6 mg, 0.94 mmol), 1-ethyl-3-(3-dimethylaminopropyl) carbonyl diimide hydrochloride (EDCI, 269.3 mg, 1.40 mmol) and 1-hydroxypyrrolidine-2,5-dione (NHS, 161.7 mg, 1.40 mmol) were dissolved in 20 mL PBS buffer and stirred at room temperature for 1 hour. Then PEI (n = 10-15) (1.00 g, 1.87 mmol) was dissolved in 5 mL PBS buffer, slowly dripped into the reaction solution, and continued to stir and react at room temperature overnight. Thin layer chromatography was used to monitor the progress of the reaction. After the reaction was completed, the crude product was dialyzed with distilled water for 2 days and then freeze-dried to obtain PN.

[0072] Test the quenching curves of coumarin derivatives (ES, MS) and PN ( Figure 1 ), array sensors were constructed according to the PN / fluorescent dye molar ratio with 70% fluorescence quenching at different pH values.

[0073] from Figure 1 A- Figure 1 F shows that each PN element has differential quenching of ES and MS.

[0074] Example 2 Preparation of 6 sensor units (C1-C6)

[0075] Three pH buffer solutions were used, namely PBS with a pH of 7.4 and a concentration of 0.01 M; glycine-NaOH with a pH of 8.5 and a concentration of 0.05 M, and glycine-NaOH with a pH of 9.5 and a concentration of 0.05 M, to dissolve and dilute the water-soluble polymer PN to a final concentration of 0.35 mM, 0.2 mM, 1.5 mM (for mixing with ES), 0.25 mM, 0.15 mM, 1.2 mM (for mixing with MS), and ES and MS were diluted with the above three pH buffer solutions to a final concentration of 0.01 mM, and the diluted PN of different concentrations was evenly mixed with ES or MS according to the table below, to obtain 6 fluorescent sensing units PN-fluorescent dyes C1-C6;

[0076] Six types of fluorescent sensing units in the sensor array

[0077]

[0078]

[0079] In the fluorescent sensing units C1, C3 and C5, the molar ratio of PN to ES is 1:1.

[0080] In the fluorescence sensing units C2, C4, and C6, the molar ratio of PN to MS is 1:1.

[0081] Example 3 Detection of different bacteria by fluorescence array sensor

[0082] Method for distinguishing different types of bacteria: Take 100 μL of 6 sensor units (C1-C6) and mix them with 100 μL of different types of bacterial liquid (final concentration OD 600 =0.05) and mixed thoroughly for 10 seconds. Bacterial species include Staphylococcus aureus (S. aureus), Bacillus cereus (B. cereus), Klebsiella pneumoniae (K. pneumoniae), Enterococcus faecalis (E. faecalis), Lactobacillus rhamnosus (L. rhamnosus GG), Lactobacillus plantarum (L. plantarum), Escherichia coli (E. coli), Serratia marcescens (S. marcescens), E. cloacae, Klebsiella pneumoniae (K. pneumoniae), Enterobacter aerogenes (E. aerogenes), Acinetobacter baumannii (A. baumannii) and Salmonella (Salmonella) (all of the above bacteria were purchased from the China General Microbiological Culture Collection Administration Center). The excitation wavelength is 368nm, and the fluorescence intensity of the complex after adding bacteria is measured at an emission wavelength of 470nm to obtain the first channel signal (ES signal); the excitation wavelength is 356nm, and the fluorescence intensity of the complex after adding bacteria is measured at an emission wavelength of 464nm to obtain the second channel signal (MS signal), and the relative fluorescence intensity change is the detection signal (I-I0) / I0. Each type of bacteria is detected once with 6 sensor units, and 6 repetitions are performed to obtain (6 repetitions × 3 pH × 2 sensor elements) = 36 groups of channels, and then the fluorescence signals of these 36 × 12 types of bacteria are used to form a data matrix (Table 1). The 6 sensor units have different fluorescence responses to different bacteria ( Figure 2 ).

[0083] The obtained fluorescence data were processed using statistical analysis software, and the fluorescence response pattern was converted into a canonical pattern using linear discriminant analysis (LDA). The LDA graph showed that the array could distinguish low-concentration, multi-species bacteria ( Figure 3 , Table 1), Jackknifed Classification Matrix showed that the array had an accuracy of 90.3% in distinguishing bacteria (Table 2).

[0084] Table 1 Data matrix and LDA results of fluorescence array sensor fluorescence response to different bacteria

[0085]

[0086]

[0087]

[0088] Note: C1~C6 represent channel 1~channel 6, Factor1~Factor6 represent processed data.

[0089] Table 2Jackknifed Classification Matrix

[0090]

[0091] The ability of the array sensor to distinguish unknown samples: Randomly select 48 unknown samples from the above bacteria. Test according to the above steps and record the change in relative fluorescence intensity. Then use linear discriminant analysis to verify the ability of the model to test unknown samples by comparing the fingerprints to distinguish the types of bacteria. 40 samples were correctly detected out of 48 unknown samples, with an accuracy rate of 83.3% (Table 3).

[0092] Table 3 Data matrix and LDA results of array sensor fluorescence response to unknown samples

[0093]

[0094]

[0095] Method for identifying mice with different IBS degrees: 100 μL of each of the six sensor units were added with intestinal flora extracted from the feces of mice with different IBS degrees (final concentration OD 600=0.005) 100 μL, shake for 10 seconds. The excitation wavelength is 368 nm, and the fluorescence intensity of the complex after adding the mouse intestinal flora is measured at an emission wavelength of 470 nm to obtain the first channel signal (ES signal); the excitation wavelength is 356 nm, and the fluorescence intensity of the complex after adding the mouse intestinal flora is measured at an emission wavelength of 464 nm to obtain the second channel signal (MS signal), and the relative fluorescence intensity change is the detection signal (I-I0) / I0. Each intestinal flora of mice with different degrees of IBS is detected once with 6 sensor units, and 6 repetitions are performed to obtain (6 repetitions × 3 pH × 2 sensor elements) = 36 data points, and then these 36 × (11 healthy samples + 10 mild IBS samples + 9 severe IBS samples) fluorescence signals are used to form a data matrix (Table 4). The 6 sensor units have different fluorescence responses to different bacteria ( Figure 4 ).

[0096] Then, the fluorescence data was processed and analyzed using statistical analysis software. The fluorescence response pattern was converted into a standard pattern using the linear discriminant analysis method (LDA). The LDA graph was used to distinguish between low-concentration and high-number IBS mice of different degrees ( Figure 5 , Table 4), Jackknifed Classification Matrix showed that the accuracy of distinguishing intestinal flora in different IBS degrees reached 84.4% (Table 5).

[0097] Table 4 Fluorescence response data and LDA results of the array sensor to the intestinal flora of mice with different IBS degrees

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] Table 5 LDA classification matrix for identifying intestinal flora in feces of mice with different IBS degrees

[0104]

[0105] Note: A represents healthy mice, B represents mild IBS mice, and C represents severe IBS mice

[0106] The ability of the array sensor to distinguish unknown samples: 120 unknown samples of intestinal flora of mice with different IBS degrees were collected from 3 types. The test was performed according to the above steps, and the changes in relative fluorescence intensity were recorded. Linear discriminant analysis was then used to verify the ability of the model to test unknown samples, and compared with the above model to distinguish the intestinal flora of mice with different IBS degrees. LDA showed that 95 samples were correctly detected out of 120 unknown samples, with an accuracy rate of 79.2% (Table 6).

[0107] Table 6 Data matrix and LDA results of array sensor fluorescence response to unknown samples

[0108]

[0109]

[0110]

[0111] Example 4 Detection of different FODMAPs by fluorescence array sensor

[0112] Method for distinguishing different types of FODMAPs: Take 100 μL of 6 sensor units (C1-C6) and mix them with 100 μL of different types of FODMAPs (final concentration of 5 mM) for 10 seconds. FODMAPs include D(+)-galactose (DGal), D-fructose (DFru), glucose (Glu), maltose (Mal), lactose (Lac), sucrose (Sur), raffinose (Raf), stachyose (Sta), galacto-oligosaccharide GOS (Gal), fructo-oligosaccharide FOS (Fru), D(+)-xylose (Dxyl), D-mannose (Man), D-sorbitol (Sor), and xylitol (Xyl). The excitation wavelength was 368 nm, and the fluorescence intensity of the complex after adding FODMAPs was measured at an emission wavelength of 470 nm to obtain the first channel signal (ES signal); the excitation wavelength was 356 nm, and the fluorescence intensity of the complex after adding FODMAPs was measured at an emission wavelength of 464 nm to obtain the second channel signal (MS signal), and the relative fluorescence intensity change was used as the detection signal (I-I0) / I0. Each FODMAPs was detected once with 6 sensor units, and 6 repetitions were performed to obtain (6 repetitions × 3 pH × 2 sensor elements) = 36 data points, and then the fluorescence signals of these 36 × 14 FODMAPs were used to form a data matrix (Table 7). The 6 sensor units have different fluorescence responses to different FODMAPs ( Figure 6 ).

[0113] Statistical analysis software was then used to process and analyze the fluorescence data. The fluorescence response pattern was converted into a standard pattern using linear discriminant analysis (LDA). LDA can be used to distinguish low-concentration, multi-type FODMAPs ( Figure 7 , Table 7), and the Jackknifed Classification Matrix showed that the accuracy of distinguishing FODMAPs reached 100% (Table 8).

[0114] Table 7 Fluorescence responses of the sensor array to 14 FODMAPs and the standard scores obtained by the LDA algorithm

[0115]

[0116]

[0117]

[0118] Table 8Jackknifed Classification Matrix

[0119]

[0120] The ability of the array sensor to distinguish unknown samples: 14 kinds of unknown samples with 56 different FODMAPs were taken and tested according to the above steps, and the relative fluorescence intensity changes were recorded. Linear discriminant analysis was then used to verify the ability of the model to test unknown samples. Compared with the above model, the results showed that 56 unknown samples were tested and 56 samples were accurately detected, with an accuracy rate of 100% (Table 9).

[0121] Table 9 Recognition results of the fluorescence array sensor for unknown samples of 14 FODMAPs

[0122]

[0123]

[0124]

[0125] Example 5: Differentiation of 10 commercially available soy milks by fluorescence array sensors

[0126] Method for distinguishing 10 commercially purchased soy milks: 100 μL of each of the 6 sensor units is transferred, and 100 μL of different commercially purchased soy milks (Silk, Vegemil, Marusan, Lactasoy, Joyoung, Yung Ho, BeiDaHuang, Soyspring, Mogart, WeiWei) is added, and shaken for 10 seconds. The excitation wavelength is 368 nm, and the fluorescence intensity of the complex after adding soy milk is measured at an emission wavelength of 470 nm to obtain the first channel signal (ES signal); the excitation wavelength is 356 nm, and the fluorescence intensity of the complex after adding soy milk is measured at an emission wavelength of 464 nm to obtain the second channel signal (MS signal), and the relative fluorescence intensity change is the detection signal (I-I0) / I0. Each type of soy milk was tested once with 6 sensor units, and 6 repetitions were performed to obtain (6 repetitions × 3 pH types × 2 sensor elements) = 36 data points. Then, the fluorescence signals of these 36 × 10 types of soy milk were used to form a data matrix (Table 10). The 6 sensor units have different fluorescence responses to different soy milks ( Figure 8 ).

[0127] Then, the fluorescence data was processed and analyzed using statistical analysis software. The fluorescence response pattern was converted into a standard pattern using the linear discriminant analysis method (LDA). The LDA graph can be used to distinguish low-concentration and multi-type soy milk ( Fig. 9 , Table 10), Classification Matrix shows that the accuracy of distinguishing soy milk reaches 100% (Table 11).

[0128] Table 10 Fluorescence responses of the sensor array to 10 commercially available soy milks and the standard scores obtained by LDA

[0129]

[0130]

[0131] Table 11Jackknifed Classification Matrix

[0132]

[0133] The ability of the array sensor to distinguish unknown samples: 40 unknown samples of 10 commercially purchased soy milks were tested according to the above steps, and the relative fluorescence intensity changes were recorded. Linear discriminant analysis was then used to verify the ability of the model to test unknown samples. Of the 40 unknown samples tested, 40 samples were correctly detected, with an accuracy rate of 100% (Table 12).

[0134] Table 12 Recognition results of the fluorescence array sensor for 10 unknown samples of commercially purchased soy milk

[0135]

[0136]

[0137] In the present invention, PN is obtained by simply modifying the high molecular polymer PEI, and is compounded with a coumarin derivative through dynamic covalent binding to quench the dye fluorescence, which is beneficial to the amplification of the detection signal of the sensor application. The above-mentioned embodiment results also prove that the preparation method of the fluorescent array sensor of the present invention is simple, low in cost, high in sensitivity, and short in detection time. It can accurately distinguish a variety of bacteria, and the accuracy rate of unknown samples is nearly 100%, with good reproducibility and strong anti-interference ability. It can be used for the detection of actual unknown samples. In addition, the sensor array operation does not require professional technicians.

[0138] The above are only embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention specification, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A dual-function fluorescence array sensor, characterized in that: The array sensor contains at least two different fluorescent sensing units, wherein the fluorescent sensing unit is a PN-coumarin dye complex formed by covalent interaction between PN and coumarin dye, wherein the PN is a high molecular polymer PEI modified by nitrobenzene boronic acid, and the coumarin dye is 6,7-dihydroxy-2H-benzopyran-2-one ES or 4-methylesculetin MS; The structural formula of the nitrobenzene boronic acid modified polymer PEI, i.e. PN, is as shown in Formula I: In formula I, n=10-15.

2. The dual-function fluorescence array sensor according to claim 1, characterized in that: The dual-function fluorescence array sensor contains a plurality of different fluorescence sensing units, and the fluorescence sensing unit is a PN-coumarin dye complex formed by mixing a PN solution with a concentration of 0.1mM-2mM and an ES or MS solution with a concentration of 0.01mM.

3. The dual-function fluorescence array sensor according to claim 1, characterized in that: The dual-function fluorescence array sensor contains 6 different fluorescence sensing units C1-C6, corresponding to 6 PN-coumarin dye complexes. The pH value, components and concentration of each component before mixing are shown in the following table: In the fluorescent sensing units C1, C3 and C5, the molar ratio of PN to ES is 1:

1. In the fluorescence sensing units C2, C4, and C6, the molar ratio of PN to MS is 1:

1.

4. The method for preparing the dual-function fluorescence array sensor according to claim 3, characterized in that: The method comprises the following steps: (1) Synthesizing PEI modified with nitrobenzene boronic acid by condensation reaction between branched PEI with a molecular weight of 10,000 and 3-boric acid-5-nitrobenzoic acid, followed by purification and impurity removal by dialysis, and obtaining PN after freeze-drying; (2) Dissolve ES and MS in methanol and dilute them with three pH buffer solutions to a final concentration of 0.01 mM. Dilute the PN synthesized in step 1) with three pH buffer solutions to a final concentration of 0.35 mM, 0.2 mM, 1.5 mM, 0.25 mM, 0.15 mM, and 1.2 mM. Mix PN solutions with concentrations of 0.35 mM, 0.2 mM, and 1.5 mM with ES solutions. Mix PN solutions with concentrations of 0.25 mM, 0.15 mM, and 1.2 mM with MS solutions to obtain 6 fluorescent sensing units C1-C6; wherein the pH values ​​of the three buffer solutions are 7.4, 8.5, and 9.5, respectively; the molar ratio of PN to ES is 1:1, and the molar ratio of PN to MS is 1:

1.

5. Use of the dual-function fluorescence array sensor as claimed in any one of claims 1 to 3 in bacteria detection.

6. The use according to claim 5, characterized in that: The bacteria include Staphylococcus aureus (S. aureus), Bacillus cereus (B. cereus), Klebsiella pneumoniae (K. pneumoniae), Enterococcus faecalis (E. faecalis), Lactobacillus rhamnosus (L. rhamnosus GG), Lactobacillus plantarum (L. plantarum), Escherichia coli (E. coli), Serratia marcescens (S. marcescens), E. cloacae, Klebsiella pneumoniae (K. pneumoniae), Enterobacter aerogenes (E. aerogenes), Acinetobacter baumannii (A. baumannii) and Salmonella.

7. A method for detecting bacteria using a dual-function fluorescent array sensor as claimed in any one of claims 1 to 3, characterized in that: The detection method specifically comprises the following steps: (1) After fully mixing different sensing units of the fluorescence array sensor and the sample to be tested, the fluorescence intensity data is detected, and the above detection steps are repeated for different sensing units; (2) using statistical analysis software to process and analyze the fluorescence data, using linear discriminant analysis to convert the fluorescence response pattern into a standard pattern, classifying the data matrix, and obtaining a two-dimensional array fingerprint of the bacteria contained in the test solution to achieve visual identification; (3) The obtained two-dimensional array distinguishing fingerprint map is used as a model to detect unknown samples, and the accuracy of the unknown sample prediction is calculated to achieve the distinction and detection of unknown bacteria.

8. Use of the dual-function fluorescence array sensor as claimed in any one of claims 1 to 3 in the detection of FODMAPs.

9. The use according to claim 8, characterized in that: The FODMAPs include one or more combinations of D(+)-galactose (DGal), D-fructose (DFru), glucose (Glu), maltose (Mal), lactose (Lac), sucrose (Sur), raffinose (Raf), stachyose (Sta), galacto-oligosaccharides GOS (Gal), fructo-oligosaccharides FOS (Fru), D(+)-xylose (xylo-oligosaccharides) (Dxyl), D-mannose (Man), D-sorbitol (Sor), and xylitol (Xyl).

10. A method for detecting FODMAPs using a dual-function fluorescence array sensor as claimed in any one of claims 1 to 3, characterized in that: The detection method specifically comprises the following steps: (1) After fully mixing different sensor units and FODMAPs samples, the fluorescence intensity data was detected, and the above detection steps were repeated for different sensor units; (2) Use statistical analysis software to process and analyze fluorescence data, use linear discriminant analysis to convert fluorescence response patterns into canonical patterns, classify FODMAPs through data matrix, and obtain a two-dimensional array fingerprint of FODMAPs in the test product to achieve visual identification; (3) The obtained two-dimensional array distinguishing fingerprint map is used as a model to detect unknown samples, and the accuracy of the unknown sample prediction is calculated to achieve the distinction and detection of unknown FODMAPs.

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