Preparation method, product and application of dual-emission fluorescence sensor

Abstract

The invention relates to the technical field of sensor preparation and antibiotic detection, in particular to a preparation method of a dual-emission fluorescence sensor, a product and application of the dual-emission fluorescence sensor. The preparation method of the dual-emission fluorescence sensor comprises the following steps: dissolving 1, 3, 5-benzene tricarboxylic acid and melamine in an organic solvent to prepare a suspension; the suspension liquid is subjected to a first heating reaction and then mixed with zinc salt to be subjected to a second heating reaction, and Zn-MBTC is obtained; the preparation method comprises the following steps: dissolving Zn-MBTC and Ru (bpy) 3 < 2 + > in water, carrying out ultrasonic treatment and oscillation, and then washing, centrifuging and drying to obtain the dual-emission fluorescence sensor. The dual-emission fluorescence sensor prepared by the method has the advantages of high sensitivity and high detection speed when being used for detecting four tetracycline antibiotics, namely doxycycline, tetracycline, oxytetracycline and gold tetracycline, and the detection limit is as low as 0.013 mu mol / L.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor preparation and antibiotic detection, and in particular to a preparation method, product and application of a dual-emission fluorescence sensor. Background Art

[0002] Tetracycline antibiotics (TCs) are a class of broad-spectrum antibiotics containing a tetracene basic skeleton. Currently, the commonly used tetracycline antibiotics mainly include tetracycline (TC), doxycycline (DOX), oxytetracycline (OTC) and tertetracycline (CTC). Due to their excellent antibacterial properties, they have been widely used in the treatment of human diseases and animal husbandry. However, tetracycline antibiotic residues in animal-derived foods have always been one of the main factors threatening food safety and human health, and can cause gastrointestinal disorders, allergic reactions and liver toxicity. The European Commission regulations stipulate that the maximum residue limit of tetracycline antibiotics in animal-derived foods is as low as μg / kg -1 level. In addition, due to the high structural similarity of DOX, TC, OTC and CTC, their specific identification is challenging. Therefore, it is of great significance to perform sensitive detection and specific identification of tetracycline antibiotic residues in animal-derived foods. Traditional methods for detecting and identifying tetracycline antibiotics mainly rely on high performance liquid chromatography, liquid chromatography-tandem mass spectrometry and immunoassay, which are complex processes, require specialized equipment and are costly. Therefore, sensitive, economical and convenient detection and identification of tetracycline antibiotics in food is an urgent need to protect food safety. Summary of the invention

[0003] Based on the above content, the present invention provides a preparation method, product and application of a dual-emission fluorescence sensor.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] One of the technical solutions of the present invention is a method for preparing a dual-emission fluorescence sensor, comprising the following steps:

[0006] Dissolving 1,3,5-benzenetricarboxylic acid and melamine in an organic solvent to prepare a suspension;

[0007] The suspension is subjected to a first heating reaction and then mixed with a zinc salt for a second heating reaction to obtain Zn-MBTC;

[0008] Combine Zn-MBTC with Ru(bpy) 3 2+The solution is dissolved in water, ultrasonicated, and shaken, and then washed, centrifuged, and dried to obtain the dual-emission fluorescence sensor (Ru@Zn-MBTC).

[0009] The second technical solution of the present invention is a dual-emission fluorescence sensor prepared by using the preparation method.

[0010] The third technical solution of the present invention is the application of the dual-emission fluorescence sensor in the detection of tetracycline antibiotics.

[0011] A fourth technical solution of the present invention is a light intelligent visualization platform, comprising the dual-emission fluorescence sensor.

[0012] The present invention discloses the following technical effects:

[0013] The presence of tetracycline antibiotic residues poses a major threat to food safety. Due to their similar chemical structures, it is challenging to identify different types of specific antibiotics in food. 2+ )-induced HOF-to-MOF scheme for the conversion of hydrogen-bonded organic frameworks (HOFs) to metal-organic frameworks (MOFs). The zinc-based MOFs prepared by this scheme have significant advantages in terms of specific surface area and pore conditions. 3 2+ As a reference signal, zinc-based MOF was used as a detection signal to construct a Ru@Zn-MBTC dual-emission fluorescence sensor. The dual-emission fluorescence sensor has the advantages of high sensitivity and fast detection speed for the detection of four tetracycline antibiotics, DOX, TC, OTC and CTC, with a detection limit as low as 0.013μmol / L.

[0014] In the presence of tetracycline antibiotics, the fluorescence spectrum of Ru@Zn-MBTC shows an enhancement peak at about 520nm and a quenching signal at 603nm. Using machine learning technology, tetracycline antibiotics are identified based on the characteristic fluorescence spectrum changes after Ru@Zn-MBTC interacts with tetracycline antibiotics. In addition, the present invention designs an optical intelligent visualization platform integrated on a smartphone, which uses the excellent color change characteristics of Ru@Zn-MBTC's fluorescence images to achieve visual detection of tetracycline antibiotics in multiple scenarios. The combination of fluorescence sensors and machine learning shows great potential in identifying and detecting tetracycline antibiotics. Combining smartphones with fluorescence sensors to quickly detect tetracycline antibiotics has broad application prospects.

[0015] The dual-emission fluorescence sensor of the present invention provides a strategy for the detection and identification of tetracycline antibiotics in animal-derived foods. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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 will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 FESEM images of (a) MA-BTC HOF, (b) Zn-MBTC, and (c) Ru@Zn-MBTC; (d) PXRD patterns of MA-BTC HOF, Zn-MBTC, and Ru@Zn-MBTC; (e) XPS spectra of Zn-MBTC and Ru@Zn-MBTC. High-resolution XPS spectra of (f) N 1s, (g) O 1s, (h) Zn 2p, (i) C 1s, and Ru 3d of Zn-MBTC and Ru@Zn-MBTC.

[0018] Figure 2 (a) The fluorescence intensity of the same concentration of Zn-MBTC (synthesized by one-pot reaction method) and Zn-MBTC (synthesized by HOF to MOF method in Example 1) when reacting with the same amount of DOX; (b) The fluorescence intensity of Zn-MBTC, Ru(bpy) 3 2+ and UV-visible absorption spectra of Ru@Zn-MBTC; (c) Zn-MBTC, Ru(bpy) 3 2+ and fluorescence emission spectra of Ru@Zn-MBTC; (d)Ru(bpy) 3 2+ , (e) Fluorescence emission spectra of Zn-MBTC and (f) Ru@Zn-MBTC before and after interaction with DOX.

[0019] Figure 3 (a) Fluorescence emission spectra of Ru@Zn-MBTC at different excitation wavelengths; (b) Fluorescence ratios of Ru@Zn-MBTC at different excitation wavelengths 518 / F 603 Linear graph; (c) Fluorescence emission spectra of Ru@Zn-MBTC at different incubation times; (d) F 518 / F 603 Linear relationship diagram of the incubation time of Ru@Zn-MBTC; (e) Fluorescence emission spectra of Ru@Zn-MBTC at different pH values; (f) F in Ru@Zn-MBTC 518 / F 603 Linear relationship graph with pH value.

[0020] Figure 4The fluorescence emission spectra of Ru@Zn-MBTC after adding different concentrations of DOX (a), TC (b), OTC (c), and CTC (d); the linear relationship between the fluorescence intensity ratio of Ru@Zn-MBTC and the concentrations of DOX (e), TC (f), OTC (g), and CTC (h).

[0021] Figure 5 (a) Fluorescence intensity ratio of Ru@Zn-MBTC after adding typical antibiotics, ions, and tetracycline antibiotics; (b) Fluorescence intensity ratio of Ru@Zn-MBTC interacting with typical antibiotics and ions in the presence of DOX.

[0022] Figure 6 (a) Fluorescence spectra of the interaction between Ru@Zn-MBTC and DOX at consistent concentrations; (b) PCA score graph of tetracycline antibiotics; (c) K-means clustering graph of tetracycline antibiotics; (d) HCA dendrite graph of tetracycline antibiotics; (e) confusion matrix of decision tree algorithm; (f) confusion matrix of random forest algorithm; (g) SVM classification graph; (h) confusion matrix of BP neural network; (i) confusion matrix of CNN.

[0023] Figure 7 (a)-(d) The linear relationship between G / R and the concentration of tetracycline antibiotics. Inset: the corresponding photos under 365nm UV light; CIE 1931 colorimetric coordinates of Ru@Zn-MBTC at different concentrations of DOX (e), TC (f), CTC (g) and OTC (h).

[0024] Figure 8 A smartphone-assisted optical intelligent visualization platform for detecting tetracycline antibiotics.

[0025] Fig. 9 FTIR spectra of Zn-MBTC and Ru@Zn-MBTC. DETAILED DESCRIPTION

[0026] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0027] It should be understood that the terms described in the present invention are only for describing a particular embodiment and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. The intermediate value in any stated value or stated range, and each smaller range between any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0028] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0029] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to the skilled artisan. The present invention description and examples are exemplary only.

[0030] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0031] The present invention proposes to synthesize zinc-based MOF using hydrogen-bonded organic framework (HOF) as a template method to better control the morphology of zinc-based MOF. HOF is a crystalline porous polymer formed by hydrogen bond self-assembly under van der Waals force and π-π stacking interaction. Hydrogen bonds are weaker than the coordination bonds that constitute MOF. If the organic ligands of zinc-based MOF are preassembled into HOF, HOF will be in the presence of zinc ions (Zn 2+ ) induced collapse, the nitrogen and oxygen atoms on the HOF surface provide Zn 2+ The template-prepared zinc-based MOF has a larger specific surface area and more active sites, which can better interact with tetracycline antibiotics. This zinc-based MOF can sensitize tetracycline antibiotics and can interact with Ru(bpy) 3 2+ The combination forms a dual-emission fluorescence sensor, which interacts with four tetracycline antibiotics and emits different fluorescence signals, providing opportunities for sensitive detection and specific identification of tetracycline antibiotics. Fluorescence spectral data usually have high-dimensional features, and machine learning algorithms can effectively process these high-dimensional features and extract important features from them. Machine learning algorithms are used to effectively distinguish the dual-emission fluorescence signals of tetracycline antibiotics and achieve specific identification of antibiotic analogs.

[0032] The present invention pre-weaves the organic ligand of MOF into HOF. 2+The dual-emission fluorescence sensor (Ru@Zn-MBTC) of the present invention uses HOF as a template to synthesize Zn-MBTC, and uses Ru(bpy) as a fluorescence detection signal. 3 2+ As a reference signal, it has ideal fluorescence properties. Using the unique fluorescence signal of the sensor, a dual-emission fluorescence sensing model was established for the quantitative determination of tetracycline antibiotics in animal-derived foods. The generated fluorescence emission spectra were analyzed using a machine learning algorithm to classify the four tetracycline antibiotics. In addition, the excellent color change advantage of the Ru@Zn-MBTC sensor was used to establish a visual detection model and build an optical intelligent visualization platform combined with a smartphone. This study proposed a new method for the synthesis of zinc-based MOFs using HOF as a template for the qualitative and quantitative detection of tetracycline antibiotics in animal-derived foods, laying a solid foundation for further development in the field of food safety.

[0033] The first aspect of the present invention provides a method for preparing a dual-emission fluorescence sensor, comprising the following steps:

[0034] Dissolving 1,3,5-benzenetricarboxylic acid and melamine in an organic solvent to prepare a suspension;

[0035] The suspension is subjected to a first heating reaction and then mixed with a zinc salt for a second heating reaction to obtain Zn-MBTC;

[0036] Combine Zn-MBTC with Ru(bpy) 3 2+ The solution is dissolved in water, ultrasonicated, and shaken, and then washed, centrifuged, and dried to obtain the dual-emission fluorescence sensor (Ru@Zn-MBTC).

[0037] In a preferred embodiment of the present invention, the molar ratio of the 1,3,5-benzenetricarboxylic acid to the melamine and zinc salt is 1:1:(1-2).

[0038] In a preferred embodiment of the present invention, the organic solvent is methanol; and the zinc salt is zinc nitrate hexahydrate.

[0039] In a preferred embodiment of the present invention, the temperature of the first heating reaction is 150° C. and the time is 12 h; the temperature of the second heating reaction is 150° C. and the time is 24 h.

[0040] In a preferred embodiment of the present invention, the Zn-MBTC and the Ru(bpy) 3 2+ The mass ratio is 5:2.

[0041] In a preferred embodiment of the present invention, the drying is specifically drying at 60° C. for 12 h under vacuum conditions.

[0042] The second aspect of the present invention provides a dual-emission fluorescence sensor prepared by the preparation method.

[0043] The third aspect of the present invention provides the use of the dual-emission fluorescence sensor in detecting tetracycline antibiotics. Specifically, the use of the dual-emission fluorescence sensor in detecting tetracycline antibiotics in animal-derived food.

[0044] A fourth aspect of the present invention provides an optical intelligent visualization platform, comprising the dual-emission fluorescence sensor.

[0045] In a preferred embodiment of the present invention, the optical intelligent visualization platform also includes a system capable of processing images to extract RGB values, determining the concentration of tetracycline antibiotics based on a linear relationship curve between the concentration of tetracycline antibiotics and the RGB values, as well as a dark box and an ultraviolet lamp.

[0046] The technical solutions described in the present invention, unless otherwise specified, are all conventional solutions in the art, and the reagents or raw materials used, unless otherwise specified, are purchased from commercial channels or have been disclosed.

[0047] The materials and reagents involved in the embodiments of the present invention are as follows:

[0048] Melamine, 1,3,5-benzenetricarboxylic acid, Zn(NO 3 ) 2 6H 2 O、Tris(2,2-bipyridyl)ruthenium chloride hexahydrate (Ru(bpy) 3 Cl 2 6H 2 O), KCl, NaCl, CaCl 2 、Na 2 CO 3 、NaHCO 3 、KNO 3 , gentamicin (GEN), lincomycin (LIN), sulfadimethoxine (SDM), sulfadiazine sodium (SDI), chloramphenicol (CHL), penicillin (PNO), and erythromycin (ERY) were purchased from Shanghai MacLean Biochemical Technology Co., Ltd. TC, OTC, DOX, CTC, formic acid, and acetonitrile were purchased from Shanghai Aladdin Biochemical Co., Ltd. (Shanghai, China). Citric acid, Na 2 HPO 4, trichloroacetic acid (TCA), and ethylenediaminetetraacetic acid (EDTA) were purchased from Tianjin Beilian Chemical Reagent Co., Ltd. (Tianjin, China). Methanol was purchased from Tianjin Komeier Chemical Reagent Co., Ltd. (Tianjin, China). Ethanol was purchased from Tianjin Fuchen Chemical Reagent Co., Ltd. (Tianjin, China). Except for formic acid and acetonitrile (chromatographic grade), the remaining chemical reagents were of analytical reagent grade without additional purification. Milk, shrimp, and egg samples were purchased from a supermarket in Baoding, China. Ultrapure water (18.25 MΩ cm) was prepared using a water purifier from Shanghai Moore Scientific Instrument Co., Ltd.

[0049] Characterizations involved in the embodiments of the present invention:

[0050] The morphology of the synthesized materials was characterized using a FEI NovaNano SEM-450 field emission scanning electron microscope (FESEM) (Waltham, USA). The powder X-ray diffraction (PXRD) spectra of the synthesized materials in the range of 5-60° were collected using a D8 ADVANCE X-ray diffractometer with a Cu target Kα as the radiation source (BrukerAXA GmbH, Karlsruhe, Germany). The KBr particle method was used to analyze the synthesized materials using a Fourier transform infrared spectrometer (PerkinElmer, Waltham, USA) in the range of 4000-400 cm -1 Surface groups within the range. In order to obtain the elemental composition, content and chemical state of the material, X-ray photoelectron spectroscopy (XPS) analysis was performed using ThermoESCALAB 250XI (Thermo Fisher, Waltham, USA). Fluorescence spectra were analyzed using a F97Pro fluorescence spectrophotometer (Shanghai Lingguang Technology Co., Ltd.) with an excitation / emission slit width of 10 nm. The absorption spectrum of the analyte was analyzed using a TU-1901 UV-visible spectrophotometer (Beijing Spectroscopy General Instrument Co., Ltd.). High performance liquid chromatography was performed using an Agilent 1290II-6460 (Santa Clara, USA).

[0051] The concentration of tetracycline antibiotics in the data set of the embodiment of the present invention is:

[0052] In order to identify tetracycline antibiotics using machine learning algorithms, the dataset is the fluorescence emission spectra of 30 different concentrations of tetracycline antibiotics interacting with Ru@Zn-MBTC. The concentrations of TC are 29, 29.1, 29.2, 29.3, 29.4, 29.5, 29.6, 29.7, 29.8, 29.9, 30, 30.1, 30.2, 30.3, 30.4, 30.5, 30.6, 30.7, 30.8, 30.9, 31, 31.1, 31.2, 31.3, 31.4, 31.5, 31.6, 31.7, 31.8 and 31.9 μmol / L. The concentrations of DOX, CTC and OTC are the same as TC.

[0053] 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.

[0054] Example 1

[0055] 1. Experimental part

[0056] Preparation of Ru@Zn-MBTC: 252 mg of 1,3,5-benzenetricarboxylic acid (BTC) and 151 mg of melamine (MA) were dissolved in 35 ml of methanol and ultrasonicated in a sealed glass bottle until a milky suspension was obtained. The prepared suspension was then poured into a 100 ml Teflon-lined pressure vessel and heated at 150 °C for 12 h to obtain MA-BTC HOF. Next, 357 mg of Zn(NO 3 ) 2 6H 2 O, and then the mixture was stirred with a magnetic stirrer for about 10 minutes, and then the solution was heated to 150°C for 24 hours. After the heating was completed, the sample was cooled to 25°C and washed three times with methanol, and then the solid product was separated by centrifugation. The obtained precipitate was dried under vacuum conditions at 60°C for 12 hours to obtain Zn-MBTC. Finally, 50 mg Zn-MBTC and 20 mg Ru(bpy) were taken. 3 2+ Dissolved in 30 ml ultrapure water, ultrasonicated for 20 min, and then shaken in a constant temperature water bath oscillator at 120 rpm and 37 ° C for 1 h. After the oscillation, the suspension was washed three times with ultrapure water to remove unbound substances. The washed product was centrifuged at 8000 rpm for 5 min to separate the precipitate. The obtained precipitate was dried under vacuum conditions at 60 ° C for 12 h to obtain the Ru@Zn-MBTC dual-emission fluorescence sensor.

[0057] Fluorescence detection of tetracycline antibiotics: In order to measure the detection ability of Ru@Zn-MBTC dual-emission fluorescence sensor for tetracycline antibiotics, tetracycline antibiotic solutions of different concentrations were added to 0.5 mg / mL Ru@Zn-MBTC aqueous solution (so that the final concentration of tetracycline antibiotics was 0, 5, 10, 15, 20, 30, 40, 50, 60 and 70 μmol / L). After the mixed solution was incubated at room temperature for 4 minutes, its fluorescence emission spectrum at an excitation wavelength of 375 nm was recorded. In order to ensure the accuracy of the results, each sample was tested three times. A linear relationship curve between the concentration of tetracycline antibiotics and the fluorescence intensity of Ru@Zn-MBTC was established.

[0058] Identification of tetracycline antibiotics: In order to evaluate the ability of Ru@Zn-MBTC to identify tetracycline antibiotics, tetracycline antibiotic solutions of different concentrations were mixed with 500 μL of Ru@Zn-MBTC aqueous solution, and ultrapure water was added to the mixed solution to make the volume of the whole system 1000 μL. The final concentration of Ru@Zn-MBTC was 0.5 mg / mL, and the final concentration of tetracycline antibiotics was as described above. The fluorescence emission spectrum of the mixed solution was recorded after incubation at room temperature for 4 min, and the 120 fluorescence emission spectra obtained were used as the data set for the machine learning algorithm. The data set was reduced in dimension using principal component analysis. Tetracycline antibiotics were identified using K-means clustering, hierarchical clustering, decision tree, random forest, support vector machine, back propagation neural network, and convolutional neural network algorithms.

[0059] Practicality of Ru@Zn-MBTC in actual samples: The applicability of Ru@Zn-MBTC in egg, milk and shrimp samples was studied to test the actual performance of Ru@Zn-MBTC in the detection of tetracycline antibiotics in animal-derived foods. Different concentrations of DOX, TC, CTC and OTC solutions were added to the treated egg, milk and shrimp samples (to make the final concentrations 0, 15, and 40 μmol / L). High performance liquid chromatography (HPLC) and fluorescence spectroscopy were used for analysis. When DOX was analyzed by HPLC, the mobile phases A and B used were 2% formic acid aqueous solution and acetonitrile, respectively, with a volume ratio of 70:30; when TC, OTC and CTC were analyzed, the mobile phases A and B used were 2% formic acid aqueous solution and acetonitrile, respectively, with a volume ratio of 85:15. The flow rate was 1.0 mL / min, the column temperature was 25, and the injection volume was 10 μL each time.

[0060] Construction of optical intelligent visualization platform: In order to realize multi-scenario detection of tetracycline antibiotics, an optical intelligent visualization platform consisting of a dark box, UV lamp, Ru@Zn-MBTC dual-emission fluorescence sensor and a smartphone was designed. When using this device to detect tetracycline antibiotics, the test solution is loaded into the sensor array, the UV lamp is turned on, and the image of the sensor array is captured through the window on the top of the dark box using a smartphone. The RGB value is extracted using the color recognition function in the smartphone, and the concentration of tetracycline antibiotics is determined based on the linear relationship curve between the concentration of tetracycline antibiotics and the RGB value.

[0061] 2. Results and Discussion

[0062] 2.1 Characterization

[0063] MA-BTC HOF is self-assembled from MA and BTC through intermolecular hydrogen bonding and interlayer π-π stacking interactions. The nitrogen- and oxygen-containing groups in MA-BTC HOF provide binding sites for metal ions, making Zn 2+ The induced HOF-TO-MOF transformation is possible. The results of field emission scanning electron microscopy (FESEM) confirmed the generation of MA-BTC HOF ( Figure 1 (a)), which indicates that the MA molecules are successfully assembled with the BTC molecules. The powder X-ray diffraction (PXRD) spectrum of MA-BTC HOF is shown in Figure 1 As shown in (d), it proves that its crystal structure is correct. The results of FESEM and PXRD jointly confirm the successful preparation of MA-BTC HOF. Then 357 mg Zn(NO 3 ) 2 6H 2 O was added to the suspension of MA-BTC HOF template and heated at 150 °C for 24 h to obtain Zn-MBTC. 2+ In the process of inducing the transformation from HOF to MOF, the morphology of the nanowires of the MA-BTC HOF template changed greatly. FESEM image of Zn-MBTC ( Figure 1 (b) demonstrates that the morphological features of the prepared Zn-MBTC are coral-like, interconnected three-dimensional nanostructures. Compared with traditional zinc-based MOFs, the specific surface area of ​​the newly formed Zn-MBTC structure is significantly increased, which may improve the sensitivity and selectivity of Zn-MBTC. The crystal structure of Zn-MBTC was further studied using PXRD patterns ( Figure 1(d)), strong diffraction peaks were observed at 10.9° and 16.1°, which were attributed to the formation of Zn-MBTC. In addition, the PXRD spectrum of Zn-MBTC lacks the characteristic diffraction peaks observed in MA-BTC HOF. The missing peaks indicate that the strong coordination between Zn and BTC ligands leads to the complete collapse of the MA-BTC HOF template. The successful preparation of Zn-MBTC indicates that the coordination between organic ligands and metal ions replaces the hydrogen bonds between organic ligands. Subsequently, the elemental composition and valence state of Zn-MBTC were analyzed by X-ray photoelectron spectroscopy (XPS). The presence of Zn, O, N and C elements was observed in Zn-MBTC ( Figure 1 (e)). Figure 1 In (f), the N1s peak is decomposed into three peaks, Zn-N (398.0 eV), CN=C (399.0 eV) and -NH 2 (400.0 eV). The three peaks at 531.4, 532.5 and 533.5 eV are respectively attributed to ZnO, CO and C=O of O1s. Figure 1 (g). Figure 1 As shown in (h), the binding energy peaks at 1022.7 and 1045.7 eV belong to Zn 2p. This is due to the Zn 2+ The above results confirm that Zn 2+ The transformation of HOF to MOF was successfully induced.

[0064] After proving the successful synthesis of Zn-MBTC, Ru(bpy) 3 2+ Doped into Zn-MBTC, Ru@Zn-MBTC dual emission fluorescence sensor was constructed. The morphology, crystal structure, element composition and valence state of Ru@Zn-MBTC were characterized. The morphology of Ru@Zn-MBTC did not change significantly compared with Zn-MBTC ( Figure 1 (c) shows that the synthesis of Ru@Zn-MBTC was successful. The coral-like cross-linked three-dimensional structure of Ru@Zn-MBTC effectively provides more binding sites for the chelation of tetracycline antibiotics with Ru@Zn-MBTC. The PXRD diffraction peaks of Ru@Zn-MBTC and Zn-MBTC have significant similarities ( Figure 1 However, a unique characteristic peak belonging to Ru is added at 43.8°Ru@Zn-MBTC, which is consistent with the literature reports, proving that Ru(bpy) 3 2+ Successfully embedded in Zn-MBTC. The FTIR spectra of Ru@Zn-MBTC and Zn-MBTC are similar ( Fig. 9), the FTIR spectrum of Ru@Zn-MBTC retains all the obvious peaks related to Zn-MBTC, and the positions of the diffraction peaks have not shifted, indicating that Ru(bpy) 3 2+ There is no interaction with Zn-MBTC. The observation of infrared spectrum provides further evidence for the successful synthesis of Ru@Zn-MBTC. From the XPS full scan spectrum, it can be seen that Ru@Zn-MBTC mainly contains Zn, O, N, C and Ru elements ( Figure 1 (e)). Figure 1 (i) is the high-resolution XPS spectra of Zn-MBTC and Ru@Zn-MBTC. The Ru 3d peak of Ru@Zn-MBTC overlaps with the C1s peak at 284.8 eV, and the relative peak intensity increases. The Ru 3d peak of Ru@Zn-MBTC is divided into two peaks at 284.4 eV and 285.0 eV, corresponding to Ru3d 5 / 2 and Ru 3d 3 / 2 The C1s, N 1s, O 1s, and Zn 2p peaks of Ru@Zn-MBTC are the same as those of Zn-MBTC. Except for the characteristic peaks of Ru, no new binding energy peaks are observed, which indicates that Ru(bpy) 3 2+ No new chemical bonds were formed with Zn-MBTC, and the characteristic peaks of Ru further confirmed that Ru(bpy) 3 2+ It was successfully incorporated into Zn-MBTC. Based on the above findings, Ru@Zn-MBTC has been successfully prepared. Ru@Zn-MBTC contains abundant functional groups, which is helpful for the recognition and detection of tetracycline antibiotics.

[0065] In order to prove that Zn-MBTC prepared using MA-BTC HOF template has better optical properties, the present invention studies the sensitivity of Zn-MBTC prepared by one-pot method and HOF-MOF template method in Example 1 to tetracycline antibiotics. The one-pot method is to mix 252 mg 1,3,5-benzenetricarboxylic acid (BTC), 151 mg melamine (MA) and 357 mg Zn(NO 3 ) 2 6H 2 O was dissolved in 35 ml of methanol and heated at 150 °C for 24 h. The heated solution was centrifuged and dried to obtain Zn-MBTC (synthesized by one-pot reaction method). An equal amount of DOX solution was added to the same concentration of Zn-MBTC (synthesized by one-pot reaction method) and Zn-MBTC (synthesized by HOF to MOF method) solutions. Figure 2As shown in (a), the fluorescence peak intensity of Zn-MBTC (synthesized by HOF to MOF method) is significantly stronger, indicating that Zn-MBTC (synthesized by HOF to MOF method) has a higher affinity for tetracycline antibiotics and can more effectively form complexes with tetracycline antibiotics. Therefore, it is known that the signal generated by the interaction between Zn-MBTC (synthesized by HOF to MOF method) and tetracycline antibiotics is more obvious and easier to detect, and the sensitivity is greatly improved. Subsequently, the optical properties of Ru@Zn-MBTC were analyzed by UV-visible spectroscopy and fluorescence spectroscopy. Ru@Zn-MBTC, Zn-MBTC and Ru(bpy) 3 2+ The UV-visible spectrum ( Figure 2 (b) shows that the absorption peak of Ru@Zn-MBTC in the range of 200-250nm is consistent with the absorption peak of Zn-MBTC, indicating that Ru(bpy) 3 2+ The addition of Ru@Zn-MBTC has no effect on the UV-visible spectrum of Zn-MBTC. In addition, the absorption peak of Ru@Zn-MBTC in the range of 250-320 nm is similar to that of Ru(bpy). 3 2+ The absorption peaks of Ru@Zn-MBTC are highly overlapped, indicating that Ru(bpy) is retained in the Ru@Zn-MBTC range. 3 2+ Optical properties of Ru@Zn-MBTC. In the spectrum of Ru@Zn-MBTC, Zn-MBTC and Ru(bpy) 3 2+ The characteristic absorption peaks are retained for Zn-MBTC and Ru(bpy) 3 2+ This provides preliminary evidence for the effective combination of Figure 2 (c) is Ru@Zn-MBTC, Zn-MBTC and Ru(bpy) 3 2+ Emission spectrum at 375nm excitation wavelength. Zn-MBTC does not show obvious fluorescence emission peak, Ru(bpy) 3 2+ It exhibits a strong fluorescence emission peak at 603nm. Ru@Zn-MBTC has the same fluorescence emission peak as Ru(bpy) at 603nm. 3 2+ The same strong emission peak and other bands are the same as those of Zn-MBTC, which further proves the successful synthesis of Ru@Zn-MBTC. Subsequently, the possibility of Ru@Zn-MBTC as a dual-emission fluorescence sensor was studied. 3 2+After adding equal amounts of DOX to Ru@Zn-MBTC, Ru(bpy) and Zn-MBTC, the fluorescence spectra were compared with those before adding DOX. 3 2+ The fluorescence intensity at 603 nm decreased ( Figure 2 (d)), while Zn-MBTC showed a new fluorescence emission peak at 518 nm ( Figure 2 (e)). Ru@Zn-MBTC effectively retains Ru(bpy) 3 2+ The fluorescence characteristics of Zn-MBTC and the fluorescence emission spectra are shown in Figure 2 As shown in (f), the fluorescence peak of Ru@Zn-MBTC at 603 nm was quenched, and a new fluorescence emission peak of Ru@Zn-MBTC appeared at 518 nm, indicating that the dual-emission fluorescence sensor was successfully constructed.

[0066] 2.2 Optimization of fluorescence detection conditions

[0067] In order to determine the optimal conditions for fluorescence detection using Ru@Zn-MBTC, a series of experiments were conducted. A certain amount of DOX was added to the Ru@Zn-MBTC aqueous solution so that the concentration of DOX in the entire system was 30μmol / L. The ratio of the fluorescence intensity of the emission spectrum of Ru@Zn-MBTC at 518nm and 603nm was calculated, and the size of the ratio was used to evaluate the ability of Ru@Zn-MBTC to detect DOX. The fluorescence emission spectrum of Ru@Zn-MBTC was recorded, and the excitation wavelength was increased from 350nm to 390nm at intervals of 5nm ( Figure 3 (a)). The change of the fluorescence intensity ratio at 518nm and 603nm is shown in Figure 3 As shown in (b). When the excitation wavelength is set to 375nm, the fluorescence intensity ratio of these wavelengths reaches the maximum. The results show that 375nm is the best excitation wavelength for fluorescence detection experiments. Optimizing the incubation time of the analysis is crucial to improving the performance of the sensor. The emission spectra of Ru@Zn-MBTC at different incubation times at 375nm excitation wavelength were recorded ( Figure 3 (c)). Figure 3 As shown in (d), after adding DOX to Ru@Zn-MBTC, the fluorescence intensity ratio at 518nm and 603nm increased rapidly and stabilized when the incubation time reached 4min, which was the optimal incubation time. The fluorescence intensity ratio at 518nm and 603nm was stable in the pH range of 3 to 10, indicating that Ru@Zn-MBTC has good chemical stability ( Figure 3 (e)-(f)).

[0068] 2.3 Fluorescence detection of tetracycline antibiotics:

[0069] Under the optimal detection conditions, different concentrations of tetracycline antibiotics were added to Ru@Zn-MBTC, and the fluorescence intensity was recorded to evaluate the sensitivity of the dual-emission fluorescence sensor constructed in the present invention. Figure 4 As can be seen from (a)-(d), with the increase of DOX, TC, OTC, and CTC concentrations, the fluorescence intensity of Ru@Zn-MBTC at 518, 520, 524, and 528 nm increases, and the fluorescence intensity at 603 nm decreases. Therefore, a fluorescence sensing model based on fluorescence intensity ratio and tetracycline antibiotic concentration was established ( Figure 4 (e)-(h)). DOX and TC show good linear relationships in the range of 0-60 μmol / L, while OTC and CTC show good linear relationships in the range of 0-55 μmol / L and 0-70 μmol / L, respectively. The four tetracycline antibiotics have different responses to the fluorescence intensity around 520 nm, and the fluorescence response of DOX is the most obvious. This may be caused by the structural differences of tetracycline antibiotics, resulting in different selectivities of Ru@Zn-MBTC for different tetracycline antibiotics. The minimum detection limits (LODs) of DOX, TC, OTC and CTC are 0.015, 0.013, 0.023 and 0.031 μmol / L, respectively. Ru@Zn-MBTC shows superior performance to traditional methods and other sensors, which is reflected in lower detection limits and wider linear response ranges (Table 1 shows the detection limits and linear response ranges of the Ru@Zn-MBTC dual-emission fluorescence sensor of the present invention and traditional sensors for tetracycline antibiotics). This characteristic makes Ru@Zn-MBTC have great potential and wide application in the detection of tetracycline antibiotics.

[0070] Table 1

[0071]

[0072]

[0073] Typical antibiotics such as gentamicin (GEN), lincomycin (LIN), sulfadimethoxine (SDM), sulfadiazine sodium (SDI), chloramphenicol (CHL), penicillin (PNO), erythromycin (ERY) and common ions (Ca 2+ , K + 、Na + , CO 3 2- 、NO 3 - , HCO 3 -) as an interference source to further verify the specificity of the constructed Ru@Zn-MBTC dual-emission fluorescence sensor, and record the emission spectrum of Ru@Zn-MBTC. In the selectivity experiment, the same concentration of interfering substances were used to replace tetracycline antibiotics. The interference experiment was to introduce DOX into the solution to which the interfering substances had been added. Figure 5 As shown in (a), after adding tetracycline antibiotics to Ru@Zn-MBTC, the fluorescence intensity ratio of Ru@Zn-MBTC is significantly enhanced. However, after adding interfering antibiotics or interfering ions to Ru@Zn-MBTC, the fluorescence intensity ratio of Ru@Zn-MBTC does not change significantly, which proves that the Ru@Zn-MBTC sensor has good selectivity for DOX, TC, OTC and CTC. In addition, when interfering substances are added to the mixed solution of Ru@Zn-MBTC and DOX, the fluorescence intensity ratio does not change significantly ( Figure 5 (b)), which proves that the sensor has good anti-interference ability in complex environments.

[0074] 2.4 Application in actual samples

[0075] The standard addition method was used to determine the different concentrations of tetracycline antibiotics in animal-derived foods to evaluate the effectiveness of the constructed Ru@Zn-MBTC dual-emission fluorescence sensor. High-performance liquid chromatography was used to detect tetracycline antibiotic residues in animal-derived food samples. Tetracycline antibiotics of different concentrations (0μmol / L, 15μmol / L, 40μmol / L) were added to the samples and the samples were treated. The concentrations of tetracycline antibiotics in animal-derived food samples were detected by high-performance liquid chromatography and fluorescence methods, and the results are shown in Tables 2-5. The recovery rate was calculated to illustrate the accuracy of Ru@Zn-MBTC in determining tetracycline antibiotics, and the relative standard deviation (RSD) was calculated to illustrate the accuracy of Ru@Zn-MBTC in determining tetracycline antibiotics. The spike recoveries were 95.14%-108.27% (DOX), 94.83%-109.03% (TC), 95.35%-101.84% (CTC) and 92.41%-110.77% (OTC). The RSDs (n=9) were: DOX (1.22%-3.67%), TC (1.50%-2.51%), CTC (1.24%-3.23%) and OTC (1.04%-3.54%). The above results show that the Ru@Zn-MBTC dual-emission fluorescence sensor has strong accuracy and reliability in detecting the content of tetracycline antibiotics in animal food.

[0076] Table 2 Analysis results of DOX determination in food samples

[0077]

[0078] Table 3 Analysis results of TC determination in food samples

[0079]

[0080] Table 4 Analysis results of CTC determination in food samples

[0081]

[0082] Table 5 Analysis results of OTC determination in food samples

[0083]

[0084]

[0085] 2.5 Qualitative identification of tetracycline antibiotics based on machine learning algorithms

[0086] In order to further verify the ability of Ru@Zn-MBTC dual-emission fluorescence sensor to identify tetracycline antibiotics, a strategy combining Ru@Zn-MBTC fluorescence spectrum with machine learning was proposed to identify tetracycline antibiotics. In this strategy, Ru@Zn-MBTC produces different fluorescence responses to four tetracycline antibiotics, which are manifested as different fluorescence spectra. Then, the machine learning algorithm analyzes the fluorescence spectra to identify four different tetracycline antibiotics. Figure 6 (a) shows the fluorescence emission spectra of DOX, TC, CTC and OTC reacting with Ru@Zn-MBTC at a concentration of 30 μmol / L. It can be found that there are certain differences in their fluorescence emission spectra. Therefore, the possibility of using machine learning algorithms to analyze fluorescence emission spectrum data to identify different types of tetracycline antibiotics is proposed. The unsupervised dimensionality reduction algorithm principal component analysis (PCA) is used to reduce the dimensionality of the data to ensure the independence of the feature attributes, focusing on the first two principal components. The reduced visualization results of the data set show ( Figure 6 In (b), samples of the same species are clustered together, indicating that the sample data are highly similar and have good reproducibility within the group. The high dispersion between different types of tetracycline antibiotics indicates that the similarity between different types of tetracycline antibiotic samples is relatively low. The above results demonstrate the potential of using fluorescence emission spectroscopy data and machine learning to identify tetracycline antibiotics.

[0087] First, K-means clustering and hierarchical clustering (HCA) are used to identify tetracycline antibiotics. These two methods do not require pre-labeling of training data, but clustering based on the characteristics of the data itself. However, K-means clustering requires a pre-set number of clusters, while HCA does not. The results of clustering the data set using K-means clustering are shown in Figure 2. Figure 6As shown in (c), CTC and TC are identified as the same category, and DOX is forced to be divided into two categories. The results of clustering the data set by HCA are shown in Figure 6 As shown in (d). It can be observed from the figure that although the dataset can be identified into four categories, the clustering results are chaotic and cannot accurately classify tetracycline antibiotics. It can be seen that despite the reduction of dimensionality, unsupervised methods still cannot provide accurate identification of tetracycline antibiotics. Therefore, in order to more accurately identify tetracycline antibiotics, we adopted a supervised machine learning algorithm. Supervised learning methods use labeled training data to train the model, which means that each training sample has a corresponding label and can achieve a higher accuracy. In this study, the entire dataset was randomly divided into training and test sets, of which 70% was the training set and 30% was the test set. Several supervised learning methods such as decision tree (DT), random forest (RF), support vector machine (SVM), BP neural network and convolutional neural network (CNN) were used to identify tetracycline antibiotics. Figure 6 (e) shows the confusion matrix generated by applying DT, indicating that the accuracy of the test set is 94.4%. RF is essentially a classifier that integrates multiple DTs. In most cases, RF performs better than a single DT in classification tasks. The confusion matrix generated by RF is shown in Figure 1. Figure 6 As shown in (f), the accuracy of the test set reaches 100%. SVM separates different categories of data by finding an optimal hyperplane in high-dimensional space and maximizes the distance between different categories, thereby realizing the recognition of tetracycline antibiotics. The particle swarm optimization algorithm (PSO) is used to find parameters, and the results are shown in Figure 6 As shown in (g), the accuracy is 100%. The BP neural network uses gradient descent to achieve arbitrary nonlinear mapping between input / output, thereby being able to identify tetracycline antibiotics. Figure 6 (h) shows the result of using BP neural network to classify tetracycline antibiotics, with an accuracy of 100%. Compared with BP neural network, CNN has fewer parameters and faster training speed. One-dimensional convolutional neural network (1D-CNN) is more suitable for processing local relationships in spectral data. The fluorescence spectrum of Ru@Zn-MBTC interacting with tetracycline antibiotics differs significantly in the range of 518-528nm. Due to the local distance difference, 1D-CNN was selected. A one-dimensional convolution layer containing 64 convolution kernels was designed to extract fluorescence curve features. The confusion matrix of the test set classified using CNN is shown in the figure below. Figure 6 As shown in (i), the recognition accuracy of tetracycline antibiotics is 100%.

[0088] In summary, the study showed that Ru@Zn-MBTC effectively identified tetracycline antibiotics using a supervised learning approach with an accuracy of 100%. This strategy highlights the powerful ability of Ru@Zn-MBTC in qualitatively identifying tetracycline antibiotics.

[0089] 2.6 Construction of optical intelligent visualization platform

[0090] Ru@Zn-MBTC has excellent fluorescence response under 365nm ultraviolet light irradiation and can be used for visual detection of tetracycline antibiotics. 3 2+ When the mass ratio of Ru@Zn-MBTC is 5:2, Ru@Zn-MBTC can be used for quantitative detection of tetracycline antibiotics with the help of fluorescence spectrophotometer. With the excellent color change of Ru@Zn-MBTC, visual detection of tetracycline antibiotics can be achieved. As the concentration of tetracycline antibiotics increases, the color of the Ru@Zn-MBTC fluorescence image changes from red to yellow-green, as shown in Figure 2. Figure 7 As shown in (a)-(d). Figure 7 (e)-(h) are CIE 1931 chromaticity diagrams of Ru@Zn-MBTC drawn based on fluorescence spectra, which further confirm that the fluorescence color of Ru@Zn-MBTC changes with the concentration of tetracycline antibiotics. The RGB values ​​of the fluorescence images of the mixtures of Ru@Zn-MBTC solutions with different concentrations of DOX, TC, CTC and OTC were extracted using a smartphone color recognition program to construct a linear relationship between G / R and the concentration of tetracycline antibiotics. DOX and G / R showed a strong linear correlation between 0-10 μmol / L and 10-60 μmol / L, and R 2 Both are 0.992( Figure 7 TC and G / R showed a good linear relationship in the range of 0-20 μmol / L and 20-60 μmol / L (R 2 =0.986,R 2 =0.991), such as Figure 7 As shown in (b). In the range of 0-20 μmol / L and 20-60 μmol / L, CTC and G / R showed a linear relationship (R 2 =0.996,R 2 =0.997)( Figure 7 (c)). OTC concentration and G / R showed a strong linear correlation between 0-10 μmol / L and 10-50 μmol / L. 2 Both are 0.993( Figure 7(d)). The detection limits of DOX, TC, CTC, and OTC were 0.028 μmol / L, 0.157 μmol / L, 0.115 μmol / L, and 0.042 μmol / L, respectively. In conclusion, the proposed Ru@Zn-MBTC sensor exhibited excellent color change and was able to effectively realize the visual detection of tetracycline antibiotics.

[0091] By utilizing the color-changing properties of Ru@Zn-MBTC fluorescence images, a light-intelligent visualization platform integrated into a smartphone was developed, which is very suitable for on-site detection of tetracycline antibiotics. The components of the light-intelligent visualization platform include two 365nm UV light sources, Ru@Zn-MBTC sensors, 3D printed dark boxes, and smartphones. Figure 8 As shown in the figure, after the smartphone acquires the image, the color information of the image is extracted and converted into RGB values ​​using an application, and the linear relationship between tetracycline antibiotics and G / R is used to achieve quantitative detection. Compared with large laboratory instruments, the combination of the optical intelligent visualization platform and Ru@Zn-MBTC improves the feasibility of on-site detection.

[0092] 3. Conclusion

[0093] In summary, the present invention proposes a new type of Ru@Zn-MBTC dual-emission fluorescence sensor, which can realize the qualitative and quantitative detection and visualization application of tetracycline antibiotics residual in animal-derived food. The Ru@Zn-MBTC dual-emission fluorescence sensor also shows good detection performance in actual food sample detection, and its spike recovery rate is between 92.41% and 110.77%. Utilizing the unique fluorescence responsiveness between the Ru@Zn-MBTC dual-emission fluorescence sensor and tetracycline antibiotics, the fluorescence spectra of Ru@Zn-MBTC and tetracycline antibiotics are trained and tested with the help of machine learning algorithms such as SVM and BP neural network, and the qualitative detection of tetracycline antibiotics is realized. The constructed optical intelligent visualization platform enables the quantitative detection of tetracycline antibiotics by Ru@Zn-MBTC dual-emission fluorescence sensor to be implemented in various scenarios outside the laboratory, which has great commercial application value.

[0094] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for preparing a dual-emission fluorescence sensor, characterized in that: The following steps are involved: Dissolving 1,3,5-benzenetricarboxylic acid and melamine in an organic solvent to prepare a suspension; The suspension is subjected to a first heating reaction and then mixed with a zinc salt for a second heating reaction to obtain Zn-MBTC; Zn-MBTC and Ru(bpy)3 2+ The dual emission fluorescence sensor is obtained by dissolving in water, ultrasonicating and shaking, and then washing, centrifuging and drying.

2. The method for preparing a dual-emission fluorescence sensor according to claim 1, characterized in that: The molar ratio of the 1,3,5-benzenetricarboxylic acid to the melamine and zinc salt is 1:1:(1-2).

3. The method for preparing a dual-emission fluorescence sensor according to claim 1, characterized in that: The organic solvent is methanol; and the zinc salt is zinc nitrate hexahydrate.

4. The method for preparing a dual-emission fluorescence sensor according to claim 1, characterized in that: The temperature of the first heating reaction is 150° C. and the time is 12 h; the temperature of the second heating reaction is 150° C. and the time is 24 h.

5. The method for preparing a dual-emission fluorescence sensor according to claim 1, characterized in that: The Zn-MBTC and the Ru(bpy)3 2+ The mass ratio is 5:

2.

6. The method for preparing a dual-emission fluorescence sensor according to claim 1, characterized in that: The drying is specifically drying at 60° C. for 12 h under vacuum conditions.

7. A dual-emission fluorescence sensor prepared according to the preparation method according to any one of claims 1 to 6.

8. Use of the dual-emission fluorescence sensor as claimed in claim 7 in detecting tetracycline antibiotics.

9. An optical intelligent visualization platform, characterized in that: Comprising the dual-emission fluorescence sensor as described in claim 7.

10. The optical intelligent visualization platform according to claim 9, characterized in that: Also included is a system capable of processing images to extract RGB values, determining the concentration of tetracycline antibiotics based on a linear relationship curve between the concentration of tetracycline antibiotics and the RGB values, as well as a dark box and an ultraviolet lamp.

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