Reagent for rapidly detecting chlortetracycline and method for rapidly detecting chlortetracycline

Abstract

The present application relates to the field of nanomaterials and analytical detection technology, in particular to a reagent for rapid detection of aureomycin and a rapid detection method of aureomycin. After adding 3-mercaptopropionic acid modified AgInS2 quantum dots to aureomycin, the fluorescence intensity and color of AgInS2 QDs change due to electrostatic interaction and band gap transition, thereby constructing a fluorescence colorimetric sensor to realize the ultra-sensitive detection of aureomycin. In order to realize the on-site detection of aureomycin, a portable device equipped with a smartphone and integrated with the AgInS2 QDs sensor is also designed. The cloud server data analysis system based on machine learning algorithm is integrated into the smartphone, which is convenient for color data collection, correction, interpretation and display. The present application utilizes the function of modern smartphones to capture and process fluorescence color signals, thereby providing a user-friendly on-site detection solution for aureomycin detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nanomaterial preparation and chemical analysis detection, and particularly relates to a reagent for rapidly detecting chlortetracycline and a method for rapidly detecting chlortetracycline. BACKGROUND

[0002] Chlortetracycline (CTC) is one of the most widely used tetracycline antibiotics in the world, which is favored due to its low price, significant efficacy and broad spectrum. However, due to the lack of awareness of the harm of veterinary drug residues among some livestock breeders, and the abuse of drugs for economic benefits, the problem of chlortetracycline residues in animal-derived food is becoming increasingly serious. Long-term intake of food containing chlortetracycline residues can seriously threaten human health, produce toxic effects such as allergic reactions, liver damage and bacterial resistance. In the face of this serious situation, many countries have clearly stipulated the maximum residue limit of antibiotics in food. Therefore, in order to ensure food safety, it is imperative to develop a rapid and accurate method for detecting chlortetracycline.

[0003] Many methods for detecting chlortetracycline have been reported, including high performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), electrochemical method and enzyme-linked immunosorbent assay (ELISA). However, these methods have defects such as long detection time, complex pretreatment, high analysis cost, need for professional operators and low sensitivity. In recent years, fluorescence sensors have attracted much attention due to their simple operation, fast speed, low detection cost, rapid response and good selectivity. However, existing fluorescence sensing methods usually require the aid of instruments, making it difficult to achieve on-site sensitive detection. Therefore, there is an urgent need to develop a portable, sensitive and economical method for on-site detection of chlortetracycline. The present application provides a new method for ultra-sensitive detection of chlortetracycline based on a machine learning assisted smartphone fluorescence visual sensing platform based on the portable characteristics of the portable fluorescence visual sensing platform. SUMMARY

[0004] To solve the above problems existing in the prior art, the purpose of the present application is to provide a reagent for rapidly detecting chlortetracycline and a method for rapidly detecting chlortetracycline, which can not only ultra-sensitively detect chlortetracycline, but also realize portable fluorescence visualization on-site detection in complex matrix.

[0005] To achieve the above purpose, the first aspect of the present application provides a reagent for rapidly detecting chlortetracycline, which is a 3-mercaptopropionic acid modified AgInS2 quantum dot solution.

[0006] The second aspect of the present application provides a preparation method of 3-mercaptopropionic acid modified AgInS2 quantum dots for rapid detection of chlortetracycline concentration, comprising: dissolving 3-mercaptopropionic acid in ultrapure water to obtain a 3-mercaptopropionic acid solution, then adding silver ions and indium ions and mixing thoroughly, then using lye to adjust the pH value to 8-9; then injecting sodium sulfide solution into the solution, and after stirring at room temperature for a preset time, heating the reaction, and after the reaction is completed, centrifuging the obtained solution to remove the precipitate to obtain a 3-mercaptopropionic acid modified AgInS2 quantum dot solution.

[0007] Further, the molar ratio of the 3-mercaptopropionic acid, silver ions, indium ions and sodium sulfide is 1:1.55-1.75:8.25-8.45:6.9-7.1;

[0008] The lye is a 0.8-1.2 mol / L NaOH solution; preferably a 1 mol / L NaOH solution;

[0009] The preset time for stirring at room temperature is 15-20 min; the temperature for the heating reaction is 95-105℃, and the time is 180-200 min.

[0010] Further, the volume of the 3-mercaptopropionic acid solution is 24-26 mL, and the concentration is 0.195-0.205 mol / L; the amount of substance of the silver nitrate solution is 0.080-0.085 mmol; and the amount of substance of the indium ions is 0.415-0.420 mmol;

[0011] And / or, the concentration of the NaOH solution is 1 mol / L, and the NaOH solution is added to adjust the pH value to 8-9;

[0012] And / or, the volume of the sodium sulfide solution is 1.95-2.05 mL, and the amount of substance is 0.341-0.359 mmol;

[0013] And / or, the preset time for stirring at room temperature is 15-20 min; the temperature for the heating reaction is 95-105℃, and the time is 180-200 min.

[0014] Further, the morphology of the 3-mercaptopropionic acid modified AgInS2 QDs solution under transmission electron microscopy is a regular spherical shape, the particle size is 2.63-4.66 nm, the strongest emission wavelength is 580 nm, and bright orange fluorescence is exhibited.

[0015] The third aspect of the present application provides a rapid detection method for chlortetracycline concentration, comprising the following steps:

[0016] The 3-mercaptopropionic acid modified AgInS2 QDs solution obtained by any one of the preparation methods is mixed with a to-be-tested chlortetracycline solution, and the concentration of the to-be-tested chlortetracycline solution is obtained according to the fluorescence intensity and / or color change;

[0017] The determination mode of the fluorescence intensity is to establish a standard relationship curve of the concentration of the chlortetracycline solution and the fluorescence enhancement degree; the determination mode of the color change is model comparison or prediction based on machine learning or comparison through an intelligent terminal.

[0018] Further, the method for establishing the standard relationship curve comprises: diluting the 3-mercaptopropionic acid modified AgInS2 QDs solution to a preset concentration, and then mixing the 3-mercaptopropionic acid modified AgInS2 QDs solution with chlortetracycline standard solutions of different concentrations, respectively; after the reaction is completed, a standard relationship curve of the concentration of the chlortetracycline solution and the fluorescence enhancement degree is established according to the concentration of the chlortetracycline solution and the fluorescence intensity change degree of the mixed solution corresponding to the concentration.

[0019] The standard relationship curve is as follows:

[0020] F / F0=4.8952C CTC +8.5546, R 2 =0.9983

[0021] Wherein, F represents the fluorescence intensity of the solution after adding chlortetracycline, F0 represents the fluorescence intensity of the solution without adding chlortetracycline, and C CTC represents the concentration of the chlortetracycline solution.

[0022] Preferably, the dilution multiple of the AgInS2 QDs solution is 10 times.

[0023] Preferably, the volume ratio of the AgInS2 QDs solution, the chlortetracycline solution and the ultrapure water is 1:1:18.

[0024] Further, the reaction time of the chlortetracycline standard solution and the AgInS2 QDs solution is 200 min, and the concentration of the chlortetracycline in the reaction system ranges from 0.05 to 5 μmol / L; preferably, the concentration of the chlortetracycline solution in the reaction system is 0.05, 0.15, 0.25, 0.35, 0.45, 1, 2, 3, 4, 5 μmol / L, respectively.

[0025] Preferably, in step 1, after the chlortetracycline solution is added to the AgInS2 QDs solution, the fluorescence color of the mixed solution changes from orange to pink and finally to blue-violet. The fluorescence intensity at 414 nm is enhanced, and the particles in the transmission electron microscope are no longer dispersed, but present obvious aggregation.

[0026] The method for constructing the model based on machine learning comprises:

[0027] The 3-mercaptopropionic acid modified AgInS2 QDs solution is diluted to a preset concentration, then mixed with different concentrations of chlortetracycline standard solution respectively to obtain a mixed solution, after the reaction is completed, the image information of the mixed solution is collected by camera shooting, the RGB value digital matrix corresponding to the photo is obtained by using MATLAB software, and finally the RF model is used to associate the RGB value of the pixel point in the mixed solution photo with the concentration of chlortetracycline solution.

[0028] Further, specifically: with the help of Photoshop software, the pixel point information in the photographed image is extracted; then, the RGB value of each pixel point is extracted using MATLAB software to obtain the corresponding digital matrix; finally, the digital matrix is combined with the RF algorithm to construct an RF model capable of realizing the prediction of chlortetracycline concentration.

[0029] Preferably, the specific operation mode is: in the RF model, the RGB value given and the corresponding chlortetracycline concentration are used to establish an original training set, samples are extracted from the original training set by self-sampling with replacement, a plurality of different training subsets are established, each training subset is used to construct a decision tree model, when all the decision trees are constructed, the RF algorithm averages or votes the prediction results of each decision tree to obtain the final regression prediction result, and a linear model is established to realize the prediction of chlortetracycline concentration.

[0030] Further, in the construction of the model based on machine learning, the reaction time of the chlortetracycline standard solution and the AgInS2 QDs solution is 200 min, and the concentration range of chlortetracycline in the reaction system is 0-500 μmol / L; preferably, the concentration of chlortetracycline solution in the reaction system is 0, 0.1, 0.2, 1, 10, 50, 100, 500 μmol / L, respectively.

[0031] Further, the concentration prediction of chlortetracycline through the intelligent terminal comprises the following steps:

[0032] Step 1: build an RGB value conversion program and the RF model of claim 9 on the intelligent terminal;

[0033] Step 2: dilute the 3-mercaptopropionic acid modified AgInS2 QDs solution to a preset concentration, then mix it with the chlortetracycline solution to be tested to obtain a mixed solution, after the reaction is completed, the image information of the mixed solution is collected by the intelligent terminal in a shooting mode, and is converted into RGB value, then the intelligent terminal predicts the concentration of the chlortetracycline solution to be tested based on the RF model described above according to the RGB value of the chlortetracycline solution to be tested;

[0034] Preferably, the intelligent terminal is a smart phone or a tablet computer.

[0035] Preferably, specifically: after adding AgInS2 QDs and different concentrations of aureomycin in the sample plate, mix uniformly. Use a smartphone to take a fluorescence photo, and accurately convert it into corresponding RGB values through an applet. Then, the extracted RGB values are uploaded to the quantitative detection interface, and the cloud machine learning algorithm (RF model) of the applet will automatically provide the predicted value of the concentration of aureomycin according to the input RGB value.

[0036] Compared with the prior art, the application has the following beneficial effects and advantages:

[0037] 1、The 3-mercapto propionic acid modified AgInS2 QDs prepared by the application can make the mixed solution have fluorescence enhancement and color change phenomena based on electrostatic interaction and band gap transition, facilitate the construction of an AgInS2 QDs fluorescence colorimetric sensor, and thus realize the visual detection of aureomycin. Moreover, the electrostatic interaction shortens the distance between the AgInS2 QDs and aureomycin, thereby improving the sensitivity of the AgInS2 QDs to aureomycin.

[0038] 2、Compared with the existing aureomycin detection method, the method for super-sensitive detection of aureomycin based on the machine learning assisted fluorescence visual sensing platform of the smartphone has the characteristics of low cost, low detection limit, high portability and rich color change. In addition, in order to realize the on-site detection of aureomycin, a portable device equipped with a smartphone integrates a cloud server data analysis system based on a machine learning algorithm into the smartphone, which facilitates color data acquisition, correction, interpretation and display. This innovative method takes advantage of the modern smartphone's ability to capture and process fluorescence color signals, thereby providing a user-friendly on-site detection solution for aureomycin detection. Compared with other detection methods, the sensor can produce more color intervals, and when used in combination with a portable fluorescence visual sensing platform, it can achieve more sensitive on-site detection of aureomycin. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is the principle and detection schematic diagram of the AgInS2 QDs fluorescence sensor of the application.

[0040] Figure 2 It is the structure characterization of AgInS2 QDs. (a) Transmission electron microscope image, (b) X-ray photoelectron spectroscopy spectrum, (c) X-ray diffraction spectrum, (d) Fourier transform infrared spectrum of AgInS2 QDs and AgInS2 QDs + aureomycin.

[0041] Figure 3For the quantitative detection of aureomycin in AgInS2 QDs. (a) Fluorescence spectra of AgInS2 QDs solution in the presence of different concentrations of aureomycin (0.05, 0.15, 0.25, 0.35, 0.45, 1, 2, 3, 4, 5 μM), (b) Linear relationship between fluorescence intensity ratio (F / F0) and aureomycin concentration, (c) CIE chromaticity diagram under the action of different concentrations of aureomycin, (d) Partially enlarged CIE chromaticity diagram.

[0042] Figure 4 For mechanism exploration. TEM images of AgInS2 QDs (a) and AgInS2 QDs + aureomycin (b), (c) Zeta potential of AgInS2 QDs and AgInS2 QDs + aureomycin, (d) UV-visible absorption spectrum and fluorescence spectrum of AgInS2 QDs and AgInS2 QDs + aureomycin.

[0043] Figure 5 For mechanism exploration. Fluorescence spectra of AgInS2 QDs (a) and AgInS2 QDs + aureomycin (c), (αhv) of AgInS2 QDs (b) and AgInS2 QDs + aureomycin (d). 1 / 2 Figure showing the change with hv.

[0044] Figure 6 In the presence of different concentrations of aureomycin (0-500 μM), (a) fluorescence photos of AgInS2 QDs (3 groups in parallel), (b) RF algorithm analysis results of fluorescence photo RGB value and aureomycin concentration (0-500 μM).

[0045] Figure 7 In the presence of different concentrations of aureomycin (0-500 μM), (a) fluorescence photos of AgInS2 QDs (3 groups in parallel), (b) RF algorithm analysis results of fluorescence photo RGB value and aureomycin concentration (0-500 μM).

[0046] Figure 8 Pictures of different concentrations of aureomycin taken by the portable sensing platform. DETAILED DESCRIPTION

[0047] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0048] The application provides a reagent for rapidly detecting aureomycin and a rapid aureomycin detection method. Based on electrostatic interaction and band gap transition, the mixed solution is subjected to fluorescence enhancement and color change, and an AgInS2 QDs fluorescence colorimetric sensor is constructed. In the process, the concentration of the aureomycin solution is directly related to the fluorescence enhancement and color change of the solution. With the increase of the concentration of the aureomycin, the fluorescence intensity of the solution gradually increases, and the fluorescence color gradually changes from orange to pink, purple and blue. Based on the above principle, the following purposes can be achieved: (1) the fluorescence enhancement degree is measured by a fluorescence spectrophotometer, a linear equation is established to predict the concentration of aureomycin in a complex matrix; (2) the RGB value of the fluorescence color of the solution is extracted, and the RF model is combined to realize the visual concentration prediction of aureomycin in a complex matrix; (3) the fluorescence photo is taken by using the portable fluorescence sensing platform developed by us, the RGB value of the fluorescence color of the solution is extracted by a small program and uploaded to the cloud, and the cloud algorithm is operated to realize the concentration prediction of aureomycin.

[0049] The method for super-sensitivity detection of aureomycin based on the machine learning assisted intelligent mobile phone fluorescence visual sensing platform will be described in detail through specific examples.

[0050] In the following examples: the aureomycin solution is prepared by adding aureomycin hydrochloride into ultrapure water.

[0051] A reagent for rapidly detecting aureomycin and a rapid aureomycin detection method, comprising the following steps:

[0052] Step (1): synthesis of 3-mercaptopropionic acid modified AgInS2 QDs

[0053] 3-mercaptopropionic acid (MPA) is dissolved in 25 mL of ultrapure water to obtain a 0.2M 3-mercaptopropionic acid solution, and then 0.083mmol of silver nitrate and 0.417mmol of indium ions (indium chloride) are added and mixed uniformly. The pH of the obtained solution is adjusted to 8.5 by using 1M sodium hydroxide. Then, 2mL of 0.35mmol Na2S is quickly injected into the solution, and the solution is stirred at room temperature for 15min and heated at 100℃ for 3h to obtain an AgInS2 QDs solution. The obtained solution is centrifuged to remove the precipitate and stored in a refrigerator at 4℃ for standby. Finally, an AgInS2 QDs solution with orange fluorescence under a 365nm ultraviolet lamp is obtained, and the maximum fluorescence emission wavelength is located at 580nm.

[0054] The prepared AgInS2 QDs are scanned by a transmission electron microscope, and the obtained TEM image is as shown in Figure 2As shown in Figure a, the prepared AgInS2 QDs exhibit near-spherical morphology and good dispersibility, with a measured lattice fringe width of 0.32 nm. The prepared AgInS2 QDs were characterized by XRD to analyze their crystal structure. Figure 2 As shown in Figure c, the XRD peaks at 2θ = 26.7°, 44.8°, and 52.3° correspond to the (112), (204), and (312) crystal planes of the AgInS2 QDs quadrilateral crystal, respectively, which are consistent with the crystal planes of the standard card JCPDS25-1330. The Fourier transform infrared spectrum of AgInS2 QDs is shown in Figure c. Figure 2 As shown in d. 3400cm -1 The broad vibrational peak at 2900 cm⁻¹ corresponds to the -OH stretching vibration. -1 The band at 1565cm is attributed to the -CH2 stretching vibration. -1 and 1385cm -1 The two absorption bands at 2570 cm⁻¹ represent the asymmetric and symmetric stretching vibrations of -COO⁻. -1 The absence of a vibrational band corresponding to the -SH group indicates that the thiol covalent bond may have broken, demonstrating that MPA successfully modified AgInS2 QDs through the covalent bond between the thiol and the metal cation. Figure 4 As shown in Figure d, under 340 nm excitation, the fluorescence spectrum of AgInS2 QDs shows an emission peak at 580 nm, while no obvious absorption peak is observed in the ultraviolet spectrum.

[0055] Step (2): Quantitative analysis of chlortetracycline using AgInS2 QDs fluorescence sensor fluorescence spectroscopy

[0056] (1) Quantitative detection of chlortetracycline by fluorescence spectroscopy

[0057] In an EP tube, 50 μL of AgInS2 QDs obtained in step (1) diluted 10 times and 50 μL of chlortetracycline solutions of different concentrations were mixed. Ultrapure water was added to maintain a total system volume of 1 mL, resulting in mixed solutions that interacted with chlortetracycline solutions of different concentrations (chlortetracycline concentrations in the system were 0.05, 0.15, 0.25, 0.35, 0.45, 1, 2, 3, 4, and 5 μmol / L). After reacting for 200 min, the fluorescence spectrum data of the mixed solutions were collected at room temperature and atmospheric pressure. A standard curve for detecting chlortetracycline was established based on the concentration of the chlortetracycline solution and the corresponding fluorescence enhancement degree of the mixed solution. Figure 3 As shown in Figure a, with the increase of chlortetracycline concentration in the system (0-5.00×10⁻⁶), -6 (mol / L), the fluorescence intensity at 414 nm gradually increases. Figure 3 b indicates that at 5.00 × 10 -8 Up to 5.00×10-6 Within the mol / L range, the fluorescence intensity (F / F0) of the system showed a good linear relationship with the concentration of chlortetracycline, and the standard curve was F / F0 = 4.8952C. CTC +8.5546(R 2 =0.9983), where F represents the fluorescence intensity of the solution after adding chlortetracycline, and F0 represents the fluorescence intensity of the solution without adding chlortetracycline. Based on LOD = 3σ / k, the detection limit (LOD) of chlortetracycline was calculated to be 1.97 nmol / L, where k is the slope of the standard curve relating F / F0 to chlortetracycline concentration, and σ is the standard deviation of the blank signal. As shown in Table 1, compared with other literature, the detection limit of the sensing system developed in this invention is lower. More importantly, as... Figure 3 c and Figure 3 As shown in Figure d, the sensor based on AgInS2 QDs exhibits rich color shifts (from orange to blue) and significant linearity.

[0058] Table 1 Comparison of the present invention with existing CTC residue detection methods

[0059]

[0060] The literature information corresponding to the five existing technologies in Table 1 is as follows:

[0061] [1]ZWLu,SR Chen,MT Chen,H.Ma,TKWang,T.Liu,JJYin,MMSun,C.Wu,GHSu,XXDai,XXWang,YYWang,HDYin,XGZhou,YZShen,HBRao,Trichromatic ratiometric fluorescent sensor based on machine learning and smartphone for visual and portable monitoring of tetracycline antibiotics,Chem.Eng.J.454(2023)140492.

[0062] [2] L. Yu, H. X. Chen, J. Yue, X. F. Chen, M. T. Sun, H. Tan, A. M. Asiri, K. A. Alamry, X. K. Wang, S. H. Wang, Metal-Organic Framework Enhances Aggregation-Induced Fluorescence of Chlortetracycline and the Application for Detection, Anal. Chem. 91 (9) (2019) 5913-5921.

[0063] [3] H. Moon, C. Lee, W. Lee, J. Kim, H. Cae, Stability of Quantum Dots, Quantum Dot Films, and Quantum Dot Light-Emitting Diodes for Display Applications, Adv. Mater. 31 (34) (2019) 1804294.

[0064] [4] L. Yu, H. X. Chen, J. Yue, X. F. Chen, M. T. Sun, H. Tan, A. M. Asiri, K. A. Alamry, X. K. Wang, S. H. Wang, Metal-Organic Framework Enhances Aggregation-Induced Fluorescence of Chlortetracycline and the Application for Detection, Anal. Chem. 91 (9) (2019) 5913-5921.

[0065] [5] C. C. Long, S. C. Liu, X. Li, J. Zhu, L. Zhang, T. P. Qing, P. Zhang, B. Feng, In-situ covalent bonding of carbon dots on two-dimensional tungsten disulfide interfaces for effective monitoring and remediation of chlortetracycline residue, Chem. Eng. J. 432 (2022) 134315.

[0066] (2) Investigation on the mechanism of AgInS2 QDs fluorescence sensor

[0067] Figure 4 a and Figure 4 Transmission electron microscopy results from sample b showed that the prepared AgInS2 QDs were well dispersed. However, after the addition of chlortetracycline, the AgInS2 QDs exhibited an aggregation tendency, with the average diameter increasing from 3.71 nm to 4.62 nm. This aggregation phenomenon may be due to the p-π conjugation between the chlorine atoms in chlortetracycline and the AgInS2 QDs. Meanwhile, as... Figure 4 As shown in Figure c, the Zeta potential of AgInS2 QDs was initially -45.65 mV, which decreased to -20.53 mV after the addition of chlortetracycline. The increase in the Zeta potential of AgInS2 QDs after the introduction of chlortetracycline is likely due to the neutralization of the negative charge associated with the carboxyl group of AgInS2 QDs by the -OH group of chlortetracycline. This change indicates that electrostatic interactions shorten the distance between AgInS2 QDs and chlortetracycline, thereby increasing the sensitivity of AgInS2 QDs to chlortetracycline. Secondly, as... Figure 2 As shown in Figure d, a comparison of the Fourier transform infrared spectra of AgInS2 QDs and AgInS2 QDs + chlortetracycline revealed that the 3400 cm⁻¹ of AgInS2 QDs... -1 1565cm -1 and 1385cm -1 The vibrational peak at the [specific location] did not shift significantly after the addition of chlortetracycline. This indicates that no new functional groups were generated during the binding of AgInS2 QDs with chlortetracycline. Subsequent UV and fluorescence spectra are shown below. Figure 4 In step d, the addition of chlortetracycline to the AgInS2 QDs solution caused a change in the absorption peak of the AgInS2 QDs, indicating a change in the system's conjugated structure. The band gap is the difference between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). For example... Figure 5 As shown in Figures b and d, the energy level differences between the LUMO and HOMO of AgInS2 QDs before and after the addition of chlortetracycline were determined by analyzing the UV-Vis diffuse reflectance spectra. Figure 5 As shown in Figures a and c, after the addition of chlortetracycline, the original fluorescence peak of AgInS2 QDs remained unchanged, while a new fluorescence peak appeared at 414 nm. The original band gap remained almost unchanged, but a new band gap of 4.53 eV was added, which may be due to band gap splitting, with a portion of the band gap undergoing transitions. Therefore, band gap transitions caused by surface defects in AgInS2 QDs play a modulating role in the fluorescence properties of quantum dots. Thus, we infer that the sensing mechanism for detecting chlortetracycline involves electrostatic interactions and band gap transitions.

[0068] Step (3): Quantitative detection of chlortetracycline by fluorescence visual sensing method

[0069] The fluorescence visual sensing technology based on AgInS2 QDs was further developed to explore its applicability in detecting chlortetracycline in complex samples. Specifically, 200 μL of AgInS2 QDs obtained in step (1) diluted 10 times and 200 μL of chlortetracycline solution with different concentrations were mixed in a 96-well plate to obtain a mixed solution interacting with chlortetracycline solutions with different concentrations (the concentration of chlortetracycline was 0, 0.1, 0.2, 1, 10, 50, 100, and 500 μmol / L). After 200 min of reaction, the fluorescence photos of the mixed solution were collected by a camera at room temperature and under normal pressure. As shown in FIG. 2a, with the gradual increase of the concentration of chlortetracycline (0-5000×10 Figure 6 -7 Under the ultraviolet lamp, the naked eye can observe that AgInS2 QDs show obvious fluorescence color changes from orange to pink, purple, and blue with the gradual increase of the concentration of chlortetracycline (0-5000×10 -7 mol / L) in the milk matrix (the above mixed solution was added to the pretreated milk solution).

[0070] The present application converts the fluorescence images corresponding to different concentrations of chlortetracycline (0-5000×10 -7 mol / L) into RGB (red, green, and blue) color values by using MATLAB software. Subsequently, the random forest (RF) algorithm is applied for quantitative analysis. The specific process is as follows:

[0071] Firstly, the collected fluorescence images are imported into PS, and 10 px×10 px pixel points (30 groups in parallel) are extracted for each concentration. Then, with the assistance of MATLAB software, each 10 px×10 px image is converted into a matrix with a size of 300 (RGB)×30 (parallel samples). Finally, we convert the images of 8 different concentrations into a matrix with a size of 300 (RGB)×240 (samples). In order to establish the best quantitative model, the RGB data of 240 samples with 8 different concentrations are randomly divided into 168 training samples and 72 prediction samples, and the Ntree and leaf number of the RF model are 150 and 7, respectively. The determination coefficient R 2 c and R 2 p are used to evaluate the correlation between the predicted concentration and the actual concentration in the training set and the prediction set, respectively. Generally speaking, R 2 c, R 2 p and R 2The closer the value is to 1, the better the model's predictive performance. RMSE (Root Mean Square Error) measures the deviation between predicted and true values ​​and is sensitive to outliers in the data. The RMSEC and RMSEP of the training and prediction sets are used to evaluate the accuracy of the quantitative model. Generally, the smaller the RMSEC and RMSEP, the better the model's accuracy. Table 2 lists the prediction results, showing that the R²c of the training set is 1.0000 and the RMSEC is 4.58. The R²p of the prediction set is 0.9999 and the RMSEP is 17.33.

[0072] Table 2. Results of chlortetracycline concentration determination based on RGB images and RF.

[0073]

[0074] like Figure 6 As shown in b, in the range of 0-5000×10 -7 Within the concentration range of mol / L, the predicted concentration of chlortetracycline showed a good linear relationship with the actual concentration, with minimal deviation. These results further demonstrate that combining RGB color values ​​with the RF algorithm model can predict the concentration of chlortetracycline in animal-derived foods, and the quantitative results are accurate and reliable.

[0075] Step (4): Visual inspection of chlortetracycline using a portable smartphone sensing platform

[0076] To broaden the application scope of this sensing strategy and make on-site detection simpler and more portable, we combined AgInS2 QDs with a self-made optical device based on a smartphone to develop an instrument-free intelligent fluorescence vision sensing platform. This optical device is equipped with a 365nm UV lamp with filtering capabilities and a sample container, serving as both a carrier and a darkroom. Figure 7 (a) It not only eliminates the influence of the distance between the sample and the excitation beam, but also effectively suppresses interference from the excitation beam and the environment, thereby improving the accuracy and feasibility of on-site monitoring. A cloud server data analysis system based on machine learning algorithms is integrated into a smartphone as a fluorescence color signal reader and substance content analyzer to extract features from fluorescence images and convert them to RGB, enabling quantitative analysis of chlortetracycline in complex matrices. Taking milk samples as an example... Figure 7As shown in FIG. 3B, 200 μL of AgInS2 QDs and 200 μL of different concentrations of chlortetracycline were mixed in a sample plate and allowed to react completely. Then, a smartphone was used to take a fluorescence photo, and the color of the fluorescence photo was accurately converted into the corresponding RGB value. Subsequently, the extracted RGB value was uploaded to the quantitative detection interface, which automatically provided the predicted value of the concentration of chlortetracycline according to the input RGB value. In order to verify the accuracy of the sensing platform, a series of chlortetracycline concentrations (1, 10, 100 and 500 μM) were selected, and three parallel fluorescence photos were taken for each concentration to facilitate the identification and determination of the app. The results are shown in Table 3, with a recovery range of 94.00% to 104.67% and a RMSEP range of 0.04 to 3.48. According to the LOD = 3.3σ, σ represents the standard deviation of the blank signal, the LOD of the portable fluorescence sensing platform for chlortetracycline is 0.69 x 10 -7 mol / L. These results show that the machine learning-assisted intelligent platform can extract color features and rapidly and accurately detect the concentration of the analyte in a simple and portable manner.

[0077] Table 3 Comparison of predicted values and measured values of the portable sensing platform in a milk matrix

[0078]

[0079]

[0080] In summary, the present application utilizes the electrostatic interaction and band gap transition of 3-mercaptopropionic acid-modified AgInS2 quantum dots in chlortetracycline to produce changes in fluorescence intensity and color, thereby achieving ultra-sensitive detection of chlortetracycline. A portable device equipped with a smartphone and integrated with an AgInS2 QDs sensor is also designed. A cloud server data analysis system based on machine learning algorithms is integrated into the smartphone to facilitate color data acquisition, correction, interpretation and display. This innovative method takes advantage of the modern smartphone's ability to capture and process fluorescence color signals, thereby providing a user-friendly on-site detection solution for chlortetracycline detection. Compared to other detection methods, the sensor can produce more color intervals, and when combined with a portable fluorescence visual sensing platform, it can achieve more sensitive on-site detection of chlortetracycline. The method of the present application has the characteristics of low cost, low detection limit, high portability and high selectivity.

[0081] The above-described embodiments only express the exact implementation of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the present patent. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present patent should be subject to the appended claims.

Claims

1. A method for rapid detection of chlortetracycline concentration, characterized by, The application relates to a method for detecting chlortetracycline by using 3-mercaptopropionic acid modified AgInS2 QDs. The method comprises the following steps: mixing a 3-mercaptopropionic acid modified AgInS2 QDs solution with a chlortetracycline solution to be detected, and obtaining the concentration of the chlortetracycline solution to be detected according to fluorescence intensity and / or color change; based on electrostatic interaction and band gap transition, the mixed solution is subjected to fluorescence enhancement and color change phenomena; with the increase of the concentration of chlortetracycline, the fluorescence intensity of the solution gradually increases, and the fluorescence color gradually changes from orange to pink, purple and blue. The fluorescence intensity is determined by establishing a standard relationship curve of the concentration of the chlortetracycline solution and the fluorescence enhancement degree; the color change is determined by model comparison or prediction based on machine learning or by realizing chlortetracycline concentration prediction through an intelligent terminal. The method for establishing the standard relationship curve comprises the following steps: diluting the 3-mercaptopropionic acid modified AgInS2 QDs solution to a preset concentration, then mixing the solution with chlortetracycline standard solutions with different concentrations, respectively, after complete reaction, establishing a standard relationship curve of the concentration of the chlortetracycline solution and the fluorescence enhancement degree according to the concentration of the chlortetracycline solution and the fluorescence intensity change degree of the mixed solution corresponding to the concentration; the reaction time of the chlortetracycline standard solution and the AgInS2 QDs solution is 200 min, the concentration range of chlortetracycline in the reaction system is 0.05-5 mu mol / L; the concentration of the chlortetracycline solution in the reaction system is 0.05, 0.15, 0.25, 0.35, 0.45, 1, 2, 3, 4 and 5 mu mol / L, respectively; and the detection limit is 0.002 mu M. The standard relationship curve is as follows: F / F0 = 4.8952 C CTC + 8.5546, R 2 = 0.9983 wherein F represents the fluorescence intensity of the solution after aureomycin is added, F0represents the fluorescence intensity of the solution when no aureomycin is added, C CTC represents the concentration of the aureomycin solution; The preparation method of the 3-mercaptopropionic acid modified AgInS2 QDs solution comprises the following steps: dissolving 3-mercaptopropionic acid in 25 mL ultrapure water to obtain a 0.2M 3-mercaptopropionic acid solution, then adding 0.083 mmol silver nitrate and 0.417 mmol indium ions, uniformly mixing, and adjusting the pH of the obtained solution to 8.5 by using 1 M sodium hydroxide; subsequently, 2 mL 0.35 mmol Na2S is quickly injected into the solution, the solution is stirred at room temperature for 15 min, and the solution is heated at 100 DEG C for 3 h to obtain an AgInS2 QDs solution.

2. The detection method according to claim 1, characterized in that, The morphology of the 3-mercaptopropionic acid modified AgInS2 QDs solution under a transmission electron microscope is a regular spherical shape, the particle size is 2.63-4.66 nm, and the strongest emission wavelength is 580 nm.

3. The method of claim 1, wherein The method for constructing the model based on machine learning comprises the following steps: The 3-mercaptopropionic acid modified AgInS2 QDs solution is diluted to a preset concentration, then mixed with chlortetracycline standard solutions with different concentrations to obtain a mixed solution, after complete reaction, the image information of the mixed solution is collected by using a camera shooting mode, the RGB value digital matrix corresponding to the photograph is obtained by using MATLAB software, and finally the RGB values of the pixel points in the mixed solution photograph are associated with the concentration of the chlortetracycline solution by using an RF model.

4. The method of claim 1, wherein Specifically, pixel point information in the photographed image is extracted by means of Photoshop software; then, the RGB value of each pixel point is extracted by means of MATLAB software, and a corresponding digital matrix is obtained; finally, the digital matrix is combined with the RF algorithm to construct an RF model capable of realizing the prediction of the concentration of aureomycin.

5. The detection method according to claim 4, characterized in that, The specific operation mode is as follows: in the RF model, an original training set is established according to the given RGB value and the corresponding concentration of aureomycin; samples are extracted from the original training set in a self-sampling manner, a plurality of different training subsets are established, each training subset is used to construct a decision tree model, when all the decision tree models are constructed, the RF algorithm averages or votes the prediction results of each decision tree to obtain the final regression prediction result, a linear model is established, and thus the prediction of the concentration of aureomycin is realized.

6. The detection method according to claim 5, characterized in that, The prediction of the concentration of aureomycin through the intelligent terminal comprises the following steps: Step 1: an RGB value conversion program and the RF model of claim 5 are built on the intelligent terminal; Step 2: the 3-mercaptopropionic acid modified AgInS2 QD solution is diluted to a preset concentration, then mixed with the aureomycin solution to be measured to obtain a mixed solution, after the reaction is completed, the image information of the mixed solution is collected by means of photographing of the intelligent terminal and converted into an RGB value, then the intelligent terminal predicts the concentration of the aureomycin solution to be measured based on the RF model of claim 5 according to the RGB value of the aureomycin solution to be measured; The intelligent terminal is a smart phone or a tablet computer.

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