Sulfur-containing compound detection method of three-color luminescent carbon quantum dot colorimetric and fluorescent dual-mode sensing array

Abstract

The invention discloses a sulfur-containing compound detection method of a three-color luminescent carbon quantum dot colorimetric and fluorescent dual-mode sensing array. Belongs to the technical field of sulfur-containing compound analysis and detection. A three-color CDs solution with high fluorescence quantum yield and tunable emission wavelength is prepared by a hydrothermal method. The method comprises the following steps: respectively mixing single sulfur-containing compounds with different concentrations and multi-element sulfur-containing compounds with different concentration ratios with a three-color CDs solution, carrying out colorimetric and fluorescence detection, normalizing the relative absorbance and the relative fluorescence intensity, training colorimetric and fluorescence data sets by using a Fisher discrimination method, and determining the concentration of the sulfur-containing compounds in the three-color CDs solution. Constructing a single sulfur-containing compound and multi-element sulfur-containing compound three-channel fluorescence and colorimetric dual-mode sensing array; a standard curve between the concentration of the single sulfur-containing compound and the main factor is established through corresponding analysis in multivariate statistical analysis, so that quantitative detection of the single sulfur-containing compound is realized; through a Fisher discriminant method or hierarchical clustering analysis (HCA), identification of multiple sulfur-containing compounds in an environmental water body sample and red wine is successfully realized.

Description

Technical Field

[0001] The present invention relates to the technical field of analysis and detection of sulfur-containing compounds, and particularly relates to a photoelectrochemical detection method for sulfur-containing compounds. Background Art

[0002] Sulfur-containing metal salts (SCMs) are widely used in the food processing and production processes. Common SCMs include Na2S, Na2SO3, Na2S2O3, and Na2S2O8, etc. Although SCMs play an important role in the food industry, excessive intake of SCMs will cause harm to the body and even lead to various diseases. For example, Na2S is a common food additive, which acts as a preservative and bleaching agent in the food processing process and is often added in the processing of pickled foods. However, excessive consumption of sodium sulfide can cause allergic reactions, such as skin itching, shortness of breath, and headache, etc., and even produce carcinogenic substances in the human body. Therefore, it is of great significance to detect sulfur-containing compounds in SCMs.

[0003] At present, the detection methods for sulfur-containing compounds mainly include enzyme-linked immunosorbent assay, high-performance liquid chromatography, capillary electrophoresis, electrochemiluminescence method, etc. These methods have high sensitivity and accuracy, but at the same time, there are limitations such as expensive laboratory instruments, high detection costs, complex sample pretreatment, and long signal processing time.

[0004] Chinese Patent (CN202010548829.0) provides a visual and photoelectrochemical detection method for sulfur ion concentration, which realizes the detection of sulfur ion concentration through a sensing material coated with an active film. However, this method can only detect a single type of S 2- ion, and the detection sensor cannot provide multi-dimensional information. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a detection method for sulfur-containing compounds with a colorimetric and fluorescence dual-mode sensing array of three-color luminescent carbon quantum dots, which can provide multi-dimensional detection information and realize the quantitative analysis of various single sulfur-containing compounds and the identification of multi-component sulfur-containing compounds.

[0006] The present invention adopts the following technical solutions: A detection method for sulfur-containing compounds with a colorimetric and fluorescence dual-mode sensing array of three-color luminescent carbon quantum dots, comprising the following steps: Step (1): Respectively perform colorimetric detection and fluorescence detection on the R-CDs, G-CDs, and B-CDs solutions to obtain the original absorbance (A0) and the original fluorescence intensity (I0); Step (2): Mix the R-CDs, G-CDs, and B-CDs solutions with single sulfur compound samples of different concentrations respectively, and mix the R-CDs, G-CDs, and B-CDs solutions with multi-sulfur compound samples of different concentration ratios respectively, and perform colorimetric and fluorescence detections respectively to obtain the measured absorbance (A) and the measured fluorescence intensity (I); Step (3): By calculating the normalized relative absorbance (A / A0) and the relative fluorescence intensity (I / I0), form a data matrix of different single sulfur compounds and a data matrix of multi-sulfur compounds corresponding to different sulfur compound sets respectively; Step (4): Construct a single sulfur compound discrimination model. The single sulfur compound discrimination model determines whether the sample contains only a single sulfur compound based on the relative absorbance and relative fluorescence intensity of the single sulfur compound sample; train the single sulfur compound discrimination model based on the data matrix of different single sulfur compounds in step (3); the single sulfur compound discrimination model is the first Fisher discrimination model; Step (5): Based on the relative absorbance and relative fluorescence intensity obtained from different single sulfur compound samples, construct a data matrix of different single sulfur compounds; perform correspondence analysis in multivariate statistical analysis on the data matrix of different single sulfur compounds respectively to obtain the standard curve of each single sulfur compound; construct a composition recognition model for different sulfur compound sets respectively. The composition recognition model identifies the composition of the sample based on the relative absorbance and relative fluorescence intensity of the sample containing multi-sulfur compounds; train the composition recognition model using the corresponding data matrix of multi-sulfur compounds; Step (6): Identify the sample to be tested.

[0007] Preferably, in step (3), the rows in the data matrix of single sulfur compounds correspond to single sulfur compound samples with different concentration ratios, and the columns correspond to the relative absorbance and relative fluorescence intensity after the sample is mixed with the R-CDs, G-CDs, and B-CDs solutions respectively.

[0008] Preferably, the sulfur compound set in step (3) refers to a set composed of two or more different single sulfur compounds. For a certain sulfur compound set, the rows in the corresponding data matrix of multi-sulfur compounds correspond to multi-sulfur compound samples with different concentration ratios that match the set, and the columns correspond to the relative absorbance and relative fluorescence intensity after the sample is mixed with the R-CDs, G-CDs, and B-CDs solutions respectively; the sulfur compound set is used to represent the "selection" of two or more single sulfides, but does not limit the concentration ratio during mixing.

[0009] Preferably, step (5) is specifically as follows: Step (5-1): Calculate the relative frequency of each element in the data matrix of a single sulfur-containing compound in the entire matrix to obtain a normalized probability matrix; Step (5-2): Based on the normalized probability matrix, calculate the marginal distributions of rows and columns, and then construct a transition matrix to describe the conditional probability relationship between row and column categories; Step (5-3): Using the transition matrix, perform R-type factor analysis for the association between variables and Q-type factor analysis for the association between samples respectively to extract the main factors, where the main factors are used to explain the common variance between observed variables and reveal the hidden association patterns between variables; the factor refers to a linear combination of observed variables, and the observed variables include relative absorbance (A / A0) and relative fluorescence intensity (I / I0); Step (5-4): According to the results of R-type and Q-type factor analyses, draw a correspondence analysis graph in the same coordinate system to show the positional relationship between sample points and variable points in the main factor space; Step (5-5): Analyze the distribution positions of single sulfur-containing compound samples with different concentrations in the correspondence analysis graph to determine their quantitative relationship with the main factor coordinates; Step (5-6): Based on the obtained quantitative relationship, establish a linear regression equation between the concentration of the single sulfur-containing compound and the main factor score to form a standard curve of the single sulfur-containing compound.

[0010] Preferably, the composition recognition model in step (5) is a second Fisher discriminant model or a hierarchical clustering analysis model; the method for training the hierarchical clustering analysis model is as follows: According to the similarity between samples in the data matrix of multi-component sulfur-containing compounds, gradually merge similar samples to form a hierarchical structure, and obtain a dendrogram for showing the clustering relationship between samples; obtain the clustering result according to the height or distance threshold of the dendrogram and calculate the center of each cluster.

[0011] Preferably, the recognition method in step (6) is as follows: First, mix it with R-CDs, G-CDs, and B-CDs solutions respectively to obtain the corresponding relative absorbance and relative fluorescence intensity, and then input the obtained relative absorbance and relative fluorescence intensity into the single sulfur-containing compound discriminant model obtained in step (4) to determine whether the sample to be tested contains only a single sulfur-containing compound; if it contains only a single sulfur-containing compound, perform quantitative analysis on the single sulfur-containing compound according to the standard curve of the single sulfur-containing compound obtained in step (5); if it does not contain only a single sulfur-containing compound, respectively identify the sample according to the multiple composition recognition models obtained in step (5), and take the result of the composition recognition model whose result does not indicate an anomaly as the recognition result.

[0012] Further preferably, when using the hierarchical clustering analysis model as the composition recognition model for recognition: according to the relative absorbance and relative fluorescence intensity of the sulfur-containing compound sample to be measured, calculate the distances between the sample to be measured and each existing clustering center in the dendrogram, and the composition corresponding to the clustering with the closest distance is the composition recognition result.

[0013] Preferably, for the fluorescence detection, the emission wavelengths of R-CDs, G-CDs, and B-CDs are 645 nm, 533 nm, and 482 nm respectively; for the colorimetric detection, the optimal absorption wavelengths of the three-color CDs in the ultraviolet-visible absorption spectrum are all 200 nm.

[0014] Preferably, the preparation method of the R-CDs is to mix N,N-dimethylformamide, citric acid, and ammonium hydroxide, stir to form a uniform suspension, transfer it to a high-pressure reaction kettle, and place the high-pressure reaction kettle in an oven at 170 °C to 190 °C for constant temperature for 7 to 9 h. After cooling, centrifugation, and microfiltration through a microporous membrane, the R-CDs stock solution is obtained, and after dilution with ethanol, the R-CDs solution is obtained; the preparation method of the G-CDs is to mix ethanol and o-phenylenediamine, stir to form a uniform suspension, transfer it to a high-pressure reaction kettle, and place the high-pressure reaction kettle in an oven at 190 °C to 210 °C for constant temperature for 9 to 11 h. After cooling, centrifugation, and microfiltration through a microporous membrane, the G-CDs stock solution is obtained, and after dilution with ethanol, the G-CDs solution is obtained; the preparation method of the B-CDs is to mix ethylenediamine (C2H8N2), CA, and H2O, stir to form a uniform suspension, transfer it to a high-pressure reaction kettle, and place the high-pressure reaction kettle in an oven at 170 °C to 190 °C for constant temperature for 4 to 6 h. After cooling, centrifugation, and microfiltration through a microporous membrane, the B-CDs stock solution is obtained, and after dilution with ethanol, the B-CDs solution is obtained.

[0015] Preferably, the single sulfur-containing compound includes: Na2S, Na2SO3, Na2SO4, Na2S2O3, and Na2S2O8; the multi-component sulfur-containing compound refers to a mixture of any two or more of the single sulfur-containing compounds.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses the method in multivariate statistical analysis to conduct exploratory analysis on data, constructs a three-channel colorimetric and fluorescence dual-mode sensing array, and realizes the recognition and quantitative detection of sulfur-containing compounds. Specifically as follows: First, three-color (red, green, blue) CDs (R-CDs, G-CDs, and B-CDs) with high fluorescence quantum yield (YQ) and tunable emission wavelengths are prepared by a hydrothermal method.

[0017] Second, different sulfur compounds are mixed with the three-color CDs to generate strong, stable, and different fluorescence and colorimetric signals, enabling different sulfur compounds to generate unique fingerprint data. A data matrix is constructed and trained, and the Fisher discriminant method is used to determine the attribution and recognition of new samples.

[0018] Third, through correspondence analysis, a standard curve between the concentration of a single sulfur compound and the main factor is established to achieve quantitative detection of a single sulfur compound; the Fisher discriminant method or the dendrogram of hierarchical cluster analysis is used to identify multiple sulfur compounds; through the Fisher discriminant method or hierarchical cluster analysis (HCA), the identification of multiple sulfur compounds in environmental water samples (lake water) and red wine is successfully achieved.

[0019] Fourth, the intersection of multivariate statistical analysis and chemistry solves the problem of difficult handling of multivariate data by traditional methods, explores the internal laws and potential connections of substances, and provides new ideas for related research. Description of the Drawings

[0020] Figure 1 This is the ultraviolet-visible absorption spectrum of the three-color CDs provided in Example 4 of the present invention. Figure 1 In it, A, B, and C correspond to R-CDs, G-CDs, and B-CDs respectively.

[0021] Figure 2 This is the excitation spectrum and emission spectrum of the three-color CDs provided in Example 4 of the present invention. Figure 2 In it, A, B, and C correspond to R-CDs, G-CDs, and B-CDs respectively.

[0022] Figure 3 This is the fingerprint map of the sensor array response of five single sulfur compounds at a concentration of 10 μM provided in Example 5 of the present invention. Among them, Figure 3 A is the fingerprint map of the relative absorbance (A / A0) of five single sulfur compounds at a concentration of 10 μM; Figure 3 B is the fingerprint map of the relative fluorescence intensity (I / I0) of five single sulfur compounds at a concentration of 10 μM.

[0023] Figure 4 This is the correspondence analysis diagram of five single sulfur compounds at a concentration of 10 μM provided in Example 5 of the present invention.

[0024] Figure 5 This is the standard curve diagram for quantitative analysis of five single sulfur compounds provided in Example 6 of the present invention.

[0025] Figure 6 This is the correspondence analysis diagram of binary mixed sulfur compounds with different concentration ratios in red wine provided in Example 7 of the present invention.

[0026] Figure 7 The dendrogram of binary mixed sulfur-containing compounds with different concentration ratios in red wine provided in Embodiment 7 of the present invention.

[0027] Figure 8 The correspondence analysis diagram of pentary mixed sulfur-containing compounds with different concentration ratios in lake water provided in Embodiment 8 of the present invention.

[0028] Figure 9 The dendrogram of pentary mixed sulfur-containing compounds with different concentration ratios in lake water provided in Embodiment 8 of the present invention. Detailed implementation manners

[0029] The following further illustrates the detailed implementation manners of the present invention in conjunction with examples. It should be noted that the detailed implementation manners described herein are only for explaining and interpreting the present invention, and are not used to limit the protection scope of the present invention.

[0030] The detection steps of the present invention are as follows: Step (1): Respectively perform colorimetric detection and fluorescence detection on the R-CDs, G-CDs, and B-CDs solutions to obtain the original absorbance (A0) and the original fluorescence intensity (I0).

[0031] Step (2): Respectively mix the R-CDs, G-CDs, and B-CDs solutions with single sulfur-containing compound samples at different concentrations, and respectively mix the R-CDs, G-CDs, and B-CDs solutions with multi-component sulfur-containing compound samples at different concentration ratios, and perform colorimetric and fluorescence detections respectively to obtain the measured absorbance (A) and the measured fluorescence intensity (I).

[0032] Step (3): By calculating the normalized relative absorbance (A / A0) and the relative fluorescence intensity (I / I0), form a data matrix of different single sulfur-containing compounds and a data matrix of multi-component sulfur-containing compounds corresponding to different sulfide sets respectively; the rows in the data matrix of single sulfur-containing compounds correspond to single sulfur-containing compound samples at different concentration ratios, and the columns correspond to the relative absorbance and relative fluorescence intensity after the samples are respectively mixed with the three solutions of R-CDs, G-CDs, and B-CDs; the sulfide set refers to a set composed of two or more different single sulfur-containing compounds. For a certain sulfide set, the rows in the corresponding data matrix of multi-component sulfur-containing compounds correspond to multi-component sulfur-containing compound samples at different concentration ratios matching the set, and the columns correspond to the relative absorbance and relative fluorescence intensity after the samples are respectively mixed with the three solutions of R-CDs, G-CDs, and B-CDs.

[0033] The sulfide set is used to represent the "selection" of two or more single sulfides, but the concentration ratio during mixing is not limited. For example, a certain sulfide set contains three single sulfur-containing compounds: Na2S, Na2SO3, and Na2SO4. Then, mixture samples composed of Na2S, Na2SO3, and Na2SO4 at different concentration ratios all match this set.

[0034] Step (4): Construct a discriminant model for single sulfur-containing compounds. The discriminant model for single sulfur-containing compounds determines whether a sample contains only a single sulfur-containing compound based on the relative absorbance and relative fluorescence intensity of the single sulfur-containing compound sample. The discriminant model for single sulfur-containing compounds is trained based on the data matrix of single sulfur-containing compounds in step (3).

[0035] The discriminant model for single sulfur-containing compounds is the first Fisher discriminant model.

[0036] Step (5): Perform correspondence analysis in multivariate statistical analysis on single sulfur-containing compound samples respectively: Construct a data matrix of single sulfur-containing compounds based on the relative absorbance and relative fluorescence intensity obtained from single sulfur-containing compound samples. Perform correspondence analysis on the data matrices of different single sulfur-containing compounds respectively to obtain a standard curve for single sulfur-containing compounds.

[0037] The specific steps of this step are as follows: Step (5-1): Calculate the relative frequency of each element in the data matrix of single sulfur-containing compounds in the entire matrix to obtain a normalized probability matrix; Step (5-2): Based on the normalized probability matrix, calculate the marginal distributions of rows and columns, and then construct a transition matrix to describe the conditional probability relationship between row and column categories; Step (5-3): Use the transition matrix to perform R-type factor analysis for the association between variables and Q-type factor analysis for the association between samples respectively, and extract the main factors. The main factors are used to explain the common variance between observed variables and reveal the hidden association patterns between variables; The factor refers to a linear combination of observed variables, and the observed variables include relative absorbance (A / A0) and relative fluorescence intensity (I / I0); Step (5-4): According to the results of R-type and Q-type factor analysis, draw a correspondence analysis graph in the same coordinate system to show the positional relationship between sample points and variable points in the main factor space; Step (5-5): Analyze the distribution positions of single sulfur-containing compound samples with different concentrations in the correspondence analysis graph to determine their quantitative relationship with the main factor coordinates; Step (5-6): Based on the obtained quantitative relationship, establish a linear regression equation between the concentration of single sulfur-containing compounds and the main factor scores to form a standard curve for single sulfur-containing compounds.

[0038] Step (6): Build a composition recognition model for different sulfide sets respectively. The composition recognition model identifies the composition of a sample based on the relative absorbance and relative fluorescence intensity of a sample containing a polyvalent sulfur compound. The composition recognition model is trained using the data matrix of the corresponding polyvalent sulfur compound.

[0039] The composition recognition model is the second Fisher discriminant model or a hierarchical clustering analysis model.

[0040] The process of training the hierarchical clustering analysis model is as follows: According to the similarity (such as Euclidean distance) between samples in the data matrix of polyvalent sulfur compounds, gradually merge similar samples to form a hierarchical structure, and obtain a dendrogram for displaying the clustering relationship between samples. Obtain the clustering result according to the height or distance threshold of the dendrogram and calculate the center of each cluster.

[0041] Step (7): For the sample to be tested: First, mix it with R-CDs, G-CDs, and B-CDs solutions respectively to obtain the corresponding relative absorbance and relative fluorescence intensity, and then input the obtained relative absorbance and relative fluorescence intensity into the single sulfur compound discriminant model obtained in step (4) to determine whether the sample to be tested contains only a single sulfur compound. If it contains only a single sulfur compound, quantitative analysis of the single sulfur compound is performed according to the single sulfur compound standard curve obtained in step (5); if it does not contain only a single sulfur compound, the sample is identified using the multiple composition recognition models obtained in step (6), and the result of the composition recognition model whose result does not indicate an abnormality is used as the recognition result.

[0042] When using the hierarchical clustering analysis model as the composition recognition model for identification: According to the relative absorbance and relative fluorescence intensity of the polyvalent sulfur compound sample to be tested, calculate the distance between the sample to be tested and each existing cluster center in the dendrogram, and the composition corresponding to the cluster with the closest distance is the composition recognition result.

[0043] To further illustrate the technical measures and effects of the present invention, the following embodiments are provided. Among them, Embodiment 1 to Embodiment 3 are preparation examples of R-CDs solution, G-CDs solution and B-CDs solution respectively, which are used to illustrate the preparation method of the three-color CDs solution. Embodiment 4 is a detection example of the maximum absorption wavelength and the best emission wavelength of the R-CDs solution, G-CDs solution and B-CDs solution prepared in Embodiment 1 to Embodiment 3, which is used to screen the maximum absorption wavelength and the best emission wavelength of the three-color CDs solution. Embodiment 5 is an embodiment of the construction and feasibility analysis of a colorimetric and fluorescence dual-mode sensing array based on nitrogen-doped three-color luminescent carbon quantum dots of the present invention, which is used to illustrate the specific construction of the colorimetric and fluorescence dual-mode sensing array and the feasibility analysis of the method. Embodiment 6 is a quantitative analysis embodiment of five single sulfur-containing compounds using the method of the present invention, which is used to illustrate the quantitative detection method of single sulfur-containing compounds. Embodiment 7 is an embodiment of the identification of multi-component sulfur-containing compounds and the analysis of actual samples using the method of the present invention, which is used to illustrate the identification of multi-component sulfur-containing compounds and the analysis of new samples. Embodiment 8 is a specific embodiment of the simultaneous detection of two sulfur-containing compounds in red wine using the method of the present invention, which is used to prove that the method of the present invention is applicable to the accurate identification of multi-component sulfur-containing compounds in red wine. Embodiment 9 is a specific embodiment of the simultaneous detection of five sulfur-containing compounds in environmental water using the method of the present invention, which is used to prove that the method of the present invention is applicable to the accurate identification of multi-component sulfur-containing compounds in environmental water.

[0044] Embodiment 1. Preparation example of R-CDs based on nitrogen-doped three-color luminescent carbon quantum dots.

[0045] Mix 10 mL of N,N-dimethylformamide (DMF), 0.96 g of citric acid (CA) and 1.5 mL of NH3·H2O, stir to form a homogeneous suspension, transfer the suspension to a stainless steel autoclave lined with polytetrafluoroethylene, and place it in a precision oven at 180 °C for 8 h. After cooling, centrifuge 3 times, and filter the dispersion through a 0.22 µm microporous membrane to obtain the stock solution of R-CDs. The stock solution of R-CDs is diluted 100 times with ethanol to obtain the R-CDs solution.

[0046] Embodiment 2. Preparation example of G-CDs based on nitrogen-doped three-color luminescent carbon quantum dots.

[0047] Mix 25 mL of C2H5OH and 0.25 g of o-phenylenediamine (OPD), stir to form a homogeneous suspension, transfer the suspension to a stainless steel autoclave lined with polytetrafluoroethylene, and place it in a precision oven at 200 °C for 10 h. After cooling, centrifuge 3 times, and filter the dispersion through a 0.22 µm microporous membrane to obtain the stock solution of G-CDs. The stock solution of G-CDs is diluted 100 times with ethanol to obtain the G-CDs solution.

[0048] Example 3: Preparation example of B-CDs based on nitrogen-doped three-color luminescent carbon quantum dots.

[0049] Mix 1.2 mL of C2H8N2, 3.5 g of CA, and 33 mL of H2O, stir to form a homogeneous suspension, transfer the suspension to a stainless-steel autoclave lined with polytetrafluoroethylene, and place it in a precision oven at 180 °C for 5 h. After cooling, centrifuge 3 times, and filter the dispersion through a 0.22 µm microporous membrane to obtain the stock solution of B-CDs. Dilute the stock solution of B-CDs 100 times with ethanol to obtain the B-CDs solution.

[0050] Example 4: Detection example of the maximum absorption wavelength and the best emission wavelength for colorimetric and fluorescence dual-mode detection based on nitrogen-doped three-color luminescent carbon quantum dots.

[0051] Measure the absorbance of the three CDs solutions respectively using a UV-visible absorption spectrometer (UV-vis); measure the fluorescence intensity using a fluorescence spectrophotometer. The dispersions of R-CDs, G-CDs, and B-CDs exhibit bright red, green, and blue fluorescence respectively. Figure 1 is the UV-visible absorption spectrogram of R-CDs, G-CDs, and B-CDs. It can be seen that obvious absorption peaks appear at 200 nm for the three-color luminescent quantum dots, corresponding to the sp 2 π-π electron transition of the C=C bond in the carbon cluster. Therefore, the optimal absorption wavelength of the UV-visible absorption spectrum of the three-color CDs is 200 nm; through * It can be seen that the optimal emission wavelengths for fluorescence detection of R-CDs, G-CDs, and B-CDs are 645 nm, 533 nm, and 482 nm respectively. Figure 2

[0052] Example 5: Example of construction and feasibility analysis of a colorimetric and fluorescence dual-mode sensing array based on nitrogen-doped three-color luminescent carbon quantum dots.

[0053] R-CDs, G-CDs, and B-CDs contain a large number of different functional groups and exhibit different binding abilities with SCMs. Select R-CDs, G-CDs, and B-CDs as the signal reporting units, and use the fluorescence intensities at the maximum emission peaks of R-CDs, λmax = 645 nm; G-CDs, λmax = 533 nm; B-CDs, λmax = 482 nm as the signal output; and use the absorbance at the maximum absorption wavelength λmax = 200 nm as the signal output.

[0054] ​Taking the colorimetric and fluorescence dual-mode sensing array of five single sulfur-containing compounds (5 SCMs) with a concentration of 10 μM as an example. The R-CDs, G-CDs, and B-CDs solutions were respectively subjected to colorimetric and fluorescence detection to obtain the original absorbance (A0) and the original fluorescence intensity (I0); the R-CDs, G-CDs, and B-CDs solutions were respectively mixed with five single sulfur-containing compounds with a concentration of 10 μM, and colorimetric and fluorescence detection were carried out in parallel 5 times (expanding the sample size) to obtain the measured absorbance (A) and the measured fluorescence intensity (I); by normalizing the relative absorbance (A / A0) and the relative fluorescence intensity (I / I0), the unique optical fingerprint of each analyte was obtained. For example: Figure 3 A is the fingerprint of the relative absorbance (A / A0) of five single sulfur-containing compounds at a concentration of 10 μM; Figure 3 B is the fingerprint of the relative fluorescence intensity (I / I0) of five single sulfur-containing compounds at a concentration of 10 μM. A data matrix (A / A0, I / I0 data set) containing 75 (3 kinds of CDs × 5 kinds of SCMs × 5 repetitions) data points was generated.

[0055] Method feasibility analysis: Correspondence analysis was performed on the data matrix, and a correspondence analysis graph was made. Whether the tight clusters formed by the five single sulfur-containing compounds in the graph overlap was used to judge whether the method could distinguish the five single sulfur-containing compounds. Figure 4 It is the correspondence analysis graph of five single sulfur-containing compounds at a concentration of 10 μM. Figure 4 It can be seen that the contributions of factor 1 and factor 2 to the total variance are 62.1% and 33.3% respectively. In the determination of the number of factors, the number of factors with a cumulative variance contribution rate > 80% is taken. The cumulative variance contribution rate of the first two factors has reached 95.4% (62.1% + 33.3%). The first two factors can already represent the vast majority of information. The first two factors are taken as the main factors, and the correspondence analysis graph is drawn with the factor loadings of the first two factors. Figure 4 It shows that five sulfur-containing compounds form 5 tight clusters, with large distances and no overlap between them. The results of 5 repeated experiments are close, indicating that the method has excellent recognition ability for sulfur-containing compounds.

[0056] Example 6. Quantitative analysis example of five single sulfur-containing compounds based on a colorimetric and fluorescence dual-mode sensing array of nitrogen-doped three-color luminescent carbon quantum dots.

[0057] The R-CDs, G-CDs, and B-CDs solutions were respectively mixed with five single sulfur-containing compounds Na2S, Na2SO3, Na2SO4, Na2S2O3, and Na2S2O8 at different concentrations (20 μM, 40 μM, 60 μM, 80 μM, 100 μM). Colorimetric and fluorescence detections were performed in parallel 5 times to obtain the measured absorbance (A), the measured fluorescence intensity (I), the normalized relative absorbance (A / A0), and the relative fluorescence intensity (I / I0), generating a unique optical fingerprint for each analyte. The fingerprint data of the five single sulfur-containing compounds Na2S, Na2SO3, Na2SO4, Na2S2O3, and Na2S2O8 ultimately generated a data matrix containing 75 (3 kinds of CDs × 5 different concentrations × 5 replicates) data points (A / A0, I / I0 data set, corresponding to the data matrix of the corresponding single sulfur-containing compound). Corresponding analysis was performed on the data matrices of the five single sulfur-containing compounds. The results showed that for each single sulfur-containing compound, the cumulative variance contribution rates of factor 1 and factor 2 were both >80%, and the number of main factors could be determined to be 2. The corresponding analysis diagrams generated by the five single sulfur-containing compounds respectively had well-separated tight clusters. When factor 2 did not exceed 40%, the concentrations of the five single sulfur-containing compounds all had a good linear relationship with factor 1 in the corresponding analysis diagram. Figure 5 This is the standard curve for the quantitative analysis of five single sulfur-containing compounds and can be used for the quantitative detection of single sulfur-containing compounds.

[0058] Example 7: Multicomponent sulfur-containing compound recognition and actual sample analysis based on a colorimetric and fluorescence dual-mode sensing array of nitrogen-doped three-color luminescent carbon quantum dots.

[0059] Mix the R-CDs, G-CDs, and B-CDs solutions with polyvalent sulfur-containing compounds (Na2SO3 / Na2SO4 = 0:10, 1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, 9:1, 10:0; Na2SO3 / Na2SO4 / Na2S2O3 = 1:2:7, 1:3:6, 2:2:6, 2:3:5, 3:3:4; Na2SO3 / Na2SO4 / Na2S2O3 / Na2S2O8 = 2:1:1:1, 1:2:1:1, 1:1:2:1, 1:1:1:2; Na2SO3 / Na2SO4 / Na2S2O3 / Na2S2O8 / Na2S = 1:2:3:4:5, 2:3:4:5:1, 3:4:5:2:1, 4:5:3:2:1, 5:4:3:2:1) respectively. Perform colorimetric and fluorescence detections in parallel 5 times to obtain the measured absorbance (A) and the measured fluorescence intensity (I), and normalize the relative absorbance (A / A0) and the relative fluorescence intensity (I / I0) to generate a unique optical fingerprint for each analyte and obtain a data matrix of polyvalent sulfur-containing compounds.

[0060] When performing the analysis of actual samples, train the first Fisher discriminant model through the data matrix combinations of different single sulfur-containing compounds obtained in Example 6, so as to determine whether the new sample contains only a single sulfur-containing compound based on the relative absorbance (A / A0) and the relative fluorescence intensity (I / I0) of the three-color CDs obtained from the actual samples.

[0061] If it is determined that the actual sample contains only a single sulfur-containing compound, quantitative analysis of the single sulfur-containing compound can be achieved through the standard curve of the single sulfur-containing compound established in Example 6. Otherwise, if it is considered that the actual sample contains polyvalent sulfur-containing compounds, the composition identification of the polyvalent sulfur-containing compounds is completed using the second Fisher discriminant model.

[0062] The composition identification of polyvalent sulfur-containing compounds can also be carried out through hierarchical clustering analysis. Hierarchical clustering analysis constructs a dendrogram based on the similarity or distance metric between data objects. After obtaining the dendrogram through hierarchical clustering analysis, according to the new sample data, the distance between the new sample and each existing cluster center can be calculated, and then the new sample is assigned to the cluster closest to it to achieve the classification and identification of the new sample. Through the data matrix of polyvalent sulfur-containing compounds obtained in Example 7, use hierarchical clustering analysis to construct a dendrogram of polyvalent sulfur-containing compounds. Based on the relative absorbance (A / A0) and the relative fluorescence intensity (I / I0) of the three-color CDs obtained from the actual samples of polyvalent sulfur-containing compounds, the distance between the actual samples of polyvalent sulfur-containing compounds and each existing cluster center in the dendrogram can be calculated, and then it is assigned to the cluster closest to it to achieve the composition identification of polyvalent sulfur-containing compounds.

[0063] Example VIII. Simultaneous detection of two sulfur-containing compounds in red wine based on a colorimetric and fluorescence dual-mode sensing array of nitrogen-doped three-color luminescent carbon quantum dots

[0064] To test the potential application of the sensing array in actual samples, red wine was selected as the test sample. Before the formal measurement, the red wine sample was centrifuged for 5 minutes and then filtered through a 0.22 µm microporous membrane. Two single sulfur-containing compounds were added to the treated red wine sample according to Na2SO3 / Na2SO4 = 0:10, 1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, 9:1, 10:0. The R-CDs, G-CDs, and B-CDs solutions were respectively mixed with binary mixed sulfur-containing compounds with different concentration ratios, and colorimetric and fluorescence detections were performed in parallel 5 times to obtain the measured absorbance (A) and the measured fluorescence intensity (I), the normalized relative absorbance (A / A0), and the relative fluorescence intensity (I / I0), generating a unique optical fingerprint for each analyte. A data matrix (A / A0, I / I0 data set) containing 165 (3 kinds of CDs × 11 different concentration ratios × 5 replicates) data points of binary mixed sulfur-containing compounds with different concentration ratios was generated. Correspondence analysis was performed on the data matrix, as Figure 6 shown, binary mixed sulfur-containing compounds with different concentration ratios formed 11 tight clusters in the red wine sample, with large distances between each other and no overlap, and the results of 5 repeated experiments were close, indicating that the sensing array showed excellent recognition and discrimination ability for the two sulfur-containing compounds in red wine. The data matrix (A / A0, I / I0 data set) containing 165 data points was trained using the Fisher discriminant method in discriminant analysis, and the simultaneous detection of the two sulfur-containing compounds in red wine was achieved through the Fisher discriminant method. In a further blind sample experiment, the unknown samples were correctly identified with an accuracy of 100%. A dendrogram of the data matrix (A / A0, I / I0 data set) was constructed by hierarchical cluster analysis (HCA), Figure 7 which is the dendrogram of binary mixed sulfur-containing compounds with different concentration ratios in red wine. It can be seen from the HCA that the parallel test samples are clustered together, but the 5 groups of samples are separated from each other and there is no crossover, indicating that HCA can correctly classify the two sulfur-containing compounds in red wine. Through the established dendrogram of binary mixed sulfur-containing compounds with different concentration ratios in red wine, the simultaneous detection of the two sulfur-containing compounds in red wine was achieved, and the unknown samples were correctly identified in the blind sample experiment with an accuracy of 100%. Therefore, accurate identification of the two sulfur-containing compounds in red wine can be achieved through the Fisher discriminant method or hierarchical cluster analysis (HCA).

[0065] Example IX. Simultaneous detection of five sulfur-containing compounds in environmental water bodies (lake water) based on a colorimetric and fluorescence dual-mode sensing array of nitrogen-doped three-color luminescent carbon quantum dots

[0066] To test the potential application of the sensing array in actual samples, environmental water body (lake water) was selected as the test sample. Before the formal measurement, the lake water sample was centrifuged for 5 minutes and then filtered through a 0.22 µm microporous membrane. Five single sulfur-containing compounds were added to the treated lake water sample according to Na2SO3 / Na2SO4 / Na2S2O3 / Na2S2O8 / Na2S = 1:2:3:4:5, 2:3:4:5:1, 3:4:5:2:1, 4:5:3:2:1, 5:4:3:2:1. The R-CDs, G-CDs, and B-CDs solutions were respectively mixed with the five-element mixed sulfur-containing compounds with different concentration ratios, and colorimetric and fluorescence detections were carried out in parallel 5 times to obtain the measured absorbance (A) and the measured fluorescence intensity (I), and the normalized relative absorbance (A / A0) and relative fluorescence intensity (I / I0) were calculated to generate a unique optical fingerprint for each analyte, resulting in a data matrix (A / A0, I / I0 data set) containing 75 data points (3 kinds of CDs × 5 different concentration ratios × 5 replicates) of five-element mixed sulfur-containing compounds. Corresponding analysis was performed on the data matrix, as Figure 8 shown, the five-element mixed sulfur-containing compounds with different concentration ratios formed 5 tight clusters in the lake water sample, with large distances and no overlap between each other, and the results of 5 repeated experiments were close, indicating that the sensing array still showed excellent recognition and discrimination ability for the five sulfur-containing compounds in the lake water. The data matrix (A / A0, I / I0 data set) of 75 data points was trained using the Fisher discriminant method in discriminant analysis, and the simultaneous detection of the five sulfur-containing compounds in the lake water sample was achieved through the Fisher discriminant method, with a 100% accuracy in the blind sample experiment. A dendrogram of the data matrix (A / A0, I / I0 data set) was constructed through hierarchical cluster analysis (HCA), Figure 9 which is the dendrogram of the five-element mixed sulfur-containing compounds with different concentration ratios in the lake water. It can be seen from the hierarchical cluster analysis (HCA) that the parallel test samples are clustered together, but the 5 groups of samples are separated from each other and there is no intersection, indicating that HCA can correctly classify the five sulfur-containing compounds in the lake water. Through the established dendrogram of the five-element sulfur-containing compounds in the lake water sample, the simultaneous detection of the five sulfur-containing compounds in the lake water was achieved. In the blind sample experiment, the unknown samples were correctly identified with a 100% accuracy. Therefore, accurate identification of the five sulfur-containing compounds in the lake water can be achieved through the Fisher discriminant method or hierarchical cluster analysis (HCA).

[0067] The R-CDs, G-CDs, and B-CDs solutions described in the present invention were obtained by diluting the stock solutions of R-CDs, G-CDs, and B-CDs 100 times with ethanol.

[0068] The multi-component sulfur-containing compound described in the present invention refers to a mixture of any two or more of the single sulfur-containing compounds.

Claims

1. A method for detecting sulfur-containing compounds using a colorimetric and fluorescence dual-mode sensing array of three-color luminescent carbon quantum dots, characterized in that Including the following steps: Step (1): Respectively perform colorimetric detection and fluorescence detection on R-CDs, G-CDs, and B-CDs solutions to obtain the original absorbance (A0) and the original fluorescence intensity (I0); Step (2): Respectively mix the R-CDs, G-CDs, and B-CDs solutions with single sulfur compound samples of different concentrations, and respectively mix the R-CDs, G-CDs, and B-CDs solutions with multi-sulfur compound samples of different concentration ratios, and perform colorimetric and fluorescence detections respectively to obtain the measured absorbance (A) and the measured fluorescence intensity (I); Step (3): By calculating the normalized relative absorbance (A / A0) and the relative fluorescence intensity (I / I0), form a data matrix of different single sulfur compounds and a data matrix of multi-sulfur compounds corresponding to different sulfide sets respectively; Step (4): Construct a single sulfur compound discrimination model, and the single sulfur compound discrimination model determines whether the sample only contains a single sulfur compound based on the relative absorbance and relative fluorescence intensity of the single sulfur compound sample; Train the single sulfur compound discrimination model based on the data matrix of different single sulfur compounds in step (3); the single sulfur compound discrimination model is the first Fisher discrimination model; Step (5): Based on the relative absorbance and relative fluorescence intensity obtained from different single sulfur compound samples, construct a data matrix of different single sulfur compounds; Respectively perform correspondence analysis in multivariate statistical analysis on the data matrices of different single sulfur compounds to obtain the standard curves of each single sulfur compound; construct a composition recognition model for different sulfide sets respectively, and the composition recognition model identifies the composition of the sample based on the relative absorbance and relative fluorescence intensity of the sample containing multi-sulfur compounds; train the composition recognition model using the corresponding data matrix of multi-sulfur compounds; Step (6): Identify the sample to be tested.

2. The method for detecting sulfur-containing compounds by the colorimetric and fluorescence dual-mode sensing array of three-color luminescent carbon quantum dots according to claim 1, characterized in that: In the data matrix of single sulfur compounds in step (3), the rows correspond to single sulfur compound samples with different concentration ratios, and the columns correspond to the relative absorbance and relative fluorescence intensity after the sample is respectively mixed with the three solutions of R-CDs, G-CDs, and B-CDs.

3. The method for detecting sulfur-containing compounds by the three-color luminescent carbon quantum dot colorimetric and fluorescence dual-mode sensing array according to claim 1, characterized in that: The sulfide set in step (3) refers to a set composed of two or more different single sulfur compounds. For a certain sulfide set, the rows in the corresponding data matrix of multi-sulfur compounds correspond to multi-sulfur compound samples with different concentration ratios that match the set, and the columns correspond to the relative absorbance and relative fluorescence intensity after the sample is respectively mixed with the three solutions of R-CDs, G-CDs, and B-CDs; the sulfide set is used to represent the "selection" of two or more single sulfides, but does not limit the concentration ratio during mixing.

4. The method for detecting sulfur-containing compounds by the three-color luminescent carbon quantum dot colorimetric and fluorescence dual-mode sensing array according to claim 1, wherein Step (5) is specifically as follows: Step (5-1): Calculate the relative frequency of each element in the data matrix of single sulfur compounds in the entire matrix to obtain a normalized probability matrix; Step (5-2): Based on the normalized probability matrix, calculate the marginal distributions of rows and columns, and then construct a transition matrix to describe the conditional probability relationship between row and column categories; Step (5-3): Using the transition matrix, perform R-type factor analysis for the associations between variables and Q-type factor analysis for the associations between samples respectively, and extract the principal factors, which are used to explain the common variance between the observed variables and reveal the hidden association patterns between variables; the factor refers to a linear combination of the observed variables, and the observed variables include relative absorbance (A / A0) and relative fluorescence intensity (I / I0). Step (5-4): According to the results of R-type and Q-type factor analyses, draw a correspondence analysis graph in the same coordinate system to show the positional relationship between the sample points and the variable points in the principal factor space. Step (5-5): Analyze the distribution positions of single sulfur-containing compound samples with different concentrations in the correspondence analysis graph to determine their quantitative relationships with the principal factor coordinates. Step (5-6): Based on the obtained quantitative relationships, establish a linear regression equation between the concentration of the single sulfur-containing compound and the principal factor score to form a standard curve for the single sulfur-containing compound.

5. The method for detecting sulfur-containing compounds by using the three-color luminescent carbon quantum dot colorimetric and fluorescence dual-mode sensing array according to claim 1, wherein: The composition recognition model in step (5) is the second Fisher discriminant model or the hierarchical clustering analysis model; the method for training the hierarchical clustering analysis model is as follows: according to the similarity between the samples in the data matrix of the multi-component sulfur-containing compounds, gradually merge the similar samples to form a hierarchical structure, and obtain a dendrogram for showing the clustering relationship between the samples; obtain the clustering results according to the height or distance threshold of the dendrogram and calculate the center of each cluster.

6. The method for detecting sulfur-containing compounds by the three-color luminescent carbon quantum dot colorimetric and fluorescence dual-mode sensing array according to claim 1, characterized in that The recognition method in step (6) is as follows: First, mix it with R-CDs, G-CDs, and B-CDs solutions respectively to obtain the corresponding relative absorbance and relative fluorescence intensity, and then input the obtained relative absorbance and relative fluorescence intensity into the single sulfur-containing compound discriminant model obtained in step (4) to determine whether the sample to be measured contains only a single sulfur-containing compound; if it contains only a single sulfur-containing compound, perform quantitative analysis on the single sulfur-containing compound according to the standard curve of the single sulfur-containing compound obtained in step (5); if it does not contain only a single sulfur-containing compound, respectively identify the sample according to the multiple composition recognition models obtained in step (5), and use the results of the composition recognition models that do not indicate abnormalities as the recognition results.

7. The method for detecting sulfur-containing compounds by the three-color luminescent carbon quantum dot colorimetric and fluorescence dual-mode sensing array according to claim 6, characterized in that: When using the hierarchical clustering analysis model as the composition recognition model for recognition: According to the relative absorbance and relative fluorescence intensity of the multi-component sulfur-containing compound sample to be measured, calculate the distances between the sample to be measured and the existing cluster centers in the dendrogram, and the composition corresponding to the cluster with the closest distance is the composition recognition result.

8. The method for detecting sulfur-containing compounds by the colorimetric and fluorescence dual-mode sensing array of the three-color luminescent carbon quantum dots according to any one of claims 1 to 7, characterized in that: For the fluorescence detection, the emission wavelengths of R-CDs, G-CDs, and B-CDs are 645 nm, 533 nm, and 482 nm respectively; for the colorimetric detection, the optimal absorption wavelengths of the ultraviolet-visible absorption spectra of the three-color CDs are all 200 nm.

9. The method for detecting sulfur-containing compounds by the colorimetric and fluorescence dual-mode sensing array of three-color luminescent carbon quantum dots according to any one of claims 1 to 7, characterized in that: The preparation method of the R-CDs is to mix N,N-dimethylformamide, citric acid and ammonium hydroxide, stir to form a uniform suspension, transfer it to a high-pressure reactor, and place the high-pressure reactor in an oven at a constant temperature of 170 °C to 190 °C for 7 to 9 h. After cooling, centrifugation, and microfiltration through a microporous membrane, the R-CDs stock solution is obtained, and the R-CDs solution is obtained after dilution with ethanol; the preparation method of the G-CDs is to mix ethanol and o-phenylenediamine, stir to form a uniform suspension, transfer it to a high-pressure reactor, and place the high-pressure reactor in an oven at a constant temperature of 190 °C to 210 °C for 9 to 11 h. After cooling, centrifugation, and microfiltration through a microporous membrane, the G-CDs stock solution is obtained, and the G-CDs solution is obtained after dilution with ethanol; the preparation method of the B-CDs is to mix ethylenediamine (C2H8N2), CA and H2O, stir to form a uniform suspension, transfer it to a high-pressure reactor, and place the high-pressure reactor in an oven at a constant temperature of 170 °C to 190 °C for 4 to 6 h. After cooling, centrifugation, and microfiltration through a microporous membrane, the B-CDs stock solution is obtained, and the B-CDs solution is obtained after dilution with ethanol.

10. The method for detecting sulfur-containing compounds by the colorimetric and fluorescence dual-mode sensing array of the three-color luminescent carbon quantum dots according to any one of claims 1 to 7, characterized in that: The single sulfur-containing compound includes: Na2S, Na2SO3, Na2SO4, Na2S2O3 and Na2S2O8; the multi-component sulfur-containing compound refers to a mixture of any two or more of the single sulfur-containing compounds.

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