Method for identifying tea producing area based on quantum dot fluorescence sensing array

Abstract

The invention belongs to the technical field of food detection, and relates to a method for identifying the producing area of tea based on a quantum dot fluorescent sensing array, which comprises the following steps: preparing InP / ZnS quantum dots with four different colors, preparing water-soluble functionalized fluorescent quantum dots by using the InP / ZnS quantum dots, constructing a four-channel fluorescent sensing array, measuring the fluorescence intensity of each tea water sample, and identifying the producing area of the tea based on the quantum dot fluorescent sensing array. Calculating the relative fluorescence intensity to obtain a fluorescence standard model spectrum of different types of tea leaves, taking to-be-detected tea leaves, obtaining a fluorescence model spectrum of the to-be-detected tea leaves, and comparing the fluorescence model spectrum with the fluorescence standard model spectrum to determine the production place of the to-be-detected tea leaves. By modifying the surface groups of the quantum dots, non-specific binding and cross response can be obtained, the construction of the quantum dot fluorescence sensing array is realized, the tea can be identified and distinguished through linear discriminant analysis, the complex pretreatment process of a tea sample is simplified, and the detection accuracy is improved. And operation is simple, cost is low, and large precise instruments are not needed.

Description

Technical Field

[0001] The invention belongs to the technical field of food detection, and relates to a detection method, in particular to a method for identifying the origin of tea leaves based on a quantum dot fluorescence sensor array. Background Art

[0002] Tea is one of the most popular beverages in the world. Tea can promote human health by providing a large number of bioactive substances that help prevent and treat certain diseases. The price of tea fluctuates greatly due to quality, which is related to the geographical origin and leaf variety. The origin attributes of tea from a specific origin influence consumers' purchasing decisions compared with other similar teas from non-specific geographical areas. However, teas from different origins (specific or non-specific) are processed through similar procedures, making them difficult to distinguish by appearance. At the same time, in the commercial market, some specific teas are mixed or adulterated with other teas, resulting in poor quality or unsatisfactory flavor. Therefore, it is necessary to quickly and effectively identify tea.

[0003] Using chemical composition as data to classify food samples is very important for identifying the geographical origin and authenticity of products. The type, quality, and quantity of active ingredients in tea chemical composition are greatly affected by the environment. Tea contains complex mineral compositions, including metal ions such as cadmium (Cd 2+ ), lead (Pb 2+ )、Zn 2+ ), iron (Fe 3+ ), chromium (Cr 6+ ) etc. Due to the differences in environmental conditions in different regions, the content (or type) of metal ions in tea leaves from different regions and different types is also different. Moreover, the content of metal ions is less affected by processing technology and storage time. Therefore, by detecting the difference in metal ion content (or type), tea leaves from different origins and different varieties can be distinguished more stably and accurately.

[0004] At present, spectral detection techniques (atomic emission spectroscopy, atomic absorption spectroscopy, atomic fluorescence spectroscopy and inductively coupled plasma mass spectrometry) are widely used for quantitative and qualitative studies of metal ions in food and water. Although these techniques have the advantages of sensitivity, reliability, and accurate quantification, they require large and sophisticated instruments, complex operating procedures, and high maintenance costs. The identification work is time-consuming and complicated, requiring professionals to complete. The use of environmentally harmful organic reagents limits their application in rapid detection and low-cost analysis and cannot achieve simultaneous detection of multiple elements. Therefore, it is challenging to explore effective methods to determine and distinguish metal ions in food.

[0005] Fluorescent sensors have attracted much attention due to their high sensitivity, simplicity, convenience, and the absence of large instruments, and are widely used in fields such as environmental monitoring and food safety testing. However, the traditional "lock-key" sensing mode requires the design and synthesis of fluorescent molecules with high specificity for metal ions, and is still limited to the detection of a single target.

[0006] A "chemical nose" sensor is broadly defined as an array-based system that works by exploiting the nonspecific binding between an analyte and a series of cross-reactive receptors, resulting in a different response to each analyte. Array sensing achieves simultaneous detection of multiple analytes in a complex environment by using multiple analytes to interact with multiple sensor elements simultaneously, and discriminates the overall changes in the composition of a complex mixture rather than identifying specific elements in it. It has been widely used in food testing and analysis, environmental monitoring, and biomedical diagnostics. Current sensing units usually require complex instrumentation or precious metal nanoparticles, so there is an urgent need to develop a low-cost, simple-to-operate detection method that can detect multiple metal ions simultaneously, which is of great significance in the field of food safety testing. Summary of the invention

[0007] The purpose of the present invention is to provide a method for identifying the origin of tea leaves based on a quantum dot fluorescence sensor array. By modifying the surface groups of quantum dots, nonspecific binding and cross-response can be obtained, and the construction of a quantum dot fluorescence sensor array can be realized. The limitation of the prior art that only a single target object can be detected can be overcome. Through linear discriminant analysis, multiple metal ions can be detected simultaneously, and tea leaves of different regions and varieties can be identified and distinguished. The method has the characteristics of high sensitivity and simple operation.

[0008] The technical solution adopted by the present invention is:

[0009] A method for identifying the origin of tea leaves based on a quantum dot fluorescence sensor array, the specific steps are as follows:

[0010] S1. Prepare InP / ZnS quantum dots of four different colors: green, yellow, orange and red.

[0011] S2. InP / ZnS quantum dots of different colors are reacted with water-soluble ligands in a methanol solution to prepare four water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors.

[0012] S3. Determine the excitation wavelength and emission wavelength of water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors respectively, and construct a four-channel fluorescence sensing array.

[0013] S4. The fluorescence intensity corresponding to the maximum emission wavelength of water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors is used as blank; tea water samples of different types of tea are prepared; the tea water samples are added to water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors and mixed, and the fluorescence intensity of each tea water sample under the condition of the four-channel fluorescence sensor array is measured after incubation at room temperature, and the relative fluorescence intensity is calculated. The calculation formula is as follows:

[0014]

[0015] Where F is the relative fluorescence intensity, F 茶 is the fluorescence intensity after adding tea water sample (fluorescence intensity of tea water sample), and F0 is the blank fluorescence intensity.

[0016] Multiple parallel measurements were performed on each tea water sample, and the relative fluorescence intensity was used to construct a data matrix of different types of tea under the conditions of a four-channel fluorescence sensor array. The fluorescence standard model spectrum of tea from different origins was obtained through data analysis.

[0017] S5. Soak the tea leaves to be tested and filter the filtrate to obtain a water sample to be tested; add the water sample to be tested into water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors and mix them evenly; detect the fluorescence intensity of the water sample to be tested under the condition of a four-channel fluorescence sensor array after incubation at room temperature, and calculate the relative fluorescence intensity; perform multiple parallel measurements to construct a data matrix of the tea leaves to be tested under the condition of a four-channel fluorescence sensor array, use the data analysis method in step S4 to obtain a fluorescence model spectrum of the tea leaves to be tested, compare the fluorescence model spectrum with the fluorescence standard model spectrum, and determine the origin of the tea leaves to be tested.

[0018] Preferably, in step S2, the water-soluble ligand contains both a carboxyl group and an amino group, and preferably, specifically cysteine, N-acetylcysteine, penicillamine and glutathione, corresponding to green InP / ZnS quantum dots, yellow InP / ZnS quantum dots, orange InP / ZnS quantum dots and red InP / ZnS quantum dots, respectively.

[0019] Preferably, in step S2, the water-soluble functionalized fluorescent InP / ZnS quantum dots are cysteine ​​functionalized green fluorescent InP / ZnS quantum dots, N-acetylcysteine ​​functionalized yellow fluorescent InP / ZnS quantum dots, penicillamine functionalized orange fluorescent InP / ZnS quantum dots and glutathione functionalized red fluorescent InP / ZnS quantum dots.

[0020] Preferably, in step S3, the excitation wavelength and emission wavelength are respectively: the excitation wavelength of cysteine ​​functionalized green fluorescent InP / ZnS quantum dots is 340 nm, and the emission wavelength is 520 nm; the excitation wavelength of N-acetylcysteine ​​functionalized yellow fluorescent InP / ZnS quantum dots is 340 nm, and the emission wavelength is 550 nm; the excitation wavelength of penicillamine functionalized orange fluorescent InP / ZnS quantum dots is 360 nm, and the emission wavelength is 580 nm; the excitation wavelength of glutathione functionalized red fluorescent InP / ZnS quantum dots is 360 nm, and the emission wavelength is 610 nm.

[0021] Preferably, in step S4, the preparation process of the tea water sample is: taking equal amounts of tea leaves of different types (different origins and different varieties) as standard tea samples, adding deionized water, soaking in a hot water bath, filtering and collecting the filtrate, and obtaining the tea water sample.

[0022] Preferably, in step S4, the fluorescence standard model spectrum is obtained by performing linear discriminant analysis on the data matrix of different types of tea using SPSS, and the data matrix is ​​converted into multiple discriminant factors. The larger the discriminant factor, the greater the contribution to sample differentiation. The two discriminant factors with the largest contribution are selected as discriminant factor 1 and discriminant factor 2, respectively. With discriminant factor 1 as the horizontal coordinate and discriminant factor 2 as the vertical coordinate, a typical two-dimensional score graph of different types of tea is drawn. The two-dimensional score graph is the fluorescence standard model spectrum of tea.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] 1) The present invention modifies the surface groups of quantum dots to obtain nonspecific binding and cross-response, thereby realizing the construction of a quantum dot fluorescence sensor array. According to the different contents and types of metal ions in the tea leaves, the linear discriminant analysis can be used to identify and distinguish tea leaves of different regions and varieties, thereby simplifying the complex pretreatment process of tea samples, eliminating the need for professional personnel to operate, and being simple to operate, low-cost, and without the need for large-scale precision instruments.

[0025] 2) Compared with the traditional "lock-key" sensing mode, the sensing array based on the multi-channel water-soluble fluorescent sensor array can realize the detection and identification of analytes in complex environments.

[0026] 3) It overcomes the limitation of existing technology that only single target can be detected, and achieves the simultaneous detection of multiple metal ions, which is of great significance in the field of food safety testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the construction of a four-channel fluorescence sensing array based on InP / ZnS quantum dots and its mechanism for identifying multiple metal ions and tea leaves.

[0028] Figure 2 Emission spectra of four water-soluble functionalized fluorescent InP / ZnS quantum dots.

[0029] Figure 3 Transmission electron microscope images of four water-soluble functionalized fluorescent InP / ZnS quantum dots.

[0030] Figure 4 This is the fingerprint of the four-channel fluorescence sensor array in response to 11 metal ions.

[0031] Figure 5 Two-dimensional score plot of linear discriminant analysis for the recognition of 11 metal ions by a four-channel fluorescence sensor array.

[0032] Figure 6 Two-dimensional score plot of linear discriminant analysis for identifying five types of tea using a four-channel fluorescence sensor array. DETAILED DESCRIPTION

[0033] The following examples are used to further describe the specific embodiments of the present invention. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention. The experimental methods in the following examples are conventional methods unless otherwise specified. The materials, reagents, etc. used in the following examples are all commercially available, with analytically pure reagents being preferred reagents.

[0034] The present invention is based on the principle of achieving different interactions with target analytes based on the subtle structural differences between metal ions and sensing elements. Four water-soluble functionalized fluorescent InP / ZnS quantum dots are used as sensing elements to construct a fluorescent sensing array, and a highly discrete metal ion database of clusters within clusters and dispersed between clusters is established. Multiple metal ions are distinguished based on the linear discriminant analysis of fluorescence response, thereby achieving multi-channel detection, identification and differentiation of tea leaves. The mechanism is as follows: Figure 1 shown.

[0035] Example 1: Differentiation of Metal Ions by Four-Channel Fluorescence Sensing Array

[0036] 1. Synthesis method of four colors of water-soluble functionalized fluorescent InP / ZnS quantum dots

[0037] A. Preparation of oil-soluble InP / ZnS core-shell quantum dots: The oil-soluble quantum dots were prepared by the method in reference (TESSIER MD, DUPONT D, DeNOLF K, et al. Economic and Size-Tunable Synthesis of InP / ZnE(E=S,Se)ColloidalQuantum Dots.[J].Chemistry of Materials, 2015, 27(13):4893-4898.). By controlling the ratio of InCl3, ZnCl2 and ZnI2, adjusting the reaction time and reaction temperature, InP / ZnS quantum dots with green fluorescence, yellow fluorescence, orange fluorescence and red fluorescence were obtained, and the maximum emission wavelengths were 520, 550, 580 and 610 nm respectively. Then the quantum dots of the four colors were dissolved in chloroform to obtain oil-soluble quantum dot chloroform solutions.

[0038] B. Green fluorescent oil-soluble quantum dots are converted into water-soluble functional fluorescent quantum dots: 20 mg of cysteine ​​is dissolved in 1 mL of methanol, and then 30 μL of a chloroform solution of green fluorescent quantum dots with an emission wavelength of 520 nm is added to the above methanol to obtain a mixed solution. Without the protection of nitrogen or argon, the mixture is stirred vigorously at room temperature for 40 minutes. After the reaction is complete, the precipitate is purified by centrifugation with ethanol to obtain a precipitate (the precipitate is a water-soluble quantum dot). The precipitate is dispersed in deionized water to obtain a water-soluble functional fluorescent quantum dot (cysteine ​​functionalized green fluorescent InP / ZnS quantum dot) with a concentration of 0.02 mg / mL, and stored in a refrigerator at 4°C.

[0039] C. Yellow fluorescent oil-soluble quantum dots are converted into water-soluble functionalized fluorescent quantum dots: 25 mg of N-acetyl-cysteine ​​is dissolved in 1 mL of methanol, and then 30 μL of chloroform solution of yellow fluorescent quantum dots with an emission wavelength of 550 nm is added to the above methanol to obtain a mixed solution. Without the protection of nitrogen or argon, the mixture is stirred vigorously at room temperature for 1 hour. After the reaction is complete, the precipitate is purified by centrifugation with ethanol to obtain a precipitate (the precipitate is a water-soluble quantum dot). The precipitate is dispersed in deionized water to obtain a water-soluble functionalized fluorescent quantum dot (N-acetyl-cysteine ​​functionalized yellow fluorescent InP / ZnS quantum dot) with a concentration of 0.02 mg / mL, and stored in a refrigerator at 4°C.

[0040] D. Orange fluorescent oil-soluble quantum dots are converted into water-soluble functional fluorescent quantum dots: 25 mg of penicillamine is dissolved in 1 mL of methanol, and then 30 μL of chloroform solution of orange fluorescent quantum dots with an emission wavelength of 580 nm is added to the above methanol to obtain a mixed solution. Without the protection of nitrogen or argon, the mixture is stirred vigorously at room temperature for 1 hour. After the reaction is complete, the precipitate is purified by centrifugation with ethanol to obtain a precipitate (the precipitate is a water-soluble quantum dot). The precipitate is dispersed in deionized water to obtain a water-soluble functional fluorescent quantum dot (penicillamine functional orange fluorescent InP / ZnS quantum dot) with a concentration of 0.02 mg / mL, and stored in a refrigerator at 4°C.

[0041] E. Red fluorescent oil-soluble quantum dots are converted into water-soluble functionalized fluorescent quantum dots: 20 mg of glutathione is weighed and dissolved in 1 mL of methanol, then 20 μL of chloroform solution of red fluorescent quantum dots with an emission wavelength of 610 nm is added to the above methanol to obtain a mixed solution, and the mixture is stirred vigorously at room temperature for 1 hour without the protection of nitrogen or argon. After the reaction is complete, the precipitate is purified by centrifugation with ethanol to obtain a precipitate (the precipitate is a water-soluble quantum dot). The precipitate is dispersed in deionized water to obtain a water-soluble functionalized fluorescent quantum dot (glutathione functionalized red fluorescent InP / ZnS quantum dot) with a concentration of 0.02 mg / mL, and stored in a refrigerator at 4°C.

[0042] Fluorescence spectra of four colors of water-soluble functionalized fluorescent InP / ZnS quantum dots are shown in Figure 2. Figure 2 As shown in the figure, it can be seen that the four colors of water-soluble functionalized fluorescent InP / ZnS quantum dots have different emission wavelengths. Water-soluble functionalized fluorescent InP / ZnS quantum dots with different emission wavelengths can locate most of the visible area.

[0043] Transmission electron microscope images of four colors of water-soluble functionalized fluorescent InP / ZnS quantum dots Figure 3 As shown in the figure, it can be seen that the four water-soluble functionalized fluorescent InP / ZnS quantum dots all present a spherical structure, uniform size distribution and good dispersibility.

[0044] 2. Construction of four-channel fluorescence sensor array

[0045] A. Take 20 μL of four colors of water-soluble functionalized fluorescent InP / ZnS quantum dots respectively and add them to a centrifuge tube, dilute to 100 μL with deionized water to obtain a four-channel fluorescent sensor array.

[0046] B. The excitation wavelengths and emission wavelengths of four colors of water-soluble functionalized fluorescent InP / ZnS quantum dots were measured: the excitation wavelength of cysteine ​​functionalized green fluorescent InP / ZnS quantum dots was 340 nm, and the emission wavelength was 520 nm; the excitation wavelength of N-acetylcysteine ​​functionalized yellow fluorescent InP / ZnS quantum dots was 340 nm, and the emission wavelength was 550 nm; the excitation wavelength of penicillamine functionalized orange fluorescent InP / ZnS quantum dots was 360 nm, and the emission wavelength was 580 nm; the excitation wavelength of glutathione functionalized red fluorescent InP / ZnS quantum dots was 360 nm, and the emission wavelength was 610 nm.

[0047] 3. Four-channel fluorescence sensing array based on water-soluble functionalized fluorescent InP / ZnS quantum dots for accurate differentiation of multiple metal ions in aqueous solution

[0048] A. Selection of metal ions: According to the various metal ions contained in tea, Ag is selected. + 、Al 3+ , Ba 2+ , Ca 2+ 、Cd 2 + , K + , Pb 2+ , Cu 2+ , Mn 2+ , Hg 2+ Cr 6+ As an experimental subject.

[0049] B. Preparation of metal ion standard solution: Accurately weigh the analytically pure standard of the required metal ion, dissolve it in deionized water, and prepare a metal ion standard solution with a molar concentration of 100 μM.

[0050] C. Obtain data matrix: Take 20 μL of water-soluble functionalized fluorescent InP / ZnS quantum dots with a concentration of 0.02 mg / mL and add them to centrifuge tubes, and dilute to 100 μL with deionized water. Then add 100 μL of metal ion standard solution, and the final volume of each centrifuge tube is 200 μL. After mixing, incubate at room temperature for 2 minutes, and then measure the fluorescence intensity of the metal ions with a fluorescence spectrophotometer, and calculate the relative fluorescence intensity. The calculation formula is as follows:

[0051]

[0052] Where F is the relative fluorescence intensity, F 离子 is the fluorescence intensity after adding metal ions, and F0 is the fluorescence intensity when no metal ions are added.

[0053] Under the conditions of a four-channel fluorescence sensing array, seven parallel experiments were conducted for 11 metal ions, and a data matrix with four emission channels × 7 repetitions × 11 metal ions was obtained.

[0054] D. After adding the same concentration of 11 heavy metal ions, the fluorescence response of the quantum dots was studied, and significant changes in the emission intensity of the quantum dots were observed. Figure 4 As shown, the results show that each metal ion produces a specific fluorescence response: Al 3+ 、Cd 2+ After adding, the fluorescence intensity of quantum dots was significantly enhanced. + Cr 6+ , Cu 2+ , Hg 2+ , Mn 2+ After the addition, there is obvious quenching, and other metal ions have no obvious enhancement and quenching effect. Therefore, different metal ions produce unique fluorescence response patterns, which indicates that the metal ions have different interactions with the four quantum dots.

[0055] E. Analysis and processing of data matrix: By using linear discriminant analysis in SPSS, the data matrix was processed and grouped to obtain a two-dimensional score map ( Figure 5 ). The results show that different metal ions have obvious distinction under the LDA discrimination model, successfully dividing the metal ions into 11 different groups, with no obvious overlap between the groups, and the recognition accuracy is 100%. Therefore, the four-channel fluorescence sensor array established by the present invention can be used to accurately identify and distinguish 11 kinds of metal ions.

[0056] Example 2: Construction of a tea fluorescence standard model spectrum based on a four-channel fluorescence sensing array of InP / ZnS quantum dots

[0057] Five kinds of tea, namely Baisha green tea, Wuzhishan green tea, Wuzhishan black tea, Jinding black tea and Partridge tea, were selected as standard samples, and the fluorescence standard model spectrum of each kind of tea was obtained.

[0058] 1. Prepare tea water sample: Weigh 1g of each tea, add the tea to 200mL of deionized water (90℃), and then soak in a hot water bath for 30 minutes. After soaking, filter the tea water with a 0.45μM micron filter membrane, take the filtrate, and then dilute it 20 times with deionized water to obtain the final tea water sample.

[0059] 2. Take 20 μL of the water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors described in Example 1 and add them to the centrifuge tubes, and dilute them to 100 μL with deionized water. Then add 100 μL of tea water samples of different teas, and the final volume of each centrifuge tube is 200 μL. After mixing, incubate at room temperature for 2 minutes, and then use a fluorescence spectrophotometer to measure the fluorescence intensity of each tea water sample under the conditions of the four-channel fluorescence sensor array, calculate the relative fluorescence intensity, and use the change in relative fluorescence intensity as the detection signal. For the five kinds of tea, seven parallel experiments were carried out, and a data matrix constructed with 4×7×5 fluorescence response data was obtained.

[0060] 3. Use the linear discriminant analysis in SPSS to process and analyze the obtained data matrix to generate a two-dimensional score map, i.e., the fluorescence standard model map. The two-dimensional score map of the five kinds of tea is shown in Figure 6 As shown, due to the differences in metal ion content and types in tea, the four-channel fluorescence sensor array has different responses to the five types of tea, there is a clear separation between different teas, and there is no obvious overlap between different groups, indicating that the four-channel fluorescence sensor array can successfully classify the five types of tea from different origins and varieties, providing a simple, efficient and stable detection method for quickly distinguishing tea of ​​different varieties and origins.

[0061] Example 3: Accurately distinguish the origin of the tea leaves to be tested

[0062] 1. The supervisor randomly selects equal amounts of samples from Baisha green tea, Jinding black tea, Wuzhishan black tea, Wuzhishan green tea and Partridge tea as the tea to be tested, shuffles the order, numbers them and records them. Three samples of each type of tea (three times in parallel) are tested, for a total of 15 tea samples to be tested. The test personnel classify the tea samples according to their origin.

[0063] The test was carried out according to the steps of Example 2 to construct a fluorescence response data matrix of 15 tea samples to be tested. The results are shown in Table 1.

[0064] Table 1 Fluorescence response data matrix of tea samples to be tested

[0065]

[0066] The above data are processed, and the results show that the tea samples numbered 1#, 3#, and 5# are in the same group, marked as Group A. The tea samples numbered 4#, 10#, and 14# are in the same group, marked as Group B. The tea samples numbered 11#, 12#, and 13# are in the same group, marked as Group C. The tea samples numbered 6#, 8#, and 15# are in the same group, marked as Group C. The tea samples numbered 2#, 7#, and 9# are in the same group, marked as Group E.

[0067] Each group of fluorescence response data is processed and analyzed to obtain the fluorescence model spectrum of each group of tea samples to be tested. The fluorescence model spectrum is compared with the fluorescence standard model spectrum obtained in Example 2, and it is concluded that Group A, Group B, Group C, Group D, and Group E are Baisha Green Tea, Jinding Black Tea, Wuzhishan Black Tea, Wuzhishan Green Tea, and Partridge Tea, respectively. The results are compared with the records of the supervisor. The grouping of the 15 tea samples to be tested is correct, and the tea origin is correctly identified, indicating that the identification method of the present application has an accuracy rate of 100%.

[0068] Although the objectives, technical solutions, beneficial effects and specific implementation plans of the present invention have been described in detail above, modifications or improvements to the above contents are obvious to professionals in the field and should therefore be included in the scope of protection of the present invention.

Claims

1. A method for identifying the origin of tea leaves based on a quantum dot fluorescence sensor array, characterized in that: The specific steps are as follows: S1, preparing InP / ZnS quantum dots of four different colors: green, yellow, orange and red; S2, reacting InP / ZnS quantum dots of different colors with water-soluble ligands in a methanol solution to prepare four water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors; S3, respectively measuring the excitation wavelength and emission wavelength of water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors, and constructing a four-channel fluorescence sensing array; S4. The fluorescence intensity corresponding to the maximum emission wavelength of water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors is used as blank; tea water samples of different types of tea are prepared; the tea water samples are added to water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors and mixed, and the fluorescence intensity of each tea water sample under the condition of the four-channel fluorescence sensor array is measured after incubation at room temperature, and the relative fluorescence intensity is calculated. The calculation formula is as follows: Where F is the relative fluorescence intensity, F 茶 is the fluorescence intensity after adding tea water sample (fluorescence intensity of tea water sample), F0 is the blank fluorescence intensity; Each tea water sample was measured multiple times in parallel, and the data matrix of tea from different origins was constructed under the condition of a four-channel fluorescence sensor array using relative fluorescence intensity. The fluorescence standard model spectrum of tea from different origins was obtained through data analysis. S5. Soak the tea leaves to be tested and filter the filtrate to obtain a water sample to be tested; add the water sample to be tested into water-soluble functionalized fluorescent InP / ZnS quantum dots of different colors and mix them evenly; detect the fluorescence intensity of the water sample to be tested under the condition of a four-channel fluorescence sensor array after incubation at room temperature, and calculate the relative fluorescence intensity; perform multiple parallel measurements to construct a data matrix of the tea leaves to be tested under the condition of a four-channel fluorescence sensor array, use the data analysis method in step S4 to obtain a fluorescence model spectrum of the tea leaves to be tested, compare the fluorescence model spectrum with the fluorescence standard model spectrum, and determine the origin of the tea leaves to be tested.

2. The method for identifying tea origin based on quantum dot fluorescence sensor array according to claim 1, characterized in that: In the step S2, the water-soluble ligand contains both a carboxyl group and an amino group.

3. The method for identifying tea origin based on quantum dot fluorescence sensor array according to claim 2, characterized in that: The water-soluble ligands are cysteine, N-acetylcysteine, penicillamine and glutathione, which correspond to green InP / ZnS quantum dots, yellow InP / ZnS quantum dots, orange InP / ZnS quantum dots and red InP / ZnS quantum dots, respectively.

4. The method for identifying tea origin based on quantum dot fluorescence sensor array according to claim 1, characterized in that: In step S2, the water-soluble functionalized fluorescent InP / ZnS quantum dots are respectively cysteine ​​functionalized green fluorescent InP / ZnS quantum dots, N-acetylcysteine ​​functionalized yellow fluorescent InP / ZnS quantum dots, penicillamine functionalized orange fluorescent InP / ZnS quantum dots and glutathione functionalized red fluorescent InP / ZnS quantum dots.

5. The method for identifying tea origin based on quantum dot fluorescence sensor array according to claim 1, characterized in that: In step S3, the excitation wavelength and emission wavelength are respectively: the excitation wavelength of cysteine ​​functionalized green fluorescent InP / ZnS quantum dots is 340nm, and the emission wavelength is 520nm; the excitation wavelength of N-acetylcysteine ​​functionalized yellow fluorescent InP / ZnS quantum dots is 340nm, and the emission wavelength is 550nm; the excitation wavelength of penicillamine functionalized orange fluorescent InP / ZnS quantum dots is 360nm, and the emission wavelength is 580nm; the excitation wavelength of glutathione functionalized red fluorescent InP / ZnS quantum dots is 360nm, and the emission wavelength is 610nm.

6. The method for identifying tea origin based on quantum dot fluorescence sensor array according to claim 1, characterized in that: In step S4, the preparation process of the tea water sample is: taking equal amounts of different types of tea leaves as standard tea samples, adding deionized water, soaking in a hot water bath, filtering and collecting the filtrate, and obtaining the tea water sample.

7. The method for identifying tea origin based on quantum dot fluorescence sensor array according to claim 1, characterized in that: In step S4, the fluorescence standard model spectrum is obtained by performing linear discriminant analysis on the tea data matrix of different origins using SPSS, and the data matrix is ​​converted into multiple discriminant factors. The larger the discriminant factor, the greater the contribution to sample differentiation. The two discriminant factors with the largest contribution are selected as discriminant factor 1 and discriminant factor 2, respectively. With discriminant factor 1 as the horizontal coordinate and discriminant factor 2 as the vertical coordinate, a typical two-dimensional score graph of different types of tea is drawn. The two-dimensional score graph is the fluorescence standard model spectrum of tea.

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