Composite fluorescent array sensor and its construction method and application

Abstract

The application discloses a kind of composite fluorescence array sensor and its construction method and application, belong to biological sensing technical field.It is with two fluorescent dyes separately and with the quenching agent based on phenylboric acid two two corresponding mixture, constructs a kind of fluorescence array sensor, for the differentiation and detection of different tea.The method of pattern recognition analysis is used to establish the "fingerprint spectrum" of different kinds of tea standard sample, then the pattern recognition of unknown kind tea sample is carried out, finally the "fingerprint spectrum" result of unknown tea sample is compared with the "fingerprint spectrum" of different kinds of tea standard sample constructed in advance, the "fingerprint spectrum" of unknown tea sample is closest to which kind of tea standard sample "fingerprint spectrum", then identify this unknown tea sample as which kind of tea category.The preparation process of the array sensor is simple, and the operation is simple;The characteristics of multiple signal channel output can reduce the interference of background signal noise, effectively improve the practical application value of the array sensor.

Description

TECHNICAL FIELD

[0001] The present application relates to a sensor and its construction method and application, in particular to a composite fluorescence array sensor and its construction method and application, belonging to the technical field of biosensing. BACKGROUND

[0002] Tea as a traditional drink in China, its safety has been concerned. Development of tea industry, to ensure the quality of tea is the most important.

[0003] The detection technology for tea origin, quality and authenticity mainly includes stable isotope and multi-mineral detection technology, chromatography technology, near infrared spectroscopy technology and fingerprint technology, etc. (Yuan et al. J Agric Sci 2013, 4, 452) Stable isotope and multi-mineral detection technology distinguishes tea origin or species by comparing the content difference of stable isotopes and mineral elements contained in tea samples. (MARCOS et al. JAAS 1998, 13, 521-5) But due to the fact that the content of elements contained in tea is easily affected by many factors such as production environment, pesticides and fertilizers, the use of such methods to identify tea authenticity needs strict selection and optimization of algorithm, even if the experimental data is obtained, its reliability cannot be guaranteed. Chromatography detection technology distinguishes tea authenticity and species by analyzing the characteristic chemical components in tea, has good separation characteristics, and high detection sensitivity. But because of its poor qualitative ability, it often needs to be used in combination with other technologies. In addition, when using such technology for detection, it is necessary to strictly select the chemical components with obvious characteristics in advance, and these characteristic chemical components are often affected by factors such as processing technology and production environment. Not only the experimental process is complicated, the experimental instrument is expensive, but also the detection rate of the model for mixed samples is poor, and the application range is limited. The various components in tea have rich structural and compositional information, therefore, near infrared spectroscopy detection technology has great advantages in tea authenticity identification. But also because it needs a large amount of data to establish a model and needs to be updated constantly, this technology is easily affected by many factors such as tea species, sample preparation and scanning wave number, and its practicability is greatly reduced. (He et al. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2012, 86, 399-404) Fingerprint detection technology contains many branches, has the characteristics of simple design, high sensitivity and intuitive detection results compared with other detection technologies. Fluorescence array sensor chip technology also belongs to one of them, which simulates the function of animal olfactory system and taste system, realizes multi-channel signal transmission of data, has broad spectrum response and interactive response, and effectively avoids the problem of over-reliance on a specific component (response substance) in other technologies.

[0004] Currently, only a small number of literatures report the detection of tea by using fluorescent array sensor, and therefore there is still a need in the art to establish a more convenient, rapid and accurate detection method. SUMMARY

[0005] The present application aims to provide a compound fluorescent array sensor with high sensitivity, high accuracy and fast detection efficiency, which simplifies the tea processing procedure while ensuring accuracy, realizes the rapid identification of tea types and quality, and involves well-known tea varieties such as green tea, black tea and oolong tea.

[0006] Technical scheme: The compound fluorescent array sensor of the present application is a liquid array sensor, which comprises N sensing units, wherein the sensing units are first fluorescent dye, second fluorescent dye, compound of first fluorescent dye and first quencher, compound of first fluorescent dye and second quencher, compound of first fluorescent dye and third quencher, compound of second fluorescent dye and first quencher, compound of second fluorescent dye and second quencher, and compound of second fluorescent dye and third quencher, wherein the first fluorescent dye and the second fluorescent dye both contain ortho-phenolic hydroxyl groups; the first quencher, the second quencher and the third quencher are phenylboronic acid-based quenchers.

[0007] Preferably, the first fluorescent dye and the second fluorescent dye are different aescin derivatives.

[0008] Preferably, in the sensing units, the first fluorescent dye is aescin, the second fluorescent dye is 4-methylescin, the first quencher is o-boronic acid-substituted bis-bipyridine, the second quencher is m-boronic acid-substituted bis-bipyridine, and the third quencher is p-boronic acid-substituted bis-bipyridine.

[0009] Specifically, the structural formula of aescin is shown in ES, and the structural formula of 4-methylescin is shown in MS:

[0010]

[0011] The structural formula of o-boronic acid-substituted bis-bipyridine is shown in 4-2O, the structural formula of m-boronic acid-substituted bis-bipyridine is shown in 4-3m, and the structural formula of p-boronic acid-substituted bis-bipyridine is shown in 4-4P:

[0012]

[0013] The structure of the first fluorescent dye is shown in ES, the structure of the second fluorescent dye is shown in MS: the structure of the complex of the first fluorescent dye and the first quencher is shown in ES-4-2O, the structure of the complex of the first fluorescent dye and the second quencher is shown in ES-4-3m, the structure of the complex of the first fluorescent dye and the third quencher is shown in ES-4-4P, the structure of the complex of the second fluorescent dye and the first quencher is shown in MS-4-2O, the structure of the complex of the second fluorescent dye and the second quencher is shown in MS-4-3m, the structure of the complex of the second fluorescent dye and the third quencher is shown in MS-4-4P:

[0014]

[0015] As a further improvement of the above-mentioned solution, in the sensing unit, the concentration of the first fluorescent dye is 0.01-0.1 mM; the concentration of the first quencher in the complex of the first fluorescent dye and the first quencher is 0.01-0.1 mM; the concentration of the second quencher in the complex of the first fluorescent dye and the second quencher is 0.1-0.5 mM; the concentration of the third quencher in the complex of the first fluorescent dye and the third quencher is 0.1-0.8 mM.

[0016] As a further improvement of the above-mentioned solution, in the sensing unit, the concentration of the second fluorescent dye is 0.01-0.1 mM; the concentration of the first quencher in the complex of the second fluorescent dye and the first quencher is 0.01-0.1 mM; the concentration of the second quencher in the complex of the second fluorescent dye and the second quencher is 0.01-0.1 mM; the concentration of the third quencher in the complex of the second fluorescent dye and the third quencher is 0.1-0.8 mM.

[0017] On the other hand, the present application provides a method for constructing the above-mentioned complex fluorescent array sensor, the sensing unit is prepared to construct the liquid array sensor, the method for preparing the sensing unit comprises:

[0018] Preparation of the first fluorescent dye sensing unit: the first fluorescent dye is diluted with a buffer solution to a mM;

[0019] Preparation of the second fluorescent dye sensing unit: the second fluorescent dye is diluted with a buffer solution to a mM;

[0020] Preparation of the complex sensing unit: the fluorescent dye is diluted with a buffer solution to a mM, then divided into several portions, and different concentrations of quenching agent are added respectively, the fluorescence intensity is measured respectively, the fluorescence titration curve is obtained, and the complex with the proportion of quenching the fluorescence intensity of the fluorescent dye to b% is selected to construct the sensing unit; wherein according to the type of the complex sensing unit, the fluorescent dye is selected as the first fluorescent dye or the second fluorescent dye, and the quenching agent is selected as the first quenching agent, the second quenching agent or the third quenching agent.

[0021] Preferably, a is 0.01-0.1, and b is 10-60.

[0022] In another aspect, the present application provides a method for identifying tea leaves by using the above-mentioned complex fluorescence array sensor, comprising the following steps:

[0023] 1) Preparation of sensing reagents: prepare the sensing reagents required by the sensing unit respectively;

[0024] 2) Construction of liquid array sensor: establish an N×M well plate array sensor according to N sensing units;

[0025] 3) Establishment of spectral data of tea leaf standard sample: add different types of tea leaf standard samples to the N×M well plate array sensor; set the excitation wavelength and the maximum emission wavelength, measure the fluorescence intensity, obtain the fluorescence spectrum intensity I of the tea leaf standard sample, and repeat the above process several times; deduct the corresponding blank value I0, and calculate the relative fluorescence change intensity by the formula (I-I0) / I0;

[0026] 4) Repeat step 3) for T different types of tea leaf samples to obtain the fluorescence spectrum intensity I, deduct the corresponding blank value I0, and calculate the corresponding fluorescence change intensity by the formula (I-I0) / I0; then analyze the corresponding fluorescence change intensity by machine learning algorithm to obtain the "fingerprint" of T different types of tea leaves;

[0027] 5) Detection of unknown tea leaf to be tested: simultaneously add the unknown tea leaf sample to be tested into the M wells of any one column of the N×M well plate array sensor, under the same excitation wavelength and maximum emission wavelength as step 3), use a multifunctional enzyme marker to measure the fluorescence intensity, obtain the fluorescence spectrum intensity I of the unknown tea leaf sample, and repeat the above process several times; deduct the corresponding blank value I0, and calculate the relative fluorescence change intensity by the formula (I-I0) / I0.

[0028] 6) Compare the "fingerprint" of the unknown tea leaf sample to be tested in step 5) with the "fingerprint" of T different types of tea leaf samples by machine learning algorithm, and identify the unknown tea leaf sample as the type of the tea leaf standard sample with which the "fingerprint" matches.

[0029] As a further improvement of the above scheme, the machine learning algorithm in step 4) is linear discriminant analysis (LDA), which is specifically as follows:

[0030] The T x N x 8 x 7 relative fluorescence intensity data obtained by repeating step 3) on T different types of tea standard samples in step 4) are analyzed and trained by the machine learning algorithm to obtain multiple discriminant functions. Two discriminant functions with the largest contribution degree are selected from the obtained multiple discriminant functions as discriminant function F1 and discriminant function F2. The value of the discriminant function F1 is taken as the X value, and the value of the discriminant function F2 is taken as the Y value. The discriminant function scatter plot of different types of tea standard samples is drawn with the X and Y values as the horizontal and vertical coordinates and 95% confidence interval reserved. The discriminant function scatter plot can be used as the "fingerprint" of the T different types of tea standard samples.

[0031] As a further improvement of the above scheme, the "fingerprint" comparison operation in step 6) is specifically as follows: the relative fluorescence intensity of the unknown tea sample to be tested is analyzed and trained by the machine learning algorithm to obtain multiple discriminant functions. Two discriminant functions with the largest contribution degree are selected from the obtained multiple discriminant functions as discriminant function F1 and discriminant function F2. The value of the discriminant function F1 is taken as the X' value, and the value of the discriminant function F2 is taken as the Y' value. The "fingerprint" of the unknown tea sample is drawn with the X' and Y' values as the horizontal and vertical coordinates and 95% confidence interval reserved. The "fingerprint" of the unknown tea sample is matched with the "fingerprint" of the T different types of tea standard samples. When the "fingerprint" of the unknown tea sample is closest to or coincides with the "fingerprint" of a certain type of tea standard sample, it is determined that the unknown sample is of that type of tea.

[0032] The application provides an application of the above-mentioned complex fluorescence array sensor in tea detection.

[0033] Further, the fluorescence response data of different sensing elements of the complex fluorescence array sensor after interaction with tea is collected.

[0034] Further, statistical analysis software SYSTAT (version 13.0) is used to process and analyze the data. Through linear discriminant analysis (LDA), the data matrix is classified, the data is visualized, the model is constructed, and the differentiation and detection of multiple proteins are realized.

[0035] Further, the tea is randomly ordered for blind test, the same steps are performed for testing, the fluorescence response data is recorded, and the linear discriminant analysis (LDA) is used to verify the detection ability of the model for unknown samples. The results show that the model can distinguish 100% of unknown samples.

[0036] As a preferred method, the method for distinguishing different varieties of tea leaves as described above is: the above-constructed bibenzylpyridine and aesculetin derivative complex array sensing unit is diluted with 0.1 mM PBS (pH = 7.4) buffer to a final concentration of 0.02 mM to prepare a new fluorescent array sensor. 100 μL of different varieties of tea leaves (tea final concentration 0.39 mg / mL) are added, including Maojian (G1), Tiegongying (G2), Biluochun (G3), Yunwu tea (G4), Jinjunmei (B1), Dianhong (B2), Zhengshanxiaozhong (B3), Qimenhongcha (B4), Darjeeling (B5), Taile (B6), Kenya (B7), Baozhe (B8), Dahongpao (W1), Tiegongying (W2), Fenghuangdancong (W3), and Luzhidonglong (W4), shaken for 10 seconds, and each tea is measured 7 times, and the fluorescence response signal is recorded.

[0037] The fluorescence response signals of the array sensing unit and the tea leaf samples are processed by using various algorithms such as linear discriminant analysis, the data matrix is classified, the two-dimensional or three-dimensional array visual fingerprint of the tea leaves is obtained, and the visual identification is realized. The obtained two-dimensional or three-dimensional array distinguishing fingerprint is used as a model to detect unknown samples, and the accuracy of the unknown sample prediction is calculated, so that the tea leaves are distinguished and detected.

[0038] Beneficial effects: Compared with the prior art, the present application has the following remarkable advantages: the tea leaf sample processing process is greatly simplified, the easily obtained commercial dye is combined with the simply synthesized quencher to construct a fluorescent array sensor, the prepared sensor has low cost, high sensitivity, short detection time, and has the ability of simultaneous detection and identification of various tea leaves. The method has short detection time, high repeatability and does not require professional technicians. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is the fluorescence titration curve of the interaction of three bibenzylpyridines (4-2O, 4-3m, 4-4P) substituted with phenylboronic acid at different positions with a certain concentration of ES, MS;

[0040] Figure 2 is the fluorescence response signal of 16 kinds of tea leaves on the 8 sensing elements constructed respectively;

[0041] Figure 3 is the visualization diagram (LDA) of the rapid identification of tea leaves by the fluorescent array sensor. DETAILED DESCRIPTION

[0042] The technical solutions of the present application are further described below in combination with the drawings.

[0043] The embodiment of the present application provides a kind of compound fluorescence array sensor, the compound fluorescence array sensor is liquid array sensor, the liquid array sensor includes N sensing units, the sensing unit is first fluorescent dye, second fluorescent dye, the compound of first fluorescent dye and first quencher, the compound of first fluorescent dye and second quencher, the compound of first fluorescent dye and third quencher, the compound of second fluorescent dye and first quencher, the compound of second fluorescent dye and second quencher, the compound of second fluorescent dye and third quencher, wherein first fluorescent dye and second fluorescent dye contain ortho phenolic hydroxyl group;First quencher, second quencher and third quencher are phenylboronic acid-based quencher.

[0044] In the embodiment of the present application, two fluorescent dyes Esculetin (ES) and 4-methylscopoletin (MS) are used alone and mixed with phenylboronic acid-based quenchers (three boronic acid-substituted bis-bipyridines: 4-2O, 4-3m, and 4-4P) to construct a fluorescence array sensor with eight sensing elements for the differentiation and detection of different tea leaves.

[0045] Specifically, the embodiment of the present application uses Esculetin as the first fluorescent dye, 4-methylscopoletin as the second fluorescent dye, boronic acid ortho-substituted bis-bipyridine as the first quencher, boronic acid meta-substituted bis-bipyridine as the second quencher, and boronic acid para-substituted bis-bipyridine as the third quencher. Among them, Esculetin is denoted as ES, 4-methylscopoletin is denoted as MS, boronic acid ortho-substituted bis-bipyridine is denoted as 4-2O, boronic acid meta-substituted bis-bipyridine is denoted as 4-3m, and boronic acid para-substituted bis-bipyridine is denoted as 4-4P. The fluorescence molecules of the eight sensing elements in the fluorescence sensor are denoted as ES, MS, ES-4-2O, ES-4-3m, ES-4-4P, MS-4-2O, MS-4-3m, and MS-4-4P, respectively.

[0046] The embodiment of the present application provides a method for constructing a fluorescence array sensor, which is as follows:

[0047] The method for constructing the fluorescence array sensing elements ES and MS is as follows: dilute ES and MS with PBS buffer (PH=7.4) to 0.02mM to obtain the fluorescence array sensing elements ES and MS, respectively.

[0048] The construction method of the fluorescent array sensing element ES-4-2O as described above is: ES is diluted to 0.02 mM with PBS buffer (pH=7.4), and different concentrations of phenylboronic acid ortho-substituted bis-bipyridine (the concentrations of phenylboronic acid ortho-substituted bis-bipyridine are 0.003, 0.006, 0.009, 0.012, 0.016, 0.02, 0.025, 0.03 mM, respectively) are added, respectively, to obtain the fluorescence titration curve of bis-bipyridine and aesculetin derivative, and the compound in which bis-bipyridine quenches the fluorescence intensity of aesculetin derivative to 30% is selected to construct the sensing element of the sensing array.

[0049] The construction method of the fluorescent array sensing element ES-4-3m as described above is: ES is diluted to 0.02 mM with PBS buffer (pH=7.4), and different concentrations of phenylboronic acid meta-substituted bis-bipyridine (the concentrations of phenylboronic acid meta-substituted bis-bipyridine are 0.01, 0.02, 0.04, 0.06, 0.08, 0.15, 0.2, 0.25, 0.35 mM, respectively) are added, respectively, to obtain the fluorescence titration curve of bis-bipyridine and aesculetin derivative, and the compound in which bis-bipyridine quenches the fluorescence intensity of aesculetin derivative to 30% is selected to construct the sensing element of the sensing array.

[0050] The construction method of the fluorescent array sensing element ES-4-4P as described above is: ES is diluted to 0.02 mM with PBS buffer (pH=7.4), and different concentrations of phenylboronic acid para-substituted bis-bipyridine (the concentrations of phenylboronic acid para-substituted bis-bipyridine are 0.02, 0.04, 0.06, 0.1, 0.15, 0.2, 0.4, 0.5, 0.6, 1 mM, respectively) are added, respectively, to obtain the fluorescence titration curve of bis-bipyridine and aesculetin derivative, and the compound in which bis-bipyridine quenches the fluorescence intensity of aesculetin derivative to 30% is selected to construct the sensing element of the sensing array.

[0051] The construction method of the fluorescent array sensing element MS-4-2O as described above is: MS is diluted to 0.02 mM with PBS buffer (pH=7.4), and different concentrations of phenylboronic acid ortho-substituted bis-bipyridine (the concentrations of phenylboronic acid ortho-substituted bis-bipyridine are 0.004, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.03, 0.05 mM, respectively) are added, respectively, to obtain the fluorescence titration curve of bis-bipyridine and aesculetin derivative, and the compound in which bis-bipyridine quenches the fluorescence intensity of aesculetin derivative to 30% is selected to construct the sensing element of the sensing array.

[0052] The method for constructing the fluorescent array sensing element MS-4-3m as described above is: MS is diluted to 0.02 mM with PBS buffer (pH = 7.4), and different concentrations of meta-substituted benzene boronic acid dipyridyl (the concentrations of meta-substituted benzene boronic acid dipyridyl are 0.004, 0.006, 0.008, 0.01, 0.015, 0.02, 0.025, 0.03, 0.05, 0.07, 0.09, 0.10 mM, respectively) are added respectively to obtain the fluorescence titration curve of dipyridyl and aesculetin derivative, and the compound of the proportion at which dipyridyl quenches the fluorescence intensity of aesculetin derivative to 30% is selected to construct the sensing element of the sensing array.

[0053] The method for constructing the fluorescent array sensing element MS-4-3m as described above is: MS is diluted to 0.02 mM with PBS buffer (pH = 7.4), and different concentrations of meta-substituted benzene boronic acid dipyridyl (the concentrations of meta-substituted benzene boronic acid dipyridyl are 0.004, 0.006, 0.008, 0.01, 0.015, 0.02, 0.025, 0.03, 0.05, 0.07, 0.09, 0.10 mM, respectively) are added respectively to obtain the fluorescence titration curve of dipyridyl and aesculetin derivative, and the compound of the proportion at which dipyridyl quenches the fluorescence intensity of aesculetin derivative to 30% is selected to construct the sensing element of the sensing array.

[0054] By the above method, the final concentrations of the PBS solutions of the reagents ES and MS are both 0.01 mM, the final concentration of 4-2O is 0.016 mM, the concentration of 4-3m is 0.15 mM when it is compounded with ES and is 0.05 mM when it is compounded with MS, and the final concentration of 4-4P is 0.4 mM.

[0055] The concentrations of the reagents determined according to the above scheme are used to prepare the sensing reagents: 0.0178 g of ES and 0.0192 g of MS are precisely weighed, respectively, and diluted to a final concentration of 0.02 mM with PBS buffer, respectively, to prepare 80 ml, respectively, and shake well for standby use. 0.0043 g of 4-2O is precisely weighed, diluted to a final concentration of 0.032 mM (40 ml) with PBS buffer, and shaken well. 0.0043 g of 4-3m is precisely weighed, diluted to a final concentration of 0.3 mM (20 ml mixed with ES) and 0.1 mM (20 ml mixed with MS) with PBS buffer, and shaken well. 0.0172 g of 4-4P is precisely weighed, diluted to a final concentration of 0.8 mM (40 ml) with PBS buffer, and shaken well. The dilutions of ES and MS are mixed with the dilutions of the three dipyridyls substituted with benzene boronic acid at different positions (4-2O, 4-3m, 4-4P) at a ratio of 1:1, shaken well, to obtain eight compound systems of ES, MS, ES-4-2O, ES-4-3m, ES-4-4P, MS-4-2O, MS-4-3m, and MS-4-4P, and standby use.

[0056] The pretreatment method of tea leaves is as follows: 0.15 g of each tea leaves is soaked in 38 mL of boiling water for 20 minutes, and is shaken once at 10 minutes. The tea liquid after soaking is filtered through a 0.22 micron filter membrane, and is diluted with distilled water to 0.39 mg / mL to prepare a tea sample.

[0057] The method for distinguishing different varieties of tea is as follows: 100 μL of each of the eight complexes ES, MS, ES-4-20, ES-4-3m, ES-4-4P, MS-4-20, MS-4-3m and MS-4-4P is added to 100 μL of pretreated tea liquid (0.39 mg / mL) of different varieties, and is shaken for 10 s. The excitation wavelengths are 368 nm (ES) and 356 nm (MS), respectively. The fluorescence response data of the complexes combined with tea are measured at the emission wavelengths of 474 nm (ES) and 466 nm (MS), respectively. The relative fluorescence intensity change is used as the detection signal (I-I0 / I0). Each variety of tea is tested with each complex for 7 times, and 7 x 16 x 8 = 896 fluorescence response data can be obtained for 16 kinds of tea (Table 1) and 8 kinds of complexes (Table 3). The 896 fluorescence response data are used to form a data matrix (Table 3). The eight sensing elements have different fluorescence responses to different varieties of tea (Fig. 1). Figure 2

[0058] Example 2, Analysis and processing of fluorescence array sensor data

[0059] The statistical analysis software SYSTAT (version 13.0) is used to process and analyze the fluorescence data. The linear discriminant analysis (LDA) is used to convert the fluorescence response mode into a standard mode. All variables are used in the model (complete mode), and the tolerance is set to 0.001. The Mahalanobis distance of each single mode in the multidimensional space to the centroid of each group is calculated, and the assignment of all categories is based on the shortest Mahalanobis distance. It can be seen from the LDA diagram that even if the concentration of different tea is low and the number of categories is large, different tea categories can be distinguished ultimately (Fig. 2). Figure 3 The Jackknifed Classification Matrix shows that the accuracy of distinguishing tea is 100% (Table 3).

[0060] Table 1 is the basic information of 16 varieties of tea

[0061]

[0062] Table 2 is the fluorescence response data array of the array sensor to different varieties of tea samples (16 kinds of tea with a concentration of 0.39 mg / mL)

[0063] ​

[0064]

[0065]

[0066]

[0067] Table 3 Classification matrix accuracy verification results

[0068]

[0069] Array sensor's ability to distinguish unknown tea samples: 16 kinds of tea were randomly ordered for blind test, a total of 80 unknown samples were blindly tested. According to the above steps, the relative fluorescence intensity change was recorded (Table 2). Then, the linear discriminant analysis was used to verify the ability of the model to test unknown samples, and to distinguish the types of tea. 80 unknown samples were tested, and 80 samples were correctly detected, with an accuracy of 100% (Table 4).

[0070] Table 4 is the fluorescence response data matrix of the array sensor to unknown tea samples

[0071]

[0072]

[0073]

[0074] In the present application, after the combination of different substituted positions of bibenzylpyridine and two kinds of aesculetin derivatives, the fluorescence of the system is significantly quenched, which is beneficial to the amplification of the detection signal in the sensor application. Three bibenzylpyridines (4-2O, 4-3m, 4-4P) corresponding to different concentrations of phenylboronic acid substituted positions and a certain concentration of ES, MS were combined to construct a fluorescence sensor array. Well-known domestic and foreign tea varieties including Maojian, Tieguanyin, Biluochun, Yunwu tea, Jinjunmei, Dianhong, Zhengshan Xiaozhong, Qimen Black Tea, Darjeeling, Taylors of Harrogate, Kenyan, Earl Grey, Dahongpao, Tieguanyin, Fenghuang, and Freezing Top Oolong were selected as analytes. Different varieties of tea were added to the fluorescence array sensor, and the concentration of each tea was 0.39 mg / mL. Each variety of tea was tested 7 times with each compound, and a data model of 7 (number of repetitions) x 16 (tea) x 8 (channels) was obtained. The data was analyzed and processed by linear discriminant analysis (LDA). The fluorescence array sensor had 100% correct discrimination ability for 16 kinds of tea and 100% correct discrimination ability for unknown samples. The present application provides a compound array sensor, which is simple to construct and easy to operate, has been applied in practice, can quickly and accurately distinguish a variety of tea samples, and can be further applied to the discrimination of more varieties of tea.

[0075] The advantages of the embodiment are as follows: the array sensor preparation method is simple, low in cost, high in sensitivity, and short in detection time period, can accurately distinguish multiple varieties of tea, has an accurate rate of 100% for distinguishing unknown samples, has good reproducibility, and can be used for actual and unknown sample detection. The sensor array operation does not require professional technicians.

Claims

1. A fluorescent array sensor for tea complex, characterized in that, The composite fluorescence array sensor is a liquid array sensor, which includes N sensing units. Each sensing unit is a first fluorescent dye, a second fluorescent dye, a complex of the first fluorescent dye and a first quencher, a complex of the first fluorescent dye and a second quencher, a complex of the first fluorescent dye and a third quencher, a complex of the second fluorescent dye and a first quencher, a complex of the second fluorescent dye and a second quencher, and a complex of the second fluorescent dye and a third quencher. Both the first and second fluorescent dyes contain ortho- and ortho-phenolic hydroxyl groups. The first, second, and third quenchers are quenchers based on phenylboronic acid. The first fluorescent dye is a fraxin derivative, the second fluorescent dye is a 4-methylesculin derivative, the first quencher is ortho-substituted bibenzylpyridine with boric acid, the second quencher is meta-substituted bibenzylpyridine with boric acid, and the third quencher is para-substituted bibenzylpyridine with boric acid.

2. The composite fluorescence array sensor according to claim 1, characterized in that, In the sensing unit, the concentration of the first fluorescent dye is 0.01-0.1 mM; the concentration of the first quencher in the complex of the first fluorescent dye and the first quencher is 0.01-0.1 mM; the concentration of the second quencher in the complex of the first fluorescent dye and the second quencher is 0.1-0.6 mM; and the concentration of the third quencher in the complex of the first fluorescent dye and the third quencher is 0.1-0.8 mM.

3. The composite fluorescence array sensor according to claim 1, characterized in that, In the sensing unit, the concentration of the second fluorescent dye is 0.01-0.1 mM; the concentration of the first quencher in the complex of the second fluorescent dye and the first quencher is 0.01-0.1 mM; the concentration of the second quencher in the complex of the second fluorescent dye and the second quencher is 0.01-0.1 mM; and the concentration of the third quencher in the complex of the second fluorescent dye and the third quencher is 0.1-0.8 mM.

4. A method for constructing a complex fluorescence array sensor according to any one of claims 1-3, characterized in that, The sensing unit is fabricated to construct a liquid array sensor. The fabrication method of the sensing unit includes: Preparation of the first fluorescent dye sensing unit: The first fluorescent dye was diluted with buffer solution to a mM; Preparation of the second fluorescent dye sensing unit: The second fluorescent dye was diluted with buffer solution to a mM; Preparation of the composite sensing unit: The fluorescent dye was diluted with buffer solution to a mM, then divided into several portions, and different concentrations of quencher were added to each portion. The fluorescence intensity was measured to obtain the fluorescence titration curve. The composite with the ratio that quenched the fluorescence intensity of the fluorescent dye to b% was selected to construct the sensing unit. The fluorescent dye was selected as the first fluorescent dye or the second fluorescent dye, and the quencher was selected as the first quencher, the second quencher, or the third quencher, depending on the type of composite sensing unit.

5. The method for constructing the complex fluorescence array sensor according to claim 4, characterized in that, a is 0.01-0.1, b is 10-60.

6. A method for identifying tea leaves using a complex fluorescence array sensor according to any one of claims 1-3, characterized in that, Includes the following steps: 1) Prepare sensing reagents: Prepare the sensing reagents required for each sensing unit; 2) Constructing a liquid array sensor: Based on N sensing units, construct an N×M orifice plate array sensor; 3) Establish spectral data for labeled tea samples: Add different types of standard tea samples to an N×M well plate array sensor; Set the excitation wavelength and the maximum emission wavelength, measure the fluorescence intensity, and obtain the fluorescence spectral intensity I of the tea standard sample. Repeat the above process several times; subtract the corresponding blank value I0, and calculate the relative fluorescence change intensity using the formula (I-I0) / I0. 4) Repeat step 3) for T different types of tea samples to obtain the fluorescence spectral intensity I, subtract the corresponding blank value I0, and calculate the corresponding fluorescence change intensity using the formula (I-I0) / I0; then analyze the corresponding fluorescence change intensity using a machine learning algorithm to obtain the "fingerprint spectrum" of T different types of tea. 5) Detection of unknown tea leaves: Add the unknown tea leaf sample to M wells in any column of the N×M well plate array sensor simultaneously. Under the same excitation wavelength and maximum emission wavelength as in step 3), measure the fluorescence intensity using a multi-functional microplate reader to obtain the fluorescence spectral intensity I of the unknown tea leaf sample. Repeat the above process several times; subtract the corresponding blank value I0, and calculate the relative fluorescence change intensity using the formula (I-I0) / I0. 6) Using a machine learning algorithm, the corresponding fluorescence change intensity of the unknown tea sample to be tested in step 5) is compared with the fingerprint spectrum of T different categories of tea samples, and the unknown tea sample is identified as the category of the tea standard sample that matches its fingerprint spectrum.

7. The method for identifying tea leaves using a complex fluorescence array sensor according to claim 6, characterized in that, The machine learning algorithm in step 4) is linear discriminant analysis, as detailed below: The T×N×8×7 relative fluorescence intensity changes obtained in step 3) of the T different types of tea standard samples in step 4) are analyzed and trained using a machine learning algorithm to obtain multiple discriminant functions. From the obtained discriminant functions, the two discriminant functions with the largest distinguishing contribution are selected as discriminant function F1 and discriminant function F2. The value of discriminant function F1 is used as the X value and the value of discriminant function F2 is used as the Y value. Using these as the x and y axes and retaining the 95% confidence interval, a scatter plot of the discriminant functions of different types of tea standard samples is plotted. This scatter plot of discriminant functions can be used as the "fingerprint spectrum" of the T different types of tea standard samples.

8. The method for identifying tea leaves using a complex fluorescence array sensor according to claim 6, characterized in that, The "fingerprint spectrum" comparison operation in step 6) is as follows: The relative fluorescence change intensity of the unknown tea sample to be tested is analyzed and trained using a machine learning algorithm to obtain multiple discriminant functions. From the obtained discriminant functions, the two discriminant functions with the largest distinguishing contribution are selected as discriminant functions F1 and F2. The value of discriminant function F1 is used as the X' value, and the value of discriminant function F2 is used as the Y' value. Using these as the horizontal and vertical axes and retaining a 95% confidence interval, the "fingerprint spectrum" of the unknown tea sample is plotted. The "fingerprint spectrum" of the unknown tea sample is matched with the "fingerprint spectrum" established by standard samples of T different categories of tea. When the "fingerprint spectrum" of the unknown tea sample is closest to or overlaps with the "fingerprint spectrum" of a certain category of tea standard sample, the unknown sample is determined to be of that category of tea.

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