CN111337451A - Device and method for detecting pesticide residue of leaf vegetables based on near-infrared characteristic spectrum - Google Patents
Device and method for detecting pesticide residue of leaf vegetables based on near-infrared characteristic spectrum Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
Abstract
The invention discloses a device and a method for detecting pesticide residue in leaf vegetables based on near-infrared characteristic spectrum, relating to the technical field of detection of pesticide residue in leaf vegetables, and comprising the steps of obtaining a leaf homogenate sample with unknown pesticide residue concentration; placing a culture dish filled with a leaf homogenate sample on a sample groove of a leaf vegetable pesticide residue detection device to obtain spectral data of the leaf homogenate sample; preprocessing the spectral data to obtain a spectral matrix and a pesticide concentration matrix corresponding to the spectral matrix; establishing a regression coefficient-spectrum wavelength relation graph according to the spectrum matrix and the pesticide concentration matrix corresponding to the spectrum matrix; determining characteristic wavelengths corresponding to pesticide residues in the leaf vegetables according to the regression coefficient-spectrum wavelength relation graph; and calculating the pesticide residue in the leaf vegetable leaves according to the spectral intensity value corresponding to the characteristic wavelength. The invention does not need complex and fussy chemical treatment in the sample preparation process, and realizes the purpose of rapid detection.
Description
Technical Field
The invention relates to the technical field of leaf vegetable pesticide residue detection, in particular to a device and a method for detecting leaf vegetable pesticide residue based on near-infrared characteristic spectrum.
Background
The pesticide can effectively prevent insect damage and diseases and improve the yield and quality of the leaf vegetables when applied in the planting process of the leaf vegetables. However, improper use and abuse of pesticides can cause excessive pesticide residues on the surfaces of leaf vegetables, and can also cause pesticide to enter the environment and pollute soil, water sources and air. Pesticide residues in the environment and food can finally enter human bodies through the food chain, and harm the health of the human bodies.
The method and the device for rapidly detecting the pesticide residue in the leaf vegetables are developed by combining the spectrum technology with the chemometrics method, are a large direction for detecting the pesticide residue, and have important significance for ecological environment protection, food safety, human health guarantee and the like.
At present, the conventional pesticide residue detection methods mainly include Gas Chromatography (GC for short), High Performance Liquid Chromatography (HPLC), Gas Chromatography-Mass Spectrometry (GC-MS for short), Ultra-High Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS for short), immunosensor, and the like. However, these traditional methods for detecting pesticide residues in leaf vegetables need to be performed in laboratories, the price of related instruments and equipment is high, and before analysis, professional technicians are required to perform complex, tedious and time-consuming pretreatment processes on samples, and a large amount of chemical reagents are consumed, so that the environment is polluted, and the requirement for rapid detection cannot be met.
The visible/near infrared spectrum is an absorption spectrum, the spectrum region of the visible/near infrared spectrum is consistent with the frequency combination of hydrogen-containing group vibration in organic molecules and the absorption region of each level of frequency doubling, and the corresponding spectrum contains organic matter component information. The visible/near infrared spectrum can be used for rapidly, nondestructively and efficiently detecting a sample, is low in cost, does not need to use a chemical reagent or excessive sample pretreatment process, and has great potential in pesticide residue detection.
Disclosure of Invention
The invention aims to provide a device and a method for detecting the pesticide residue in leaf vegetables based on a near-infrared characteristic spectrum, which do not need complicated chemical treatment in the sample preparation process and realize the aim of rapid detection.
In order to achieve the purpose, the invention provides the following scheme:
a leaf vegetable pesticide residue detection device based on near-infrared characteristic spectrum comprises a shell; the shell comprises a cavity, a light source, a sample groove, a central controller, a spectrometer and a spectrum signal receiving probe, wherein the light source is arranged on the side surface of the cavity and forms a set angle with the vertical direction;
the central controller is connected with an external computer through a connecting wire arranged at the top of the shell;
the centers of the central controller, the spectrometer and the spectrum signal receiving probe are all on the same vertical line with the center of the sample groove.
Optionally, the cavity is V-shaped, and the inner surface of the cavity is completely black.
Optionally, the light source device further comprises a switch connected with the light source, installed outside the housing, and used for controlling the light source; wherein the light source is a multi-characteristic wavelength light source.
Optionally, the set angle is 30 °.
A method for detecting the pesticide residue of leaf vegetables based on a near-infrared characteristic spectrum comprises the following steps:
obtaining a leaf homogenate sample with known pesticide name and unknown pesticide residue concentration;
determining the characteristic wavelength of the light source corresponding to the leaf homogenate sample according to a corresponding relation database of pesticide names and the characteristic wavelength of the light source, and replacing the light source of the characteristic wavelength corresponding to the leaf homogenate sample with the light source in the original leaf vegetable pesticide residue amount detection device to obtain the leaf vegetable pesticide residue amount detection device corresponding to the leaf homogenate sample;
placing the culture dish filled with the blade homogenate sample on a sample groove of a leaf vegetable pesticide residue detection device corresponding to the blade homogenate sample, and enabling the center of the culture dish to be over against a spectral signal receiving probe of the leaf vegetable pesticide residue detection device corresponding to the blade homogenate sample so as to obtain spectral data of the blade homogenate sample;
preprocessing the spectral data to obtain a spectral matrix corresponding to the blade homogenate sample; the spectrum matrix comprises a spectrum intensity value corresponding to the characteristic wavelength of the light source selected by the blade homogenate sample;
and calculating the pesticide residue in the leaf vegetable according to the spectral intensity value corresponding to the light source characteristic wavelength selected by the leaf homogenate sample.
Optionally, the establishing process of the pesticide name and light source characteristic wavelength corresponding relation database is as follows:
preparing leaf vegetable products without pesticide application;
preparing a pesticide standard solution with a known pesticide name;
preparing leaf homogenate standard samples with different concentrations according to the pesticide standard solution and the leaf vegetable;
placing culture dishes containing blade homogenate standard samples with different concentrations on a sample groove of a leaf vegetable pesticide residue detection device in sequence, and enabling the center of the culture dish to be over against a spectral signal receiving probe of the leaf vegetable pesticide residue detection device so as to obtain spectral data corresponding to the blade homogenate standard samples with different concentrations;
sequentially preprocessing the spectral data corresponding to the blade homogenate standard samples with different concentrations to obtain a plurality of groups of matrix data; each group of matrix data comprises a spectrum matrix and a pesticide concentration matrix corresponding to the spectrum matrix;
establishing a regression coefficient-spectrum wavelength relation graph corresponding to each group of matrix data according to the matrix data;
determining the characteristic wavelengths of the light source selected by the leaf homogenate standard samples with different concentrations according to all the regression coefficient-spectrum wavelength relation graphs;
and preferentially selecting the light source characteristic wavelengths selected by the blade homogenate standard samples with different concentrations, determining the light source characteristic wavelength corresponding to the final blade homogenate standard sample, and further establishing a corresponding relation database of pesticide names and the light source characteristic wavelengths.
Optionally, the establishing a regression coefficient-spectrum wavelength relation graph corresponding to each set of matrix data according to the matrix data specifically includes:
in The UnscramblerX10.4 software, establishing a partial least squares regression model according to The spectrum matrix and The pesticide concentration matrix corresponding to The spectrum matrix, and further obtaining a regression coefficient of The spectrum data under each spectrum wavelength;
and establishing a regression coefficient-spectrum wavelength relation graph by taking the spectrum wavelength as an x axis and the regression coefficient as a y axis.
Optionally, the determining, according to all the regression coefficient-spectral wavelength relationship maps, the characteristic wavelengths of the light source selected by the leaf homogenate standard samples of different concentrations specifically includes:
and in the regression coefficient-spectrum wavelength relation graph, taking the spectrum wavelengths corresponding to the wave crests and the wave troughs as the light source characteristic wavelengths corresponding to the pesticide residues in the leaf vegetables to obtain the light source characteristic wavelengths selected by the leaf homogenate standard samples with different concentrations.
Optionally, the preprocessing is performed on the spectral data to obtain a spectral matrix corresponding to the blade homogenate sample, and the method specifically includes:
and (3) preprocessing The spectral data by adopting a standard normal variation algorithm or a multivariate scattering correction algorithm in The UnscamblbergerX 10.4 software to obtain a spectral matrix corresponding to The blade homogenate sample.
Optionally, the calculating the pesticide residue in the leaf vegetable according to the spectral intensity value corresponding to the light source characteristic wavelength selected by the leaf homogenate sample specifically includes:
determining spectral intensity values and pesticide concentration values corresponding to light source characteristic wavelengths selected by blade homogenate standard samples with different concentrations;
establishing a multiple linear regression model according to the spectral intensity value and the pesticide concentration value corresponding to the light source characteristic wavelength selected by the leaf homogenate standard samples with different concentrations to obtain a relational expression of the spectral intensity value and the pesticide true value in the leaf vegetables, and further establishing a corresponding relational database of pesticide names and the relational expression;
and determining a relational expression between the spectral intensity value corresponding to the leaf homogenate sample and the actual pesticide value in the leaf vegetables according to the relational database between the pesticide name and the relational expression, substituting the spectral intensity value corresponding to the light source characteristic wavelength selected by the leaf homogenate sample into the relational expression between the spectral intensity value corresponding to the leaf homogenate sample and the actual pesticide value in the leaf vegetables, and determining the pesticide residue in the leaf vegetables.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
compared with the traditional chemical detection method, the detection method provided by the invention does not need complex and tedious chemical treatment in the sample preparation process, only needs to break the wall of the leaf vegetables and stir and then put into a culture dish, and is simple to operate. And the detection device is simple to operate and portable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic view of the structure of a device for detecting the pesticide residue in vegetables based on near-infrared characteristic spectrum according to an embodiment;
FIG. 2 is a schematic structural diagram of a device for detecting pesticide residues in vegetables based on near-infrared characteristic spectrum in an embodiment in use;
FIG. 3 is a schematic flow chart of a device for detecting pesticide residues in vegetables based on near-infrared characteristic spectrum according to the second embodiment;
FIG. 4 is a graph of three original spectra of the example;
FIG. 5 is a graph of the spectrum after pretreatment in the third example;
FIG. 6 is a plot of the triple partial least squares regression coefficient versus spectral wavelength for the examples;
FIG. 7 is a schematic structural diagram of a light source with three characteristic wavelengths according to an embodiment;
FIG. 8 is a graph of the four original spectra of the example;
FIG. 9 is a graph of the spectrum after the pretreatment of the fourth example;
FIG. 10 is a graph of the four partial least squares regression coefficient versus spectral wavelength for the example;
fig. 11 is a schematic structural diagram of a four-multi-characteristic wavelength light source according to an embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a device and a method for detecting the pesticide residue in leaf vegetables based on a near-infrared characteristic spectrum, which do not need complicated chemical treatment in the sample preparation process and realize the aim of rapid detection.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example one
As shown in fig. 1 and fig. 2, the device for detecting the pesticide residue in leafy vegetables based on near-infrared characteristic spectrum provided by the present embodiment includes a cylindrical housing 1 and a cover 11. The cylindrical shell 1 comprises a cavity 9 similar to a V shape, a multi-characteristic-wavelength light source 3 arranged on the side face of the cavity 9 and forming an angle of 30 degrees with the vertical direction, a sample groove 10 arranged at the center of the bottom of the cavity 9, a central controller 5 arranged at the top of the cavity 9, a spectrometer 4 connected below the central controller 5, and a spectrum signal receiving probe 2 connected below the spectrometer 4.
The centers of the central controller 5, the spectrometer 4 and the spectral signal receiving probe 2 are all on the same vertical line with the center of the sample cell 10.
The spectrum signal receiving probe 2 is connected with the spectrometer 4, the spectrometer 4 is connected with the central controller 5, and the central controller 5 is connected with the light source 3 through wires; the top of the cylindrical shell 1 is provided with a USB wire 6 connected with the central controller 5, and the USB wire 6 is used for connecting the software inside the computer 7.
The inner surface of the cavity 9 is completely black, so as to provide a dark environment for spectrum detection, and simultaneously reduce the reflection of light on the surface of the cavity 9, and prevent interference on spectrum information detection.
The detection device further comprises a switch 12 connected to the light source 3 and mounted outside the cylindrical housing 1 and controlling the light source.
When the device for detecting the pesticide residue in the leaf vegetables based on the near-infrared characteristic spectrum is used, the power supply is provided by the computer 7.
Example two
The detection device provided by the first application embodiment provides a method for detecting the pesticide residue of leaf vegetables based on a near-infrared characteristic spectrum, and as shown in fig. 3, the method comprises the following steps:
step 101: leaf homogenate samples of known pesticide name and unknown pesticide residue concentration were obtained. The method specifically comprises the following steps:
leaf vegetable samples of known pesticide names and unknown pesticide residue concentrations were prepared. The method specifically comprises the following steps: cutting leaf vegetables with known pesticide names and unknown pesticide residue concentrations into pieces, putting the cut leaves into a wall breaking stirrer, fully breaking the walls, and stirring for 5 minutes to obtain leaf vegetable homogenate, namely obtaining the leaf vegetable sample with the known pesticide names and unknown pesticide residue concentrations.
And adding a magnetic stirrer into a beaker filled with leaf vegetable samples with known pesticide names and unknown pesticide residue concentrations, and placing the beaker on the magnetic stirrer for fully stirring to obtain leaf homogenate samples with known pesticide names and unknown pesticide residue concentrations.
A mL of a leaf homogenate sample with a known pesticide name and an unknown pesticide residue concentration is extracted by a needle cylinder, injected into a culture dish with the diameter of 35mm, and the culture dish is slightly vibrated on a table top to flatten the surface of the sample in the culture dish.
Step 102: and determining the characteristic wavelength of the light source corresponding to the leaf homogenate sample according to the corresponding relation database of the pesticide name and the characteristic wavelength of the light source, and replacing the light source in the original leaf vegetable pesticide residue amount detection device with the light source with the characteristic wavelength corresponding to the leaf homogenate sample to obtain the leaf vegetable pesticide residue amount detection device corresponding to the leaf homogenate sample. The method specifically comprises the following steps:
preparing leaf vegetable products without pesticide application; the method specifically comprises the following steps: cutting leaf vegetables without pesticide application into pieces, and putting into a wall breaking stirrer to break the wall and stir for 5 minutes to obtain leaf vegetable homogenate, namely obtaining the leaf vegetable product without pesticide application.
Preparing a pesticide standard solution with a known pesticide name; the method specifically comprises the following steps: obtaining n mg of pesticide standard product with known pesticide name, using acetonitrile or acetone or other organic solvent capable of dissolving pesticide to make the volume of the pesticide standard product be constant to m mL volumetric flask, making the pesticide standard product be fully dissolved out, so as to obtain pesticide standard solution with known pesticide name.
Preparing blade homogenate standard samples with different concentrations according to the pesticide standard solution and leaf vegetable products; the method specifically comprises the following steps: firstly, weighing the mass of the dry clean beaker, pouring about k mL of the leaf vegetable without pesticide application into the dry clean beaker, and reading the mass of the leaf vegetable without pesticide application by adopting a weighing method. The reading quality is the amount of pesticide standard solution to be added for subsequent calculation. Secondly, adding a magnetic stirrer into a beaker containing the non-pesticide-applied leaf vegetable, placing the beaker on a magnetic stirrer for stirring, adding a pesticide standard solution with a known pesticide name into the stirred non-pesticide-applied leaf vegetable according to the mass of the non-pesticide-applied leaf vegetable in the beaker to prepare 9 concentration gradient blade homogenate standard samples with the concentrations of 0mg/kg, 0.5mg/kg, 1mg/kg, 5mg/kg, 10mg/kg, 30mg/kg, 50mg/kg, 70mg/kg and 100mg/kg respectively, and continuing to stir by using the magnetic stirrer until the samples are fully dissolved. Then, amL leaf homogenate standard samples are extracted by a needle cylinder and injected into a culture dish with the diameter of 35mm, the culture dish is lightly vibrated on a table top, the surface of the leaf homogenate standard samples in the culture dish is smooth, and the leaf homogenate standard samples with different concentrations are prepared.
Placing the culture dishes filled with the blade homogenate standard samples with different concentrations on a sample groove of a leaf vegetable pesticide residue detection device in sequence, and enabling the center of the culture dish to be over against a spectrum signal receiving probe of the leaf vegetable pesticide residue detection device so as to obtain spectrum data corresponding to the blade homogenate standard samples with different concentrations. The process of acquiring the spectral data is the same as that of step 103.
Sequentially preprocessing the spectral data corresponding to the blade homogenate standard samples with different concentrations to obtain a plurality of groups of matrix data; each group of matrix data comprises a spectrum matrix and a pesticide concentration matrix corresponding to the spectrum matrix. The preprocessing is the same as the preprocessing of step 104.
Establishing a regression coefficient-spectrum wavelength relation graph corresponding to each group of matrix data according to the matrix data; the number of the regression coefficient-spectrum wavelength relation graphs is the same as that of the matrix data sets. The method specifically comprises the following steps: in The unscrambler X10.4 software, a Partial Least Squares Regression (PLSR) model is established according to The preprocessed spectrum matrix X and The corresponding pesticide concentration matrix Y, and then regression coefficients of The spectrum data under various spectrum wavelengths are obtained; and establishing a regression coefficient-spectrum wavelength relation graph by taking the spectrum wavelength as an x axis and the regression coefficient as a y axis.
And determining the characteristic wavelength of the light source selected by the leaf homogenate standard samples with different concentrations according to all regression coefficient-spectrum wavelength relation graphs. The method specifically comprises the following steps: selecting the spectral wavelengths corresponding to the wave crests and the wave troughs from the regression coefficient-spectral wavelength relation graph as the characteristic wavelengths corresponding to the pesticide residues in the leaf vegetables to obtain the light source characteristic wavelengths corresponding to the leaf homogenate standard samples with different concentrations. The spectral wavelengths corresponding to the peaks and the troughs are selected because the absolute value of the regression coefficient corresponding to the peaks and the troughs is the largest, and the larger the regression coefficient is, the larger the contribution degree of the variable to the model is.
And preferentially selecting the light source characteristic wavelengths selected by the blade homogenate standard samples with different concentrations, determining the light source characteristic wavelength corresponding to the final blade homogenate standard sample, and further establishing a corresponding relation database of pesticide names and the light source characteristic wavelengths.
And determining the characteristic wavelength of the light source corresponding to the leaf homogenate sample according to the corresponding relation database of the pesticide name and the characteristic wavelength of the light source, and replacing the light source in the original leaf vegetable pesticide residue amount detection device with the light source with the characteristic wavelength corresponding to the leaf homogenate sample to obtain the leaf vegetable pesticide residue amount detection device corresponding to the leaf homogenate sample.
Step 103: and placing the culture dish filled with the blade homogenate sample on a sample groove of a leaf vegetable pesticide residue detection device corresponding to the blade homogenate sample, and enabling the center of the culture dish to be over against a spectral signal receiving probe of the leaf vegetable pesticide residue detection device corresponding to the blade homogenate sample so as to obtain spectral data of the blade homogenate sample.
The method specifically comprises the following steps: placing the culture dish on a sample groove, enabling the center of the culture dish to be opposite to the spectral signal receiving probe, carrying out black-and-white board correction on a spectrometer, collecting a reflection spectrum curve of a blade homogenate sample in the culture dish in a dark environment, repeating the operation three times, transmitting the reflection spectrum curve to a computer by the spectrometer, and calculating the average value of the three reflection spectrum curves to serve as spectral data of the blade homogenate sample.
The ASD field Spectrum 4 high resolution spectrometer (Analytical Spectral Devices, Inc., Boulder, Colorado, USA) used in this example has a Spectral range of 350-.
Step 104: preprocessing the spectral data to obtain a spectral matrix corresponding to the blade homogenate sample; the spectral matrix includes spectral intensity values corresponding to characteristic wavelengths of the selected light source from the leaf homogenate sample.
The spectral data obtained in The previous step are preprocessed in The unscrambler x10.4 software using Standard Normal Variance (SNV) or Multivariate Scatter Correction (MSC) algorithms. The purpose of the pre-processing is to reduce spectral noise, random noise and redundant information, reduce baseline drift of the sample reflectance spectral curve and solve the spectrum non-repeat problem.
Step 105: and calculating the pesticide residue in the leaf vegetable according to the spectral intensity value corresponding to the light source characteristic wavelength selected by the leaf homogenate sample. The method specifically comprises the following steps:
on the basis of the step 102, spectral intensity values and pesticide concentration values corresponding to the light source characteristic wavelengths selected by the leaf homogenate standard samples with different concentrations are determined.
Establishing a multiple linear regression model according to the spectral intensity value and the pesticide concentration value corresponding to the light source characteristic wavelength selected by the leaf homogenate standard samples with different concentrations to obtain a relational expression of the spectral intensity value and the pesticide true value in the leaf vegetables, and further establishing a corresponding relational database of pesticide names and the relational expression.
According to the pesticide name and relational expression corresponding relation database, determining a relational expression between the spectral intensity value corresponding to the leaf homogenate sample with the known pesticide name and unknown pesticide residue concentration and the pesticide true value in the leaf vegetables, substituting the spectral intensity value corresponding to the light source characteristic wavelength selected by the leaf homogenate sample into the relational expression between the spectral intensity value corresponding to the leaf homogenate sample and the pesticide true value in the leaf vegetables, and determining the pesticide residue in the leaf vegetables.
EXAMPLE III
Chlorpyrifos is an organophosphorus pesticide widely applied to nearly 100 countries and regions around the world and is commonly used for controlling various insect pests in agricultural production, but the chlorpyrifos has strong toxicity and can cause neurodevelopmental injury to human beings, particularly children. Therefore, how to rapidly and accurately detect the chlorpyrifos residual quantity in the leaf vegetables is a problem to be solved urgently at present.
The scheme for detecting the residual quantity of chlorpyrifos in leaf vegetables is described by taking pakchoi as an object of an embodiment.
Step 1: weighing 100mg of chlorpyrifos standard substance, and using acetonitrile to fix the volume to a 100mL volumetric flask so as to fully dissolve out the chlorpyrifos to obtain a chlorpyrifos standard solution.
Step 2: cutting fresh pakchoi leaf, putting into a wall-breaking blender, breaking wall sufficiently, and stirring for 5 min to obtain a homogenate of pakchoi leaf.
And step 3: sample homogenates containing different chlorpyrifos concentrations were prepared as follows:
weighing the mass of the dry clean beaker, pouring about 100mL of the Chinese cabbage leaf homogenate, and reading the mass of the Chinese cabbage leaf homogenate by adopting a weighing method. The reading quality is the amount of chlorpyrifos standard solution that needs to be added for subsequent calculation.
Adding a magnetic stirrer into the beaker filled with the Chinese cabbage leaf homogenate, and placing the beaker on a magnetic stirrer for stirring.
According to the quality of the leaf homogenate of the pakchoi in the beaker, adding a chlorpyrifos standard solution into the stirred leaf homogenate of the pakchoi to prepare 9 leaf homogenates of the pakchoi to be detected with concentration gradients of 0mg/kg, 0.5mg/kg, 1mg/kg, 5mg/kg, 10mg/kg, 30mg/kg, 50mg/kg, 70mg/kg and 100mg/kg respectively, namely the sample homogenate, and continuously stirring by using a magnetic stirrer. The stirring is to mix the chlorpyrifos standard solution and the Chinese cabbage leaf homogenate evenly.
And 4, step 4: 4mL of sample homogenate with different concentrations is extracted by a needle cylinder and is respectively injected into different culture dishes with the diameter of 35mm, and the culture dishes are slightly vibrated on a table top, so that the surfaces of the sample homogenate in the culture dishes are smooth.
And 5: placing the culture dish on a sample groove, enabling the center to be opposite to the spectral signal receiving probe, correcting a black-and-white board of the spectrometer, collecting a reflection spectral curve of sample homogenate in the culture dish in a dark environment, repeating the steps for three times as shown in figure 4, transmitting spectral information to a computer by the spectrometer, and calculating the average value of the three spectral curves to be used as the spectral curve of the sample.
The ASD field Spectrum 4 high resolution spectrometer (Analytical Spectral Devices, Inc., Boulder, Colorado, USA) used had a Spectral range of 350-.
Step 6: the spectral curve obtained in The previous step was pre-processed in The unscrambler x10.4 software using Standard Normal Variation (SNV), and The pre-processed spectral curve is shown in fig. 5. The purpose of preprocessing is to reduce spectral noise, random noise and redundant information, reduce baseline drift of the sample spectral curve and solve the problem of spectral non-repeatability.
And 7: in The unscrambler X10.4 software, a Partial Least Squares Regression (PLSR) model is established for The spectrum matrix X after SNV pretreatment and The corresponding chlorpyrifos concentration matrix Y, and regression coefficients of The spectrum data at each spectrum wavelength are obtained.
A graph of the relationship between the regression coefficient and the spectral wavelength is established by taking the spectral wavelength as the x-axis and the regression coefficient as the y-axis, as shown in fig. 6. Selecting spectral wavelengths corresponding to wave crests and wave troughs as characteristic wavelengths for detecting chlorpyrifos pesticide residue in the leaves of the pakchoi, namely 510nm, 567nm, 660nm, 695nm and 1161 nm.
The wavelengths corresponding to the peaks and the troughs are selected because the absolute value of the regression coefficient corresponding to the peaks and the troughs is the largest, and the larger the regression coefficient is, the larger the contribution degree of the variable to the model is.
And 8: and (4) establishing a multiple linear regression model based on the spectrums corresponding to the 5 characteristic wavelengths obtained in the step (7) and the corresponding chlorpyrifos concentration to obtain the relation between the spectrum peak value and the real value of the chlorpyrifos pesticide in the leaves of the Chinese cabbage.
The relationship is:
Y＝-18.7415-129.3202x1-129.4850x2-119.2168x3+353.0265x4-44.3132x5
the correlation coefficient of the formula is R2＝0.9820。
In the formula: y is the residual quantity of chlorpyrifos pesticide, and the unit is mg/kg (kg is the mass of homogenate to be detected, mg is the residual mass of chlorpyrifos pesticide), x1、x2、x3、x4、x5510nm, 567nm, respectively,Spectral intensity values at 660nm, 695nm, 1161 nm.
The detection effect of the chlorpyrifos pesticide residue in the leaf vegetables is good (taking the Chinese cabbage as an example, R)2＝0.9820)。
The light source characteristic wavelength and the spectral peak value corresponding to the chlorpyrifos pesticide are determined in the steps, and the relation formula of the real value of the chlorpyrifos pesticide in the pakchoi leaves is determined.
The operating process of the detection device provided by the invention is described by taking the pakchoi as an embodiment and combining the embodiment one.
Step 1: dark current and white board correction are performed. The cover is opened, a white board with the diameter the same as the caliber of the sample groove is placed in the sample groove, the detection device is placed on a horizontal desktop, the sample groove of the detection device is aligned with and completely covers the white board, the detection device is connected to a computer, and dark current information is obtained in computer software. And then, turning on a light source switch, turning on a light source with a characteristic wavelength corresponding to the chlorpyrifos pesticide, acquiring whiteboard information in computer software, and carrying out dark current and whiteboard correction.
Step 2: taking part of the sample homogenate to a culture dish as a sample to be detected, taking out the white board, and placing the culture dish containing the sample to be detected on a sample groove.
And step 3: collecting the spectral intensity values of the sample homogenate under each characteristic wavelength, repeating the steps for three times, transmitting the spectral information to a computer by a spectrometer, and calculating the average value of three spectral curves to be used as the spectral curve of the sample homogenate.
The composition of the multi-characteristic-wavelength light source 3 in the first embodiment is shown in fig. 7, and according to the result of the detection method in the present embodiment, the light source is a combination of five visible/near-infrared luminescent light sources 3-1 having peak wavelengths of 510nm, 567nm, 660nm, 695nm, and 1161nm, respectively.
And 4, step 4: the raw spectra are pre-processed, specifically, the spectral pre-processing method adopted is Standard Normal Variation (SNV).
y＝-18.7415-129.3202x1-129.4850x2-119.2168x3+353.0265x4-44.3132x5
in the formula: y is the residual quantity of chlorpyrifos pesticide, and the unit is mg/kg (kg is the mass of homogenate to be detected, mg is the residual mass of chlorpyrifos pesticide), x1、x2、x3、x4、x5The spectral intensity values are respectively at 510nm, 567nm, 660nm, 695nm and 1161 nm.
Example four
Carbendazim is a pesticide widely applied to the world and is commonly used for controlling various insect pests in agricultural production, but the carbendazim is extremely toxic and can cause neurodevelopment injury to human beings, particularly children. Therefore, how to rapidly and accurately detect the residual amount of carbendazim in the leaf vegetables is a problem to be solved urgently at present.
The technical scheme of the invention is described by taking the Chinese cabbage as an object of an embodiment.
Step 1: weighing 25mg of carbendazim standard substance, and using acetone to fix the volume to a volumetric flask of 250mL to fully dissolve the carbendazim to obtain a carbendazim standard solution.
Step 2: cutting fresh pakchoi leaf, putting into a wall-breaking blender, breaking wall sufficiently, and stirring for 5 min to obtain a homogenate of pakchoi leaf.
And step 3: sample homogenates containing different carbendazim concentrations were prepared as follows:
weighing the mass of the dry clean beaker, pouring about 100mL of the Chinese cabbage leaf homogenate, and reading the mass of the Chinese cabbage leaf homogenate by adopting a weighing method. The read quality is the amount of carbendazim standard solution that needs to be added for subsequent calculations.
Adding a magnetic stirrer into the beaker filled with the Chinese cabbage leaf homogenate, and placing the beaker on a magnetic stirrer for stirring.
Adding carbendazim standard solution into the stirred Chinese cabbage leaf homogenate according to the quality of the Chinese cabbage leaf homogenate in the beaker to prepare 9 concentration gradient Chinese cabbage leaf homogenates to be detected, namely sample homogenates, with the concentrations of 0mg/kg, 0.5mg/kg, 1mg/kg, 5mg/kg, 10mg/kg, 30mg/kg, 50mg/kg, 70mg/kg and 100mg/kg respectively, and continuously stirring by using a magnetic stirrer. The stirring is to mix the carbendazim standard solution and the vegetable leaf homogenate uniformly.
And 4, step 4: 4mL of sample homogenate with different concentrations is extracted by a needle cylinder and is respectively injected into different culture dishes with the diameter of 35mm, and the culture dishes are slightly vibrated on a table top, so that the surfaces of the sample homogenate in the culture dishes are smooth.
And 5: placing the culture dish on a sample groove, enabling the center to be opposite to the spectral signal receiving probe, correcting a black-and-white board of the spectrometer, collecting a reflection spectral curve of sample homogenate in the culture dish in a dark environment, repeating the steps for three times as shown in figure 8, transmitting spectral information to a computer by the spectrometer, and calculating the average value of the three spectral curves to be used as the spectral curve of the sample.
The ASD field Spectrum 4 high resolution spectrometer (Analytical Spectral Devices, Inc., Boulder, Colorado, USA) used had a Spectral range of 350-.
Step 6: the spectral curve obtained in The previous step was preprocessed in The Unscrambler X10.4 software using The Multivariate Scatter Correction (MSC) algorithm, and The preprocessed spectral curve is shown in fig. 9. The purpose of preprocessing is to reduce spectral noise, random noise and redundant information, reduce baseline drift of the sample spectral curve and solve the problem of spectral non-repeatability.
And 7: in The unscrambler X10.4 software, a Partial Least Squares Regression (PLSR) model is established for The spectrum matrix X after MSC pretreatment and The corresponding carbendazim concentration matrix Y, and regression coefficients of The spectrum data at each spectrum wavelength are obtained.
A graph of the relationship between the regression coefficient and the spectral wavelength is established with the spectral wavelength as the x-axis and the regression coefficient as the y-axis, as shown in fig. 10. Selecting the spectral wavelengths corresponding to the wave crests and the wave troughs as characteristic wavelengths for detecting the carbendazim pesticide residue in the leaves of the Chinese cabbage, namely 460nm, 516nm, 571nm, 611nm, 656nm, 698nm, 892nm, 962nm, 1133nm, 1172nm, 1291nm, 1692nm and 1729 nm.
The wavelengths corresponding to the peaks and the troughs are selected because the absolute value of the regression coefficient corresponding to the peaks and the troughs is the largest, and the larger the regression coefficient is, the larger the contribution degree of the variable to the model is.
And 8: and (4) establishing a multiple linear regression model based on the spectrums corresponding to the 13 characteristic wavelengths obtained in the step (7) and the corresponding carbendazim concentration to obtain the relation between the spectrum peak value and the true value of the carbendazim pesticide in the leaf of the Chinese cabbage.
The relationship is:
y＝-747.5698+432.2893x1-101.3953x2+1033.2830x3-1482.6500x4+692.6411x5+219.1137x6+875.1214x7+818.2443x8+728.0732x9-2665.6876x10+2055.9998x11-2483.1551x12+3665.1958x13
the correlation coefficient of the formula is R2＝0.9977。
In the formula: y is the residual amount of carbendazim pesticide in mg/kg (kg is the mass of homogenate to be measured and mg is the residual mass of carbendazim pesticide), and x1、x2、x3、x4、x5、x6、x7、x8、x9、x10、x11、x12、x13The spectral intensity values are respectively at 460nm, 516nm, 571nm, 611nm, 656nm, 698nm, 892nm, 962nm, 1133nm, 1172nm, 1291nm, 1692nm and 1729 nm.
The light source characteristic wavelength and the spectrum peak value corresponding to the carbendazim pesticide are determined in the steps, and the relation formula of the true value of the carbendazim pesticide in the Chinese cabbage leaves is determined.
The operating process of the detection device provided by the invention is described by taking the pakchoi as an embodiment and combining the embodiment one.
Step 1: dark current and white board correction are performed. The cover is opened, a white board with the diameter the same as the caliber of the sample groove is placed in the sample groove, the detection device is placed on a horizontal desktop, the sample groove of the detection device is aligned with and completely covers the white board, the detection device is connected to a computer, and dark current information is obtained in computer software. And then, turning on a light source switch, turning on a light source with a characteristic wavelength corresponding to the carbendazim pesticide, acquiring whiteboard information in computer software, and carrying out dark current and whiteboard correction.
Step 2: taking part of the sample homogenate to a culture dish as a sample to be detected, taking out the white board, and placing the culture dish containing the sample to be detected on a sample groove.
And step 3: collecting the spectral intensity values of the sample homogenate under each characteristic wavelength, repeating the steps for three times, transmitting the spectral information to a computer by a spectrometer, and calculating the average value of three spectral curves to be used as the spectral curve of the sample homogenate.
Referring to fig. 11, the composition of the multi-characteristic wavelength light source 3 in the first embodiment is shown, and according to the result of the detection method in the present embodiment, the light source is a combination of thirteen visible/near infrared light emitting sources 3-2 having peak wavelengths of 460nm, 516nm, 571nm, 611nm, 656nm, 698nm, 892nm, 962nm, 1133nm, 1172nm, 1291nm, 1692nm, and 1729 nm.
And 4, step 4: the raw spectrum is preprocessed, and specifically, the adopted spectrum preprocessing method is Multivariate Scattering Correction (MSC).
y＝-747.5698+432.2893x1-101.3953x2+1033.2830x3-1482.6500x4+692.6411x5+219.1137x6+875.1214x7+818.2443x8+728.0732x9-2665.6876x10+2055.9998x11-2483.1551x12+3665.1958x13
in the formula: y is the residual amount of carbendazim pesticide in mg/kg (kg is the mass of homogenate to be measured and mg is the residual mass of carbendazim pesticide), and x1、x2、x3、x4、x5、x6、x7、x8、x9、x10、x11、x12、x13Are respectively 460nm,516nm, 571nm, 611nm, 656nm, 698nm, 892nm, 962nm, 1133nm, 1172nm, 1291nm, 1692nm, and 1729 nm.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A leaf vegetable pesticide residue detection device based on near-infrared characteristic spectrum is characterized by comprising a shell; the shell comprises a cavity, a light source, a sample groove, a central controller, a spectrometer and a spectrum signal receiving probe, wherein the light source is arranged on the side surface of the cavity and forms a set angle with the vertical direction;
the central controller is connected with an external computer through a connecting wire arranged at the top of the shell;
the centers of the central controller, the spectrometer and the spectrum signal receiving probe are all on the same vertical line with the center of the sample groove.
2. The device for detecting the pesticide residue on the leaf vegetables based on the near-infrared characteristic spectrum as claimed in claim 1, wherein the cavity is V-shaped, and the inner surface of the cavity is completely black.
3. The device for detecting the pesticide residue on the leaf vegetables based on the near-infrared characteristic spectrum is characterized by further comprising a switch which is connected with the light source, is arranged outside the shell and is used for controlling the light source; wherein the light source is a multi-characteristic wavelength light source.
4. The device for detecting the pesticide residue on the leaf vegetables based on the near-infrared characteristic spectrum as claimed in claim 1, wherein the set angle is 30 °.
5. A method for detecting the pesticide residue of leaf vegetables based on a near-infrared characteristic spectrum is characterized by comprising the following steps:
obtaining a leaf homogenate sample with known pesticide name and unknown pesticide residue concentration;
determining the characteristic wavelength of the light source corresponding to the leaf homogenate sample according to a corresponding relation database of pesticide names and the characteristic wavelength of the light source, and replacing the light source of the characteristic wavelength corresponding to the leaf homogenate sample with the light source in the original leaf vegetable pesticide residue amount detection device to obtain the leaf vegetable pesticide residue amount detection device corresponding to the leaf homogenate sample;
placing the culture dish filled with the blade homogenate sample on a sample groove of a leaf vegetable pesticide residue detection device corresponding to the blade homogenate sample, and enabling the center of the culture dish to be over against a spectral signal receiving probe of the leaf vegetable pesticide residue detection device corresponding to the blade homogenate sample so as to obtain spectral data of the blade homogenate sample;
preprocessing the spectral data to obtain a spectral matrix corresponding to the blade homogenate sample; the spectrum matrix comprises a spectrum intensity value corresponding to the characteristic wavelength of the light source selected by the blade homogenate sample;
and calculating the pesticide residue in the leaf vegetable according to the spectral intensity value corresponding to the light source characteristic wavelength selected by the leaf homogenate sample.
6. The method for detecting the leaf vegetable pesticide residue based on the near-infrared characteristic spectrum of claim 5, wherein the establishment process of the database of the correspondence between the pesticide name and the light source characteristic wavelength is as follows:
preparing leaf vegetable products without pesticide application;
preparing a pesticide standard solution with a known pesticide name;
preparing leaf homogenate standard samples with different concentrations according to the pesticide standard solution and the leaf vegetable;
placing culture dishes containing blade homogenate standard samples with different concentrations on a sample groove of a leaf vegetable pesticide residue detection device in sequence, and enabling the center of the culture dish to be over against a spectral signal receiving probe of the leaf vegetable pesticide residue detection device so as to obtain spectral data corresponding to the blade homogenate standard samples with different concentrations;
sequentially preprocessing the spectral data corresponding to the blade homogenate standard samples with different concentrations to obtain a plurality of groups of matrix data; each group of matrix data comprises a spectrum matrix and a pesticide concentration matrix corresponding to the spectrum matrix;
establishing a regression coefficient-spectrum wavelength relation graph corresponding to each group of matrix data according to the matrix data;
determining the characteristic wavelengths of the light source selected by the leaf homogenate standard samples with different concentrations according to all the regression coefficient-spectrum wavelength relation graphs;
and preferentially selecting the light source characteristic wavelengths selected by the blade homogenate standard samples with different concentrations, determining the light source characteristic wavelength corresponding to the final blade homogenate standard sample, and further establishing a corresponding relation database of pesticide names and the light source characteristic wavelengths.
7. The method for detecting the pesticide residue on the leaf vegetables based on the near-infrared characteristic spectrum according to claim 6, wherein the establishing of the regression coefficient-spectrum wavelength relation graph corresponding to each group of matrix data according to the matrix data specifically comprises:
in the TheUnscamblbergerX10.4 software, establishing a partial least squares regression model according to the spectrum matrix and the pesticide concentration matrix corresponding to the spectrum matrix, and further obtaining a regression coefficient of the spectrum data under each spectrum wavelength;
and establishing a regression coefficient-spectrum wavelength relation graph by taking the spectrum wavelength as an x axis and the regression coefficient as a y axis.
8. The method for detecting the pesticide residue on the leaf vegetables based on the near infrared characteristic spectrum as claimed in claim 6, wherein the determining of the characteristic wavelengths of the light source selected by the standard samples of the leaf homogenate with different concentrations according to all the regression coefficient-spectrum wavelength relationship maps specifically comprises:
and in the regression coefficient-spectrum wavelength relation graph, taking the spectrum wavelengths corresponding to the wave crests and the wave troughs as the light source characteristic wavelengths corresponding to the pesticide residues in the leaf vegetables to obtain the light source characteristic wavelengths selected by the leaf homogenate standard samples with different concentrations.
9. The method for detecting the pesticide residue in the leaf vegetables based on the near-infrared characteristic spectrum of claim 5, wherein the preprocessing is performed on the spectral data to obtain a spectral matrix corresponding to the leaf homogenate sample, and specifically comprises:
and (3) preprocessing the spectral data by adopting a standard normal variation algorithm or a multivariate scattering correction algorithm in the TheUnscambleblerX10.4 software to obtain a spectral matrix corresponding to the blade homogenate sample.
10. The method for detecting the pesticide residue in the leaf vegetables based on the near-infrared characteristic spectrum as claimed in claim 6, wherein the calculating of the pesticide residue in the leaf vegetables according to the spectral intensity value corresponding to the light source characteristic wavelength selected by the leaf homogenate sample specifically comprises:
determining spectral intensity values and pesticide concentration values corresponding to light source characteristic wavelengths selected by blade homogenate standard samples with different concentrations;
establishing a multiple linear regression model according to the spectral intensity value and the pesticide concentration value corresponding to the light source characteristic wavelength selected by the leaf homogenate standard samples with different concentrations to obtain a relational expression of the spectral intensity value and the pesticide true value in the leaf vegetables, and further establishing a corresponding relational database of pesticide names and the relational expression;
and determining a relational expression between the spectral intensity value corresponding to the leaf homogenate sample and the actual pesticide value in the leaf vegetables according to the relational database between the pesticide name and the relational expression, substituting the spectral intensity value corresponding to the light source characteristic wavelength selected by the leaf homogenate sample into the relational expression between the spectral intensity value corresponding to the leaf homogenate sample and the actual pesticide value in the leaf vegetables, and determining the pesticide residue in the leaf vegetables.
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