CN108267438A - A kind of raman spectral signal analysis method for remaining blended - Google Patents
A kind of raman spectral signal analysis method for remaining blended Download PDFInfo
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Abstract
The invention discloses a kind of raman spectral signal analysis methods for the residual blended for belonging to fruits and vegetables agricultural product security detection technique field.The raman spectral signal for remaining blended is decomposed into the raman spectral signal of single pesticide using self-modeling mixture analytic approach by this method; and pass through and establish single pesticide prediction model to residual blended progress quantitative analysis; there is accuracy height, strong applicability, a kind of accurate, reliable, non-contact, lossless novel rapid detection method is provided for fruits and vegetables agricultural product harmful chemicals residual.
Description
Technical field
The invention belongs to fruits and vegetables agricultural product security detection technique field, more particularly to a kind of Raman light for remaining blended
Spectrum signal analysis method.
Background technology
In recent years, Food Quality Safety more and more causes social concerns.Pesticide is agriculturally being used as one kind
In prevention disease pest and coordinate plant growth, the medicament of weeding, crop can be promoted, had in agricultural production to pass
Consequence.But since there is pesticide diseases, the long-term consumptions such as carcinogenic, teratogenesis, early ageing can be enriched in human body, therefore
In China, Pesticide Residue is especially prominent.
Raman spectroscopy is a spectral analysis technique to be grown up based on Raman scattering effect, it is possible to provide molecule
Vibration or rotation information, detection limit it is low, can effectively exclude water and be used widely to the interference of testing result.In recent years
Come, Raman spectroscopy by feat of its without sample pre-treatments, it is quick, easy to operate not damaged the advantages that quality of agricultural product pacify
It is developed rapidly in complete analysis.
But often simultaneously containing there are many most of in poisonous and harmful substance, such as the apple of market sale in agricultural product
Simultaneously containing two or more pesticide residue, Raman spectroscopy can detect many kinds of substance, but due to most of material molecules simultaneously
Complicated, raman characteristic peak is intensive, leads to signal overlap, quantitative analysis difficult.
In consideration of it, how harmful chemicals residuals a variety of in fruits and vegetables agricultural product to be carried out using Raman spectroscopy quick, accurate
True non-destructive testing becomes the current technical issues that need to address.
Invention content
The purpose of the present invention is to provide a kind of raman spectral signal analysis method for remaining blended, particular technique sides
Case is as follows：
A kind of raman spectral signal analysis method for remaining blended is that will be remained using self-modeling mixture analytic approach
The raman spectral signal of blended is decomposed into the raman spectral signal of single pesticide, and passes through and establish single pesticide prediction model
Quantitative analysis is carried out to residual blended.
It the described method comprises the following steps：
(1) Raman spectrum detection is carried out to the sample for remaining Multiple Pesticides；
(2) it determines the characteristic peak of pesticide to be measured in Raman spectrum obtained by step (1), obtains the drawing of sample carryover blended
Graceful spectral signal；
(3) raman spectral signal obtained using self-modeling mixture analytic approach decomposition step (2) is obtained single pesticide and drawn
Graceful spectral signal；
(4) single pesticide prediction model is established, quantitative analysis is carried out to sample carryover blended.
Pesticide variety >=2 kind in step (1) the residual blended.
Different pesticides have respectively independent spy in the raman spectral signal for the residual blended that the step (2) obtains
Reference number, and feature peak-to-peak signal has overlapping；Self-modeling mixture analytic approach is according to pesticide variety by characteristic peak signal overlap
It remains blended Raman spectrum to decompose, obtains single pesticide raman spectral signal, and obtained single pesticide Raman will be decomposed
Spectral signal is compared with pesticide standard Raman spectrum, and verification self-modeling mixture analytic approach is applicable in field of pesticide detection
Property.
Feature letter in the single pesticide raman spectral signal that the step (3) obtains only containing a certain pesticide to be measured
Number, and pass through and probe into residual blended other type pesticide concentrations to the shadow of a certain pesticide raman spectral signal to be measured
It rings, verifies the stability and applicability of single pesticide Raman spectrum decomposed using self-modeling mixture analytic approach.
According to a certain kind pesticide Raman signal to be measured obtained under different known concentrations, the prediction model of the pesticide is established,
Then quantitative analysis is carried out to pesticide concentration each in sample carryover blended.
Single pesticide prediction model is pesticide concentration and characteristic peak signal intensity and/or degrees of offset in the step (4)
Between relationship.
Beneficial effects of the present invention are：
SMA method decomposed spectrum provided by the invention can be ensured that the accurate with complete of signal, while eliminate a variety of agricultures
Medicine mixes the influence to feature peak intensity, the single pesticide prediction model qualitative and quantitative analysis residual agriculture established on this basis
There is accuracy height, strong applicability during concentration；For fruits and vegetables agricultural product harmful chemicals residual provide it is a kind of accurate, can
It leans on, is non-contact, lossless novel rapid detection method.
Description of the drawings
Fig. 1 is a kind of flow diagram of raman spectral signal analysis method for remaining blended provided by the invention；
Fig. 2 is that SMA method decomposes the raman spectral signal data summarization figure that apple remains blended in embodiment 1；Its
In 2 (a) be remain blended Raman spectrum, 2 (b) -2 (d) is respectively the decomposition Raman of Acetamiprid, chlopyrifos, carbendazim
Spectrum；
Fig. 3 is 2 Chlorpyrifos of embodiment and carbendazim changes of contents to Acetamiprid characteristic peak 634cm-1Intensity effect；
Fig. 4 is that the stability of 2.1 Acetamiprid Raman spectrum of embodiment and applicability analysis data are converged；Wherein 4 (a) is pyridine worm
The identical sample carryover blended Raman spectrogram of amidine content, 4 (b) decompose raman spectral signal for Acetamiprid, and 4 (c) is pyridine
Worm amidine characteristic peak 634cm-1Partial enlarged view, 4 (d) be decomposed spectrum characteristic peak 634cm-1Intensity (open circles "○") with only
The characteristic peak 634cm of the sample spectra of Acetamiprid containing 0.3mg/kg-1Intensity (solid squares " █ ") compares；
Fig. 5 is that the stability of 2.2 chlopyrifos Raman spectrum of embodiment and applicability analysis data are converged；Wherein 5 (a) is poisons with poison
The identical sample carryover blended Raman spectrogram of tick content, 5 (b) decompose raman spectral signal for chlopyrifos, and 5 (c) is poison
Dead tick characteristic peak 680cm-1Partial enlarged view, 5 (d) be decomposed spectrum characteristic peak 680cm-1Intensity (open circles "○") with only
The characteristic peak 680cm of the sample spectra of chlopyrifos containing 0.3mg/kg-1Intensity (solid squares " █ ") compares；
Fig. 6 is that the stability of 2.3 carbendazim Raman spectrum of embodiment and applicability analysis data are converged；Wherein 6 (a) is more bacterium
The identical sample carryover blended Raman spectrogram of clever content, 6 (b) decompose raman spectral signal for carbendazim, and 6 (c) is more
Bacterium spirit characteristic peak 1231cm-1Partial enlarged view, 6 (d) be decomposed spectrum characteristic peak 1231cm-1Intensity (open circles "○") with
Contain only the characteristic peak 1231cm of 0.3mg/kg carbendazim sample spectras-1Intensity (solid squares " █ ") compares；
Fig. 7 is 4 quantitative analytical data summary view of embodiment；Wherein 7 (a) be sample carryover blended Raman spectrogram, 7
(b) it is Acetamiprid predicted value and the correlation analysis of measured value, 7 (c) is the correlation analysis of chlopyrifos predicted value and measured value,
7 (d) is the correlation analysis of carbendazim predicted value and measured value.
Specific embodiment
The present invention provides a kind of raman spectral signal analysis methods for remaining blended, below in conjunction with the accompanying drawings and implement
The present invention is described further for example.
Self-modeling mixture analytic approach (Self-modeling Mixture Analysis, SMA) is a system in the present invention
Row algorithm, for spectral mixing data to be decomposed into pure component spectrum and its contribution, without using about the previous of mixture
Information.By acquiring a series of information of pure component under different situations, certain one-component characteristic model is established, using existing more
The feature of a pure component, decomposition of the mixture information, so as to obtain in spectral mixture the contribution of one-component spectrum and with it is pure
The orthogonal interference information of component and contribution.SMA method mainly utilizes the purity function (Eigenvector of PLS_Toolbox
Research, Wenatchee, WA, USA) it realizes, briefly, exactly spectrum is decomposed using the method that vector decomposes.
Flow diagram according to a kind of raman spectral signal analysis method of residual blended as shown in Figure 1 is established
Single pesticide prediction model.
Embodiment 1：Utilize self-modeling mixture analytic approach (Self-modeling Mixture Analysis, SMA) point
Solve the raman spectral signal of apple residual blended
To remaining the apple sample progress Raman signal acquisition of various concentration Acetamiprid, chlopyrifos, carbendazim, wherein apple
In fruit Acetamiprid concentration range be 0~15.31mg/kg, chlopyrifos concentration range be 0~15.52mg/kg, carbendazim concentration model
It encloses for 0~10.53mg/kg.It is smooth and airPLS methods deduct fluorescence background, the mixed spectra after being corrected by S-G
Shown in signal such as Fig. 2 (a), the characteristic peak of 3 kinds of pesticides can be observed.
In this study, 3 kinds of pesticides have the independent characteristic signal of oneself, and have the situation of Partial Feature overlap of peaks, lead to
It crosses SMA method to decompose the characteristic peak of 3 kinds of pesticides, to ensure each ingredient to be decomposed with the presence of its independent characteristic signal.
The mixed spectra signal of various concentration remains of pesticide is decomposed respectively using SMA method, respectively obtains low concentration 0.5mg/
Kg, two kinds of Acetamiprids represented under concentration of high concentration 10mg/kg, chlopyrifos, carbendazim decomposition raman spectral signal, Acetamiprid,
Chlopyrifos, the decomposition Raman spectrum of carbendazim are specific as shown in Fig. 2 (b), 2 (c), 2 (d)；
By the decomposed spectrum signal that compares the spectral signal of standard sample of pesticide and extracted from mixed spectra signal it is found that
Ratio between the feature peak shift of decomposed spectrum signal during high concentration, peak shape and each characteristic peak, substantially and standard spectrum
It is consistent.Decomposed spectrum during low concentration, compared to standard spectrum, some characteristic peaks disappear, but several obvious spies
Sign peak is all clear and legible, for example the principal character peak of Acetamiprid is 634cm-1、1111cm-1And 2164cm-1, the spy of chlopyrifos
Sign peak is 348cm-1、621cm-1And 680cm-1, the characteristic peak of carbendazim is 632cm-1、1231cm-1、1278cm-1And
1522cm-1.It can be seen that SMA method can go out the raman spectral signal of the lower 3 kinds of pesticides of various concentration with successful decomposition, and to the greatest extent may be used
Can ensure signal it is accurate with it is complete.
Embodiment 2：The stability and applicability of single pesticide Raman spectrum
By probing into residual blended other type pesticide concentrations to a certain pesticide raman spectral signal to be measured
It influences, i.e., by probing into influencing each other for blended Raman signal, verifies what is decomposed in embodiment 1 using SMA method
The stability and applicability of single pesticide Raman spectrum.
One group of apple sample for remaining Acetamiprid, chlopyrifos, carbendazim is prepared, including 12 sample spots, wherein pyridine worm
Amidine is 0.3mg/kg, and the content of chlopyrifos is chosen and increased with sample number into spectrum gradual in 0~16.12mg/kg ranges inside gradient
Increase, the content of carbendazim are chosen in 0~10.04mg/kg ranges inside gradient and increase gradually increase with sample number into spectrum.It adopts respectively
It in 12 samples of collection after the Raman spectral information of blended, is decomposed using SMA method, extracts Acetamiprid in mixed spectra
Raman information, obtain chlopyrifos under various concentration, carbendazim Acetamiprid characteristic peak 634cm-1Strength Changes data, specifically such as
Shown in Fig. 3, wherein zero represents Acetamiprid characteristic peak 634cm-1Intensity, Δ represent chlopyrifos concentration, and represents carbendazim concentration.
From figure 3, it can be seen that when chlopyrifos, carbendazim pesticide concentration is relatively low, when being respectively less than 2mg/kg, Acetamiprid feature
Peak 634cm-1Place's signal strength remains stable substantially；When chlopyrifos, carbendazim pesticide concentration further increase, Acetamiprid characteristic peak
634cm-1Place's signal strength continuously decreases.Illustrate that, when the remains of pesticide concentration of mixing is higher, phase can occur for the signal of mixture
Mutual interference, when reason is that pesticide concentration incrementally increases, it is molten that Multiple Pesticides compete silver in raman spectral signal acquisition simultaneously
The active site that glue provides, the pesticide of high concentration causes number of active sites to be greatly reduced, therefore the work that other pesticides obtain
Property number of loci is less, and feature peak-to-peak signal is accordingly weaker；And when pesticide concentration all than it is relatively low when, occupied silver sol activity
Number of loci is less, and there is no apparent competitive relation between pesticide, therefore pesticide feature peak-to-peak signal is not interfered by other pesticides.
And in the fruits and vegetables of actual purchase, pesticide concentration therein is generally relatively low, substantially below 1.5mg/kg, therefore only needs
Blended signal influences each other under discussion low concentration.
Embodiment 2.1：Less than the Acetamiprid Raman spectrum in 1.5mg/kg concentration ranges, decomposed using SMA method
Stability and applicability
36 apple samples are prepared, the content for keeping Acetamiprid is the content model of 0.3mg/kg, chlopyrifos and carbendazim
It encloses for 0~1.5mg/kg, chooses 6 concentration gradients respectively in the range, combination of two obtains 36 samples, is tested to ensure
Accuracy, pesticide is uniformly applied to apple surface.The Raman spectral information of 36 samples is acquired respectively, if Fig. 4 (a) is it
In a sample blended Raman spectrogram, decomposed using SMA method, and extract the drawing of Acetamiprid in mixed spectra
(Fig. 4 (c) is Acetamiprid characteristic peak 634cm shown in graceful information signal such as Fig. 4 (b)-1Partial enlarged view).
The Acetamiprid characteristic peak 634cm that 36 apple sample mixing spectral analysis are obtained-1Intensity is summarized, such as Fig. 4
(d) in shown in open circles "○", and in the apple sample Raman signal with only being prepared containing a kind of pesticide of 0.3mg/kg Acetamiprids
Characteristic peak 634cm-1Signal strength compares and analyzes (in such as Fig. 4 (d) shown in solid squares " █ "), is specifically shown in 4 (d)；To two
Group data carry out one-way analysis of variance, and as shown in table 1, when confidence level α is 0.05, gained p value is 0.65, i.e. two groups of numbers
Otherness is not notable between, this illustrates Raman signal of the presence to Acetamiprid of surveyed blended Chlorpyrifos and carbendazim
It has no significant effect.
Embodiment 2.2：Less than the chlopyrifos Raman spectrum in 1.5mg/kg concentration ranges, decomposed using SMA method
Stability and applicability
Using the method identical with embodiment 2.1, the content of the chlopyrifos of 36 apple samples is 0.3mg/kg, pyridine worm
The content range of amidine and carbendazim is 0~1.5mg/kg, chooses 6 concentration gradients respectively in the range, and combination of two obtains
36 samples.
Data are made a concrete analysis of as shown in figure 5, the blended Raman spectrogram of wherein 5 (a) for one of sample, 5 (b)
For the chlopyrifos Raman information signal extracted from mixed spectra, 5 (c) is chlopyrifos characteristic peak 680cm-1Partial enlarged view, 5
(d) it is decomposed spectrum characteristic peak 680cm-1Intensity (open circles "○") and the characteristic peak for containing only 0.3mg/kg chlopyrifos sample spectras
680cm-1Intensity (solid squares " █ ") compares.One-way analysis of variance is carried out to two groups of data, as shown in table 1, in confidence water
When flat α is 0.05, gained p value is 0.62, i.e. otherness is not notable between two groups of data, this illustrates pyridine worm in surveyed blended
The presence of amidine and carbendazim has no significant effect the Raman signal of chlopyrifos.
Embodiment 2.3：Less than the carbendazim Raman spectrum in 1.5mg/kg concentration ranges, decomposed using SMA method
Stability and applicability
Using the method identical with embodiment 2.1, the content of the carbendazim of 36 apple samples is 0.3mg/kg, pyridine worm
The content range of amidine and chlopyrifos is 0~1.5mg/kg, chooses 6 concentration gradients respectively in the range, and combination of two obtains
36 samples.
Data are made a concrete analysis of as shown in fig. 6, the blended Raman spectrogram of wherein 6 (a) for one of sample, 6 (b)
Carbendazim to be extracted from mixed spectra decomposes raman spectral signal, and 6 (c) is carbendazim characteristic peak 1231cm-1Part put
Big figure, 6 (d) are decomposed spectrum characteristic peak 1231cm-1Intensity (open circles "○") is with containing only 0.3mg/kg carbendazim sample spectras
Characteristic peak 1231cm-1Intensity (solid squares " █ ") compares.One-way analysis of variance is carried out to two groups of data, as shown in table 1,
When confidence level α is 0.05, gained p value is 0.79, i.e. otherness is not notable between two groups of data, this illustrates to survey mixing agriculture
The presence of Acetamiprid and chlopyrifos has no significant effect the Raman signal of chlopyrifos in medicine.
1 single pesticide spectrum of table and blended extraction spectral differences specific analysis
From embodiment 2.1,2.2,2.3 as can be seen that in less than 1.5mg/kg concentration ranges, between blended mutually
Influence smaller can be ignored, therefore have one using the single pesticide Raman spectrum that SMA method decomposes in embodiment 1
Fixed stability and applicability can establish single pesticide prediction model by the single pesticide Raman spectrum under various concentration.
Embodiment 3：Establish single pesticide prediction model
One group of sample is prepared, wherein choosing different Acetamiprid concentration points, chlopyrifos and more bacterium in the range of 0~1.5mg/kg
The concentration ＜ 1.5mg/kg of spirit, concentration variation or constant, to ensure the accuracy of experiment, apple is uniformly applied to by pesticide
Surface.Sample is detected using Raman spectrum, and the Raman spectrum of blended is decomposed using SMA method, obtains different Acetamiprids
Acetamiprid Raman spectrum under concentration, establishes Acetamiprid prediction model：
Acetamiprid concentration y (mg/kg)=0.001098x1+0.002295x2-0.00328x3+ 0.6683, wherein x1For
634cm-1Locate feature peak intensity, x2For 1114cm-1Locate feature peak intensity, x3For 2167cm-1Locate feature peak intensity.
Similarly, chlopyrifos prediction model is：
Chlopyrifos concentration y (mg/kg)=- 0.0053x1+0.013x2- 0.72, wherein x1For 621cm-1Locate feature peak intensity,
x2For 680cm-1Locate feature peak intensity.
Carbendazim prediction model is：
Carbendazim concentration y (mg/kg)=- 0.000512x1+0.00735x2-0.00392x3+ 0.554, wherein x1For
621cm-1Locate feature peak intensity, x2For 1231cm-1Locate feature peak intensity, x3For 1525cm-1Locate feature peak intensity.
Embodiment 4：Quantitative analysis is carried out to sample carryover blended
Preparation 8 while the apple samples for remaining 3 kinds of Acetamiprid, chlopyrifos, carbendazim pesticides, wherein Acetamiprid are poisoned with poison
Tick, carbendazim concentration in the range of 0~1.5mg/kg.Using the method for gas chromatography-mass spectrography to pyridine in 8 samples
Worm amidine, chlopyrifos, carbendazim are detected, and obtain measured value.
Raman signal scanning is carried out to above-mentioned 8 apple samples, the Raman signal of each 25 points of sample collection takes it flat
Equal spectrum represents the Raman information of the sample, fluorescence background deduction is carried out to Raman spectrum, as a result as shown in Fig. 7 (a).
Each pesticide concentration of prediction model quantitative analysis obtained using embodiment 3：First in SMA method exploded view 7 (a)
Shown hybrid Raman spectrum obtains the decomposition raman spectral signal under 3 kinds of pesticide various concentrations, is obtained using embodiment 3 pre-
The concentration of Acetamiprid, chlopyrifos, carbendazim in 8 samples of model quantitative is surveyed, and obtained with gas chromatography-mass spectrography
Each sample pesticide concentration carries out correlation analysis, and each pesticide predicted value as shown in Fig. 7 (b) -7 (d) is distributed with measured value scatterplot,
Wherein 7 (b), 7 (c), 7 (d) represent Acetamiprid, chlopyrifos, carbendazim respectively；From Fig. 7 (b) -7 (d) it is found that Acetamiprid, poisoning with poison
The relative coefficient of tick, carbendazim predicted value and measured value is respectively 0.893,0.926,0.938.
SMA is not utilized to decompose hybrid Raman signal, it is obtained Acetamiprid, chlopyrifos, more directly using mixed spectra modeling
The related coefficient of bacterium spirit predicted value and measured value is respectively 0.781,0.720,0.772；It follows that it is decomposed using SMA method
After spectrum, the concentration of prediction model prediction blended established using single pesticide has feasibility, and correlation has very big
It improves, predicted value precision is significantly improved.
Claims (5)
1. a kind of raman spectral signal analysis method for remaining blended, which is characterized in that the described method comprises the following steps：
(1) Raman spectrum detection is carried out to the sample for remaining Multiple Pesticides；
(2) it determines the characteristic peak of pesticide to be measured in Raman spectrum obtained by step (1), obtains the Raman light of sample carryover blended
Spectrum signal；
(3) raman spectral signal obtained using self-modeling mixture analytic approach decomposition step (2) obtains single pesticide Raman light
Spectrum signal；
(4) single pesticide prediction model is established, quantitative analysis is carried out to sample carryover blended.
2. raman spectral signal analysis method according to claim 1, which is characterized in that step (1) the residual mixing
Pesticide variety >=2 kind in pesticide.
3. raman spectral signal analysis method according to claim 1, which is characterized in that the step (2) obtains residual
Different pesticides in the raman spectral signal of blended is stayed to have respectively independent characteristic signal, and feature peak-to-peak signal has weight
It is folded.
4. raman spectral signal analysis method according to claim 1, which is characterized in that the list that the step (3) obtains
Characteristic signal in one pesticide raman spectral signal only containing a certain pesticide to be measured.
5. raman spectral signal analysis method according to claim 1, which is characterized in that single agriculture in the step (4)
Relationship of the medicine prediction model between pesticide concentration and characteristic peak signal intensity and/or degrees of offset.
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| CN108982468A (en) * | 2018-07-13 | 2018-12-11 | 浙江大学 | A kind of Raman analysis method of trace impurity in p-chlorotoluene |
| CN111122536A (en) * | 2019-12-18 | 2020-05-08 | 盐城工学院 | Method for predicting content of each pesticide in mixed pesticide solution based on surface enhanced Raman spectroscopy |
| CN111912823A (en) * | 2020-06-30 | 2020-11-10 | 淮阴工学院 | Multi-component pesticide residue fluorescence detection analysis method |
| CN112461808A (en) * | 2019-09-06 | 2021-03-09 | 苏州市农产品质量安全监测中心 | Detection method and kit for detecting carbendazim in agricultural products |
| CN113624740A (en) * | 2021-08-12 | 2021-11-09 | 浙江大学 | Establishment method of fruit and vegetable surface pesticide residue rapid detection model and fruit and vegetable surface pesticide residue rapid detection method |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108982468A (en) * | 2018-07-13 | 2018-12-11 | 浙江大学 | A kind of Raman analysis method of trace impurity in p-chlorotoluene |
| CN108982468B (en) * | 2018-07-13 | 2020-02-28 | 浙江大学 | Raman analysis method for trace impurities in p-chlorotoluene |
| CN112461808A (en) * | 2019-09-06 | 2021-03-09 | 苏州市农产品质量安全监测中心 | Detection method and kit for detecting carbendazim in agricultural products |
| CN111122536A (en) * | 2019-12-18 | 2020-05-08 | 盐城工学院 | Method for predicting content of each pesticide in mixed pesticide solution based on surface enhanced Raman spectroscopy |
| CN111912823A (en) * | 2020-06-30 | 2020-11-10 | 淮阴工学院 | Multi-component pesticide residue fluorescence detection analysis method |
| CN113624740A (en) * | 2021-08-12 | 2021-11-09 | 浙江大学 | Establishment method of fruit and vegetable surface pesticide residue rapid detection model and fruit and vegetable surface pesticide residue rapid detection method |
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