CN110567909B - Method for detecting content of sex pheromone in trap chip - Google Patents
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Abstract
The invention provides a method for detecting the content of a sex pheromone in a chip of a trap, which comprises the following steps: (1) extracting the standard trapper chip by using an organic solvent to obtain a standard solution, and diluting the standard solution into a plurality of gradient solutions with gradient concentrations; (2) collecting spectra of a standard solution and a plurality of gradient solutions by using a near-infrared spectrometer; (3) fitting the sample spectrum obtained in the step (2) with a corresponding ratio concentration value based on a semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) to establish a quantitative detection model of the change of the content of the sex pheromone in the sex pheromone trap; (4) and (4) carrying out quantitative analysis on the content of the sex pheromone in the trapper chip to be detected by using the model obtained in the step (3). The method is simple and rapid to operate.
Description
Technical Field
The invention relates to the field of spectrum detection, in particular to a method for detecting the content of a sex pheromone in a chip of a trap.
Background
The tobacco house pests refer to a plurality of pests which are harmful to tobacco leaves and tobacco products in tobacco houses and tobacco storage places, in China, more than 30 tobacco house pests exist, the most harmful pests include 2 tobacco beetles and tobacco pink borers, and the most harmful pests are serious in part of regions. The tobacco bin pests have great loss to the tobacco leaves in the storage period, and the tobacco leaves are completely unusable due to the low level and the high level. Worldwide losses due to the infestation by tobacco beetles and tobacco meal borers are estimated to be about 1% annually. The direct insect damage rate of tobacco stored in China is 1.64 percent each year.
At present, the control method of the tobacco storing pests is to utilize a sex pheromone trapper to monitor so as to know the distribution and the density of the tobacco beetles and the tobacco pink borers, make clean and sanitary work, reasonably use insecticide and insect growth regulators and fumigate by phosphine. It can be seen that the stability of the sex pheromone content of the sex pheromone trap is important, and if the sex pheromone content of the sex pheromone trap is too low to cause the sex pheromone trap to fail in advance, the situation of the insect cannot be faithfully reflected.
Disclosure of Invention
The present disclosure provides a method for detecting the content of a sex pheromone in a trap chip.
In some aspects, there is provided a method of detecting the content of a sex pheromone in a trap chip, comprising:
(1) extracting the standard trapper chip by using an organic solvent to obtain a standard solution, and diluting the standard solution into a plurality of gradient solutions with gradient concentrations;
(2) collecting spectra of a standard solution and a plurality of gradient solutions by using a near-infrared spectrometer;
(3) fitting the sample spectrum obtained in the step (2) with a corresponding ratio concentration value based on a semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) to establish a quantitative detection model of the change of the content of the sex pheromone in the sex pheromone trap;
(4) carrying out quantitative analysis on the content of the sex pheromone in the trapper chip to be detected by using the model obtained in the step (3);
the organic solvent is composed of acetone and NN-dimethylformamide, and the volume ratio of the acetone to the NN-dimethylformamide is 18.5-19.5: 1.
Preferably, the volume ratio of acetone to NN-dimethylformamide is 19: 1.
The specific organic solvent formulation, i.e., the specific volume ratio of acetone to NN-dimethylformamide, is critical to obtain accurate detection of the amount of the sex pheromone in the trap chip. Deviation from the above formula does not lead to accurate detection results.
In some embodiments, a step of pre-treating the spectrum is further included between steps (2) and (3), the pre-treating being selected from the group consisting of: one or more of first order derivatives, second order derivatives, vector normalization, multivariate signal correction, and spectral smoothing.
In some embodiments, a step of pre-processing the spectrum is further included between steps (2) and (3), the method of pre-processing comprising one or more of:
-eliminating the differences due to sample inhomogeneities by using Multivariate Signal Correction (MSC);
the influence of baseline drift is eliminated by adopting first-order differential processing, and spectral profile change with higher resolution and clearer resolution than the original spectrum is obtained; and
-smoothing the spectrum using Savitzky-Golay filtering with a segment length of 9 and an interval of 5, eliminating high frequency noise and preserving useful low frequency information.
In some embodiments, the method for establishing a model for quantitatively detecting the change in the content of the sex pheromone in the sex pheromone trap in the step (3) comprises the steps of:
inputting spectral data;
setting initial parameters, executing a semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR), and estimating unmarked samples;
and (3) executing a semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) to obtain the optimal model parameters, and establishing a spectrum quantitative analysis model.
In some embodiments, between steps i and ii, a cross-validation method is used, and about 10% of the sample is reserved as a test sample; after the model is established, the test sample is input into the model for detection, and the performance of the model is evaluated.
In some embodiments, step (3) comprises the following operations,
establishing a quantitative model: adding 190 parts of sample into different acetone ratios, scanning the spectrum, preprocessing the spectrum data, and selecting 8634-4102 cm-1In the optimal spectrum section in the wave number range, 20 samples are reserved as test samples by adopting a cross validation method;
setting initial parameters: the iteration number M is 100, the number N of the initialized population is 25, p is 0.6, and the search range is: α ═ 0, 100], γ [0, 1000], λ ═ 0, 1000 ];
executing QPSO-LSS3VR algorithm, estimating unmarked sample, selecting detection root mean square error RMSEC and decision coefficient R for model detection performance2Carrying out evaluation; calculated, alpha is [0, 3.9 ]]，γ[0，118]，λ＝[0，42.5]The time is optimal;
wherein n is the number of samples,
for the predicted value of the test sample, yiIs the measured value of the test sample.
Using a coefficient of determination (R)2) As a criterion for evaluating the correlation between the actually measured values of the calibration set data and the predicted values (the closer the value is to 1, the better the correlation). The model with good prediction performance has R close to 12Lower RMSEC value.
In some embodiments, the semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) is designed as follows:
a) the TQ software is adopted to perform dimensionality reduction processing on the sample spectrum, and high-dimensional data is mapped to a low-dimensional space, so that distance measurement is facilitated;
b) computing a sample N in a set N of unlabeled samplesiAnd the samples M in the marked sample set MjDistance d (n) ofi，mj)。
c) Solving each unlabeled sample n according to KNN algorithmiThe set of k labeled neighbors of (a) is M'.
d) Taking the average value of all marked samples in M' and estimating the unmarked sample niThe initial estimate of (d) is: t isn＝Tl×(1+rand(0,1))
Wherein, TlRepresenting the mean of the labeled samples in the collection.
e) For the unlabeled sample selected for training, replacing the labeled value with the detection value currently given by the model; the values are kept unchanged for unlabeled samples that are not selected for the next iteration.
In some embodiments, the step (1) includes selecting 200 qualified trap chips which are just produced, adding 2000ml of organic solvent (composed of acetone and NN-dimethylformamide with a volume ratio of 19:1), after soaking and extracting for 24 hours, pouring out and uniformly stirring the solution, averagely dividing the solution into 200 parts, taking 10 parts of the solution as reference liquid, adding a certain amount of acetone into the other 190 parts of the solution respectively to prepare a preparation solution containing 70-99.9 vol% of reference liquid, setting the reference liquid value as 100, and taking the percentage of the prepared solution containing the reference liquid as a corresponding value.
In some embodiments, the step (2) comprises collecting the sample spectrum of the sample prepared by the above method by using a liquid transmission sampling module of a near infrared spectrometer, wherein the wave number is in the range of 10000-3800 cm-1Taking the background in the instrument as reference, the sample and reference are scanned for 70 times, and the resolution is 8cm-1。
In some aspects, there is provided a method of determining the validity of a trap chip, comprising the steps of:
extracting a trapper chip to be detected by using an organic solvent to obtain a solution to be detected;
detecting the content of the sex pheromone in a solution to be detected by using the method disclosed by any one of the disclosures to obtain a detected content;
and comparing the detected content with a preset threshold, judging that the trap chip is invalid when the detected content is less than the preset threshold, and judging that the trap chip is valid when the detected content is greater than the preset threshold.
In some embodiments, a Least squares support vector machine (LS-SVM) is a machine learning method. The documents Suykens, Johan A K.Least squares support vector machines [ J ]. International Journal of Circuit Theory & Applications,2002,27(6): 605-.
In some embodiments, the semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) is a semi-supervised SVR algorithm based on quantum-particle swarm optimization. The literature is based on semi-supervised and transfer learning near infrared spectroscopy modeling studies [ D ]. china oceanic university, 2012.
In some embodiments, the modeling step of the near infrared spectrum quantitative analysis based on the regression algorithm of the semi-supervised least squares support vector machine is as follows:
the first step is as follows: inputting spectrum data, and carrying out preprocessing such as noise reduction on the spectrum;
the second step: reserving 10% of samples as test samples by adopting a cross validation method;
the third step: setting initial parameters, executing a QPSO-LSS3VR algorithm, and estimating unmarked samples;
the fourth step: executing a QPSO-LSS3VR algorithm to obtain optimal model parameters, and establishing a spectrum quantitative analysis model;
the fifth step: and inputting the test sample into the model for detection, and evaluating the performance of the model.
Description of the terms
The sex pheromone (sex pheromone) refers to a chemical substance secreted by animals to communicate information between two sexes of the same species. Acting as a sex attractant.
Advantageous effects
By adopting the technical scheme, the problem that the concentration change of the sex pheromone in the sex pheromone trapper cannot be measured for the first time so as to evaluate the effectiveness of the sex pheromone trapper is solved, and the sex pheromone trapper has the advantages of simplicity and quickness in operation, no damage to the sex pheromone trapper, reusability and the like.
The inventor adopts specific spectrum acquisition parameters, specific spectrum preprocessing parameters, specific QPSO-LSS3VR algorithm parameters and specific quantitative model establishing parameters to obtain the best effect.
The fabrication of the modeled samples is also unique in the disclosed methods, e.g., some embodiments extract the standard trap chip with an organic solvent (consisting of acetone and NN-dimethylformamide in a volume ratio of 18.5 to 19.5:1) to obtain a standard solution, and dilute the standard solution into a plurality of gradient solutions of gradient concentration.
Drawings
FIG. 1 is a schematic view of some embodiments of the method for detecting the content of a sex pheromone in a trap chip according to the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to examples, but those skilled in the art will appreciate that the following examples are only illustrative of the present invention and should not be construed as limiting the scope of the present invention. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products commercially available.
FIG. 1 shows a schematic flow chart of a method for detecting the content of a sex pheromone in a trap chip according to the present disclosure, comprising:
102, collecting spectrums of a standard solution and a plurality of gradient solutions by using a near-infrared spectrometer;
103, fitting the sample spectrum obtained in the step (2) with a corresponding ratio concentration value based on a semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) to establish a quantitative detection model of the content change of the sex pheromone in the sex pheromone trap;
104, carrying out quantitative analysis on the content of the sex pheromone in the trapper chip to be detected by using the model obtained in the step (3);
the organic solvent was acetone and NN-Dimethylformamide (DMF) as follows 19: the obtained solution was mixed at a volume ratio of 1.
Embodiments of the present invention will be described in detail with reference to example 1
Example 1
The method for measuring the change in the concentration of sex pheromone in a sex pheromone trap is described in detail below:
(1) modeling sample preparation: 200 just produced qualified trapper chips are selected, 2000ml of solvent (acetone and NN-Dimethylformamide (DMF) are mixed according to the volume ratio of 19:1) is added, after 24 hours of soaking and extraction, the solution is poured out and evenly stirred, and is averagely divided into 200 parts, 10 parts of the solution are used as reference liquid, a certain amount of acetone is added into the other 190 parts of the solution respectively to prepare preparation liquid containing 70% -99.9% of the reference liquid, the reference liquid value is set to be 100, and the matching liquid value is used as a corresponding value according to the percentage of the reference liquid.
(2) Collecting a sample spectrum: collecting the spectrum of the sample by using a liquid transmission sampling module of a near-infrared spectrometer, wherein the wave number is 10000-3800 cm-1Taking the background in the instrument as reference, the sample and reference are scanned for 70 times, and the resolution is 8cm-1；
(3) Spectrum pretreatment: in order to reduce the noise and baseline drift of the near-infrared spectrogram, the near-infrared spectrogram is preprocessed by adopting one or more combinations of vector normalization, first derivative, second derivative, multivariate signal correction and spectrum smoothing methods; in the examples the sample spectra were subjected to the following methods to obtain the desired results:
a, eliminating differences caused by sample nonuniformity by adopting Multivariate Signal Correction (MSC);
b, eliminating the influence of baseline drift by adopting first-order differential processing to obtain spectral profile change with higher resolution and clearer resolution than the original spectrum;
c, filtering and smoothing the spectrum by adopting a Saviz-Golay (Savitzky-Golay) with the section length of 9 and the interval of 5, eliminating high-frequency noise and reserving useful low-frequency information;
(4) QPSO-LSS3VR algorithm design:
a) all sample spectra are subjected to dimensionality reduction by adopting TQ software, and high-dimensional data are mapped to a low-dimensional space, so that distance measurement is facilitated;
b) computing a sample N in a set N of unlabeled samplesiAnd the samples M in the marked sample set MjDistance d (n) ofi，mj)。
c) Solving each unlabeled sample n according to KNN algorithmiK sets M' of labeled neighbors.
d) Taking the average value of all marked samples in M' and estimating the unmarked sample niInitial estimation ofThe values are: t isn＝Tl×(1+rand(0,1))
Wherein, TlRepresenting the mean of the labeled samples in the collection.
e) For the unlabeled sample selected for training, replacing the labeled value with the detection value currently given by the model; the values are kept unchanged for unlabeled samples that are not selected for the next iteration.
(5) Establishing a quantitative model, namely adding 190 parts of samples with different acetone ratios, scanning a spectrum, preprocessing spectrum data, and selecting 8634-4102 cm-1Reserving 20 samples as test samples in the optimal spectrum section of the wave number range by adopting a cross validation method; setting initial parameters: the iteration number M is 100, the number of initialized group individuals N is 25, p is 0.6, and the search range is: α ═ 0, 100]，γ[0，1000]，λ＝[0，1000]. Executing QPSO-LSS3VR algorithm, estimating unmarked sample, selecting detection root mean square error RMSEC and decision coefficient R for model detection performance2Carrying out evaluation; calculated, alpha is [0, 3.9 ]]，γ[0，118]，λ＝[0，42.5]The time is optimal; establishing a spectrum quantitative analysis model according to the optimized parameters, wherein the model result is as follows: RMSEC ═ 5.61, R2＝0.9665。
20 prepared test samples are taken; the samples are added into a mathematical model as a verification set, and the detection result is compared with the ratio of actual preparation for analysis, and the results are shown in the following table 1.
TABLE 1 near-infrared model validation results for detecting changes in the content of sex pheromones
As shown in Table 1, the average relative error obtained by the detection of the method is only 5.18 percent, and the method has the advantage of high accuracy.
The result shows that the near infrared data model established by adopting the semi-supervised learning method based on the quantum particle swarm optimization-least squares support vector machine regression algorithm can well, quickly and accurately detect the change condition of the content of the sex pheromone in the sex pheromone trapper under the condition of less modeling samples, thereby judging whether the drug effect is invalid or not.
While specific embodiments of the invention have been described in detail, those skilled in the art will understand that: many modifications and variations of the details are possible in light of the overall teachings of the disclosure, and such variations are within the scope of the invention. The full scope of the invention is given by the appended claims and any equivalents thereof.
Claims (7)
1. A method of detecting the amount of a sex pheromone in a trap chip comprising:
(1) extracting the standard trapper chip by using an organic solvent to obtain a standard solution, and diluting the standard solution into a plurality of gradient solutions with gradient concentrations;
(2) collecting spectra of a standard solution and a plurality of gradient solutions by using a near-infrared spectrometer;
(3) fitting the sample spectrum obtained in the step (2) with a corresponding ratio concentration value based on a semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) to establish a quantitative detection model of the change of the content of the sex pheromone in the sex pheromone trap;
(4) carrying out quantitative analysis on the content of the sex pheromone in the trapper chip to be detected by using the model obtained in the step (3);
the organic solvent is composed of acetone and N, N-dimethylformamide, and the volume ratio of the acetone to the N, N-dimethylformamide is 18.5-19.5: 1;
the step (3) includes the operations of,
establishing a quantitative model: adding 190 parts of sample into different acetone proportions, scanning the spectrum, preprocessing the spectrum data, and selecting 4102-8634 cm-1In the optimal spectrum section in the wave number range, 20 samples are reserved as test samples by adopting a cross validation method;
setting initial parameters: the iteration number M is 100, the number N of the initialized population is 25, p is 0.6, and the search range is: α ═ 0, 100], γ ═ 0, 1000, λ ═ 0, 1000;
executing QPSO-LSS3VR algorithm, estimating unmarked sample, selecting detection root mean square error RMSEC and decision coefficient R for model detection performance2Carrying out evaluation; by calculation, α ═ 0, 3.9]，γ＝[0，118]，λ＝[0，42.5]The time is optimal;
establishing a spectrum quantitative analysis model according to the optimized parameters, wherein the model result is as follows: RMSEC ═ 5.61, R2＝0.9665；
Selecting 200 qualified trapper chips which are just produced, adding 2000ml of mixed proportioning liquid of acetone and N, N-dimethylformamide, soaking and extracting for 24 hours, pouring out and uniformly stirring the solution, averagely dividing the solution into 200 parts, taking 10 parts of the solution as reference liquid, adding a certain amount of acetone into the other 190 parts of the solution respectively to prepare a preparation liquid containing 70-99.9% of the reference liquid by volume percent, setting the value of the reference liquid as 100, and taking the value of the proportioning liquid as a corresponding value according to the volume percent of the reference liquid;
the semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) is designed according to the following method:
a) the TQ software is adopted to perform dimensionality reduction processing on the sample spectrum, and high-dimensional data is mapped to a low-dimensional space, so that distance measurement is facilitated;
b) computing a sample N in a set N of unlabeled samplesiAnd the samples M in the marked sample set MjD (n) ofi，mj)；
c) Solving each unmarked sample n according to KNN algorithmiA set of k labeled neighbors M';
d) taking the average value of all marked samples in M' and estimating the unmarked sample niThe initial estimate of (a) is: t isn＝Tl×(1+rand(0,1))
Wherein, TlRepresenting the mean of the marked samples in the set;
e) for the unlabeled sample selected for training, replacing the labeled value with the detection value currently given by the model; the values are kept unchanged for unlabeled samples that are not selected for the next iteration.
2. The method of claim 1, further comprising between steps (2) and (3) a step of pre-processing the spectrum by a method selected from the group consisting of: one or more of first order derivatives, second order derivatives, vector normalization, multivariate signal correction, and spectral smoothing.
3. The method of claim 1, further comprising between steps (2) and (3) a step of pre-processing the spectrum, the pre-processing method comprising one or more of:
-eliminating the differences due to sample inhomogeneity using Multivariate Signal Correction (MSC);
the influence of baseline drift is eliminated by adopting first-order differential processing, and spectral profile change with higher resolution and clearer resolution than the original spectrum is obtained; and
-smoothing the spectrum using Savitzky-Golay filtering with a segment length of 9 and an interval of 5, eliminating high frequency noise and preserving useful low frequency information.
4. The method according to claim 1, wherein the method for establishing the model for quantitatively detecting the change in the content of the sex pheromone in the sex pheromone trap in the step (3) comprises the following steps:
i. inputting spectral data;
setting initial parameters, executing a semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR), and estimating unmarked samples;
and iii, executing a semi-supervised least squares support vector machine regression algorithm (QPSO-LSS3VR) to obtain the optimal model parameters, and establishing a spectrum quantitative analysis model.
5. The method of claim 4, wherein between steps i and ii, 10% of the sample is reserved for testing by using a cross-validation method; after the model is established, the test sample is input into the model for detection, and the performance of the model is evaluated.
6. The method of claim 1, wherein step (2) comprises subjecting the prepared sample to a treatment with near redThe liquid transmission sampling module of the external spectrometer collects sample spectra, and the collection wave number range is 3800-10000 cm-1Taking the background in the instrument as reference, the sample and reference are scanned for 70 times, and the resolution is 8cm-1。
7. A method of determining the validity of a trap chip, comprising the steps of:
extracting a trapper chip to be detected by using an organic solvent to obtain a solution to be detected;
detecting the content of the sex pheromone in a solution to be detected by using the method of any one of claims 1 to 6 to obtain a detected content;
and comparing the detected content with a preset threshold, judging that the trap chip is invalid when the detected content is less than the preset threshold, and judging that the trap chip is valid when the detected content is greater than the preset threshold.
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