CN104297202A - Method for quantitatively determining pesticide residue in grains by use of THz-TDS (terahertz time-domain spectroscopy) frequency domain spectrum - Google Patents
Method for quantitatively determining pesticide residue in grains by use of THz-TDS (terahertz time-domain spectroscopy) frequency domain spectrum Download PDFInfo
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Abstract
The invention relates to a method for quantitatively determining pesticide residue in grains by use of THz-TDS (terahertz time-domain spectroscopy) frequency domain spectrum. The method comprises the steps of grinding a grain sample to be detected, tabletting to obtain a grain tablet sample to be detected, testing the sample by THz-TDS to obtain terahertz time-domain spectroscopy, carrying out Fourier transforming to obtain frequency domain spectrum, selecting a wave band interval with good frequency domain spectrum repeatability as a feature band, randomly dividing the frequency domain spectrum of the grain tablet sample to be detected in the feature band into a training set sample frequency domain spectrum and a validation set sample frequency domain spectrum, building a frequency domain spectrum quantitative analysis model by a partial least squares regression method to obtain the quantitative detection value of each grain sample to be detected. The method can really and effectively detect the pesticide residue in grains rapidly, accurately and quantitatively, wherein the average value of the predicted correlation coefficients of the frequency domain spectrum quantitative analysis model can reach 0.995.
Description
Technical field
The present invention relates to a kind of method that the THz-TDS of utilization frequency domain spectra quantitatively detects Pesticide Residues In Grain, belong to Pesticides Testing technical field.
Background technology
Along with the fast development of modern agriculture, agricultural and agricultural product are to the demand of agricultural chemicals and rely on growing.In addition some practitioners lack pesticide knowldgde, and long-term a large amount of agricultural chemicals that uses even is abused, and makes agricultural chemicals cause impact greatly and harm to environment and human health.For crops, after the applications of pesticide to crops, often have a small amount of residual in crops, remains of pesticide of ingesting for a long time can have a strong impact on health, also can cause significant damage to health and ecologic environment.Particularly in recent years, the teratogenesis that food Pesticide Residues exceeds standard and causes is disabled and poisoning etc. also more and more receives publicity, and the residual and metabolin of agricultural chemicals vestige is all found in soil, water and agricultural product.Thus, under the prerequisite vigorously advocating scientifical use agricultural chemicals, how to detect the food because agricultural chemicals causes fast and accurately, the problem such as pollution of environment just seems very important and urgent.
All there are corresponding method of detection and standard in countries in the world to agricultural product and Pesticide maximum residue limit, existing Pesticides Testing method mainly contains vapor-phase chromatography, high performance liquid chromatography, internal standard method for gas chromatography technology, liquid chromatography and mass spectrometric hyphenated technique, immunization, hexavalent chrome bio-removal etc., but said method all also exists sample pre-treatments complexity, detection time is long, testing cost is high, require higher to testing staff, cannot the deficiency such as on-line checkingi.
Terahertz emission (also claiming " THz radiation ") refers to that frequency is at 0.1THz-10THz, the electromagnetic wave of wavelength between 0.03-3mm, its wave band is between microwave and infrared ray, be the region of macroelectronics to the transition of microcosmic photonics, in electromagnetic spectrum, occupy very special position.The transition of the large molecule of much polarity between vibrational energy level is just in time in Terahertz frequency range, and therefore, the tera-hertz spectra of biomolecule can reflect by molecule or the intrinsic property of low frequency diaphragm that causes of intermolecular collective vibration and lattice vibration.Terahertz electromagnetic wave has lower photon energy, when carrying out sample detection, can not produce harmful photoionization, is a kind of effective lossless detection method.
Chinese patent literature CN103472032A discloses a kind of method utilizing terahertz time-domain spectroscopic technology to detect quadracycline, it comprises the steps: (1) by quadracycline powder and high-density polyethylene powder mixed grinding in varing proportions, be pressed into disk-shaped wafers one by one with sheeter, obtain the quadracycline compressing tablet containing different quality number percent; (2) in 0.1-3.5THz band limits, the terahertz time-domain spectroscopy of the quadracycline compressing tablet of different quality number percent is gathered one by one by terahertz time-domain spectroscopy system; (3) using the time domain waveform of nitrogen as with reference to signal, using the time domain waveform of quadracycline compressing tablet as sample signal, carry out Fourier transform respectively, obtain the frequency domain distribution of two kinds of signals, utilize formula to obtain absorption coefficient and the refractive index of compressing tablet sample; (4) set up calibration model according to the mass percent of each compressing tablet sample and the absorption spectra of correspondence thereof, carry out checking and the evaluation of calibration model.Although said method can realize carrying out quantitative and qualitative analysis detection to microbiotic quadracycline in food, but in Sample Preparation Procedure, by microbiotic quadracycline to be detected and high-density polyethylene powder mixed pressuring plate sample, tygon is utilized to be the content of " background " detection of antibiotics, what thus the method finally detected is the relative value being present in antibiotic content in tygon, this just causes differing greatly with the detection sample of reality, have impact on the authenticity detecting sample; Meanwhile, introduce new foreign matter tygon, unavoidably will impact the accuracy of pattern detection, also waste medicine simultaneously.In addition, now more people is the process utilizing Terahertz absorption spectra, thus qualitative or quantitative studies agricultural chemicals, does not utilize the useful information that THz-TDS technology obtains more fully.
Summary of the invention
Technical matters to be solved by this invention provides a kind of THz-TDS of utilization frequency domain spectra directly Pesticide Residues In Grain to be carried out to the method quantitatively detected.
For solving the problems of the technologies described above, the present invention is achieved by the following technical solutions:
Utilize THz-TDS frequency domain spectra quantitatively to detect a method for Pesticide Residues In Grain, it is characterized in that, comprise the steps:
(1) get the rear compressing tablet of grain samples to be measured grinding, obtain grain press sheet compression to be measured;
(2) getting the target pesticide sample for detecting, mixing with tygon according to the ratio of mass ratio 1:4, and compressing tablet after grinding, obtain containing target agricultural chemicals and poly mixed pressuring plate sample;
(3) THz-TDS spectroscopic system is utilized to test one by one described grain press sheet compression to be measured and described mixed pressuring plate sample, obtain the terahertz time-domain spectroscopy of each sample, terahertz time-domain spectroscopy signal under collection nitrogen atmosphere is as reference signal, and the terahertz time-domain spectroscopy signal gathering described grain press sheet compression to be measured and mixed pressuring plate sample is under the same conditions as sample signal, Reference Signal and sample signal are respectively through the frequency domain spectra signal Er of the frequency domain spectra signal Es and sample that obtain reference after Fourier transform:
In formula, ω represents frequency,
the phase differential of representative sample signal and reference signal, ρ is the ratio of the amplitude mode of sample signal and reference signal;
(4) according to the frequency domain spectra signal of described mixed pressuring plate sample, the good wave band of selected reappearance is interval as characteristic wave bands, and is training set sample frequency domain spectra and checking collection sample frequency domain spectra by the frequency domain spectra random division of described grain press sheet compression to be measured under described characteristic wave bands;
(5) utilize partial least-square regression method to set up the Quantitative Analysis Model of described training set sample frequency domain spectra and described checking collection sample frequency domain spectra, obtain the quantitative detected value of each described grain samples to be measured.
Described agricultural chemicals is pesticide.
Described pesticide is Acetamiprid, and described Acetamiprid frequency domain spectra characteristic wave bands is 0.1-2.0THz.
Described pesticide is sevin, and the frequency domain spectra characteristic wave bands of described sevin is 0.5-2.0THz.
Described pesticide is Imidacloprid, and the frequency domain spectra characteristic wave bands of described Imidacloprid is at 0.1-2.0THz.
In described step (5), partial least-square regression method is utilized to set up the Quantitative Analysis Model of described training set sample frequency domain spectra and described checking collection sample frequency domain spectra, the number of principal components of described Acetamiprid frequency domain spectra Quantitative Analysis Model is 2, the number of principal components of described sevin frequency domain spectra Quantitative Analysis Model is 4, and the main cause subnumber of described Imidacloprid frequency domain spectra Quantitative Analysis Model is 6.
In described step (3), the test condition of described THz-TDS is: detected temperatures is 19 DEG C, and with nitrogen as a reference, the scanning step motor interval of spectrometer scanning system is-1mm-2mm, and step-length is 0.01mm.
In described step (4), the division of described training set sample and described checking collection sample utilizes self-service Latin partition method to carry out;
The concrete computation process of described self-service Latin partition method is as follows:
A () tentation data collection has n sample, sample is randomly ordered, extracts N number of sample out from data centralization at every turn afterwards;
B this data set is divided into the m group of equal sizes by (), m=n/N;
C (), with first group for checking collection, residue m-1 group is training set, namely use n-N training set Sample Establishing analytical model, and prediction is drawn out of the N number of sample come;
D (), with second group for checking collection, residue m-1 group is training set, namely use n-N training set Sample Establishing analytical model, and prediction is drawn out of the N number of sample come;
E () by that analogy, carries out m time altogether, until all groups are drawn out of once and carry out only being predicted as.
When utilizing described self-service Latin partition method to divide described frequency domain spectra, repeat partition and calculate 10 times.
In described step (3), each sample duplicate measurements 3 times, each sample is the circle sheet of diameter 13mm, thickness 1.2mm.
In described step (5), the concrete Computing Principle of described partial least-square regression method is as follows:
Deflected secondary air is based upon the model on independent variable X and dependent variable Y matrix basis, by setting up the linear regression model (LRM) of latent variable about the latent variable of dependent variable of independent variable, and then the relation between reaction independent variable and dependent variable;
X＝TP
T+E＝∑t
ap
a t；
Y＝UQ
T+F＝∑u
aq
a t；
In formula: T is the score matrix of X, P is the loading matrix of X, and E is the residual matrix of X, t
afor score vector, p
afor corresponding load vectors, U is the score matrix of Y, and Q is the loading matrix of Y, and F is the residual matrix of Y, u
afor score vector, q
afor corresponding load vectors;
Partial least squares regression extracts respective latent variable respectively in X and Y, and they are respectively the linear combination of independent variable and dependent variable, should meet the following conditions simultaneously:
A () two groups of latent variable farthest carry the variation information of independent variable and dependent variable respectively;
B () covariance therebetween maximizes.
Described partial least squares regression is one and in iterative computation, utilizes mutually the information of the other side with the method for process of iteration progressively extract component, and iteration is constantly according to the remaining information adjustment t of X, Y each time
a, u
acarry out the second constituents extraction of taking turns, until the element absolute value in remaining matrix is approximately zero, algorithm stops.The coefficient obtained thus can better reflect the relation of X and Y.
Technique scheme of the present invention has the following advantages compared to existing technology:
(1) THz-TDS of utilization frequency domain spectra of the present invention quantitatively detects the method for Pesticide Residues In Grain, by the grain press sheet compression to be measured that compressing tablet after grain samples grinding to be measured is obtained, direct employing THz-TDS spectroscopic system is tested it, obtain terahertz time-domain spectroscopy, frequency domain spectra is obtained through Fourier transform, direct afterwards good for reappearance in frequency domain spectra wave band is decided to be characteristic wave bands, and be training set sample frequency domain spectra and checking collection sample frequency domain spectra by the frequency domain spectra random division of described grain press sheet compression to be measured under described characteristic wave bands, Quantitative Analysis Model is set up by partial least-square regression method, obtain the quantitative detected value of each described grain samples to be measured, the method of the invention can directly utilize the effective information of frequency domain spectra signal to carry out quantitative test, without the need to being further converted to absorption coefficient spectrum, thus simplify data processing step, and do not need in the described sample for detecting to mix other any materials, sample preparation is simple, can truly, effectively realize quantitatively detecting fast and accurately Pesticide Residues In Grain, the mean value of the prediction related coefficient (Rv) of described Quantitative Analysis Model be up to 0.995.
(2) THz-TDS of utilization frequency domain spectra of the present invention quantitatively detects the method for Pesticide Residues In Grain, in described step (4), adopt Latin partition method to carry out division obtain described training set sample frequency domain spectra and described checking collection sample frequency domain spectra, its reason is: described Latin partition method is a kind of method of testing model robustness, in computation process, first sample is randomly ordered, extract N number of sample out from data centralization at every turn afterwards, the N number of sample be drawn out of also is predicted with remaining sample, ensure each sample for and only for 1 time prediction, ensure that the compressing tablet sample of different quality mark is concentrated at training set and checking to occur with same ratio, thus realize evaluating without inclined institute's established model predictive ability, make qualification model more reliable, analysis result has more statistical significance.
(3) THz-TDS of utilization frequency domain spectra of the present invention quantitatively detects the method for Pesticide Residues In Grain, in described step (5), all adopt partial least-square regression method to set up Quantitative Analysis Model to described training set sample frequency domain spectra and described checking collection sample frequency domain spectra, and adopt correction root-mean-square error (RMSEC), predicted root mean square error (RMSEP), prediction related coefficient (R
v) as model performance pass judgment on foundation, RMSEC, RMSEP are less, R
vlarger, model is better.
Accompanying drawing explanation
In order to make content of the present invention be more likely to be clearly understood, below in conjunction with accompanying drawing, the present invention is further detailed explanation, wherein,
Fig. 1 is the frequency domain spectra of Acetamiprid described in the embodiment of the present invention 1-tygon mixed pressuring plate sample;
Fig. 2 is the frequency domain spectra in part grain samples 0.1-2.0THz characteristic wave bands to be measured interval described in the embodiment of the present invention 1;
Fig. 3 is the prediction square error of analytical model described in the embodiment of the present invention 1 and the graph of a relation of number of principal components;
Fig. 4 is the graph of a relation described in the embodiment of the present invention 1 between analytical model predicted value and experiment value;
Fig. 5 is the frequency domain spectra of sevin described in the embodiment of the present invention 2-tygon mixed pressuring plate sample;
Fig. 6 is the frequency domain spectra in part grain samples 0.5-2.0THz characteristic wave bands to be measured interval described in the embodiment of the present invention 2;
Fig. 7 is the graph of a relation of analytical model prediction square error and number of principal components described in the embodiment of the present invention 2;
Fig. 8 is the graph of a relation described in the embodiment of the present invention 2 between analytical model predicted value and experiment value.
Fig. 9 is the Terahertz frequency domain spectra of Imidacloprid described in the embodiment of the present invention 3-tygon mixed pressuring plate sample;
Figure 10 is the frequency domain spectra of grain samples to be measured in 0.1-2.0THz characteristic wave bands interval described in the embodiment of the present invention 3;
Figure 11 is the graph of a relation of cross validation root-mean-square error described in the embodiment of the present invention 3 and PLS main cause subnumber;
Figure 12 is the graph of a relation described in the embodiment of the present invention 3 between model prediction concentration and actual concentrations.
Embodiment
Embodiment 1
The present embodiment provides a kind of THz-TDS of utilization frequency domain spectra quantitatively to detect the method for Acetamiprid content in wheat samples in conjunction with Chemical Measurement, wherein, prepare after the cake wheat flour (Binzhou Taiyu Wheat Industry Co., Ltd.) of described " wheat samples " employing not containing any residues of pesticides itself adds known quantity Acetamiprid (Beijing North Na Chuanlian Bioteknologisk Institut provides), and adopt the inventive method to detect described " wheat samples " as blind sample, concrete steps are as follows:
(1) described cake wheat flour is put into comminutor to pulverize, sieve (200 order), puts into baking oven and dry, and obtains just wheat powder processed; To just mix according to different quality ratio with Acetamiprid powder by wheat powder processed, be transferred to further grinding in agate mortar and obtain described " wheat samples " fine powder, wherein, in described " wheat samples ", Acetamiprid mass percentage is followed successively by 0%, 0.5%, 1.0%, 1.5%, 2.0%, 2.5%, 3.0%, 3.5%, 4.0%, 4.5%, 5.5%, 6.0%, 6.5%, 7.0%, 9.0%, 12.0%, 14.0%, 20.0%, 25.0%, 27.0%;
Take " wheat samples " fine powder described in above-mentioned often kind and be about 200mg, be placed in the mould of Specac company, under the pressure of 5t, 3-4min is kept with sheeter, thus described " wheat samples " fine powder is pressed into the circle sheet of diameter 13mm, thickness 1.2mm, obtain the grain press sheet compression to be measured containing different quality number percent Acetamiprid, described grain press sheet compression two to be measured surface be parallel, smooth surface and do not have crack;
(2) Acetamiprid powder is mixed according to mass ratio 1:4 with polyethylene powders, be transferred in agate mortar and grind further, adopt the method identical with step (1) to suppress and obtain described mixed pressuring plate sample;
(3) the THz-TDS spectroscopic system adopted is described below:
Femtosecond laser oscillator Output of laser centre wavelength 800nm, pulsewidth 35fs, repetition frequency 74MHz.Femtosecond laser is by after 1/2 wave plate and polarizing prism, and beam energy is divided into two, and one, as pump light, shines directly on the GaAs photoelectric traverse of large aperture and produces THz radiation, utilize 4 piece 90 after optical delay line
°off axis paraboloidal mirror is collected and calibration THz ripple, is finally focused on ZnTe crystal transmitted through silicon chip; It is two as probe, converges and is irradiated on ZnTe crystal with the THz ripple conllinear produced after film reflection, utilize electro optic sampling method to detect THz wave through condenser lens.Voltage signal carries out de-noising by lock-in amplifier and amplifies process, finally carries out sampling, processing the time domain electric field waveform obtaining terahertz pulse with data acquisition software.
Test one by one described grain press sheet compression to be measured and described mixed pressuring plate sample (totally 21 compressing tablets) with above-mentioned THz-TDS spectroscopic system, each compressing tablet duplicate measurements obtains the terahertz time-domain spectroscopy of each sample for 3 times, averages; The test condition of described THz-TDS is: temperature is 19 DEG C, and with nitrogen as a reference, the scanning step motor interval of spectrometer scanning system is-1mm-2mm, and step-length is the frequency range of 0.01mm, THz-TDS system is 0.1-3.0THz;
Terahertz time-domain spectroscopy signal under collection nitrogen atmosphere is as reference signal, and the terahertz time-domain spectroscopy signal gathering described grain press sheet compression to be measured and mixed pressuring plate sample is under the same conditions as sample signal, Reference Signal and sample signal are respectively through the frequency domain spectra signal Er of the frequency domain spectra signal Es and sample that obtain reference after Fourier transform:
Wherein ω represents frequency,
the phase differential of representative sample and reference signal, ρ is the ratio of the amplitude mode of sample and reference signal;
(4) frequency domain spectra of described Acetamiprid-tygon mixed pressuring plate sample is illustrated in figure 1, can find out, in 0.1-3.0THz wave band, Acetamiprid is positioned at frequency 0.74THz, 0.76THz, 0.83THz, 1.18THz, 1.60THz, 1.68THz place existence 6 characteristic absorption, therefore, select 0.1-2.0THz as characteristic wave bands, in described characteristic wave bands interval, the reappearance of frequency domain spectra is better;
Be illustrated in figure 2 the frequency domain spectra of described part grain samples to be measured in 0.1-2.0THz characteristic wave bands interval, the mass percent of Acetamiprid is respectively 0.5%, and 6%, 7%, 9%, 12%, 25%;
For the frequency domain spectra of 0.1-2.0THz characteristic wave bands, adopt Latin partition method to be divided into training set sample frequency domain spectra and checking collection sample frequency domain spectra, N value gets 5 here; So 20 samples are divided into 4 groups at random, 15 samples are as training set, and 5 samples are as checking collection, and each partition, all ensures each sample only as 1 checking collection, and each sample is as training set 3 times, repeats partition 10 times;
Wherein, described Latin partition method, being described as follows of concrete computation process:
A () tentation data collection has n sample, sample is randomly ordered;
B this data set is divided into the m group (m=n/N) of equal sizes by ();
C (), with first group for checking collection, residue m-1 group is training set, namely use n-N training set Sample Establishing analytical model, and prediction is drawn out of the N number of sample come;
D (), with second group for checking collection, residue m-1 group is training set, namely use n-N training set Sample Establishing analytical model, and prediction is drawn out of the N number of sample come;
E () by that analogy, carries out m time altogether, until all groups are drawn out of once and only carry out being predicted as;
(5) adopt partial least-square regression method to set up Quantitative Analysis Model to described training set sample and described checking collection sample frequency domain spectra, its concrete principles illustrated is as follows:
Deflected secondary air is based upon the model on independent variable X and dependent variable Y matrix basis, by setting up the linear regression model (LRM) of latent variable about the latent variable of dependent variable of independent variable, and then the relation between reaction independent variable and dependent variable;
X＝TP
T+E＝∑t
ap
a t；
Y＝UQ
T+F＝∑u
aq
a t；
In formula: T is the score matrix of X, P is the loading matrix of X, and E is the residual matrix of X, t
afor score vector, p
afor corresponding load vectors, U is the score matrix of Y, and Q is the loading matrix of Y, and F is the residual matrix of Y, u
afor score vector, q
afor corresponding load vectors;
Partial least squares regression extracts respective latent variable respectively in X and Y, and they are respectively the linear combination of independent variable and dependent variable, should meet the following conditions simultaneously:
A () two groups of latent variable farthest carry the variation information of independent variable and dependent variable respectively;
B () covariance therebetween maximizes;
Partial least squares regression be one with the method for process of iteration progressively extract component.In iterative computation, utilize mutually the information of the other side, iteration is constantly according to the remaining information adjustment t of X, Y each time
a, u
acarry out the second constituents extraction of taking turns, until the element absolute value in remaining matrix is approximately zero, algorithm stops.The coefficient obtained thus can better reflect the relation of X and Y.
Be illustrated in figure 3 the graph of a relation of described model prediction square error and number of principal components, when number of principal components is 2, the predicted value of model and experiment value are very close, are illustrated in figure 4 the graph of a relation between described model predication value and experiment value, and prediction related coefficient can reach 0.995.
Utilize Latin partition method to verify institute's established model, root-mean-square error (RMSEC) will be corrected, foundation that predicted root mean square error (RMSEP), prediction related coefficient (Rv) are passed judgment on as model performance; Under different partition number of times, the correction root-mean-square error (RMSEC) calculated, predicted root mean square error (RMSEP), prediction related coefficient (Rv) are as shown in table 1.
Table 1-partial least squares regression sets up the results of property of analytical model
As can be seen from table 1, data equally, and RMSEC, RMSEP of above-mentioned analytical model are smaller, R
vcomparatively large, thus illustrate that the model that the inventive method is set up is reliably feasible, can be used for quantitatively detecting Acetamiprid in wheat samples.
Embodiment 2
The present embodiment provides one to utilize THz-TDS frequency domain spectra quantitatively to detect sevin content in rice sample in conjunction with Chemical Measurement, wherein, prepare after the rice (production of Jilin Jia Fu meter industry company limited) of described " rice sample " employing not containing any residues of pesticides itself adds known quantity sevin (Beijing North Na Chuanlian Bioteknologisk Institut provides), and adopt the inventive method to detect described " rice sample " as blind sample, concrete steps are as follows:
(1) described rice is put into comminutor to pulverize, sieve (100 order), puts into baking oven and dry, obtain rice powder; Rice powder is mixed according to different quality ratio with sevin powder, is transferred to further grinding in agate mortar and obtains described " rice sample " fine powder, wherein, in described " rice sample ", sevin mass percentage is followed successively by 0%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1.0%, 2.0%, 3.0%, 4.0%, 5.0%, 6.0%, 7.0%, 8.0%;
Take " rice sample " fine powder described in above-mentioned often kind and be about 200mg, be placed in the mould of Specac company, under the pressure of 5t, 3-4min is kept with sheeter, thus described " rice sample " fine powder is pressed into the circle sheet of diameter 13mm, thickness 1.2mm, obtain the grain press sheet compression to be measured containing different quality number percent sevin, described grain press sheet compression two to be measured surface be parallel, smooth surface and do not have crack;
(2) sevin powder is mixed according to mass ratio 1:4 with polyethylene powders, be transferred in agate mortar and grind further, adopt the method identical with step (1) to suppress and obtain described mixed pressuring plate sample;
(3) the THz-TDS spectroscopic system adopted is described below:
Femtosecond laser oscillator Output of laser centre wavelength 800nm, pulsewidth 35fs, repetition frequency 74MHz.Femtosecond laser is by after 1/2 wave plate and polarizing prism, and beam energy is divided into two, and one, as pump light, shines directly on the GaAs photoelectric traverse of large aperture and produces THz radiation, utilize 4 piece 90 after optical delay line
°off axis paraboloidal mirror is collected and calibration THz ripple, is finally focused on ZnTe crystal transmitted through silicon chip; It is two as probe, converges and is irradiated on ZnTe crystal with the THz ripple conllinear produced after film reflection, utilize electro optic sampling method to detect THz wave through condenser lens.Voltage signal carries out de-noising by lock-in amplifier and amplifies process, finally carries out sampling, processing the time domain electric field waveform obtaining terahertz pulse with data acquisition software.
Test one by one described grain press sheet compression to be measured and described mixed pressuring plate sample (totally 16 compressing tablets) with above-mentioned THz-TDS spectroscopic system, each compressing tablet duplicate measurements obtains the terahertz time-domain spectroscopy of each sample for 3 times, averages; The test condition of described THz-TDS is: temperature is 19 DEG C, and with nitrogen as a reference, the scanning step motor interval of spectrometer scanning system is-1mm-2mm, and step-length is the frequency range of 0.01mm, THz-TDS system is 0.1-3.0THz;
Terahertz time-domain spectroscopy signal under collection nitrogen atmosphere is as reference signal, and the terahertz time-domain spectroscopy signal gathering described grain press sheet compression to be measured and mixed pressuring plate sample is under the same conditions as sample signal, Reference Signal and sample signal are respectively through the frequency domain spectra signal Er of the frequency domain spectra signal Es and sample that obtain reference after Fourier transform:
Wherein ω represents frequency,
the phase differential of representative sample and reference signal, ρ is the ratio of the amplitude mode of sample and reference signal;
(4) frequency domain spectra of described sevin-tygon mixed pressuring plate sample is illustrated in figure 5, can find out, in 0.1-3.0THz wave band, sevin is positioned at frequency 0.9THz place existence 1 characteristic absorption, therefore, select 0.5-2.0THz as characteristic wave bands, in described characteristic wave bands interval, the reappearance of frequency domain spectra is better;
Be illustrated in figure 6 the frequency domain spectra in described part grain samples 0.5-2.0THz characteristic wave bands to be measured interval, the mass percent of Acetamiprid is respectively 0.8%, and 0.9%, 2.0%, 6.0%, 7.0%.
For the frequency domain spectra of 0.5-2.0THz characteristic wave bands, adopt Latin partition method to be divided into training set sample frequency domain spectra and checking collection sample frequency domain spectra, N value gets 4 here; So 15 samples are divided into 4 groups at random, each partition, all ensure each sample only as 1 checking collection, and each sample is as training set 3 times, repeats partition 10 times;
Wherein, described Latin partition method, being described as follows of concrete computation process:
A () tentation data collection has n sample, sample is randomly ordered;
B this data set is divided into the m group (m=n/N) of equal sizes by ();
C (), with first group for checking collection, residue m-1 group is training set, namely use n-N training set Sample Establishing analytical model, and prediction is drawn out of the N number of sample come;
D (), with second group for checking collection, residue m-1 group is training set, namely use n-N training set Sample Establishing analytical model, and prediction is drawn out of the N number of sample come;
E () by that analogy, carries out m time altogether, until all groups are drawn out of once and only carry out being predicted as;
(5) adopt partial least-square regression method to set up Quantitative Analysis Model to described training set sample and described checking collection sample frequency domain spectra, its concrete principles illustrated is as follows:
Deflected secondary air is based upon the model on independent variable X and dependent variable Y matrix basis, by setting up the linear regression model (LRM) of latent variable about the latent variable of dependent variable of independent variable, and then the relation between reaction independent variable and dependent variable;
X＝TP
T+E＝∑t
ap
a t；
Y＝UQ
T+F＝∑u
aq
a t；
In formula: T is the score matrix of X, P is the loading matrix of X, and E is the residual matrix of X, t
afor score vector, p
afor corresponding load vectors, U is the score matrix of Y, and Q is the loading matrix of Y, and F is the residual matrix of Y, u
afor score vector, q
afor corresponding load vectors;
Partial least squares regression extracts respective latent variable respectively in X and Y, and they are respectively the linear combination of independent variable and dependent variable, should meet the following conditions simultaneously:
A () two groups of latent variable farthest carry the variation information of independent variable and dependent variable respectively;
B () covariance therebetween maximizes;
Partial least squares regression be one with the method for process of iteration progressively extract component.In iterative computation, utilize mutually the information of the other side, iteration is constantly according to the remaining information adjustment t of X, Y each time
a, u
acarry out the second constituents extraction of taking turns, until the element absolute value in remaining matrix is approximately zero, algorithm stops.The coefficient obtained thus can better reflect the relation of X and Y.
Be illustrated in figure 7 the prediction square error of described analytical model and the graph of a relation of number of principal components, select number of principal components to be 4 according to Fig. 7.By Latin partition method, institute's established model is verified, root-mean-square error (RMSEC) will be corrected, foundation that predicted root mean square error (RMSEP), prediction related coefficient (Rv) are passed judgment on as model performance, result is as shown in table 2.
Be illustrated in figure 8 the graph of a relation between the predicted value of described analytical model and experiment value, illustrate the predicted value of model and experiment value very close, prediction related coefficient can reach 0.959.
Table 2-partial least squares regression sets up the results of property of analytical model
As can be seen from table 2, data equally, and RMSEC, RMSEP of above-mentioned analytical model are smaller, R
vcomparatively large, thus illustrate that the model that the inventive method is set up is reliably feasible, can be used for quantitatively detecting sevin in little rice sample.
Embodiment 3
The present embodiment provides one to utilize Terahertz frequency domain spectra quantitatively to detect Imidacloprid content in rice sample, wherein, prepare after the rice (Zhangjiakou City inspection and quarantine bureau of Hebei province provides) of described " rice sample " to be measured employing not containing any residues of pesticides itself adds known quantity Imidacloprid (Beijing North Na Chuanlian Bioteknologisk Institut provides), and adopt the inventive method to detect described " rice sample " to be measured as blind sample, concrete steps are as follows:
(1) described rice is put into comminutor to pulverize, sieve after grinding (100 orders, particle diameter≤150 μm), put into baking oven and dry, obtain rice powder; Rice powder is mixed according to different quality ratio with Imidacloprid powder, is transferred to further grinding in agate mortar and obtains described " rice sample " to be measured fine powder, wherein, in described " rice sample " to be measured, Imidacloprid mass percentage is followed successively by 0%, 0.99%, 1.5%, 1.97%, 2.50%, 3.00%, 3.50%, 4.00%, 4.50%, 5.01%, 5.50%, 5.91%, 6.51%, 7.00%, 7.50%, 8.00%, 8.51%, 9.00%, 9.50%, 10.00%, 11.99%, 14.02%, 15.01%;
Take " rice sample " fine powder described in above-mentioned often kind and be about 170mg, be placed in the mould of Specac company, under the pressure of 5t, 3-4min is kept with sheeter, thus described " rice sample " fine powder is pressed into diameter 13mm, circle sheet that thickness is about 1.0mm, obtain the grain press sheet compression to be measured containing different quality number percent Imidacloprid, described grain press sheet compression two to be measured surface be parallel, smooth surface and do not have crack.
(2) Imidacloprid powder is mixed according to mass ratio 1:4 with polyethylene powders, be transferred in agate mortar and grind further, adopt the method identical with step (1) to suppress and obtain described mixed pressuring plate sample;
(3) application is based on the transmission-type terahertz time-domain spectroscopy system of photoconductive antenna, adopt the Mai Tai type femtosecond laser oscillator of U.S. Spectra-Physics (spectrum-physics) brand as external lasing light emitter, laser pulse centre wavelength 800nm, output power > 500mW, under 20 DEG C of conditions, Terahertz frequency range is 0.3-3.0THz, with nitrogen as a reference, adopt transmission measurement pattern, gather the terahertz time-domain spectroscopy signal of described grain press sheet compression to be measured as sample signal E
sam(t), and before the time-domain spectroscopy data in Terahertz region gathering each press sheet compression, first gather the time-domain spectroscopy signal in the Terahertz region under the nitrogen atmosphere not placing press sheet compression as reference signal E
ref(t); Reference Signal E
ref(t) and sample signal E
samt () is respectively through the frequency-region signal E obtaining reference after Fast Fourier Transform (FFT)
ref(ω) and the frequency-region signal E of sample
sam(ω).
When carrying out above-mentioned data acquisition, the sample spectra of each described grain press sheet compression to be measured repeated acquisition 3 times under equivalent environment, the mean value finally getting 3 times, as subsequent treatment frequency domain spectra data used, finally obtains 23 groups of frequency domain spectra data.
(4) be illustrated in figure 9 employing the inventive method and measure Imidacloprid-poly Terahertz frequency domain spectra, can find out, be the frequency domain spectra characteristic peak that 0.89THz, 1.26THz, 1.50THz place exists Imidacloprid in frequency.
Be the frequency domain spectra of grain samples described to be measured in effective frequency range interval of the different Imidacloprid mass percentage described in this patent as shown in Figure 10, in the frequency range of 0.1-2.0THz, contain the frequency domain spectra information of the overwhelming majority of pesticide imidacloprid, therefore can as effective frequency range of application Terahertz frequency domain spectra technical Analysis rice Pesticides Imidacloprid.In addition, as can be seen from Figure 10, in the scope that Imidacloprid massfraction is 0.00%-15.01%, the frequency domain spectra characteristic peak of Imidacloprid and the massfraction change in direct ratio of Imidacloprid, so the frequency domain spectra in the characteristic spectra of Imidacloprid can be used in the quantitative test of grain Pesticides Imidacloprid.
In the validity feature wave band of selected grain press sheet compression described to be measured, by the frequency domain spectra of described grain press sheet compression to be measured, above-mentioned 23 groups of frequency domain spectra data, self-service Latin partition method is adopted to be divided into training set sample frequency domain spectra and checking collection sample frequency domain spectra, partition number N is selected to get 4, get wherein 3/4 as training set sample, 1/4 as checking collection sample, be specially: first sample to be tested is divided into 4 parts, select wherein 1 part of conduct checking collection sample, all the other 3 parts as training set sample, it should be noted that, in each calculating, each sample is only for once predicting checking.
(5) training set data collection is utilized, quantitative calibration models is set up in conjunction with partial least squares regression, adopt leave one cross validation, determine the main cause subnumber of partial least squares regression, as shown in Figure 11, when main gene is 6, the root-mean-square error of cross validation is minimum, RMSECV=0.00418 (MSECV=1.747 × 10
-5), adopt 6 main genes when therefore setting up quantitative calibration models.The concrete principle of described partial least squares regression is as follows:
First partial least squares regression decomposes the Terahertz frequency domain spectra matrix X of sample and concentration matrix Y, and its model tormulation is as follows:
In above-mentioned expression formula, t
kthe score of i-th main gene that (n × 1) is matrix X; p
kthe load of i-th main gene that (1 × m) is matrix X; u
k(n × 1) is the score of i-th main gene of concentration matrix Y, q
k(1 × p) is the load of i-th main gene of concentration matrix Y; K is main cause subnumber.T and U is the score matrix of X and Y matrix respectively, P and Q is the loading matrix of X and Y matrix respectively, Ex and E
ythen the PLS regression criterion matrix of X and Y matrix respectively.
Afterwards T and U two score matrix T and U are done linear regression:
U＝TB
B＝(T
TT)
-1T
TY
Last when predicting, first obtain the score matrix T ＇ of unknown sample frequency domain spectra matrix X ＇ according to the loading matrix P of matrix X, then can be obtained the concentration prediction matrix Y ＇ of unknown sample by following formula:
Y＇＝T＇BQ
The concentration prediction value y ＇ of the unknown sample in concentration prediction matrix Y ＇ can be obtained thus;
(5) checking collection data set is utilized, under best main cause subnumber, verify the estimated performance of the partially minimum second metering calibration model set up, the actual concentrations of different sample and the comparing result of prediction concentrations as shown in table 3, result shows: within the scope of the Imidacloprid massfraction of 0-15%, and the prediction square error of partial least square model is MSEV=2.0561 × 10
-5, squared correlation coefficient R
2=0.9870.Be the graph of a relation between described model prediction concentration and actual concentrations as shown in figure 11, can know and find out that the correlativity between described model prediction concentration and actual concentrations is satisfactory.
The different sample actual concentrations value of table 3-and prediction concentrations value
As can be seen here, the MSEV of above-mentioned analytical model prediction is less, R
2up to 0.9870, thus illustrate that the model that the inventive method is set up is reliably feasible, can be used for quantitatively detecting rice sample Pesticides Imidacloprid.
Adopt the method for foregoing description, not only quantitatively can detect the Acetamiprid in wheat and the sevin in rice, Imidacloprid, also can be generalized to the grain samples of other kind and the detection of other agricultural chemicals.In the detection of the sample of other kind grain, also need sample preparation powdered compressing tablet.The agricultural chemicals detected should have characteristic absorption in Terahertz frequency range.
Detection method of the present invention can be accurate, easy the farm chemical ingredients in grain is detected, above-mentioned research project obtains the subsidy of the great scientific instrument special project (2012YQ140005) of state natural sciences fund (21275101) and country.
Obviously, above-described embodiment is only for clearly example being described, and the restriction not to embodiment.For those of ordinary skill in the field, can also make other changes in different forms on the basis of the above description.Here exhaustive without the need to also giving all embodiments.And thus the apparent change of extending out or variation be still among the protection domain of the invention.
Claims (10)
1. utilize THz-TDS frequency domain spectra quantitatively to detect a method for Pesticide Residues In Grain, it is characterized in that, comprise the steps:
(1) get the rear compressing tablet of grain samples to be measured grinding, obtain grain press sheet compression to be measured;
(2) getting the target pesticide sample for detecting, mixing with tygon according to the ratio of mass ratio 1:4, and compressing tablet after grinding, obtain containing target agricultural chemicals and poly mixed pressuring plate sample;
(3) THz-TDS spectroscopic system is utilized to test one by one described grain press sheet compression to be measured and described mixed pressuring plate sample, obtain the terahertz time-domain spectroscopy of each sample, terahertz time-domain spectroscopy signal under collection nitrogen atmosphere is as reference signal, and the terahertz time-domain spectroscopy signal gathering described grain press sheet compression to be measured and mixed pressuring plate sample is under the same conditions as sample signal, Reference Signal and sample signal are respectively through the frequency domain spectra signal Er of the frequency domain spectra signal Es and sample that obtain reference after Fourier transform:
In formula, ω represents frequency,
the phase differential of representative sample signal and reference signal, ρ is the ratio of the amplitude mode of sample signal and reference signal;
(4) according to the frequency domain spectra signal of described mixed pressuring plate sample, the good wave band of selected reappearance is interval as characteristic wave bands, and is training set sample frequency domain spectra and checking collection sample frequency domain spectra by the frequency domain spectra random division of described grain press sheet compression to be measured under described characteristic wave bands;
(5) utilize partial least-square regression method to set up the Quantitative Analysis Model of described training set sample frequency domain spectra and described checking collection sample frequency domain spectra, obtain the quantitative detected value of each described grain samples to be measured.
2. the THz-TDS of utilization frequency domain spectra according to claim 1 quantitatively detects the method for Pesticide Residues In Grain, it is characterized in that, described agricultural chemicals is pesticide.
3. the THz-TDS of utilization frequency domain spectra according to claim 2 quantitatively detects the method for Pesticide Residues In Grain, it is characterized in that, described pesticide is Acetamiprid, and described Acetamiprid frequency domain spectra characteristic wave bands is 0.1-2.0THz.
4. the THz-TDS of utilization frequency domain spectra according to claim 2 quantitatively detects the method for Pesticide Residues In Grain, it is characterized in that, described pesticide is sevin, and the frequency domain spectra characteristic wave bands of described sevin is 0.5-2.0THz.
5. the THz-TDS of utilization frequency domain spectra according to claim 2 quantitatively detects the method for Pesticide Residues In Grain, it is characterized in that, described pesticide is Imidacloprid, and the frequency domain spectra characteristic wave bands of described Imidacloprid is at 0.1-2.0THz.
6. quantitatively detect the method for Pesticide Residues In Grain according to the arbitrary described THz-TDS frequency domain spectra that utilizes of claim 1-5, it is characterized in that, in described step (5), partial least-square regression method is utilized to set up the Quantitative Analysis Model of described training set sample frequency domain spectra and described checking collection sample frequency domain spectra, the number of principal components of described Acetamiprid frequency domain spectra Quantitative Analysis Model is 2, the number of principal components of described sevin frequency domain spectra Quantitative Analysis Model is 4, and the main cause subnumber of described Imidacloprid frequency domain spectra Quantitative Analysis Model is 6.
7. quantitatively detect the method for Pesticide Residues In Grain according to the arbitrary described THz-TDS frequency domain spectra that utilizes of claim 1-6, it is characterized in that, in described step (3), the test condition of described THz-TDS is: detected temperatures is 19 DEG C, with nitrogen as a reference, the scanning step motor interval of spectrometer scanning system is-1mm-2mm, and step-length is 0.01mm.
8. quantitatively detect the method for Pesticide Residues In Grain according to the arbitrary described THz-TDS frequency domain spectra that utilizes of claim 1-7, it is characterized in that, in described step (4), the division of described training set sample and described checking collection sample utilizes self-service Latin partition method to carry out;
The concrete computation process of described self-service Latin partition method is as follows:
A () tentation data collection has n sample, sample is randomly ordered, extracts N number of sample out from data centralization at every turn afterwards;
B this data set is divided into the m group of equal sizes by (), m=n/N;
C (), with first group for checking collection, residue m-1 group is training set, namely use n-N training set Sample Establishing analytical model, and prediction is drawn out of the N number of sample come;
D (), with second group for checking collection, residue m-1 group is training set, namely use n-N training set Sample Establishing analytical model, and prediction is drawn out of the N number of sample come;
E () by that analogy, carries out m time altogether, until all groups are drawn out of once and carry out only being predicted as.
When utilizing described self-service Latin partition method to divide described frequency domain spectra, repeat partition and calculate 10 times.
9. quantitatively detect the method for Pesticide Residues In Grain according to the arbitrary described THz-TDS frequency domain spectra that utilizes of claim 1-8, it is characterized in that, in described step (3), each sample duplicate measurements 3 times, each sample is the circle sheet of diameter 13mm, thickness 1.2mm.
10. quantitatively detect the method for Pesticide Residues In Grain according to the arbitrary described THz-TDS frequency domain spectra that utilizes of claim 1-9, it is characterized in that, in described step (5), the concrete Computing Principle of described partial least-square regression method is as follows:
Deflected secondary air is based upon the model on independent variable X and dependent variable Y matrix basis, by setting up the linear regression model (LRM) of latent variable about the latent variable of dependent variable of independent variable, and then the relation between reaction independent variable and dependent variable;
X＝TP
T+E＝∑t
ap
a t；
Y＝UQ
T+F＝∑u
aq
a t；
In formula: T is the score matrix of X, P is the loading matrix of X, and E is the residual matrix of X, t
afor score vector, p
afor corresponding load vectors, U is the score matrix of Y, and Q is the loading matrix of Y, and F is the residual matrix of Y, u
afor score vector, q
afor corresponding load vectors;
Partial least squares regression extracts respective latent variable respectively in X and Y, and they are respectively the linear combination of independent variable and dependent variable, should meet the following conditions simultaneously:
A () two groups of latent variable farthest carry the variation information of independent variable and dependent variable respectively;
B () covariance therebetween maximizes.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107290302A (en) * | 2017-07-12 | 2017-10-24 | 中央民族大学 | Chromium quantitative detecting method in edible gelatin based on terahertz time-domain spectroscopy |
| CN107727608A (en) * | 2017-11-17 | 2018-02-23 | 深圳市太赫兹系统设备有限公司 | The detection method of Indomethacin |
| CN109470647A (en) * | 2019-01-20 | 2019-03-15 | 南京林业大学 | A kind of measurement method of vapor Terahertz absorption spectra |
| CN114264627A (en) * | 2021-12-20 | 2022-04-01 | 河南工业大学 | Terahertz sensor and using method thereof |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7605371B2 (en) * | 2005-03-01 | 2009-10-20 | Osaka University | High-resolution high-speed terahertz spectrometer |
| JP2010038809A (en) * | 2008-08-07 | 2010-02-18 | Murata Mfg Co Ltd | Terahertz spectroscopic device |
| CN103411912A (en) * | 2013-05-22 | 2013-11-27 | 首都师范大学 | Method for identifying Chinese herbal medicine by using THz-TDS (terahertz-total dissolved solids) in combination with fuzzy rule expert system |
| CN103424372A (en) * | 2013-08-26 | 2013-12-04 | 青岛科技大学 | Solid pesticide classification method based on decision tree algorithm |
| CN103969214A (en) * | 2014-04-17 | 2014-08-06 | 首都师范大学 | Method for utilizing terahertz band infrared spectrum technology to detect content of pesticides in foodstuffs |
-
2014
- 2014-09-28 CN CN201410510382.2A patent/CN104297202B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7605371B2 (en) * | 2005-03-01 | 2009-10-20 | Osaka University | High-resolution high-speed terahertz spectrometer |
| JP2010038809A (en) * | 2008-08-07 | 2010-02-18 | Murata Mfg Co Ltd | Terahertz spectroscopic device |
| CN103411912A (en) * | 2013-05-22 | 2013-11-27 | 首都师范大学 | Method for identifying Chinese herbal medicine by using THz-TDS (terahertz-total dissolved solids) in combination with fuzzy rule expert system |
| CN103424372A (en) * | 2013-08-26 | 2013-12-04 | 青岛科技大学 | Solid pesticide classification method based on decision tree algorithm |
| CN103969214A (en) * | 2014-04-17 | 2014-08-06 | 首都师范大学 | Method for utilizing terahertz band infrared spectrum technology to detect content of pesticides in foodstuffs |
Non-Patent Citations (5)
| Title |
|---|
| 刘欢等: "基于太赫兹时域光谱技术的饼干水分定量分析", 《食品安全质量检测学报》 * |
| 王森: "《在线分析仪器手册》", 30 September 2008, 北京:化学工业出版社 * |
| 花月芳: "基于太赫兹时域光谱技术的农药定性和定量分析", 《中国博士学位论文全文数据库 工程科技Ⅰ辑》 * |
| 袁德成主编: "《过程控制工程》", 28 February 2013, 北京：机械工业出版社 * |
| 马冶浩: "基于THz-TDS的固体农药混合定量分析及其分类", 《中国优秀硕士学位论文全文数据库 工程科技I辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107290302A (en) * | 2017-07-12 | 2017-10-24 | 中央民族大学 | Chromium quantitative detecting method in edible gelatin based on terahertz time-domain spectroscopy |
| CN107727608A (en) * | 2017-11-17 | 2018-02-23 | 深圳市太赫兹系统设备有限公司 | The detection method of Indomethacin |
| CN109470647A (en) * | 2019-01-20 | 2019-03-15 | 南京林业大学 | A kind of measurement method of vapor Terahertz absorption spectra |
| CN114264627A (en) * | 2021-12-20 | 2022-04-01 | 河南工业大学 | Terahertz sensor and using method thereof |
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