CN109520965A - A method of lysine content is detected based near infrared spectrum characteristic extractive technique - Google Patents
A method of lysine content is detected based near infrared spectrum characteristic extractive technique Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 63
- 239000004472 Lysine Substances 0.000 title claims abstract description 48
- KDXKERNSBIXSRK-UHFFFAOYSA-N Lysine Natural products NCCCCC(N)C(O)=O KDXKERNSBIXSRK-UHFFFAOYSA-N 0.000 title claims abstract description 40
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 22
- 235000018977 lysine Nutrition 0.000 claims abstract description 39
- 238000001514 detection method Methods 0.000 claims abstract description 25
- VJDRZAPMMKFJEA-JEDNCBNOSA-N (2s)-2,6-diaminohexanoic acid;sulfuric acid Chemical compound OS(O)(=O)=O.NCCCC[C@H](N)C(O)=O VJDRZAPMMKFJEA-JEDNCBNOSA-N 0.000 claims abstract description 21
- 238000001228 spectrum Methods 0.000 claims abstract description 20
- 238000005259 measurement Methods 0.000 claims abstract description 14
- 230000003044 adaptive effect Effects 0.000 claims abstract description 10
- 238000012360 testing method Methods 0.000 claims abstract description 8
- 230000002860 competitive effect Effects 0.000 claims abstract description 6
- 238000012937 correction Methods 0.000 claims abstract description 6
- 238000004611 spectroscopical analysis Methods 0.000 claims description 12
- 230000003595 spectral effect Effects 0.000 claims description 8
- 238000005070 sampling Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 5
- 238000002203 pretreatment Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 5
- 238000012614 Monte-Carlo sampling Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 238000004497 NIR spectroscopy Methods 0.000 claims description 2
- 238000013075 data extraction Methods 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 claims description 2
- 238000012417 linear regression Methods 0.000 claims description 2
- 238000010238 partial least squares regression Methods 0.000 claims description 2
- DWNBOPVKNPVNQG-LURJTMIESA-N (2s)-4-hydroxy-2-(propylamino)butanoic acid Chemical compound CCCN[C@H](C(O)=O)CCO DWNBOPVKNPVNQG-LURJTMIESA-N 0.000 claims 1
- 235000001014 amino acid Nutrition 0.000 abstract description 11
- 150000001413 amino acids Chemical class 0.000 abstract description 10
- 239000000126 substance Substances 0.000 abstract description 7
- 238000005903 acid hydrolysis reaction Methods 0.000 abstract description 6
- KDXKERNSBIXSRK-YFKPBYRVSA-N L-lysine Chemical compound NCCCC[C@H](N)C(O)=O KDXKERNSBIXSRK-YFKPBYRVSA-N 0.000 description 44
- 235000019766 L-Lysine Nutrition 0.000 description 8
- 241001465754 Metazoa Species 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 239000000654 additive Substances 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000004445 quantitative analysis Methods 0.000 description 3
- VEXZGXHMUGYJMC-UHFFFAOYSA-N Hydrochloric acid Chemical compound Cl VEXZGXHMUGYJMC-UHFFFAOYSA-N 0.000 description 2
- 230000000996 additive effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000004615 ingredient Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 125000001176 L-lysyl group Chemical group [H]N([H])[C@]([H])(C(=O)[*])C([H])([H])C([H])([H])C([H])([H])C(N([H])[H])([H])[H] 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- -1 amino-acid salt Chemical class 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 235000020776 essential amino acid Nutrition 0.000 description 1
- 239000003797 essential amino acid Substances 0.000 description 1
- 238000011049 filling Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000007062 hydrolysis Effects 0.000 description 1
- 238000006460 hydrolysis reaction Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 244000144972 livestock Species 0.000 description 1
- 238000011068 loading method Methods 0.000 description 1
- 208000030159 metabolic disease Diseases 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 230000000050 nutritive effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 235000018102 proteins Nutrition 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000010298 pulverizing process Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000001694 spray drying Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
Abstract
The method of quick detection lysine content of the present invention, is detected based near infrared spectrum characteristic extractive technique.Near infrared spectrum characteristic extractive technique described herein, it is that data information relevant to lysine in sample spectra is extracted by competitive adaptive weighted algorithm, near infrared correction is established, so as to quick, efficient, lysine in accurate detection feed addictive L-lysine sulfate content.The method of quick detection lysine content of the present invention, by extracting characteristic relevant to test substance, reduce the data volume for establishing calibration model, improve the accuracy of near-infrared modeling efficiency and model, detection batch samples that can be faster and better, testing cost is saved, also significantly improving acid-hydrolysis method measurement amino acid in standard " measurement of amino acid in feed " (GB/T 18246-2000), time-consuming, high consumption, the demanding disadvantage of instrumentation.
Description
Technical field
The invention belongs to feed detection technique fields, and in particular to one kind is based near infrared spectrum characteristic extractive technique
Quickly detect the method for lysine content in feed addictive L-lysine sulfate.
Background technique
Feed addictive refers to a small amount of or micro substance added in Feed Manufacturing processing, use process, although dosage
Seldom but effect is significant.Feed addictive is the raw material that modern feed industry necessarily uses, to strengthen basal feed nutritive value,
Improve breeding performonce fo animals, guarantee animal health, save feed cost and improve livestock products quality etc. has apparent effect
Fruit.
L-lysine is one of eight kinds of essential amino acids required in humans and animals body and that itself cannot be synthesized, people and is moved
Lysine shortage will cause organism metabolic disorder in object, cause certain physiological function diseases.In animal feeding, it is often necessary to
L-lysine is added in feed as exogenous amino acid.L-lysine belongs to one kind of organic matter, in order to maintain the steady of substance
Determine and be readily transported use, it is often necessary to salt use is made, and the inorganic constituents in lysine salt molecule will not destroy substance
Organic structure, in the case where retaining L-lysine function, it is easier to storage and transport.L-lysine sulfate is dynamic at present
One of the amino acids additive being most widely used in object production, not only can be improved the utilization rate of protein in feed,
Animal feed conversion rate can also be improved, the nutrition of more general equilibrium can be provided for growth of animal.
L-lysine sulfate is yellowish-brown mobility powder, has specific smell and moisture absorption, passes through biofermentation
Method is made, and by 65%L- lysine sulphate made of spray drying, wherein the content of L-lysine is to L-lysine sulfate
The effect of feed addictive has critically important influence.Currently, always containing about L-lysine in L-lysine sulphate additives
Unified country or professional standard, enterprise mostly use " measurement of amino acid in feed " (GB/T 18246- not yet for the measurement of amount
2000) lysine total content in the acid-hydrolysis method measurement product in, acid-hydrolysis method are needed sample in 6mol/L hydrochloric acid, 110 DEG C
Environment in hydrolyze 22-24h.Although this method precision is relatively high, Parallel testing sample relative deviation < 5% was detected
But there is long sample hydrolysis time, instrument cost valuableness in journey and the defects of level requirement is high is operated to inspector, especially singly
Sample detection time-consuming takes around the time of 2d, causes analysis speed slower, is not suitable for the quick detection and analysis of batch samples.
In addition, leading to the disunity of target level of product quality due to the difference of each enterprise's production technology, factory product is be easy to cause to form
Ingredient, L-lysine content, granular size, product colour etc. are irregular, while L-lysine sulfate is in transport, storage
It is easy to deliquesce during hiding and agglomerates, is even rotten, these can all become L-lysine content inspection in limitation L-lysine sulfate
The unfavorable factor of survey, and seriously affect the accuracy of testing result.
Near-infrared spectral analysis technology can quickly be estimated using organic chemicals in the optical characteristics of near infrared spectrum
One or more chemical composition contents in sample, have the advantages that quickly, conveniently, accurately, can analyze simultaneously it is multi-component.Due to
Near-infrared analysis is the spectral signal for obtaining sample, can even be measured in former container sometimes, not need other examinations
Therefore agent will not generate any pollution in test process, belong to environmentally protective detection method.In recent years, domestic and international expert answers
Relatively broad research, but amino-acid salt have been carried out to the detection of aminoacid ingredient in food, feed with near-infrared spectrum technique
And its correlative study of product has not been reported.The present invention exactly on the basis of using near infrared cheracteristics, has carried out fast
The technique study of L-lysine content in speed measurement L-lysine sulfate feed addictive.
Summary of the invention
For this purpose, a kind of based near infrared spectrum characteristic extraction skill technical problem to be solved by the present invention lies in providing
The method that art quickly detects lysine content in feed addictive, with solution, lysine detection cycle is longer in the prior art and examines
Survey the unstable problem of accuracy.
In order to solve the above technical problems, of the present invention a kind of based on the detection of near infrared spectrum characteristic extractive technique
The method for establishing model of lysine content, includes the following steps:
(1) sample to be tested for collecting separate sources, carries out pre-treatment respectively, spare；
(2) lysine content of each sample to be tested is measured respectively with art methods；
(3) near infrared spectrum information collection is carried out to sample to be tested obtained in step (1) respectively, obtains calibration set sample
Full spectroscopic data；
(4) the full spectroscopic data of calibration set sample in step (3) is pre-processed, and to pretreated spectroscopic data into
The extraction of row characteristic information, to establish calibration model；
(5) based on the lysine content measured in step (2), the accuracy of the calibration model of foundation is verified.
In the step (1), the pre-treatment step be sample to be tested is smashed it through into 60 meshes by Cyclone mill, and
5h is handled in 105 DEG C of baking oven.
In the step (2), the lysine content detecting step is according to " measurement of amino acid in feed " (GB/T
Acid-hydrolysis method measures lysine content in 18246-2000).
In the step (3), the step of the near infrared spectrum information collection for using near-infrared diffusing reflection mode to each
Sample to be tested is scanned to obtain spectroscopic data；The scanning mode is continuous wavelength infrared diaphanoscopy, spectra collection wavelength
For 1000nm-2500nm, resolution ratio 10nm, scanning times 32 times, three times then spectrum is averaged for each sample acquisition, each
The sweep time of the sample to be tested is 1min.
In the step (4), the Pretreated spectra step is to be handled and gone scattering processing, variable using variable standardization
At least one of standardization, multiplicative scatter correction, Second Derivative Methods.
In the step (4), the spectroscopic data carries out characteristic information data extraction step using competitive adaptive weight
Weighting algorithm (CARS) extracts characteristic information data, specifically comprises the following steps:
(a) sample: model is sampled based on Monte Carlo sampling method, and each CARS sampling in, require from
A certain amount of sample is randomly selected in sample sets as calibration set, to establish PLS model；
(b) remove variable based on decaying exponential function: it is assumed that surveyed sample spectrum battle array is X (m × p), m is sample number, and p is
Variable number, the true value matrix of SSC are y (m × 1), then PLS regression model are as follows:
Y=Xb+e；
In formula, b indicates that the coefficient vector of p dimension, e indicate prediction residual；
Wherein, b=Wc=[b1, b2..., bp]TThe linear combination coefficient of score matrix and X (W expression), i-th yuan in b
The absolute value of element | bi| (1≤i≤p) indicates contribution of i-th of variable to SSC value, and variable corresponding to the bigger expression of the value is in SSC
Prediction in it is more important；
Utilization index attenuation function removes by force | bi| it is worth relatively small wavelength points, and is sampled using MC, is adopted in i-th
After sample operation, the storage rate of variable point is calculated by following exponential function:
ri=ae-ki；
In formula, a and k indicate constant respectively at the 1st time and n-th MCS, whole p variables and only 2 variable in sample set
Participate in modeling, i.e. r1=1 and rN=2/p, so that the calculation formula of a and k is as follows:
A=(2/P)1/(N-1)；
In formula, ln indicates natural logrithm, and variable number p is 1499, and setting MC is sampled 50 times；
(c) further variable is screened based on adaptive weight weight sampling technology, by evaluating each variable point
Weight wiVariable Selection is carried out, the calculating of weighted value is as follows:
(d) by the RMSECV value for the new variable subset for calculating and generating more every time, with the smallest change of RMSECV value
Quantum collection is as optimal variable subset；And full spectroscopic data is carried out with spectral signature information data building calibration model with this excellent
Choosing.
In the step (4), the step of establishing calibration model used chemometrics method is multiple linear
The Return Law.
In the step (6), the verification step includes: one group of verifying collection sample of acquisition, utilizes established straightening die
Type obtains the predicted value of verifying collection lysine content, and the actual value based on art methods measurement compares evaluation, counts
The related coefficient and variance for calculating predicted value and actual value, the accuracy of the calibration model is evaluated with this.
The invention also discloses the method for establishing model in detection feed addictive in lysine content field
Using.
The invention also discloses it is a kind of based near infrared spectrum characteristic extractive technique detection lysine content method,
Including the near-infrared spectroscopy required according to the method building, and sample containing lysine is contained under this condition
The step of amount detection.
The sample containing lysine is L-lysine sulfate feed addictive.
The method of quick detection lysine content of the present invention is carried out based near infrared spectrum characteristic extractive technique
Detection.Near infrared spectrum characteristic extractive technique described herein is to extract sample light by competitive adaptive weighted algorithm
Data information relevant to lysine, establishes near infrared correction in spectrum, so as to quick, efficient, accurate detection feed
The content of lysine in additive L-lysine sulfate.The method of quick detection lysine content of the present invention, by mentioning
Take characteristic relevant to test substance, reduce the data volume for establishing calibration model, improve near-infrared modeling efficiency and
The accuracy of model, detection batch samples that can be faster and better have saved testing cost, have also significantly improved and be obviously improved
In standard " measurement of amino acid in feed " (GB/T 18246-2000) acid-hydrolysis method measurement amino acid time-consuming, high consumption,
The demanding disadvantage of instrumentation.
Detailed description of the invention
In order to make the content of the present invention more clearly understood, it below according to specific embodiments of the present invention and combines
Attached drawing, the present invention is described in further detail, wherein
Fig. 1 is the near-infrared primary light spectrogram of L-lysine sulfate sample after dries pulverizing；
Fig. 2 is the spectral signature information data extracted through competitive adaptive weight weighting algorithm (CARS)；
Fig. 3 is sample to be tested predicted value and actual value correlation scatter plot.
Specific embodiment
Embodiment 1
The present embodiment detects feed addictive L-lysine sulfate sample using near infrared spectrum characteristic extractive technique
The content of lysine in this.
The model for quickly detecting lysine content based near infrared spectrum characteristic extractive technique described in the present embodiment is built
Cube method specifically comprises the following steps:
(1) sample collection and preparation
The feed addictive L-lysine sulfate sample in source known to 57 parts is taken, which comes from enterprise；By every part
Sample carries out smashing it through 60 meshes using Cyclone mill；Then it is placed in processing 5h in 105 DEG C of baking ovens again to be dried, as dries
Crush state sample to be tested.
(2) measurement of sample lysine content
Every part of sample is placed in processing 5h in 105 DEG C of baking ovens to dry, then by the sample after drying according to " in feed
The measurement of amino acid " acid-hydrolysis method measurement lysine in (GB/T 18246-2000) content.
(3) sample near infrared spectra collection
It is made using SupNIR-2700 type grating near infrared spectrometer (production of optically focused Science and Technology Co., Ltd.) to above-mentioned
Oven-dried condition under L-lysine sulfate crush sample carry out spectra collection.Near infrared spectrometer preheating is opened at 25 DEG C
30min is swept before sample every time using air spectrum as background spectrum.Wherein, the near infrared spectrometer acquisition mode is adopted for diffusing reflection
Collection, spectra collection range are 1000nm-2500nm, and resolution ratio 10nm, scanning times are 32 times.When filling sample, each sample
Height will maintain an equal level with the edge of sample cell, and to guarantee that sample-loading amount is consistent, each sample multiple scanning 3 times asks its average light to set a song to music
Line.The sample to be tested original spectrum curve collected is as shown in Figure 1.
(4) extraction of near infrared spectrum characteristic and model construction
Handle and go scattering to handle (SNVDT), variable standardization (SNV), multiplicative scatter correction using variable standardization
(MSC), second derivative method is pre-processed to the full spectrum of sample correction collection is obtained in step (3).Then using competitive adaptive
Weighting algorithm (CARS) should be weighed and be extracted characteristic information in step (3) after sample preprocessing in spectroscopic data, from every spectrum
It is extracted 31 in 1499 number of wavelengths strong points, extracts result as shown in the following table 1 and Fig. 2, specific extraction step includes:
(a) it samples: model being sampled based on Monte Carlo sampling method (Monte Carlo sampling, MCS), and
In each CARS sampling, require to randomly select a certain amount of sample from sample sets as calibration set, to establish PLS mould
Type；
(b) variable is removed based on decaying exponential function (exponentially decreasing function, EDP): false
Fixed surveyed sample spectrum battle array is X (m × p), and m is sample number, and p is variable number, and the true value matrix of SSC is y (m × 1), then PLS is returned
Return model are as follows:
Y=Xb+e；
In formula, b indicates that the coefficient vector of p dimension, e indicate prediction residual；
Wherein, b=Wc=[b1, b2..., bp]TThe linear combination coefficient of score matrix and X (W expression), i-th yuan in b
The absolute value of element | bi| (1≤i≤p) indicates contribution of i-th of variable to SSC value, and variable corresponding to the bigger expression of the value is in SSC
Prediction in it is more important；
Utilization index attenuation function removes by force | bi| it is worth relatively small wavelength points, and is sampled using MC, is adopted in i-th
After sample operation, the storage rate of variable point is calculated by following exponential function:
ri=ae-ki；
In formula, a and k indicate constant respectively at the 1st time and n-th MCS, whole p variables and only 2 variable in sample set
Participate in modeling, i.e. r1=1 and rN=2/p, so that the calculation formula of a and k is as follows:
A=(2/P)1/(N-1)；
In formula, ln indicates natural logrithm, and variable number p is 1499, and setting MC is sampled 50 times；
(c) further right based on adaptive weight weight sampling technology (adaptive reweighted sampling, ARS)
Variable is screened, the rule of " survival of the fittest " in the technical modelling Darwinian evolution, by evaluating each variable point
Weight wiVariable Selection is carried out, the calculating of weighted value is as follows:
(d) by the RMSECV value for the new variable subset for calculating and generating more every time, with the smallest change of RMSECV value
Quantum collection is as optimal variable subset；And full spectroscopic data is carried out with spectral signature information data building calibration model with this excellent
Choosing.
The Pretreated spectra and characteristic information data are extracted, and are run in Matlab and Pls_toolbox software
's.Calibration model is constructed using the spectral signature information data combination multiple linear regression method (MLR) after extraction, it is as a result as follows
Shown in table 2.
Spectral signature wavelength of the table 1 after CARS is extracted
2 L-lysine sulfate Quantitative Analysis Model verification result of table
(5) verifying and sample measures of calibration model
Other verifying collection samples of acquisition 18, repeat step (1) to (3), and the calibration set mould established using step (4)
Type prediction verifying collection sample lysine content, specific verification result is as shown in table 3 below, L-lysine sulfate Quantitative Analysis Model
Verifying collection prediction result see the table below 4, and verifying collection sample to be tested predicted value is with actual value correlation scatter plot as shown in attached drawing 3.
3 L-lysine sulfate Quantitative Analysis Model verification result of table
Table 4 verifies model sample true value and predicted value contrast table
| Sample number into spectrum | True value | Predicted value | Deviation |
| 1 | 51.87 | 52.42 | 0.55 |
| 2 | 52.64 | 52.41 | -0.23 |
| 3 | 53.03 | 53.70 | 0.67 |
| 4 | 54.11 | 53.45 | -0.66 |
| 5 | 54.61 | 56.10 | 1.49 |
| 6 | 54.91 | 56.29 | 1.38 |
| 7 | 55.85 | 56.03 | 0.18 |
| 8 | 56.34 | 56.22 | -0.12 |
| 9 | 56.77 | 58.49 | 1.72 |
| 10 | 57.03 | 57.74 | 0.71 |
| 11 | 57.44 | 56.35 | -1.09 |
| 12 | 57.72 | 57.68 | -0.04 |
| 13 | 57.83 | 58.05 | 0.22 |
| 14 | 58.15 | 57.89 | -0.26 |
| 15 | 58.54 | 58.61 | 0.07 |
| 16 | 58.72 | 59.91 | 1.19 |
| 17 | 59.30 | 59.96 | 0.66 |
| 18 | 60.33 | 60.43 | 0.10 |
From can be seen that in the result of table 3 and 4, lysine content near-infrared in the L-lysine sulfate that the present invention constructs is fixed
Analysis model accuracy with higher is measured, can be used for the content detection of lysine.
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments.It is right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of variation or
It changes.There is no necessity and possibility to exhaust all the enbodiments.And it is extended from this it is obvious variation or
It changes still within the protection scope of the invention.
Claims (10)
1. a kind of method for establishing model based near infrared spectrum characteristic extractive technique detection lysine content, feature exist
In including the following steps:
(1) sample to be tested for collecting separate sources, carries out pre-treatment respectively, spare；
(2) lysine content of each sample to be tested is measured respectively with art methods；
(3) near infrared spectrum information collection is carried out to sample to be tested obtained in step (1) respectively, obtains the full light of calibration set sample
Modal data；
(4) the full spectroscopic data of calibration set sample in step (3) is pre-processed, and pretreated spectroscopic data is carried out special
The extraction of data information is levied, to establish calibration model；
(5) based on the lysine content measured in step (2), the accuracy of the calibration model of foundation is verified.
2. method for establishing model according to claim 1, it is characterised in that: in the step (1), the pre-treatment step
For sample to be tested is smashed it through 60 meshes by Cyclone mill, and 5h is handled in 105 DEG C of baking oven.
3. method for establishing model according to claim 1 or 2, it is characterised in that: in the step (3), the near-infrared
The step of spectral information acquires is that near-infrared diffusing reflection mode is used to be scanned to obtain spectroscopic data each sample to be tested；Institute
Stating scanning mode is continuous wavelength infrared diaphanoscopy, and spectra collection wavelength is 1000nm-2500nm, resolution ratio 10nm, scanning
Number 32 times, three times then spectrum is averaged for each sample acquisition, and the sweep time of each sample to be tested is 1min.
4. method for establishing model according to claim 1-3, it is characterised in that: in the step (4), the light
Spectrum pre-treatment step is to be handled and gone scattering processing, variable standardization, multiplicative scatter correction, second dervative using variable standardization
At least one of method.
5. method for establishing model according to claim 1-4, it is characterised in that: in the step (4), the light
Modal data carries out characteristic information data extraction step and extracts feature letter using competitive adaptive weight weighting algorithm (CARS)
Data are ceased, are specifically comprised the following steps:
(a) it samples: model being sampled based on Monte Carlo sampling method, and in each CARS sampling, require from sample
Concentration randomly selects a certain amount of sample as calibration set, to establish PLS model；
(b) remove variable based on decaying exponential function: it is assumed that surveyed sample spectrum battle array is X (m × p), m is sample number, and p is variable
Number, the true value matrix of SSC are y (m × 1), then PLS regression model are as follows:
Y=Xb+e；
In formula, b indicates that the coefficient vector of p dimension, e indicate prediction residual；
Wherein, b=Wc=[b1, b2..., bp]TThe linear combination coefficient of score matrix and X (W expression), i-th element is exhausted in b
To value | bi| (1≤i≤p) indicates contribution of i-th of variable to SSC value, prediction of the variable corresponding to the bigger expression of the value in SSC
In it is more important；
Utilization index attenuation function removes by force | bi| it is worth relatively small wavelength points, and is sampled using MC, samples and transport in i-th
After calculation, the storage rate of variable point is calculated by following exponential function:
ri=ae-ki；
In formula, when a and k indicate constant respectively at the 1st time with n-th MCS, whole p variables and only 2 variables participation in sample set
Modeling, i.e. r1=1 and rN=2/p, so that the calculation formula of a and k is as follows:
A=(2/P)1/(N-1)；
In formula, ln indicates natural logrithm, and variable number p is 1499, and setting MC is sampled 50 times；
(c) further variable is screened based on adaptive weight weight sampling technology, by the weight w for evaluating each variable pointi
Variable Selection is carried out, the calculating of weighted value is as follows:
(d) by the RMSECV value for the new variable subset for calculating and generating more every time, with the smallest variable of RMSECV value
Collection is used as optimal variable subset；And full spectroscopic data and spectral signature information data building calibration model are carried out with this preferred.
6. method for establishing model according to claim 1-5, it is characterised in that: described to build in the step (4)
The step of vertical calibration model, used chemometrics method was multiple linear regression method.
7. method for establishing model according to claim 1-6, it is characterised in that: described to test in the step (6)
Card step includes: one group of verifying collection sample of acquisition, and established calibration model is utilized to obtain the prediction of verifying collection lysine content
The related coefficient of value, and the actual value based on art methods measurement compares evaluation, calculating predicted value and actual value and
Variance evaluates the accuracy of the calibration model with this.
8. the described in any item method for establishing model of claim 1-7 are in detection feed addictive in lysine content field
Using.
9. a kind of method based near infrared spectrum characteristic extractive technique detection lysine content, which is characterized in that including
According to near-infrared spectroscopy needed for the described in any item method buildings of claim 1-7, and under this condition to containing bad
Propylhomoserin sample carries out the step of content detection.
10. the method according to claim 9 based near infrared spectrum characteristic extractive technique detection lysine content,
It is characterized in that, the sample containing lysine is L-lysine sulfate feed addictive.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110567937A (en) * | 2019-09-11 | 2019-12-13 | 重庆大学 | Competitive self-adaptive heavy-weighted key data extraction method for Raman spectrum analysis of insulating oil |
| CN112461784A (en) * | 2020-11-16 | 2021-03-09 | 山东大学 | Near infrared spectrum-based drug cocrystal detection method and application thereof |
| CN114166780A (en) * | 2021-11-16 | 2022-03-11 | 华中农业大学 | Intermediate infrared rapid batch detection method for content of free lysine in milk |
| CN116793991A (en) * | 2023-08-22 | 2023-09-22 | 青岛理工大学 | Glutamic acid concentration measurement method based on near infrared spectrum and mixing loss |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102967578A (en) * | 2012-11-08 | 2013-03-13 | 中国农业科学院农业质量标准与检测技术研究所 | Method for obtaining near-infrared spectrum of beef sample online and application thereof in evaluating beef quality |
| CN107655851A (en) * | 2017-09-18 | 2018-02-02 | 中国农业科学院农业质量标准与检测技术研究所 | A kind of method based on near-infrared spectrum technique quick detection lysine content |
| CN108195793A (en) * | 2016-12-08 | 2018-06-22 | 中国农业机械化科学研究院 | The universal model construction method of plant-derived feedstuff amino acid content |
| CN108663339A (en) * | 2018-05-15 | 2018-10-16 | 南京财经大学 | Corn online test method of going mouldy based on spectrum and image information fusion |
-
2018
- 2018-11-20 CN CN201811386962.XA patent/CN109520965A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102967578A (en) * | 2012-11-08 | 2013-03-13 | 中国农业科学院农业质量标准与检测技术研究所 | Method for obtaining near-infrared spectrum of beef sample online and application thereof in evaluating beef quality |
| CN108195793A (en) * | 2016-12-08 | 2018-06-22 | 中国农业机械化科学研究院 | The universal model construction method of plant-derived feedstuff amino acid content |
| CN107655851A (en) * | 2017-09-18 | 2018-02-02 | 中国农业科学院农业质量标准与检测技术研究所 | A kind of method based on near-infrared spectrum technique quick detection lysine content |
| CN108663339A (en) * | 2018-05-15 | 2018-10-16 | 南京财经大学 | Corn online test method of going mouldy based on spectrum and image information fusion |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110567937A (en) * | 2019-09-11 | 2019-12-13 | 重庆大学 | Competitive self-adaptive heavy-weighted key data extraction method for Raman spectrum analysis of insulating oil |
| CN112461784A (en) * | 2020-11-16 | 2021-03-09 | 山东大学 | Near infrared spectrum-based drug cocrystal detection method and application thereof |
| CN114166780A (en) * | 2021-11-16 | 2022-03-11 | 华中农业大学 | Intermediate infrared rapid batch detection method for content of free lysine in milk |
| CN114166780B (en) * | 2021-11-16 | 2024-02-13 | 华中农业大学 | Mid-infrared rapid batch detection method for content of free lysine in milk |
| CN116793991A (en) * | 2023-08-22 | 2023-09-22 | 青岛理工大学 | Glutamic acid concentration measurement method based on near infrared spectrum and mixing loss |
| CN116793991B (en) * | 2023-08-22 | 2023-11-10 | 青岛理工大学 | Glutamic acid concentration measurement method based on near infrared spectrum and mixing loss |
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