CN106018337A - Method for determination of phytic acid content of cotton seed powder - Google Patents
Method for determination of phytic acid content of cotton seed powder Download PDFInfo
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- CN106018337A CN106018337A CN201610640917.7A CN201610640917A CN106018337A CN 106018337 A CN106018337 A CN 106018337A CN 201610640917 A CN201610640917 A CN 201610640917A CN 106018337 A CN106018337 A CN 106018337A
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- 238000000034 method Methods 0.000 title claims abstract description 112
- 239000000843 powder Substances 0.000 title claims abstract description 70
- 229940068041 phytic acid Drugs 0.000 title claims abstract description 62
- 239000000467 phytic acid Substances 0.000 title claims abstract description 62
- IMQLKJBTEOYOSI-GPIVLXJGSA-N Inositol-hexakisphosphate Chemical compound OP(O)(=O)O[C@H]1[C@H](OP(O)(O)=O)[C@@H](OP(O)(O)=O)[C@H](OP(O)(O)=O)[C@H](OP(O)(O)=O)[C@@H]1OP(O)(O)=O IMQLKJBTEOYOSI-GPIVLXJGSA-N 0.000 title claims abstract description 61
- IMQLKJBTEOYOSI-UHFFFAOYSA-N Phytic acid Natural products OP(O)(=O)OC1C(OP(O)(O)=O)C(OP(O)(O)=O)C(OP(O)(O)=O)C(OP(O)(O)=O)C1OP(O)(O)=O IMQLKJBTEOYOSI-UHFFFAOYSA-N 0.000 title claims abstract description 61
- 235000002949 phytic acid Nutrition 0.000 title claims abstract description 61
- 229920000742 Cotton Polymers 0.000 title abstract description 14
- 235000012343 cottonseed oil Nutrition 0.000 claims abstract description 82
- 238000012937 correction Methods 0.000 claims abstract description 39
- 238000001228 spectrum Methods 0.000 claims abstract description 31
- 238000004611 spectroscopical analysis Methods 0.000 claims abstract description 17
- 238000000227 grinding Methods 0.000 claims abstract description 5
- 238000002329 infrared spectrum Methods 0.000 claims description 22
- 210000000582 semen Anatomy 0.000 claims description 17
- 238000003556 assay Methods 0.000 claims description 14
- 238000011425 standardization method Methods 0.000 claims description 12
- 238000002790 cross-validation Methods 0.000 claims description 7
- 238000012843 least square support vector machine Methods 0.000 claims description 6
- 238000009499 grossing Methods 0.000 claims description 4
- 238000002360 preparation method Methods 0.000 claims description 4
- 238000012706 support-vector machine Methods 0.000 claims description 4
- 238000000862 absorption spectrum Methods 0.000 claims description 3
- 230000011514 reflex Effects 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 2
- 238000003379 elimination reaction Methods 0.000 claims description 2
- 239000006227 byproduct Substances 0.000 abstract description 7
- 238000012545 processing Methods 0.000 abstract description 5
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- 238000002203 pretreatment Methods 0.000 abstract description 3
- 238000004255 ion exchange chromatography Methods 0.000 abstract 1
- 238000007873 sieving Methods 0.000 abstract 1
- 239000000523 sample Substances 0.000 description 66
- 239000000126 substance Substances 0.000 description 10
- 241000219146 Gossypium Species 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 5
- 238000011156 evaluation Methods 0.000 description 5
- 150000002500 ions Chemical class 0.000 description 5
- 238000005259 measurement Methods 0.000 description 4
- 235000018102 proteins Nutrition 0.000 description 4
- 108090000623 proteins and genes Proteins 0.000 description 4
- 102000004169 proteins and genes Human genes 0.000 description 4
- 230000003595 spectral effect Effects 0.000 description 4
- 239000000203 mixture Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- VEXZGXHMUGYJMC-UHFFFAOYSA-N Hydrochloric acid Chemical compound Cl VEXZGXHMUGYJMC-UHFFFAOYSA-N 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 2
- 235000001014 amino acid Nutrition 0.000 description 2
- 150000001413 amino acids Chemical class 0.000 description 2
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- 229930003231 vitamin Natural products 0.000 description 2
- 150000003722 vitamin derivatives Chemical class 0.000 description 2
- HNSDLXPSAYFUHK-UHFFFAOYSA-N 1,4-bis(2-ethylhexyl) sulfosuccinate Chemical compound CCCCC(CC)COC(=O)CC(S(O)(=O)=O)C(=O)OCC(CC)CCCC HNSDLXPSAYFUHK-UHFFFAOYSA-N 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 238000010987 Kennard-Stone algorithm Methods 0.000 description 1
- 244000061176 Nicotiana tabacum Species 0.000 description 1
- 235000002637 Nicotiana tabacum Nutrition 0.000 description 1
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- 244000046052 Phaseolus vulgaris Species 0.000 description 1
- 235000010627 Phaseolus vulgaris Nutrition 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- 108010073771 Soybean Proteins Proteins 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
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- 238000005251 capillar electrophoresis Methods 0.000 description 1
- 238000004587 chromatography analysis Methods 0.000 description 1
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- 239000004459 forage Substances 0.000 description 1
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- 239000011707 mineral Substances 0.000 description 1
- 238000001320 near-infrared absorption spectroscopy Methods 0.000 description 1
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- 230000000050 nutritive effect Effects 0.000 description 1
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- 238000005457 optimization Methods 0.000 description 1
- 150000002894 organic compounds Chemical class 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 231100000614 poison Toxicity 0.000 description 1
- 230000007096 poisonous effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
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- 235000009566 rice Nutrition 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
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- 235000019710 soybean protein Nutrition 0.000 description 1
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
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Abstract
The invention discloses a method for determination of phytic acid content of cotton seed powder. The method comprises collecting different kinds of cottonseed samples planted in different regions, carrying out sample husking, grinding, sieving and moisture balance on the samples, collecting full spectrum data, pretreating the near infrared spectroscopy data through a plurality of pretreatment methods, accurately determining sample phytic acid content through high performance ion chromatography, constructing the optimal PLS model in a full spectrum range through a full cross-validated method, carrying out variable selection on the spectroscopic data, building multiple correction models through a multivariate calibration regression method, building a near infrared spectroscopy correction model and detecting phytic acid content of cotton seed powder through the model. The method utilizes a Buchi NIR Flex-N500 Fourier transform near infrared spectrometer to acquire a spectrogram of cotton seed powder, has a fast determination speed and high accuracy, is environmentally friendly, convenient and efficient and has an important meaning for cultivation of low-phytic acid cotton and promotion of cotton side product processing and utilization.
Description
Technical field
The invention discloses the assay method of a kind of agricultural byproducts content, particularly relate to a kind of Cottonseed powder and plant
The assay method of acid content.
Background technology
Semen Gossypii is the principal by product of Cotton Gossypii, and whole nation annual output reaches more than 10,000,000 tons, produces cottonseed cake per year and reaches
More than 6000000 tons, widely distributed, the stock number whole world first.Rich in substantial amounts of protein in Semen Gossypii
(27.83～45.60%) and fat (28.24～44.05%), obtain Oleum Gossypii semen and cottonseed cake through squeezing after shelling.
Oleum Gossypii semen edible, cottonseed cake can be as poultry and livestock feedstuff.Protein content in cottonseed cake is only second to
Bean cake, compares with Semen Tritici aestivi with rice, and protein content exceeds 5～8 times.17 can be obtained after cottonseed cake hydrolysis
Planting aminoacid, from the point of view of necessary aminoacid, cottonseed protein is close with soybean protein；From vitamin and mineral
From the perspective of, cottonseed cake contains abundant B and E vitamin, and phosphorus content is up to 0.83～1.04%.Therefore cotton
Seedcake not only can alleviate what China's protein resource lacked as the forage protein source of poultry and aquatic animal
Present situation, but also feed cost can be reduced, increase economic efficiency.But, due to antinutritional factor in Semen Gossypii
The existence of phytic acid, Semen Gossypii nutrient substance fails sufficiently to be comprehensively utilized, particularly have impact on cottonseed cake conduct
The nutritive value of animal feed.Therefore, the phytic acid content in Accurate Determining Semen Gossypii is for cultivating low phytic acid Cotton Gossypii
The processing and utilization of kind and the cotton side-product of promotion is significant.
And measure phytic acid content in Semen Gossypii at present based on conventional chemical method, such as the sedimentation method, spectrophotometric
Method, titrimetry, ion exchange, high performance liquid chromatography, high-efficient ion chromatogram method, high performance capillary electrophoresis
And nuclear magnetic resonance method etc..But these traditional methods exist, and preparation of samples is loaded down with trivial details, reagent toxicity is relatively big, analysis
The problem such as time length, sensitivity is low and testing cost is high.Near-infrared spectrum technique (Near Infrared
Spectroscopy, NIRS) refer to wavelength C-H, N-H in organic compound in the range of 780～2526nm,
The frequency multiplication of the groups such as O-H and S-H and the produced absorption spectrum of sum of fundamental frequencies vibration.Since the nineties in 20th century,
Along with near-infrared spectrum technique and the fast development of Chemical Measurement, near-infrared spectrum technique is successfully applied to
The analysis of many industry products such as food, medicine, Nicotiana tabacum L., feedstuff and petrochemical industry measures.Particularly exist
On the attributional analysis of agricultural byproducts, because of it quickly, without pre-treatment, non-destructive and multicomponent calmly simultaneously
The advantages such as component analysis test and be more widely applied.
Summary of the invention
In Cottonseed powder, phytic acid content is the important indicator affecting Semen Gossypii comprehensive utilization, according to conventional chemical side
It is long for analysis time that method measures phytic acid content, and testing cost is high, and consumes the most poisonous chemical reagent, pollutes
Environment, safety is low, hinders the Evaluation of Comprehensive Utilization of Cottonseed powder nutrient substance.It is an object of the invention to pin
To not enough present in existing chemical analysis technology, it is provided that the assay method of phytic acid content in a kind of Cottonseed powder.
The present invention uses near infrared spectrum and Chemical Measurement quickly to measure phytic acid content in Cottonseed powder,
Efficiently solve the problems referred to above, provide one fast and efficiently for the detection of phytic acid content in Cottonseed powder
Analysis method, has convenience, green, accurately advantage.
The technical solution used in the present invention is:
1) preparation is at the Cottonseed powder sample of the different cultivars of different regions plantation, is carried out by Semen Gossypii (cotton seeds)
Pretreatment, obtains Cottonseed powder sample；
2) near infrared spectrometer collection is utilized to obtain the near infrared spectrum data of Cottonseed powder sample；
3) for step 2) near infrared spectrum data of Cottonseed powder sample that collects carries out 11 kinds of methods respectively
Spectroscopic data pretreatment, then record the phytic acid content of Cottonseed powder sample by high-efficient ion chromatogram method (HPIC),
The near infrared spectrum data obtained in conjunction with 11 kinds of preprocess methods and phytic acid content, use offset minimum binary respectively
Method (PLS) sets up the near infrared correction of Cottonseed powder phytic acid content；
4) select prediction related coefficient (R2) value and residue pre-from all near infrared correction of above-mentioned steps
Survey deviation (RPD) value maximum and predicted root mean square error (RMSEP) value and cross validation root-mean-square error
(RMSECV) model that value is minimum is as optimal full spectrum PLS model；
5) utilize Variable Selection to step 4) in the spectroscopic data of optimal full spectrum PLS model carry out
Variable selection, use Multivariate Correction homing method set up the spectroscopic data after variable selection and its phytic acid content it
Between multiple near infrared spectrum calibration models；
6) from all near infrared spectrum calibration models of above-mentioned steps, select prediction related coefficient (R2) value and remain
Remaining prediction deviation (RPD) value is maximum and predicted root mean square error (RMSEP) value and cross validation root-mean-square error
(RMSECV) model that value is minimum is as optimum Cottonseed powder phytic acid content near infrared correction；
7) use step 1) and 2) described same procedure collection Cottonseed powder sample to be measured near infrared spectrum data,
By above-mentioned steps 6) constructed by optimum Cottonseed powder phytic acid content near infrared correction detect Cottonseed powder to be measured
Sample, obtains its phytic acid content.
Described step 1) in pretreatment concrete the most in the following ways: by cottonseed delinting, dry, peel off and again
After drying, with sample grinding machine, Cottonseed is clayed into power, cross 60 mesh sieves, obtain Cottonseed powder sample.
Described step 2) utilize near infrared spectrometer collection Cottonseed powder sample spectrum to concretely comprise the following steps:
2.1) spectroscopic data is obtained with near infrared spectrometer collection after every part of sample is filled in three times sample scanning, the reddest
The collection wave-length coverage of external spectrum instrument is 4000-10000cm-1, every 4cm-1Gather reflex strength (R), altogether
Count 1501 spectrum points, average after multiple scanning 64 times；The applied sample amount of dress sample scanning is that 3.5g is left every time
The right side, sample cell is the cylinder of a diameter of 1cm, and the scanning of dress sample is all to enter under 25 ± 0.5 DEG C of temperature conditionss every time
OK；
2.2) then calculate the average light spectrum of three spectroscopic datas of every part of sample, then average light spectrum is converted
For log (1/ average light spectrum), obtain the near-infrared absorption spectrum of Cottonseed powder sample.
Described step 3) in 11 kinds of preprocess methods be respectively Savitzky-Golay smoothing techniques, first differential
Method, variable standardization method, multiplicative scatter correction method, Savitzky-Golay be smooth+first differential method,
Savitzky-Golay is smooth+and variable standardization method, Savitzky-Golay be smooth+multiplicative scatter correction method,
Rank differential+variable standardization method, first differential+multiplicative scatter correction method, Savitzky-Golay smooth+variable
Standardization+first differential method, Savitzky-Golay smooth+multiplicative scatter correction+first differential method.
Preferably, described optimal full spectrum PLS model uses Savitzky-Golay and smooths+variable mark
The model that standardization+first differential method obtains, Savitzky-Golay smooths+variable standardization+first differential method tool
Body is to first pass through Savitzky-Golay to smooth the signal to noise ratio improving spectrum analysis signal, recycles variable standardization
Eliminate the impact on spectrum of Semen Gossypii granular size, surface scattering and change in optical path length, finally use first differential
Eliminate the drift that spectrum co-wavelength is unrelated.
Described step 5) in variable selection use based on DSMC without information variable elimination
(MC-UVE) method.
Described step 5) in Multivariate Correction homing method specifically include offset minimum binary (PLS) method, least square
Support vector machine (LS-SVM) method and Weighted Least Squares Support Vector Machines (WLS-SVM) method.
Preferably step 6) in optimum Cottonseed powder phytic acid content near infrared correction use based on Monte Carlo
Least square method supporting vector machine (MC-UVE-LS-SVM) model eliminated without information variable.
Described step 7) in optimum Cottonseed powder phytic acid content near infrared correction use based on Monte Carlo
Least square method supporting vector machine (MC-UVE-LS-SVM) model eliminated without information variable.
In full spectroscopic data, all there is significant dependency in the most all of spectral variables with objective trait,
Wherein there may be substantial amounts of without information variable.Therefore, the present invention uses MC-UVE method to become spectrum
The selection of amount, compared with full spectrum PLS model, this model eliminates redundant variables, simplifies calibration model, carries
The high arithmetic speed of model.
Compared with prior art, the invention has the beneficial effects as follows:
1. the present invention utilizes B ü chi NIR Flex-N500 ft-nir spectrometer (Switzerland's step fine jade public affairs
Department) gather the spectrogram of Cottonseed powder, use Unscrambler V9.7 and matlab R2011a software to light
Modal data is analyzed, and have studied modeling and the application process of phytic acid content in Cottonseed powder, and its spectroscopic data is more
Add accurately.
2. to measure accuracy the highest for the present invention, is a kind of green, efficient, convenient, assay method accurately,
Processing and utilization for cultivating low phytic acid cotton variety with promote cotton side-product is significant and is worth.
Accompanying drawing explanation
Fig. 1 is Cottonseed powder sample phytic acid content scattergram in the inventive method.
Fig. 2 is Cottonseed powder near-infrared primary light spectrogram in the inventive method.
Fig. 3 is near infrared light spectrogram after the optimum pretreatment of Cottonseed powder in the inventive method.
Fig. 4 be in the inventive method Monte Carlo without the selection figure of optimal threshold in information variable selection course.
Fig. 5 is in the inventive method between Cottonseed powder sample phytic acid chemical measurements and near infrared spectrum predictive value
Dependency graph.
Detailed description of the invention
The present invention will be further described with embodiment below in conjunction with the accompanying drawings.
The specific embodiment of the present invention is as follows:
1) the choosing of sample
Sample is the Semen Gossypii of the different cultivars taking from 10 the planted in different ecological areas plantations in the whole nation in 2014, including Zhejiang
The different ecological growing areas such as Jiang Hangzhou, Wuhu, Yancheng, Jiangsu Province, Lixian County, Hunan, totally 280 Semen Gossypii samples
This.
2) preparation of Cottonseed powder sample
Semen Gossypii through lint, dry, peel off and again dry after, with sample grinding machine, Cottonseed is clayed into power, cross 60 mesh
Sieve, obtains Cottonseed powder sample；
3) sample spectra acquisition
Every part of Cottonseed powder sample is loaded in sample cell in three times, obtains spectroscopic data after scanning, calculate every part
The average light spectrum of three spectroscopic datas of sample, and it is converted into log (1/R).Sample primary light spectrogram, such as figure
Shown in 2.
Near infrared spectra collection condition: utilize B ü chi NIR Flex-N500 ft-nir spectrometer
(Bu Qi company of Switzerland) gathers the spectrogram of Cottonseed powder, and the collection wave-length coverage of near infrared spectrometer is
4000-10000cm-1, every 4cm-1Gathering reflex strength (R), 1501 spectrum points, repeat to sweep altogether
Average after retouching 64 times；The applied sample amount of dress sample scanning is about 3.5g every time, and sample cell is a diameter of 1cm
Cylinder, cylinder height is 5cm；The scanning of dress sample is all to carry out under 25 ± 0.5 DEG C of temperature conditionss every time, adopts
With Unscrambler V9.7 and matlab R2011a software, spectroscopic data is analyzed.
4) Pretreated spectra
Utilize Savitzky-Golay smoothing techniques, first differential method, variable standardization method, multiplicative scatter correction method,
Savitzky-Golay is smooth+and first differential method, Savitzky-Golay be smooth+variable standardization method,
Savitzky-Golay is smooth+multiplicative scatter correction method, first differential+variable standardization method, first differential+many
Unit's scatter correction method, Savitzky-Golay be smooth+variable standardization+first differential method, Savitzky-Golay
11 kinds of processing methods such as smooth+multiplicative scatter correction+first differential method carry out pretreatment to original spectrum respectively.
5) phytic acid content during high-efficient ion chromatogram method (HPIC) measures Cottonseed powder
Semen Gossypii through lint, dry, peel off and again dry after, with sample grinding machine, Cottonseed is clayed into power, cross 60 mesh
Sieve, obtains Cottonseed powder sample；
First with dehydrated alcohol Cottonseed powder sample carried out water-bath ungrease treatment, then with hydrochloric acid through water-bath, cooling and
The centrifugal phytic acid extracted in Cottonseed powder sample, then passes sequentially through two Cleanert IC chromatography of ions pre-treatments
Post and water system filter membrane purification, carry out HPIC and detect its content；Chromatographic condition is: use DIONEX
ICS-3000 ion chromatograph, AG16-HC Guaed (4 × 50mm) protect and AS16-HC
Analytical (4 × 250mm) detached dowel, leacheate are KOH, flow rate of mobile phase is 1.0ml/min, enters
Sample amount is the condition detection of 100 μ L.The phytic acid content percentage composition measured represents.
The HPIC analysis result of phytic acid percentage composition in 280 parts of Cottonseed powder samples that the present embodiment mainly provides
See Fig. 1；Owing to sample comes from different regions, different cultivars, the content of phytic acid has larger difference, is shown in Table 1,
Wherein sample material phytic acid content is big (0.4302-1.8391%) across width, has good representativeness, for setting up
Spectral model provides condition.
Phytic acid content distributional difference in table 1 Cottonseed powder
| Composition | Minima | Maximum | Meansigma methods | Standard deviation |
| Phytic acid | 0.4302 | 1.8391 | 1.2250 | 0.3059 |
6) calibration set and checking collect choosing of sample
Utilize near infrared spectrometer gather above-mentioned sample spectrum, for near-infrared model for, calibration set and
Forecast set sample must can represent the data distribution situation of original sample, and the sample content model of calibration set
Enclose forecast set sample content scope to be comprised, use Kennard-Stone algorithm by Cottonseed powder sample according to 3:1
Ratio be divided into calibration set sample and forecast set sample, 280 parts of samples will be carried out diversity, be corrected
210 parts of sample of collection, it was predicted that 70 parts of sample of collection, sets up described near-infrared model, calibration set and forecast set sample
This distribution such as table 2.
Phytic acid content distribution in table 2 calibration set and forecast set sample
| Sample sets | Sample number | Minima | Maximum | Meansigma methods | Standard deviation |
| Calibration set | 210 | 0.4302 | 1.8391 | 1.2429 | 0.3101 |
| Forecast set | 70 | 0.4506 | 1.7015 | 1.1712 | 0.2883 |
From table 2 it can be seen that calibration set sample phytic acid content scope is wide, comprise forecast set sample phytic acid content
Scope, is suitable for the structure of near infrared correction.
7) the selecting and the structure of PLS model of preprocess method
For the calibration set sample of 210 parts, full cross validation is used to set up PLS model in full spectral region,
Investigating the impact on PLS model of 11 kinds of different preprocessing procedures respectively, 11 kinds of preprocess methods are respectively
For Savitzky-Golay smoothing techniques, first differential method, variable standardization method, multiplicative scatter correction method,
Savitzky-Golay is smooth+and first differential method, Savitzky-Golay be smooth+variable standardization method,
Savitzky-Golay is smooth+multiplicative scatter correction method, first differential+variable standardization method, first differential+many
Unit's scatter correction method, Savitzky-Golay be smooth+variable standardization+first differential method, Savitzky-Golay
Smooth+multiplicative scatter correction+first differential method.
The most all with prediction related coefficient (R2) and remaining predicted deviation (RPD) value is maximum, prediction root-mean-square
Error (RMSEP) and cross validation root-mean-square error (RMSECV) value minimum choose the pre-place of optimum
Reason method is as the preprocess method of modeling, and in the present invention, optimum preprocess method is that Savitzky-Golay smooths
+ variable standardization+first differential method, Fig. 3 is original spectrum spectrum after optimum preprocess method processes
Figure.The model reference metrics evaluation of 11 kinds of preprocess method foundation is shown in Table 3.
The PLS model parameter evaluation index that 3 11 kinds of preprocess methods of table are set up
In table 3: Control indicates without pretreatment；SG represents that Savitzky-Golay smooths；1D represents one
Rank differential；SNV represents variable standardization；MSC represents multiplicative scatter correction；RMSECV represents intersection
Checking root-mean-square error (the least effect of numerical value is the best)；RMSEP represents predicted root mean square error, and (numerical value is more
Little effect is the best)；R2Represent prediction related coefficient (R2> 0.9 represent can substitute completely tradition assay method)；
RPD represents remaining predicted deviation (RPD > 2.5 represents that the robustness of model is good).
8) foundation of near infrared correction and optimization
Pretreated spectroscopic data is imported in matlab software, first calculate according to press value optimal latent
Variable number, then utilizes Monte Carlo to select without information variable and Multivariate Correction homing method sets up model,
Wherein Multivariate Correction homing method specifically includes offset minimum binary (PLS) method, least square method supporting vector machine
(LS-SVM) method and Weighted Least Squares Support Vector Machines (WLS-SVM) method.Wherein Fig. 4 is Meng Teka
Sieve is 5 without the determination of optimal threshold in information variable system of selection processing procedure, optimal threshold.
It is to become without information based on Monte Carlo that optimum Cottonseed powder phytic acid content near infrared correction of the present invention uses
Least square method supporting vector machine (MC-UVE-LS-SVM) model that amount eliminates, this model R2With RPD value
The highest, RMSECV and RMSEP value is minimum, and model evaluation parameter is shown in Table 4.
46 kinds of phytic acid content near-infrared model parameter evaluation indexs of table
In table 4: PLS represents partial least square method；LS-SVM represents least square method supporting vector machine；
WLS-SVM represents Weighted Least Squares Support Vector Machines；MC-UVE represents that Monte Carlo is without information variable
Eliminate；RMSECV represents cross validation root-mean-square error (the least effect of numerical value is the best)；RMSEP table
Show predicted root mean square error (the least effect of numerical value is the best)；R2Represent prediction related coefficient (R2> 0.9 expression
Tradition assay method can be substituted completely)；RPD represents that (RPD > 2.5 represents the steady of model to remaining predicted deviation
Strong property is good).
As can be seen here, the embodiment of the inventive method wherein sample material phytic acid content is big across width
(0.4302-1.8391%), there is good representativeness, be suitable near infrared spectrum modeling；The most different in advance by comparison
Processing method, the preprocess method obtaining optimum is that Savitzky-Golay smooths+variable standardization+first differential
Method；Use this preprocess method to set up different phytic acid calibration models, determine that optimum Cottonseed powder sample phytic acid contains
Amount near infrared correction is the least square method supporting vector machine eliminated without information variable based on Monte Carlo
(MC-UVE-LS-SVM) model, this model R2The highest with RPD value, RMSECV and RMSEP value
Minimum, it is possible to measure the phytic acid content in Cottonseed powder accurately.Its phytic acid chemical measurements and near infrared spectrum
Dependency graph between predictive value, as it is shown in figure 5, wherein diagonal represent optimal predict the outcome (pre-
Measured value=chemical score), sample point, closer to diagonal, illustrates that the effect of model is the best, and vice versa.
9) prepare Cottonseed powder sample to be measured, gather the near infrared spectrum of Cottonseed powder sample to be measured under the same conditions
Data, detect Cottonseed powder sample to be measured with the optimum near infrared spectrum calibration model constructed by above-mentioned steps,
To its phytic acid content.
The innovation of the present invention is, utilizes near infrared spectrometer, as long as gathering the spectrogram of Cottonseed powder,
Process spectral information according to optimum preprocess method, utilize above-mentioned optimum calibration model, just can quickly measure
The content of phytic acid in Cottonseed powder, this quick, green, accurate, the assay method of convenient environment friendly, for training
The processing and utilization educating low phytic acid cotton variety and promote cotton side-product is significant and is worth.
Claims (6)
1. the assay method of phytic acid content in a Cottonseed powder, it is characterised in that comprise the steps:
1) preparation is at the Cottonseed powder sample of the different cultivars of different regions plantation, Semen Gossypii is carried out pretreatment, obtains
Cottonseed powder sample；
2) near infrared spectrometer collection is utilized to obtain the near infrared spectrum data of Cottonseed powder sample；
3) for step 2) near infrared spectrum data of Cottonseed powder sample that collects carries out 11 kinds of methods respectively
Spectroscopic data pretreatment, then record the phytic acid content of Cottonseed powder sample by high-efficient ion chromatogram method (HPIC),
The near infrared spectrum data obtained in conjunction with 11 kinds of preprocess methods and phytic acid content, use offset minimum binary respectively
Method (PLS) sets up the near infrared correction of Cottonseed powder phytic acid content；
4) select prediction related coefficient (R2) value and residue pre-from all near infrared correction of above-mentioned steps
Survey deviation (RPD) value maximum and predicted root mean square error (RMSEP) value and cross validation root-mean-square error
(RMSECV) model that value is minimum is as optimal full spectrum PLS model；
5) utilize Variable Selection to step 4) in the spectroscopic data of optimal full spectrum PLS model carry out
Variable selection, use Multivariate Correction homing method set up the spectroscopic data after variable selection and its phytic acid content it
Between multiple near infrared spectrum calibration models；
6) from all near infrared spectrum calibration models of above-mentioned steps, select prediction related coefficient (R2) value and remain
Remaining prediction deviation (RPD) value is maximum and predicted root mean square error (RMSEP) value and cross validation root-mean-square error
(RMSECV) model that value is minimum is as optimum Cottonseed powder phytic acid content near infrared correction；
7) use step 1) and 2) described same procedure collection Cottonseed powder sample to be measured near infrared spectrum data,
By above-mentioned steps 6) constructed by optimum Cottonseed powder phytic acid content near infrared correction detect Cottonseed powder to be measured
Sample, obtains its phytic acid content.
The assay method of phytic acid content in a kind of Cottonseed powder the most according to claim 1, it is characterised in that:
Described step 1) in pretreatment concrete the most in the following ways: by cottonseed delinting, dry, peel off and dry again
After, with sample grinding machine, Cottonseed is clayed into power, cross 60 mesh sieves, obtain Cottonseed powder sample.
The assay method of phytic acid content in a kind of Cottonseed powder the most according to claim 1, it is characterised in that:
Described step 2) utilize near infrared spectrometer collection Cottonseed powder sample spectrum to concretely comprise the following steps:
2.1) spectroscopic data is obtained with near infrared spectrometer collection after every part of sample is filled in three times sample scanning, the reddest
The collection wave-length coverage of external spectrum instrument is 4000-10000cm-1, every 4cm-1Gather reflex strength (R), altogether
Count 1501 spectrum points, average after multiple scanning 64 times；The applied sample amount of dress sample scanning is that 3.5g is left every time
The right side, sample cell is the cylinder of a diameter of 1cm, and the scanning of dress sample is all to enter under 25 ± 0.5 DEG C of temperature conditionss every time
OK；
2.2) then calculate the average light spectrum of three spectroscopic datas of every part of sample, then average light spectrum is converted
For log (1/ average light spectrum), obtain the near-infrared absorption spectrum of Cottonseed powder sample.
The assay method of phytic acid content in a kind of Cottonseed powder the most according to claim 1, it is characterised in that:
Described step 3) in 11 kinds of preprocess methods be respectively Savitzky-Golay smoothing techniques, first differential method,
Variable standardization method, multiplicative scatter correction method, Savitzky-Golay be smooth+first differential method,
Savitzky-Golay is smooth+and variable standardization method, Savitzky-Golay be smooth+multiplicative scatter correction method,
Rank differential+variable standardization method, first differential+multiplicative scatter correction method, Savitzky-Golay smooth+variable
Standardization+first differential method, Savitzky-Golay smooth+multiplicative scatter correction+first differential method.
The assay method of phytic acid content in a kind of Cottonseed powder the most according to claim 1, it is characterised in that:
Described step 5) in variable selection use based on DSMC without information variable elimination (MC-UVE)
Method.
The assay method of phytic acid content in a kind of Cottonseed powder the most according to claim 1, it is characterised in that:
Described step 5) in Multivariate Correction homing method specifically include offset minimum binary (PLS) method, least square support
Vector machine (LS-SVM) method and Weighted Least Squares Support Vector Machines (WLS-SVM) method.
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