CN110231302A - A kind of method of the odd sub- seed crude fat content of quick measurement - Google Patents
A kind of method of the odd sub- seed crude fat content of quick measurement Download PDFInfo
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- 238000004566 IR spectroscopy Methods 0.000 claims 1
- 238000002790 cross-validation Methods 0.000 abstract description 9
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- 229960004488 linolenic acid Drugs 0.000 description 1
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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
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
-
- 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
-
- 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
- G01N21/47—Scattering, i.e. diffuse reflection
- G01N21/4738—Diffuse reflection, e.g. also for testing fluids, fibrous materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
Abstract
The invention discloses a kind of methods of the odd sub- seed crude fat content of quickly measurement, belong to crude fat detection field.The method of the present invention measures the practical crude fat content of sample as reference value using national standard, sample is scanned simultaneously in fixed spectra collection method, determine number of principal components, select preprocessing procedures specific, suitable for odd sub- seed crude fat, based on Partial Least Squares, the calibration model between spectral information and reference value is constructed.The content information of each ingredient can be quickly obtained using established calibration model, and established model prediction accuracy with higher is verified by cross-validation method.The quantitative analysis of the method for the present invention crude fat suitable for odd sub- seed, method is accurate, time-saving and efficiency.
Description
Technical field
The present invention relates to a kind of methods of the odd sub- seed crude fat content of quickly measurement, belong to crude fat detection field.
Background technique
Odd Asia seed (Chia Seed) is the seed of chia (Salvia Hispanica L.), small in size, is thin
The close relative of lotus plant families.It is odd sub- cold-resistant, it is grown in the sandy or Rocks, Soils of hot Arid Area, its initial growth is in ink
The High aititude desert area of western brother.As a " star's food ", " super food ", odd Asia seed be most initially American-European popular,
Gradually incoming domestic afterwards, national health State Family Planning Commission is new raw-food material in the odd sub- seed of approval in 2014, and in addition to directly eating, surprise is sub-
Seed as raw material or auxiliary material be also widely used for cereal meal replacement powder, cereal bars, biscuit, bread, Yoghourt, corn-dodger and jam it
In.But odd Asia seed does not have plantation at home, and the sub- seed of the surprise on domestic market all relies on import, and sample collection is not easy.
In recent years, people are more and more interested as the substitution source of vegetable oil in unconventional plant, for example, as Qi Ya or
New plant as flax contains a large amount of greases with good nutrition value.Compared with other cereal, odd Asia seed fat content
Higher, odd Asia seed oil content is between 25-38%, and saturated fatty acid content is low, and the polyunsaturated fatty acid with high-content (accounts for
The 82% of total fluid composition), wherein linolenic acid about 66%, linoleic acid about 20%.Use to odd sub- seed oil is also food, drink and sex-man's prime wants
To odd sub- one of major consumers approach, add it in different food formulas, health care tonic or skin care item.Odd Asia seed and
Odd Asia seed oil has been found to can be safely used in animal feed, to increase the polyunsaturated fatty acid in meat and egg products
With reduction cholesterol levels
The crude fat content of odd Asia seed is to influence the Fundamentals of odd sub- seed quality comparison, is odd sub- seed processing, processing, receives
The important indicator that must be detected in purchase, transit link.In conjunction with the reason of sample collection aspect, the measurement of odd Asia seed crude fat content
Mainly there are physical squeezing method, soxhlet extraction, supercritical fluid extraction, ultrasonic wave added to shift to an earlier date method.Although this method chemistry point
It is more accurate to analyse result, but operation is not only time-consuming but also laborious, detection cycle is long, and sample must carry out destructive processing, effect
Rate is not high.So seeking a kind of quick, effective, lossless detection method measuring with important to odd sub- seed crude fat content
Realistic meaning.
Near infrared spectrum (Near Infrared Spectroscopy, NIRS) be based on visible light and middle infrared spectrum it
Between a Duan Guangpu, it is hydric group (C-H, N-H, O-H) that wave-length coverage 780nm-2526nm, which belongs to molecular vibration spectrum,
The frequency multiplication and sum of fundamental frequencies of vibration absorb, and are a kind of effective carriers for obtaining hydric group characteristic information.With adding for Chemical Measurement
Enter, near-infrared spectrum technique has more application in the food inspections such as agricultural product, and its feature is: testing cost is low, fastly
Speed, harmless sample to be tested are a kind of food quality analytical technologies of maturation, it can be achieved that on-line checking.This is by the technology
Analyzing and monitor in real time for odd sub- seed crude fat content becomes a kind of possibility.
The current report having using near infrared spectrum detection crude fat content, such as the Xiao Qingqing of Ji'nan University are (close red
External spectrum is used for the quick analysis of corn moisture and crude fat, Ji'nan University) by comparing full wave offset minimum binary (PLS)
Model and at equal intervals combination Partial Least Squares (EC-PLS), discovery establish crude fat quantitative model result more at EC-PLS
Good, selecting optimal starting point wave number, wave number number and space-number is respectively 4568cm-1, 22,10, consider from the manufacture of instrument, greatly
The earth saves cost, its application is better than full spectrum in actual production；Although this method can accurately measure jade
Crude fat content in rice, but since physical environment difference, crude fat absorption bands differ greatly, utilize the wave in this method
Section selection data can not establish general, accurate linearity correction model.(near infrared spectroscopy quickly detects thick in tealeaves Xiong Lihua
The research of fiber, crude fat and ash content, East China University of Science) using near infrared spectroscopy establish crude fat content mould in tealeaves
Type selects scanning range for 4150~6200cm-1, 6800~8000cm-1Region as the spectral regions for establishing quantitative model
Domain；This method is not directed to the selection of characteristic wave bands, it is difficult to guarantee that the region is best modeled wave band.In addition, Gansu Agriculture University
Show in oil cake of flax seed nutrient context of detection by many experiments exploration in year fragrant (CN105675538): oil cake of flax seed can be in 400-
Spectral information is acquired within the scope of 2498nm, then establishes crude fat calibration mould using the various pretreatments of improvement least square method collocation
Type；However, this method and not yet explicitly how characteristic spectrum data construct calibration model, and the wave band in this method can not equally yet
Effectively, crude fat content in odd sub- seed is accurately predicted.Therefore, it develops in a kind of quick, effective, lossless and accurate odd sub- seed
Crude fat content detection method is highly important.
Summary of the invention
The purpose of the invention is to overcome crude fat content in existing odd sub- seed time-consuming and laborious in the analysis process, detection
Period is long, sample broke test, ineffective disadvantage, provide it is a kind of based near infrared spectrum, be suitable for odd sub- seed slightly
The quality of odd sub- seed can be evaluated in determination of fat method, accurate modeling, simple and rapid detection, for odd sub- seed processing
The identification of quality.
Since crude fat contains C-H group in odd sub- seed, there is spy in the level-one frequency multiplication of near infrared region and second level frequency multiplication area
Absorption band is levied, the molecular structure information for including near infrared spectrum can be very good characterization target quality concentration feature, pre- to locate
Spectrum after reason is associated with actually measured crude fat content reference value, establishes calibration model using Partial Least Squares, this
Identical pretreated spectrum can be called in calibration model, obtain surprise by sample by the atlas of near infrared spectra of the odd sub- seed of scanning
Accurate crude fat content percentage value in sub- seed.
The first purpose of the invention is to provide a kind of methods of the odd sub- seed crude fat content of measurement, and the method includes such as
Lower step:
(1) in 4000-10000cm-1The odd sub- seed sample of diffusing reflection scanning, obtains near infrared spectrum in full spectrum Spectral range
Figure；
(2) in gained atlas of near infrared spectra choose specific band range, to the spectral information within the scope of specific band into
Row pretreatment, obtains peak intensity angle value；The pretreated mode includes multiplicative scatter correction (MSC), standard normal variation
(SNV), first derivative (1st), second dervative (2nd), one or more of SG is smooth and ND is smooth；The specific band
Range includes 4400-7600cm-1、4400-5500cm-1、5700-6500cm-1、7500-7600cm-1One of or it is a variety of.
(3) by crude fat content in the odd sub- seed sample of national standard method measurement as reference value, and Partial Least Squares is utilized,
The peak intensity angle value obtained with step (2) establishes calibration model；
(4) sample to be tested obtains peak intensity angle value through step (1), (2), according to the calibration model in step (3), is calculated
Crude fat content in sample to be tested sample.
In one embodiment of the invention, the preferred 4400-7600cm of specific band range in the step (2)-1、
4400-5500cm-1、5700-6500cm-1、7500-7600cm-1One of or it is a variety of.
In one embodiment of the invention, the further preferred 4400- of specific band range in the step (2)
7600cm-1Or 4400-5500cm-1、5700-6500cm-1And 7500-7600cm-1Combination.
In one embodiment of the invention, the further preferred 4400- of specific band range in the step (2)
7600cm-1。
In one embodiment of the invention, the step (1) is that odd sub- seed sample is placed in specimen cup, in spectrum area
Diffusing reflection scans in range, resolution ratio 4-16cm-1, scanning times 16-64 times 4-8 times of gain, obtain near infrared spectrum.
In one embodiment of the invention, the step (1) is that odd sub- seed sample is placed in specimen cup, in spectrum area
Diffusing reflection scans in range, resolution ratio 8cm-1, scanning times 32 times, 8 times of gain, obtain near infrared spectrum.
In one embodiment of the invention, the pretreated preferred first derivative of mode in the step (2), Huo Zhebiao
Combination or multiplicative scatter correction, first derivative and the SG that quasi- normal state changes, first derivative and SG are smooth smooth combination.
In one embodiment of the invention, the further preferably more single orders of pretreated mode are led in the step (2)
Number.
In one embodiment of the invention, using sample in MSC removal near-infrared diffusing reflection spectrum in the pretreatment
Noise caused by the mirror-reflection and inhomogeneities of product and spectrum it is not repeated；Disappeared using first derivative and/or second dervative
Except baseline and improve resolution ratio, reduction noise jamming；Using SG is smooth and/or ND smoothly improves the signal-to-noise ratio of effective information.
In one embodiment of the invention, calibration model is established with Partial Least Squares in the step (3), in conjunction with
Principal component analysis carries out dimensionality reduction compression to spectral information.
In one embodiment of the invention, the number of principal components of the calibration model is 6.
Second object of the present invention is that above-mentioned detection method is applied to crude fat content in the odd sub- seed of real-time detection to become
In change.
Third object of the present invention is applied to above-mentioned detection method in odd sub- seed processing quality monitoring field.This hair
Bright beneficial effect is:
Near infrared spectrum combination chemometrics method is introduced the lossless inspection of odd sub- seed crude fat content by the present invention for the first time
It surveys, for the quantitative analysis of crude fat in odd sub- seed, method is accurate, time-saving and efficiency.
For the present invention compared with traditional chemical analysis method, detection speed is fast, does not damage sample, and whole continuous mode is not necessarily to one
Minute, it is a kind of convenient convenience, environmentally protective detection method.
Near-infrared spectrum technique is introduced into odd sub- building for seed database model by the method for the present invention may be implemented online reality
When detection odd sub- seed crude fat content in process variation, be extremely to have to the stability for ensuring odd sub- seed processing quality
Meaning.
Detailed description of the invention
Fig. 1 is the flow chart that near-infrared spectrum technique establishes crude fat content model in odd sub- seed；
Fig. 2 is the original atlas of near infrared spectra of odd sub- seed；
Fig. 3 is variation diagram of the RMSECV with number of principal components；
Fig. 4 is the predicted value of calibration set sample figure related to reference value；
Fig. 5 is the predicted value figure related to reference value of verifying collection sample；
Fig. 6 is the result figure of sample interior cross validation.
Specific embodiment
Below by way of examples to near-infrared spectroscopy of the present invention establish and application process furtherly
Bright, which should not be construed as limitation of the present invention.
Near-infrared spectroscopy of the present invention is established and application process such as Fig. 1, specific as follows:
1. the collection and classification of representative sample:
According to the growing environment and growth cycle feature of odd sub- seed, the sample on each odd sub- seed main product ground, sample are collected respectively
Product amount reaches 103 parts, including the ground such as Mexico, Australia, Argentina, Bolivia, Ecuador, Nicaragua, Peru
Area, collecting the period is greater than 2 years (odd sub- be pennyroyal annual plant), multiple batches of acquisition, collect type include Hei Qiya seed and
Bai Qiya seed two types.Sample is divided into Calibration and verification sample collection, every collection covers each area, batch and color
The sub- seed of the surprise of type, effectively expands the range of sample data.Sample carries out pressing national standard method survey while spectra collection immediately
Crude fat content in Ding Qiya seed establishes odd sub- seed sample crude fat content database.
2. the acquisition of instrument condition and sample spectra:
Instrument: near infrared spectrum is closely red by the Antaris II purchased from scientific and technological (China) Co., Ltd of Thermo Fisher
The scanning of outer analysis instrument is collected, which is RESULT-Integration equipped with spectra collection software；Modeling software is TQ
Analyst is scientific and technological (China) the Co., Ltd exploitation of Thermo Fisher.Meanwhile being furnished with InGaAs detector.
The acquisition of sample spectra: near infrared spectrum spectrogram is carried out to odd sub- seed sample to be measured under conditions of 25 DEG C ± 2 DEG C
Acquisition, weigh 25g seed in standard sample cup, scan odd sub- seed sample with irreflexive mode in the Spectral range of selection
The near infrared spectrum of product, resolution ratio 8cm-1, scanning times 32 times, gain 8 ×, same sample pours out specimen cup, fills cup again, such as
This scanning three times or more, takes average spectrum as the standard spectrum of the sample.
3. with reference to the measurement of Value Data:
The crude fat of odd sub- seed is measured by GB 5009.6-2016 " fatty measurement in national food safety standard food "
Content, each sample detection three times, are averaged.
4. sample sets divide:
103 samples are divided into two groups, one group is calibration set, for establishing quantitative model；Another group, as verifying collection, is used
In the Stability and veracity of testing model.In order to avoid due to sample grouping it is unreasonable caused by deviation, subset select such as
Lower progress: for every 4 samples, random selection 3 are used as calibration set, and remaining sample is used as forecast set.Therefore, this experiment is answered
77 are used as calibration set, in addition 26 composition verifyings collect.As shown in Table 1, the content range of calibration set sample covers verifying
The content range for collecting sample, illustrates that the group result is preferable.
The sample number and content of 1 calibration set of table and verifying collection
| Sample sets | Content unit | Sample number | Content range | Average content | Standard deviation |
| Calibration set | g/g | 77 | 26.4~36.7 | 33.0 | 1.7 |
| Verifying collection | g/g | 26 | 26.5~36.5 | 33.2 | 1.8 |
5. establishing calibration model:
Spectrum is pre-processed using modeling software, extracts the characteristic spectrum of the odd sub- seed crude fat content of concurrent big expression
Information.Preprocessing procedures include: to cause for the mirror-reflection of sample and inhomogeneities in removal near-infrared diffusing reflection spectrum
Noise and spectrum it is not repeated use MSC；First derivative and second dervative are used to eliminate baseline and improving resolution ratio,
To reduce noise jamming；The signal-to-noise ratio for improving effective information uses SG smoothly and ND is smooth.In combination with principal component analysis to light
Spectrum information carries out dimensionality reduction compression, establishes calibration model with Partial Least Squares.
6. cross-validation:
Regulation all samples are calibration set sample, carry out cross-validation to model using leaving-one method, that is, leave portion
For sample as sample to be predicted, remaining sample participates in this sample crude fat content of modeling and forecasting, and so circulation measures repeatedly
The relational graph of 103 parts of predicted values and practical crude fat content value, with coefficient RCVWith cross validation root-mean-square error (Root
Mean Square Error of Cross Validation, RMSECV) assessment models.
7. carrying out sample prediction:
Under stable environmental condition, the halogen tungsten lamp light source in near-infrared analyzer issues light radiation, is radiated at odd sub- seed
On sample, the light that diffusing reflection comes out is integrated ball collection, converts digital data transmission near infrared spectrum by detector and arrives
Computer, then digital signal is analyzed with the calibration model for having been established and verifying, contain to obtain crude fat in odd sub- seed
The data of amount.
Embodiment 1: the building of odd Asia seed crude fat content calibration model
(1) near-infrared is carried out to odd sub- seed sample (be divided into calibration set and verifying collects) to be measured under conditions of 25 DEG C ± 2 DEG C
The acquisition of spectrum spectrogram weighs 25g seed in standard sample cup, in 4000-10000cm-1With irreflexive in Spectral range
Mode scans the near infrared spectrum of odd sub- seed sample, resolution ratio 8cm-1, scanning times 32 times, gain 8 ×, same sample pours out sample
Product cup, fills cup again, and so scanning three times or more, takes average spectrum as the standard spectrum of the sample.
(2) it chooses to 4400-7600cm in standard spectrum-1Spectral information in Spectral range is pre-processed, described pre-
Processing mode is 1stSmoothly；
(3) odd sub- seed sample is obtained the practical crude fat of sample and is contained by the crude fat content in the odd sub- seed of national standard method measurement
Magnitude；Extract 4400-7600cm-1All-wave section establishes Partial Least Squares and establishes calibration model, obtains predicted value and reality is thick
The linear model of fat content value；It is respectively that 0.292,0.475, RPD is that R, which is 0.9671, RMSEC and RMSEP, in PLS modeling
6.2, the preferable model of preprocess method prediction accuracy with higher can be used for the quantitative determination of actual sample.Figure 4 and 5 point
It is not the predicted value figure related to practical crude fat content value of calibration set and verifying collection sample, wherein calibration set and verifying collection
Coefficient RCAnd RPRespectively 0.9865 and 0.9671, root-mean-square error is respectively 0.292 and 0.475.
Embodiment 2: the verifying of odd Asia seed crude fat content calibration model
If all samples are calibration set sample, cross-validation is carried out to model using leaving-one method, that is, leaves a sample
For product as sample to be predicted, remaining sample participates in this sample crude fat content of modeling and forecasting, and so circulation measures 103 repeatedly
Part predicted value, with cross validation related coefficient (RCV) and RMSECV assessment models.
Fig. 6 is to be predicted with the calibration set reference value (national standard measures sample crude fat content) after above-mentioned cross validation with NIR
The related figure of value can intuitively find out, there is no the larger point for deviateing fitting a straight line, linear fit is good very much from figure.Wherein,
RCVFor 0.9486, RMSECV 0.612.RMSECV is smaller, RCVGreater than 0.9, show to combine partially most using near-infrared spectrum technique
Small square law can carry out quantitative analysis to crude fat content in odd sub- seed.
Embodiment 3: the influence that Pretreated spectra mode models PLS
Pretreatment mode in step (2) is replaced with pretreatment mode as shown in Table 2 by reference implementation example 1, other
Part is constant, carries out PLS modeling.
Table 2 selects the sub- seed crude fat content PLS modeling result of the surprise of different pretreatments method
Wherein: Raw: original spectrum；1st: first derivative；2nd: second dervative；MSC: multiplicative scatter correction；SNV: standard
Normal state variation；ND is smooth: Norris derivative fiter；SG is smooth: Savitzky-Golay filter；Main composition because
Subnumber is matched optimized parameter condition under each pretreatment mode.
Spectrogram is optimized by various preprocessing procedures, analysis software automatically selects wave-number range and principal component
Number, as a result by coefficient R and root-mean-square error (RMSEC and RMSEP) come the superiority and inferiority of evaluation model, R (RpGreater than threshold value 0.7)
Bigger, RMSEC and RMSEP are smaller, then the preprocess method is preferable.Whether judgment models are suitable for practice examining, relation analysis
Error (RPD) is important index: RPD is the ratio of standard deviation and root-mean-square error, for proving the predictive ability of model；
RPD numerical value difference is smaller, 0.1 difference may be because the representativeness of collected sample distribution and dimensional discrepancy is poor, can also
Can because model data processing mode select it is bad, cause the linear fit of reference value and predicted value poor, root-mean-square error compared with
Greatly, 0.1 difference illustrates entirely different accuracy result；As RPD >=3, explanation works well, and model built can be used for
It is actually detected；If RPD < 3, illustrate to detect it is not good enough, precision up for improve.
Concrete outcome is as shown in table 2, and best preprocess method is 1st, PLS modeling in R be 0.9508, RMSEC and RMSEP
Respectively 0.220,0.593, RPD 8.2, RPD are greater than 3, illustrate model prediction accuracy with higher, can be used for practical sample
The quantitative determination of product.
Embodiment 4: different pretreatments wave band, which models surprise Asia seed crude fat content PLS, to be influenced
Reference implementation example 1 selects best pretreatment mode 1st, the wave band in step (2) is replaced with into wave as shown in table 3
Number range, other conditions are constant, carry out PLS modeling.
Table 3 selects odd sub- seed crude fat content PLS modeling result under different-waveband range
Select 4000-10000cm-1All-wave number interval establishes Partial Least Squares and establishes calibration model, obtain predicted value with
The linear model of practical crude fat content value；R is 0.9108, RPD less than 3 in PLS modeling, shows all-wave number drag accuracy
It is not good enough.Comprehensively consider table 2 and table 3, specific wave number 4400-7600cm-1Under, R 0.9671, RMSEC and RMSEP are respectively
0.292,6.2 0.475, RPD, and there is higher prediction accuracy in the preferable model of preprocess method, it is more conducive to reality
The accurate quantitative analysis of sample measures.
In addition, number of principal components is also one of evaluation parameter of calibration model, works as root-mean-square error when being modeled using PLS
(RMSECV) it is reduced with the increase of number of principal components when almost stable, obtaining best number of principal components is 6 (see Fig. 3), this
When contribution rate 99%.
Claims (10)
1. a kind of method of crude fat content in odd sub- seed of measurement, which is characterized in that described method includes following steps:
(1) in 4000-10000cm-1The odd sub- seed sample of diffusing reflection scanning, obtains atlas of near infrared spectra in full spectrum Spectral range；
(2) specific band range is chosen in gained atlas of near infrared spectra, the spectral information within the scope of specific band is carried out pre-
Processing, obtains peak intensity angle value；The pretreated mode includes multiplicative scatter correction (MSC), standard normal changes, single order is led
One or more of number, second dervative, SG are smooth and ND is smooth；The specific band range includes 4400-7600cm-1、
4400-5500cm-1、5700-6500cm-1、7500-7600cm-1One of or it is a variety of；
(3) by crude fat content in the odd sub- seed sample of national standard method measurement as reference value, and it is based on Partial Least Squares, with step
Suddenly the peak intensity angle value that (2) obtain establishes calibration model；
(4) odd sub- seed sample to be measured obtains peak intensity angle value through step (1), (2), according to the calibration model in step (3), calculates
To the crude fat content in sample to be tested sample.
2. the method according to claim 1, wherein specific band range is 4400- in the step (2)
7600cm-1、4400-5500cm-1、5700-6500cm-1、7500-7600cm-1One of or it is a variety of.
3. method according to claim 1 or 2, which is characterized in that specific band range is 4400- in the step (2)
7600cm-1, or be 4400-5500cm-1、5700-6500cm-1、7500-7600cm-1Combination.
4. method according to claim 1 to 3, which is characterized in that pretreated mode is one in the step (2)
Order derivative perhaps standard normal changes, first derivative and SG are smooth combination or multiplicative scatter correction, first derivative and SG
Smooth combination.
5. method according to claim 1 to 4, which is characterized in that pretreated mode is one in the step (2)
Order derivative.
6. -5 any method according to claim 1, which is characterized in that the step (1) is to be placed in odd sub- seed sample
In specimen cup, diffusing reflection is scanned in full spectrum Spectral range, resolution ratio 4-16cm-1, scanning times 16-64 times, gain 4-8
Times, obtain near infrared spectrum.
7. -6 any method according to claim 1, which is characterized in that the step (1) is to be placed in odd sub- seed sample
In specimen cup, diffusing reflection is scanned in full spectrum Spectral range, resolution ratio 8cm-1, scanning times 32 times, 8 times of gain, obtain close
Infrared spectroscopy.
8. -7 any method according to claim 1, which is characterized in that using the stability of leaving-one method verifying calibration model
With reliability.
9. any detection method of claim 1-8 answering in terms of crude fat content variation in the odd sub- seed of real-time detection
With.
10. application of any detection method of claim 1-8 in odd sub- seed processing quality monitoring field.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112213282A (en) * | 2020-09-15 | 2021-01-12 | 吉林省农业科学院 | Method for detecting content of crude fat in cyperus esculentus by applying near-infrared grain analyzer |
| CN112782117A (en) * | 2020-12-30 | 2021-05-11 | 太仓安佑生物科技有限公司 | Method for measuring content of fatty acid in flaxseed |
| CN113484270A (en) * | 2021-06-04 | 2021-10-08 | 中国科学院合肥物质科学研究院 | Construction and detection method of single-grain rice fat content quantitative analysis model |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101887018A (en) * | 2009-05-13 | 2010-11-17 | 山东省花生研究所 | Method for nondestructively measuring main fatty acid content of peanut seeds |
| CN102759515A (en) * | 2012-04-26 | 2012-10-31 | 江苏大学 | Method for rapidly determining oil contents of agricultural products by using mid-infrared spectrometer based on horizontal attenuated total reflection (ATR) |
| CN105181642A (en) * | 2015-10-12 | 2015-12-23 | 华中农业大学 | Near-infrared detection method for peanut quality and application |
| CN105675538A (en) * | 2016-01-04 | 2016-06-15 | 甘肃农业大学 | Method for detection of linseed cake nutrients |
-
2019
- 2019-06-10 CN CN201910495860.XA patent/CN110231302A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101887018A (en) * | 2009-05-13 | 2010-11-17 | 山东省花生研究所 | Method for nondestructively measuring main fatty acid content of peanut seeds |
| CN102759515A (en) * | 2012-04-26 | 2012-10-31 | 江苏大学 | Method for rapidly determining oil contents of agricultural products by using mid-infrared spectrometer based on horizontal attenuated total reflection (ATR) |
| CN105181642A (en) * | 2015-10-12 | 2015-12-23 | 华中农业大学 | Near-infrared detection method for peanut quality and application |
| CN105675538A (en) * | 2016-01-04 | 2016-06-15 | 甘肃农业大学 | Method for detection of linseed cake nutrients |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112213282A (en) * | 2020-09-15 | 2021-01-12 | 吉林省农业科学院 | Method for detecting content of crude fat in cyperus esculentus by applying near-infrared grain analyzer |
| CN112782117A (en) * | 2020-12-30 | 2021-05-11 | 太仓安佑生物科技有限公司 | Method for measuring content of fatty acid in flaxseed |
| CN113484270A (en) * | 2021-06-04 | 2021-10-08 | 中国科学院合肥物质科学研究院 | Construction and detection method of single-grain rice fat content quantitative analysis model |
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