CN109374548A - A method of quickly measuring nutritional ingredient in rice using near-infrared - Google Patents
A method of quickly measuring nutritional ingredient in rice using near-infrared Download PDFInfo
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- 235000007164 Oryza sativa Nutrition 0.000 title claims abstract description 56
- 235000009566 rice Nutrition 0.000 title claims abstract description 56
- 235000016709 nutrition Nutrition 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 19
- 239000004615 ingredient Substances 0.000 title claims abstract description 17
- 240000007594 Oryza sativa Species 0.000 title 1
- 241000209094 Oryza Species 0.000 claims abstract description 55
- 230000003595 spectral effect Effects 0.000 claims abstract description 34
- 102000004169 proteins and genes Human genes 0.000 claims abstract description 30
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 30
- 150000001720 carbohydrates Chemical class 0.000 claims abstract description 29
- 238000001228 spectrum Methods 0.000 claims abstract description 29
- 238000001514 detection method Methods 0.000 claims abstract description 15
- 230000035764 nutrition Effects 0.000 claims abstract description 15
- 239000000203 mixture Substances 0.000 claims abstract description 13
- 239000000843 powder Substances 0.000 claims abstract description 11
- 238000001914 filtration Methods 0.000 claims abstract description 6
- 230000009467 reduction Effects 0.000 claims abstract description 4
- 238000002790 cross-validation Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims 1
- 229910052799 carbon Inorganic materials 0.000 claims 1
- 210000004885 white matter Anatomy 0.000 claims 1
- 238000005259 measurement Methods 0.000 abstract description 14
- 239000000126 substance Substances 0.000 abstract description 14
- 238000011282 treatment Methods 0.000 abstract description 2
- 235000013305 food Nutrition 0.000 description 8
- 238000002329 infrared spectrum Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000002203 pretreatment Methods 0.000 description 3
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000010561 standard procedure Methods 0.000 description 2
- 102100040396 Transcobalamin-1 Human genes 0.000 description 1
- 101710124861 Transcobalamin-1 Proteins 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000003333 near-infrared imaging Methods 0.000 description 1
- 238000000746 purification Methods 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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- 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
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- 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
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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
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- 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
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- 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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- 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
- G01N2021/3129—Determining multicomponents by multiwavelength light
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- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
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Abstract
The invention discloses a kind of method for quickly measuring nutritional ingredient in rice using near-infrared, the nutritional ingredient includes protein, fat, carbohydrate and moisture, comprising the following steps: (1) acquires rice sample；(2) content of each nutrition composition of rice sample is measured as reference value using national standard respectively；Rice sample is randomly divided into calibration set and verifying collection, acquires the spectral information of rice powder；(3) the constituency spectrum of corresponding each nutrition composition is chosen, wherein the constituency spectrum of corresponding protein and carbohydrate is handled using first derivative, the constituency spectrum of corresponding fat and moisture is handled using second dervative；Noise reduction filtering again；(4) quick detection model is established；(5) verifying of model.It is short using method minute of the invention, without chemical treatment, green non-pollution and measurement while each nutrition composition may be implemented.
Description
Technical field
The present invention relates to rice nutrition composition detection fields, are quickly measured in rice more particularly, to a kind of using near-infrared
The method of nutritional ingredient.
Background technique
Protein, fat, carbohydrate and moisture are the important indicators for evaluating rice quality.National Standard Method of Determination exists
The deficiencies of process is cumbersome, condition is not easy to control, reagent consumption is big, time-consuming.Near-infrared spectrum analysis is to rapidly develop in recent years
A kind of green analytical technology, have sample pre-treatments are simple, analysis speed is fast, without chemical reagent, non-destructive and mostly at
New approach is provided for the quick detection of rice nutrition ingredient the advantages that dividing while analyzing.
Near infrared spectrum refers to that between visible light and mid-infrared light, spectral wavelength ranges 780nm~2500nm is
12820cm-1~4000cm-1Spectrum area, main spectral peak is the frequency multiplication and conjunction of the hydric groups such as C-H, N-H and O-H in material molecule
Produced by frequency vibration absorbs, spectral characteristic stablizes the qualitative and quantitative analysis for being suitable for complicated natural goods.The near infrared light of substance
There is inherent to contact for the content of the protein of spectrum information and substance, fat, carbohydrate and moisture etc., uses stoichiometry
The two is associated by method, establishes the quantitative relationship between the two to get quantitative model；Pass through the close of acquisition unknown sample
Infrared spectrogram can be obtained by containing for protein in unknown sample, fat, carbohydrate and moisture etc. according to quantitative model
Amount.Therefore, NIR technology is compared with traditional chemical analysis with unique advantage, but the protein of rice, rouge
Near infrared light spectrum information in terms of fat, carbohydrate and moisture is rare to be had been reported that, at the same measure protein in rice, fat,
Carbohydrate and the near-infrared method of moisture content are more the absence of research.Additionally due to the information that near infrared spectrum provides is very
It is many and diverse, the problem of more accurately detection model becomes urgent need to resolve is established how to efficiently use its spectral information.
Summary of the invention
Nutritional ingredient in rice is quickly measured using near-infrared technical problem to be solved by the invention is to provide a kind of
Method, the correlation between the predicted value and chemical measurements of the quick detection model of foundation is high, and multicomponent may be implemented
While measure.
The technical solution used in the present invention is:
The present invention provides a kind of method that nutritional ingredient in rice is quickly measured using near-infrared, and the nutritional ingredient includes
Protein, fat, carbohydrate and moisture, comprising the following steps:
(1) rice sample is acquired, powder is broken into；
(2) content of each nutrition composition of rice sample is measured as reference value using national standard respectively；By rice sample
It is randomly divided into calibration set and verifying collection, using the spectral information of near infrared spectrometer acquisition rice powder, the spectral information is
4000.00cm-1~10000.00cm-1The rice near infrared light spectrum information of range；
(3) the constituency spectrum that corresponding each nutrition composition is chosen from collected rice near infrared light spectrum information, wherein right
The constituency spectrum of protein and carbohydrate is answered to handle using first derivative, the constituency spectrum of corresponding fat and moisture uses two
Order derivative processing；Savitzky-golay filter noise reduction filtering is recycled, signal-to-noise ratio is improved；
(4) foundation of model: keeping light path constant, is built using minimum square law partially to the information of reference value and chosen spectrum
Vertical association, establishes the quick detection model of rice protein, fat, carbohydrate and moisture respectively；
(5) verifying of model: judging the feasibility of quick detection model using external certificate or cross-validation, verifying
Index includes coefficient R2With root mean square RMSECV.
Preferably, in step (3), the constituency spectral region of the corresponding protein is 4037.65cm-1~5169.48cm-1And 5381.35cm-1~8917.50cm-1；The constituency spectral region of the corresponding fat is 5360.35cm-1~8980.50cm-1；The constituency spectral region of the corresponding carbohydrate is 4030.05cm-1~4471.97cm-1And 5335.15cm-1~
8847.81cm-1；The constituency spectral region of the corresponding moisture is 4008.28cm-1~4343.85cm-1And 4428.01cm-1~
7439.20cm-1。
Preferably, the acquisition parameter in step (2) when the spectral information of acquisition rice powder are as follows: be in spectral resolution
8cm-1, scan 64 times under 2x gain, the spectral background information of acquisition in every four hours.
Preferably, the near infrared spectrometer is Antares ‖ type Fourier Transform Near Infrared instrument.
It is further preferred that utilizing the integrating sphere mould of Antares ‖ type Fourier Transform Near Infrared instrument in step (2)
The spectral information of block acquisition rice powder.
The beneficial effects of the present invention are:
The present invention carries out crushing pre-treatment to rice sample, improves the uniformity and representative of collected near infrared spectrum
Property.The light path of collected near infrared spectrum is not necessarily to carry out multiplicative scatter correction, but remains constant.Until sample measure
Shi Wuxu repeats to model, and the spectral information of acquisition is directly participated in model prediction, simplifies the treatment process to spectral information.Choosing
The spectral region of each nutrition composition of correspondence is used using full Spectral range as the spectrum for establishing model, reduces model
Calculation amount.Different from utilizing near-infrared all to use single order to the spectral information of acquisition when measuring multicomponent simultaneously in the prior art
Derivative carries out pretreated mode, and the present invention passes through lot of experiments, carries out different spectrum using for Different Nutrition ingredient
Pretreated mode obtains optimal Pretreated spectra by comparing the combination of different pretreatments and different modeling methods
Method.I.e. for the spectrum of the near infrared prediction model for establishing protein, carbohydrate using at first derivative
Reason, the near infrared prediction model for establishing fat and moisture is handled using second dervative, so that the quick detection established
Correlation between the predicted value and chemical measurements of model is high, improves the precision of prediction model.
Detailed description of the invention
Fig. 1 is rice atlas of near infrared spectra；
Fig. 2 is protein near-infrared prediction model chemical measurements and predicted value dependency graph；
Fig. 3 is fatty near-infrared prediction model chemical measurements and predicted value dependency graph；
Fig. 4 is carbohydrate near-infrared prediction model chemical measurements and predicted value dependency graph；
Fig. 5 is moisture near-infrared prediction model chemical measurements and predicted value dependency graph；
Fig. 6 is protein near-infrared prediction model cross-validation result figure；
Fig. 7 is fatty near-infrared prediction model cross-validation result figure；
Fig. 8 is carbohydrate near-infrared prediction model cross-validation result figure；
Fig. 9 is moisture near-infrared prediction model cross-validation result figure.
Specific embodiment
It is clearly and completely described below with reference to technical effect of the embodiment to design and generation of the invention, with
It is completely understood by the purpose of the present invention, feature and effect.Obviously, described embodiment is that a part of the invention is implemented
Example, rather than whole embodiments, based on the embodiment of the present invention, those skilled in the art is not before making the creative labor
Other embodiments obtained are put, the scope of protection of the invention is belonged to.
Embodiment 1
Protein in rice, fat, carbohydrate and moisture are quickly measured using near-infrared the present embodiment provides a kind of
Method, comprising the following steps:
(1) 75 kinds of representative rice samples are acquired, break into powder with disintegrating machine, it is spare.
(2) content of each nutrition composition of rice sample is measured respectively using national standard as reference value, specific measurement side
Formula are as follows: pass through " GB 5009.5-2016. national food safety standard: the measurement of Protein in Food ", " GB 5009.6-
2010. food national safety standards: fatty measurement in food ", " 5009.3-2016. food national safety standard of GB: food
The measurement of moisture in product ", " 5009.4-2016. food national safety standard of GB: the measurement of ash content in food " measure it is 75 big
The content of the rice protein of sample, fat, moisture and ash content, then the content of carbohydrate is calculated, surveyed protein, rouge
The chemical score of fat, moisture and carbohydrate content as modeling.
75 rice samples are randomly divided into 63 calibration sets and 12 verifying collection, it is close using Antares ‖ type Fourier transformation
The spectral information of the rice powder of the integrating sphere module acquisition correction collection of infrared spectrometer.It is 8cm in spectral resolution-1, 2x increase
Benefit lower scanning 64 times, the spectral background information of acquisition in every four hours obtains 4000.00cm-1~10000.00cm-1Range
Rice near infrared light spectrum information, as shown in Figure 1.
(3) the constituency spectrum of corresponding each nutrition composition is chosen from collected rice near infrared light spectrum information, selection
Spectral region is as shown in table 1.
The corresponding constituency spectral region of each nutrition composition in 1 rice of table
It is handled by near infrared spectrum of the derivative to rice sample, amplifies the effective information of spectrum, specific processing side
Formula are as follows: the constituency spectrum of corresponding protein and carbohydrate is handled using first derivative, the constituency light of corresponding fat and moisture
Spectrum is handled using second dervative.
Savitzky-golay filter noise reduction filtering is recycled, noise jamming is removed, purification effective information is modeled
The near infrared spectrum used, the savitzky-golay filtering parameter used are as shown in table 2.
2 protein of table, fat, carbohydrate and moisture savitzky-golay filtering parameter
| Component | Data point | Multinomial power |
| Protein | 9 | 3 |
| Fat | 7 | 3 |
| Carbohydrate | 5 | 3 |
| Moisture | 7 | 3 |
(4) foundation of model: according to the cross validation root mean square RESECV of Monte Carlo Cross-Validation with because subnumber LV's
Variation determine minimum inclined two multiply regression model because of subnumber, for establishing used near infrared prediction model in the present embodiment
Because subnumber protein, fat and carbohydrate be 8, moisture because subnumber is 6.It keeps light path constant, recycles minimum
Inclined square law is associated with reference value with the foundation of the information of chosen spectrum, establish respectively rice protein, fat, carbohydrate and
The quick detection model of moisture, as a result as shown in Figure 2-5.According to the coefficient R of verifying collection and calibration set2And root mean square
The feasibility of RMSECV judgment models, as the result is shown the calibration set R of protein2For 0.9875, RMSECV 0.118；Fat
Calibration set R2For 0.9997, RMSECV 0.00895；The calibration set R of total reducing sugar2For 0.9950, RMSECV 0.0949；Moisture
Calibration set R2For 0.9802, RMSECV 0.134.R2Value is all larger than 0.98, RMSECV and is respectively less than 0.15, shows that National Standard Method measures
There is good correlation between value and model predication value, the prediction effect of model is good.
(5) verifying of model:
External certificate: rice sample is collected to 12 verifyings for having neither part nor lot in modeling and is predicted, and by predicted value and each component
Chemical score be compared, specific data are shown in Table 3.
Prediction result of the 3 near infrared detection model of table to verifying collection each nutrition composition content of rice sample
Verify the set pair analysis model external certificate the results show that protein R2For 0.9890, RMSECV 0.149；Fat
R2For 0.9751, RMSECV 0.145；The R of carbohydrate2For 0.9845, RMSECV 0.167；The R of moisture2For
0.9727, RMSECV 0.298.R2Value is all larger than 0.97, RMSECV and is respectively less than 0.30, shows chemical measurements and model prediction
There is good correlation between value, the prediction effect of model is good.
Cross-validation: two groups of samples are chosen from modeling sample as verifying, other samples participate in modeling, as a result
As Figure 6-9, the results show that the R of protein2For 0.9768, RMSECV 0.160；The R of fat2It is 0.9319, RMSECV
It is 0.139；The R of carbohydrate2For 0.9380, RMSECV 0.330；The R of moisture2For 0.9228, RMSECV 0.261.R2
Value is all larger than 0.92, and RMSECV value is respectively less than 0.35, shows there is good correlation between chemical measurements and model predication value
Property, the prediction effect of model is good, can be used for the quick detection of rice protein, fat, carbohydrate and moisture.
(6) detection of sample to be tested: establishing workflow using Thermo RESULT integration software, calls big
Rice protein, fat, carbohydrate and moisture quick detection model, realize to being surveyed while nutritional ingredient in rice to be measured
It is fixed.
Claims (5)
1. a kind of method for quickly measuring nutritional ingredient in rice using near-infrared, the nutritional ingredient include protein, fat,
Carbohydrate and moisture, which comprises the following steps:
(1) rice sample is acquired, powder is broken into；
(2) content of each nutrition composition of rice sample is measured as reference value using national standard respectively；Rice sample is random
It is divided into calibration set and verifying collection, using the spectral information of near infrared spectrometer acquisition rice powder, the spectral information is
4000.00cm-1~10000.00cm-1The rice near infrared light information of range；
(3) the constituency spectrum of corresponding each nutrition composition is chosen from collected rice near infrared light spectrum information, wherein corresponding egg
The constituency spectrum of white matter and carbohydrate is handled using first derivative, and the constituency spectrum of corresponding fat and moisture is led using second order
Number processing；Savitzky-golay filter noise reduction filtering is recycled, signal-to-noise ratio is improved；
(4) foundation of model: keeping light path constant, is established and is closed to the information of reference value and chosen spectrum using minimum square law partially
Connection, establishes the quick detection model of rice protein, fat, carbohydrate and moisture respectively；
(5) verifying of model: judging the feasibility of quick detection model using external certificate or cross-validation, verifies index
Including coefficient R2With root mean square RMSECV.
2. the method according to claim 1 for quickly measuring nutritional ingredient in rice using near-infrared, which is characterized in that step
Suddenly in (3), the constituency spectral region of the corresponding protein is 4037.65cm-1~5169.48cm-1And 5381.35cm-1~
8917.50cm-1；The constituency spectral region of the corresponding fat is 5360.35cm-1~8980.50cm-1；The corresponding carbon aquation
The constituency spectral region for closing object is 4030.05cm-1~4471.97cm-1And 5335.15cm-1~8847.81cm-1；The correspondence
The constituency spectral region of moisture is 4008.28cm-1~4343.85cm-1And 4428.01cm-1~7439.20cm-1。
3. the method according to claim 1 or 2 for quickly being measured nutritional ingredient in rice using near-infrared, feature are existed
Acquisition parameter in, step (2) when the spectral information of acquisition rice powder are as follows: in spectral resolution be 8cm-1, under 2x gain
Scanning 64 times, the spectral background information of acquisition in every four hours.
4. the method according to claim 1 or 2 for quickly being measured nutritional ingredient in rice using near-infrared, feature are existed
In the near infrared spectrometer is Antares ‖ type Fourier Transform Near Infrared instrument.
5. the method according to claim 4 for quickly measuring nutritional ingredient in rice using near-infrared, which is characterized in that step
Suddenly the spectrum of the integrating sphere module acquisition rice powder in (2) using Antares ‖ type Fourier Transform Near Infrared instrument is believed
Breath.
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Cited By (6)
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| CN110779897A (en) * | 2019-11-08 | 2020-02-11 | 湖北民族大学 | Method for determining inorganic selenium in nutritional rice flour |
| CN112285057A (en) * | 2020-11-27 | 2021-01-29 | 常州金坛江南制粉有限公司 | Method for rapidly detecting water content of water-milled glutinous rice flour based on near infrared spectrum technology |
| CN113189042A (en) * | 2021-05-13 | 2021-07-30 | 大连工业大学 | Method for rapidly detecting protein content of infant supplementary food nutrition bag |
| 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 |
| CN114324233A (en) * | 2021-11-16 | 2022-04-12 | 贵州省生物技术研究所(贵州省生物技术重点实验室、贵州省马铃薯研究所、贵州省食品加工研究所) | Near-infrared nondestructive online quality detection method and system for nutritional ingredients of agricultural products |
| CN115508305A (en) * | 2022-03-01 | 2022-12-23 | 河北省畜牧良种工作总站(河北省种畜禽质量监测站) | Intermediate infrared rapid batch detection method for monounsaturated fatty acid in milk |
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| CN112285057A (en) * | 2020-11-27 | 2021-01-29 | 常州金坛江南制粉有限公司 | Method for rapidly detecting water content of water-milled glutinous rice flour based on near infrared spectrum technology |
| CN113189042A (en) * | 2021-05-13 | 2021-07-30 | 大连工业大学 | Method for rapidly detecting protein content of infant supplementary food nutrition bag |
| 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 |
| CN114324233A (en) * | 2021-11-16 | 2022-04-12 | 贵州省生物技术研究所(贵州省生物技术重点实验室、贵州省马铃薯研究所、贵州省食品加工研究所) | Near-infrared nondestructive online quality detection method and system for nutritional ingredients of agricultural products |
| CN115508305A (en) * | 2022-03-01 | 2022-12-23 | 河北省畜牧良种工作总站(河北省种畜禽质量监测站) | Intermediate infrared rapid batch detection method for monounsaturated fatty acid in milk |
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