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 PDF

Info

Publication number
CN109374548A
CN109374548A CN201811352253.XA CN201811352253A CN109374548A CN 109374548 A CN109374548 A CN 109374548A CN 201811352253 A CN201811352253 A CN 201811352253A CN 109374548 A CN109374548 A CN 109374548A
Authority
CN
China
Prior art keywords
rice
spectrum
constituency
infrared
moisture
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201811352253.XA
Other languages
Chinese (zh)
Inventor
从彦丽
黄林森
唐旭蔚
刘冬
孙海燕
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Polytechnic
Original Assignee
Shenzhen Polytechnic
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Polytechnic filed Critical Shenzhen Polytechnic
Priority to CN201811352253.XA priority Critical patent/CN109374548A/en
Publication of CN109374548A publication Critical patent/CN109374548A/en
Pending legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3554Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for determining moisture content
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3563Investigating 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
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N2021/3129Determining multicomponents by multiwavelength light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N2021/3196Correlating located peaks in spectrum with reference data, e.g. fingerprint data
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N2021/3595Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR

Landscapes

  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

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

A method of quickly measuring nutritional ingredient in rice using near-infrared
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.
CN201811352253.XA 2018-11-14 2018-11-14 A method of quickly measuring nutritional ingredient in rice using near-infrared Pending CN109374548A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811352253.XA CN109374548A (en) 2018-11-14 2018-11-14 A method of quickly measuring nutritional ingredient in rice using near-infrared

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811352253.XA CN109374548A (en) 2018-11-14 2018-11-14 A method of quickly measuring nutritional ingredient in rice using near-infrared

Publications (1)

Publication Number Publication Date
CN109374548A true CN109374548A (en) 2019-02-22

Family

ID=65385007

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811352253.XA Pending CN109374548A (en) 2018-11-14 2018-11-14 A method of quickly measuring nutritional ingredient in rice using near-infrared

Country Status (1)

Country Link
CN (1) CN109374548A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102279168A (en) * 2011-07-20 2011-12-14 浙江大学 Near-infrared spectroscopic technology-based method for fast and undamaged analysis of nutritional quality of whole cottonseed
CN102879340A (en) * 2012-09-27 2013-01-16 江苏徐州甘薯研究中心 Method for quickly detecting nutritional quality of root/stem crops on basis of near-infrared spectrum
JP2013072726A (en) * 2011-09-27 2013-04-22 Chikuno Shokuhin Kogyo Kk Method for quantifying triacylglycerol in brown rice using near-infrared spectroscopy
CN105181643A (en) * 2015-10-12 2015-12-23 华中农业大学 Near-infrared inspection method for rice quality and application thereof
CN105588819A (en) * 2016-03-11 2016-05-18 江西出入境检验检疫局检验检疫综合技术中心 Method for conducting near-infrared rapid detection on component content in infant formula rice flour
CN105675548A (en) * 2015-12-31 2016-06-15 深圳市芭田生态工程股份有限公司 Method for determining main nutrition components in rice through using spectroscopy
CN106092962A (en) * 2016-08-17 2016-11-09 山西省农业科学院农作物品种资源研究所 A kind of near infrared spectroscopy quickly detects the method for millet crude protein content
CN106770016A (en) * 2017-01-16 2017-05-31 中国科学院合肥物质科学研究院 The Protein quantitative analysis NIR transmitted spectrum measuring methods of single seed paddy seed
CN106908408A (en) * 2017-03-01 2017-06-30 四川农业大学 A kind of assay method of Itanlian rye crude protein content
CN107655852A (en) * 2017-09-29 2018-02-02 广东出入境检验检疫局检验检疫技术中心 The near infrared spectrum quick determination method of essential nutrient in baby formula milk powder
CN108169168A (en) * 2017-12-19 2018-06-15 信阳师范学院 Test and analyze rice grain protein content mathematical model and construction method and application

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102279168A (en) * 2011-07-20 2011-12-14 浙江大学 Near-infrared spectroscopic technology-based method for fast and undamaged analysis of nutritional quality of whole cottonseed
JP2013072726A (en) * 2011-09-27 2013-04-22 Chikuno Shokuhin Kogyo Kk Method for quantifying triacylglycerol in brown rice using near-infrared spectroscopy
CN102879340A (en) * 2012-09-27 2013-01-16 江苏徐州甘薯研究中心 Method for quickly detecting nutritional quality of root/stem crops on basis of near-infrared spectrum
CN105181643A (en) * 2015-10-12 2015-12-23 华中农业大学 Near-infrared inspection method for rice quality and application thereof
CN105675548A (en) * 2015-12-31 2016-06-15 深圳市芭田生态工程股份有限公司 Method for determining main nutrition components in rice through using spectroscopy
CN105588819A (en) * 2016-03-11 2016-05-18 江西出入境检验检疫局检验检疫综合技术中心 Method for conducting near-infrared rapid detection on component content in infant formula rice flour
CN106092962A (en) * 2016-08-17 2016-11-09 山西省农业科学院农作物品种资源研究所 A kind of near infrared spectroscopy quickly detects the method for millet crude protein content
CN106770016A (en) * 2017-01-16 2017-05-31 中国科学院合肥物质科学研究院 The Protein quantitative analysis NIR transmitted spectrum measuring methods of single seed paddy seed
CN106908408A (en) * 2017-03-01 2017-06-30 四川农业大学 A kind of assay method of Itanlian rye crude protein content
CN107655852A (en) * 2017-09-29 2018-02-02 广东出入境检验检疫局检验检疫技术中心 The near infrared spectrum quick determination method of essential nutrient in baby formula milk powder
CN108169168A (en) * 2017-12-19 2018-06-15 信阳师范学院 Test and analyze rice grain protein content mathematical model and construction method and application

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
H. L. WANG ET A.: "Quantitative Analysis of Fat Content in Rice by Near-Infrared Spectroscopy Technique", 《CEREAL CHEM.》 *
XING LIU ET AL.: "Rapid Determination of Fat, Protein and Amino Acid Content in Coix Seed Using Near-Infrared Spectroscopy Technique", 《FOOD ANAL. METHODS》 *
于清丽: "近红外光谱技术在婴幼儿营养米粉快速检测中的应用研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》 *
徐泽林等: "红外光谱法测定大米中的淀粉含量", 《农产品加工》 *
李俊辉等: "我国稻米食味品质的研究现状与发展趋势", 《中国稻米》 *
王传梁等: "基于近红外漫反射技术的大米脂肪含量的研究", 《粮油加工》 *
王海莲等: "稻米脂肪含量近红外光谱分析技术研究", 《中国农业科学》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Similar Documents

Publication Publication Date Title
CN109374548A (en) A method of quickly measuring nutritional ingredient in rice using near-infrared
Li et al. Recent advances in nondestructive analytical techniques for determining the total soluble solids in fruits: a review
Zhu et al. Ripeness evaluation of ‘Sun Bright’tomato using optical absorption and scattering properties
Muik et al. Direct, reagent-free determination of free fatty acid content in olive oil and olives by Fourier transform Raman spectrometry
Başlar et al. Determination of protein and gluten quality-related parameters of wheat flour using near-infrared reflectance spectroscopy (NIRS)
Ouyang et al. Real-time monitoring of process parameters in rice wine fermentation by a portable spectral analytical system combined with multivariate analysis
Lu et al. Quantitative measurements of binary amino acids mixtures in yellow foxtail millet by terahertz time domain spectroscopy
Li et al. Identification of aged-rice adulteration based on near-infrared spectroscopy combined with partial least squares regression and characteristic wavelength variables
CN110646407A (en) Method for rapidly detecting content of phosphorus element in aquatic product based on laser-induced breakdown spectroscopy technology
Malvandi et al. Non-destructive measurement and real-time monitoring of apple hardness during ultrasonic contact drying via portable NIR spectroscopy and machine learning
CN103558167A (en) Method for rapidly measuring content of sodium chloride in salted meat
CN102393376A (en) Support vector regression-based near infrared spectroscopy for detecting content of multiple components of fish ball
CN102937575B (en) Watermelon sugar degree rapid modeling method based on secondary spectrum recombination
CN109520965A (en) A method of lysine content is detected based near infrared spectrum characteristic extractive technique
Arslan et al. NIR spectroscopy coupled chemometric algorithms for rapid antioxidants activity assessment of Chinese dates (Zizyphus Jujuba Mill.)
Chen et al. Rapid identification of three varieties of Chrysanthemum with near infrared spectroscopy
CN108169168A (en) Test and analyze rice grain protein content mathematical model and construction method and application
CN110231302A (en) A kind of method of the odd sub- seed crude fat content of quick measurement
CN104316492A (en) Method for near-infrared spectrum measurement of protein content in potato tuber
Rohman et al. Simultaneous quantitative analysis of two functional food oils, extra virgin olive oil and virgin coconut oil using FTIR spectroscopy and multivariate calibration
Li et al. Study on a two‐dimensional correlation visible–near infrared spectroscopy kinetic model for the moisture content of fresh walnuts stored at room temperature
CN102519903B (en) Method for measuring whiteness value of Agaricus bisporus by using near infrared spectrum
CN107328733A (en) A kind of method of the content of starch added in quick detection minced fillet
Peng et al. Rapid detection of adulteration of glutinous rice as raw material of Shaoxing Huangjiu (Chinese Rice Wine) by near infrared spectroscopy combined with chemometrics
Dong et al. Nondestructive method for analysis of the soybean quality

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20190222