CN107917897A - The method of the special doctor's food multicomponent content of near infrared ray - Google Patents
The method of the special doctor's food multicomponent content of near infrared ray Download PDFInfo
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- CN107917897A CN107917897A CN201711461693.4A CN201711461693A CN107917897A CN 107917897 A CN107917897 A CN 107917897A CN 201711461693 A CN201711461693 A CN 201711461693A CN 107917897 A CN107917897 A CN 107917897A
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- 238000000034 method Methods 0.000 title claims abstract description 42
- 235000013305 food Nutrition 0.000 title claims abstract description 37
- 235000013325 dietary fiber Nutrition 0.000 claims abstract description 24
- 102000004169 proteins and genes Human genes 0.000 claims abstract description 24
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 24
- 238000012795 verification Methods 0.000 claims abstract description 19
- 230000003595 spectral effect Effects 0.000 claims abstract description 12
- 238000011156 evaluation Methods 0.000 claims abstract description 8
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 8
- 238000002203 pretreatment Methods 0.000 claims abstract description 5
- 238000004611 spectroscopical analysis Methods 0.000 claims abstract description 4
- 238000001228 spectrum Methods 0.000 claims description 15
- 238000002790 cross-validation Methods 0.000 claims description 10
- 238000002512 chemotherapy Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- 239000012491 analyte Substances 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000003018 immunoassay Methods 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 238000012360 testing method Methods 0.000 claims 1
- 238000009659 non-destructive testing Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 abstract description 2
- 239000003814 drug Substances 0.000 description 4
- 238000012417 linear regression Methods 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 235000012054 meals Nutrition 0.000 description 2
- 235000016709 nutrition Nutrition 0.000 description 2
- 230000035764 nutrition Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000012628 principal component regression Methods 0.000 description 2
- 238000010561 standard procedure Methods 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 102000004190 Enzymes Human genes 0.000 description 1
- 108090000790 Enzymes Proteins 0.000 description 1
- 238000004497 NIR spectroscopy Methods 0.000 description 1
- 238000000944 Soxhlet extraction Methods 0.000 description 1
- 102000013081 Tumor Necrosis Factor Ligand Superfamily Member 13 Human genes 0.000 description 1
- 108010065323 Tumor Necrosis Factor Ligand Superfamily Member 13 Proteins 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000007062 hydrolysis Effects 0.000 description 1
- 238000006460 hydrolysis reaction Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 208000030159 metabolic disease Diseases 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
Abstract
A kind of method using the special doctor's food multicomponent content of near infrared ray, step are：（1）Choose representative special doctor's food and carry out the special doctor's multi-component feasibility of food of preliminary identification near infrared spectrum detection；（2）Using national standard determination sample protein, fat, dietary fiber content, as with reference to be worth；（3）Sample is randomly divided into calibration set and verification collects, gathers the atlas of near infrared spectra of sample；（4）The different pretreatments method of its near-infrared spectrogram is screened；（5）The light spectral of modeling is made choice；（6）Spectroscopic data of the sample spectrogram after pretreatment, in selected wave-length coverage is associated with corresponding protein, fat, dietary fiber content using multivariate calibration methods, establishes calibration model；（7）The verification and evaluation of calibration model.The present invention can realize it is special while curing food multicomponent content, quickly, Non-Destructive Testing.
Description
Technical field
The present invention relates to analysis technical field, and in particular to while special medicine purposes formula food multicomponent content,
Quickly, Non-Destructive Testing.
Background technology
Special medicine purposes formula food (Food for special medical purpose, hereinafter referred to as special doctor's food
Product) refer to meet limited feed, Disorder of Digestion and A orption, metabolic disorder or particular disease states crowd to nutrient or meals
Special requirement, specially process the formula food being formulated.Spy doctor's food can play battalion as a kind of enteral nutrition preparation
Support the effect supported.《Special medicine purposes formula food registration management method》In require enterprise detectability high, spy doctor's food
Product manufacturing enterprise should possess whole project lot-by-lot inspection abilities according to special doctor's food national Specification.And special doctor's food nutrition
Component is numerous (such as protein, fat, dietary fiber), and the conventional method that national standard is provided is (such as：Kjeldahl's method, rope
Family name's extraction method etc.) sample need to be pre-processed, it is cumbersome time-consuming, and need a large amount of reagents, it is impossible to measure at the same time.So have
Necessity establishes a kind of fast and effectively detection method under the premise of accuracy and accuracy is met.
Near-infrared spectrum technique combines multiple subjects such as computer technology, spectral technique and Chemical Measurement, is in recent years
To develop swift and violent non-destructive testing technology.Wave-length coverage is 780~2500nm, predominantly can contain H in the generation vibration of this wave band
Group, such as O-H, C-H, N-H, S-H, therefore near-infrared can be to the organic matter of the group containing H (such as：Protein, fat, meals are fine
Dimension etc.) carry out qualitative or quantitative analysis.The comparison of near infrared spectroscopy and national standard method is shown in Table 1, but there is no to be used
In the assay of special medicine purposes formula food.
The comparison of 1 near infrared spectrum of table and national standard method therefor
The content of the invention
Present invention aims at provide it is a kind of using near infrared spectrum it is quick, at the same time, the special doctor's food multicomponent of non-destructive determination
The method of content, and there is higher accuracy and accuracy.
Technical solution is as follows：
Using the method for the special doctor's food multicomponent content of near infrared ray, comprise the following steps：
(1) choose representative special doctor's food come preliminary identification near-infrared be used for it is special cure food multi-analyte immunoassay can
Row.Representativeness refers to that the protein, fat, dietary fiber content of sample can cover desired excursion, and at this
A scope is even variation.The content range of special doctor's Protein in Food of selection is 1%~10%, fatty content range
For 1.5%~6%, the content range of dietary fiber is 2%~11%.
The sample number of spy doctor's food is 80~100 in the formal established model of near-infrared, and the present invention selects 30 samples
Verify that near-infrared is used for the feasibility of special doctor's food multi-analyte immunoassay, for formally establishing model provides necessary basis.
(2) standard GB/T 5009.5-2016, GB5009.6-2016, GB5009.88-2014 determination sample is respectively adopted
Protein, fat, the content of dietary fiber, as chemical score.
(3) 5 are pressed:Sample is randomly divided into 1 ratio calibration set and verification collects, and gathers special doctor's food calibration set and verification collects
Atlas of near infrared spectra, acquisition parameter is respectively resolution ratio：14~18nm；Measuring speed：2000~6000ms；Pendulous frequency：3
~10 times；Spectra collection mode：Transmission；Measurement temperature：25℃.
(4) pretreatment of spectrum：In order to eliminate spectrum noise, interference of the other factors to spectrum is reduced, it is necessary to spectrum
Pre-processed, be evaluation with correlation coefficient r, cross validation root-mean-square error (RMSECV), predicted root mean square error (RMSEP)
Index, screens the different pretreatments method of near-infrared spectrogram with chemo metric software ChemoStudio.
The different pretreatments method includes：Average centralization, Principal Component Analysis (PCA), mahalanobis distance method, standard
Normal variate conversion (SNV), multiplicative scatter correction (MSC), smoothing processing, derivative method.It is because using first derivative and smoothly pre-
Processing method has higher r values, relatively low RMSECV and RMSEP, therefore selects first derivative and smooth preprocess method pair
Near-infrared spectrogram is pre-processed.
(5) suitable Spectral range is selected:The light spectral of modeling is made choice, influence of noise can be reduced, improves fortune
Calculate efficiency and the stability of model.The spectrum range big with sample component response to be measured can be picked out from full spectrum to be built
Mould.The Spectral range selected in the present invention is 1550~1850nm/2050~2350nm.
(6) sample spectrogram is pre-processed by first derivative and smoothly using multivariate calibration methods, in selected wavelength model
Spectroscopic data when enclosing is associated with corresponding protein, fat, dietary fiber content, establishes calibration model.
The multivariate calibration methods are divided into linear regression and nonlinear regression.Wherein linear regression method mainly has polynary
Linear regression (MLR), principal component regression (PCR), Partial Least Squares return (PLSR), and non-linear regression method has artificial neuron
Network (ANN) and support vector machines (SVM) etc..Returned in the present invention using Partial Least Squares.
(7) verification and evaluation of calibration model：Using staying, a cross-validation method is verified or model is tested in external certificate
Card, verification index have correlation coefficient r, cross validation root-mean-square error (RMSECV), predicted root mean square error (RMSEP).
Brief description of the drawings
Fig. 1 is the original atlas of near infrared spectra of sample；
Fig. 2 is protein calibration model figure, r=0.996；
Fig. 3 is fatty calibration model figure, r=0.998；
Fig. 4 is dietary fiber calibration model figure, r=0.989.
Embodiment
With reference to specific embodiment, the invention will be further described.
Embodiment 1 measures special doctor's Protein in Food, fat, the content of dietary fiber
(1) 30 representative special doctor's food are chosen.The content range of protein is 1%~10%, and fatty contains
It is 1.5%~6% to measure scope, and the content of dietary fiber is 2%~11%.Each sample specific protein, fat, dietary fiber
Content is shown in Table 2.
2 each sample protein of table, fat, dietary fiber content
(2) kjeldahl determination in standard GB/T 5009.5-2016, GB5009.6-2016, GB5009.88-2014 is used
Method, soxhlet extraction, enzyme hydrolysis method measure the protein, fat, dietary fiber content of each sample respectively, as chemical score.
(3) selection of verification collection：Sample is randomly divided into calibration set and verification collects, wherein calibration set 26, verification collection 4
It is a.Choose 5,6,7, No. 19 samples in this EXPERIMENTAL EXAMPLE 1 for verification to collect, calibration set is used to model, and verification collection is built for verification
The accuracy of mould.
The collection of spectrum：30 are directly scanned near infrared spectrometer (Fourier Transform Near Infrared instrument, Bo Er, B311)
A sample, obtain near-infrared spectrogram (be sample 1, sample 10, sample 15, sample 20, the near-infrared spectrogram of sample 22 see Fig. 1,
Remaining is not arranged).Spectra collection parameter is respectively resolution ratio：14~18nm；Measuring speed：2000~6000ms；Pendulous frequency：3
~10 times；Spectra collection mode：Transmission；Measurement temperature：25℃.
(4) pretreatment of spectrum：With correlation coefficient r, cross validation root-mean-square error (RMSECV), predicted root mean square error
(RMSEP) it is evaluation index, with the difference of the chemo metric software ChemoStudio near-infrared spectrograms obtained to step (4)
Preprocess method is screened, because having higher r values, relatively low RMSECV using first derivative and smooth preprocess method
And RMSEP.Therefore 1 experiment of embodiment selects first derivative and smooth preprocess method to pre-process near-infrared spectrogram.
(5) selection of Spectral range：Missed with correlation coefficient r, cross validation root-mean-square error (RMSECV), prediction root mean square
Poor (RMSEP) is evaluation index, and the near-infrared spectrogram obtained to step (4) with chemo metric software ChemoStudio is not
Co-wavelength scope is screened.Embodiment 1 select Spectral range for：1550~1850nm/2050~2350nm.
(6) foundation of calibration model：(PLSR) is returned using Partial Least Squares to lead 30 sample spectrograms by single order
Number and smooth pretreatment, the spectroscopic data in 1550~1850nm/2050 of wavelength~2350nm and corresponding protein, fat
Fat, dietary fiber content are associated, and establish calibration model, see Fig. 2-4.
(7) verification and evaluation of calibration model：4 verification collection samples for having neither part nor lot in modeling are predicted, and will prediction
For value compared with the chemical score of each component, specific data are shown in Table 3.
3 near-infrared quantitative model of table collects verification sample protein matter, fat, the prediction result of dietary fiber content
Wherein protein, fat, the correlation coefficient r of dietary fiber are followed successively by 0.996,0.988,0.989, and cross validation is equal
Square error (RMSECV) is followed successively by 0.27,0.26,0.51, predicted root mean square error (RMSEP) is followed successively by 0.48,0.33,
0.62.Protein, fat, each parameter of dietary fiber near-infrared quantitative model are shown in Table 4.
Each near-infrared model parameter of table april protein, fat, dietary fiber
| Model | Preprocessing procedures | r | RMSEC | RMSEP | Spectral range (nm) |
| Protein | It is first derivative, smooth | 0.996 | 0.27 | 0.48 | 1550~1850/2050~2350 |
| Fat | It is first derivative, smooth | 0.988 | 0.26 | 0.33 | 1550~1850/2050~2350 |
| Dietary fiber | It is first derivative, smooth | 0.989 | 0.51 | 0.62 | 1550~1850/2050~2350 |
(8) analysis of unknown sample：Spectral scan is carried out to special doctor's food of unknown content, calls calibration model to carry out
Protein, fat, dietary fiber content measure.
Claims (5)
1. using the method for the special doctor's food multicomponent content of near infrared ray, comprise the following steps：
(1) choose representative special doctor's food come preliminary identification near infrared spectrum be used for it is special cure food multi-analyte immunoassay can
Row；The representativeness refers to that the protein, fat, dietary fiber content of sample can cover desired excursion, and
It is even variation in this scope；
（2）The content of determination sample protein, fat, dietary fiber is distinguished using national standard, as with reference to value；
（3）By 5:Sample is randomly divided into 1 ratio calibration set and verification collects, and the special doctor's food calibration set of collection and verification collect near
Infrared spectrogram, acquisition parameter are respectively resolution ratio：14 ~ 18nm；Measuring speed：2000 ~ 6000ms；Pendulous frequency：3 ~
10 times；Spectra collection mode：Transmission；Measurement temperature：25℃；
（4）The pretreatment of spectrum：With related coefficientr, cross validation root-mean-square error, predicted root mean square error be evaluation index,
The different pretreatments method of near-infrared spectrogram is screened with chemo metric software ChemoStudio；Because using single order
Derivative and smooth preprocess method have higherrValue, relatively low cross validation root-mean-square error, predicted root mean square error, because
This selects first derivative and smooth preprocess method to pre-process near-infrared spectrogram；
（5）Select Spectral range:The light spectral of modeling is made choice, component response to be measured with sample is picked out from full spectrum
It is worth big spectrum range to be modeled；
（6）Using multivariate calibration methods by sample spectrogram by first derivative and smooth pretreatment, in selected wave-length coverage
Spectroscopic data be associated with corresponding protein, fat, dietary fiber content, establish calibration model;
(7) verification and evaluation of calibration model：Using staying cross-validation method verification or external certificate to verify model, test
Card index has related coefficientr, cross validation root-mean-square error, predicted root mean square error.
2. the method using the special doctor's food multicomponent content of near infrared ray described in claim 1, it is characterised in that step
Suddenly（1）Described in desired excursion refer to：The content range of special doctor's Protein in Food of selection is 1% ~ 10%, fat
The content range of fat is 1.5% ~ 6%, and the content range of dietary fiber is 2% ~ 11%.
3. the method using the special doctor's food multicomponent content of near infrared ray described in claim 1, it is characterised in that step
Suddenly（3）Described in different pretreatments method include：Average centralization, Principal Component Analysis, mahalanobis distance method, standard normal become
Change of variable, multiplicative scatter correction, smoothing processing, derivative method.
4. the method using the special doctor's food multicomponent content of near infrared ray described in claim 1, it is characterised in that step
Suddenly（6）In, the multivariate calibration methods return for Partial Least Squares.
5. the method using the special doctor's food multicomponent content of near infrared ray described in claim 1, it is characterised in that step
Suddenly（5）In, the Spectral range of selection is the nm of 1550 ~ 1850 nm/2050 ~ 2350.
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Cited By (6)
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| CN111540417A (en) * | 2019-11-20 | 2020-08-14 | 杭州华东医药集团新药研究院有限公司 | Crystal form quantification method of canagliflozin |
| CN111751364A (en) * | 2020-06-28 | 2020-10-09 | 浙江省农业科学院 | Method for rapidly determining water-soluble protein and total sugar of royal jelly |
| CN112798555A (en) * | 2020-12-25 | 2021-05-14 | 江苏大学 | Modeling method for improving adaptability of coarse protein correction model of wheat flour |
| 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 |
| CN113670840A (en) * | 2021-08-13 | 2021-11-19 | 华南农业大学 | Rapid nondestructive testing method for insoluble dietary fiber content in fresh-cut bamboo shoots |
| CN113945539A (en) * | 2021-10-18 | 2022-01-18 | 江西农业大学 | GWAS (glow wire optical network analysis) -based method and system for predicting quality of near infrared spectrum characteristic waveband after screening |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111540417A (en) * | 2019-11-20 | 2020-08-14 | 杭州华东医药集团新药研究院有限公司 | Crystal form quantification method of canagliflozin |
| CN111751364A (en) * | 2020-06-28 | 2020-10-09 | 浙江省农业科学院 | Method for rapidly determining water-soluble protein and total sugar of royal jelly |
| CN111751364B (en) * | 2020-06-28 | 2023-05-23 | 浙江省农业科学院 | Method for rapidly determining water-soluble protein and total sugar of royal jelly |
| CN112798555A (en) * | 2020-12-25 | 2021-05-14 | 江苏大学 | Modeling method for improving adaptability of coarse protein correction model of wheat flour |
| 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 |
| CN113670840A (en) * | 2021-08-13 | 2021-11-19 | 华南农业大学 | Rapid nondestructive testing method for insoluble dietary fiber content in fresh-cut bamboo shoots |
| CN113945539A (en) * | 2021-10-18 | 2022-01-18 | 江西农业大学 | GWAS (glow wire optical network analysis) -based method and system for predicting quality of near infrared spectrum characteristic waveband after screening |
| CN113945539B (en) * | 2021-10-18 | 2023-07-04 | 江西农业大学 | Method and system for predicting quality of near infrared spectrum characteristic wave band based on GWAS (Global positioning System) screening |
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Application publication date: 20180417 |