CN107121405A - One kind increases health-care oral liquid online test method - Google Patents
One kind increases health-care oral liquid online test method Download PDFInfo
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- 239000007788 liquid Substances 0.000 title claims abstract description 93
- 238000010998 test method Methods 0.000 title claims abstract description 19
- 238000000034 method Methods 0.000 claims abstract description 59
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 35
- 229920001282 polysaccharide Polymers 0.000 claims abstract description 34
- 150000004676 glycans Chemical class 0.000 claims abstract description 33
- 239000005017 polysaccharide Substances 0.000 claims abstract description 33
- 238000004445 quantitative analysis Methods 0.000 claims abstract description 27
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- 239000007787 solid Substances 0.000 claims description 28
- 238000004611 spectroscopical analysis Methods 0.000 claims description 14
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- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000009795 derivation Methods 0.000 claims description 7
- 230000002068 genetic effect Effects 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
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- 238000005516 engineering process Methods 0.000 abstract description 11
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 15
- 238000005259 measurement Methods 0.000 description 11
- 239000000243 solution Substances 0.000 description 9
- QAOWNCQODCNURD-UHFFFAOYSA-N Sulfuric acid Chemical compound OS(O)(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-N 0.000 description 8
- 238000012937 correction Methods 0.000 description 8
- 239000012286 potassium permanganate Substances 0.000 description 8
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 6
- VEXZGXHMUGYJMC-UHFFFAOYSA-N Hydrochloric acid Chemical compound Cl VEXZGXHMUGYJMC-UHFFFAOYSA-N 0.000 description 6
- 239000000047 product Substances 0.000 description 6
- 238000004448 titration Methods 0.000 description 6
- 239000003153 chemical reaction reagent Substances 0.000 description 4
- BERDEBHAJNAUOM-UHFFFAOYSA-N copper(I) oxide Inorganic materials [Cu]O[Cu] BERDEBHAJNAUOM-UHFFFAOYSA-N 0.000 description 4
- 238000002790 cross-validation Methods 0.000 description 4
- 229940112669 cuprous oxide Drugs 0.000 description 4
- KRFJLUBVMFXRPN-UHFFFAOYSA-N cuprous oxide Chemical compound [O-2].[Cu+].[Cu+] KRFJLUBVMFXRPN-UHFFFAOYSA-N 0.000 description 4
- 239000000706 filtrate Substances 0.000 description 4
- 239000011521 glass Substances 0.000 description 4
- 229940039790 sodium oxalate Drugs 0.000 description 4
- 238000010200 validation analysis Methods 0.000 description 4
- 241001269238 Data Species 0.000 description 3
- 238000004497 NIR spectroscopy Methods 0.000 description 3
- 239000002253 acid Substances 0.000 description 3
- XGGLLRJQCZROSE-UHFFFAOYSA-K ammonium iron(iii) sulfate Chemical compound [NH4+].[Fe+3].[O-]S([O-])(=O)=O.[O-]S([O-])(=O)=O XGGLLRJQCZROSE-UHFFFAOYSA-K 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
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- RTZKZFJDLAIYFH-UHFFFAOYSA-N Diethyl ether Chemical compound CCOCC RTZKZFJDLAIYFH-UHFFFAOYSA-N 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 2
- LPQOADBMXVRBNX-UHFFFAOYSA-N ac1ldcw0 Chemical compound Cl.C1CN(C)CCN1C1=C(F)C=C2C(=O)C(C(O)=O)=CN3CCSC1=C32 LPQOADBMXVRBNX-UHFFFAOYSA-N 0.000 description 2
- 238000001816 cooling Methods 0.000 description 2
- ZOMNIUBKTOKEHS-UHFFFAOYSA-L dimercury dichloride Chemical class Cl[Hg][Hg]Cl ZOMNIUBKTOKEHS-UHFFFAOYSA-L 0.000 description 2
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- BAUYGSIQEAFULO-UHFFFAOYSA-L iron(2+) sulfate (anhydrous) Chemical compound [Fe+2].[O-]S([O-])(=O)=O BAUYGSIQEAFULO-UHFFFAOYSA-L 0.000 description 2
- 230000003647 oxidation Effects 0.000 description 2
- 238000007254 oxidation reaction Methods 0.000 description 2
- 238000012856 packing Methods 0.000 description 2
- CTYRPMDGLDAWRQ-UHFFFAOYSA-N phenyl hydrogen sulfate Chemical compound OS(=O)(=O)OC1=CC=CC=C1 CTYRPMDGLDAWRQ-UHFFFAOYSA-N 0.000 description 2
- 239000002244 precipitate Substances 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- ZNCPFRVNHGOPAG-UHFFFAOYSA-L sodium oxalate Chemical compound [Na+].[Na+].[O-]C(=O)C([O-])=O ZNCPFRVNHGOPAG-UHFFFAOYSA-L 0.000 description 2
- 239000006228 supernatant Substances 0.000 description 2
- 239000012085 test solution Substances 0.000 description 2
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- MYKOKMFESWKQRX-UHFFFAOYSA-N 10h-anthracen-9-one;sulfuric acid Chemical compound OS(O)(=O)=O.C1=CC=C2C(=O)C3=CC=CC=C3CC2=C1 MYKOKMFESWKQRX-UHFFFAOYSA-N 0.000 description 1
- OQVYMXCRDHDTTH-UHFFFAOYSA-N 4-(diethoxyphosphorylmethyl)-2-[4-(diethoxyphosphorylmethyl)pyridin-2-yl]pyridine Chemical compound CCOP(=O)(OCC)CC1=CC=NC(C=2N=CC=C(CP(=O)(OCC)OCC)C=2)=C1 OQVYMXCRDHDTTH-UHFFFAOYSA-N 0.000 description 1
- 235000001674 Agaricus brunnescens Nutrition 0.000 description 1
- QGZKDVFQNNGYKY-UHFFFAOYSA-O Ammonium Chemical compound [NH4+] QGZKDVFQNNGYKY-UHFFFAOYSA-O 0.000 description 1
- 244000003416 Asparagus officinalis Species 0.000 description 1
- 235000005340 Asparagus officinalis Nutrition 0.000 description 1
- 229920000742 Cotton Polymers 0.000 description 1
- 241000233866 Fungi Species 0.000 description 1
- 244000241872 Lycium chinense Species 0.000 description 1
- 235000015468 Lycium chinense Nutrition 0.000 description 1
- 244000197580 Poria cocos Species 0.000 description 1
- 235000008599 Poria cocos Nutrition 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- RJGDLRCDCYRQOQ-UHFFFAOYSA-N anthrone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3CC2=C1 RJGDLRCDCYRQOQ-UHFFFAOYSA-N 0.000 description 1
- 239000012496 blank sample Substances 0.000 description 1
- 229910052927 chalcanthite Inorganic materials 0.000 description 1
- 238000009614 chemical analysis method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
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- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
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- 238000013100 final test Methods 0.000 description 1
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- 239000012263 liquid product Substances 0.000 description 1
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- 238000000691 measurement method Methods 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 238000010986 on-line near-infrared spectroscopy Methods 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- VZOPRCCTKLAGPN-ZFJVMAEJSA-L potassium;sodium;(2r,3r)-2,3-dihydroxybutanedioate;tetrahydrate Chemical compound O.O.O.O.[Na+].[K+].[O-]C(=O)[C@H](O)[C@@H](O)C([O-])=O VZOPRCCTKLAGPN-ZFJVMAEJSA-L 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
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- KZDMWVBUOTWRDX-UHFFFAOYSA-J sodium cerium(3+) tetrahydroxide Chemical compound [OH-].[Na+].[Ce+3].[OH-].[OH-].[OH-] KZDMWVBUOTWRDX-UHFFFAOYSA-J 0.000 description 1
- 229940074446 sodium potassium tartrate tetrahydrate Drugs 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000000967 suction filtration Methods 0.000 description 1
- 150000003467 sulfuric acid derivatives Chemical class 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/28—Electrolytic cell components
- G01N27/30—Electrodes, e.g. test electrodes; Half-cells
- G01N27/302—Electrodes, e.g. test electrodes; Half-cells pH sensitive, e.g. quinhydron, antimony or hydrogen electrodes
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- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Electrochemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Molecular Biology (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention belongs to Chinese herbal medicament oral liquid Quality Control Technology field, disclose a kind of online test method for increasing health-care oral liquid, this method is that the optimal Quantitative Analysis Model for increasing health-care oral liquid sample quality index components is set up using near infrared spectrum combination PLS, and sample quality index is predicted according to the optimal Quantitative Analysis Model of quality index composition.Online test method of the present invention is simple to operate, quickly, and accuracy is high, product quality can be detected in real time, realized that the real-time online for increasing health-care oral liquid Key Quality Con trolling index is quickly determined, be not required to pre-process sample on detecting step, directly sample is detected, it is economic and environment-friendly；The 12 day time that chemical method is detected is shorten to 15 30 seconds in detection time；Due to no material consumption in detection process in testing cost, strong support is provided to increase health-care oral liquid production process quality monitoring, is the quantum jump in Chinese herbal medicament oral liquid polysaccharide control technology.
Description
Technical field
The invention belongs to Chinese herbal medicament oral liquid Quality Control Technology field, and in particular to one kind increases health-care oral liquid on-line checking
Method, the method that the Multiple components content for increasing health-care oral liquid is especially determined near infrared spectroscopy combination Chemical Measurement.
Background technology
It is according to Traditional Chinese medical theory, with modern study achievement and practical experience, from a variety of natural plants to increase health-care oral liquid
In thing extract immunomodulator-complex polysaccharide, then be equipped with a variety of help classes such as Poria cocos, white fungus, asparagus, mushroom, the fruit of Chinese wolfberry and
Clearing heat and detoxicating, medicine-food two-purpose natural plants etc. are refined to be formed.Increasing health-care oral liquid can effectively adjust, strengthen body immunity,
Improve Abwehrkraft des Koepers, prevent the invasion and attack of disease, strengthen and improve constitution.
Polyoses content is the efficacy measures for increasing health-care oral liquid, and Methods in Determination of Polysaccaride Content is marked currently without unified country
Standard, assay method is more, to same product, and the method that different regions, different documents are used is different, and the result drawn is just not
Together, thus formulate quality standard and control desired value it is different.Current polyoses content detection frequently with method have Gao Meng
Sour potassium titration, Phenol-sulphate acid method, sulfuric acid anthrone method, the main gap of each method come from the difference of pre-treating method, and it shows
Color method not is very big to the result gap that same sample is determined.Phenol-sulphate acid method is mainly used to determine all monose and all kinds of
Polysaccharide, will first precipitate polysaccharides, the interference of removal monose when carrying out polysaccharide determination.Sulfuric acid anthrone method be mainly used in determine hexose and
Polysaccharide containing hexose, if being then other sugar in green for hexose in blueness.Direct titrimetric method measurement result is higher, forms sediment
The polysaccharide such as powder have interference effect to it.The polysaccharide value scope distribution of complex polysaccharide is wide, increases the polysaccharide Distribution value scope of health-care oral liquid
From 300 to 800mg/100mL so that the quality control of raw material containing polysaccharide and product is difficult to grasp, and the current country can only be carried out slightly
The control of polysaccharide.
At present no matter which kind of chemical assay, be required to pre-process sample, it is necessary to consume a large amount of chemical reagent, it is right
Environment also results in certain pollution.And because pretreatment operation step is more, the time is long, human error influences on final testing result
It is larger, cause the determination of polysaccharide result of final products to fluctuate larger, detection cycle length (1-2 days), be unfavorable for increasing and be good for orally
Liquid product is detected and the monitoring of production process fast mass.
The content of the invention
In view of this, it is accurate it is an object of the invention to provide a kind of quick online test method for increasing health-care oral liquid
Really, real-time online detection increase health-care oral liquid polyoses content, soluble solid content and and solution pH value, so as to supervise online
Control increases the quality of health-care oral liquid, and strong support is provided to increase health-care oral liquid production process quality monitoring.
To realize the purpose of the present invention, the invention provides following technical scheme：
A kind of online test method for increasing health-care oral liquid, is set up using near infrared spectrum combination PLS and increases strong mouth
The optimal Quantitative Analysis Model of liquid sample quality index components is taken, according to the optimal Quantitative Analysis Model of quality index composition to sample
Quality figureofmerit is predicted.
It is of the present invention increase health-care oral liquid online test method can with on-line checking increase health-care oral liquid polyoses content, can
Dissolubility solid content and pH value.
Wherein, the specific method for setting up optimal Quantitative Analysis Model comprises the following steps：
(1) nir instrument collection increases the near infrared spectrum data of health-care oral liquid sample, while collecting sample；
(2) chemical score for the quality index composition for increasing health-care oral liquid sample is determined；
(3) spectroscopic data for being collected into step (1) uses the choosing method of SPXY training set samples to the sample of collection
It is trained the packet of collection and checking collection；
(4) near infrared spectrum data to training set pre-processed and wave band selection；
(5) with PLS by obtained by the chemical score of the quality index composition measured by step (2) and step (4)
The near infrared spectrum data of training set be associated, set up the Quantitative Analysis Model of quality index composition；
(6) Quantitative Analysis Model is verified with checking collection spectroscopic data, determines the optimal quantitative of quality index composition
Analysis model.
Near infrared spectrum (Near-Infrared Spectroscopy, NIR), wave-length coverage is 780~2526nm
(12820~3959cm-1).NIR absorption bands are absorbed and formed by the frequency multiplication and sum of fundamental frequencies of the stretching vibration of the functional group such as-CH ,-NH ,-OH
's.Near infrared spectroscopy have the advantages that quickly, low consumption, without it is destructive, almost without sample pretreatment.NIR can be replaced
Traditional chemical analysis method is offline or on-line period is measured.The method of the invention utilizes On-line near infrared analyzer instrument on-line measurement
Probe, detection product quality data, carry out the regulation and control of operation operating mode in time in real time.
Wherein during specific production of the invention increases health-care oral liquid, near-infrared probe is goed deep into oral liquid preparing tank
Real time scan detection is carried out to sample, sample is collected while spectrum is gathered, the measure for subsequent quality index.The present invention
The near infrared spectrum data of the increasing health-care oral liquid of each season different batches is gathered, data time is collected and is up to 1 year, contain not
Same month, the increasing health-care oral liquid of different batches, the sample representativeness of collection are strong.
Wherein, it is preferred that use the silent winged generation that science and technology near infrared spectrometer Nicolet Antaris II collection spectrum of match.
The near infrared spectra collection condition is preferably：Transflector type collection spectrum, using air as reference, collection wavelength is 1000-
2500nm, scanning times are 128, and resolution ratio is 16cm-1, absorbance data form is SPA, each sample multiple scanning three times,
Calculate three averaged spectrums and be used as sample near infrared spectrum data.
(2) of the present invention the step of set up calibration model are using chemical method to increasing each quality in health-care oral liquid sample
Index is measured.It is preferred that, the assay method of each quality index is respectively：
The polyoses content for increasing health-care oral liquid sample is measured using permanganimetric method；
The soluble solid content for increasing health-care oral liquid sample is measured using compound microcapsule；
The pH value for increasing health-care oral liquid sample is measured using acidometer.
Specifically, the content principle for using titration of potassium permanganate polysaccharide is made with liquor potassic permanganate titration oxidation
With the ferrous salt of rear generation, according to potassium permanganate consumption, cuprous oxide content is calculated, then tables look-up and must reduce sugar amount, is finally rolled over
It is counted as polyoses content.
Specifically, the principle that the content of soluble solid is determined using compound microcapsule is to be treated at 20 DEG C with refractometer measurement
The index of refraction of sample measuring liquid, and table look-up or the direct content for reading soluble solid from refractometer.
Specifically, using using glass electrode as indicator electrode, saturated calomel electrode carries out pH for the acidometer of reference electrode
The measure of value.The sample in appropriate packing container is put into beaker or will directly inserted electrodes into after inner wrapping uncapping during measure
Wherein.
(3) of the present invention the step of set up calibration model are using sample set partitioning based on
The spectroscopic data that step (1) is collected into by the choosing method (SPXY methods) of joint x-y distance training set samples is carried out
Packet.SPXY methods have considered the difference between spectroscopic data and chemical score, and the sample being collected into is divided into automatically to be used for
Set up the training data of model and the checking data for model external certificate.
(4) of the present invention the step of set up calibration model are pre-processed and ripple to the near infrared spectrum data of training set
The selection of section.For each index components, different pretreatments are carried out to spectrum, optimal preprocess method is therefrom found out,
Reuse genetic algorithm modeling wave band is in optimized selection, set up the Quantitative Analysis Model of each index components.Modeling used
Software is Matlab tool boxes PLS_toolbox.
Wherein the present invention eliminates the noise of spectrum, overlap peak, baseline drift etc. using multiple spectrum preprocess method.
It is preferred that, step (4) described preprocess method be first derivation, second order derivation, first differential, second-order differential,
At least one of detrend, baseline, MSC, SNV.
It is preferred that, band selection method of the present invention is interval partial least square (ipls), leapfrog (Random at random
Frog), competitive adaptive weight weighting algorithm (CARS), Monte Carlo are without information variable null method (MCUVE), genetic algorithm
At least one of (GA).
To different index components, optimal preprocess method is different with effective wave band of modeling.
In some embodiments, preprocess method used in the polysaccharide model is baseline, and effective wave band is wave number
183 wave numbers between 73-778；Soluble solid model preprocessing method is first derivation, and effective wave band is between 79-778
175 wave numbers；PH model preprocessings method is baseline, and effective wave band is 204 wave numbers between 82-777.
Further, with PLS by the chemical score of the quality index composition measured by step (2) and step (4)
The near infrared spectrum data of the training set of gained is associated, and sets up the Quantitative Analysis Model of quality index composition, then with testing
Card collection spectroscopic data is verified to Quantitative Analysis Model, determines the optimal Quantitative Analysis Model of quality index composition.
Quantitative Analysis Model optimal first is relevant with model parameter.Model parameter includes coefficient correlation (R2), correction error
Root mean square (RMSEC), cross validation error root mean square (RMSECV), validation error root mean square (RMSEP).R2Closer to 1,
RMSEC and RMSEP closer to, illustrate the chemical scores of each index components and model predication value closer to, i.e., both correlation it is better；
RMSEC, RMSECV, RMSEP are smaller, then model is more accurate, i.e., precision of prediction is higher.Preference pattern parameter R2Closer to 1,
RMSEC and RMSEP is closer to RMSEC, RMSECV, RMSEP smaller Quantitative Analysis Model is to primarily determine that best model.
Further by verifying the near infrared spectrum data of collection, primarily determine that best model enters to quality index with described
Row prediction, carries out error calculation with chemical score by predicted value, progressively model is modified, until gained model meets every miss
Difference is required.
It is preferred that, the predicted value of checking collection and the error of chemical score of each quality index should be respectively less than 10%.
Further, the relative error for increasing the prediction of health-care oral liquid polyoses content should be less than 10%, increase health-care oral liquid soluble
The relative error of solid content prediction should be less than 5%, and the relative error for increasing health-care oral liquid pH predictions should be less than 3%.
In some embodiments, the optimal Quantitative Analysis Model of foundation is integrated into an on-line checking software,
Line near infrared spectrometer online acquisition increases the atlas of near infrared spectra of health-care oral liquid, and spectroscopic data is real-time transmitted to is integrated with most
Inside the software of good Quantitative Analysis Model, and the matter that this batch increases health-care oral liquid sample is gone out by optimal Quantitative Analysis Model real-time estimate
Parameter is measured, on-line monitoring increases the quality of health-care oral liquid.
From above-mentioned technical side, the invention provides a kind of online test method for increasing health-care oral liquid, this method is to adopt
The optimal Quantitative Analysis Model for increasing health-care oral liquid sample quality index components is set up near infrared spectrum combination PLS,
Sample quality index is predicted according to the optimal Quantitative Analysis Model of quality index composition.On-line checking side of the present invention
Method is simple to operate, quickly, and accuracy is high, and product quality can be detected in real time, realizes and increases health-care oral liquid Key Quality Con trolling index
Real-time online is quickly determined, and is not required to pre-process sample on detecting step, directly sample is detected, economic ring
Protect；The 1-2 days times that chemical method is detected are shorten to 15-30 seconds in detection time；Due to detection process in testing cost
Middle no material consumption, can save 50,000 yuan of consumptive material expense every year, and strong branch is provided to increase health-care oral liquid production process quality monitoring
Hold, be the quantum jump in Chinese herbal medicament oral liquid polysaccharide control technology.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described.
Fig. 1 effectively models wave band figure to increase health-care oral liquid polysaccharide model；
To increase, health-care oral liquid polysaccharide model training set measured value is related to near-infrared predicted value to scheme Fig. 2；
Fig. 3 collects measured value and near-infrared predicted value relative error figure to increase health-care oral liquid polysaccharide model training set, checking；Its
Middle calibration data are training set data, and validation data are checking collection data；
Fig. 4 effectively models wave band figure to increase health-care oral liquid soluble solid model；
To increase, health-care oral liquid soluble solid model training collection measured value is related to near-infrared predicted value to scheme Fig. 5；
Fig. 6 is relative with near-infrared predicted value to increase health-care oral liquid soluble solid model training collection, checking collection measured value
Error Graph；Wherein calibration data are training set data, and validation data are checking collection data；
Fig. 7 effectively models wave band figure to increase health-care oral liquid pH value model；
To increase, health-care oral liquid pH model training collection measured values are related to near-infrared predicted value to scheme Fig. 8；
Fig. 9 collects measured value and near-infrared predicted value relative error figure to increase health-care oral liquid pH model trainings collection, checking；Wherein
Calibration data are training set data, and validation data are checking collection data.
Embodiment
The invention discloses a kind of online test method for increasing health-care oral liquid.Those skilled in the art can be used for reference in herein
Hold, be suitably modified technological parameter realization.In particular, all similar replacements and change are to those skilled in the art
For be it will be apparent that they are considered as being included in the present invention.The method and product of the present invention is by preferably implementing
Example is described, and related personnel can substantially not depart from present invention, method described herein entered in spirit and scope
Row is changed or suitably changed with combining, to realize and apply the technology of the present invention.
Wherein (a) determination of polysaccharide is referred to《The measure of reduced sugar in food》The methods of-GB/T 5009.7-2008 first are high
Potassium manganate titration；
Cleaning Principle：After removing protein, " polysaccharide in sample is reduced sugar by acidolysis ", reduced sugar mantoquita also
Originally it was that cuprous oxide is oxidized to mantoquita after cuprous oxide plus ammonium ferric sulfate, with raw after liquor potassic permanganate titration oxidation
Into ferrous salt, according to potassium permanganate consumption, calculate cuprous oxide content, then table look-up and must reduce sugar amount, be finally converted to many
Sugared content；
Reagent：
Fei Linshi liquid first:Weigh 34.91g and analyze pure CuSO4·5H2O is diluted to 500ml in the water boiled；
Fei Linshi liquid second:Weigh 173g and analyze pure sodium potassium tartrate tetrahydrate and 50g analysis pure cerium hydroxide sodium in the water boiled,
It is diluted to 500ml；
Sulfuric acid solution (2mol/L):Concentrated sulfuric acid 112ml is measured, is slowly added into the case where being stirred continuously in suitable quantity of water, after cooling
It is diluted with water to 1000ml；
Hydrochloric acid solution (25%):Concentrated hydrochloric acid 675.7ml is measured, 1000ml is diluted with water to；
Ammonium ferric sulfate solution:135g ammonium ferric sulfates are weighed in suitable quantity of water, and are diluted to 1000ml；
Potassium permanganate titrand (0.1N):[weigh 3.3gKMnO4 and 20-30 points are boiled in 1000ml water, slowly
Clock, cooling was filtered with acidproof filtration funnel, is stored in brown bottle after the dark place closed preservation a few days.
Demarcation：Precision is weighed to be dried to the benchmark sodium oxalate 0.2g of constant weight through 105 DEG C, is dissolved in the cold water that 250ml newly boils
With concentrated sulfuric acid 10ml, this liquid about 25ml is rapidly added from buret, after fading, 65 DEG C is heated to and (is careful not to more than 90
DEG C, otherwise sodium oxalate can be decomposed), it is in blush to multiply pyrotitration to solution, and keeps 30s colour-fast, while making blank test.When
When titration ends, solution temperature should be not less than 55 DEG C.
Calculation formula：
In formula：The equivalent concentration of N-Standard Potassium Permanganate Solution, N；Potassium permanganate volume, ml are consumed during V-demarcation；
Potassium permanganate volume, ml are consumed during V0-blank；The milliequivalent of 0.067-sodium oxalate；The quality of m-sodium oxalate, g.
Sample treatment：
Weigh:Accurate measuring 25ml increases health-care oral liquid and is placed in Centrifuge Cup；
Wine sinks:95% ethanol (analysis is pure) that addition is three times in taken oral liquid weight makes the polysaccharide precipitation in oral liquid；
Separation:Centrifuge Cup is moved into centrifuge and is centrifuged (3000r/min, 10min), supernatant is removed, then uses
Sediment in 75% ethanol 10ml washing Centrifuge Cups, centrifuges and supernatant is discarded, then with 50ml water sediment again
Wash in 250ml boiling flasks；
Hydrolysis:25% hydrochloric acid 15ml is added, back hydrolysis 2.5 hours in boiling water bath；
Constant volume:Hydrolyzate is filled into 100ml volumetric flasks, flask and filter paper are fixed respectively with a small amount of distilled water flushing three times
Hold to scale.
Measurement of the polysaccharide content：
It is each to draw Fei Linshi liquid first, second 25ml, it is placed in 250ml triangular flasks, it is accurate to add sample hydrolyzate 10ml, then add
Entering 40ml distilled water makes total measurement (volume) be adjusted to 100+1ml；50ml water is drawn in addition, respectively adds 25ml Fei Linshi liquid solution A and second
Liquid, does blank sample；
Heat sample liquid, control boiled in 4 minutes, then keep boiling 2 minutes (heat time must strictly observe, permit
Permitted there are+15 seconds errors)；
Sample liquid multiplies heat G4 acid funnel suction filtrations into bottle,suction, and washs triangles with 60 DEG C of hot water 100ml fractions time
Bottle and precipitation, filtrate in bottle,suction is discarded, and is cleaned；
Ammonium ferric sulfate solution 50ml is taken, fraction dissolves red precipitate in funnel, and solution is evacuated in bottle,suction,
Again with 60 DEG C of hot water 50ml fraction time washing funnels (3-4 times)；
Merging filtrate, and 2mol/L sulfuric acid 20ml are added, it is titrated to blush with 0.1N potassium permanganate titrand
And it is terminal to keep 30s colour-fast.
(b) measure of soluble solid content：Compound microcapsule
Principle：The index of refraction of sample measuring liquid is treated with refractometer measurement at 20 DEG C, and table look-up or from refractometer directly read can
The content of dissolubility solid content.
Instrument：Digital refractometer：Measurement range 0%~80%, accuracy ± 0.1%.
The preparation of reagent：Testing sample is smashed to pieces, filtrate is extruded with four layers of gauze and discards initial several drops, collect filtrate and supply
Test is used.
Determination step：By specification correction refractometer before determining；Separate refractometer two sides prism, dipped in absorbent cotton ether or
Ethanol is cleaned；The glass bar for melting circle with end dips test solution 2~3 and dripped, and drips and (notes not making glass bar in refractometer prism facets center
Touch minute surface)；Rapid closing prism, stands 1min, makes test solution uniform bubble-free, and full of the visual field；Alignment light source, passes through eyepiece
Viewing objective.Regulation indicates rule, the visual field is divided into light and shade two, then rotary fine adjustment spiral, makes terminator clear, and make its point
Boundary line is just on the right-angled intersection point of objective lens.Call the score number or the index of refraction in the eyepiece visual field are read, and records prism temperature；
If eye piece reading scale label is percentage, as soluble solid content (%)；The difference of same sample measured value twice, no
It should be greater than 0.5%.The arithmetic mean of instantaneous value determined twice is taken as a result, being accurate to one decimal place.
(c) measure of pH value：Use using glass electrode as indicator electrode, saturated calomel electrode is the acidometer of reference electrode
It is measured.The sample in appropriate packing container is put into beaker or will directly inserted electrodes into after inner wrapping uncapping during measure
Wherein.
For a further understanding of the present invention, below in conjunction with the embodiment of the present invention, to the technical side in the embodiment of the present invention
Case is clearly and completely described, it is clear that described embodiment is only a part of embodiment of the invention, rather than all
Embodiment.Based on the embodiment in the present invention, those of ordinary skill in the art institute under the premise of creative work is not made
The every other embodiment obtained, belongs to the scope of protection of the invention.
Unless otherwise specified, reagent and instrument involved in the embodiment of the present invention are commercially available prod, can be passed through
Commercial channel purchase is obtained.
Embodiment 1, increasing health-care oral liquid polyoses content are quickly determined
(1) collection of health-care oral liquid near infrared spectrum data is increased.
The not same month is gathered in production line using silent winged your the science and technology near infrared spectrometer Nicolet Antaris II of generation of match
Part, the near infrared spectrum data totally 225 batches of the increasing health-care oral liquid of different batches.Use sample set
Partitioningbased on joint x-y distance (SPXY) methods are instructed to 225 batches of near infrared spectrum datas
Practice the packet of collection and checking collection, training set and checking collection ratio are 4：Best results are modeled when 1, so training set 180 after packet
Batch, checking collection 45 batches.
(2) determine and increase health-care oral liquid polyoses content
225 batches are increased and is good for oral sample laboratory common detection methods detection polyoses content, reference data is used as.Polysaccharide
Content range is：386.00-663.14(mg/100ml)
(3) the effectively selection of modeling wave band
Wave band function is selected using the PLS_toolbox genetic algorithms carried,
From baseline preprocess methods, window width carries out the selection of effective wave band from 1-50, dealt with objects as 180
Training set is criticized, selected best modeled wave band is 183 wave numbers between wave number 73-778 when window width is 1.Increase health-care oral liquid many
Sugared model effectively models wave band figure and sees accompanying drawing 1.
(4) PLS is associated near infrared spectrum and polysaccharide value
Baseline is carried out using PLS (PLS) on modeling wave band to the spectroscopic data of training set in advance to locate
Reason, is closed effective modeling wave band of the spectroscopic data gathered and correspondence reference data by the method for Chemical Measurement
Connection, sets up the quantitative model of polysaccharide.Increase health-care oral liquid polysaccharide model training set measured value it is related to near-infrared predicted value scheme and
Polysaccharide model training set, checking collection measured value and near-infrared predicted value relative error figure are shown in accompanying drawing 2,3.
(5) prediction of the model to checking collection
In the near-infrared polysaccharide model that the near infrared spectrum data input that 45 batches of checkings collect sample has been built up, it is verified
The polysaccharide predicted value of collection, predicts the outcome and is shown in Table 1.
Table 1 increases 45 batches of checking collection sample polysaccharide of health-care oral liquid and predicted the outcome
The model coefficient correlation (Corr.Coeff.) set up is 0.687018, and correction mean square deviation (RMSEC) is
20.3911, cross validation mean square deviation (RMSECV) is 24.638, and correction mean square deviation (RMSEP) is 34.4854.Model is to 45 groups
During what checking collected predicts the outcome, remove outside the higher data of one group of polysaccharide value, the relative error of all data is controlled 10%
Within, maximum absolute deviation of mean is -76.2948, and mean absolute deviation is 26.84115；Maximum relative error is -11.958%, is put down
Equal relative error is 4.6699%, and the deviation that predicts the outcome is very small.
Embodiment 2 increases health-care oral liquid soluble solid content and quickly determined
(1) collection of health-care oral liquid near infrared spectrum data is increased
The not same month is gathered in production line using silent winged your the science and technology near infrared spectrometer Nicolet Antaris II of generation of match
Part, the near infrared spectrum data totally 242 batches of the increasing health-care oral liquid of different batches.Use sample set
Partitioningbased on joint x-y distance (SPXY) methods are instructed to 242 batches of near infrared spectrum datas
Practice the packet of collection and checking collection, training set 182 batches after packet, checking collection 60 batches.
(2) determine and increase health-care oral liquid soluble solid content
242 batches are increased and is good for oral sample laboratory common detection methods detection soluble solid content, reference is used as
Data.Soluble solid content scope is：7.95-8.73 (%).
(3) the effectively selection of modeling wave band
Select wave band function using the PLS_toolbox genetic algorithms carried, from first derivation window width for 15 it is pre-
Processing method, window width carries out the selection of effective wave band from 1-50, deals with objects as 182 batches of training sets, selected best modeled
Wave band is 175 wave numbers between wave number 79-778 when window width is 3.Increase health-care oral liquid soluble solid model effectively to model
Wave band is shown in accompanying drawing 4.
(4) PLS is associated near infrared spectrum and soluble solid content
First derivation window is carried out on modeling wave band to the spectroscopic data of training set using PLS (PLS) wide
Spend the pretreatment for 15, by Chemical Measurement method is by effective modeling wave band of the spectroscopic data gathered and correspondingly refers to
Data are associated, and set up the quantitative model of soluble solid.Increase the actual measurement of health-care oral liquid soluble solid model training collection
Value figure related to near-infrared predicted value and model training collection, checking collection measured value and near-infrared predicted value relative error are shown in accompanying drawing
5、6。
(5) prediction of the model to checking collection
In the near-infrared soluble solid model that the near infrared spectrum data input that 60 batches of checkings collect sample has been built up,
The soluble solid content predicted value of collection is verified, predicts the outcome and is shown in Table 2.
Table 2 increases 60 batches of checking collection sample solid contents of health-care oral liquid and predicted the outcome
The model coefficient correlation (Corr.Coeff.) set up is 0.394798, and correction mean square deviation (RMSEC) is
0.0801941, cross validation mean square deviation (RMSECV) is 0.0898427, and correction mean square deviation (RMSEP) is 0.137281.Model
To in the predicting the outcome of 60 blind samples, the relative error of all data is controlled within 5%, maximum absolute deviation of mean for-
0.42798, mean absolute deviation is 0.11321；Maximum relative error is -4.902%, and average relative error is 1.3386%,
The deviation that predicts the outcome is very small.
Embodiment 3 increases health-care oral liquid pH value and quickly determined
(1) collection of health-care oral liquid near infrared spectrum data is increased
The not same month is gathered in production line using silent winged your the science and technology near infrared spectrometer Nicolet Antaris II of generation of match
Part, the near infrared spectrum data totally 240 batches of the increasing health-care oral liquid of different batches.Use sample set
Partitioningbased on joint x-y distance (SPXY) methods are instructed to 240 batches of near infrared spectrum datas
Practice the packet of collection and checking collection, training set 181 batches after packet, checking collection 59 batches.
(2) determine and increase health-care oral liquid pH value
240 batches are increased and is good for oral sample laboratory common detection methods detection pH value, reference data is used as.PH value range
For：4.50-4.65.
(3) the effectively selection of modeling wave band
Wave band function is selected using the PLS_toolbox genetic algorithms carried, from baseline preprocess methods, window is wide
The selection that effective wave band is carried out from 1-50 is spent, is dealt with objects as 181 batches of training sets, selected best modeled wave band is that window width is
204 wave numbers when 3 between wave number 82-777.
Effectively modeling wave band figure is shown in accompanying drawing 7.
(4) PLS is associated near infrared spectrum and soluble solid content
Carry out baseline pre- place on modeling wave band to the spectroscopic data of training set using PLS (PLS)
Reason, is closed effective modeling wave band of the spectroscopic data gathered and correspondence reference data by the method for Chemical Measurement
Connection, sets up the quantitative model of pH value.PH model training collection measured values it is related to near-infrared predicted value figure and pH model trainings collection,
Checking collection measured value is shown in accompanying drawing 8,9 with near-infrared predicted value relative error.
(5) prediction of the model to checking collection
In the near-infrared pH value model that the near infrared spectrum data input that 59 batches of checkings collect sample has been built up, it is verified
The pH value predicted value of collection, predicts the outcome and is shown in Table 3.
Table 3 increases 59 batches of checking collection sample pHs of health-care oral liquid and predicted the outcome
The model coefficient correlation (Corr.Coeff.) set up is 0.606768, and correction mean square deviation (RMSEC) is
0.0203438, cross validation mean square deviation (RMSECV) is 0.0270743, and correction mean square deviation (RMSEP) is 0.129052.Model
To in the predicting the outcome of 60 blind samples, the relative error of all data is controlled within 2%, and maximum absolute deviation of mean is
0.078053, mean absolute deviation is 0.011344；Maximum relative error is 1.7345%, and average relative error is
0.6187%, the deviation that predicts the outcome is very small.
Claims (10)
1. one kind increases health-care oral liquid online test method, it is characterised in that built using near infrared spectrum combination PLS
The vertical optimal Quantitative Analysis Model for increasing health-care oral liquid sample quality index components, according to the optimal quantitative analysis of quality index composition
Model is predicted to sample quality index.
2. require described online test method according to right 1, it is characterised in that the quality index is polyoses content, solvable
At least one of property solid content, pH value.
3. described online test method is required according to right 1, it is characterised in that the specific side for setting up optimal Quantitative Analysis Model
Method comprises the following steps：
(1) nir instrument collection increases the near infrared spectrum data of health-care oral liquid sample, while collecting sample；
(2) chemical score for the quality index composition for increasing health-care oral liquid sample is determined；
(3) spectroscopic data for being collected into step (1) is carried out using the choosing method of SPXY training set samples to the sample of collection
The packet of training set and checking collection；
(4) near infrared spectrum data to training set pre-processed and wave band selection；
(5) PLS is used by the instruction obtained by the chemical score of the quality index composition measured by step (2) and step (4)
The near infrared spectrum data for practicing collection is associated, and sets up the Quantitative Analysis Model of quality index composition；
(6) Quantitative Analysis Model is verified with checking collection spectroscopic data, determines the optimal quantitative analysis of quality index composition
Model.
4. online test method according to claim 3, it is characterised in that step (1) the near infrared spectra collection bar
Part is：Transflector type collection spectrum, using air as reference, collection wavelength is 1000-2500nm, and scanning times are 128, is differentiated
Rate is 16cm-1, absorbance data form is SPA, and each sample multiple scanning three times calculates three averaged spectrums near as sample
Ir data.
5. online test method according to claim 3, it is characterised in that step (2) the increasing health-care oral liquid sample
The assay method of each quality index is respectively：
The polyoses content for increasing health-care oral liquid sample is measured using permanganimetric method；
The soluble solid content for increasing health-care oral liquid sample is measured using compound microcapsule；
The pH value for increasing health-care oral liquid sample is measured using acidometer.
6. online test method according to claim 3, it is characterised in that step (4) described preprocess method is single order
At least one of derivation, second order derivation, first differential, second-order differential, detrend, baseline, MSC, SNV.
7. online test method according to claim 3, it is characterised in that between step (4) described band selection method is
Every PLS (ipls), leapfrog (Random Frog) at random, and competitive adaptive weight weighting algorithm (CARS), illiteracy are special
Carlow is without at least one of information variable null method (MCUVE), genetic algorithm (GA).
8. online test method according to claim 3, it is characterised in that preprocess method used in the polysaccharide model is
Baseline, effective wave band is 183 wave numbers between wave number 73-778；Soluble solid model preprocessing method is asked for single order
Lead, effective wave band is 175 wave numbers between 79-778；PH model preprocessings method is baseline, and effective wave band is 82-777
Between 204 wave numbers.
9. online test method according to claim 3, it is characterised in that the optimal Quantitative Analysis Model determines method
For preference pattern parameter R2Closer to 1, RMSEC and RMSEP closer to RMSEC, RMSECV, RMSEP smaller quantitative analysis mould
Type；By verifying the near infrared spectrum data of collection, quality index is predicted with the Quantitative Analysis Model, by predicted value
Error calculation is carried out with chemical score, model is modified using error, until gained model meets every error requirements.
10. online test method according to claim 9, it is characterised in that the predicted value of the checking collection of each quality index
10% should be respectively less than with the error of chemical score.
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| CN111599416A (en) * | 2020-06-04 | 2020-08-28 | 广东省生物工程研究所（广州甘蔗糖业研究所） | Method for rapidly determining formula and using amount of sweetener and application thereof |
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