CN105606548A - Work method of database and computing server - Google Patents
Work method of database and computing server Download PDFInfo
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- CN105606548A CN105606548A CN201610059287.4A CN201610059287A CN105606548A CN 105606548 A CN105606548 A CN 105606548A CN 201610059287 A CN201610059287 A CN 201610059287A CN 105606548 A CN105606548 A CN 105606548A
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- 238000000034 method Methods 0.000 title claims abstract description 34
- 239000000126 substance Substances 0.000 claims abstract description 54
- 238000002835 absorbance Methods 0.000 claims abstract description 26
- 238000013506 data mapping Methods 0.000 claims abstract description 21
- 238000004611 spectroscopical analysis Methods 0.000 claims description 53
- 238000004364 calculation method Methods 0.000 claims description 48
- 238000001514 detection method Methods 0.000 claims description 48
- 239000000203 mixture Substances 0.000 claims description 22
- 238000001228 spectrum Methods 0.000 claims description 21
- 239000000463 material Substances 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 14
- 230000015572 biosynthetic process Effects 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 238000012271 agricultural production Methods 0.000 claims description 5
- 238000004445 quantitative analysis Methods 0.000 claims description 4
- 238000010183 spectrum analysis Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 238000004451 qualitative analysis Methods 0.000 claims description 3
- 239000002689 soil Substances 0.000 claims description 3
- 235000013399 edible fruits Nutrition 0.000 claims description 2
- 235000013305 food Nutrition 0.000 claims description 2
- 238000007619 statistical method Methods 0.000 claims 1
- 235000013311 vegetables Nutrition 0.000 claims 1
- 230000003595 spectral effect Effects 0.000 abstract description 14
- 238000013098 chemical test method Methods 0.000 abstract 4
- 244000061456 Solanum tuberosum Species 0.000 description 24
- 235000002595 Solanum tuberosum Nutrition 0.000 description 24
- 241000209140 Triticum Species 0.000 description 19
- 235000021307 Triticum Nutrition 0.000 description 19
- 229920002472 Starch Polymers 0.000 description 9
- 238000002329 infrared spectrum Methods 0.000 description 9
- 235000019698 starch Nutrition 0.000 description 9
- 239000008107 starch Substances 0.000 description 9
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 description 8
- 241001269238 Data Species 0.000 description 8
- 229920002678 cellulose Polymers 0.000 description 8
- 239000001913 cellulose Substances 0.000 description 8
- 244000005700 microbiome Species 0.000 description 7
- 238000013499 data model Methods 0.000 description 6
- ZZZCUOFIHGPKAK-UHFFFAOYSA-N D-erythro-ascorbic acid Natural products OCC1OC(=O)C(O)=C1O ZZZCUOFIHGPKAK-UHFFFAOYSA-N 0.000 description 4
- 229930003268 Vitamin C Natural products 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 235000019154 vitamin C Nutrition 0.000 description 4
- 239000011718 vitamin C Substances 0.000 description 4
- 238000012360 testing method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 244000241235 Citrullus lanatus Species 0.000 description 1
- 235000012828 Citrullus lanatus var citroides Nutrition 0.000 description 1
- 244000017020 Ipomoea batatas Species 0.000 description 1
- 235000002678 Ipomoea batatas Nutrition 0.000 description 1
- 235000011430 Malus pumila Nutrition 0.000 description 1
- 235000015103 Malus silvestris Nutrition 0.000 description 1
- 238000004497 NIR spectroscopy Methods 0.000 description 1
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- 238000012937 correction Methods 0.000 description 1
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- 230000002452 interceptive effect Effects 0.000 description 1
- 238000002381 microspectrum Methods 0.000 description 1
- 235000013573 potato product Nutrition 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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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
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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
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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
- G01N2021/3129—Determining multicomponents by multiwavelength light
Abstract
The invention discloses a work method of a database and a computing server. N sets of spectral data of object samples and n sets of corresponding chemical testing data of the object samples are input into the same database according to object varieties to form a plurality of primary databases, the computing server forms data mapping sets of the sets of spectral data and the sets of chemical testing data of the object samples according to the object sample varieties, absorbance values of 2-100 wavelengths are selected from the data mapping sets and correspond to the chemical testing data, K formulae of absorbance changes of 2-100 wavelengths and chemical testing data changes in qualitative and quantitative relations are determined, and the K formulae are embedded into the computing server; new spectral data is input into the computing server, the computing server automatically matches the formulae according to the varieties of input objects to be tested and components, needing to be detected, of the input objects to be tested, and the content of the substance components needing to be detected is calculated according to the new spectral data. The work method is fast and small in error.
Description
Technical field
The invention belongs to material detection field, particularly relate to the method for utilizing spectral detection chemical composition, concreteRelate to the method for work of a kind of database and calculation server.
Background technology
Control and the Data Management Analysis system of modern near infrared spectroscopy instrument are the important component parts of instrument. OneAs by instrument control, adopt spectrum and two software systems of spectral manipulation analysis and corresponding hardware device and form. The formerMajor function is the duty of control instrument each several part, sets the relevant parameters of spectra collection, as spectral measurementThe sweep limits of mode, scanning times, setting spectrum etc., set the duty of detector and accept detectorSpectral signal. The spectrum that spectral manipulation analysis software mainly gathers detector is processed, and realizes qualitativeOr quantitative analysis. To specific sample system, the difference of near infrared spectrum characteristic peak is also not obvious, need to pass throughThe processing of spectrum reduces so that eliminates the interference of each side factor to spectral information, then from the very micro-spectrum letter of differenceIn breath, extract the qualitative or quantitative information of sample, everything all will be by powerful spectroscopic data Treatment AnalysisSoftware is realized.
Near-infrared spectral analysis technology analysis speed is fast, is that computer calculates knot because spectral measurement speed is very fastAlso very fast reason of fruit speed. But the efficiency of near-infrared spectrum analysis depend on data model that instrument is equipped with,Quantity, and method of work between calculation server and database, the degree of accuracy of data model is depended on and is set up numberSize and the operation rule of the data volume according to model during based on modeling, efficient between calculation server and databaseRunning depend on that calculation server is transferred data in database and computing after the efficiency of data storage.
CN101556242B discloses a kind of with method for discriminating microorganism by utilizing Fourier infrared spectrum, comprises cultivationContrast microorganism; Gather the infared spectrum of contrast microorganism; 3000 ?2300cm‐1With 1300 to 700cm‐1One or more spectral coverages in interval are set up microorganism and are differentiated model; Under above-mentioned identical condition, cultivate and treat micrometerBiology, gathers the infared spectrum of microorganism to be measured, by infared spectrum substitution microorganism differentiate in model, determine to be measuredThe ownership of microorganism.
In current method, because the foundation of model carries out according to the pattern of collection of illustrative plates, or according to local numberAccording to what carry out, or on stoichiometric basis, carrying out match spectrum data, all there is modeling in these methodsRear adjustment difficulty is large, and basic data is incomplete, causes the correction of data model and the renewal of formula and changes difficultyDegree is large, and between calculation server and database, data corruption easily occurs in running simultaneously, and newly detects data and can not getUpgrade, cause testing result error greatly and the low defect of data model turnover rate.
Summary of the invention
For solving the problems of the technologies described above, the invention provides the method for work of a kind of database and calculation server,The method is utilized the operational formula of setting up of the spectrum multi-wavelength characteristic information of material and many material informations corresponding relation.Main process is to have set up after spectroscopic data and chemical detection data, and input database, enters by calculation serverThe mapping of row spectroscopic data and chemical data, finds the wavelength combination information that represents its rule according to mapping, and willWavelength combination information and substance composition and content information are set up many sets of data formula, then will overlap mathematical formulae embedding moreEnter calculation server, calculation server and database are closely interactive, carry out the excellent of novel substance detection and mathematical formulaeChange.
Concrete, the invention provides the method for work of a kind of database and calculation server, it is characterized in that,The n group spectroscopic data of object sample and the corresponding n group of each object sample chemical detection data are pressed to kind of object recordEnter same database and form multiple primary database, primary database is connected with calculation server, calculation serverThe data that receive primary database are pressed object sample type by many groups spectroscopic data of each object sample and many groupizationsLearn and detect the set of data formation data-mapping, from data-mapping set, choose the absorbance of 2-100 wavelengthIt is corresponding that numerical value and chemical detection data are carried out, and determines that 2-100 wavelength absorbance changes with chemical detection data to becomeChange K the formula with quantitative and qualitative analysis relation, K formula embedded to calculation server; By new spectrum numberAccording to input database with input to calculation server, calculation server according to the examined object kind of input andThe composition Auto-matching formula of required detection, realizes according to new spectroscopic data and calculates containing of material composition to be detectedAmount, a described 2-100 wavelength is selected from wavelength value or the wave-length coverage in 700-2500nm, wherein, chemistry inspectionSurvey data and comprise T kind composition and content detection thereof, T >=1, K >=T, n >=50; Described object sample is foodOne or more of thing class, agricultural production category, soil class.
Specifically, the invention provides the method for work of a kind of database and calculation server, the method comprises as followsStep:
Step I: irradiate object sample A to be detected with light source1, then collect object sample A1ReflectSpectrum, adopt spectral analysis apparatus to determine wavelength and the absorbance of collected spectrum, form object sample A1Spectroscopic data;
Step II: to object sample A1Carry out chemical analysis, analyze its T kind composition and content, form objectThe chemical detection data of sample; T represents the quantity of composition, namely does the analysis of several compositions, when to objectWhen analysing protein and starch, T is 2, if increase soluble sugar, T is 3. T is greater than etc.In 1, ordinary circumstance does not limit greatest measure, as long as conditions permit can be done complete point to the composition of objectAnalyse, like that T may reach 20, and even 30;
Step II I: by object A1Spectroscopic data and the same database of chemical detection data typing, form dataMapping X1;
Step IV: repeat above-mentioned steps I, Step II and Step II I, to object sample A2To An+1Carry out nInferior repetition, forms n group spectroscopic data and corresponding n group chemical detection data, by spectroscopic data and chemical detectionThe same database of data typing, forms the data-mapping set of the n group data-mapping of object sample A1;
Step V: the spectroscopic data in data-mapping set in above-mentioned database is chosen to the suction of 2-100 wavelengthIt is corresponding that luminosity numerical value and chemical detection data are carried out, and determines that 2-100 wavelength absorbance changes and chemical detection numberThere is K formula of quantitative and qualitative analysis relation according to variation; A described 2-100 wavelength is selected from 700-2500nmWavelength value or wave-length coverage; The K of above-mentioned steps formula embedded to calculation server. Wherein K represents public affairsThe quantity of formula, generally K >=1, in order to analyze separately multicomponent, K value is greater than T value, namelyThe quantity of formula is necessarily greater than the quantity of composition, sometimes in order to carry out multicomponent analysis simultaneously, needs K value fullThe following relational expression of foot:
Wherein C represents knockdown implication.
Accurate in order to consider the detection of each composition or the combination of multiple composition, need formula for subsequent use, alsoBeing for abnormal data, there is imponderable situation in formula, tackles formula for subsequent use and carries out computing, now examinesWhile considering formula for subsequent use, K value needs to meet following relational expression:
Wherein C represents knockdown implication.
Step VI: K the formula that other object sample repeating steps I is become to each object sample to step V-arrangementAnd embed calculation server;
Step VII, gathers the spectroscopic data of object fresh sample Y, by its input database with input to computing clothesWhen business device, and in calculation server, input examined object kind Y and need the composition detecting, fortuneCalculate server according to Auto-matching formula, the content that calculates each composition in kind of object Y obtains fresh sampleLearn data, this chemical data is outputed to display end and database simultaneously, and in database with object fresh sampleThe spectroscopic data of Y forms measurement data mapping, and this measurement data mapping is upgraded with existing data-mapping formationData-mapping set is for the new object sample detection of statistical analysis integrated information;
Step VIII: K formula on database and the calculation server forming to step VII according to step I,Database is connected with calculation server, the data input pin of database and data output end, setting are set simultaneouslyThe data input pin of calculation server and data output end, the spectroscopic data model of formation object, wherein T >=1,K≥T。
Preferably, n is more than or equal to 100. N represents sample detection quantity, and n value is larger, spectroscopic data andThe quantity of chemical detection data is larger, can make data in mapping (enum) data set better support the foundation of formula,The n value herein limiting refers to sets up the minimum sample detection amount that model needs, and maximum detection limit is unrestricted, onlyWant conditions permit, sample detection amount can be increased to more than 1000, or even more than 10000.
Preferably, in said method, the wave-length coverage of spectrum is 700-2500nm. Preferably, the ripple of spectrumLong scope is 800-1800nm, or the wave-length coverage of spectrum be 1500 ?2500, or in 700-2500nmThe wave-length coverage of any range.
Preferably, in said method, object is that the basic identical but component content difference value of chemical composition exists20% with interior similar object. Described component content difference value refer to component content in each agricultural product sample definitelyThe percentage of the ratio of the mean value of component content in value and each agricultural product sample.
Preferably, object is food class, agricultural production category, soil class etc., preferably agricultural production category, for example horseBell potato stem tuber, wheat seed, watermelon, leaf vegetables, apple etc. For example set up the data model of potato,In said method, object sample is potato sample, and can not select Ipomoea batatas sample.
Preferably, in said method, spectroscopic data is all wavelengths of nanometer integer level wavelength and the number of absorbanceAccording to set. That is to say, spectroscopic data is not only a figure or several wavelength data, but selectedThe absorbance of all wavelengths in scope, even the absorbance of some wavelength is zero, also will be recorded into spectroscopic dataIn.
Preferably, in said method, spectroscopic data be wavelength be 800 ?the ripple of 1001 wavelength of 1800nmThe data acquisition system of length and absorbance.
Preferably, in said method, spectroscopic data be wavelength be 1500 ?the wavelength of 1001 wavelength of 2500Data acquisition system with absorbance.
Chemical measurement data of the present invention are also stoichiometry data, refer to by the national standard of CucumberRow is measured the chemical data obtaining. The for example content of starch in potato, need to be according to national standard or rowIndustry standard is measured, and also can adopt the instrument that meets GB certainty of measurement to measure.
Beneficial effect
Compared with prior art, the present invention has following beneficial effect:
The kind of 1, not only many groups spectroscopic data of many kinds of substance and many group chemical detection data being pressed to material separatelyIn independent input database, by substance classes, spectroscopic data and chemical detection data are shone upon, avoided orderBefore only by not defect independently of the data that cause in spectroscopic data typing chemical detection data processor, be difficult to reachUpgrade and change to formula, having hindered the flexible variation of formula, also avoiding number between calculation server and databaseAccording to the confusion of calling, can not only realize each substance classes according to spectroscopic data chemistry component content, also andCan also realize the easy detection to same material heterogeneity, even can also be poor to material component content of the same raceQuick higher than 20% time of different value accurately detected. Meanwhile, each material is minimum for the data of setting up data model50 groups of spectroscopic datas and 50 groups of corresponding chemical detection data, the larger foundation of data base unit weight of setting up data modelData model accuracy higher.
2, in the data-mapping set in database, from 700-2500nm, choose 2 ?100 wavelength datas,By 2 ?absorbance numerical value under 100 wavelength datas and chemical detection data carry out correspondingly, determine 2-100 rippleThe quantitative and qualitative analysis relation of apneusis light varience and chemical detection data variation. Because the wavelength data in databaseComprehensively, therefore choose 2 ?100 wavelength datas carry out corresponding and set up formula more just with chemical measurement dataVictory, 2 ?100 wavelength datas can convert, abandoned the selection of single wavelength section in current technology, shapeBecome uncertainty analysis, inaccurate to the location of wavelength. In addition, from 700-2500nm, choose 2 ?100 ripplesLong data has improved the accuracy of the data model of setting up, and passes through data after further having reduced input spectrum dataThe error of model chemistry composition and content data.
3, K formula of calculation server carrying can be according to the material to be detected and becoming that it need detect of inputPoint and the spectroscopic data Auto-matching formula of material to be detected, calculate fast material to be detected and need detect compositionContent. Owing to independently setting up database and corresponding mapping database, therefore calculation server according to the kind of materialWith the computing of the database of each material be also independently, therefore, calculation server can be according to the kind of material to be detectedThe composition that class, spectroscopic data, need detect can calculate the content of the each composition of this material fast and accurately.
In realizing the operation independent of formula, also ensure that operation result is independently in defeated time database,And select when wavelength data, to there is no need whole wavelength datas to compare, only in server, compare2 ?100 wavelength datas, comparison efficiency is high, does not affect again original spectrum data and is entered in database.Increase for spectroscopic data and chemical detection data base unit weight, and 100 ripples of 2 ? of choosing from 700-2500nmLong data, the quantity of formula and formula is improved accordingly and is increased, and is also conducive to further result of calculationAccuracy.
Detailed description of the invention
The method of work of embodiment 1 database and calculation server
Material: potato tubers, from potato product, randomly draw 300 potato tubers, transverse cuts,Light source irradiates and spectroscopic data is collected for potato cross section.
Wheat obtains at random 200 parts from wheat products, and every part is placed in the container of 10 centimetres of diameters, heightDegree 2cm, light source irradiates and spectroscopic data is collected the upper plane for the medium and small dung heap of container.
Equipment: light source irradiation unit, spectrum gathering-device and spectral analysis apparatus are integral device or split equipment,Market is bought and is obtained.
Irradiate potato tubers with light source, then collect the near infrared spectrum that potato tubers reflects, spectrumScope be 800 ?1800nm, adopt near-infrared spectrum analysis device analysis spectral absorbance, form potato ballStem 800 ?the near infrared spectrum data of 1800nm, this spectroscopic data has 1001 light wave absorbance datas.
Potato tubers is carried out to chemical analysis, analyze potato tubers content of starch, Vitamin C content,Content of cellulose, the chemical detection data of formation potato tubers;
By the spectroscopic data of potato tubers and the same database of chemical detection data typing, form the 1st group of dataMapping;
Extract again 299 groups, potato sample, only according to the method described above to randomly draw 299 groups of potato tubersVertical processing, obtains 299 groups of spectroscopic datas and 299 groups of corresponding chemical detection data, by spectroscopic data withThe same database of chemical detection data typing, 300 groups of data-mappings of formation potato sample;
In 300 groups of data-mappings in above-mentioned potato sample data storehouse, spectroscopic data is chosen 4 wavelength modelsThe absorbance numerical value (1200-1300nm, 1400-1600nm, 1000-1100nm, 2000-2300nm) enclosing,The unification of absorbance numerical value and the chemical detection data of 4 wavelength are carried out corresponding, determine that 4 wavelength absorbances changeThere are 4 formula of synchronized relation with chemical detection data variation, 4 formula of above-mentioned steps are embedded to computingsServer.
Irradiate wheat with light source, then collect the near infrared spectrum that wheat reflects, spectral region is1500 ?2500nm, adopt near-infrared spectrum analysis device analysis spectral absorbance, form wheat1500 ?the near infrared spectrum data of 2500nm, this spectroscopic data has 1001 light wave absorbance datas.
Wheat is carried out to chemical analysis, analyze content of starch, protein content, the content of cellulose of wheat, shapeBecome the chemical detection data of wheat;
By the spectroscopic data of wheat and the same database of chemical detection data typing, form the 1st group of data-mapping;
Extract again 199 groups of wheat samples, randomly draw 08 group of wheat is independently located according to the method described aboveReason, obtains 199 groups of spectroscopic datas and 199 groups of corresponding chemical detection data, by spectroscopic data and chemical detectionThe same database of data typing, 200 groups of data-mappings of formation wheat samples;
In 200 groups of data-mappings in above-mentioned wheat samples database, spectroscopic data is chosen 5 wave-length coveragesAbsorbance numerical value (1400-1600nm, 1000-1100nm, 2000-2300nm, 900-950nm,1700-1900nm), the unification of absorbance numerical value and the chemical detection data of 5 wavelength are carried out corresponding, determine 5Wavelength absorbance changes with chemical detection data variation and has 5 formula of synchronized relation, and by above-mentioned steps5 formula embed calculation server.
Gather new potato tubers spectroscopic data typing potato database and input to calculation server, simultaneouslyChoose 4 wave-length coverages (1200-1300nm, 1400-1600nm, 1000-1100nm, 2000-2300nm)Typing calculation server, and in calculation server, input examined object kind potato and need the one-tenth detectingPoint (content of starch, Vitamin C content, content of cellulose), calculation server is according to Auto-matching formula,Calculate content of starch in potato, Vitamin C content, content of cellulose. Simultaneously by the shallow lake of potato tubersPowder content, Vitamin C content, content of cellulose output to display end, and while input database, and are countingAccording to forming test data mapping with the new spectroscopic data gathering in storehouse.
According to 4 formula on above-mentioned steps established data storehouse and calculation server, by database and computing clothesBusiness device is connected, arrange simultaneously database data input pin and data output end, the data of calculation server are setInput and data output end, the spectroscopic data model of formation potato tubers.
Gather again new wheat spectroscopic data typing wheat database and input to calculation server, choosing 5 simultaneouslyIndividual wave-length coverage (1400-1600nm, 1000-1100nm, 2000-2300nm, 900-950nm, 1700-1900nm)Typing calculation server, and in calculation server, input examined object kind wheat and need the composition detecting(content of starch, protein content, content of cellulose), calculation server, according to Auto-matching formula, calculatesGo out content of starch in wheat, protein content, content of cellulose. Simultaneously by the content of starch of wheat stem tuber, dimensionRaw plain C content, content of cellulose output to display end, and input database simultaneously, and in database with newThe spectroscopic data gathering forms test data mapping.
According to 5 formula on above-mentioned steps established data storehouse and calculation server, by database and computing clothesBusiness device is connected, arrange simultaneously database data input pin and data output end, the data of calculation server are setInput and data output end, the spectroscopic data model of formation wheat.
Claims (9)
1. a method of work for database and calculation server, is characterized in that, by the n group of object sampleThe corresponding n group of spectroscopic data and each object sample chemical detection data are pressed the same database shape of kind of object typingBecome multiple primary database, primary database is connected with calculation server, and calculation server receives primary databaseData press object sample type by many groups spectroscopic data of each object sample and many group chemical detection data formationsData-mapping set, from data-mapping set, chooses absorbance numerical value and the chemical detection of 2-100 wavelengthData are carried out correspondence, determine that 2-100 wavelength absorbance changes and chemical detection data variation has qualitative and fixedThe K of a magnitude relation formula, embeds calculation server by K formula; By new spectroscopic data input database andInput to calculation server, calculation server is according to the examined object kind of input and the composition of required detection thereofAuto-matching formula, realizes the content that calculates material composition to be detected according to new spectroscopic data, described 2-100Individual wavelength is selected from wavelength value or the wave-length coverage in 700-2500nm, and wherein, chemical detection data comprise T kindComposition and content detection thereof, T >=1, K >=T, n >=50; Described object sample be food class, agricultural production category,One or more of soil class.
2. a method of work for database and calculation server, is characterized in that, the method comprises following stepRapid:
Step I: irradiate object sample A to be detected with light source1, then collect object sample A1ReflectSpectrum, adopt spectral analysis apparatus to determine wavelength and the absorbance of collected spectrum, form object sample A1Spectroscopic data;
Step II: to object sample A1Carry out chemical analysis, analyze its T kind composition and content, form objectThe chemical detection data of sample;
Step II I: by object A1Spectroscopic data and the same database of chemical detection data typing, form dataMapping X1;
Step IV: repeat above-mentioned steps I, Step II and Step II I, to object sample A2To An+1Carry out nInferior repetition, forms n group spectroscopic data and corresponding n group chemical detection data, by spectroscopic data and chemical detectionThe same database of data typing, forms object sample A1The data-mapping set of n group data-mapping;
Step V: the spectroscopic data in data-mapping set in above-mentioned database is chosen to the suction of 2-100 wavelengthIt is corresponding that luminosity numerical value and chemical detection data are carried out, and determines that 2-100 wavelength absorbance changes and chemical detection numberThere is K formula of quantitative and qualitative analysis relation according to variation; A described 2-100 wavelength is selected from 700-2500nmWavelength value or wave-length coverage; The K of above-mentioned steps formula embedded to calculation server;
Step VI: K the formula that other object sample repeating steps I is become to each object sample to step V-arrangementAnd embed calculation server;
Step VII, gathers the spectroscopic data of object fresh sample Y, by its input database with input to computing clothesWhen business device, and in calculation server, input examined object kind Y and need the composition detecting, fortuneCalculate server according to Auto-matching formula, the content that calculates each composition in kind of object Y obtains fresh sampleLearn data, this chemical data is outputed to display end and database simultaneously, and in database with object fresh sampleThe spectroscopic data of Y forms measurement data mapping, and this measurement data mapping is upgraded with existing data-mapping formationData-mapping set is for the new object sample detection of statistical analysis integrated information;
Step VIII: K formula on database and the calculation server forming to step VII according to step I,Database is connected with calculation server, the data input pin of database and data output end, setting are set simultaneouslyThe data input pin of calculation server and data output end, the spectroscopic data model of formation object, wherein T >=1,K≥T。
3. method according to claim 1 and 2, is characterized in that, n is more than or equal to 100; StepIn VI, other object sample repeating steps I is become to K formula of each object sample and embeds to transport to step V-arrangementWhile calculating server, comprise object sample Y is carried out to same processing.
4. method according to claim 1 and 2, is characterized in that the wave-length coverage of spectrum is700-2500nm; Preferably, the wave-length coverage of spectrum is 800-1800nm, or the wave-length coverage of spectrum is1500‐2500。
According to claim 1 ?method described in 3 any one, it is characterized in that object is chemical compositionBasic identical but the similar object of component content difference value in 20%.
6. method according to claim 1, is characterized in that described agricultural production category includes but not limited to grainFood, fruit, vegetables etc.
According to claim 1 ?method described in 5, it is characterized in that spectroscopic data is nanometer integer level wavelengthAll wavelengths and the data acquisition system of absorbance.
8. method according to claim 2, it is characterized in that spectroscopic data be wavelength be 800 ?1800nmThe wavelength of 1001 wavelength and the data acquisition system of absorbance, or described spectroscopic data be wavelength be 1500 ?2500The wavelength of 1001 wavelength and the data acquisition system of absorbance.
According to claim 2 ?method described in 8 any one, it is characterized in that T >=1, K >=T, preferred,K value meets following relational expression:
Wherein C represents knockdown implication.
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| CN (1) | CN105606548B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106872397A (en) * | 2016-12-29 | 2017-06-20 | 深圳市芭田生态工程股份有限公司 | A kind of method based on existing calibration model quick detection agricultural product chemical constituent |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040084623A1 (en) * | 2002-11-06 | 2004-05-06 | Yicheng Long | NIR spectroscopy method for analyzing chemical process components |
| EP1992939A1 (en) * | 2007-05-16 | 2008-11-19 | National University of Ireland, Galway | A kernel-based method and apparatus for classifying materials or chemicals and for quantifying the properties of materials or chemicals in mixtures using spectroscopic data. |
| CN103959292A (en) * | 2011-09-23 | 2014-07-30 | 陶氏益农公司 | Chemometrics for near infrared spectral analysis |
| CN104020127A (en) * | 2014-06-18 | 2014-09-03 | 山东烟草研究院有限公司 | Method for rapidly measuring inorganic element in tobacco by near infrared spectrum |
| CN104849232A (en) * | 2015-04-27 | 2015-08-19 | 中国农业科学院蜜蜂研究所 | Method for rapid detection of contents of water and protein in royal jelly |
| CN105241826A (en) * | 2015-10-13 | 2016-01-13 | 惠州Tcl移动通信有限公司 | Intelligent mobile terminal and food detection method using the same |
-
2016
- 2016-01-28 CN CN201610059287.4A patent/CN105606548B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040084623A1 (en) * | 2002-11-06 | 2004-05-06 | Yicheng Long | NIR spectroscopy method for analyzing chemical process components |
| EP1992939A1 (en) * | 2007-05-16 | 2008-11-19 | National University of Ireland, Galway | A kernel-based method and apparatus for classifying materials or chemicals and for quantifying the properties of materials or chemicals in mixtures using spectroscopic data. |
| CN103959292A (en) * | 2011-09-23 | 2014-07-30 | 陶氏益农公司 | Chemometrics for near infrared spectral analysis |
| CN104020127A (en) * | 2014-06-18 | 2014-09-03 | 山东烟草研究院有限公司 | Method for rapidly measuring inorganic element in tobacco by near infrared spectrum |
| CN104849232A (en) * | 2015-04-27 | 2015-08-19 | 中国农业科学院蜜蜂研究所 | Method for rapid detection of contents of water and protein in royal jelly |
| CN105241826A (en) * | 2015-10-13 | 2016-01-13 | 惠州Tcl移动通信有限公司 | Intelligent mobile terminal and food detection method using the same |
Non-Patent Citations (1)
| Title |
|---|
| 郑立华等: "基于近红外光谱的土壤参数快速分析系统", 《光谱学与光谱分析》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106872397A (en) * | 2016-12-29 | 2017-06-20 | 深圳市芭田生态工程股份有限公司 | A kind of method based on existing calibration model quick detection agricultural product chemical constituent |
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