CN105628639A - Method for measuring vegetable oil and fat in agricultural product by utilization of spectroscopic method - Google Patents
Method for measuring vegetable oil and fat in agricultural product by utilization of spectroscopic method Download PDFInfo
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- 238000004611 spectroscopical analysis Methods 0.000 title claims abstract description 69
- 235000015112 vegetable and seed oil Nutrition 0.000 title claims abstract description 60
- 239000008158 vegetable oil Substances 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 40
- 235000019871 vegetable fat Nutrition 0.000 title claims abstract description 9
- 238000001514 detection method Methods 0.000 claims abstract description 87
- 239000000126 substance Substances 0.000 claims abstract description 65
- 238000013499 data model Methods 0.000 claims abstract description 32
- 230000003595 spectral effect Effects 0.000 claims abstract description 28
- 238000004364 calculation method Methods 0.000 claims abstract description 19
- 238000005259 measurement Methods 0.000 claims abstract description 9
- 235000013311 vegetables Nutrition 0.000 claims abstract description 8
- 102000004895 Lipoproteins Human genes 0.000 claims description 58
- 108090001030 Lipoproteins Proteins 0.000 claims description 58
- 238000001228 spectrum Methods 0.000 claims description 12
- 238000002835 absorbance Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 7
- 239000000470 constituent Substances 0.000 claims description 7
- 235000013399 edible fruits Nutrition 0.000 claims description 6
- 238000010521 absorption reaction Methods 0.000 claims description 5
- 238000013506 data mapping Methods 0.000 claims description 4
- 235000013339 cereals Nutrition 0.000 claims description 2
- 235000021384 green leafy vegetables Nutrition 0.000 claims description 2
- 238000010183 spectrum analysis Methods 0.000 claims description 2
- 235000001674 Agaricus brunnescens Nutrition 0.000 description 22
- 210000000582 semen Anatomy 0.000 description 21
- 235000014113 dietary fatty acids Nutrition 0.000 description 9
- 229930195729 fatty acid Natural products 0.000 description 9
- 239000000194 fatty acid Substances 0.000 description 9
- 235000020995 raw meat Nutrition 0.000 description 9
- 150000004665 fatty acids Chemical class 0.000 description 8
- 238000013507 mapping Methods 0.000 description 8
- 239000003925 fat Substances 0.000 description 6
- 235000019197 fats Nutrition 0.000 description 6
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 5
- 239000002253 acid Substances 0.000 description 4
- 229910052799 carbon Inorganic materials 0.000 description 4
- 150000002333 glycines Chemical class 0.000 description 4
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 3
- GHVNFZFCNZKVNT-UHFFFAOYSA-N decanoic acid Chemical compound CCCCCCCCCC(O)=O GHVNFZFCNZKVNT-UHFFFAOYSA-N 0.000 description 3
- 150000004668 long chain fatty acids Chemical class 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- 238000000944 Soxhlet extraction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 150000004667 medium chain fatty acids Chemical class 0.000 description 2
- 235000021003 saturated fats Nutrition 0.000 description 2
- 150000004666 short chain fatty acids Chemical class 0.000 description 2
- 235000021122 unsaturated fatty acids Nutrition 0.000 description 2
- 150000004670 unsaturated fatty acids Chemical class 0.000 description 2
- WRIDQFICGBMAFQ-UHFFFAOYSA-N (E)-8-Octadecenoic acid Natural products CCCCCCCCCC=CCCCCCCC(O)=O WRIDQFICGBMAFQ-UHFFFAOYSA-N 0.000 description 1
- LQJBNNIYVWPHFW-UHFFFAOYSA-N 20:1omega9c fatty acid Natural products CCCCCCCCCCC=CCCCCCCCC(O)=O LQJBNNIYVWPHFW-UHFFFAOYSA-N 0.000 description 1
- HVCOBJNICQPDBP-UHFFFAOYSA-N 3-[3-[3,5-dihydroxy-6-methyl-4-(3,4,5-trihydroxy-6-methyloxan-2-yl)oxyoxan-2-yl]oxydecanoyloxy]decanoic acid;hydrate Chemical compound O.OC1C(OC(CC(=O)OC(CCCCCCC)CC(O)=O)CCCCCCC)OC(C)C(O)C1OC1C(O)C(O)C(O)C(C)O1 HVCOBJNICQPDBP-UHFFFAOYSA-N 0.000 description 1
- QSBYPNXLFMSGKH-UHFFFAOYSA-N 9-Heptadecensaeure Natural products CCCCCCCC=CCCCCCCCC(O)=O QSBYPNXLFMSGKH-UHFFFAOYSA-N 0.000 description 1
- 239000005632 Capric acid (CAS 334-48-5) Substances 0.000 description 1
- 241001672694 Citrus reticulata Species 0.000 description 1
- 240000002319 Citrus sinensis Species 0.000 description 1
- 235000005976 Citrus sinensis Nutrition 0.000 description 1
- 229930186217 Glycolipid Natural products 0.000 description 1
- 239000005642 Oleic acid Substances 0.000 description 1
- ZQPPMHVWECSIRJ-UHFFFAOYSA-N Oleic acid Natural products CCCCCCCCC=CCCCCCCCC(O)=O ZQPPMHVWECSIRJ-UHFFFAOYSA-N 0.000 description 1
- 241000220324 Pyrus Species 0.000 description 1
- VMHLLURERBWHNL-UHFFFAOYSA-M Sodium acetate Chemical compound [Na+].CC([O-])=O VMHLLURERBWHNL-UHFFFAOYSA-M 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 235000004626 essential fatty acids Nutrition 0.000 description 1
- 235000019387 fatty acid methyl ester Nutrition 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 150000002431 hydrogen Chemical class 0.000 description 1
- QXJSBBXBKPUZAA-UHFFFAOYSA-N isooleic acid Natural products CCCCCCCC=CCCCCCCCCC(O)=O QXJSBBXBKPUZAA-UHFFFAOYSA-N 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000021281 monounsaturated fatty acids Nutrition 0.000 description 1
- 229920005615 natural polymer Polymers 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 235000014593 oils and fats Nutrition 0.000 description 1
- ZQPPMHVWECSIRJ-KTKRTIGZSA-N oleic acid Chemical compound CCCCCCCC\C=C/CCCCCCCC(O)=O ZQPPMHVWECSIRJ-KTKRTIGZSA-N 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 235000021017 pears Nutrition 0.000 description 1
- 150000003904 phospholipids Chemical class 0.000 description 1
- 235000021391 short chain fatty acids Nutrition 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
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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- Spectroscopy & Molecular Physics (AREA)
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- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
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Abstract
The invention discloses a method for measuring vegetable oil and fat in an agricultural product by utilization of a spectroscopic method. The method comprises the following steps of measuring a vegetable oil-fat content of each of n samples of a same agricultural product by utilization of a chemical detection method so as to obtain corresponding vegetable oil-fat chemical detection data, and performing nondestructive spectral measurement on the n samples of the same agricultural product under a spectral range of 800 to 2500 nm so as to obtain corresponding vegetable oil-fat spectral data, wherein n is not smaller than 50; building a data model for agricultural product vegetable oil-fat detection by utilization of the spectral data and the chemical detection data, and embedding the data model into a data operation server; collecting the spectral data of a to-be-detected agricultural product, and inputting the collected spectral data into the data operation server; matching the data model and performing calculation according to the variety of the product required for being detected so as to obtain the vegetable oil-fat content of the detected agricultural product by utilization of the operation server.
Description
Technical field
The invention belongs to material detection field, the method particularly relating to utilize spectral detection chemical composition, it is specifically related to and a kind of utilizes the method for Vegetable oil lipoprotein in spectrographic determination agricultural product.
Background technology
Vegetable oil lipoprotein is the natural polymer being bound up by fatty acid and glycerol, is distributed widely in nature. Every fat general designation Vegetable oil lipoprotein from plant seed, sarcocarp and other extracting section gained.
Wherein, fatty acid is a compounds three kinds elementary composition by carbon, hydrogen, oxygen, is the main component of neutral fat, phospholipid and glycolipid. Fatty acid radical can be classified as again short-chain fatty acid (shortchainfattyacids according to the difference of carbon chain lengths, SCFA), carbon number in its carbochain is less than 6, also referred to as volatile fatty acid (volatilefattyacids, VFA); Medium-chain fatty acid (Midchainfattyacids, MCFA), refers to that in carbochain, carbon number is the fatty acid of 6-12, and main component is sad (C8) and capric acid (C10); Long-chain fatty acid (Longchainfattyacids, LCFA), in its carbochain, carbon number is more than 12. Animal can synthesize required satisfied fatty acid and this kind of unsaturated fatty acid containing only 1 double bond of oleic acid, polyenoid fatty acid containing 2 or more than 2 double bonds then must obtain from plant, therefore the latter is called essential fatty acid, wherein linolenic and linoleic is most important.
In life, fat content in agricultural product is increasingly paid attention to by consumer, especially for foster survivor, therefore, the requirement of its fat is also increasingly paid close attention to by consumer when selecting agricultural product, and at present measuring of agricultural product fat is often used expensive experimental apparatus analysis based on destructive chemical analysis or at laboratory, these methods are required for damaging agricultural product, and can not carrying out Site Test Analysis, test process is loaded down with trivial details, be unfavorable for consumer directly, quickly know the situation of content of fatty acid in agricultural product. Meanwhile, there is not patented technology openly to utilize the method for total fat, saturated fat (acid), unsaturated fatty acids (acid) in spectrographic determination agricultural product at present, especially agricultural product are detected by agricultural product under nondestructive state.
Summary of the invention
In order to overcome the various defects existing for said determination method, the invention provides and a kind of utilize the method for Vegetable oil lipoprotein in spectrographic determination agricultural product, the method comprises the steps:
A. utilize chemical detection method to measure Vegetable oil lipoprotein content in n sample of same agricultural product, obtain corresponding Vegetable oil lipoprotein chemical detection data, wherein n >=50;
B. it is under 800-2500nm in spectral region, n sample of same agricultural product is carried out nondestructive spectral measurement, obtains corresponding Vegetable oil lipoprotein spectroscopic data, wherein n >=50;
C. spectroscopic data and chemical detection data are utilized to set up the data model of agricultural product Vegetable oil lipoprotein detection, data model embedding data calculation server;
D. under 800-2500nm, carry out spectroscopic data collection for agricultural product to be checked, by collected spectroscopic data input data operation server, select to need the agricultural product kind of detection simultaneously;
E. calculation server is gone forward side by side row operation according to the product variety matched data model of required detection, it is thus achieved that the Vegetable oil lipoprotein content of detected agricultural product.
The method that described step C data model is set up, comprises the steps:
Step I: the device launch spot launching spectral collection of holding concurrently with light source irradiates agricultural samples A1 to be detected, and collect the agricultural samples A1 spectrum reflected, adopt spectral analysis apparatus to determine wavelength and the absorbance of collected spectrum, form the spectroscopic data of agricultural samples A1;
Step II: agricultural samples A1 carries out chemical analysis, analyzes Vegetable oil lipoprotein content, forms the chemical detection data of agricultural samples;
Step III: by the spectroscopic data of agricultural product A1 and the same data base of chemical detection data inputting, forms data and maps X1;
Step IV: repeat the above steps I, step II and step III, agricultural samples A2 to An+1 carries out n time repeat, form n group spectroscopic data and corresponding n group chemical detection data, by spectroscopic data and the same data base of chemical detection data inputting, form the data mapping set that n group data map;
Step V: the absorption values that the spectroscopic data in data mapping set in above-mentioned data base is chosen 2-100 wavelength carries out corresponding with chemical detection data, it is determined that the quantitative relationship of 2-100 wavelength absorbance change and chemical detection data variation;
Step VI: the quantitative relationship of above-mentioned steps is embedded calculation server, gather the spectroscopic data of agricultural product fresh sample AX, while its input database, 2-100 the wavelength typing calculation server that selecting step V determines, calculate and do not carry out actually detected agricultural product fresh sample chemical data, this chemical data is exported display end and data base simultaneously, and in data base, forms measurement data mapping with the spectroscopic data of agricultural product fresh sample AX;
Step VII: according to the quantitative relationship on step I to the step VI data base formed and calculation server, data base is connected with calculation server, data input pin and data output end, the data input pin arranging calculation server and the data output end of data base are set simultaneously, form the spectroscopic data model of agricultural product.
Above-mentioned steps IV sets up the method for data mapping set specifically:
1) in spectroscopic data input spectrum data base, data strip is set up according to nanoscale, each nanoscale wavelength is defined as a data strip, by in each nanoscale wavelength data and Wavelength strength data inputting data base, forming the spectroscopic data bar in spectra database, the nano wave length quantity k in spectral region is correspondingly formed the spectroscopic data bar k of respective numbers; Such as wave-length coverage is 1000-1500 nanometer, then have 501 spectroscopic data bars, and k is 501, and each spectroscopic data bar includes wavelength and intensity;
2) in chemical detection data input chemline, chemical detection data are set up data strip by the quantity of detected composition, data strip is set up according to composition, each composition is defined as a data bar, by in each Components Name and component content input database, forming the compositional data bar in chemline, the quantity of composition is correspondingly formed the compositional data bar of respective numbers; Such as, having composition in 5 in the chemical detection data of object, then have 5 data bar, respectively Y1, Y2 ... Y5, each data strip includes Components Name and component content; Or chemical detection data are carried out permutation and combination, then using all permutation and combination as data strip data database, permutation and combination;
3) by all the components data strip in a spectroscopic data bar correspondence chemical data table in spectral catalogue, formed and map data set, the principle of correspondence is a spectroscopic data bar corresponding each compositional data bar respectively, forms the mapping data set that single spectrum is corresponding with multicomponent; Such as spectroscopic data bar is X1000, and compositional data bar is Y1, Y2, Y3, Y4, Y5, then be { X1000Y1, X1000Y2, X1000Y3, X1000Y4, X1000Y5} for the mapping data set that the single spectrum of 1000 nanometers is corresponding with multicomponent;
According to the method that above-mentioned foundation maps data set, all spectroscopic data bars in spectral catalogue are carried out with all the components data strip in chemical data table corresponding respectively, form the set of all mapping data sets, be mapping data acquisition system; Such as spectroscopic data bar is 501, compositional data bar is 5, comprising 501 �� 5=2505 data in spectroscopic data that then one-time detection is formed and the mapping data acquisition system of chemical detection data, this 2505 data is the mapping data acquisition system of this detection of object.
If the different samples of this object are carried out n detection, then form n and map data acquisition system, n is mapped in data acquisition system one independent data base of unified input, then form this object Mapping data base.
Above-mentioned n is be more than or equal to 50, it is more preferable to, n is be more than or equal to 100.
Preferably, the wave-length coverage of described spectrum is 800-1800nm, or the wave-length coverage of spectrum is 1500-2500.
Preferably, described agricultural product are the vegetable fat constituent content difference value same agricultural products at 0-50%. Described vegetable fat constituent content difference value refers to the absolute value of Vegetable oil lipoprotein content and the percent of the ratio of the meansigma methods of Vegetable oil lipoprotein content in each agricultural samples in each agricultural samples.
Preferably, described spectroscopic data is wavelength is the data acquisition system of the wavelength of 1001 wavelength of 800-1800nm and intensity, or the data acquisition system of the wavelength of 1001 wavelength of spectroscopic data to be wavelength be 1500-2500 and intensity.
Preferably, described agricultural product include leaf vegetables, fruits, grain class, tubers, fruit vegetables. It is further preferred that described fruits are mandarin orange, Citrus sinensis Osbeck, Fructus Mali pumilae, pears, Fructus Fragariae Ananssae, Fructus actinidiae chinensis. Described fruit vegetables is Fructus Cucumidis sativi, Fructus Solani melongenae, Fructus Lycopersici esculenti, Fructus Colocasiae Esculentae, Semen Armeniacae Amarum, Radix Raphani, Fructus Luffae.
Preferably, in described step A, chemical detection method is GB/T22223-2008.
In the method for the present invention, spectroscopic data is the light energy of the different wave length collected by spectral collection device, is converted into spectroscopic data by light inverted signal device, and spectroscopic data generally requires have spectral intensity, even if certain wavelength light intensity of wave is zero, then it is also required to record at spectroscopic data.
Compared with prior art, there is advantages that
1, when the present invention detects, only need to carry out agricultural product simply cleaning or cutting, when the skin depth of agricultural product is more than more than 1mm, just agricultural product are cut and obtain section, the section obtained is carried out spectral measurement and obtains fatty acid methyl ester concrete in agricultural product, the content (including all taking advantage of a situation and trans fatty acid configuration) of total fat, various single saturated fat (acid) content and monounsaturated fatty acid (acid) content. Oils and fats analysis contained by agricultural product is comprehensive. Agricultural product are only cut by the method when necessary, do not need agricultural product carry out chemical treatment or more mechanical treatment. Detection method is simpler and convenient.
2, adopt spectral technique analysis to measure, analyze speed fast, can detect at any time, convenient and swift;
3, the spectroscopic data of the present invention and the mapping method of chemical detection data take into full account the component characteristic of different agricultural product, it is possible to measure Vegetable oil lipoprotein content in different agricultural product as required simultaneously;
4, the modeling method between spectroscopic data provided by the invention and chemical composition data can conveniently update basic database, it is provided that big and reliable data, improves detection degree of accuracy, reduces personal error.
5, the spectroscopic data that the data model that the inventive method is set up is based on is the spectroscopic data reflected be more than or equal to each position of 50 samples and sample for agricultural product, the spectroscopic data collected is complete, and the content of Vegetable oil lipoprotein in agricultural product can be measured accurately by spectroscopic data model without correction. Meanwhile, in the present invention, vegetable fat constituent content difference value is in 0-50% suitable in agricultural product for same data model, and such testing result is more accurate.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearly understand, below in conjunction with embodiment, the present invention is further elaborated, but the scope of protection of present invention is not limited to the scope that embodiment is expressed.
Embodiment 1
A kind of utilizing the method for Vegetable oil lipoprotein in spectrographic determination dried mushrooms, the method is specific as follows:
Adopt GB/T22223-2008 to measure Vegetable oil lipoprotein content in 100 dried mushrooms, obtain corresponding Vegetable oil lipoprotein chemical detection data; It is under 800-2300nm in spectral region, corresponding 100 dried mushrooms is carried out nondestructive spectral measurement, obtains corresponding Vegetable oil lipoprotein spectroscopic data; Spectroscopic data and chemical detection data are utilized to set up the data model of agricultural product Vegetable oil lipoprotein detection, data model embedding data calculation server; Wherein, the spectroscopic data of the data model setting up the detection of agricultural product Vegetable oil lipoprotein is to have chosen 4 wave-length coverage (respectively 2000-2300nm, 1300-1400nm, 1000-1100nm, absorption values 900-950nm) carries out corresponding with corresponding chemical detection data, determines the quantitative relationship of these 4 wavelength absorbance changes and chemical detection data variation.
Carrying out spectroscopic data collection under 800-2300nm for 30 dried mushrooms to be checked, by collected spectroscopic data input data operation server, select the agricultural product kind needing detection is dried mushrooms simultaneously; Calculation server is gone forward side by side row operation according to the product variety dried mushrooms matched data model of required detection, it is thus achieved that the Vegetable oil lipoprotein content of detected dried mushrooms. Simultaneously, being also directed to these 30 dried mushrooms to be checked adopts GB/T22223-2008 to carry out chemical detection, found that vegetable fat constituent content difference value is 5.5% in dried mushrooms, these 30 dried mushrooms use the error of the Vegetable oil lipoprotein content that chemical detection and spectrographic determination obtain less than 0.23mg/100g.
Carrying out spectroscopic data collection under 800-2300nm for 30 new fresh mushrooms to be checked, by collected spectroscopic data input data operation server, select the agricultural product kind needing detection is dried mushrooms simultaneously; Calculation server is gone forward side by side row operation according to the product variety dried mushrooms matched data model of required detection, it is thus achieved that detected the Vegetable oil lipoprotein content of new fresh mushroom. Simultaneously, being also directed to these 30 new fresh mushrooms to be checked adopts GB/T22223-2008 to carry out chemical detection, found that new fresh mushroom is 152.9% (more than 50%) with the vegetable fat constituent content difference value in dried mushrooms, these 30 dried mushrooms use the difference value of the Vegetable oil lipoprotein content that chemical detection and spectrographic determination obtain at about 73.6mg/100g. Illustrating that new fresh mushroom and the vegetable fat constituent content difference value in dried mushrooms are more than 50%, difference value is excessive, it is adaptable to the data model measuring dried mushrooms is not suitable for measuring new fresh mushroom.
Embodiment 2
A kind of utilizing the method for Vegetable oil lipoprotein in the dry Semen Glycines of spectrographic determination, the method is specific as follows:
Adopt GB/T22223-2008 to measure Vegetable oil lipoprotein content in 100 dry Semen Glyciness, obtain corresponding Vegetable oil lipoprotein chemical detection data; It is under 800-2400nm in spectral region, corresponding 100 dry Semen Glyciness is carried out nondestructive spectral measurement, obtains corresponding Vegetable oil lipoprotein spectroscopic data; Spectroscopic data and chemical detection data are utilized to set up the data model of agricultural product Vegetable oil lipoprotein detection, data model embedding data calculation server; Wherein, the spectroscopic data of the data model setting up the detection of agricultural product Vegetable oil lipoprotein is to have chosen 5 wave-length coverage (respectively 2000-2300nm, 1300-1400nm, 1000-1100nm, 900-950nm, absorption values 980-1200nm) carries out corresponding with corresponding chemical detection data, determines the quantitative relationship of these 5 wavelength absorbance changes and chemical detection data variation. Carrying out spectroscopic data collection under 800-2400nm for 30 dry Semen Glyciness to be checked, by collected spectroscopic data input data operation server, select the agricultural product kind needing detection is dry Semen Glycines simultaneously; Calculation server is gone forward side by side row operation according to the dry Semen Glycines matched data model of product variety of required detection, it is thus achieved that detected the Vegetable oil lipoprotein content of dry Semen Glycines. Meanwhile, it is also directed to these 30 dry Semen Glyciness to be checked and adopts GB/T22223-2008 to carry out chemical detection, found that these 30 dry Semen Glyciness use the error of Vegetable oil lipoprotein content that chemical detection and spectrographic determination obtain less than 0.32mg/100g.
Embodiment 3
A kind of utilizing spectrographic determination to spend the method for Vegetable oil lipoprotein in raw meat in vain, the method is specific as follows:
Adopt GB/T22223-2008 to measure 30 and spend Vegetable oil lipoprotein content in raw meat in vain, obtain corresponding Vegetable oil lipoprotein chemical detection data; It is under 900-2300nm in spectral region, spends raw meat in vain to corresponding 30 and carry out nondestructive spectral measurement, obtain corresponding Vegetable oil lipoprotein spectroscopic data; Spectroscopic data and chemical detection data are utilized to set up the data model of agricultural product Vegetable oil lipoprotein detection, data model embedding data calculation server; Wherein, the spectroscopic data of the data model setting up the detection of agricultural product Vegetable oil lipoprotein is to have chosen 4 wave-length coverage (respectively 2000-2300nm, 1300-1400nm, 1000-1100nm, 900-950nm,) absorption values carry out corresponding with corresponding chemical detection data, determine the quantitative relationship of this 4 wavelength absorbance change and chemical detection data variation. Under 900-2300nm, carry out spectroscopic data collection for 25 raw meat of spending in vain to be checked, by collected spectroscopic data input data operation server, select the agricultural product kind needing detection for spending raw meat in vain simultaneously; Calculation server is spent raw meat matched data model in vain according to the product variety of required detection and is gone forward side by side row operation, it is thus achieved that detected the Vegetable oil lipoprotein content spending raw meat in vain. Simultaneously, being also directed to these 25 raw meat of spending in vain to be checked adopts GB/T22223-2008 to carry out chemical detection, found that these 25 are spent the error of the Vegetable oil lipoprotein content that raw meat uses chemical detection and spectrographic determination to obtain in vain more than 67.9mg/100g, illustrate that the error based on the Vegetable oil lipoprotein content setting up its measuring samples of data model less than 50 samples is big.
Embodiment 4
A kind of utilizing the method for Vegetable oil lipoprotein in spectrographic determination Semen Armeniacae Amarum, the method is specific as follows:
The chemical detection method utilizing soxhlet extraction measures Vegetable oil lipoprotein content in 300 Semen Armeniacae Amarums, obtains corresponding Vegetable oil lipoprotein chemical detection data; It is under 800-2500nm in spectral region, corresponding 300 Semen Armeniacae Amarums is carried out nondestructive spectral measurement, obtains corresponding Vegetable oil lipoprotein spectroscopic data; Spectroscopic data and chemical detection data are utilized to set up the data model of agricultural product Vegetable oil lipoprotein detection, data model embedding data calculation server; Wherein, the spectroscopic data of the data model setting up the detection of agricultural product Vegetable oil lipoprotein is to have chosen 5 wavelength (respectively 2000-2300nm, 1300-1400nm, 1000-1100nm, 900-950nm, absorption values 980-1200nm) carries out corresponding with corresponding chemical detection data, determines the quantitative relationship of these 5 wavelength absorbance changes and chemical detection data variation.
Carrying out spectroscopic data collection under 800-2500nm for 35 Semen Armeniacae Amarums to be checked, by collected spectroscopic data input data operation server, select the agricultural product kind needing detection is Semen Armeniacae Amarum simultaneously; Calculation server is gone forward side by side row operation according to the product variety Semen Armeniacae Amarum matched data model of required detection, it is thus achieved that the Vegetable oil lipoprotein content of detected Semen Armeniacae Amarum.
Meanwhile, it is also directed to these 35 Semen Armeniacae Amarums to be checked and adopts soxhlet extractions to carry out chemical detection, found that these 35 Semen Armeniacae Amarums use the error of Vegetable oil lipoprotein content that chemical detection and spectrographic determination obtain less than 0.24mg/100g.
But with GB/T22223-2008,35 Semen Armeniacae Amarums to be checked are carried out chemical detection, found that these 35 Semen Armeniacae Amarums use the error of Vegetable oil lipoprotein content that GB/T22223-2008 and above-mentioned spectrographic determination obtain more than 45.7mg/100g.
The announcement of book and instruction according to the above description, above-mentioned embodiment can also be modified and revise by those skilled in the art in the invention. Therefore, the invention is not limited in detailed description of the invention disclosed and described above, should also be as some modifications and changes of the present invention falling in the scope of the claims of the present invention. Although additionally, employ some specific terms in this specification, but these terms are intended merely to convenient explanation, and the present invention does not constitute any restriction.
Claims (8)
1. utilizing a method for Vegetable oil lipoprotein in spectrographic determination agricultural product, the method comprises the steps:
A. utilize chemical detection method to measure Vegetable oil lipoprotein content in n sample of same agricultural product, obtain corresponding Vegetable oil lipoprotein chemical detection data, wherein n >=50;
B. it is under 800-2500nm in spectral region, n sample of same agricultural product is carried out nondestructive spectral measurement, obtains corresponding Vegetable oil lipoprotein spectroscopic data, wherein n >=50;
C. spectroscopic data and chemical detection data are utilized to set up the data model of agricultural product Vegetable oil lipoprotein detection, data model embedding data calculation server;
D. under 800-2500nm, carry out spectroscopic data collection for agricultural product to be checked, by collected spectroscopic data input data operation server, select to need the agricultural product kind of detection simultaneously;
E. calculation server is gone forward side by side row operation according to the product variety matched data model of required detection, it is thus achieved that the Vegetable oil lipoprotein content of detected agricultural product.
2. method according to claim 1, it is characterised in that the wave-length coverage of described spectrum is 800-1800nm, or the wave-length coverage of spectrum is 1500-2500nm.
3. method according to claim 1 and 2, it is characterised in that described agricultural product are the vegetable fat constituent content difference value same agricultural products at 0-50%.
4. method according to claim 1, it is characterised in that n is be more than or equal to 100, it is preferred that n is be more than or equal to 200.
5. method according to claim 1, it is characterised in that the method that described step C data model is set up comprises the steps:
Step I: the device launch spot launching spectral collection of holding concurrently with light source irradiates agricultural samples A to be detected1, and collect agricultural samples A1The spectrum reflected, adopts spectral analysis apparatus to determine wavelength and the absorbance of collected spectrum, forms agricultural samples A1Spectroscopic data;
Step II: to agricultural samples A1Carry out chemical analysis, analyze Vegetable oil lipoprotein content, form the chemical detection data of agricultural samples;
Step III: by agricultural product A1Spectroscopic data and the same data base of chemical detection data inputting, formed data map X1;
Step IV: repeat the above steps I, step II and step III, to agricultural samples A2To An+1Carry out n time to repeat, form n group spectroscopic data and corresponding n group chemical detection data, by spectroscopic data and the same data base of chemical detection data inputting, form the data mapping set that n group data map;
Step V: the absorption values that the spectroscopic data in data mapping set in above-mentioned data base is chosen 2-100 wavelength carries out corresponding with chemical detection data, it is determined that the quantitative relationship of 2-100 wavelength absorbance change and chemical detection data variation;
Step VI: the quantitative relationship of above-mentioned steps is embedded calculation server, gathers agricultural product fresh sample AXSpectroscopic data, while its input database, 2-100 the wavelength typing calculation server that selecting step V determines, calculate and do not carry out actually detected agricultural product fresh sample chemical data, this chemical data is exported display end and data base simultaneously, and in data base with agricultural product fresh sample AXSpectroscopic data formed measurement data map;
Step VII: according to the quantitative relationship on step I to the step VI data base formed and calculation server, data base is connected with calculation server, data input pin and data output end, the data input pin arranging calculation server and the data output end of data base are set simultaneously, form the spectroscopic data model of agricultural product.
6. method according to claim 5, it is characterized in that, described spectroscopic data is wavelength is the data acquisition system of the wavelength of 1001 wavelength of 800-1800nm and intensity, or the data acquisition system of the wavelength of 1001 wavelength of spectroscopic data to be wavelength be 1500-2500 and intensity.
7. method according to claim 1, it is characterised in that agricultural product include leaf vegetables, fruits, grain class, tubers, fruit vegetables.
8. method according to claim 1, it is characterised in that in described step A, chemical detection method is GB/T22223-2008.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102353648A (en) * | 2011-10-24 | 2012-02-15 | 深圳市芭田生态工程股份有限公司 | Method for detecting fertilizer components |
| CN104865222A (en) * | 2015-04-30 | 2015-08-26 | 北京林业大学 | Nondestructive testing method of content of fatty acid in peony seeds |
| CN104914068A (en) * | 2015-03-19 | 2015-09-16 | 哈尔滨商业大学 | Spectrum rapid detection method of trans-fatty acid content in grease |
| CN105044050A (en) * | 2015-07-07 | 2015-11-11 | 中国农业大学 | Rapid quantitative analysis method for metallic elements in crop straw |
-
2015
- 2015-12-31 CN CN201511028187.7A patent/CN105628639A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102353648A (en) * | 2011-10-24 | 2012-02-15 | 深圳市芭田生态工程股份有限公司 | Method for detecting fertilizer components |
| CN104914068A (en) * | 2015-03-19 | 2015-09-16 | 哈尔滨商业大学 | Spectrum rapid detection method of trans-fatty acid content in grease |
| CN104865222A (en) * | 2015-04-30 | 2015-08-26 | 北京林业大学 | Nondestructive testing method of content of fatty acid in peony seeds |
| CN105044050A (en) * | 2015-07-07 | 2015-11-11 | 中国农业大学 | Rapid quantitative analysis method for metallic elements in crop straw |
Non-Patent Citations (1)
| Title |
|---|
| 宋志强 等: "近红外光谱技术在食用植物油脂检测中的应用", 《武汉工业学院学报》 * |
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