CN107121406A - A kind of adulterated discrimination method of grape-kernel oil based near infrared spectrum - Google Patents
A kind of adulterated discrimination method of grape-kernel oil based near infrared spectrum Download PDFInfo
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- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 29
- 238000012850 discrimination method Methods 0.000 title claims abstract description 12
- 239000003921 oil Substances 0.000 claims abstract description 36
- 235000019198 oils Nutrition 0.000 claims abstract description 36
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 13
- 241000131894 Lampyris noctiluca Species 0.000 claims abstract description 8
- 235000019483 Peanut oil Nutrition 0.000 claims abstract description 8
- 235000019486 Sunflower oil Nutrition 0.000 claims abstract description 8
- 239000000312 peanut oil Substances 0.000 claims abstract description 8
- 239000003549 soybean oil Substances 0.000 claims abstract description 8
- 235000012424 soybean oil Nutrition 0.000 claims abstract description 8
- 239000002600 sunflower oil Substances 0.000 claims abstract description 8
- 235000005687 corn oil Nutrition 0.000 claims abstract description 7
- 239000002285 corn oil Substances 0.000 claims abstract description 7
- 238000010348 incorporation Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 12
- 235000009754 Vitis X bourquina Nutrition 0.000 claims description 2
- 235000012333 Vitis X labruscana Nutrition 0.000 claims description 2
- 240000006365 Vitis vinifera Species 0.000 claims description 2
- 235000014787 Vitis vinifera Nutrition 0.000 claims description 2
- 230000005856 abnormality Effects 0.000 claims description 2
- 235000015112 vegetable and seed oil Nutrition 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 5
- 238000000862 absorption spectrum Methods 0.000 abstract 1
- 238000001228 spectrum Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 239000008157 edible vegetable oil Substances 0.000 description 2
- 238000004611 spectroscopical analysis Methods 0.000 description 2
- OYHQOLUKZRVURQ-NTGFUMLPSA-N (9Z,12Z)-9,10,12,13-tetratritiooctadeca-9,12-dienoic acid Chemical compound C(CCCCCCC\C(=C(/C\C(=C(/CCCCC)\[3H])\[3H])\[3H])\[3H])(=O)O OYHQOLUKZRVURQ-NTGFUMLPSA-N 0.000 description 1
- VZRRCQOUNSHSGB-UHFFFAOYSA-N 4-hydroxy-4,6,6-trimethylbicyclo[3.1.1]heptan-3-one Chemical compound C1C2C(C)(C)C1CC(=O)C2(O)C VZRRCQOUNSHSGB-UHFFFAOYSA-N 0.000 description 1
- 238000002965 ELISA Methods 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 238000010987 Kennard-Stone algorithm Methods 0.000 description 1
- 241000254158 Lampyridae Species 0.000 description 1
- 240000008042 Zea mays Species 0.000 description 1
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 1
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 1
- 238000002835 absorbance Methods 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000003712 anti-aging effect Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 description 1
- 235000005822 corn Nutrition 0.000 description 1
- GVJHHUAWPYXKBD-UHFFFAOYSA-N d-alpha-tocopherol Natural products OC1=C(C)C(C)=C2OC(CCCC(C)CCCC(C)CCCC(C)C)(C)CCC2=C1C GVJHHUAWPYXKBD-UHFFFAOYSA-N 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005670 electromagnetic radiation Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 239000008169 grapeseed oil Substances 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000001320 near-infrared absorption spectroscopy Methods 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- ISWSIDIOOBJBQZ-UHFFFAOYSA-N phenol group Chemical group C1(=CC=CC=C1)O ISWSIDIOOBJBQZ-UHFFFAOYSA-N 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 229960001295 tocopherol Drugs 0.000 description 1
- 229930003799 tocopherol Natural products 0.000 description 1
- 235000010384 tocopherol Nutrition 0.000 description 1
- 239000011732 tocopherol Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- GVJHHUAWPYXKBD-IEOSBIPESA-N α-tocopherol Chemical compound OC1=C(C)C(C)=C2O[C@@](CCC[C@H](C)CCC[C@H](C)CCCC(C)C)(C)CCC2=C1C GVJHHUAWPYXKBD-IEOSBIPESA-N 0.000 description 1
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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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- 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
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- 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
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
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- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
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Abstract
The present invention relates to a kind of adulterated discrimination method of the grape-kernel oil based near infrared spectrum, pure grape-kernel oil and incorporation soybean oil, peanut oil, corn oil, the near-infrared absorption spectrum figure of the grape-kernel oil of sunflower oil are gathered first；Secondly near-infrared spectrum wavelength is carried out using glowworm swarm algorithm combination successive projection algorithm it is preferred, will it is preferred after wavelength according to importing ELM models；FA SPA ELM discrimination models are finally set up, unknown sample is predicted.The present invention is with detection speed is fast, classification accuracy is high, simple operation and other advantages.
Description
Technical field
The present invention relates to the adulterated discriminating field of grape-kernel oil, particularly a kind of grape-kernel oil based near infrared spectrum is mixed
False discrimination method.
Background technology
Grape-kernel oil contains a variety of functional components such as linoleic acid, many phenolic alcohol, tocopherol and OPC, with anti-aging,
The multiple efficacies, its nutrition having such as strengthen immunity, enhancing development, elimination serum cholesterol, treatment angiocardiopathy
Grape-kernel oil is set to turn into a kind of high-class healthy edible oil with pharmaceutical value.Because the grape-kernel oil market price is higher, driven by interests
Dynamic criminal often mixes cheap edible oil in grape-kernel oil.In order to ensure the quality of grape-kernel oil, protection consumption is safeguarded
The rights and interests of person, find it is a kind of can quick detection and differentiate grape-kernel oil mix puppet method have great importance.
Method currently for the adulterated detection in grape-kernel oil mainly has, enzyme-linked immuno-sorbent assay, strip method,
Round pcr, the method such as genetic chip, but have that experimentation is numerous and diverse, complex operation is, it is necessary to the shortcomings of adding chemical reagent.
Therefore, it is necessary to find a kind of simpler, quick adulterated grape-kernel oil discrimination method.Near-infrared spectral analysis technology is one
The lossless Fast Detection Technique of green is planted, without sample pretreatment, without destroying sample, is expected to turn into a kind of Portugal of simple and fast
The adulterated discrimination method of grape seed oil.
Near infrared spectrometer（Near Infrared Spectrum Instrument, NIRS）It is between visible ray（Vis）
It is infrared with（MIR）Between electromagnetic radiation as waves, near infrared spectrum is defined as 780-2526nm region, is people in absorption
The first non-visible light area found in spectrum.Near infrared spectrum and hydric group in organic molecule（O-H、N-H、C-H）Shake
Dynamic sum of fundamental frequencies is consistent with the uptake zone of frequencys multiplication at different levels, by scanning the near infrared spectrum of sample, can obtain in sample organic point
The characteristic information of sub- hydric group, and using near-infrared spectrum technique analysis sample have easily and fast, efficiently, accurately and
Cost is relatively low, does not destroy sample, does not consume chemical reagent, free from environmental pollution, the advantages of instrument is portable, therefore the technology is by more
Carry out the favor of more people.
But full spectroscopic data has the characteristics such as wavelength points are numerous, data investigation is serious, these characteristics can be influenceed based on number
According to adulterated discriminating model accuracy and speed, even result in infeasible.Spectroscopic data carries out wavelength selection and rejected without intelligence wave
Long and interference wavelength, can be prevented effectively from model overfitting, set up a robustness height, easily explanation, high-precision calibration model,
Greatly improve the estimated performance of model.Therefore, optimal wavelength combination can be effectively extracted with glowworm swarm algorithm, but extracted
Higher synteny is still suffered between the data of the wavelength combination gone out, the wavelength preferably gone out to glowworm swarm algorithm using successive projection method
Combination progress two is less preferred, finally gives optimal several wavelength points.In real work, it is only necessary to know sample classification and
Credit rating, and the problem of number of components contained in sample is with its content is required no knowledge about, at this time need pattern-recongnition method.Closely
The qualitative foundation of infrared spectrum is mainly spectrum in itself, identical because spectrum reflects the Nomenclature Composition and Structure of Complexes information of authentic sample
Or approximate sample has same or like spectrum.In recent years, because ELM calculators have method easily to use, amount of calculation is small, excellent
The features such as different Generalization Capability, is widely applied in the analysis of near infrared spectrum.
The content of the invention
In view of this, the purpose of the present invention is to propose to a kind of adulterated discrimination method of the grape-kernel oil based near infrared spectrum,
Wavelength is carried out preferably to the near infrared spectrum of collection using FA-SPA methods, it is real that the wavelength after recycling preferably sets up ELM models
Referring now to the adulterated discriminating of grape-kernel oil, be conducive to hitting the adulterated behavior of grape-kernel oil.
The present invention is realized using following scheme：A kind of adulterated discrimination method of grape-kernel oil based near infrared spectrum, specifically
Comprise the following steps：
Step S1：Collect soybean oil, peanut oil, corn oil, the grape of sunflower oil of pure grape-kernel oil and incorporation different content
Seed oil is used as sample；
Step S2：Sample is carried out near infrared spectrum scanning, all band collection of illustrative plates of its near infrared spectrum is gathered, and rejecting abnormalities
Sample data；Its medium wave band scanning range is：1000-2300nm；
Step S3：According to pure grape-kernel oil and the grape-kernel oil for mixing soybean oil, peanut oil, corn oil, sunflower oil near red
The frequency multiplication and sum of fundamental frequencies absworption peak of outskirt, utilize glowworm swarm algorithm（Firefly algorithm, FA）Near-infrared spectrum wavelength is become
Amount progress is preliminary preferred, then passes through successive projection method（Successive projections algorithm, SPA）Carry out wavelength
It is two less preferred, regard final preferred wavelength variable as extreme learning machine（Extreme learning machine, ELM）Build
The input variable of mould, obtains FA-SPA-ELM discrimination models；
Step S4：For unknown detected sample, its near infrared spectrum is scanned, the FA-SPA-ELM established using step S3
Discrimination model, predicts its generic.
Further, it is described preliminary preferred in step S3, filter out 710 wavelength.
Further, in step S3, described two is less preferred, filters out 17 wavelength.
Compared with prior art, the present invention has following beneficial effect：Detection speed of the present invention is fast, and FA-SPA-ELM can be fast
Speed differentiates the adulterated species of grape-kernel oil, and with high accuracy, simple operation and other advantages, has a good application prospect.
Brief description of the drawings
Fig. 1 is soybean oil, peanut oil, the corn of grape-kernel oil pure described in the embodiment of the present invention and incorporation different content
Oil, the near-infrared original absorbance spectrogram of the grape-kernel oil sample of sunflower oil.
Fig. 2 is the iteration result of glowworm swarm algorithm in the embodiment of the present invention.
Fig. 3 is 710 Wavelength distribution figures of glowworm swarm algorithm primary election extraction in the embodiment of the present invention.
Fig. 4 is FA+SPA algorithms RMSE convergence curve figures when selecting different variable numbers in the embodiment of the present invention.
Fig. 5 is 17 selected FA+SPA in embodiment of the present invention characteristic wavelength distribution maps.
Embodiment
Below in conjunction with the accompanying drawings and embodiment the present invention will be further described.
A kind of adulterated discrimination method of the grape-kernel oil based near infrared spectrum is present embodiments provided, following step is specifically included
Suddenly：
（1）Collect pure grape-kernel oil and incorporation soybean oil, peanut oil, corn oil, the grape-kernel oil sample each 31 of sunflower oil
Individual sample, when temperature is 25 DEG C, under the conditions of humidity is 30 % or so, utilizes the Fourier transformation near infrared lights of Nicolet 6700
Instrument (Thermo Fischer Scient Inc., Thermo Fisher) gathers its abosrption spectrogram, scanning range 1000-2500 nm, such as
Fig. 1.
（2）The spectral information under 1000-2300nm characteristic wave bands is chosen, with glowworm swarm algorithm to near-infrared spectrum wavelength
Primary election is carried out, 710 wavelength are filtered out, wherein as shown in Fig. 2 carrying out essence to the wavelength after primary election by successive projection algorithm again
Choosing, it is selected after wavelength number be 17 as shown in figure 3, set up extreme learning machine model using the wavelength after selected, pass through school
The continuous training pattern of data just collected, is optimal the parameter of model, obtains optimal model.Wherein, Fig. 4 calculates for FA+SPA
Method RMSE convergence curve figures when selecting different variable numbers.Fig. 5 is 17 selected characteristic wavelength distribution maps of FA+SPA.
（3）By totally 155 samples using KS algorithms respectively to pure grape-kernel oil and incorporation soybean oil, peanut oil, corn
Oil, the grape-kernel oil of sunflower oil divide calibration set and forecast set, 100 samples of calibration set, 55 samples of forecast set.
（4）Set up FA-SPA-ELM models.For unknown sample, scanner near infrared spectrum, it is possible to utilize foundation
Good FA-SPA-ELM models, predict that it belongs to, accuracy rate such as following table, and success rate prediction is with forecast set in calibration set
100%。
The foregoing is only presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, should all belong to the covering scope of the present invention.
Claims (3)
1. a kind of adulterated discrimination method of grape-kernel oil based near infrared spectrum, it is characterised in that：Comprise the following steps：
Step S1：Collect soybean oil, peanut oil, corn oil, the grape of sunflower oil of pure grape-kernel oil and incorporation different content
Seed oil is used as sample；
Step S2：Sample is carried out near infrared spectrum scanning, all band collection of illustrative plates of its near infrared spectrum is gathered, and rejecting abnormalities
Sample data；Its medium wave band scanning range is：1000-2300nm；
Step S3：According to pure grape-kernel oil and the grape-kernel oil for mixing soybean oil, peanut oil, corn oil, sunflower oil near red
The frequency multiplication and sum of fundamental frequencies absworption peak of outskirt, it is preliminary to the progress of near-infrared spectrum wavelength variable preferred using glowworm swarm algorithm, then pass through
Two less preferred, the input changes that final preferred wavelength variable is modeled as extreme learning machine that successive projection method carries out wavelength
Amount, obtains FA-SPA-ELM discrimination models；
Step S4：For unknown detected sample, its near infrared spectrum is scanned, the FA-SPA-ELM established using step S3
Discrimination model, predicts its generic.
2. the adulterated discrimination method of a kind of grape-kernel oil based near infrared spectrum according to claim 1, it is characterised in that：
It is described preliminary preferred in step S3, filter out 710 wavelength.
3. the adulterated discrimination method of a kind of grape-kernel oil based near infrared spectrum according to claim 1, it is characterised in that：
In step S3, described two is less preferred, filters out 17 wavelength.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107884526A (en) * | 2017-11-07 | 2018-04-06 | 雅安蒋氏蜜蜂园有限公司 | A kind of honey parameter detection method and system based on deep learning |
| CN108051394A (en) * | 2018-02-07 | 2018-05-18 | 武汉轻工大学 | Detecting Methods for Adulteration in Sesame Oil based near infrared spectrum |
| CN108509997A (en) * | 2018-04-03 | 2018-09-07 | 深圳市药品检验研究院（深圳市医疗器械检测中心） | A method of Chemical Pattern Recognition is carried out to the true and false that Chinese medicine Chinese honey locust is pierced based on near-infrared spectrum technique |
| CN108573205A (en) * | 2017-09-26 | 2018-09-25 | 中科智文（北京）科技有限公司 | A kind of Khotan jade seed material micro-image intelligent identifying system |
| CN109342359A (en) * | 2018-10-25 | 2019-02-15 | 中国科学院上海技术物理研究所 | A kind of rapid detection method of other micro constitutent concentration mixed in pair of pesticide |
| CN109507138A (en) * | 2018-11-22 | 2019-03-22 | 天津工业大学 | A kind of binary grape seed oil based on uv-vis spectra and extreme learning machine mixes pseudo- quantitative analysis method |
| CN110646375A (en) * | 2019-09-06 | 2020-01-03 | 赣南医学院 | Camellia oil adulteration identification method |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108573205A (en) * | 2017-09-26 | 2018-09-25 | 中科智文（北京）科技有限公司 | A kind of Khotan jade seed material micro-image intelligent identifying system |
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| CN108051394A (en) * | 2018-02-07 | 2018-05-18 | 武汉轻工大学 | Detecting Methods for Adulteration in Sesame Oil based near infrared spectrum |
| CN108051394B (en) * | 2018-02-07 | 2020-10-02 | 武汉轻工大学 | Sesame oil adulteration detection method based on near infrared spectrum |
| CN108509997A (en) * | 2018-04-03 | 2018-09-07 | 深圳市药品检验研究院（深圳市医疗器械检测中心） | A method of Chemical Pattern Recognition is carried out to the true and false that Chinese medicine Chinese honey locust is pierced based on near-infrared spectrum technique |
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| CN109342359A (en) * | 2018-10-25 | 2019-02-15 | 中国科学院上海技术物理研究所 | A kind of rapid detection method of other micro constitutent concentration mixed in pair of pesticide |
| CN109507138A (en) * | 2018-11-22 | 2019-03-22 | 天津工业大学 | A kind of binary grape seed oil based on uv-vis spectra and extreme learning machine mixes pseudo- quantitative analysis method |
| CN110646375A (en) * | 2019-09-06 | 2020-01-03 | 赣南医学院 | Camellia oil adulteration identification method |
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