CN203811536U - Multi-source information fusion-based rapid hogwash oil detection device - Google Patents
Multi-source information fusion-based rapid hogwash oil detection device Download PDFInfo
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- CN203811536U CN203811536U CN201420203260.4U CN201420203260U CN203811536U CN 203811536 U CN203811536 U CN 203811536U CN 201420203260 U CN201420203260 U CN 201420203260U CN 203811536 U CN203811536 U CN 203811536U
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- 230000004927 fusion Effects 0.000 title claims abstract description 14
- 238000001514 detection method Methods 0.000 title abstract description 21
- 239000013307 optical fiber Substances 0.000 claims abstract description 14
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- 150000002367 halogens Chemical class 0.000 claims abstract description 10
- 229910052724 xenon Inorganic materials 0.000 claims abstract description 10
- FHNFHKCVQCLJFQ-UHFFFAOYSA-N xenon atom Chemical compound [Xe] FHNFHKCVQCLJFQ-UHFFFAOYSA-N 0.000 claims abstract description 10
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 10
- 239000000377 silicon dioxide Substances 0.000 claims description 5
- 230000001066 destructive effect Effects 0.000 abstract description 2
- 230000003287 optical effect Effects 0.000 abstract 1
- 238000011897 real-time detection Methods 0.000 abstract 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 44
- FMMWHPNWAFZXNH-UHFFFAOYSA-N Benz[a]pyrene Chemical compound C1=C2C3=CC=CC=C3C=C(C=C3)C2=C2C3=CC=CC2=C1 FMMWHPNWAFZXNH-UHFFFAOYSA-N 0.000 description 34
- DCXXMTOCNZCJGO-UHFFFAOYSA-N tristearoylglycerol Chemical compound CCCCCCCCCCCCCCCCCC(=O)OCC(OC(=O)CCCCCCCCCCCCCCCCC)COC(=O)CCCCCCCCCCCCCCCCC DCXXMTOCNZCJGO-UHFFFAOYSA-N 0.000 description 21
- 235000012000 cholesterol Nutrition 0.000 description 20
- TXVHTIQJNYSSKO-UHFFFAOYSA-N BeP Natural products C1=CC=C2C3=CC=CC=C3C3=CC=CC4=CC=C1C2=C34 TXVHTIQJNYSSKO-UHFFFAOYSA-N 0.000 description 17
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 16
- 239000000126 substance Substances 0.000 description 14
- 229910001385 heavy metal Inorganic materials 0.000 description 13
- 229910052751 metal Inorganic materials 0.000 description 11
- 239000002184 metal Substances 0.000 description 11
- 238000002536 laser-induced breakdown spectroscopy Methods 0.000 description 9
- 238000000034 method Methods 0.000 description 9
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 8
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 8
- 229910052785 arsenic Inorganic materials 0.000 description 8
- RQNWIZPPADIBDY-UHFFFAOYSA-N arsenic atom Chemical compound [As] RQNWIZPPADIBDY-UHFFFAOYSA-N 0.000 description 8
- 229910052804 chromium Inorganic materials 0.000 description 8
- 239000011651 chromium Substances 0.000 description 8
- 239000008157 edible vegetable oil Substances 0.000 description 8
- 229910052742 iron Inorganic materials 0.000 description 8
- WPBNNNQJVZRUHP-UHFFFAOYSA-L manganese(2+);methyl n-[[2-(methoxycarbonylcarbamothioylamino)phenyl]carbamothioyl]carbamate;n-[2-(sulfidocarbothioylamino)ethyl]carbamodithioate Chemical compound [Mn+2].[S-]C(=S)NCCNC([S-])=S.COC(=O)NC(=S)NC1=CC=CC=C1NC(=S)NC(=O)OC WPBNNNQJVZRUHP-UHFFFAOYSA-L 0.000 description 8
- BBEAQIROQSPTKN-UHFFFAOYSA-N pyrene Chemical compound C1=CC=C2C=CC3=CC=CC4=CC=C1C2=C43 BBEAQIROQSPTKN-UHFFFAOYSA-N 0.000 description 8
- 230000003595 spectral effect Effects 0.000 description 8
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- 238000002189 fluorescence spectrum Methods 0.000 description 5
- 238000002329 infrared spectrum Methods 0.000 description 5
- 125000005605 benzo group Chemical group 0.000 description 4
- GVEPBJHOBDJJJI-UHFFFAOYSA-N fluoranthrene Natural products C1=CC(C2=CC=CC=C22)=C3C2=CC=CC3=C1 GVEPBJHOBDJJJI-UHFFFAOYSA-N 0.000 description 4
- 238000002835 absorbance Methods 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
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- 239000003921 oil Substances 0.000 description 3
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- 229930195730 Aflatoxin Natural products 0.000 description 1
- XWIYFDMXXLINPU-UHFFFAOYSA-N Aflatoxin G Chemical compound O=C1OCCC2=C1C(=O)OC1=C2C(OC)=CC2=C1C1C=COC1O2 XWIYFDMXXLINPU-UHFFFAOYSA-N 0.000 description 1
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- 238000002290 gas chromatography-mass spectrometry Methods 0.000 description 1
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- 238000004128 high performance liquid chromatography Methods 0.000 description 1
- 238000001095 inductively coupled plasma mass spectrometry Methods 0.000 description 1
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- 239000002689 soil Substances 0.000 description 1
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- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The utility model discloses a multi-source information fusion-based rapid hogwash oil detection device. The device comprises a computer (13), a near infrared spectrometer (14), a fluorescent spectrometer (15), a multi-channel CCD (Charge Coupled Device) spectrometer (16), an optical path switching device (17), an optical fiber (12), a focusing lens I (11), an objective table (9), a halogen light lamp (7), an xenon arc lamp (4), a focusing lens II (3) and a laser (2). The multi-source information fusion-based rapid hogwash oil detection device has the advantages of being free from sample pretreatment, and capable of rapidly performing real-time detection and having the non-destructive performance, and can be applicable to rapid detection on different sources of hogwash oil.
Description
Technical field
The utility model relates to agricultural product/food safety detection technical field, relates in particular to a kind of hogwash fat device for fast detecting based on Multi-source Information Fusion.
Background technology
Hogwash fat is called again swill oil, for leftovers, the leftovers in the greasy floating thing in sewer or restaurant, the hotel upper strata oil slick after collecting, through the oil of simply processing, extracting.The process hygienic conditions such as hogwash fat recovery, processing and refinement are severe, cause containing in hogwash fat the severe overweights such as multiple poisonous and harmful element, aflatoxin, heavy metal, bacterium.Illegal businessman, for reaping staggering profits, mixes hogwash fat qualified edible vegetable oil or directly sells as qualified edible vegetable oil, and harm people's is healthy.
At present, the existing detection method of hogwash fat has vapor-phase chromatography, high performance liquid chromatography, inductively coupled plasma mass spectrometry, GC-MS(gas chromatography-mass spectrography), conductimetric method etc.Hogwash fat source is complicated, and the hogwash fat that said method only can be used for a certain particular source detects, and lacks applicability widely.In addition, there is loaded down with trivial details, the consuming time length of operating process, high in cost of production shortcoming, can not realize field quick detection.
Utility model content
The purpose of this utility model is to provide a kind of hogwash fat device for fast detecting based on Multi-source Information Fusion.
The utility model adopts following technical scheme:
Hogwash fat device for fast detecting based on Multi-source Information Fusion of the present utility model comprises computing machine, near infrared spectrometer, fluorescence spectrophotometer, hyperchannel CCD spectrometer, light path switching device, optical fiber, condenser lens I, objective table, halogen light modulation, xenon arc lamp, condenser lens II and laser constitution; Computing machine is connected with near infrared spectrometer, fluorescence spectrophotometer and hyperchannel CCD spectrometer respectively, near infrared spectrometer, fluorescence spectrophotometer and hyperchannel CCD spectrometer are connected with light path switching device respectively, light path switching device connects optical fiber, optical fiber below is provided with condenser lens I, condenser lens I below is objective table, one side of objective table is provided with halogen light modulation, and top is provided with xenon arc lamp and laser instrument, and laser instrument front is provided with condenser lens II.
Objective table is provided with the silica ware that holds sample.
Between laser instrument and condenser lens II, be provided with catoptron.
Good effect of the present utility model is as follows:
Hogwash fat device for fast detecting based on Multi-source Information Fusion of the present utility model has advantages of without sample pretreatment, non-destructive, detects in real time fast, and goes for the fast detecting of separate sources hogwash fat.
Brief description of the drawings
Fig. 1 is the schematic diagram of the hogwash fat device for fast detecting based on Multi-source Information Fusion of the present utility model
In figure: 1, laser beam; 2, laser instrument; 3, condenser lens II; 4, xenon arc lamp; 5, xenon arc lamp light beam; 6, Halogen lamp LED light beam; 7, Halogen lamp LED; 8, silica ware; 9, objective table; 10, sample; 11, condenser lens I; 12, optical fiber; 13, computing machine; 14, near infrared spectrometer; 15, fluorescence spectrophotometer; 16, hyperchannel CCD spectrometer; 17, light path switching device; 18, catoptron.
Embodiment
The following examples are to describe in further detail of the present utility model.
This specific embodiment is taking 3 edible vegetable oil samples as example, be respectively qualified edible vegetable oil (cholesterol, triacylglycerol polymkeric substance, benzo (a) pyrene, total content of beary metal is all lower than the threshold value of setting), (cholesterol level is 50 mg/kg to hogwash fat 1, higher than the threshold value of setting, triacylglycerol polymkeric substance, benzo (a) pyrene, total content of beary metal is all lower than the threshold value of setting), (content of beary metal is 5 mg/kg to hogwash fat 2, higher than the threshold value of setting, cholesterol, triacylglycerol polymkeric substance, benzo (a) pyrene content is all lower than the threshold value of setting).Qualified edible vegetable oil, hogwash fat 1 and hogwash fat 2 are designated as respectively to sample A, sample B and sample C.
As shown in Figure 1, the hogwash fat method for quick based on Multi-source Information Fusion of the present embodiment, pack 8 li of the silica wares that are positioned on objective table 9 into using sample A as sample 10, adjust light path switching device 17, optical fiber 12 is only communicated with near infrared spectrometer 14, opening Halogen lamp LED 7 makes Halogen lamp LED light beam 6 irradiate the sample 10 that is positioned at 8 li of silica wares, the light penetrating from sample 10 surfaces is converged by condenser lens I 11, collected by near infrared spectrometer 14 by optical fiber 12 again, thereby obtain the near infrared spectrum of sample 10, finally the near infrared spectrum of sample 10 is saved in computing machine 13.
Close Halogen lamp LED 6, adjust light path switching device 17, optical fiber 12 is only communicated with fluorescence spectrophotometer 15, open xenon arc lamp 4, make xenon arc lamp light beam 5 irradiate sample 10, the light penetrating from sample 10 surfaces is converged by condenser lens I 11, then is collected by fluorescence spectrophotometer 15 by optical fiber 12, thereby the Three-Dimensional Synchronous Fluorescence Spectra that obtains sample 10, is finally saved in the Three-Dimensional Synchronous Fluorescence Spectra of sample 10 in computing machine 13.
Close xenon arc lamp 4, adjust light path switching device 17, optical fiber 12 is only communicated with hyperchannel CCD spectrometer 16, open laser instrument 2, make laser beam 1 irradiate sample 10, the light penetrating from sample 10 surfaces is converged by condenser lens I 11, then is collected by hyperchannel CCD spectrometer 16 by optical fiber 12, thereby obtains the Laser-induced Breakdown Spectroscopy of sample 10.Finally the Laser-induced Breakdown Spectroscopy of sample 10 is saved in computing machine 13.Close laser instrument 2.
By above-mentioned steps, obtain the near infrared of sample B and sample C, three-dimensional synchronous fluorescence and Laser-induced Breakdown Spectroscopy again.
The near infrared spectrum of sample A is carried out to gaussian filtering processing, extract the absorbance of the cholesterol of sample A and the characteristic spectrum wavelength of triacylglycerol polymkeric substance, and be input to respectively in cholesterol and triacylglycerol polymeric detection model, obtain cholesterol and the triacylglycerol polymer content of sample A.
The Three-Dimensional Synchronous Fluorescence Spectra of sample A is carried out to standard normalized, extract peak value and the curve shape parameter of benzo (a) the pyrene characteristic peak of sample A.The peak value of characteristic peak and curve shape parameter are input in benzo (a) pyrene detection model, obtain benzo (a) the pyrene content of sample A.
Noise and the background signal of the Laser-induced Breakdown Spectroscopy of deduction sample A, extract the intensity of iron, manganese, chromium, zinc, arsenic, plumbous heavy metal element characteristic spectral line, and characteristic spectral line intensity is input in corresponding heavy metal detection model, obtain iron, manganese, chromium, zinc, arsenic, the lead content of sample A, and calculate total content of beary metal.
By above-mentioned steps, the near infrared to sample B and sample C, three-dimensional synchronous fluorescence and Laser-induced Breakdown Spectroscopy are carried out same treatment, obtain cholesterol, triacylglycerol polymkeric substance, benzo (a) pyrene and the total content of beary metal of sample B and sample C.
Cholesterol, triacylglycerol polymkeric substance, benzo (a) pyrene and the total content of beary metal of sample A, sample B and the sample A that detection model is obtained compare with the threshold value of setting respectively.Cholesterol, triacylglycerol polymkeric substance, benzo (a) pyrene and the total content of beary metal of sample A is all less than the threshold value of setting, and the cholesterol level of sample B is greater than the threshold value of setting, and the total metal contents in soil of sample C is greater than the threshold value of setting.Therefore sample A is detected as non-hogwash fat, and sample B and sample C are detected as hogwash fat.The threshold value of described setting, cholesterol threshold value is 15 mg/kg, and triacylglycerol polymkeric substance threshold value is 0.03 g/g, and benzo (a) pyrene threshold value is 100 μ g/kg, and total heavy metal threshold value is 1.2 mg/kg.
The concrete steps of the hogwash fat method for quick based on Multi-source Information Fusion of the present utility model are as follows:
(1) near infrared of collecting sample, three-dimensional synchronous fluorescence and Laser-induced Breakdown Spectroscopy successively;
(2) near infrared spectrum of sample is carried out to gaussian filtering processing, extract the absorbance of the characteristic spectrum wavelength of sample cholesterol and triacylglycerol polymkeric substance, and be input to respectively in cholesterol and triacylglycerol polymeric detection model, obtain cholesterol and the triacylglycerol polymer content of sample;
(3) Three-Dimensional Synchronous Fluorescence Spectra of sample is carried out to standard normalized, extract peak value and the curve shape parameter of benzo (a) pyrene characteristic peak, the peak value of characteristic peak and curve shape parameter are input in benzo (a) pyrene detection model, obtain benzo (a) the pyrene content of sample;
(4) noise and the background signal of deduction sample Laser-induced Breakdown Spectroscopy, extract the intensity of iron, manganese, chromium, zinc, arsenic, plumbous heavy metal element characteristic spectral line, and characteristic spectral line intensity is input in corresponding heavy metal detection model, obtain iron, manganese, chromium, zinc, arsenic, the lead content of sample, and calculate total content of beary metal;
(5) the sample cholesterol, triacylglycerol polymkeric substance, benzo (a) pyrene and the total content of beary metal that detection model are obtained compare with the threshold value of setting respectively, and in the time that above-mentioned a certain content exceedes the threshold value of setting, this sample is detected as hogwash fat.The threshold value of described setting, cholesterol threshold value is 15 mg/kg, and triacylglycerol polymkeric substance threshold value is 0.03 g/g, and benzo (a) pyrene threshold value is 100 μ g/kg, and total heavy metal threshold value is 1.2 mg/kg.
The foundation of described cholesterol and triacylglycerol polymeric detection model comprises the following steps:
(1) obtain the edible vegetable oil sample of separate sources different content cholesterol and triacylglycerol polymkeric substance, the near infrared spectrum of collecting sample;
(2) spectrum is carried out to gaussian filtering processing, adopt the heavy weighting algorithm of genetic algorithm binding competition self-adaptation to obtain respectively the characteristic spectrum wavelength of cholesterol and triacylglycerol polymkeric substance;
(3) adopt national standard method to measure respectively cholesterol and the triacylglycerol polymer content in sample; Described national standard method is GB/T 22220-2008 and GB/T 26636-2011;
(4) application of chaos radial base neural net carries out the absorbance of the characteristic spectrum wavelength of cholesterol and triacylglycerol polymkeric substance and corresponding real content value associated, sets up respectively cholesterol and triacylglycerol polymeric detection model.
The foundation of described benzo (a) pyrene detection model comprises the following steps:
(1) obtain the edible vegetable oil sample of separate sources different content benzo (a) pyrene, the Three-Dimensional Synchronous Fluorescence Spectra of collecting sample;
(2) spectrum is carried out to standard normalized, contrast the level line spectrogram of different sample spectrum, determine the spectral signature peak of benzo (a) pyrene, extract peak value and the curve shape parameter of characteristic peak;
(3) adopt national standard method to measure benzo (a) the pyrene content in sample; Described national standard method is GB/T 5009.27-2003;
(4) apply sparse partial least square method and the peak value at benzo (a) pyrene spectral signature peak and curve shape parameter and its real content value are carried out associated, set up benzo (a) pyrene detection model.
The foundation of described heavy metal detection model comprises the following steps:
(1) obtain the edible vegetable oil sample of separate sources different content heavy metal, the Laser-induced Breakdown Spectroscopy of collecting sample;
(2) noise and the background signal of deduction sample Laser-induced Breakdown Spectroscopy, determines the characteristic spectral line of iron, manganese, chromium, zinc, arsenic, plumbous heavy metal element by standard of comparison and similarity measure method;
(3) adopt national standard method to measure respectively iron, manganese, chromium, zinc, arsenic, the plumbous content of beary metal in sample; Described national standard method is GB/T 5009.90-2003, GB/T 5009.14-2003, GB/T 5009.123-2003, GBT5009.11-2003 and GB 5009.12-2010;
(4) application Multiple Kernel Learning matrixing least square method supporting vector machine carries out the intensity of the characteristic spectral line of iron, manganese, chromium, zinc, arsenic, plumbous heavy metal element and contents of heavy metal elements accordingly associated, sets up respectively iron, manganese, chromium, zinc, arsenic, plumbous heavy metal element detection model.
Although illustrated and described embodiment of the present utility model, for the ordinary skill in the art, be appreciated that in the situation that not departing from principle of the present utility model and spirit and can carry out multiple variation, amendment, replacement and modification to these embodiment, scope of the present utility model is limited by claims and equivalent thereof.
Claims (3)
1. the hogwash fat device for fast detecting based on Multi-source Information Fusion, is characterized in that: described device comprises computing machine (13), near infrared spectrometer (14), fluorescence spectrophotometer (15), hyperchannel CCD spectrometer (16), light path switching device (17), optical fiber (12), condenser lens I (11), objective table (9), halogen light modulation (7), xenon arc lamp (4), condenser lens II (3) and laser instrument (2) composition, computing machine (13) respectively with near infrared spectrometer (14), fluorescence spectrophotometer (15) is connected with hyperchannel CCD spectrometer (16), near infrared spectrometer (14), fluorescence spectrophotometer (15) is connected with light path switching device (17) respectively with hyperchannel CCD spectrometer (16), light path switching device (17) connects optical fiber (12), optical fiber (12) below is provided with condenser lens I (11), condenser lens I (11) below is objective table (9), one side of objective table (9) is provided with halogen light modulation (7), top is provided with xenon arc lamp (4) and laser instrument (2), laser instrument (2) front is provided with condenser lens II (3).
2. the hogwash fat device for fast detecting based on Multi-source Information Fusion according to claim 1, is characterized in that: objective table (9) is provided with the silica ware (8) that holds sample (10).
3. the hogwash fat device for fast detecting based on Multi-source Information Fusion according to claim 1, is characterized in that: between laser instrument (2) and condenser lens II (3), be provided with catoptron (18).
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103913435A (en) * | 2014-04-24 | 2014-07-09 | 江西农业大学 | Method and device for quickly detecting hogwash oil based on multi-source information fusion |
| CN105675520A (en) * | 2016-04-08 | 2016-06-15 | 上海赛诚医药科技有限公司 | Multisource spectrograph |
| CN107044959A (en) * | 2017-02-16 | 2017-08-15 | 江苏大学 | Micro- multi-modal fusion spectral detection system |
| CN113390820A (en) * | 2021-05-17 | 2021-09-14 | 西派特（北京）科技有限公司 | Multi-source spectrum light fuel oil quality detection system |
| US20220260497A1 (en) * | 2020-03-17 | 2022-08-18 | Zhejiang University | Gas mixture-based libs signal enhancement apparatus and heavy metal detection method |
-
2014
- 2014-04-24 CN CN201420203260.4U patent/CN203811536U/en not_active Expired - Lifetime
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103913435A (en) * | 2014-04-24 | 2014-07-09 | 江西农业大学 | Method and device for quickly detecting hogwash oil based on multi-source information fusion |
| CN103913435B (en) * | 2014-04-24 | 2017-02-01 | 江西农业大学 | Method and device for quickly detecting hogwash oil based on multi-source information fusion |
| CN105675520A (en) * | 2016-04-08 | 2016-06-15 | 上海赛诚医药科技有限公司 | Multisource spectrograph |
| CN105675520B (en) * | 2016-04-08 | 2018-05-18 | 上海赛诚医药科技有限公司 | A kind of multi-source optical spectrum instrument |
| CN107044959A (en) * | 2017-02-16 | 2017-08-15 | 江苏大学 | Micro- multi-modal fusion spectral detection system |
| US20220260497A1 (en) * | 2020-03-17 | 2022-08-18 | Zhejiang University | Gas mixture-based libs signal enhancement apparatus and heavy metal detection method |
| CN113390820A (en) * | 2021-05-17 | 2021-09-14 | 西派特（北京）科技有限公司 | Multi-source spectrum light fuel oil quality detection system |
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