CN107091827A - Gutter oil low amounts blends the efficient lossless discrimination method of edible oil - Google Patents
Gutter oil low amounts blends the efficient lossless discrimination method of edible oil Download PDFInfo
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- CN107091827A CN107091827A CN201710317526.6A CN201710317526A CN107091827A CN 107091827 A CN107091827 A CN 107091827A CN 201710317526 A CN201710317526 A CN 201710317526A CN 107091827 A CN107091827 A CN 107091827A
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- 239000003921 oil Substances 0.000 title claims abstract description 146
- 239000008157 edible vegetable oil Substances 0.000 title claims abstract description 31
- 238000012850 discrimination method Methods 0.000 title claims abstract description 6
- 239000000203 mixture Substances 0.000 title claims description 11
- 238000000034 method Methods 0.000 claims abstract description 78
- 238000001237 Raman spectrum Methods 0.000 claims abstract description 42
- 230000001360 synchronised effect Effects 0.000 claims abstract description 31
- 238000007621 cluster analysis Methods 0.000 claims abstract description 23
- 238000002156 mixing Methods 0.000 claims abstract description 23
- 238000005516 engineering process Methods 0.000 claims abstract description 20
- 230000008859 change Effects 0.000 claims abstract description 12
- 238000001069 Raman spectroscopy Methods 0.000 claims description 40
- 239000007789 gas Substances 0.000 claims description 21
- 238000001228 spectrum Methods 0.000 claims description 21
- 238000001514 detection method Methods 0.000 claims description 17
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 14
- 230000005284 excitation Effects 0.000 claims description 7
- 229910052710 silicon Inorganic materials 0.000 claims description 7
- 239000010703 silicon Substances 0.000 claims description 7
- -1 silicon lipid Chemical class 0.000 claims description 7
- XKRFYHLGVUSROY-UHFFFAOYSA-N Argon Chemical compound [Ar] XKRFYHLGVUSROY-UHFFFAOYSA-N 0.000 claims description 6
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 6
- 238000009833 condensation Methods 0.000 claims description 4
- 229910052786 argon Inorganic materials 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 238000001344 confocal Raman microscopy Methods 0.000 claims description 3
- 239000001307 helium Substances 0.000 claims description 3
- 229910052734 helium Inorganic materials 0.000 claims description 3
- SWQJXJOGLNCZEY-UHFFFAOYSA-N helium atom Chemical compound [He] SWQJXJOGLNCZEY-UHFFFAOYSA-N 0.000 claims description 3
- 229910052757 nitrogen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 238000010926 purge Methods 0.000 claims 1
- 230000005180 public health Effects 0.000 abstract description 4
- 230000007246 mechanism Effects 0.000 abstract 1
- 235000019198 oils Nutrition 0.000 description 126
- 238000007670 refining Methods 0.000 description 32
- 239000000047 product Substances 0.000 description 30
- 235000012424 soybean oil Nutrition 0.000 description 25
- 239000003549 soybean oil Substances 0.000 description 25
- 230000003595 spectral effect Effects 0.000 description 16
- 238000004364 calculation method Methods 0.000 description 5
- 239000004519 grease Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 235000015112 vegetable and seed oil Nutrition 0.000 description 4
- 239000008158 vegetable oil Substances 0.000 description 4
- 244000068988 Glycine max Species 0.000 description 3
- 235000010469 Glycine max Nutrition 0.000 description 3
- 230000005494 condensation Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 239000004065 semiconductor Substances 0.000 description 3
- 238000007664 blowing Methods 0.000 description 2
- 238000005336 cracking Methods 0.000 description 2
- 235000021393 food security Nutrition 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 238000005057 refrigeration Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical group [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000007445 Chromatographic isolation Methods 0.000 description 1
- 240000008415 Lactuca sativa Species 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 235000019482 Palm oil Nutrition 0.000 description 1
- 235000019483 Peanut oil Nutrition 0.000 description 1
- 235000019484 Rapeseed oil Nutrition 0.000 description 1
- 235000019774 Rice Bran oil Nutrition 0.000 description 1
- 235000019486 Sunflower oil Nutrition 0.000 description 1
- 241000949456 Zanthoxylum Species 0.000 description 1
- 239000010828 animal waste Substances 0.000 description 1
- 235000013361 beverage Nutrition 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 239000000828 canola oil Substances 0.000 description 1
- 235000019519 canola oil Nutrition 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 235000019864 coconut oil Nutrition 0.000 description 1
- 239000003240 coconut oil Substances 0.000 description 1
- 235000005687 corn oil Nutrition 0.000 description 1
- 239000002285 corn oil Substances 0.000 description 1
- 239000002537 cosmetic Substances 0.000 description 1
- 235000012343 cottonseed oil Nutrition 0.000 description 1
- 239000002385 cottonseed oil Substances 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 235000013365 dairy product Nutrition 0.000 description 1
- 238000004332 deodorization Methods 0.000 description 1
- 235000014113 dietary fatty acids Nutrition 0.000 description 1
- 230000032050 esterification Effects 0.000 description 1
- 238000005886 esterification reaction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 229930195729 fatty acid Natural products 0.000 description 1
- 239000000194 fatty acid Substances 0.000 description 1
- 150000004665 fatty acids Chemical class 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 235000015203 fruit juice Nutrition 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 239000010806 kitchen waste Substances 0.000 description 1
- 235000021388 linseed oil Nutrition 0.000 description 1
- 239000000944 linseed oil Substances 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000004006 olive oil Substances 0.000 description 1
- 235000008390 olive oil Nutrition 0.000 description 1
- 239000003973 paint Substances 0.000 description 1
- 239000002540 palm oil Substances 0.000 description 1
- 239000000312 peanut oil Substances 0.000 description 1
- 229920001296 polysiloxane Polymers 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000010453 quartz Substances 0.000 description 1
- 238000012958 reprocessing Methods 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 239000008165 rice bran oil Substances 0.000 description 1
- 235000005713 safflower oil Nutrition 0.000 description 1
- 239000003813 safflower oil Substances 0.000 description 1
- 235000012045 salad Nutrition 0.000 description 1
- 235000011803 sesame oil Nutrition 0.000 description 1
- 239000008159 sesame oil Substances 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 238000004611 spectroscopical analysis Methods 0.000 description 1
- 239000002600 sunflower oil Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000012780 transparent material Substances 0.000 description 1
- 239000000341 volatile oil Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- 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
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
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- Health & Medical Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The present invention proposes a kind of edible oil discrimination method of high sense, the two-dimentional synchronous related and/or two-dimentional asynchronous related Raman spectrum of reflection C H stretching vibration change mechanisms is extracted in described technical scheme using alternating temperature control technology, oil sample discriminating is carried out using hierarchial-cluster analysis.This method have amount of samples it is small, without pretreatment, sample nondestructive wound, simple and efficient to handle, finger print information is abundant, sense is strong the features such as, not only can Fast Identification go out certified products edible oil and refined gutter oil, and the blending oil sample of mass ratio as little as 5% is blended to gutter oil also with very high sense, it is remarkably improved the identification efficiency and convincingness to palming off edible oil, cracked down on counterfeit goods for edible oil, safety of protecting public health is significant.
Description
Technical field
The invention belongs to edible oil determination method technical field, and in particular to one kind differentiates trench based on Raman spectrum
Oil and gutter oil blend the clustering method of edible oil.
Background technology
The adulterated serious threat food security of edible oil and public health, related " cracking down on counterfeit goods " research are significant.Refining
The mode of appearance of gutter oil and refined one-level soybean oil, salad oil are very much like, especially when refined gutter oil is with minor proportion
When being blended with certified products edible oil, differentiate that difficulty is very big, it has also become food safety supervision examine and the difficult point cracked down on counterfeit goods of edible oil and
Emphasis.Prior art for the relatively low personation oil sample of gutter oil mixing proportion distinguishing ability also than relatively limited, and operate numerous
It is miscellaneous.By taking red, orange, green, blue, yellow (ROGBY) as an example, it is mainly analyzed the content of fatty acid and classification in oil product, not only needs to carry out oil sample
The pre-treatment of a series of complex such as esterification is operated, and identification efficiency and convincingness also have much room for improvement.
For illegal manufacturer, when blending mass ratio of the gutter oil in edible oil is less than 5%, its withholding
Space has been extremely limited.Therefore, in adulterated edible oil, the blending mass ratio of gutter oil is usually above 5%.Can if developing
The oil sample that mass ratio as little as 5% is blended to gutter oil realizes and quick and precisely reflects method for distinguishing, has for edible oil discriminating of cracking down on counterfeit goods
Important application value.
The content of the invention
The purpose of the present invention is the problem of threatening food security and public health for edible oil is adulterated, to research and develop a kind of superelevation
The edible oil Undamaged determination method of sense, not only can Fast Identification go out certified products edible oil and refined gutter oil, and to trench
The blending oil sample of oil blending mass ratio as little as 5% also with very high sense, be with a wide range of applications with it is important
Practical value.
The present invention is for refining gutter oil blending certified products edible oil harm public health and differentiates the problem of difficulty is larger, carries
Go out the efficient lossless discrimination method that a kind of gutter oil low amounts based on alternating temperature Raman technology blends edible oil, mirror of the present invention
Other method comprises the following steps：
In the sample cell that oil sample to be checked is packaged in the transparent materials such as glass, quartz, in the range of+100 DEG C to -40 DEG C,
Confocal micro Raman spectrum under condition of different temperatures is determined with 5-10 DEG C of temperature interval respectively, c h bond vibration is then based on
The change of characteristic peak, asks and calculates and draw two-dimentional synchronous related Raman spectrum and/or two-dimentional asynchronous related Raman spectrum；Will be two-dimentional synchronous
And/or two-dimentional asynchronous related Raman modal data is converted into the vector point in hyperspace, and blind sample is carried out using hierarchial-cluster analysis
Differentiate.In continuous mode, sample cell is purged using incoagulable gas under cryogenic conditions, to prevent sample cell in spectra collection
During condensation or frosting.
Incoagulable gas refers under described cryogenic conditions, when being purged to sample cell, in room temperature to -40 DEG C of temperature
In the range of will not occur to condense from the gas gathered without interference spectrum, for example：Nitrogen, argon gas, helium, oxygen etc. or its
Mixture.For in technique described above scheme, its excitation wavelength used can for 785,632.8,532,514.5,
488th, Raman spectrum excitation source conventional 325nm etc., wherein it is preferred that excitation light wave a length of 532nm, 514.5nm or 488nm.
For in technique described above scheme, spectra collection scope preferably is 500-4000cm-1, the light being more highly preferred to
Spectral limit is 2850-2975cm-1。
For in technique described above scheme, temperature range preferably is+100 DEG C to -40 DEG C, the temperature being more highly preferred to
Scope is+15 DEG C to -25 DEG C.
For in technique described above scheme, in described two-dimensional correlation Raman spectrum, generally, drawing two dimension same
Step related Raman spectrum can be used to hierarchial-cluster analysis discriminating；Preferably to compare or corroborating each other, it can also paint simultaneously
The synchronous related Raman spectrum of system two dimension and two-dimentional asynchronous related Raman spectrum.
For in technique described above scheme, the hierarchial-cluster analysis can be bright using Chebyshev's distance, quadravalence
Koffsky distance is criterion；It is 1 that the standardized method of two-dimentional modal data, which includes Z score and/or standard deviation,；Clustering method is
Attached method between Ward methods, farthest neighbors method, group, attached method, median method or centroid method in group, wherein it is preferred that in the case of,
Described hierarchial-cluster analysis use the bright Koffsky criterion of quadravalence, and clustering method is Ward methods and farthest neighbors method and two dimension
Modal data carries out Z scoring criterias.
For in technique described above scheme, described oil sample is pure edible vegetable oil, adulterated vegetable oil, gutter oil are mixed
Convert vegetable oil or trench oil samples.
Wherein：The gutter oil is generally kitchen waste grease, frying waste grease, animal waste grease or more grease
Mixture reprocessing oil product.Refining gutter oil outward appearance near colorless in the embodiment of the present invention, is by rectifying, takes off
The gutter oil obtained after the processing of the process for refining such as color, deodorization.Vegetable oil of the present invention refer to it is all can be adulterated by gutter oil
Oil product, includes but are not limited to the product oil or crude oil of following oil products：Soybean oil, rapeseed oil, olive oil, peanut oil, sesame oil,
Canola oil, palm oil, sunflower oil, corn oil, coconut oil, rice bran oil, cottonseed oil, Rice oil, safflower oil, linseed oil,
At least one of Zanthoxylum essential oil or edible blend oil.In the embodiment of the present invention, selection outward appearance and refining trench oil pole are close just
Product one-level soybean oil is measuring samples, while have detected the oil sample that refining gutter oil blending mass ratio is 5%, is experimentally confirmed
The superelevation sense of the method for the invention.
Fig. 1 of the present invention is to use organic efficient the cannot-harm-detection device based on alternating temperature Raman technology, utilizes the present apparatus pair
The oil samples such as vegetable oil or gutter oil that embodiment 1 is provided have carried out alternating temperature Raman determination experiment.In fact, dress of the present invention
Put for other liquid type samples, such as dairy products, flavouring, beverage, fruit juice, drinks, cosmetics, blood, fuel oil etc.
It can be detected.
Device of the present invention includes the sample stage with groove structure, and the groove of the sample stage is arranged above out
Window, the side of the sample stage is provided with the socket of one or more sample cells be arrangeding in parallel, and the socket is communicated with groove；
Heat-insulated and water vapor cover is provided with the outside of the sample stage, the side wall of water vapor cover, which is provided with protection gas, to be entered
Mouthful passage, uplifting window is provided with the top surface of water vapor cover, and thus the protection gas open a window discharge；
In the bottom of sample stage, thermal conductive silicon lipid layer, temperature change member, thermal conductive silicon lipid layer and heat exchanger are disposed with.
Device of the present invention, in addition to temperature controller, the temperature controller are connected with temperature element, and voltage and current adjustment
Constant current supply, wherein, the temperature element can insert the side wall of sample stage, the temperature for detecting and controlling sample stage.For
For oil sample used in the embodiment of the present invention, by the detection and control of temperature element, realize with 5-10 DEG C of temperature interval
The purpose of the confocal micro Raman spectrum under condition of different temperatures is determined respectively.
The measuring samples filled in sample cell insert the part of groove by socket, and the exciting light projected by Raman object lens can
The measuring samples in sample cell are irradiated to by this windowing, sample signal also can arrive at Raman object lens by this windowing；
For the organic efficient the cannot-harm-detection device of use described above based on alternating temperature Raman technology, specifically, institute
It can be semiconductor chilling plate to state temperature change member.For oil sample used in the embodiment of the present invention, temperature range preferably
For+100 DEG C to -40 DEG C, the temperature range being more highly preferred to is+15 DEG C to -25 DEG C.
For the organic efficient the cannot-harm-detection device of use described above based on alternating temperature Raman technology, specifically, institute
It can be thermal resistance or thermocouple to state temperature element.
For the organic efficient the cannot-harm-detection device of use described above based on alternating temperature Raman technology, specifically, this
The size of invention described device can be matched with existing Raman spectrometer size；The exciting light that excitation source is sent passes through confocal
Optical routing Raman object lens are projected, then uplifting window, the groove uplifting window of sample stage by water vapor cover, are irradiated in sample cell
Oil sample, the sample signal being excited is by the groove uplifting window of sample stage, the uplifting window of water vapor cover, Raman object lens, altogether
After burnt light path, beam splitting system, reaching detector is simultaneously recorded.For use described above based on the organic of alternating temperature Raman technology
Thing efficient lossless detection means, specifically, the protection gas uses incoagulable gas under cryogenic conditions, such as：Nitrogen,
Argon gas, helium, oxygen etc. or its mixture.In detection process, by protecting air-blowing to sweep sample cell, for excluding near sample cell
Vapor, in order to avoid frosting or condensation occur under cryogenic for sample cell；
For the organic efficient the cannot-harm-detection device of use described above based on alternating temperature Raman technology, specifically, working as
During the voltage forward direction connection of temperature change member, its upper surface is chill surface, and lower surface is heats face, and its heat is timely by heat exchanger
Export, can carry out controllable temperature refrigeration to sample stage；When the voltage reversal connection of temperature change member, its upper surface is to heat face, under
Surface is chill surface, and its cold is exported in time by heat exchanger, can carry out controllable temperature heating to sample stage.
Usefulness of the present invention
The present invention, to the sensitiveness of temperature, introduces temperature using the stretching vibration of c h bond on Long carbon chain in edible oil ingredient
Perturbation factor, extracts the original fingerprint information comprising fluorescence and Raman signatures simultaneously using microscopic confocal Raman spectrometer, asks calculation
And two-dimentional synchronous spectrum and two-dimentional asynchronous spectrum are drawn, and blind sample discriminating is carried out using system cluster analysis.
The present invention hinders without carrying out chromatographic isolation or pretreatment, the extraction of spectral fingerprint information to sample to sample nondestructive,
Sample finger print information enriches, and sense is very strong, easy to operate quick, without other reagents.For the oil sample tool of low-doped amount
There is very high sense, good mutual supplement with each other's advantages can be formed with other discrimination methods, identification efficiency and convincingness is significantly improved,
Cracked down on counterfeit goods for edible oil significant.
Brief description of the drawings
Fig. 1 is the instrumentation diagram that Raman spectrum is gathered under condition of different temperatures used in the present invention.Wherein：1. sample
Platform, 2. sample cells, 3. oil samples, 4. sample stage uplifting windows, 5. heat-insulated and water vapor covers, 6. protection gas access roades are opened on 7.
Window, 8. temperature change members (such as semiconductor chilling plate), 9. temperature elements (such as thermal resistance or thermocouple), 10. temperature controllers, 11.
The constant current supply of voltage and current adjustment, 12. thermal conductive silicon lipid layer, 13. thermal conductive silicon lipid layer, 14. heat exchangers, 15. exciting lights,
16. sample signal.
The structural representation of Fig. 2 temperature controlled sample platforms used in the present invention.Wherein：(A) top view, (B) side view.
Fig. 3 is the synchronous related Raman spectrum of two dimension of certified products soybean oil, (A) blind sample, (B) standard specimen.
Fig. 4 is the synchronous related Raman spectrum of two dimension that 1# refines gutter oil, (A) blind sample, (B) standard specimen.
Fig. 5 is the synchronous related Raman spectrum of two dimension that 2# refines gutter oil, (A) blind sample, (B) standard specimen.
Fig. 6 is two dimension synchronization related Raman spectrum of the 1# refining trench oil qualities than the adulterated oil for 5%, (A) blind sample, (B)
Standard specimen.
Fig. 7 is two dimension synchronization related Raman spectrum of the 2# refining trench oil qualities than the adulterated oil for 5%, (A) blind sample, (B)
Standard specimen.
Fig. 8 is to use Ward methods to two-dimentional synchronous related Raman spectrum, and hierarchial-cluster analysis knot is carried out by Z scoring criteriaizations
Really, (A) uses the bright Koffsky of quadravalence apart from criterion, and (B) is using Chebyshev apart from criterion.
Fig. 9 is to use Ward methods to two-dimentional synchronous related Raman spectrum, and system is carried out for 1 standardized method by standard deviation
Cluster analysis result, (A), using the bright Koffsky of quadravalence apart from criterion, (B) is using Chebyshev apart from criterion.
Figure 10 is to use farthest neighbors method to two-dimentional synchronous related Raman spectrum, and carrying out system by Z scoring criteriaizations gathers
Alanysis result, (A), using the bright Koffsky of quadravalence apart from criterion, (B) is using Chebyshev apart from criterion.
Figure 11 is to use farthest neighbors method to two-dimentional synchronous related Raman spectrum, passes through the standardized method that standard deviation is 1
Hierarchial-cluster analysis result is carried out, (A), using the bright Koffsky of quadravalence apart from criterion, (B) is using Chebyshev apart from criterion.
Figure 12 is the two-dimentional asynchronous related Raman spectrum of certified products soybean oil, (A) blind sample, (B) standard specimen.
Figure 13 is the two-dimentional asynchronous related Raman spectrum that 1# refines gutter oil, (A) blind sample, (B) standard specimen.
Figure 14 is the two-dimentional asynchronous related Raman spectrum that 2# refines gutter oil, (A) blind sample, (B) standard specimen.
Figure 15 is two-dimentional asynchronous related Raman spectrum of the 1# refining trench oil qualities than the adulterated oil for 5%, (A) blind sample, (B)
Standard specimen.
Figure 16 is two-dimentional asynchronous related Raman spectrum of the 2# refining trench oil qualities than the adulterated oil for 5%, (A) blind sample, (B)
Standard specimen.
Figure 17 is to use Ward methods to two-dimentional asynchronous related Raman spectrum, and hierarchial-cluster analysis are carried out by Z scoring criteriaizations
As a result, (A) uses the bright Koffsky of quadravalence apart from criterion, and (B) is using Chebyshev apart from criterion.
Figure 18 is to use Ward methods to two-dimentional asynchronous related Raman spectrum, is for 1 standardized method by standard deviation
System cluster analysis result, (A), using the bright Koffsky of quadravalence apart from criterion, (B) is using Chebyshev apart from criterion.
Figure 19 is to use farthest neighbors method to two-dimentional asynchronous related Raman spectrum, and carrying out system by Z scoring criteriaizations gathers
Alanysis result, (A), using the bright Koffsky of quadravalence apart from criterion, (B) is using Chebyshev apart from criterion.
Figure 20 is to use farthest neighbors method to two-dimentional asynchronous related Raman spectrum, passes through the standardized method that standard deviation is 1
Hierarchial-cluster analysis result is carried out, (A), using the bright Koffsky of quadravalence apart from criterion, (B) is using Chebyshev apart from criterion.
Embodiment
Following non-limiting examples can make one of ordinary skill in the art be more fully understood the present invention, but not with
Any mode limits the present invention.
Two kinds of refining trench oil samples 1# and 2# used in the present embodiment come from Department of Public Security of Guangdong Province's " strike gutter oil
The gutter oil that special campaigns " are taken over, outward appearance near colorless is transparent, and used certified products one-level Refined Soybean oil samples are purchased from
Supermarket.Oil sample in view of blending mass ratio as little as 5% to exquisite gutter oil, still lacks effective discriminating means, chooses at present
Certified products one-level soybean oil and 1# refining gutter oil, using gutter oil mass ratio for 5% proportions blending oil sample, hereinafter with
" soybean oil blending gutter oil 1 " is marked；Certified products one-level soybean oil and 2# refining gutter oils are chosen, using gutter oil mass ratio as 5%
Proportions blending oil sample, hereinafter with " soybean oil blending gutter oil 2 " mark.For certified products one-level soybean oil, 1# and 2#
Refining trench oil samples, soybean oil blending gutter oil 1 and soybean oil blending gutter oil 2 prepare standard specimen and blind sample respectively.
Embodiment 1
Fig. 1 is to use organic efficient the cannot-harm-detection device based on alternating temperature Raman technology, carries out alternating temperature Raman and determines real
The schematic diagram tested.In figure, described device includes the sample stage 1 with groove structure, and the groove of the sample stage 1 is arranged above
Have a windowing 4, the side of the sample stage 1 is provided with the socket of one or more sample cells 2 be arrangeding in parallel, the socket with it is recessed
Groove is communicated；The oil sample 3 filled in sample cell 2 inserts the part of groove by socket.The exciting light 15 that excitation source is sent passes through
Confocal optical path is projected by Raman object lens, then uplifting window 7, the groove uplifting window 4 of sample stage 1 by water vapor cover 5, is irradiated to
Oil sample 3 in sample cell 2, the sample signal 16 that is excited by the groove uplifting window 4 of sample stage 1, water vapor cover 5 on open
After window 7, Raman object lens, confocal optical path, beam splitting system, reaching detector is simultaneously recorded.
The outside of sample stage 1 is provided with heat-insulated and water vapor cover 5, and the side wall of water vapor cover 5 is provided with protection
Gas access road 6, the protection gas is used under cryogenic conditions in incoagulable gas, detection process, by protecting air-blowing
Sample cell is swept, for excluding the vapor near sample cell 2, in case frosting or condensation occur under cryogenic for sample cell 2；
The top surface of water vapor cover 5 is provided with uplifting window 7, thus the protection gas open a window 7 discharges；
In the bottom surface of sample stage 1, it is disposed with thermal conductive silicon lipid layer 12, temperature change member 8 (such as semiconductor chilling plate), leads
Hot silicone grease layer 13 and heat exchanger 14.
Device of the present invention, in addition to temperature controller 10, the temperature controller 10 be connected with temperature element 9 (for example thermal resistance or
Thermocouple), and voltage and current adjustment constant current supply 11, wherein, the temperature element 9 can insert the side wall of sample stage 1, use
In the temperature for detecting and controlling sample stage 1.
When the voltage forward direction connection of temperature change member 8, its upper surface is chill surface, lower surface to heat face, its heat by
Heat exchanger 14 is exported in time, can carry out controllable temperature refrigeration to sample stage 1；When the voltage reversal connection of temperature change member 8, thereon
Surface is heats face, and lower surface is chill surface, and its cold is exported in time by heat exchanger 14, can carry out controllable temperature to sample stage 1
Heating.
Embodiment 2
Using Horiba JY XploRA microscopic confocal Raman spectrometers, in+15 DEG C to -25 DEG C temperature ranges, it is with 5 DEG C
Interval, standard specimen and blind sample that gutter oil, 3# and 4# blend oil sample are refined to certified products one-level refined soybean oil, 1# and 2#, is adopted respectively
Collect the Raman spectrum finger print data under different temperatures.Excitation wavelength is 532nm, and optical filter is set to 100%, and aperture and slit divide
Wei not be 500 μm and 200 μm, grating is 1800T, object lens 50 ×, detection range is 2850-2975cm-1, CCD-detector temperature for-
70 DEG C, time for exposure 1s, each spectrogram is cumulative 20 times；Protection gas is high-purity N2Gas, flow velocity 60ml/min.All samples use phase
Same test condition, spectroscopic data is handled without any fluorescence background deduction, seeks the two-dimentional synchronous spectrum data of calculation.
Fig. 3 A and B are the synchronous related Raman spectrum of the two dimension of the blind sample of certified products soybean oil and standard specimen, as illustrated, the spectral line of the two
Feature is substantially similar.
Fig. 4 A and B are the synchronous related Raman spectrum of the two dimension of blind sample and standard specimen that 1# refines gutter oil, as illustrated, the two
Spectral line characteristic is substantially similar.
Fig. 5 A and B are the synchronous related Raman spectrum of the two dimension of blind sample and standard specimen that 2# refines gutter oil, as illustrated, the two
Spectral line characteristic is substantially similar.
Comparison diagram 3, Fig. 4, Fig. 5, it can be seen that the related drawing synchronous to the two dimension of 1# and 2# refining gutter oils of certified products soybean oil
The spectral line difference of graceful spectrum is obvious.
Fig. 6 A related drawings synchronous to the two dimension of blind sample and standard specimen that B is the adulterated oil that 1# refining trench oil quality ratios are 5%
Graceful spectrum, as illustrated, the spectral line characteristic of the two is substantially similar.
Fig. 7 A related drawings synchronous to the two dimension of blind sample and standard specimen that B is the adulterated oil that 2# refining trench oil quality ratios are 5%
Graceful spectrum, as illustrated, the spectral line characteristic of the two is substantially similar.
As can be seen that doping 1# refining gutter oil and doping 2# refining gutter oil sample spectrogram with certified products soybean oil
Spectrogram there is more apparent difference, and the synchronous related Raman spectrum of two dimension of two kinds of oil samples of doping variety classes gutter oil also has
Difference.
The two-dimentional synchronous spectrum data calculated will be asked to be converted into the vector point in hyperspace, be using Ward methods
Clustering of uniting differentiates with blind sample.
Fig. 8 A and B are given using Ward methods, by Z scoring criterias and take respectively the bright Koffsky distance of quadravalence with
Chebyshev's distance carries out the result of hierarchial-cluster analysis to the synchronous related Raman spectrum of above-mentioned two dimension.As illustrated, certified products soybean
The blind sample that oil, 1# refine gutter oil, 2# refining gutter oils and two kinds of adulterated oils obtains accurate recognition.
Fig. 9 A and B are given using Ward methods, by standard deviation for 1 standardized method and take quadravalence Ming Kefu respectively
This cardinal distance is from the result that hierarchial-cluster analysis are carried out with being composed with a distance from Chebyshev to the synchronous related Raman of above-mentioned two dimension.As schemed
Show, the blind sample that certified products soybean oil, 1# refine gutter oil, 2# refining gutter oils and two kinds of adulterated oils obtains accurate recognition.
Embodiment 3
The collection of Raman spectral information is carried out to oil sample using method same as Example 2, and seeks the synchronous phase of calculation two dimension
Close modal data；Two-dimentional synchronous spectrum data are converted into the vector point in hyperspace, are using farthest neighbors method
Clustering methodology of uniting differentiates with blind sample.
Figure 10 A and B are given using farthest neighbors method, take by Z scoring criterias and respectively quadravalence Ming Kefusi
Cardinal distance is from the result that hierarchial-cluster analysis are carried out with being composed with a distance from Chebyshev to the synchronous related Raman of above-mentioned two dimension.As illustrated,
The blind sample that certified products soybean oil, 1# refine gutter oil, 2# refining gutter oils and two kinds of adulterated oils obtains accurate recognition.
Figure 11 A and B are given using farthest neighbors method, by standard deviation for 1 standardized method and take four respectively
The bright Koffsky distance of rank composes the result for carrying out hierarchial-cluster analysis with Chebyshev's distance to the synchronous related Raman of above-mentioned two dimension.
As illustrated, the blind sample of certified products soybean oil, 1# refining gutter oil, 2# refining gutter oils and two kinds of adulterated oils is accurately distinguished
Know.
Embodiment 4
The collection of Raman spectral information is carried out to oil sample using method same as Example 2, and seeks the two-dimentional asynchronous phase of calculation
Close modal data.
Figure 12 A and B are the two-dimentional asynchronous related Raman spectrum of the blind sample of certified products soybean oil and standard specimen, as illustrated, the spectrum of the two
Line feature is substantially similar.
Figure 13 A and B are that 1# refines the blind sample of gutter oil and the two-dimentional asynchronous related Raman spectrum of standard specimen, as illustrated, the two
Spectral line characteristic it is substantially similar.
Figure 14 A and B are that 2# refines the blind sample of gutter oil and the two-dimentional asynchronous related Raman spectrum of standard specimen, as illustrated, the two
Spectral line characteristic it is substantially similar.
Comparison diagram 12, Figure 13, Figure 14, it can be seen that certified products soybean oil is two-dimentional asynchronous related to 1# and 2# refining gutter oils
The spectral line difference of Raman spectrum is obvious.
The two-dimentional asynchronous related drawing of Figure 15 A and the blind sample and standard specimen that B is the adulterated oil that 1# refining trench oil quality ratios are 5%
Graceful spectrum, as illustrated, the spectral line characteristic of the two is substantially similar.
The two-dimentional asynchronous related drawing of Figure 16 A and the blind sample and standard specimen that B is the adulterated oil that 2# refining trench oil quality ratios are 5%
Graceful spectrum, as illustrated, the spectral line characteristic of the two is substantially similar.
As can be seen that doping 1# refining gutter oils and doping 2# refine the sample spectrogram of gutter oil, with certified products soybean
There is more apparent difference in the spectrogram of oil, and the two-dimentional asynchronous related Raman spectrum of two kinds of oil samples of doping variety classes gutter oil also has
It is variant.
The two-dimentional asynchronous spectrum data calculated will be asked to be converted into the vector point in hyperspace, be using Ward methods
Clustering of uniting differentiates with blind sample.
Figure 17 A and B are given using Ward methods, take by Z scoring criterias and respectively the bright Koffsky distance of quadravalence
The result of hierarchial-cluster analysis is carried out to above-mentioned two-dimentional asynchronous related Raman spectrum with Chebyshev's distance.As illustrated, certified products is big
The blind sample that soya-bean oil, 1# refine gutter oil, 2# refining gutter oils and two kinds of adulterated oils obtains accurate recognition.
Figure 18 A and B are given using Ward methods, by standard deviation for 1 standardization and take quadravalence Ming Kefusi respectively
Cardinal distance is from the result with carrying out hierarchial-cluster analysis with a distance from Chebyshev to above-mentioned two-dimentional asynchronous related Raman spectrum.As illustrated,
The blind sample that certified products soybean oil, 1# refine gutter oil, 2# refining gutter oils and two kinds of adulterated oils obtains accurate recognition.
Embodiment 5
The collection of Raman spectral information is carried out to oil sample using method same as Example 2, and seeks the two-dimentional asynchronous phase of calculation
Close modal data；Two-dimentional asynchronous spectrum data are converted into the vector point in hyperspace, are using farthest neighbors method
Clustering methodology of uniting differentiates with blind sample.
Figure 19 A and B are given using farthest neighbors method, take by Z scoring criterias and respectively quadravalence Ming Kefusi
Cardinal distance is from the result that hierarchial-cluster analysis are carried out with being composed with a distance from Chebyshev to the synchronous related Raman of above-mentioned two dimension.As illustrated,
The blind sample that certified products soybean oil, 1# refine gutter oil, 2# refining gutter oils and two kinds of adulterated oils obtains accurate recognition.
Figure 20 A and B are given using farthest neighbors method, by standard deviation for 1 standardization and take quadravalence bright respectively
Koffsky distance composes the result for carrying out hierarchial-cluster analysis with Chebyshev's distance to the synchronous related Raman of above-mentioned two dimension.As schemed
Shown, the blind sample that certified products soybean oil, 1# refine gutter oil, 2# refining gutter oils and two kinds of adulterated oils obtains accurate recognition.
Claims (10)
1. a kind of gutter oil low amounts based on alternating temperature Raman technology blends the efficient lossless discrimination method of edible oil, its feature exists
In：It the described method comprises the following steps：
Oil sample to be checked is packaged in transparent sample cell, in the range of+100 DEG C to -40 DEG C, with 5-10 DEG C of temperature interval point
Confocal micro Raman spectrum that Ce Ding be under condition of different temperatures, based on the change at c h bond vibration performance peak, asks and calculates and draw two
Tie up synchronous and/or two-dimentional asynchronous related Raman spectrum；Two-dimentional synchronous and/or two-dimentional asynchronous related Raman modal data is converted into multidimensional
Vector point in space, is differentiated using hierarchial-cluster analysis；During said determination, using incoagulable gas under cryogenic conditions
Body purges sample cell with anti-condensation or frosting.
2. the efficient lossless of the gutter oil low amounts blending edible oil according to claim 1 based on alternating temperature Raman technology differentiates
Method, it is characterised in that：Described incoagulable gas is selected from nitrogen, argon gas, helium, oxygen etc. or its mixture.
3. the efficient lossless of the gutter oil low amounts blending edible oil according to claim 1 based on alternating temperature Raman technology differentiates
Method, it is characterised in that：Described oil sample detection excitation wavelength is selected from 785,632.8,532,514.5,488,325nm.
4. the efficient lossless of the gutter oil low amounts blending edible oil according to claim 1 based on alternating temperature Raman technology differentiates
Method, it is characterised in that：Described oil sample detection spectrum acquisition range is 500-4000cm-1。
5. the efficient lossless of the gutter oil low amounts blending edible oil according to claim 4 based on alternating temperature Raman technology differentiates
Method, it is characterised in that：Described oil sample detection spectrum acquisition range is 2850-2975cm-1。
6. the efficient lossless of the gutter oil low amounts blending edible oil according to claim 1 based on alternating temperature Raman technology differentiates
Method, it is characterised in that：Described oil sample detection temperature scope is+100 DEG C to -40 DEG C.
7. the efficient lossless of the gutter oil low amounts blending edible oil according to claim 6 based on alternating temperature Raman technology differentiates
Method, it is characterised in that：Described oil sample detection temperature scope is+15 DEG C to -25 DEG C.
8. the efficient lossless of the gutter oil low amounts blending edible oil according to claim 1 based on alternating temperature Raman technology differentiates
Method, it is characterised in that：The hierarchial-cluster analysis use the bright Koffsky distance of Chebyshev's distance, quadravalence for criterion；Two
It is 1 to tie up the standardized method of modal data to include Z score and/or standard deviation；Clustering method is Ward methods, farthest neighbors method, group
Between attached method, group in attached method, median method or centroid method.
9. the efficient lossless of the gutter oil low amounts blending edible oil according to claim 8 based on alternating temperature Raman technology differentiates
Method, it is characterised in that：Described hierarchial-cluster analysis use the bright Koffsky criterion of quadravalence, and clustering method is for Ward methods and most
Remote neighbors method and two-dimentional modal data carry out Z scoring criterias.
10. a kind of organic efficient the cannot-harm-detection device based on alternating temperature Raman technology, it is characterised in that：Described device includes tool
The sample stage of fluted structure, the groove of the sample stage is arranged above windowing, and the side of the sample stage is provided with one
The socket of individual or multiple sample cells be arrangeding in parallel, the socket is communicated with groove；
Heat-insulated and water vapor cover is provided with the outside of the sample stage, the side wall of water vapor cover, which is provided with protection gas entrance, to be led to
Road, uplifting window is provided with the top surface of water vapor cover, and thus the protection gas open a window discharge；
In the bottom of sample stage, thermal conductive silicon lipid layer, temperature change member, thermal conductive silicon lipid layer and heat exchanger are disposed with；
Also include temperature controller, the temperature controller is connected with temperature element and constant current supply, wherein, the temperature element can insert sample
The side wall of sample platform, the temperature for detecting and controlling sample stage.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110095450A (en) * | 2019-05-20 | 2019-08-06 | 大连海事大学 | A kind of olive oil low amounts blends the Undamaged determination method of soybean oil or corn oil |
| CN113252762A (en) * | 2021-04-13 | 2021-08-13 | 西北农林科技大学 | Method for rapidly detecting special virgin olive oil adulterated olive pomace oil |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU693804A1 (en) * | 1978-04-03 | 1982-12-30 | Предприятие П/Я В-8851 | Cryostat for x-ray diffractometer |
| SU1641146A1 (en) * | 1988-10-03 | 1992-06-30 | Предприятие П/Я В-2892 | Thermal table for realization of low-temperature thermal table for realization of low-temperature probe measurements |
| CA2241337A1 (en) * | 1996-01-12 | 1997-07-17 | David R. Gagnon | Spectroscopic sample holder |
| US6389306B1 (en) * | 1998-04-24 | 2002-05-14 | Lightouch Medical, Inc. | Method for determining lipid and protein content of tissue |
| CN205719980U (en) * | 2016-04-26 | 2016-11-23 | 东北大学 | A kind of electrochemical in-situ raman spectroscopy measurement microscopic heating stand and sample cell system |
| CN106483148A (en) * | 2016-10-11 | 2017-03-08 | 中国科学院上海应用物理研究所 | A kind of thermal station of ray microprobe, thermal station device and its experimental technique |
| CN206696183U (en) * | 2017-05-08 | 2017-12-01 | 大连海事大学 | Organic efficient the cannot-harm-detection device based on alternating temperature Raman technology |
-
2017
- 2017-05-08 CN CN201710317526.6A patent/CN107091827A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU693804A1 (en) * | 1978-04-03 | 1982-12-30 | Предприятие П/Я В-8851 | Cryostat for x-ray diffractometer |
| SU1641146A1 (en) * | 1988-10-03 | 1992-06-30 | Предприятие П/Я В-2892 | Thermal table for realization of low-temperature thermal table for realization of low-temperature probe measurements |
| CA2241337A1 (en) * | 1996-01-12 | 1997-07-17 | David R. Gagnon | Spectroscopic sample holder |
| US6389306B1 (en) * | 1998-04-24 | 2002-05-14 | Lightouch Medical, Inc. | Method for determining lipid and protein content of tissue |
| CN205719980U (en) * | 2016-04-26 | 2016-11-23 | 东北大学 | A kind of electrochemical in-situ raman spectroscopy measurement microscopic heating stand and sample cell system |
| CN106483148A (en) * | 2016-10-11 | 2017-03-08 | 中国科学院上海应用物理研究所 | A kind of thermal station of ray microprobe, thermal station device and its experimental technique |
| CN206696183U (en) * | 2017-05-08 | 2017-12-01 | 大连海事大学 | Organic efficient the cannot-harm-detection device based on alternating temperature Raman technology |
Non-Patent Citations (3)
| Title |
|---|
| 孙鸿 等: ""低碳醇合成中反应温度、催化剂组成对碳链增长因子α的影响"", 《运城高等专科学校学报》, vol. 19, no. 3, pages 35 - 36 * |
| 逯来玉;周晓林;姬广富;刘科;常景;: "高温下季戊四醇晶体的结构和振动性质的分子动力学模拟(英文)", no. 05 * |
| 陈达;许云涛;李奇峰;于苓;金涌;徐可欣;: "基于多尺度二维相关拉曼光谱的橄榄油掺杂检测", vol. 14, no. 01, pages 60 - 65 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110095450A (en) * | 2019-05-20 | 2019-08-06 | 大连海事大学 | A kind of olive oil low amounts blends the Undamaged determination method of soybean oil or corn oil |
| CN110095450B (en) * | 2019-05-20 | 2021-08-27 | 大连海事大学 | Nondestructive identification method for low-content olive oil blended with soybean oil or corn oil |
| CN113252762A (en) * | 2021-04-13 | 2021-08-13 | 西北农林科技大学 | Method for rapidly detecting special virgin olive oil adulterated olive pomace oil |
| CN113252762B (en) * | 2021-04-13 | 2022-12-13 | 西北农林科技大学 | Method for rapidly detecting special virgin olive oil adulterated olive pomace oil |
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