CN102636510B - Proton nmr spectra detects the method for edible oil quality - Google Patents
Proton nmr spectra detects the method for edible oil quality Download PDFInfo
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Abstract
The invention discloses a kind of method utilizing proton nmr spectra to detect edible oil quality, belong to food detection method field.First the method carries out NMR signal collection for the oils sample of different quality under identical parameter, then integration is carried out to the collection of illustrative plates collected, utilize integrated value to calculate the data such as content and iodine number of the various fatty acid such as saturated fatty acid, oleic acid, linoleic acid, find out the feature of different grease; Reperformance test is carried out to same quality oil sample; Set up oils attribute test database according to the test data of different quality oils, and form analytical test standard; Finally, by the test data in database, Quality Identification is carried out to unknown oils sample.The inventive method have simple to operate, accuracy is high, reproducible, quick economic dispatch advantage, the discriminating being applicable to certified products edible oil or mixing pseudo-edible oil detects, and can differentiate the kind of edible oil.
Description
Technical field
The invention belongs to food detection method field, be specifically related to the method for nuclear magnetic resonance map detection and identification edible oil quality.
Background technology
Along with developing rapidly of the tertiary industry, China's catering trade scale expanding day, the waste grease of discharging in food and drink waste water is increasing, and lawless person illegally reclaims refinement to these waste greases, poisonous " waste oil " just gets back to dining table again, is detrimental to health.Waste oil generally comprises swill oil, frying oil and discarded animal fat, about the quick detection of waste oil, there are technology or the method for several types in the market, there is the paper chromatography based on detecting conductivity and polar compound total amount, thin-layer chromatography, sensor method for quick, also the method for quick based on detecting heavy-metal pollution thing is had, also have based on detection waste oil acid value, the method for quick such as peroxide value, these methods respectively have superiority in application, the qualitative detection of waste oil can be carried out to a certain extent, but also existing defects, the ratio of mixing waste oil in edible oil generally needs to reach relatively high degree and just can be detected or easily occur false positive (" development and research of waste oil detection technique ", Food technology is with economical, 2011, 36 (1): 41-44, ), (" discrimination method of waste oil ", CN102393426A), such as electrical conductivity method detect lower limit be waste oil addition more than 20%, the lower limit that detects of cholesterol method is that the addition of waste oil is more than 10%, thin layer chromatography is for some not purified oil (such as sesame oil, Chinese prickly ash wet goods) and the oil (as chilli oil) adding other compositions, easily there is false positive, the method that exclusion chromatography (HPSEC) measures triglyceride oxypolymer is applicable to the detection of frying oil, but lower to the accuracy rate of swill oil.Therefore, in order to ensure edible oil quality safety, can differentiate in the urgent need to a kind of the reliable detection method that waste oil mixes pseudo-edible oil fast.Utilize proton nmr spectra to certified products edible oil with mix pseudo-edible oil and carry out discriminating and yet there are no bibliographical information.
Summary of the invention
The object of the invention is to provide that a kind of test speed is fast, accuracy is high, the proton nmr spectra that utilizes that is with low cost, favorable reproducibility differentiates the method for edible oil quality.
The principle of foundation of the present invention is: the edible oil of each kind is all made up of the fatty glyceride substantially determined, even if blending stock, the definite composition of wherein various oil is also known by manufacturer, can calculate its fatty glyceride composition accordingly.But waste oil, due to the problem in source, causes its complicated component changeable, form even if the producer can not talk clearly it, this is the feature that it cannot change.According to this feature, by setting up and the nuclear magnetic resonance map of comparison edible oil, can whether doped with waste oil in the easy edible oil and fat of detection rapidly.
For realizing the object of the invention, first the present invention carries out proton nmr spectra collection for the oils sample of the different quality of variety classes under identical parameters, then integration is carried out to collection of illustrative plates, after the integrated value obtained being calculated according to formula, obtain the eigenwert of different grease; Set up oils qualitative data storehouse according to the proton nmr spectra integrated value of different quality oils or eigenwert, form analytical test standard; By the test data in database, Quality Identification is carried out to unknown oils sample.
The concrete implementation step of the inventive method is as follows:
(1) standard model proton nmr spectra data acquisition: under the same terms, carries out the test of more than twice proton nmr spectra to the certified products edible oil sample of same quality, draws front and back twice or the error of repeatedly test data, correction error; Then repeated proton nmr spectra test is carried out to the certified products edible oil sample of same quality;
(2) standard model data processing: carry out integration to testing the certified products edible oil sample proton nmr spectra obtained, record the integrated value of its hydrogen nuclear magnetic resonance spectrum signature group, utilize its integrated value do X-Y scheme, the three-dimensional plot of integration data or its integrated value is substituted into the eigenwert that formulae discovery obtains certified products edible oil, make the PCA analysis chart of its eigenwert;
(3) foundation of database: under the same terms, tests the proton nmr spectra of a large amount of variety classes different quality certified products edible oils sample, repeats step (2) and process, set up edible oils attribute test database;
(4) sample is differentiated: under the same terms, nuclear magnetic resonance test is carried out to unknown edible oils sample, utilize its hydrogen nuclear magnetic resonance spectrogram of data processing method process of step (2), the edible oils attribute test database model set up with step (3) is compared, Qualitative Identification certified products edible oil or mix pseudo-edible oil, and certified products edible oil kind is judged.
Described certified products edible oil is respectively the mediation wet goods of soybean oil, peanut oil, sesame oil, palm oil, corn oil, rapeseed oil, cottonseed oil, sunflower oil or known composition.
Carry out integration to proton nmr spectra according to shown in accompanying drawing 1, have nine groups of integrated values, the chemical shift of nine groups of protons is respectively: 1,2-5.34ppm, 3-4.23ppm, 4-2.77ppm, 5-2.31ppm, 6-2.04ppm, 7-1.61ppm, 8-1.28ppm, 9-0.98ppm, 10-0.89ppm.
Described edible oils attribute test database is the integration data X-Y scheme of the hydrogen nuclear magnetic resonance spectrum signature group of various conventional certified products edible oil and fat or the PCA analysis chart of three-dimensional plot and various conventional edible certified products oil eigenwert.
Compared with surveying fake method with traditional grease, this method has the following advantages: 1. simplify experimental procedure.In this method, sample does not need to carry out pre-treatment, can directly test; Sample is without the need to weighing quantitatively, because integral area display itself is exactly each group of peak-to-peak ratio; 2. degree of accuracy is high.Collection of illustrative plates collection, process and undertaken by computing machine, and integrating range is also fixing, avoids the error that human factor causes.3. quick economical.Only test detection sample during detection, after carrying out algebraic manipulation, compare with database, detection speed is fast, and consuming time short, whole test can complete in 5 minutes, reduced workload and testing cost.4. widely applicable.Database is later all applicable to the instrument test data of different model different magnetic field.
The method is not only applicable to various edible oil whether doped with the detection of waste oil, and kind differentiation can be carried out to various edible oil, prevent the edible oil pretending to be price high with cheap edible oil, there is the features such as test speed is fast, accuracy is high, with low cost, the work of edible oils quartile length can be actually used in, can differentiate that waste oil mixes pseudo-edible oil effectively, containment is not sent out retailer and is pretended to be expensive edible oil with cheap edible oil, for the healthy diet of people provides quality assurance.
Accompanying drawing explanation
The integrated value Information Monitoring figure of the corresponding proton in each peak in Fig. 1 certified products edible oil nuclear magnetic resonance figure; 1:C in figure
h=CH, all unsaturated fatty acids, 2:C
h-OCOR triglyceride, 3:C
h 2 -OCOR triglyceride, 4:CH=CH-C
h 2-CH=CH
2linoleic acid plus linolenic acid chain, 5:C
h 2the all fatty acid chains of-COOH, 6:C
h 2 the all unsaturated fatty acid chains of-CH=CH, 7:C
h 2 -CH
2the all fatty acid chains of COOH, 8:(C
h 2 )
nall fatty acid chains, 9:CH=CH-CH
2-C
h 3leukotrienes chain, 10:CH
2cH
2cH
2-C
h 3all fatty acid chains except leukotrienes;
Fig. 2 is containing the soybean oil at No. 9 peaks and the X-Y scheme database of rapeseed oil; In figure ▲ and soybean oil, ■ rapeseed oil;
Fig. 3 is not containing the various grease X-Y scheme databases at No. 9 peaks; △ peanut oil in figure, ▼ sesame oil, ◆ palm oil, ● corn oil, ◇ cottonseed oil, ▽ sunflower oil;
Fig. 4 utilizes and judges edible oil sample containing No. 9 peak X-Y scheme database analysises; In figure ▲ and soybean oil, ■ rapeseed oil, ★ adds the rapeseed oil of waste oil;
Fig. 5 utilizes and does not judge edible oil sample containing the X-Y scheme database analysis at No. 9 peaks; △ peanut oil in figure, ▼ sesame oil, ◆ palm oil, ● corn oil, ◇ cottonseed oil, ▽ sunflower oil, ★ adds the sunflower oil of 10% waste oil;
The three-dimensional plot database of the various conventional certified products edible oil of Fig. 6; In figure ▲ and soybean oil, △ peanut oil, ▼ sesame oil, ◆ palm oil, ● corn oil, ◇ cottonseed oil, zero rapeseed oil, ▽ sunflower oil;
Fig. 7 analysis chart of three-dimensional plot database to edible oil quality; In figure ▲ and soybean oil, △ peanut oil, ▼ sesame oil, ◆ palm oil, ● corn oil, ◇ cottonseed oil, zero rapeseed oil, ▽ sunflower oil, ★ adds the peanut oil of 10% waste oil;
The PCA analysis chart database-1 of Fig. 8 various certified products edible oil eigenwert; D soybean oil in figure, H peanut oil, X sesame oil, Y corn oil, K sunflower oil;
The PCA analysis chart database-2 of Fig. 9 various certified products edible oil eigenwert; Z palm oil in figure, M cottonseed oil, C rapeseed oil;
Figure 10 utilizes PCA analysis to judge D soybean oil in edible oil sample quality figure-1, figure, H peanut oil, X sesame oil, Y corn oil, K sunflower oil, and DG soybean oil adds 5% waste oil, and XG sesame oil adds 5% waste oil;
Figure 11 utilizes PCA analysis to judge D soybean oil in edible oil sample quality figure-2, figure, H peanut oil, X sesame oil, Y corn oil, K sunflower oil, and WX mixes pseudo-sesame oil;
Figure 12 utilizes and judges edible blend oil sample containing No. 9 peak X-Y scheme database analysises, in figure ▲ soybean oil, ■ rapeseed oil, ● soybean oil, peanut oil, the blending stock of sesame oil 7:2:1 weight ratio, ★ soybean oil, peanut oil, the blending stock of sesame oil 7:2:1 weight ratio adds 10% waste oil.
Embodiment
One, test material
Soybean oil, peanut oil, sesame oil, palm oil, corn oil, cottonseed oil, rapeseed oil, sunflower oil, sample hose, deuterochloroform (CDCl
3).
Two, test apparatus
BRUKERAvance300 NMR spectrometer with superconducting magnet.
Three, the preparation of test specimen
Get soybean oil 20 microlitre, add 0.6mLCDCl
3dissolving is placed in
Φin 5mm sample hose, obtain testing sample solution.Using such method obtains 14 portions of soybean oils, 14 portions of peanut oil, 12 parts of sesame oil, 7 parts of palm oils, 6 portions of corn oils, 7 portions of cottonseed oils, 7 portions of rapeseed oils, 7 portions of sunflower oils, 8 portions of blending stocks.
Four, process of the test
1. collection of illustrative plates measures
Condition determination: temperature: 295K, CDCl
3for internal lock, each collection of illustrative plates scans 16 times, spectrum width 20ppm, recurrent interval 2s, pulse train: zg30.
By above-mentioned obtained sample solution working sample in nuclear magnetic resonance analyser
1hNMR collection of illustrative plates, obtains sample free damping signal (FID signal).
2. collection of illustrative plates process and integration
The sample F ID signal obtained by said determination carries out Fourier transform, and line phase of going forward side by side corrects and baseline correction, using the chemical shift 0ppm of TMS as interior mark, and calibration graph.
After correction, proton nmr spectra test is carried out to above-mentioned all kinds of certified products edible oil, carry out integration for soybean oil respectively according to shown in accompanying drawing 1, have nine groups of integrated values, the chemical shift of nine groups of protons is respectively: 1,2-5.34ppm, 3-4.23ppm, 4-2.77ppm, 5-2.31ppm, 6-2.04ppm, 7-1.61ppm, 8-1.28ppm, 9-0.98ppm, 10-0.89ppm, A, B, C, D, E, F, G, H, I represent the area of above-mentioned nine groups of integrations respectively.
The foundation of 3 standard databases
3.1 directly utilize integral area
According to different certified products edible oil grouping, integral area is utilized to set up the database (table 1-9) of different certified products edible oil.
3.2 utilize integrated value to calculate the eigenwerts such as grease composition and iodine number
Bring associated quad value into following formula to calculate:
[ω-3UFA]=H/(H+I)
[ω-6 linoleic acid]=(3C-4H)/3D
[MUFA]=E/2D-H/(H+I)-(3C-4H)/3D
[SFA]=1-E/2D
[IV]=[(A-B/4)/D]×86
In above-mentioned formula, [ω-3UFA] is the ratio of omega-3 unsaturated fatty acid in grease; [ω-6 linoleic acid] is the ratio of linoleic acid in grease; [MUFA] is the ratio of monounsaturated fatty acids in grease; [SFA] is the ratio of saturated fatty acid in grease; The iodine number that [IV] is grease.The result of calculation of various certified products grease eigenwert is listed in table 10-table 18 respectively.
4. data analysis
Two groups or three groups of choosing in the integral area of edible oil and fat hydrogen nuclear magnetic resonance spectrum signature group are parameter, draw X-Y scheme or the three-dimensional plot of the integration data of various conventional certified products edible oil and fat hydrogen nuclear magnetic resonance spectrum signature group, or the grease eigenwert input SIMCA-P11.5 calculated is carried out to principal component analysis (PCA) and obtains PCA analysis chart
,one or more comparisons of the analysis result of unknown sample and above-mentioned X-Y scheme, three-dimensional plot and PCA analysis chart are identified.
The integral area of table 1 soybean oil
A | B | C | D | E | F | G | H | I |
2.41 | 1.00 | 0.96 | 1.52 | 2.47 | 1.68 | 12.79 | 0.21 | 2.10 |
2.40 | 1.00 | 0.96 | 1.52 | 2.49 | 1.70 | 12.79 | 0.18 | 2.09 |
2.41 | 1.00 | 0.96 | 1.52 | 2.48 | 1.73 | 12.79 | 0.20 | 2.08 |
2.41 | 1.00 | 0.95 | 1.52 | 2.49 | 1.72 | 12.82 | 0.19 | 2.11 |
2.41 | 1.00 | 0.95 | 1.51 | 2.48 | 1.67 | 12.83 | 0.21 | 2.10 |
2.40 | 1.00 | 0.94 | 1.52 | 2.48 | 1.67 | 12.81 | 0.20 | 2.11 |
2.40 | 1.00 | 0.96 | 1.52 | 2.47 | 1.70 | 12.76 | 0.19 | 2.09 |
2.40 | 1.00 | 0.96 | 1.51 | 2.48 | 1.72 | 12.80 | 0.18 | 2.09 |
2.41 | 1.00 | 0.96 | 1.52 | 2.48 | 1.71 | 12.87 | 0.20 | 2.10 |
2.38 | 1.00 | 0.94 | 1.51 | 2.47 | 1.69 | 12.84 | 0.20 | 2.09 |
2.47 | 1.00 | 1.01 | 1.56 | 2.50 | 1.74 | 12.48 | 0.20 | 1.98 |
2.49 | 1.00 | 1.00 | 1.53 | 2.53 | 1.76 | 12.79 | 0.20 | 2.11 |
2.43 | 1.00 | 0.97 | 1.55 | 2.51 | 1.79 | 12.85 | 0.18 | 2.06 |
2.41 | 1.00 | 0.90 | 1.49 | 2.49 | 1.68 | 13.63 | 0.22 | 2.33 |
The integral area of table 2 peanut oil
A | B | C | D | E | F | G | H | I |
1.93 | 1.00 | 0.54 | 1.50 | 2.33 | 1.67 | 14.57 | 0.00 | 2.28 |
1.93 | 1.00 | 0.54 | 1.51 | 2.34 | 1.68 | 14.54 | 0.00 | 2.26 |
1.94 | 1.00 | 0.54 | 1.49 | 2.34 | 1.62 | 14.56 | 0.00 | 2.27 |
1.93 | 1.00 | 0.54 | 1.52 | 2.34 | 1.67 | 14.53 | 0.00 | 2.25 |
1.93 | 1.00 | 0.54 | 1.51 | 2.34 | 1.64 | 14.59 | 0.00 | 2.27 |
1.93 | 1.00 | 0.54 | 1.52 | 2.34 | 1.67 | 14.58 | 0.00 | 2.25 |
1.94 | 1.00 | 0.55 | 1.51 | 2.34 | 1.76 | 14.65 | 0.00 | 2.27 |
1.96 | 1.00 | 0.56 | 1.51 | 2.34 | 1.69 | 14.53 | 0.00 | 2.27 |
1.92 | 1.00 | 0.52 | 1.51 | 2.38 | 1.60 | 14.68 | 0.00 | 2.28 |
1.97 | 1.00 | 0.56 | 1.50 | 2.36 | 1.57 | 14.53 | 0.00 | 2.25 |
2.02 | 1.00 | 0.60 | 1.55 | 2.44 | 1.72 | 14.34 | 0.00 | 2.24 |
1.97 | 1.00 | 0.56 | 1.55 | 2.44 | 1.77 | 14.39 | 0.00 | 2.21 |
1.99 | 1.00 | 0.56 | 1.53 | 2.38 | 1.71 | 14.83 | 0.00 | 2.31 |
2.02 | 1.00 | 0.56 | 1.51 | 2.44 | 1.71 | 14.63 | 0.00 | 2.40 |
The integral area of table 3 sesame oil
A | B | C | D | E | F | G | H | I |
2.09 | 1.00 | 0.64 | 1.51 | 2.54 | 1.59 | 13.70 | 0.00 | 2.27 |
2.09 | 1.00 | 0.64 | 1.50 | 2.51 | 1.62 | 13.62 | 0.00 | 2.25 |
2.12 | 1.00 | 0.66 | 1.50 | 2.52 | 1.59 | 13.63 | 0.00 | 2.28 |
2.12 | 1.00 | 0.66 | 1.52 | 2.53 | 1.65 | 13.63 | 0.00 | 2.26 |
2.08 | 1.00 | 0.63 | 1.52 | 2.59 | 1.58 | 13.78 | 0.00 | 2.30 |
2.09 | 1.00 | 0.64 | 1.53 | 2.60 | 1.63 | 13.84 | 0.00 | 2.27 |
2.09 | 1.00 | 0.63 | 1.53 | 2.59 | 1.62 | 13.81 | 0.00 | 2.30 |
2.08 | 1.00 | 0.64 | 1.53 | 2.53 | 1.61 | 13.80 | 0.00 | 2.27 |
2.14 | 1.00 | 0.68 | 1.53 | 2.62 | 1.64 | 13.67 | 0.00 | 2.27 |
2.14 | 1.00 | 0.68 | 1.53 | 2.60 | 1.64 | 13.68 | 0.00 | 2.29 |
2.13 | 1.00 | 0.67 | 1.53 | 2.51 | 1.75 | 13.34 | 0.00 | 2.18 |
2.20 | 1.00 | 0.67 | 1.53 | 2.61 | 1.64 | 14.36 | 0.00 | 2.46 |
The palmitic integral area of table 4
A | B | C | D | E | F | G | H | I |
0.98 | 1.00 | 0.13 | 1.50 | 1.29 | 1.89 | 15.94 | 0.00 | 2.14 |
0.96 | 1.00 | 0.11 | 1.49 | 1.27 | 1.63 | 16.37 | 0.00 | 2.25 |
1.04 | 1.00 | 0.12 | 1.47 | 1.37 | 1.66 | 16.41 | 0.00 | 2.36 |
0.97 | 1.00 | 0.10 | 1.49 | 1.28 | 1.68 | 16.29 | 0.00 | 2.32 |
0.98 | 1.00 | 0.09 | 1.46 | 1.28 | 1.67 | 16.45 | 0.00 | 2.41 |
0.98 | 1.00 | 0.10 | 1.48 | 1.30 | 1.62 | 16.36 | 0.00 | 2.36 |
1.13 | 1.00 | 0.14 | 1.47 | 1.54 | 1.60 | 15.99 | 0.00 | 2.31 |
The integral area of table 5 corn oil
A | B | C | D | E | F | G | H | I |
2.40 | 1.00 | 0.79 | 1.51 | 2.59 | 1.82 | 13.88 | 0.00 | 2.61 |
2.35 | 1.00 | 0.78 | 1.53 | 2.49 | 1.82 | 14.07 | 0.00 | 2.56 |
2.43 | 1.00 | 0.80 | 1.55 | 2.65 | 1.86 | 13.33 | 0.00 | 2.62 |
2.46 | 1.00 | 0.76 | 1.55 | 2.66 | 1.83 | 14.13 | 0.00 | 2.59 |
2.44 | 1.00 | 0.77 | 1.55 | 2.69 | 1.80 | 13.98 | 0.00 | 2.60 |
2.40 | 1.00 | 0.80 | 1.57 | 2.58 | 1.83 | 13.84 | 0.00 | 2.63 |
The integral area of table 6 cottonseed oil
A | B | C | D | E | F | G | H | I |
2.09 | 1.00 | 0.82 | 1.56 | 2.12 | 1.77 | 12.94 | 0.00 | 2.18 |
2.11 | 1.00 | 0.82 | 1.55 | 2.14 | 1.91 | 13.12 | 0.00 | 2.25 |
2.23 | 1.00 | 0.87 | 1.56 | 2.29 | 1.79 | 13.14 | 0.00 | 2.26 |
2.09 | 1.00 | 0.86 | 1.55 | 2.31 | 1.87 | 12.96 | 0.00 | 2.22 |
2.16 | 1.00 | 0.86 | 1.56 | 2.22 | 1.90 | 13.54 | 0.00 | 2.24 |
2.11 | 1.00 | 0.85 | 1.56 | 2.16 | 1.81 | 13.22 | 0.00 | 2.25 |
2.00 | 1.00 | 0.87 | 1.55 | 2.28 | 1.80 | 13.18 | 0.00 | 2.25 |
The integral area of table 7 rapeseed oil
A | B | C | D | E | F | G | H | I |
2.22 | 1.00 | 0.60 | 1.55 | 2.82 | 1.72 | 14.08 | 0.27 | 2.12 |
2.22 | 1.00 | 0.58 | 1.55 | 2.82 | 1.72 | 13.98 | 0.27 | 2.12 |
2.23 | 1.00 | 0.58 | 1.55 | 2.72 | 1.71 | 14.11 | 0.28 | 2.13 |
2.24 | 1.00 | 0.62 | 1.55 | 2.70 | 1.70 | 14.54 | 0.28 | 2.12 |
2.20 | 1.00 | 0.61 | 1.56 | 2.83 | 1.72 | 14.01 | 0.28 | 2.11 |
2.21 | 1.00 | 0.59 | 1.55 | 2.84 | 1.72 | 14.05 | 0.27 | 2.11 |
2.20 | 1.00 | 0.58 | 1.55 | 2.70 | 1.71 | 14.17 | 0.27 | 2.12 |
The integral area of table 8 sunflower oil
A | B | C | D | E | F | G | H | I |
2.45 | 1.00 | 0.92 | 1.55 | 2.66 | 1.74 | 12.93 | 0.00 | 2.25 |
2.45 | 1.00 | 0.98 | 1.55 | 2.66 | 1.74 | 13.03 | 0.00 | 2.24 |
2.52 | 1.00 | 0.98 | 1.59 | 2.65 | 1.74 | 12.94 | 0.00 | 2.25 |
2.43 | 1.00 | 0.94 | 1.55 | 2.70 | 1.75 | 12.95 | 0.00 | 2.25 |
2.43 | 1.00 | 0.96 | 1.54 | 2.60 | 1.75 | 12.96 | 0.00 | 2.25 |
2.46 | 1.00 | 0.93 | 1.54 | 2.59 | 1.74 | 13.11 | 0.00 | 2.24 |
2.44 | 1.00 | 0.93 | 1.55 | 2.66 | 1.74 | 12.88 | 0.00 | 2.25 |
Table 9 soybean oil: peanut oil: the integral area of sesame oil 7:2:1 weight ratio blending stock
A | B | C | D | E | F | G | H | I |
2.18 | 1.00 | 0.93 | 1.50 | 2.34 | 1.99 | 11.58 | 0.07 | 1.65 |
2.28 | 1.00 | 0.90 | 1.52 | 2.44 | 1.86 | 12.34 | 0.13 | 1.88 |
2.16 | 1.00 | 0.89 | 1.48 | 2.31 | 1.97 | 11.62 | 0.13 | 1.81 |
2.16 | 1.00 | 0.92 | 1.52 | 2.33 | 1.89 | 11.51 | 0.08 | 1.67 |
2.26 | 1.00 | 0.89 | 1.49 | 2.37 | 1.83 | 12.23 | 0.14 | 1.98 |
2.22 | 1.00 | 0.92 | 1.48 | 2.39 | 1.86 | 11.80 | 0.12 | 1.79 |
2.17 | 1.00 | 0.85 | 1.40 | 2.27 | 2.02 | 11.38 | 0.16 | 1.86 |
2.13 | 1.00 | 0.88 | 1.42 | 2.31 | 2.10 | 11.12 | 0.09 | 1.69 |
Table 10 calculates the eigenwert of soybean oil
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0909 | 0.4474 | 0.2742 | 0.1875 | 122.21 |
0.0793 | 0.4737 | 0.2661 | 0.1809 | 121.64 |
0.0877 | 0.4561 | 0.2719 | 0.1842 | 122.21 |
0.0826 | 0.4583 | 0.2781 | 0.1809 | 122.21 |
0.0909 | 0.4437 | 0.2866 | 0.1788 | 123.02 |
0.0866 | 0.4430 | 0.2862 | 0.1842 | 121.64 |
0.0833 | 0.4649 | 0.2643 | 0.1875 | 121.64 |
0.0793 | 0.4768 | 0.2651 | 0.1788 | 122.45 |
0.0870 | 0.4561 | 0.2727 | 0.1842 | 122.21 |
0.0873 | 0.4459 | 0.2846 | 0.1821 | 121.31 |
0.0913 | 0.4786 | 0.2334 | 0.1967 | 122.70 |
0.0870 | 0.4784 | 0.2604 | 0.1742 | 125.91 |
0.0804 | 0.4716 | 0.2594 | 0.1886 | 121.11 |
0.0860 | 0.4072 | 0.3416 | 0.1651 | 124.42 |
Table 11 calculates the eigenwert of peanut oil
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0000 | 0.3600 | 0.4167 | 0.2233 | 96.32 |
0.0000 | 0.3576 | 0.4172 | 0.2252 | 95.68 |
0.0000 | 0.3624 | 0.4228 | 0.2148 | 97.54 |
0.0000 | 0.3553 | 0.4145 | 0.2303 | 95.05 |
0.0000 | 0.3576 | 0.4172 | 0.2252 | 95.68 |
0.0000 | 0.3553 | 0.4145 | 0.2303 | 95.05 |
0.0000 | 0.3642 | 0.4106 | 0.2252 | 96.25 |
0.0000 | 0.3709 | 0.4040 | 0.2252 | 97.39 |
0.0000 | 0.3444 | 0.4437 | 0.2119 | 95.11 |
0.0000 | 0.3733 | 0.4133 | 0.2133 | 98.61 |
0.0000 | 0.3871 | 0.4000 | 0.2129 | 98.21 |
0.0000 | 0.3613 | 0.4258 | 0.2129 | 95.43 |
0.0000 | 0.3660 | 0.4118 | 0.2222 | 97.80 |
0.0000 | 0.3709 | 0.4371 | 0.1921 | 100.81 |
Table 12 calculates the eigenwert of sesame oil
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0000 | 0.4238 | 0.4172 | 0.1589 | 104.79 |
0.0000 | 0.4267 | 0.4100 | 0.1633 | 105.49 |
0.0000 | 0.4400 | 0.4000 | 0.1600 | 107.21 |
0.0000 | 0.4342 | 0.3980 | 0.1678 | 105.80 |
0.0000 | 0.4145 | 0.4375 | 0.1480 | 103.54 |
0.0000 | 0.4183 | 0.4314 | 0.1503 | 103.42 |
0.0000 | 0.4118 | 0.4346 | 0.1536 | 103.42 |
0.0000 | 0.4183 | 0.4085 | 0.1732 | 102.86 |
0.0000 | 0.4444 | 0.4118 | 0.1438 | 106.24 |
0.0000 | 0.4444 | 0.4052 | 0.1503 | 106.24 |
0.0000 | 0.4379 | 0.3824 | 0.1797 | 105.67 |
0.0000 | 0.4379 | 0.4150 | 0.1471 | 109.61 |
Table 13 calculates palmitic eigenwert
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0000 | 0.0836 | 0.3472 | 0.5692 | 41.76 |
0.0000 | 0.0703 | 0.3537 | 0.5760 | 40.64 |
0.0000 | 0.0809 | 0.3861 | 0.5330 | 46.24 |
0.0000 | 0.0652 | 0.3658 | 0.5689 | 41.64 |
0.0000 | 0.0639 | 0.3756 | 0.5605 | 43.27 |
0.0000 | 0.0656 | 0.3738 | 0.5606 | 42.53 |
0.0000 | 0.0975 | 0.4284 | 0.4741 | 51.74 |
Table 14 calculates the eigenwert of corn oil
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0000 | 0.5251 | 0.3313 | 0.1435 | 122.40 |
0.0000 | 0.5117 | 0.2993 | 0.1890 | 118.00 |
0.0000 | 0.5174 | 0.3353 | 0.1474 | 120.75 |
0.0000 | 0.4878 | 0.3692 | 0.1430 | 122.46 |
0.0000 | 0.4997 | 0.3693 | 0.1311 | 121.75 |
0.0000 | 0.5105 | 0.3140 | 0.1755 | 118.16 |
Table 15 calculates the eigenwert of cottonseed oil
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0000 | 0.5260 | 0.1549 | 0.3191 | 101.23 |
0.0000 | 0.5271 | 0.1624 | 0.3106 | 103.01 |
0.0000 | 0.5621 | 0.1749 | 0.2630 | 109.45 |
0.0000 | 0.5557 | 0.1883 | 0.2560 | 101.89 |
0.0000 | 0.5501 | 0.1645 | 0.2853 | 105.46 |
0.0000 | 0.5433 | 0.1488 | 0.3079 | 102.49 |
0.0000 | 0.5579 | 0.1744 | 0.2677 | 96.57 |
Table 16 calculates the eigenwert of rapeseed oil
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.1146 | 0.1479 | 0.6448 | 0.0927 | 109.31 |
0.1126 | 0.1397 | 0.6581 | 0.0897 | 109.21 |
0.1145 | 0.1395 | 0.6222 | 0.1238 | 110.01 |
0.1165 | 0.1602 | 0.5938 | 0.1295 | 110.72 |
0.1170 | 0.1532 | 0.6380 | 0.0918 | 107.36 |
0.1142 | 0.1468 | 0.6544 | 0.0846 | 108.76 |
0.1144 | 0.1383 | 0.6172 | 0.1302 | 107.89 |
Table 17 calculates the eigenwert of sunflower oil
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0000 | 0.5925 | 0.2668 | 0.1407 | 122.32 |
0.0000 | 0.6293 | 0.2282 | 0.1425 | 121.87 |
0.0000 | 0.6144 | 0.2218 | 0.1638 | 122.96 |
0.0000 | 0.6072 | 0.2649 | 0.1279 | 121.11 |
0.0000 | 0.6218 | 0.2216 | 0.1566 | 122.04 |
0.0000 | 0.5997 | 0.2387 | 0.1616 | 122.93 |
0.0000 | 0.6000 | 0.2581 | 0.1419 | 121.45 |
Table 18 calculates the eigenwert of table 9 blending stock
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0429 | 0.5501 | 0.1833 | 0.2237 | 110.24 |
0.0624 | 0.4841 | 0.2591 | 0.1945 | 114.80 |
0.0683 | 0.4849 | 0.2293 | 0.2175 | 111.31 |
0.0431 | 0.5375 | 0.1832 | 0.2362 | 108.15 |
0.0675 | 0.4707 | 0.2574 | 0.2044 | 115.80 |
0.0605 | 0.5146 | 0.2299 | 0.1951 | 114.16 |
0.0781 | 0.4547 | 0.2808 | 0.1864 | 118.37 |
0.0522 | 0.5302 | 0.2288 | 0.1888 | 113.40 |
For better illustrating the present invention, for the differentiation of grease, enumerate embodiment as follows:
embodiment 1 directly utilizes integral area to be differentiated edible oil by X-Y scheme
Standard model carries out proton nmr spectra test according to the method described above and carries out integration, find through experiment, soybean oil and rapeseed oil is only had to have No. 9 peaks in conventional edible oil, therefore according to or without No. 9 peaks, grease can be divided into two classes, utilize the 2 d plane picture that integrated value A and C does, as accompanying drawing 2 and accompanying drawing 3, as standard database.
Can be separated completely by the visible various grease of accompanying drawing, grease to be measured is carried out proton nmr spectra test according to the method described above and carries out integration, utilize the standard database of its integrated value A and C and foundation to compare and judge.
The waste oil sample adding 5% as us in rapeseed oil carries out proton nmr spectra test according to the method described above and carries out integration, obtains data as follows:
Table 19 adds the integral area of the rapeseed oil of 5% waste oil
A | B | C | D | E | F | G | H | I |
2.10 | 1.00 | 0.66 | 1.56 | 2.66 | 1.83 | 12.52 | 0.15 | 1.76 |
No. 9 peaks are had owing to only having soybean oil and rapeseed oil, therefore only need to utilize accompanying drawing 2 as standard database, by contrasting known with standard database, see accompanying drawing 4, the sample adding waste oil drops on outside soybean oil and rapeseed oil characteristic area, and clearly showing is not pure rapeseed oil.
Table 20 adds the integral area of the sunflower oil of 5% waste oil
A | B | C | D | E | F | G | H | I |
2.31 | 1.00 | 0.90 | 1.48 | 2.50 | 1.68 | 11.88 | 0.00 | 2.06 |
The same, there is no the Fig. 3 at No. 9 peaks as standard database, by testing sample and its contrast known, the sunflower oil adding waste oil drops on outside each edible oil and fat standard feature region, as accompanying drawing 5, can obviously differentiate out not to be pure sunflower oil.
embodiment 2 directly utilizes integral area to be differentiated grease by three-dimensional plot
Standard model carries out proton nmr spectra test according to the method described above and carries out integration, and utilize integrated value A, C, E do three-dimensional plot, various edible oil can separate completely, as accompanying drawing 6, as standard database.
Edible oil to be measured carried out proton nmr spectra test according to the method described above and carries out integration, utilizing its integrated value A, C, E and standard database accompanying drawing 6 to compare and judge.Such as
Table 21 is as follows respectively containing the integration data of two peanut oil samples of 10% waste oil
A | B | C | D | E | F | G | H | I |
1.91 | 1.00 | 0.62 | 1.43 | 2.23 | 1.97 | 12.57 | 0.00 | 1.99 |
1.87 | 1.00 | 0.68 | 1.50 | 2.27 | 2.07 | 12.28 | 0.00 | 1.60 |
Its distribution in standard database three-dimensional plot accompanying drawing 6, as Fig. 7, drops on outside each edible oil standard characteristic area, can obviously differentiate out that this sample is not pure peanut oil.
embodiment 3 differentiates grease by carrying out PCA analysis
Standard model carries out proton nmr spectra test according to the method described above and carries out anomalous integral calculating, the eigenwert of edible oil is utilized to carry out PCA analysis as variable, each group can be separated completely, as the PCA analysis chart data of standard edible oil eigenwert, as accompanying drawing 8 and accompanying drawing 9.
Edible oil to be measured carried out proton nmr spectra test according to the method described above and carries out anomalous integral calculating, carrying out PCA analysis using its eigenwert as variable, by judging oil quality with standard model storehouse accompanying drawing 8 and accompanying drawing 9 comparison.Such as
Table 22 adds integral area and the eigenwert of the soybean oil of 5% waste oil
A | B | C | D | E | F | G | H | I |
2.29 | 1.00 | 0.89 | 1.44 | 2.36 | 1.78 | 12.34 | 0.21 | 2.07 |
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0906 | 0.4280 | 0.2998 | 0.1816 | 121.77 |
Table 23 adds integral area and the eigenwert of the sesame oil of 5% waste oil
A | B | C | D | E | F | G | H | I |
2.05 | 1.00 | 0.72 | 1.52 | 2.50 | 1.78 | 12.56 | 0.00 | 1.88 |
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0000 | 0.4772 | 0.3462 | 0.1766 | 102.18 |
Analysis chart, as accompanying drawing 10, can be found out, in Figure 10, the Data distribution8 of DG and XG difference criterion distance soybean oil D and sesame oil X is comparatively far away, judges that these two kinds of oil are not all soybean oil and the sesame oil of certified products.
Table 24 mixes integral area and the eigenwert of pseudo-sesame oil
A | B | C | D | E | F | G | H | I |
2.20 | 1.00 | 0.74 | 1.56 | 2.7 | 1.70 | 13.58 | 0.00 | 2.33 |
2.18 | 1.00 | 0.73 | 1.51 | 2.52 | 1.68 | 13.36 | 0.00 | 2.30 |
[ω-3 UFA] | [ω-6 linoleic acid] | [MUFA] | [SFA] | IV |
0.0000 | 0.4744 | 0.3910 | 0.1346 | 107.50 |
0.0000 | 0.4834 | 0.3510 | 0.1656 | 109.92 |
As accompanying drawing 11 obviously can find out that these two kinds of oil samples are not the sesame oil of certified products.
Embodiment 4 is to the judgement of the blending stock of known composition
Utilize integration data in table 9 to set up the database of the blending stock (soybean oil: peanut oil: sesame oil weight ratio 7:2:1) of known composition, utilize the X-Y scheme analysis with No. 9 peaks to judge to add the blending stock of 10% waste oil, as accompanying drawing 12.
Table 257:2:1 weight ratio blending stock adds the integral area of 10% waste oil
A | B | C | D | E | F | G | H | I |
2.09 | 1.00 | 0.84 | 1.47 | 2.25 | 1.95 | 11.69 | 0.11 | 1.74 |
Sample drops on outside this blending stock standard feature region, can obviously differentiate out that this sample does not meet known proportion mediation oil composition.
Claims (4)
1. the method for a proton nmr spectra detection edible oil quality, it is characterized in that, realize as follows: (1) standard model proton nmr spectra data acquisition: under the same terms, more than twice proton nmr spectra test is carried out to the certified products edible oil sample of same quality, draw front and back twice or the error of repeatedly test data, correction error; Then repeated proton nmr spectra test is carried out to the certified products edible oil sample of same quality;
(2) standard model data processing: test in the certified products edible oil sample proton nmr spectra obtained and comprise nine groups of proton peak, respectively integration is carried out to these nine groups of peaks, record the integrated value of its hydrogen nuclear magnetic resonance spectrum signature group, utilize its integrated value do X-Y scheme, the three-dimensional plot of integration data or its integrated value is substituted into the eigenwert that following formulae discovery obtains certified products edible oil, make the major component PCA analysis chart of its eigenwert:
[ω-3UFA]=H/(H+I)
[ω-6 linoleic acid]=(3C-4H)/3D
[MUFA]=E/2D-H/(H+I)-(3C-4H)/3D
[SFA]=1-E/2D
[IV]=[(A-B/4)/D]×86
In above-mentioned formula, [ω-3UFA] is the ratio of omega-3 unsaturated fatty acid in grease; [ω-6 linoleic acid] is the ratio of linoleic acid in grease; [MUFA] is the ratio of monounsaturated fatty acids in grease; [SFA] is the ratio of saturated fatty acid in grease; The iodine number that [IV] is grease; A, B, C, D, E, H, I represent the area of above-mentioned nine groups of proton peak wherein seven groups of proton peak integrations respectively;
(3) foundation of database: under the same terms, tests the proton nmr spectra of a large amount of variety classes different quality certified products edible oil, repeats step (2) and processes, set up edible oils attribute test database;
(4) sample is differentiated: under the same terms, nuclear magnetic resonance test is carried out to unknown edible oils sample, utilize its hydrogen nuclear magnetic resonance spectrogram of data processing method process of step (2), the edible oils attribute test database model set up with step (3) is compared, Qualitative Identification certified products edible oil or mix pseudo-edible oil, and certified products edible oil kind is judged.
2. proton nmr spectra according to claim 1 detects the method for edible oil quality, it is characterized in that, described edible oils attribute test database is the X-Y scheme of the integration data of various conventional certified products edible oil or the major component PCA analysis chart of three-dimensional plot and various conventional certified products edible oil eigenwert.
3. proton nmr spectra according to claim 1 and 2 detects the method for edible oil quality, it is characterized in that, described certified products edible oil is respectively the blending stock of soybean oil, peanut oil, sesame oil, palm oil, corn oil, rapeseed oil, cottonseed oil, sunflower oil or known composition.
4. proton nmr spectra according to claim 1 and 2 detects the method for edible oil quality, and it is characterized in that, the eigenwert of described certified products edible oil comprises leukotrienes, linoleic acid, oleic acid, saturated fatty acid, monounsaturated fatty acids and iodine number.
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