CN102495164B - Comprehensive classifying model authenticating method for geographical indication protected vinegar product - Google Patents
Comprehensive classifying model authenticating method for geographical indication protected vinegar product Download PDFInfo
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Abstract
The invention provides a comprehensive classifying model authenticating method for a geographical indication protected vinegar product. The method comprises the following steps: A) detecting characteristic components of the geographical indication protected vinegar product; B) carrying out variable optimization by correlation analysis; C) establishing classifying models of four types of variables; D) establishing a comprehensive classifying model of the geographical indication protected vinegar product; and E) carrying out true and false authentication on an unknown vinegar sample. According to combination of chromatograph, mass spectrum measurement technique and chemometric method, the invention provides the comprehensive classifying model authenticating method for the geographical indication protected vinegar product, discloses overall characteristics of the geographical indication protected vinegar product, can conduct effective authentication on a geographical indication protected vinegar product, solves a problem of difficulty in carrying out true and false authentication on the geographical indication protected vinegar products in market and provides reference for authentication and protection of geographical indication protected products.
Description
Technical field
The present invention relates to the authentication method of vinegar geographical indication protection product, relate in particular to a kind of comprehensive classifying model authenticating method of vinegar geographical indication protection product.
Background technology
The polytrophic acid condiment of one that Cu Shi China people like very much, has the history of brewageing of 5,000 years in China.The output of the annual vinegar of China exceedes 2,600 ten thousand kilograms, and the consumption figure of every day is 3,200,000 kilograms.Wherein Shanxi mature vinegar, Zhenjiang sweet (mature) vinegar reputation rank among the best in Chinese four your name's vinegar, and have applied for geography symbol product protection, are representing deep history and cultural, very popular, and the market demand is large.Although Shanxi mature vinegar and Zhenjiang sweet (mature) vinegar have relevant standard to protect; but too simple to the regulation of various component contents in standard; the method that there is no really plays the effect of protection geography symbol product, needs badly and improves correlated quality control index and criterion to implement more effective protection.
At present the research of vinegar geographical indication protection product is mainly concentrated on the analysis of chemical composition, as the analysis of amino acid, organic acid, trace element and functional component.There is scholar to propose by a few chemical composition or physical attribute, its quality to be evaluated, the for example analysis of ligustrazine, organic acid, reducing sugar equal size in Shanxi mature vinegar, 5 hydroxymethyl furfural in high effective liquid chromatography for measuring vinegar, gas chromatography determination ethanol, inductively coupled plasma mass spectrometry is measured lead etc.
Existing research is mainly confined to the relation of certain constituent of vinegar and the quality of vinegar, for example distinguish the quality grade of product by the content of total acid and ester, carry out the index analysis of single or minority, lack the systematic study of constituent and geography symbol product relation, can not disclose the global feature of geography symbol product, also cannot effectively identify the true and false of vinegar geography symbol product.There is no at present the systematic Study to vinegar geography symbol product discrimination method, can not effectively identify vinegar geography symbol product, be unfavorable for effective protection of vinegar geography symbol product.
Summary of the invention
For solving problems of the prior art; the present invention carries out groping of testing conditions and quantitatively detects amino acid, polyvalent alcohol, organic acid and inorganic elements four major types variable in vinegar geographical indication protection product; the relation of composition in vinegar and geography symbol product has been carried out to systematic study; determine the characteristic of division factor; set up the compressive classification model of vinegar geographical indication protection product; a kind of comprehensive classifying model authenticating method of vinegar geographical indication protection product is provided, can effectively identifies the true and false of vinegar geographical indication protection product.
A comprehensive classifying model authenticating method for vinegar geographical indication protection product, comprises the steps:
A) detection of vinegar geographical indication protection product characteristic component: respectively vinegar geographical indication protection product and control sample are carried out to the detection of four major types variable from amino acid, polyvalent alcohol, organic acid and four aspects of inorganic elements;
B) adopt correlation analysis to carry out the optimization of variable: respectively the related coefficient between any two pairs of variablees in all types of variablees of described four major types variable to be calculated, deleted the high variable of correlativity, carry out the optimization of variable;
C) set up respectively the disaggregated model of four major types variable: based on the content of variable after amino acid, polyvalent alcohol, organic acid and the optimization of inorganic elements four major types, adopt PCA to analyze rejecting abnormalities sample the preliminary feasibility that judges disaggregated model, set up respectively the disaggregated model of four major types variable by Fisher criterion;
D) foundation of the compressive classification model of vinegar geographical indication protection product: adopt mahalanobis distance to calculate the weight coefficient of each variable in described disaggregated model, delete the little variable of weight coefficient, determine the characteristic of division factor, carry out after linear discriminant analysis, set up a compressive classification model based on four major types variable, obtain discriminant critical value;
E) true and false of unknown vinegar sample is differentiated: the concentration that detects the characteristic of division factor described in unknown vinegar sample, use described compressive classification model to calculate predicted value, by the comparison of described predicted value and described discriminant critical value, carry out the true and false of unknown vinegar sample and differentiate.
Adopt technique scheme, use chromatogram, the technology such as mass spectrum are carried out groping of testing conditions and quantitatively detect the characteristic component of vinegar geographical indication protection product, apply two class linear discriminant methods under comprehensive PCA principal component analysis (PCA) and Fisher criterion, by chromatogram, mass spectrum detection combines with chemometrics method and has set up the compressive classification model of vinegar geographical indication protection product, a kind of comprehensive classifying model authenticating method of vinegar geographical indication protection product is provided, investigate the difference of vinegar geographical indication protection product and other vinegar products, disclose the global feature of vinegar geographical indication protection product, can effectively identify vinegar geographical indication protection product, for qualification and the protection of geographical indication protection product provide reference.
Owing to adopting technique scheme; having overcome existing research only detects and analyzes the variable of single in vinegar or minority; can not disclose the global feature of vinegar geography symbol product; cannot effectively be applied to the authenticity of vinegar geography symbol product; be unfavorable for the shortcoming of the protection of vinegar geography symbol product, having solved prior art cannot carry out the effectively technical matters of qualification to the true and false of vinegar geographical indication protection product.
As a further improvement on the present invention; described vinegar geographical indication protection product is Shanxi mature vinegar geographical indication protection product, and the described characteristic of division factor comprises Co, As, Al, Mg, Ca, arabite, erythritol, sorbierite, proline, lysine and pyruvic acid.These characteristic of division factors as main, have been taken into account other types variable to identify inorganic elements that accuracy rate is high and polyvalent alcohol type variable in the disaggregated model of four major types variable simultaneously, more can reflect the global feature of Shanxi mature vinegar geographical indication protection product.
As a further improvement on the present invention, described compressive classification model is:
A=-2.9970 × 10
-3c
co+ 1.0957 × 10
-4c
as+ 1.9331 × 10
-3c
al+ 3.8446 × 10
-5c
mg+ 8.0572 × 10
-5c
ca+ 1.3194 × 10
-4c
arabite-8.1130 × 10
-5c
erythritol-2.1542 × 10
-4c
sorbierite+ 5.9381 × 10
-6c
proline-3.3950 × 10
-5c
lysine-4.8603 × 10
-5c
pyruvic acid, described discriminant critical value is 0.0043.This compressive classification model has been considered the weight coefficient of each variable, by this compressive classification model, can effectively identify Shanxi mature vinegar geographical indication protection product, highly sensitive.Discriminant critical value calculates gained by the weighted mean value of Fisher two class discriminant function averages, and the sample that is greater than critical value is Shanxi mature vinegar geographical indication protection product, and the sample that is less than critical value is not Shanxi mature vinegar geographical indication protection product.
As a further improvement on the present invention; described vinegar geographical indication protection product is Zhenjiang sweet (mature) vinegar geographical indication protection product, and the described characteristic of division factor comprises threonine, serine, glycocoll, lysine, Ba, erythritol, xylitol and lactic acid.These characteristic of division factors as main, have been taken into account other types variable to identify amino acid type variable that accuracy rate is high in the disaggregated model of four major types variable simultaneously, more can reflect the global feature of Zhenjiang sweet (mature) vinegar geographical indication protection product.
As a further improvement on the present invention, described compressive classification model is: B=1.4462 × 10
-4c
threonine+ 6.5185 × 10
-5c
serine-1.9386 × 10
-4c
glycocoll+ 1.2814 × 10
-5c
lysine+ 9.2693 × 10
-6c
ba-1.0712 × 10
-4c
erythritol-4.7652 × 10
-5c
xylitol-2.3420 × 10
-8c
lactic acid, described discriminant critical value is 0.001476.This compressive classification model has been considered the weight coefficient of each variable, by this compressive classification model, can effectively identify Zhenjiang sweet (mature) vinegar geographical indication protection product, highly sensitive.Discriminant critical value calculates gained by the weighted mean value of Fisher two class discriminant function averages; the sample that is greater than critical value is Zhenjiang sweet (mature) vinegar geographical indication protection product, and the sample that is less than critical value is not Zhenjiang sweet (mature) vinegar geographical indication protection product.
Compared with prior art, the invention has the beneficial effects as follows: use chromatogram, the technology such as mass spectrum are carried out groping of testing conditions and quantitatively detect the characteristic component of vinegar geographical indication protection product, apply two class linear discriminant methods under comprehensive PCA principal component analysis (PCA) and Fisher criterion, by chromatogram, mass spectrum detection combines with chemometrics method, set up the compressive classification model of vinegar geographical indication protection product, a kind of comprehensive classifying model authenticating method of vinegar geographical indication protection product is provided, investigate the difference of vinegar geographical indication protection product and other vinegar products, get rid of the interference of food additives etc., various types of characteristic components are considered all sidedly, disclose the global feature of vinegar geographical indication protection product, can effectively identify vinegar geographical indication protection product, for qualification and the protection of geographical indication protection product provide reference.Meanwhile, provide the characteristic of division factor and the compressive classification model of Shanxi mature vinegar geographical indication protection product, reflected the global feature of Shanxi mature vinegar geographical indication protection product, be conducive to qualification and the protection of Shanxi mature vinegar geographical indication protection product; The characteristic of division factor and the compressive classification model of Zhenjiang sweet (mature) vinegar geographical indication protection product are provided; the global feature that has reflected Zhenjiang sweet (mature) vinegar geographical indication protection product, is conducive to qualification and the protection of Zhenjiang sweet (mature) vinegar geographical indication protection product.
Brief description of the drawings
Fig. 1 is the qualification result schematic diagram of Shanxi mature vinegar geographical indication protection product compressive classification model for sample.
Fig. 2 is the qualification result schematic diagram of Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification model for sample.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further details.
The analysis of embodiment mono-polyvalent alcohol.
Instrument condition: Trace GC-DSQ, purchased from Thermo company of the U.S.; Roto Fix 32A type hydro-extractor, purchased from German Laboroto company; 4003 type Rotary Evaporators, purchased from German Laboroto company; DHG-9203A type electric heating constant-temperature blowing drying box, purchased from Shanghai Yi Heng Science and Technology Ltd..
Chromatographic condition: chromatographic column: DB-35ms post, 30m × 0.25mm × 0.25 μ m; Injector temperature: 250 DEG C; Transmission line temperature: 240 DEG C; Program temperature: 70 DEG C keep 1min, rises to 190 DEG C with 10 DEG C/min speed, then rises to 210 DEG C with the speed of 5 DEG C/min, then rise to 220 DEG C and keep 5min with the speed of 3 DEG C/min, then rises to 250 DEG C and keep 2min with the speed of 15 DEG C/min; Carrier gas: helium, constant current, 1.0mL/min; Splitless injecting samples, sampling volume 1 μ L.
Mass spectrum parameter: ionization pattern: electron bombardment ionization source (EI), energy is 70eV; Ion source temperature is 280 DEG C; Analyzer (electron-multiplier) voltage is 1427V; Solvent delay is 8min; Scan mode: adopt quick full scan and the mode of selecting ion scan to combine; Full scan scope is m/z 50-300; Selecting characteristic ion is m/z 115,145,187.
The processing of sample:
A) extraction of sample: take 1.000g sample, put in 10mL color comparison tube, add 8mL acetonitrile, water constant volume is to 10mL.Shake up vibration, transfer in 15mL centrifuge tube, the centrifugal 5min of 4000r/min, gets supernatant 5mL rotary evaporation in heart bottle and does near, 105 DEG C of oven dry;
B) sample is derivative: get 1mL pyridine, 0.1g oxammonium hydrochloride, adds above-mentioned heart bottle, bottle stopper beyond the Great Wall, 90 DEG C of oximate 0.5h.Get 1mL acetic anhydride and add in oximate afterproduct, react 1.5h at 135 DEG C.Take out heart bottle, put to room temperature, add 5mL water, 10mL methenyl choloride, vortex mixing 30s, makes two to be separated.Get organic phase, add a small amount of anhydrous sodium sulfate jolting, more than placing 15min, by organic phase upper machine after 0.45 μ m membrane filtration;
C) preparation of blank sample: measure the content operation of acetonitrile-water (V/V=8: 2) 5mL, in cucurbit, is spin-dried for, in 105 DEG C of oven dry, all the other press B), as qualitative investigation;
D) standard working curve is drawn: adopting deionized water preparation erythritol, arabite, xylitol, inositol, sweet mellow wine, sorbitol concentration is 1000mg/L mixed standard solution, uses the standard solution of deionized water stepwise dilution to variable concentrations when use.Get that concentration is respectively 0.1,1.0,5.0,50.0,100.0, the standard solution 1.00mL of 200.0mg/L in cucurbit, be spin-dried for, in 105 DEG C of oven dry, all the other press B) content operation.
Embodiment bis-organic acid analyses.
Instrument condition: E2695 type high performance liquid chromatograph, purchased from Waters company of the U.S.; Sep-Pak C18 solid-phase extraction column, MAX solid-phase extraction column, purchased from Waters company of the U.S., first activate with methyl alcohol, the each 5mL of water before use; ENVI-Carb solid-phase extraction column, purchased from Supelco company of the U.S., first activates with 5mL methyl alcohol before use.Mobile phase: 25mmol/LKH2PO4 solution, pH=2.6; Chromatographic column: Prevail Organic acid 250mm × 4.6mm × 5 μ m; Flow velocity: 1mL/min; Column temperature: 25 DEG C; Sample size: 10 μ L; Detect wavelength: 210nm.
The processing of sample: measure 2.0mL vinegar sample, water is settled to 25.0mL as sample solution, for subsequent use.C18SPE column purification: get 5mL sample solution by SPE post, discard the 3mL of initial outflow, collect efflux 2mL thereafter, upper machine is measured after 0.45 μ m membrane filtration.
The analysis of embodiment triamido acid.
Instrument condition: the L-8900 of Hitachi automatic amino acid analyzer, purchased from HIT; Analytical column: HITACHI2622SC-PH ion analysis post, 4.6mmID × 60mmL; Column temperature: 57 DEG C; Reaction column temperature: 135 DEG C; Damping fluid flow velocity: 0.4mL/min; Reactant liquor flow velocity: 0.35mL/min; Ultraviolet detects wavelength: a passage, 570nm; Second channel, 440nm.
The processing of sample: by 25 times of 0.02mol/L hydrochloric acid dilutions for vinegar sample, upper machine is measured after 0.45 μ m membrane filtration.
The analysis of embodiment tetra-inorganic elements.
Instrument condition: THERMO Xii quadrupole rod icp ms, inductively coupled plasma emission spectrometer, purchased from power & light company of the U.S., instrument condition is respectively in table 1, table 2; CEM MARs high flux airtight microwave digestion system, purchased from U.S. CE M company, instrument condition is in table 3; Mili-Q water purification machine, purchased from Millipore Corp. of the U.S..
The processing of sample: take 2.5g sample in digester, add 4mL nitric acid and 1mL 30% hydrogen peroxide, micro-wave digestion is carried out in sealing, clears up Parameter Conditions referring to table 3.Digestion solution is settled to 25mL, and after 0.45 μ m membrane filtration, upper machine is measured, and does reagent blank twice simultaneously.
Table 1 icp ms acquisition parameter
Table 2 inductive coupling plasma emission spectrograph acquisition parameter
| Element | Wavelength/nm |
| Al | 394.4 |
| Ca | 393.3 |
| Cu | 324.7 |
| Fe | 239.5 |
| K | 766.4 |
| Mg | 279.5 |
| Mn | 257.6 |
| Na | 589.5 |
| Ti | 323.4 |
| Zn | 202.5 |
Table 3 micro-wave digestion Parameter Conditions
| Step | Power W | Temperature/DEG C | Retention time/min | Time-to-climb/min |
| 1 | 800 | 120 | 10 | 20 |
| 2 | 800 | 190 | 10 | 20 |
The foundation of embodiment five Shanxi mature vinegar geographical indication protection product compressive classification models.
Amount to 62 for the sample of setting up Shanxi mature vinegar geographical indication protection product compressive classification model; wherein; 37, Shanxi mature vinegar geographical sign protection sample (produce and produce that family provides or local supermarket is bought by locality), 25 of control sample (comprising the vinegar sample in the places of production such as Guangdong, Hunan, Shanxi, Zhenjiang, Sichuan).
Correlation of variables analysis, the PCA using in the present invention analyzes and the foundation of Fisher discrimination model is all write handling procedure in science software for calculation Matlab 6.5.
Amino acid, polyvalent alcohol, organic acid and inorganic elements four major types variable (totally 71 variablees) are carried out to data processing: wherein component concentration is the variable of " 0 ", in data analysis process, does not study; In amino acid, be added on vinegar the inside because glutamic acid can be used as food additives, cause indivedual sample glutamic acid to be far longer than other sample content, therefore glutamic acid data do not participate in modeling in data analysis and modeling process; In inorganic elements, part contains the sxemiquantitative data variable that is less than test analysis detection lower limit, does not also participate in modeling; In all types of variablees of four major types variable, calculate the related coefficient between all two pairs of variablees, delete correlativity highland variable, carry out the optimization of variable.Table 4 is data analysis of the present invention variable used.
Table 4 data analysis variable used
Based on the content of variable after amino acid, polyvalent alcohol, organic acid and the optimization of inorganic elements four major types, adopt PCA to analyze rejecting abnormalities sample the preliminary feasibility that judges disaggregated model, set up respectively the disaggregated model of four major types variable by Fisher criterion.Wherein, the False Rate minimum of inorganic elements disaggregated model to control sample; next is polyvalent alcohol and amino acid; and the False Rate of organic acid disaggregated model is slightly large; difference between inorganic elements and polyvalent alcohol type variable energy reflected well Shanxi mature vinegar geographical indication protection product and control sample is described in four major types variable.Therefore; to reflect that the inorganic elements of geography information and the polyol component of production technology information are as main optimization object; with due regard to amino acid and organic acid composition; the weight coefficient of each variable in the disaggregated model of employing mahalanobis distance calculating four major types variable; delete the little variable of weight coefficient, the characteristic of division factor of determining Shanxi mature vinegar geographical indication protection product is Co, As, Al, Mg, Ca, arabite, erythritol, sorbierite, proline, lysine and pyruvic acid.Carry out after linear discriminant analysis, set up a Shanxi mature vinegar geographical indication protection product compressive classification model based on four major types variable: A=-2.9970 × 10
-3c
co+ 1.0957 × 10
-4c
as+ 1.9331 × 10
-3c
al+ 3.8446 × 10
-5c
mg+ 8.0572 × 10
-5c
ca+ 1.3194 × 10
-4c
arabite-8.1130 × 10
-5c
erythritol-2.1542 × 10
-4c
sorbierite+ 5.9381 × 10
-6c
proline-3.3950 × 10
-5c
lysine-4.8603 × 10
-5c
pyruvic acid, discriminant critical value calculates gained by the weighted mean value of Fisher two class discriminant function averages, above formula discriminant critical value is 0.0043, adopt chromatogram, the technology such as mass spectrum are to Co in unknown sample, As, Al, Mg, Ca, arabite, erythritol, sorbierite, proline, the concentration of lysine and pyruvic acid detects, use Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification model to calculate the predicted value of unknown sample, the sample that predicted value is greater than critical value is Shanxi mature vinegar geographical indication protection product, the sample that predicted value is less than critical value is not Shanxi mature vinegar geographical indication protection product.
The foundation of embodiment six Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification models.
Amount to 74 for the sample of setting up Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification model; wherein; 47, Zhenjiang sweet (mature) vinegar geographical sign protection sample (produce and produce that family provides or local supermarket is bought by locality), 27 of control sample (comprising the vinegar sample in the places of production such as Guangdong, Hunan, Shanxi, Zhenjiang, Sichuan).
Amino acid, polyvalent alcohol, organic acid and inorganic elements four major types variable (totally 71 variablees) are carried out to data processing: wherein component concentration is the variable of " 0 ", in data analysis process, does not study; In amino acid, be added on vinegar the inside because glutamic acid can be used as food additives, cause indivedual sample glutamic acid to be far longer than other sample content, therefore glutamic acid data do not participate in modeling in data analysis and modeling process; In inorganic elements, part contains the sxemiquantitative data variable that is less than test analysis detection lower limit, does not also participate in modeling; In all types of variablees of four major types variable, calculate the related coefficient between all two pairs of variablees, delete correlativity highland variable, carry out the optimization of variable, data analysis variable used is as shown in table 4.
Based on the content of variable after amino acid, polyvalent alcohol, organic acid and the optimization of inorganic elements four major types, adopt PCA to analyze rejecting abnormalities sample the preliminary feasibility that judges disaggregated model, set up respectively the disaggregated model of four major types variable by Fisher criterion.Wherein, the False Rate minimum of Amino Acid Classification model to control sample, is secondly organic acid and inorganic elements.Therefore; taking amino acid composition as main optimization object; the with due regard to composition of organic acid, inorganic elements and polyvalent alcohol; the weight coefficient of each variable in the disaggregated model of employing mahalanobis distance calculating four major types variable; delete the little variable of weight coefficient, the characteristic of division factor of determining Zhenjiang sweet (mature) vinegar geographical indication protection product is threonine, serine, glycocoll, lysine, Ba, erythritol, xylitol and lactic acid.Carry out after linear discriminant analysis, set up a Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification model based on four major types variable: B=1.4462 × 10
-4c
threonine+ 6.5185 × 10
-5c
serine-1.9386 × 10
-4c
glycocoll+ 1.2814 × 10
-5c
lysine+ 9.2693 × 10
-6c
ba-1.0712 × 10
-4c
erythritol-4.7652 × 10
-5c
xylitol-2.3420 × 10
-8c
lactic acid, discriminant critical value calculates gained by the weighted mean value of Fisher two class discriminant function averages, and above formula discriminant critical value is 0.001476.Adopt the technology such as chromatogram, mass spectrum to detect the concentration of threonine, serine, glycocoll, lysine, Ba, erythritol, xylitol and lactic acid in unknown sample; use Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification model to calculate the predicted value of unknown sample; the sample that predicted value is greater than critical value is Zhenjiang sweet (mature) vinegar geographical indication protection product, and the sample that predicted value is less than critical value is not Zhenjiang sweet (mature) vinegar geographical indication protection product.
The checking of embodiment seven Shanxi mature vinegar geographical indication protection product compressive classification models.
After Shanxi mature vinegar geographical indication protection product compressive classification model is set up, adopt different Zhenjiang sweet (mature) vinegar geographical indication protection product noise made in coughing or vomiting left mature vinegar geographical indication protection product and other the non-geographical sign protection samples close with production technology of production technology to differentiate checking to this compressive classification model.Totally 129, sample; wherein; 37, Shanxi mature vinegar geographical sign protection sample, 92, non-Shanxi mature vinegar geographical sign protection sample (comprising 47, Zhenjiang sweet (mature) vinegar sample, 11, the left mature vinegar sample of noise made in coughing or vomiting, 34, other non-geographical sign protection samples).
To the differentiation result of 129 verification samples as shown in Figure 1, wherein, ground is designated as the abbreviation of geographical indication protection product to Shanxi mature vinegar geographical indication protection product compressive classification model.Have 6 verification samples and differentiate mistake, comprise 1 Shanxi mature vinegar geographical sign protection sample and 5 other samples differentiation mistakes, False Rate is 4.7%.From the differentiation situation of all types of vinegar samples; particularly all differentiate correct for the different left mature vinegar of noise made in coughing or vomiting in the similar only place of production of production technology; as can be seen here; the compressive classification model of setting up taking Co, As, Al, Mg, Ca, arabite, erythritol, sorbierite, proline, lysine and pyruvic acid as characterization factor can well reflect the feature of Shanxi mature vinegar geographical indication protection product, and the computing of the test result based on above 11 characterization factors and compressive classification model can be carried out correct differentiation to Shanxi mature vinegar geographical indication protection product.
The checking of embodiment eight Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification models.
After Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification model is set up, the Shanxi mature vinegar geographical indication protection product that employing production technology is different and the left mature vinegar geographical indication protection product of noise made in coughing or vomiting and other non-geographical sign protection samples are differentiated checking to this compressive classification model.Totally 131, sample; wherein; 47, Zhenjiang sweet (mature) vinegar geographical sign protection sample, 84, non-Zhenjiang sweet (mature) vinegar geographical sign protection sample (comprising 37, Shanxi mature vinegar sample, 11, the left mature vinegar sample of noise made in coughing or vomiting, 36, other non-geographical sign protection samples).
To the differentiation result of 131 verification samples as shown in Figure 2, wherein, ground is designated as the abbreviation of geographical indication protection product to Zhenjiang sweet (mature) vinegar geographical indication protection product compressive classification model.Have 12 verification samples and differentiate mistake, comprise 6 Zhenjiang sweet (mature) vinegar geographical sign protection samples and 6 other samples differentiation mistakes, False Rate is 9.2%.From the differentiation situation of all types of vinegar samples; particularly all differentiate correct for the different left mature vinegar of noise made in coughing or vomiting in the similar only place of production of production technology; as can be seen here; the compressive classification model of setting up taking threonine, serine, glycocoll, lysine, Ba, erythritol, xylitol and lactic acid as characterization factor can well reflect the feature of Zhenjiang sweet (mature) vinegar geographical indication protection product, and the computing of the test result based on above 8 characterization factors and compressive classification model can be carried out correct differentiation to Zhenjiang sweet (mature) vinegar geographical indication protection product.
Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, can also make some simple deduction or replace, all should be considered as belonging to protection scope of the present invention.
Claims (1)
1. a comprehensive classifying model authenticating method for vinegar geographical indication protection product, is characterized in that: comprise the steps:
A) detection of vinegar geographical indication protection product characteristic component: respectively vinegar geographical indication protection product and control sample are carried out to the detection of four major types variable from amino acid, polyvalent alcohol, organic acid and four aspects of inorganic elements;
B) adopt correlation analysis to carry out the optimization of variable: respectively the related coefficient between any two pairs of variablees in all types of variablees of described four major types variable to be calculated, deleted the high variable of correlativity, carry out the optimization of variable;
C) set up respectively the disaggregated model of four major types variable: based on the content of variable after amino acid, polyvalent alcohol, organic acid and the optimization of inorganic elements four major types, adopt PCA to analyze rejecting abnormalities sample the preliminary feasibility that judges disaggregated model, set up respectively the disaggregated model of four major types variable by Fisher criterion;
D) foundation of the compressive classification model of vinegar geographical indication protection product: adopt mahalanobis distance to calculate the weight coefficient of each variable in described disaggregated model, delete the little variable of weight coefficient, determine the characteristic of division factor, carry out after linear discriminant analysis, set up a compressive classification model based on four major types variable, obtain discriminant critical value;
E) true and false of unknown vinegar sample is differentiated: detect the concentration of the characteristic of division factor described in unknown vinegar sample, use described compressive classification model to calculate predicted value, by the comparison of described predicted value and described discriminant critical value, carry out the true and false of unknown vinegar sample and differentiate;
Described vinegar geographical indication protection product is Shanxi mature vinegar geographical indication protection product or zhenjiang vinegar geographical indication protection product, and the corresponding characteristic of division factor of described Shanxi mature vinegar comprises Co, As, Al, Mg, Ca, arabite, erythritol, sorbierite, proline, lysine and pyruvic acid; The corresponding characteristic of division factor of described zhenjiang vinegar comprises threonine, serine, glycocoll, lysine, Ba, erythritol, xylitol and lactic acid;
The compressive classification model of described Shanxi mature vinegar is A,
a=-2.9970 × 10
-3 c co+ 1.0957 × 10
-4 c as+ 1.9331 × 10
-3 c al+ 3.8446 × 10
-5 c mg+ 8.0572 × 10
-5 c ca+ 1.3194 × 10
-4 c arabite-8.1130 × 10
-5 c erythritol-2.1542 × 10
-4 c sorbierite+ 5.9381 × 10
-6 c proline-3.3950 × 10
-5 c lysine-4.8603 × 10
-5 c pyruvic acid, described discriminant critical value is 0.0043;
The compressive classification model of described zhenjiang vinegar is B,
b=1.4462 × 10
-4 c threonine+ 6.5185 × 10
-5 c serine-1.9386 × 10
-4 c glycocoll+ 1.2814 × 10
-5 c lysine+ 9.2693 × 10
-6 c ba-1.0712 × 10
-4 c erythritol-4.7652 × 10
-5 c xylitol-2.3420 × 10
-8 c lactic acid, described discriminant critical value is 0.001476.
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