CN105758897A - White peony fragrance grade discriminating method based on electronic nose detection information - Google Patents
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- CN105758897A CN105758897A CN201610186734.2A CN201610186734A CN105758897A CN 105758897 A CN105758897 A CN 105758897A CN 201610186734 A CN201610186734 A CN 201610186734A CN 105758897 A CN105758897 A CN 105758897A
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- 238000001514 detection method Methods 0.000 title claims abstract description 27
- 239000003205 fragrance Substances 0.000 title claims abstract description 27
- 244000236658 Paeonia lactiflora Species 0.000 title claims abstract description 25
- 235000008598 Paeonia lactiflora Nutrition 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 16
- 239000007789 gas Substances 0.000 claims description 11
- 229910044991 metal oxide Inorganic materials 0.000 claims description 8
- 150000004706 metal oxides Chemical class 0.000 claims description 8
- 230000004069 differentiation Effects 0.000 claims description 6
- 239000000796 flavoring agent Substances 0.000 claims description 6
- 235000019634 flavors Nutrition 0.000 claims description 6
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 4
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 4
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 4
- 235000013305 food Nutrition 0.000 claims description 3
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 2
- UCKMPCXJQFINFW-UHFFFAOYSA-N Sulphide Chemical compound [S-2] UCKMPCXJQFINFW-UHFFFAOYSA-N 0.000 claims description 2
- 150000001335 aliphatic alkanes Chemical class 0.000 claims description 2
- 150000001412 amines Chemical class 0.000 claims description 2
- 229910021529 ammonia Inorganic materials 0.000 claims description 2
- -1 biogas Natural products 0.000 claims description 2
- 238000010411 cooking Methods 0.000 claims description 2
- 229930195733 hydrocarbon Natural products 0.000 claims description 2
- 150000002430 hydrocarbons Chemical class 0.000 claims description 2
- 239000001257 hydrogen Substances 0.000 claims description 2
- 229910052739 hydrogen Inorganic materials 0.000 claims description 2
- 229910000037 hydrogen sulfide Inorganic materials 0.000 claims description 2
- 239000003350 kerosene Substances 0.000 claims description 2
- 239000005416 organic matter Substances 0.000 claims description 2
- 239000003960 organic solvent Substances 0.000 claims description 2
- 244000269722 Thea sinensis Species 0.000 abstract description 6
- 238000007477 logistic regression Methods 0.000 abstract description 5
- 238000004458 analytical method Methods 0.000 abstract description 4
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 230000001953 sensory effect Effects 0.000 abstract description 2
- 238000013441 quality evaluation Methods 0.000 abstract 1
- 235000013616 tea Nutrition 0.000 description 8
- VIKNJXKGJWUCNN-XGXHKTLJSA-N norethisterone Chemical compound O=C1CC[C@@H]2[C@H]3CC[C@](C)([C@](CC4)(O)C#C)[C@@H]4[C@@H]3CCC2=C1 VIKNJXKGJWUCNN-XGXHKTLJSA-N 0.000 description 4
- 238000003380 quartz crystal microbalance Methods 0.000 description 3
- 229920001940 conductive polymer Polymers 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000010897 surface acoustic wave method Methods 0.000 description 2
- 235000009024 Ceanothus sanguineus Nutrition 0.000 description 1
- 240000003553 Leptospermum scoparium Species 0.000 description 1
- 235000015459 Lycium barbarum Nutrition 0.000 description 1
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000002322 conducting polymer Substances 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 210000000697 sensory organ Anatomy 0.000 description 1
- 229910052709 silver Inorganic materials 0.000 description 1
- 239000004332 silver Substances 0.000 description 1
- 230000035943 smell Effects 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 235000020334 white tea Nutrition 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
- G01N27/12—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body in dependence upon absorption of a fluid; of a solid body in dependence upon reaction with a fluid, for detecting components in the fluid
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- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Fats And Perfumes (AREA)
Abstract
The invention relates to a white peony fragrance grade discriminating method based on electronic nose detection information.The method comprises the steps of sample preparation, collection of detection information and building of a Logistic regression discrimination connection function and the discriminating result probability of all grades of samples.According to the discriminating method, the problem that manual review cannot be carried out for a long time due to sensory fatigue is effectively solved, discriminating result errors of different review staff are well eliminated, and the stability and uniformity of white peony fragrance grade discriminating results are improved; the whole review process is convenient and fast, consumed time is short, and the discriminating time for each sample is shorter than 5 min after the sample is manufactured; model analysis results prove that stability is good, and the uniformity is 90% higher than manual review result uniformity.The white peony fragrance grade discriminating method has the good practical value, and has the great significance in application of an automatic control means in the white peony production process and final tea product quality evaluation expression and selling circulation.
Description
Technical field
The present invention relates to a kind of tea grades method of discrimination, be specifically related to a kind of white peony fragrance Grade Judgment based on detection by electronic nose information, belong to food technology field.
Background technology
White peony belongs to Ramulus et Folium Mussaendae Pubescentis class, generally adopts the big Ramulus et Folium Mussaendae Pubescentis such as Fuding white tea, Fuding big milli tea, the big Ramulus et Folium Mussaendae Pubescentis in Fuan, big Ramulus et Folium Mussaendae Pubescentis of having stable political situation or Fujian Shuihsien's tea tree breed one bud one leaf, the two tender tips of leaf to make.Because its greenery press from both sides silver color pekoe bud likeness in form flower, after brewing, tender shoots in greenery holder, as if open at the beginning of bud, therefore named " white peony ".
Fragrance grade evaluation needs " sense organ " that rely on the person of evaluating to complete traditionally, not only evaluate the cycle longer, and it is easily subject to the interference of various subjectivity and extraneous factor, " concordance " and " quantization " of data is all not enough, it is difficult to objective and stably characterizes its actual quality, it is therefore necessary to finding a kind of standardization and easy-operating means realize the stabilizing determination of the fragrant grade of white peony milli.
Electronic Nose is a kind of typical Artificial Olfactory (ArtificialOlfactorySystem, AOS), and it is made up of certain selective electrochemical sensor permutation and suitable identification device, can recognise that simple and complicated abnormal smells from the patient.It is different from general chemistry instrument, obtained is not sample composition is qualitative and quantitative result, but give the Global Information of volatile ingredient in sample, i.e. " finger print data ", it can monitor the odor profile of ad-hoc location continuously, in real time in long time, by the comparison with the signal in the data base utilizing standard specimen to set up, it is identified and judgement.
Difference according to gas sensor material type, substantially Electronic Nose can be divided into three major types, it is metal-oxide (MetalOxideSensors respectively, MOS), conductive polymer subtype (ConductingPolymer), mass type: as application quartz crystal microbalance sensor (QuartzCrystalMicrobalance, and surface acoustic wave sensor (SurfaceAcousticWave, SAW) QCM).
Metal-oxide Electronic Nose is utilized to carry out the differentiation of white peony fragrance grade, namely Electronic Nose is obtained " finger print data " and is carried out classification prediction by its mathematics essence, the analysis method of such problem is had a lot by statistics, and common solution mainly has: " Logistic regression analysis " and " techniques of discriminant analysis ".When output variable is two classified variable, adopt " binary logistic regression analysis ", when output variable is many classified variables, generally adopt " multinomial Logistic regression analysis ".When " multinomial Logistic regression analysis " is used for solving one or more covariants with an ordered categorization dependent variable relation, the method is also referred to as " grade regression analysis ".
Summary of the invention
It is an object of the invention to provide a kind of white peony fragrance Grade Judgment based on detection by electronic nose information.
The purpose of the present invention is achieved through the following technical solutions.
1, a kind of white peony fragrance Grade Judgment based on detection by electronic nose information, it is characterised in that method of discrimination is as follows:
(1) sample preparation: weigh 5.0g Tea Samples to be checked in detection bottle, be placed under 45 DEG C of baking ovens and balance 15min, adopt metal-oxide Electronic Nose to detect；Described metal-oxide Electronic Nose is made up of 10 sensors, and different types of gas is produced hindrance function by each sensor, and concrete corresponding gas is respectively as follows: S1, ammonia, amine；S2, hydrogen sulfide, sulfide；S3, hydrogen；S4, ethanol, organic solvent；S5, volatilization gas in cooking food process；S6, methane, biogas, hydrocarbons；S7, imflammable gas；S8, volatile organic matter；S9, oxynitride, gasoline, kerosene；S10, alkane, imflammable gas；
(2) acquisition testing information: starting Electronic Nose and obtain the detection information of white peony fragrance, after this detection information being derived, choosing " S1 " therein sensor response value is variable X1, choosing " S3 " therein sensor response value is variable X2, choosing " S6 " therein sensor response value is variable X3, choosing " S7 " therein sensor response value is variable X4, choosing " S9 " therein sensor response value is variable X5；Described acquisition testing information, with the acquisition sample aroma component peak signal of telecommunication build detection information；
(3) building Logistic and return differentiation connectivity function, expression formula is respectively as follows:
Link1 level=-13.380+ (2.288X1-6.378X2-5.977X3+6.376X4-9.952X5)；
Link2 level=-9.521+ (2.288X1-6.378X2-5.977X3+6.376X4-9.952X5)；
Described " Link1 level " representative sample flavour grade is the connectivity function of 1 grade；" Link2 level " representative sample flavour grade is the connectivity function of 2 grades；
(4) each rating sample differentiates that probability of outcome formula is:
P1 grade=1/ 1+Exp (-Link1 level)；
P2 grades=1/ 1+Exp (-Link2 level)-P1 grade；
P3 grades=1-P1 grade-P2 grades；
Described P1 grade、P2 grades、P3 gradesNumerical value, the probability of each flavour grade judged for " detection by electronic nose information " ownership, the grade of the P corresponding to its maximum, for the fragrance grade of sample.
Advantages of the present invention and beneficial effect:
1, the white peony fragrance Grade Judgment based on metal-oxide detection by electronic nose information of the present invention, efficiently solves the problem that " manually evaluating " process can not carry out for a long time due to " sensory fatigue "；
2, the relative immobility of discrimination standard, eliminates the differentiation resultant error that difference is evaluated between personnel preferably, improves stability and the concordance of the fragrant grade assessment result of white peony milli；
3, evaluating overall process convenient and swift, elapsed time is short, and the overall process that the sample prepared completes to differentiate is less than 5min；
4, complete based on data to build owing to discrimination model adopts the large sample amount tea sample that the senior person of evaluating of multidigit carries out under kilter to evaluate, simultaneously, modal analysis results shows that model has good stability, with the concordance of artificial assessment result also above 90%, therefore this differentiation result has good practical value, the concordance realizing white peony fragrance ranking score grade standard can be assisted well, objectivity and versatility, to the application of Automated condtrol means in Tea Production process, and the evaluation table of final tea products quality addresses sale circulation and clears away the obstacles.
Detailed description of the invention
In order to fully disclose the white peony fragrance Grade Judgment based on detection by electronic nose information of the present invention, it is illustrated below in conjunction with embodiment.
Embodiment 1, a kind of fragrant Grade Judgment of white peony milli based on detection by electronic nose information, comprise the following steps:
(1) sample preparation: weigh 5.0g Tea Samples to be checked in detection bottle, be placed under 45 DEG C of baking ovens and balance 15min, adoptSmartnose metal-oxide Electronic Nose detects；
(2) acquisition testing information: start Electronic Nose and obtain the detection information of white peony fragrance, after this detection information is derived, " S1 " therein sensor response value variable X1=3.44；" S3 " sensor response value variable X2=2.32；" S6 " sensor response value variable X3=2.01；" S7 " sensor response value variable X4=2.26；" S9 " sensor response value variable X5=2.26；
(3) building Logistic and return differentiation connectivity function, expression formula is respectively as follows:
Link1 level=-13.380+ (2.288X1-6.378X2-5.977X3+6.376X4-9.952X5)；
Link2 level=-9.521+ (2.288X1-6.378X2-5.977X3+6.376X4-9.952X5)；
Described " Link1 level " representative sample fragrance grade is the connectivity function of 1 grade；" Link2 level " representative sample fragrance grade is the connectivity function of 2 grades, and after bringing data into, result is as follows:
Link1 level=-13.380+ (2.288 × 4.088-6.378 × 1.589-5.977 × 2.502+6.376 × 2.781-9.952 × 1.371)；
Link2 level=-9.521+ (2.288 × 4.088-6.378 × 1.589-5.977 × 2.502+6.376 × 2.781-9.952 × 1.371)；
(4) each rating sample differentiates that probability of outcome formula is:
P1 grade=1/ 1+Exp (-Link1 level)=1/ 1+Exp (-0.447)=0.610；
P2 grades=1/ 1+Exp (-Link2 level)-P1 grade=0.038；
P3 grades=1-P1 grade-P2 grades=1-0.610-0.038=0.352；
Described P1 grade、P2 grades、P3 gradesNumerical value, the probability of each fragrance grade judged for " detection by electronic nose information " ownership, the grade of the P corresponding to its maximum, for the fragrance grade of sample.
Result of calculation is, P1 grade=1.350 × 10-11, P2 grades=0.999, P3 grades=6.267 × 10-10, because 0.999 > 1.350 × 10-11>6.267×10-10, so P2 grades>P1 grade>P3 grades, therefore, this tea sample fragrance grade is judged as the maximum probability of 2 grades, reaches 99.9%, therefore determines that the fragrance grade of this white peony is 2 grades.
Claims (3)
1. the white peony fragrance Grade Judgment based on detection by electronic nose information, it is characterised in that method of discrimination is as follows:
(1) sample preparation: weigh 5.0g Tea Samples to be checked in detection bottle, be placed under 45 DEG C of baking ovens and balance 15min, adopt metal-oxide Electronic Nose to detect；
(2) acquisition testing information: starting Electronic Nose and obtain the detection information of white peony fragrance, after this detection information being derived, choosing " S1 " therein sensor response value is variable X1, choosing " S3 " therein sensor response value is variable X2, choosing " S6 " therein sensor response value is variable X3, choosing " S7 " therein sensor response value is variable X4, choosing " S9 " therein sensor response value is variable X5；
(3) building Logistic and return differentiation connectivity function, expression formula is respectively as follows:
Link1 level=-13.380+ (2.288X1-6.378X2-5.977X3+6.376X4-9.952X5)；
Link2 level=-9.521+ (2.288X1-6.378X2-5.977X3+6.376X4-9.952X5)；
Described " Link1 level " representative sample flavour grade is the connectivity function of 1 grade；" Link2 level " representative sample flavour grade is the connectivity function of 2 grades；
(4) each rating sample differentiates that probability of outcome formula is:
P1 grade=1/ 1+Exp (-Link1 level)；
P2 grades=1/ 1+Exp (-Link2 level)-P1 grade；
P3 grades=1-P1 grade-P2 grades；
Described P1 grade、P2 grades、P3 gradesNumerical value, the probability of each flavour grade judged for " detection by electronic nose information " ownership, the grade of the P corresponding to its maximum, for the fragrance grade of sample.
2. a kind of white peony fragrance Grade Judgment based on detection by electronic nose information according to claim 1, it is characterised in that described acquisition testing information, with the acquisition sample aroma component peak signal of telecommunication build detection information.
3. a kind of white peony fragrance Grade Judgment based on detection by electronic nose information according to claim 1, it is characterized in that described Electronic Nose is metal-oxide Electronic Nose, it is made up of 10 sensors, different types of gas is produced hindrance function by each sensor, concrete corresponding gas is respectively as follows: S1, ammonia, amine；S2, hydrogen sulfide, sulfide；S3, hydrogen；S4, ethanol, organic solvent；S5, volatilization gas in cooking food process；S6, methane, biogas, hydrocarbons；S7, imflammable gas；S8, volatile organic matter；S9, oxynitride, gasoline, kerosene；S10, alkane, imflammable gas.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106353217A (en) * | 2016-08-12 | 2017-01-25 | 泉州市旭丰粉体原料有限公司 | Odor test method |
| CN107843695A (en) * | 2017-10-31 | 2018-03-27 | 华东理工大学 | Tobacco and the electronic nose instrument evaluation method of tobacco product aesthetic quality |
| CN111707728A (en) * | 2020-06-29 | 2020-09-25 | 闽江师范高等专科学校 | Method for identifying white peony tea with different grades based on HS-PTR-TOF-MS |
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| US20140023557A1 (en) * | 2012-07-20 | 2014-01-23 | Eps Bio Technology Corp. | Electronic nose device |
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- 2016-03-29 CN CN201610186734.2A patent/CN105758897B/en not_active Expired - Fee Related
Patent Citations (4)
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| WO2014143291A2 (en) * | 2012-12-21 | 2014-09-18 | Research Triangle Institute | An encased polymer nanofiber-based electronic nose |
| CN103134850A (en) * | 2013-03-01 | 2013-06-05 | 河南农业大学 | Tea quality rapid detection apparatus and detection method based on characteristic fragrance |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106353217A (en) * | 2016-08-12 | 2017-01-25 | 泉州市旭丰粉体原料有限公司 | Odor test method |
| CN107843695A (en) * | 2017-10-31 | 2018-03-27 | 华东理工大学 | Tobacco and the electronic nose instrument evaluation method of tobacco product aesthetic quality |
| CN107843695B (en) * | 2017-10-31 | 2018-08-28 | 华东理工大学 | The electronic nose instrument evaluation method of tobacco and tobacco product aesthetic quality |
| CN111707728A (en) * | 2020-06-29 | 2020-09-25 | 闽江师范高等专科学校 | Method for identifying white peony tea with different grades based on HS-PTR-TOF-MS |
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Granted publication date: 20180710 |