CN101487825B - Method for recognizing tea kinds and/or grades - Google Patents
Method for recognizing tea kinds and/or grades Download PDFInfo
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- CN101487825B CN101487825B CN2009100788288A CN200910078828A CN101487825B CN 101487825 B CN101487825 B CN 101487825B CN 2009100788288 A CN2009100788288 A CN 2009100788288A CN 200910078828 A CN200910078828 A CN 200910078828A CN 101487825 B CN101487825 B CN 101487825B
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Abstract
The invention discloses a method for distinguishing varieties and/or levels of tea, which utilizes heracles electronic nose to test and analyze the varieties and/or the levels of the tea. By adopting the method, the preliminary treatment of the sample is simple; the whole experiment process causes no pollution since no organic solvent is used; the testing speed is rapid, and the accuracy rate of recognition is high; the method has simple steps and convenient operation, and is convenient for large scale popularization and application.
Description
Technical field
The present invention relates to a kind of method of discerning tea kinds and/or grade.
Background technology
At home and abroad, the tea leaf quality great majority are by people's subjective appreciation, because the origin cause of formation of the global feature information of tea aroma and structure are very complicated, quality of tea leaves is not just can express by the quantification of certain composition, and it is the concentrated expression of various compositions in the tealeaves.Change of external conditions and self emotional change can influence people's sensory sensitivity, thereby influence the accuracy of evaluation result.
Electronic Nose (Electronic Nose), claim artificial olfactory analysis system (Artificial Olfactory) again, be analysis, identification and the detection technique of a kind of novelty of growing up the nineties in 20th century, the artificial intelligence system of forming by sensor-based system and automatic mode recognition system of discerning at various smells, its the similar people's of principle of work nose is so be referred to as " Electronic Nose ".Electronic Nose has merged the current new results in active fields such as materialogy, electronic signal, applied mathematics, sensing and computer science the most, once producing great attention and the common attention that has been subjected to external scientific circles.
A kind of novel electronic nose has appearred again at present, HERACLES Electronic Nose, and it is a kind of artificial intelligence smell recognition system of making according to the principle of gas chromatography.It is compared with traditional sensor type Electronic Nose, has the advantages that detection is quicker, the analyzing and testing scope is bigger, and has avoided the sensor intoxicating phenomenon of sensor type Electronic Nose.
Summary of the invention
The invention provides a kind of method of discerning tea kinds and/or grade.
The method of identification tea kinds provided by the invention and/or grade is with HERACLES Electronic Nose the kind and/or the grade of tealeaves to be measured to be discerned.
In the described method, the injector temperature of HERACLES Electronic Nose can be 180-300 ℃, as 180-250 ℃ or 250-300 ℃.
In the described method, the detector temperature of HERACLES Electronic Nose can be 190-300 ℃, as 190-280 ℃ or 280-300 ℃.
In the described method, the test column heating schedule of HERACLES Electronic Nose can be: 40 ℃ keep 1-10s, are warmed up to 260 ℃ with the speed of 1-10 ℃/s and keep 1-15s.The test column heating schedule specifically can be 40 ℃ and keeps 1-2s, is warming up to 260 ℃ with the speed of 1-5 ℃/s and keeps 1-5s.The test column heating schedule specifically also can be 40 ℃ and keeps 2-10s, is warming up to 260 ℃ with the speed of 5-10 ℃/s and keeps 5-15s.
In the described method, the sample injection time of HERACLES Electronic Nose can be 1-100s, as 1-20s or 20-100s.
In the described method, the input mode of HERACLES Electronic Nose specifically can be the static headspace gas sample introduction.
When HERACLES Electronic Nose adopted the static headspace gas sample introduction, the parameter that relates to was as follows:
The static headspace gas occurrence temperature can be 50-100 ℃, as 50-80 ℃ or 80-100 ℃.
The static headspace gas time of origin can be 10-40min, as 10-20min or 20-40min.
The static headspace gas sample size can be 1ml-5ml, as 1ml-3ml or 3ml-5ml.
In the described method, the sample that is used to detect specifically can be water and extracts the extract that tealeaves to be measured obtains.
The present invention is applied to HERACLES Electronic Nose the identification of tea kinds and/or grade first, and by a large amount of tests, has obtained best parameter setting scope, and when adopting the parameter in this scope, discrimination index is very high.Take the inventive method identification tea leaf quality, have following advantage: sample pre-treatments is simple; Whole experiment is not utilized organic solvent, and is pollution-free; Detection speed was fast, and the corresponding steps of static headspace gas generation step required time and gas chromatography is approaching, but the analysis recognition time of each sample only needs about 3 minutes, much smaller than 0.5-1 hour of gas chromatography; The recognition accuracy height; Step is simple, easy to operate, is convenient to large-scale promotion application.
Embodiment
Following embodiment is convenient to understand better the present invention, but does not limit the present invention.Experimental technique among the following embodiment if no special instructions, is conventional method.Reagent among the following embodiment if no special instructions, is can buy from routine biochemistry reagent and obtains.
Used Electronic Nose is the HERACLES ElectronicNose of French Alpha MOS instrument company in following examples.
Tealeaves sample used in following examples is as follows:
The tealeaves sample of embodiment 1 to embodiment 3 (four kinds of tealeaves, every kind of tealeaves is got 5 samples) is as follows:
Jasmine tea: Fujian jasmine tea (one-level); Green tea: Xinyang Maojian Tea (summer tea one-level); Black tea: Keemun black tea (one-level); Blue or green tea: Anxi Tieguanyin Tea (one-level); Above sample is commercially available in bulk.
The tealeaves sample of embodiment 4 to embodiment 6 is as follows:
The tippy tea sample of five grades of mill camellia field, Xinyang: m240, m180, m110, m80, m20; Each grade is got 5 samples.
The tealeaves sample of embodiment 7 to embodiment 9 is as follows:
The tippy tea sample of five grades on tea plantation, lion river port, Xinyang: s treasure, the pure bud of s, s bud, s spring tail, s two adopt bud; Each grade is got 5 samples.
The recognition capability of Electronic Nose to sample represented in following discrimination index (Discrimination Index is called for short DI), calculated by following formula:
DI=[1-(the gross space areas of the summation of each cohort spatial area/all cohorts of containing)] * 100.
In principal component analysis (PCA) (PCA) collection of illustrative plates of sample, if when not superposeing between the cohort, DI be on the occasion of, when between the cohort stack being arranged, DI is a negative value, shows between the sample and can not distinguish fully.The maximal value of DI is 100, and DI just shows effective differentiation between 80-100, and the DI value is big more, and it is good more to distinguish.
The identification of embodiment 1, variety classes tealeaves
Each tealeaves sample detects respectively, and is specific as follows:
Brew 5g tealeaves to be measured 5 minutes with the 250mL boiling water, obtain tea.5ml tea is put into sample bottle (the sample bottle specification is 10ml), then sample is positioned over the static headspace gas generating means, the static headspace gas occurrence temperature is 50 ℃, and the static headspace gas time of origin is 40min.Extract static headspace gas with the Electronic Nose sample introduction needle then, be expelled in the Electronic Nose injection port, the static headspace gas sample size is 1ml.
Being provided with of Electronic Nose is as follows: 180 ℃ of injector temperatures; 190 ℃ of detector temperatures; Sample injection time 1s; The post heating schedule is: 40 ℃ keep 1s, are warmed up to 260 ℃ with the speed of 1 ℃/s and keep 1s.
Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 91.435%, 2.656%, is 91 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 94.091%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 91, and this illustrates that different types of sample can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt method of the present invention to discern different types of tealeaves, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
The identification of embodiment 2, variety classes tealeaves
Each tealeaves sample detects respectively, and is specific as follows:
Brew 5g tealeaves to be measured 5 minutes with the 250mL boiling water, obtain tea.5ml tea is put into sample bottle (the sample bottle specification is 10ml), then sample is positioned over the static headspace gas generating means, the static headspace gas occurrence temperature is 80 ℃, and the static headspace gas time of origin is 20min.Extract static headspace gas with the Electronic Nose sample introduction needle then, be expelled in the Electronic Nose injection port, the static headspace gas sample size is 3ml.
Being provided with of Electronic Nose is as follows: 250 ℃ of injector temperatures; 280 ℃ of detector temperatures; Sample injection time 20s; The post heating schedule is: 40 ℃ keep 2s, are warmed up to 260 ℃ with the speed of 5 ℃/s and keep 5s.
Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 95.715%, 2.146%, is 97 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 97.861%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 97, and this illustrates that different types of sample can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt method of the present invention to discern different types of tealeaves, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
The identification of embodiment 3, variety classes tealeaves
Each tealeaves sample detects respectively, and is specific as follows:
Brew 5g tealeaves to be measured 5 minutes with the 250mL boiling water, obtain tea.5ml tea is put into sample bottle (the sample bottle specification is 10ml), then sample is positioned over the static headspace gas generating means, the static headspace gas occurrence temperature is 100 ℃, and the static headspace gas time of origin is 10min.Extract static headspace gas with the Electronic Nose sample introduction needle then, be expelled in the Electronic Nose injection port, the static headspace gas sample size is 5ml.
Being provided with of Electronic Nose is as follows: 300 ℃ of injector temperatures; 300 ℃ of detector temperatures; Sample injection time 100s; The post heating schedule is: 40 ℃ keep 10s, are warmed up to 260 ℃ with the speed of 10 ℃/s and keep 15s.
Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 91.334%, 3.203%, is 92 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 94.537%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 92, and this illustrates that different types of sample can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt method of the present invention to discern different types of tealeaves, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
The identification of embodiment 4, different brackets tealeaves
Method is with embodiment 1.Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 90.832%, 3.146%, is 90 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 93.978%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 90, and the sample of this explanation different brackets can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt the tealeaves of method identification different brackets of the present invention, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
The identification of embodiment 5, different brackets tealeaves
Method is with embodiment 2.Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 96.805%, 2.747%, is 98 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 99.552%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 98, and the sample of this explanation different brackets can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt the tealeaves of method identification different brackets of the present invention, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
The identification of embodiment 6, different brackets tealeaves
Method is with embodiment 3.Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 92.665%, 3.897%, is 94 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 96.547%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 94, and the sample of this explanation different brackets can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt the tealeaves of method identification different brackets of the present invention, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
The identification of embodiment 7, different brackets tealeaves
Method is with embodiment 1.Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 90.262%, 2.186%, is 89 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 92.448%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 89, and the sample of this explanation different brackets can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt the tealeaves of method identification different brackets of the present invention, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
The identification of embodiment 8, different brackets tealeaves
Method is with embodiment 2.Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 94.312%, 3.708%, is 97 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 98.02%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 97, and the sample of this explanation different brackets can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt the tealeaves of method identification different brackets of the present invention, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
The identification of embodiment 9, different brackets tealeaves
Method is with embodiment 3.Each sample carries out revision test 6 times.
Through principal component analysis (PCA) (PCA) software that Electronic Nose carries the data of being obtained are analyzed.Analysis result shows: the accumulation variance contribution ratio of first principal component (PC1) and Second principal component, (PC2) is 91.221%, 2.132%, is 93 by the two-dimensional map discrimination index (DI) that PC1 (X-axis)-PC2 (Y-axis) is constituted.Wherein the accumulation variance contribution ratio sum of PC1 and PC2 is 93.353%, and greater than 85%, this explanation PC1 and PC2 have comprised the very big quantity of information of sample, can reflect the Global Information of sample.Discrimination index (DI) is 93, and the sample of this explanation different brackets can well be distinguished, and recognition result conforms to actual, and recognition result is correct.In 6 repeated experiments, the discrimination index unanimity, repeatability is fabulous.Adopt the tealeaves of method identification different brackets of the present invention, it is short to analyze recognition time, and sample pre-treatments is simple, does not also utilize organic solvent in the experimentation.
Claims (2)
1. a method of discerning tea kinds and/or grade is with HERACLES Electronic Nose the kind and/or the grade of tealeaves to be measured to be discerned;
The injector temperature of described HERACLES Electronic Nose is 250-300 ℃; Detector temperature is 280-300 ℃; The test column heating schedule is: 40 ℃ keep 1-10s, are warmed up to 260 ℃ with the speed of 1-10 ℃/s and keep 1-15s; Sample injection time is 20-100s; Input mode is the static headspace gas sample introduction; The static headspace gas occurrence temperature is 80-100 ℃; The static headspace gas time of origin is 20-40min; The static headspace gas sample size is 3ml-5ml.
2. the method for claim 1, it is characterized in that: in the described method, the sample that is used to detect is that water extracts the extract that tealeaves to be measured obtains.
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| CN2009100788288A CN101487825B (en) | 2009-03-03 | 2009-03-03 | Method for recognizing tea kinds and/or grades |
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| CN2009100788288A CN101487825B (en) | 2009-03-03 | 2009-03-03 | Method for recognizing tea kinds and/or grades |
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| CN101487825B true CN101487825B (en) | 2011-11-16 |
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Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101692053B (en) * | 2009-10-09 | 2012-01-18 | 江苏大学 | Multi-sensing information fusion based instrumental intelligent evaluation method for quality of famous tea |
| CN105021748A (en) * | 2014-04-29 | 2015-11-04 | 中国科学院大连化学物理研究所 | Tea volatile aroma component analysis method |
| CN104849387A (en) * | 2015-05-22 | 2015-08-19 | 福建中烟工业有限责任公司 | Tea brewing fragrance substance detection method, tea cover cup fragrance collecting method and tea cover cup fragrance collecting device |
| CN104849320A (en) * | 2015-06-04 | 2015-08-19 | 安徽农业大学 | Yellow bud tea aroma grade sorting method by use of electronic nose |
| CN104897729A (en) * | 2015-06-04 | 2015-09-09 | 安徽农业大学 | Sorting method of storage time of melon slice tea by using electronic nose |
| CN105181761B (en) * | 2015-08-26 | 2018-02-16 | 安徽农业大学 | A kind of method using electronic nose quick discriminating tealeaves radiation absorber amount |
| CN105866223B (en) * | 2016-03-29 | 2018-06-29 | 福建农林大学 | A kind of Wuyi cliff tea flavour Grade Judgment based on electronic tongues detection information |
| CN106018623A (en) * | 2016-07-14 | 2016-10-12 | 上海应用技术学院 | Identification method for nitrogen-containing compound in Keemun black tea |
| CN106124680A (en) * | 2016-07-14 | 2016-11-16 | 上海应用技术学院 | The authentication method of nitrogen-containing compound in a kind of Radix Campylotropis Hirtella (Herba Myrsines Africanae) tea |
| CN107273421B (en) * | 2017-05-16 | 2020-10-23 | 浙江大学 | High-accuracy mode identification and detection method for aroma type and quality of tea |
| CN109164188A (en) * | 2018-10-16 | 2019-01-08 | 云南清莲生物科技有限公司 | A method of distinguishing different producing area and fermented type oolong tea |
| CN113466409A (en) * | 2021-06-24 | 2021-10-01 | 上海应用技术大学 | Method for identifying different production processes of Zhengshan Xiaochong black tea through electronic nose |
| CN114586546A (en) * | 2022-03-14 | 2022-06-07 | 西南大学 | Automatic strawberry picking device based on electronic nose and image recognition and control method thereof |
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