CN114279991B - White spirit brand identification method - Google Patents
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Abstract
The invention relates to a method for identifying white spirit brands, which relates to the technical field of white spirit identification and comprises the steps of collecting ultraviolet spectrum data and near infrared spectrum data of a white spirit sample, carrying out dimension reduction treatment, extracting ultraviolet characteristic values and near infrared characteristic values of the white spirit sample, respectively mixing the white spirit sample with k kinds of quantum dots to collect three-dimensional fluorescence spectrum data, taking the k kinds of quantum dots as new one-dimensional degree data, expanding the three-dimensional fluorescence spectrum data to corresponding four-dimensional fluorescence spectrum data, carrying out pretreatment, carrying out mathematical decomposition, extracting fluorescence characteristic values, fusing three characteristic values of the white spirit sample, obtaining fusion characteristics of the white spirit sample, dividing the white spirit sample into a training set and a testing set, taking the classified sample fusion characteristics as input of a classifier, and outputting classification results by the classifier to achieve identification of the white spirit brands.
Description
Technical Field
The invention relates to the technical field of white spirit identification, in particular to a white spirit brand identification method.
Background
The white spirit is a traditional fermentation product, the brewing process is complex, the formed aroma types are various, the white spirit with higher value often becomes a main target of adulteration under the driving of benefits, for example, the traditional white spirit brand identification can use sensory evaluation methods, chromatographic methods, mass spectrometry, electronic noses, electronic tongues and the like, but the rapid identification of a large number of white spirit brands cannot be realized by the methods.
Disclosure of Invention
The technical problems solved by the invention are as follows: the method for identifying the brands of the white spirit solves the problem that the prior art cannot realize rapid identification of the brands of the white spirit in large quantities.
The invention solves the technical problems by adopting the technical scheme that: the method for identifying the brands of the white spirit comprises the following steps:
s01, selecting m white spirit samples with different brands, wherein each brand has n white spirit samples, and acquiring ultraviolet spectrum data and near infrared spectrum data of each white spirit sample;
s02, reducing the dimension of ultraviolet spectrum data to obtain ultraviolet characteristic values of all white spirit samples, and reducing the dimension of near infrared spectrum data to obtain near infrared characteristic values of all white spirit samples;
s03, acquiring three-dimensional fluorescence spectrum data of a mixture of the same white spirit sample and k quantum dots, and then taking the k quantum dots as data of a new dimension, so that the three-dimensional fluorescence spectrum data are expanded to corresponding four-dimensional fluorescence spectrum data, preprocessing the four-dimensional fluorescence spectrum data, and performing mathematical decomposition to obtain concentration scores of the white spirit sample as fluorescence characteristic values;
s04, fusing the ultraviolet characteristic value, the fluorescence characteristic value and the near infrared characteristic value of the white spirit sample to obtain the fusion characteristic of the white spirit sample;
s05, dividing m multiplied by n samples into a training set and a testing set, taking the divided sample fusion characteristics as input of a classifier, and outputting a classification result by the classifier to achieve identification of white spirit brands.
Further, in step S02, the dimension reduction uses a PCA method.
Further, k is a positive integer greater than or equal to 2.
Further, in step S03, the preprocessing eliminates raman scattering in the spectrum of the sample to be measured by subtracting the blank data, and eliminates rayleigh scattering in the spectrum of the sample to be measured by interpolation.
Further, in step S03, the mathematical decomposition uses an AWRCQLD algorithm.
The invention has the beneficial effects that: according to the method for identifying the brands of the white spirit, the ultraviolet spectrum data and the near infrared spectrum data of the white spirit sample are collected, the dimension reduction treatment is carried out, the ultraviolet characteristic value and the near infrared characteristic value of the white spirit sample are extracted, the white spirit sample is respectively mixed with k kinds of quantum dots to collect three-dimensional fluorescence spectrum data, k kinds of quantum dots are used as new one-dimensional data, the three-dimensional fluorescence spectrum data are expanded to corresponding four-dimensional fluorescence spectrum data, the pretreatment is carried out, the mathematical decomposition is carried out, the fluorescence characteristic value is extracted, the three characteristic values of the white spirit sample are fused, the fusion characteristic of the white spirit sample is obtained, the white spirit sample is divided into a training set and a test set, the classified sample fusion characteristic is used as the input of a classifier, and the classification result is output by the classifier to achieve the identification of the brands of the white spirit, and the problem that the rapid identification of the brands of the white spirit in a large scale cannot be realized in the prior art is solved.
Drawings
FIG. 1 is a schematic flow chart of the method for identifying the brand of white spirit.
Detailed Description
The method for identifying the brands of the white spirit disclosed by the invention, as shown in the attached figure 1, comprises the following steps of:
s01, selecting m white spirit samples with different brands, wherein each brand has n white spirit samples, and acquiring ultraviolet spectrum data and near infrared spectrum data of each white spirit sample;
s02, reducing the dimension of ultraviolet spectrum data to obtain ultraviolet characteristic values of all white spirit samples, and reducing the dimension of near infrared spectrum data to obtain near infrared characteristic values of all white spirit samples;
s03, acquiring three-dimensional fluorescence spectrum data of a mixture of the same white spirit sample and k quantum dots, and then taking the k quantum dots as data of a new dimension, so that the three-dimensional fluorescence spectrum data are expanded to corresponding four-dimensional fluorescence spectrum data, preprocessing the four-dimensional fluorescence spectrum data, and performing mathematical decomposition to obtain concentration scores of the white spirit sample as fluorescence characteristic values;
s04, fusing the ultraviolet characteristic value, the fluorescence characteristic value and the near infrared characteristic value of the white spirit sample to obtain the fusion characteristic of the white spirit sample;
s05, dividing m multiplied by n samples into a training set and a testing set, taking the divided sample fusion characteristics as input of a classifier, and outputting a classification result by the classifier to achieve identification of white spirit brands.
Further, in step S02, the dimension reduction uses a PCA method.
Further, k is a positive integer greater than or equal to 2.
Further, in step S03, the preprocessing eliminates raman scattering in the spectrum of the sample to be measured by subtracting the blank data, and eliminates rayleigh scattering in the spectrum of the sample to be measured by interpolation.
Further, in step S03, the mathematical decomposition uses an AWRCQLD algorithm.
Examples
Taking 20 different brands of white spirit samples, each brand has 20 white spirit samples, and 3 kinds of quantum dots are taken as examples, namely m=20, n=20 and k=3.
The first step: and collecting ultraviolet spectrum data and near infrared spectrum data of 400 white spirit samples.
The ultraviolet spectrum data acquisition method comprises the following steps: taking 400 mu L of white spirit sample as a reference, adding the white spirit sample into a cuvette, and recording the ultraviolet spectrum of the white spirit sample in the wavelength range of 200-350nm by using a UV-2700 ultraviolet spectrophotometer, wherein the scanning speed of the UV-2700 ultraviolet spectrophotometer is high, and the slit width is 5nm.
The near infrared spectrum data acquisition method comprises the following steps: near infrared spectrometer of model II Antaris Fourier transform at 4000-12000cm -1 And collecting the near infrared spectrum of the white spirit in the wave number range. The transmission mode is selected for spectrum scanning, the scanning times are 32 times, and the resolution is 8cm -1 。
Second step: and reducing the dimension of the ultraviolet spectrum data by using a PCA method to obtain an ultraviolet spectrum data characteristic value a1, and reducing the dimension of the near infrared spectrum data by using the PCA method to obtain an infrared spectrum data characteristic value a2.
Third step: three-dimensional fluorescence spectrum data of the same white spirit sample and three quantum dot mixtures are obtained, then three quantum dots are used as data of a new dimension, the three-dimensional fluorescence spectrum data are expanded to corresponding four-dimensional fluorescence spectrum data, the four-dimensional fluorescence spectrum data are preprocessed, mathematical decomposition is carried out, and the concentration score of the white spirit sample is obtained and used as a fluorescence characteristic value a3.
The three-dimensional fluorescence spectrum data acquisition method comprises the following steps: taking 600 mu l of white spirit sample and 30 mu l of first quantum dot solution in a cuvette, obtaining three-dimensional fluorescence data of the white spirit sample and the first quantum dot by using an FLS970 fluorescence spectrometer, taking 600 mu l of white spirit sample and 30 mu l of second quantum dot solution in the cuvette, obtaining three-dimensional fluorescence data of the white spirit sample and the second quantum dot by using the FLS970 fluorescence spectrometer, taking 600 mu l of white spirit sample and 30 mu l of third quantum dot solution in the cuvette, and obtaining three-dimensional fluorescence data of the white spirit sample and the third quantum dot by using the FLS970 fluorescence spectrometer.
FLS970 fluorescence spectrometer parameters were set as follows: the excitation wavelength is a wavelength which is within the range of 200-600nm and is separated by 5 nm; emission wavelengths are wavelengths within the range of 215-600nm, which are spaced 5nm apart; the scanning speed is 30000nm/min; the detector voltage was 700V; the width of the excitation and emission slits was 5nm.
On the basis of obtaining three-dimensional fluorescence data of the same white spirit sample and three quantum dots respectively, three quantum dots are used as data of a new dimension, so that three-dimensional fluorescence spectrum data are expanded to corresponding four-dimensional fluorescence spectrum data, namely four-dimensional fluorescence spectrum data of the white spirit sample are obtained, the four-dimensional fluorescence spectrum data are preprocessed, raman scattering in a spectrum of a detected sample is eliminated by subtracting blank data, rayleigh scattering in the spectrum of the detected sample is eliminated by an interpolation method, and then mathematical decomposition is carried out by adopting an AWRCQLD algorithm, so that a characteristic value a3 of the four-dimensional fluorescence spectrum data is obtained.
Fourth step: and fusing the ultraviolet characteristic value, the fluorescence characteristic value and the near infrared characteristic value of the white spirit sample to obtain the fusion characteristic of the white spirit sample.
Specifically, the characteristic value a1 of ultraviolet spectrum data, the characteristic value a2 of infrared spectrum data and the characteristic value a3 of four-dimensional fluorescence spectrum data are fused into the characteristic a= (a 1, a2, a 3), and 400 fusion characteristics corresponding to 400 white spirit samples are obtained.
And fifthly, dividing 400 samples into a training set and a testing set, taking the divided sample fusion characteristics as input of a classifier, and outputting a classification result by the classifier to achieve identification of white spirit brands.
Specifically, 14 of 20 sample fusion characteristics of each white spirit in 20 white spirits are divided into training sets, the remaining 6 are used as test sets, a ten-fold cross validation method and a network search algorithm are adopted to search the optimal punishment factors and the optimal nuclear parameters of the classifier respectively, the classifier is trained by the training sets, the classifier is validated by the test sets, the classifier capable of accurately identifying the brand of the white spirits is obtained, and the identification of the brand of the white spirits is realized by the output result of the classifier.
In this embodiment, the classifier capable of accurately identifying brands of white spirits can accurately identify brands of 20 selected white spirits, so that the identification of the 20 white spirits can be realized.
Claims (5)
1. The method for identifying the brands of the white spirit is characterized by comprising the following steps of:
s01, selecting m white spirit samples with different brands, wherein each brand has n white spirit samples, and acquiring ultraviolet spectrum data and near infrared spectrum data of each white spirit sample;
s02, reducing the dimension of ultraviolet spectrum data to obtain ultraviolet characteristic values of all white spirit samples, and reducing the dimension of near infrared spectrum data to obtain near infrared characteristic values of all white spirit samples;
s03, acquiring three-dimensional fluorescence spectrum data of a mixture of the same white spirit sample and k quantum dots, and then taking the k quantum dots as data of a new dimension, so that the three-dimensional fluorescence spectrum data are expanded to corresponding four-dimensional fluorescence spectrum data, preprocessing the four-dimensional fluorescence spectrum data, and performing mathematical decomposition to obtain concentration scores of the white spirit sample as fluorescence characteristic values;
s04, fusing the ultraviolet characteristic value, the fluorescence characteristic value and the near infrared characteristic value of the white spirit sample to obtain the fusion characteristic of the white spirit sample;
s05, dividing m multiplied by n samples into a training set and a testing set, taking the divided sample fusion characteristics as input of a classifier, and outputting a classification result by the classifier to achieve identification of white spirit brands.
2. The method for identifying white spirit brands according to claim 1, wherein in step S02, said dimension reduction is performed by PCA.
3. The method for identifying white spirit brands according to claim 1, wherein in step S03, k is a positive integer greater than or equal to 2.
4. The method according to claim 1, wherein in step S03, the preprocessing eliminates raman scattering in the spectrum of the sample under test by subtracting blank data, and eliminates rayleigh scattering in the spectrum of the sample under test by interpolation.
5. The method for identifying white spirit brands according to claim 1, wherein in step S03, said mathematical decomposition uses AWRCQLD algorithm.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101825572A (en) * | 2010-06-11 | 2010-09-08 | 重庆大学 | Method for differentiating Chinese spirits with different flavor types with fluorescein |
| CN101936891A (en) * | 2010-08-30 | 2011-01-05 | 泸州老窖股份有限公司 | Method for identifying Chinese spirits with different flavors by ultraviolet spectroscopy |
| CN102099671A (en) * | 2008-05-20 | 2011-06-15 | 大学健康网络 | Device and method for fluorescence-based imaging and monitoring |
| CA3097861A1 (en) * | 2011-01-21 | 2012-07-26 | Labrador Diagnostics Llc | Systems and methods for sample use maximization |
| CN103234918A (en) * | 2013-04-12 | 2013-08-07 | 泸州品创科技有限公司 | Method for discriminating liquor odor by liquid array taste sense bionic sensor |
| EP2698404A1 (en) * | 2011-04-12 | 2014-02-19 | Dai Nippon Printing Co., Ltd. | Microparticles, particle sets, anti-counterfeiting ink, anti-counterfeiting toner, anti-counterfeiting sheet and anti-counterfeiting medium |
| CN104502320A (en) * | 2014-12-29 | 2015-04-08 | 江南大学 | Method for identifying strong flavor Baijiu by combining three-dimensional fluorescence spectrum with PCA-SVM |
| CN104677875A (en) * | 2015-03-24 | 2015-06-03 | 江南大学 | Method for identifying baijiu of different brands by combining three-dimensional fluorescence spectrum and parallel factor |
| CN108410202A (en) * | 2018-02-12 | 2018-08-17 | 成都驷博生物科技有限公司 | A kind of preparation method of seven methine cyanine dyes of quinoline |
| WO2018175565A1 (en) * | 2017-03-22 | 2018-09-27 | Lawrence Livermore National Security, Llc | System and method for controlling depth of imaging in tissues using fluorescence microscopy under ultraviolet excitation following staining with fluorescing agents |
| CN111735804A (en) * | 2020-07-01 | 2020-10-02 | 北京百漾科技股份有限公司 | Ratio type fluorescence method for distinguishing fen-flavor raw pulp from liquid-method white spirit and solid-liquid-method white spirit |
| CN112280556A (en) * | 2020-11-14 | 2021-01-29 | 西北农林科技大学 | Preparation of phosphate radical responsive carbon quantum dots and application of phosphate radical responsive carbon quantum dots in fingerprint fluorescence identification |
| CN112697745A (en) * | 2021-01-20 | 2021-04-23 | 西安电子科技大学 | Method for measuring alcohol content of white spirit |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9964489B2 (en) * | 2014-09-16 | 2018-05-08 | Lawrence Livermore National Security, Llc | System and method for controlling depth of imaging in tissues using fluorescence microscopy under ultraviolet excitation following staining with fluorescing agents |
-
2021
- 2021-12-30 CN CN202111646763.XA patent/CN114279991B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102099671A (en) * | 2008-05-20 | 2011-06-15 | 大学健康网络 | Device and method for fluorescence-based imaging and monitoring |
| CN101825572A (en) * | 2010-06-11 | 2010-09-08 | 重庆大学 | Method for differentiating Chinese spirits with different flavor types with fluorescein |
| CN101936891A (en) * | 2010-08-30 | 2011-01-05 | 泸州老窖股份有限公司 | Method for identifying Chinese spirits with different flavors by ultraviolet spectroscopy |
| CA3097861A1 (en) * | 2011-01-21 | 2012-07-26 | Labrador Diagnostics Llc | Systems and methods for sample use maximization |
| EP2698404A1 (en) * | 2011-04-12 | 2014-02-19 | Dai Nippon Printing Co., Ltd. | Microparticles, particle sets, anti-counterfeiting ink, anti-counterfeiting toner, anti-counterfeiting sheet and anti-counterfeiting medium |
| CN103234918A (en) * | 2013-04-12 | 2013-08-07 | 泸州品创科技有限公司 | Method for discriminating liquor odor by liquid array taste sense bionic sensor |
| CN104502320A (en) * | 2014-12-29 | 2015-04-08 | 江南大学 | Method for identifying strong flavor Baijiu by combining three-dimensional fluorescence spectrum with PCA-SVM |
| CN104677875A (en) * | 2015-03-24 | 2015-06-03 | 江南大学 | Method for identifying baijiu of different brands by combining three-dimensional fluorescence spectrum and parallel factor |
| WO2018175565A1 (en) * | 2017-03-22 | 2018-09-27 | Lawrence Livermore National Security, Llc | System and method for controlling depth of imaging in tissues using fluorescence microscopy under ultraviolet excitation following staining with fluorescing agents |
| CN108410202A (en) * | 2018-02-12 | 2018-08-17 | 成都驷博生物科技有限公司 | A kind of preparation method of seven methine cyanine dyes of quinoline |
| CN111735804A (en) * | 2020-07-01 | 2020-10-02 | 北京百漾科技股份有限公司 | Ratio type fluorescence method for distinguishing fen-flavor raw pulp from liquid-method white spirit and solid-liquid-method white spirit |
| CN112280556A (en) * | 2020-11-14 | 2021-01-29 | 西北农林科技大学 | Preparation of phosphate radical responsive carbon quantum dots and application of phosphate radical responsive carbon quantum dots in fingerprint fluorescence identification |
| CN112697745A (en) * | 2021-01-20 | 2021-04-23 | 西安电子科技大学 | Method for measuring alcohol content of white spirit |
Non-Patent Citations (7)
| Title |
|---|
| He M et al.Identification of liquors from the same brand based on ultraviolet, near-infrared and fluorescence spectroscopy combined with chemometrics.《FOOD CHEMISTRY》.2023,第400卷第1-9页. * |
| Jiang ZY et al.Building a Bridge from Papermaking to Solar Fuels.《ANGEWANDTE CHEMIE-INTERNATIONAL EDITION》.2019,第58卷(第42期),第14850-14854页. * |
| Li JW et al.A novel acid-sensitive quantum dot sensor array for the identification of Chinese baijiu.《ANALYTICAL METHODS》.2020,第12卷(第39期),第4789-4797页. * |
| Yang Ping et al.Quantum Dots (QDs) Based Baijiu Fluorescent Identification Sensor Array Jointly Verified by Multivariate Analysis and Radial Basis Function Neural Network.《Analytical Methods》.2019,第1-30页. * |
| 朱明等.白酒生产中快速检测技术的研究进展.《酿酒科技》.2023,第2卷(第2期),第95-101+106页. * |
| 陈飞等.浓香型白酒基酒的现状和发展趋势.2017,第36卷(第10期),第5-8页. * |
| 韦露露等.基于白酒风味的现代分析技术及其生物活性研究进展.《中国酿造》.2021,第40卷(第9期),第12-18页. * |
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