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Baijiu age detection method based on 0-order sparse learning TSK fuzzy model

A fuzzy model and detection method technology, applied in the detection field, can solve the problem of not finding liquor age timing and identification devices and methods, and achieve the effect of overcoming the limitations of traditional physical and chemical analysis and artificial sensory evaluation and high detection sensitivity

Inactive Publication Date: 2018-10-12
JIANGNAN UNIV
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Problems solved by technology

So far, there is no report on the aging timing and identification device and method for all liquors in China

Method used

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  • Baijiu age detection method based on 0-order sparse learning TSK fuzzy model
  • Baijiu age detection method based on 0-order sparse learning TSK fuzzy model
  • Baijiu age detection method based on 0-order sparse learning TSK fuzzy model

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Embodiment

[0029] A method for detecting the age of liquor with 0-order sparse learning TSK fuzzy model, comprising the following steps:

[0030] 1) Using the electronic nose to detect the volatile substances of the liquor to be tested with known liquor age, and obtain the original frequency signal; respectively measure 5 bottles of 1-year-old liquor samples, 5 bottles of 2-year-old liquor samples, and 3-year-old liquor samples 5 bottles, 5 bottles of 5-year-old liquor samples, 5 bottles of 7-year-old liquor samples, 5 bottles of 10-year-old liquor samples, shake each standard sample well, take 5mL and put it in a 20mL airtight sample of the fast gas chromatography electronic nose analysis system In the bottle, use the fast gas chromatography electronic nose fingerprint analysis system to collect the original frequency signal of the liquor in the 20mL airtight sample bottle, and establish the standard sample set of the model;

[0031] 2), adopt the 0-order sparse learning TSK fuzzy model...

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Abstract

The invention discloses a Baijiu age detection method based on a 0-order sparse learning TSK fuzzy model, wherein the method comprises the following steps: 1) detecting Baijiu volatile substances of to-be-detected Baijiu with known Baijiu age by an electronic nose, to obtain an original frequency signal; 2) processing the frequency signal obtained in the step 1) by the 0-order sparse learning TSKfuzzy model based on fuzzy subspace cluster, and establishing a Baijiu identification model; and 3) detecting the Baijiu volatile substances of the to-be-detected Baijiu by the electronic nose, and judging the Baijiu age by the identification model based on the 0-order sparse learning TSK fuzzy model and obtained in the step 2). The Baijiu age detection method based on the 0-order sparse learningTSK fuzzy model can quickly, conveniently and accurately judge the Baijiu age, has high detection sensitivity, can overcome the limitations of traditional physical and chemical analysis and artificialsensory evaluation, and provides an effective and simple new method for Baijiu quality evaluation.

Description

technical field [0001] The invention relates to a detection method, in particular to a liquor age detection method based on 0-order sparse learning TSK fuzzy model. Background technique [0002] "Chen" is the evaluation standard of good wine in the traditional culture of the Chinese nation. Old wine refers to high-quality liquor that has been stored for a long time. The age of liquor is the most important factor affecting the flavor of liquor. At present, the identification technology of wine age in caves in China is very backward. There are mainly electrolyte analysis and identification methods of liquor and volatility coefficient identification methods. These identification techniques have great limitations and inaccuracies. As well as different production processes, it is impossible to form a consistent identification method. Moreover, these identification methods are very troublesome. It is necessary to obtain the parameters of many components in the wine, and through c...

Claims

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Application Information

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IPC IPC(8): G01N30/02G01N30/86G06N7/02
CPCG01N30/02G01N30/8686G06N7/023
Inventor 邓赵红蒋亦樟王士同
Owner JIANGNAN UNIV
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