Quantitative forecasting method for fresh and sweet aroma type of cigarettes

Abstract

The invention discloses a quantitative forecasting method for the fresh and sweet aroma type of cigarettes on the basis of detection of content of 17 volatile components in cigarettes. Firstly, the content of the 17 volatile components in the cigarettes is measured with an independently developed and established sample treatment method and a GC-MS/MS analysis method, and the measured content is introduced into a regression equation, the value of Y fresh and sweet aroma is calculated, that is, the forecasted value of the score of the quantitative forecasting method for the green aroma type of the cigarettes is calculated and ranges from 0 to 10. The quantitative forecasting method for the fresh and sweet aroma type of the cigarettes is better fit with sensory actual smoke panel test results, the forecasted mean absolute error is 0.1329 scores, and favorable technical support is provided for keeping the sensory quality of the cigarettes stable.

Description

technical field [0001] The invention belongs to the field of quantitative prediction and control of the green flavor type of cigarettes, and specifically relates to a method for quantitatively predicting the green flavor type of cigarettes based on the detection of the content of 17 volatile components in shredded tobacco and combining the regression coefficient to construct a regression model method. Background technique [0002] The key to the stability and improvement of cigarette product quality lies in the stability and improvement of sensory quality. How to establish an evaluation method for cigarette sensory quality has always been a research hotspot in the industry. At present, the evaluation of the sensory quality of cigarettes is mainly carried out through the sensory evaluation of experts, and relevant national and industry standards have been issued and implemented. For example, GB 5606.4-2005 "Cigarette Part 4: Sensory Technical Requirements" and YC / T 497-2014 ...

AI Technical Summary

Problems solved by technology

For example, Mo Hongwei from Harbin Engineering University provided a method for predicting cigarette sensory evaluation and smoke index through immune neural network algorithm (CN 101419209 A), but the ingredients included in the calculation modeling are only total sugar, More than 10 conventional chemical composition indicators such as total nicotine and reducing sugar, which cannot represent the overall sensory characteristics of cigarettes

Hu Shengguo from Yizhong Tobacco (Group) Co., Ltd. invented a BP neural network model combined with fuzzy set method, and used the model to predict the sensory evaluation and smoke index of cigarettes (CN 1525394 A), but it is used for There are more than 110 chemical components to be modeled and calculated, and the qualitative and quantitative index detection is cumbersome, and the calculation is difficult, which leads to the disadvantages of low or unreliable prediction results

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