Method for quantitatively predicting cigarette cocoa aroma type

Abstract

The invention discloses a method for quantitatively predicting the cigarette cocoa aroma type based on the detected contents of 19 volatile components in cigarettes. The method includes the steps that firstly, the contents of 19 volatile components in tobacco shreds are measured according to a sample handling method established through independent development and the GC-MS / MS analysis method; then, the measured contents are substituted into a regression equation to calculate the numerical value of the cocoa aroma Y, namely, the predicted value of the score obtained in the method for quantitatively predicting the cigarette cocoa aroma type, wherein the score ranges from 0 to 10. The method for quantitatively predicting the cigarette cocoa aroma type has high goodness of fit with the actual sensory smoke panel test result, and the score of the mean absolute error of prediction is 0.1108; a beneficial technical support is provided for maintaining the stability of the sensory quality of cigarettes.

Description

technical field [0001] The invention belongs to the field of quantitative prediction and control of cocoa flavor type of cigarettes, and specifically relates to a method for quantitatively predicting cocoa flavor type of cigarettes based on the detection content of 19 volatile components in shredded tobacco of cigarettes and a method of constructing a regression model in combination with regression coefficients. 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...

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 of 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 difficult to calculate

This leads to disadvantages such as low or unreliable prediction results.

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