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Chinese Agricultural Science Bulletin ›› 2024, Vol. 40 ›› Issue (1): 128-134.doi: 10.11924/j.issn.1000-6850.casb2023-0579

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Relationship Between Sensory Quality and Chemical Component of Flue-cured Tobacco in Shandong Province and Establishment of Quality Prediction Models

ZHOU Xiansheng1(), LI Xiaoyang2, LIU Zhiguang1, ZHOU Xiaoyu1, QIU Chengyu1, ZHUANG Zhilin3,4, CAO Jianmin3()   

  1. 1 China Tobacco Shandong Industry Co., Ltd., Jinan 250013
    2 Qingdao University, Qingdao, Shandong 266071
    3 Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, Shandong 266101
    4 Graduate School, Chinese Academy of Agricultural Sciences, Beijing 100081
  • Received:2023-08-15 Revised:2023-11-10 Online:2024-01-05 Published:2023-12-29

Abstract:

In order to explore the relationship between chemical components and sensory quality of flue-cured tobacco in Shandong Province, a sensory quality evaluation and determination of 23 chemical indicators in five categories, including conventional components, organic acids, alkaloids, monosaccharides and polyphenols, were conducted on representative flue-cured tobacco leaves from six regions in Shandong Province. Multiple statistical methods such as simple correlation, analysis of variance, principal component analysis, and regression analysis were used for data analysis. Six key quality indicators including total sugar, chlorine, chlorogenic acid, nornicotine, citric acid/nicotine, and oleic acid were screened. The sensory quality prediction model of Shandong tobacco leaves was constructed. The absolute difference between the predicted value and the actual value was 0.43-3.88 points. The relative error was -5.66%-6.32%, between actual value and predicted value of the verification sample, and the average error was 3.18%. The sensory quality prediction model based on key chemical components can provide certain technical support for the objective quality evaluation of flue-cured tobacco in Shandong Province.

Key words: flue-cured tobacco, multivariate statistical analysis, quality evaluation, chemical components, prediction model