Tobacco substitution and cigarette blend maintenance based on near-infrared spectral similarity

LI Shitou1 LIAO Fu1 HE Wenmiao1 ZHANG Lili1 TIE Jinxin1 LI Yongsheng1 HAO Xianwei1 TIAN Yunong1 BI Yiming1 WU Jizhong1 WANG Hui1 XU Qingquan1

(1.Technology Center, China Tobacco Zhejiang Industrial Co., Ltd.)

【Abstract】Near-infrared spectroscopic spectral similarity was investigated and applied to tobacco substitution and cigarette blend maintenance. By means of a local spectral pre-processing and an ensemble spectral similarity algorithm, any interference on baseline and scattering in the spectrum was eliminated and the technique was used for characterizing tobacco leaf based on spectral similarity to aid cigarette blending. The results showed that the matching rates according to the spectral similarity with target strips were 82.0% and 75.7% viewed from the producing area and stalk position respectively. The average differences in main chemical components, such as total sugar and nicotine, were less than 5%. There was no significant difference in chemical components and sensory indicators between the original blend and a simulated blend obtained through combining strip similarity with strip combination similarity.

【Keywords】 Cigarette blend; Near-infrared spectrum; Spectral similarity; Sensory evaluation; Tobacco leaf substitution; Formula maintenance ;

【DOI】

【Funds】 Science and Technology Project of China Tobacco Zhejiang Industrial Co., Ltd. (ZJZY2015A005)

Download this article

(Translated by ZHAO B)

    References

    [1] Zhang J P, Chen J H, Shu R X, et al. Tobacco characteristics identification and blending formula study by using NIRS [J]. Acta Tabacaria Sinica, 2007, 13 (5): 1–5 (in Chinese).

    [2] Li Q L, Chen K Y, Deng X H, et al. Method of tobacco substitution based on differential analysis of tobacco pyrolysis [J]. Tobacco Science & Technology, 2018, 51 (8): 77–84 (in Chinese).

    [3] Hao X W, Tie J X, He W M, et al. Style simulation and substitution of Brazilian tobacco based on near-infrared spectrum and sensory evaluation [J]. Tobacco Science & Technology, 2018, 51 (10): 83–89 (in Chinese).

    [4] Luo X G, Wang N, Zhang Z L, et al. Application of association rule mining method to intelligent maintenance of tobacco blend of cigarette [J]. Acta Tabacaria Sinica, 2018, 24 (3): 21–29 (in Chinese).

    [5] Yun Y H, Li H D, Deng B C, et al. An overview of variable selection methods in multivariate analysis of near-infrared spectra [J]. TrAC Trends in Analytical Chemistry, 2019, 113: 102–115.

    [6] Wang J J, Luo L P, Li H, et al. Simultaneous determination of N, Cl, P and K in tobacco by FT-NIR spectrometry [J]. Tobacco Science & Technology, 2004 (12): 24–27 (in Chinese).

    [7] Yang K, Cai J Y, Zhang C P, et al. Analysis of tobacco site features using near-infrared spectroscopy and projection model [J]. Spectroscopy and Spectral Analysis, 2014, 34 (12): 3277–3280 (in Chinese).

    [8] Duan Y Q, Tao Y, Zhe W, et al. Application of near-infrared spectroscopy in determination of the producing areas of tobacco leaf [J]. Journal of Yunnan University (Natural Sciences), 2011, 33 (1): 77–82 (in Chinese).

    [9] Shu R X, Cai J Y, Yang Z Y, et al. Analysis of tobacco style features using near-infrared spectroscopy and projection model [J]. Spectroscopy and Spectral Analysis, 2014, 34 (10): 2764–2768 (in Chinese).

    [10] Engel J, Gerretzen J, Szymańska E, et al. Breaking with trends in pre-processing? [J]. TrACTrends in Analytical Chemistry, 2013, 50: 96–106.

    [11] Savitzky A, Golay M J E. Smoothing and differentiation of data by simplified least squares procedures [J]. Analytical Chemistry, 1964, 36 (8): 1627–1639.

    [12] Geladi P, MacDougall D, Martens H. Linearization and scatter-correction for near-infrared reflectance spectra of meat [J]. Applies Spectroscopy, 1985, 39 (3): 491–500.

    [13] Barnes R J, Dhanoa M S, Lister S J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra [J]. Applied Spectroscopy, 1989, 43 (5): 772–777.

    [14] Bi Y M, Yuan K L, Xiao W Q, et al. A Local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation [J]. Analytica Chimica Acta, 2016, 909: 30–40.

    [15] Van Der Meer F, Bakker W. Cross correlogram spectral matching: application to surface mineralogical mapping by using AVIRIS data from Cuprite, Nevada [J]. Remote Sensing of Environment, 1997, 61 (3): 371–382.

    [16] Frewen B E, Merrihew G E, Wu C C, et al. Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries [J]. Analytical Chemistry, 2006, 78 (16): 5678–5684.

    [17] Chang C I. An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis [J]. IEEE Transactions on Information Theory, 2000, 46 (5): 1927–1932.

    [18] Bi Y M, Li S T, Zhang L L, et al. Quality evaluation of flue-cured tobacco by near-infrared spectroscopy and spectral similarity method [J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 215: 398–404.

    [19] YC/T 138-1998 Tobacco and tobacco products—The sensory evaluation methods [S] (in Chinese).

This Article

ISSN:1002-0861

CN:41-1137/TS

Vol 53, No. 02, Pages 88-93

February 2020

Downloads:9

Share
Article Outline

Abstract

  • 1 Materials and methods
  • 2 Results and analysis
  • 3 Conclusion
  • References