近红外光谱烟叶质量等级快速检测与等级特征分析

doi:10.3964/j.issn.1000-0593(2021)03-0943-05

Abstract
References
Related Citation (15)

Download: PDF (1159 KB)
Export: BibTeX | EndNote (RIS)

Abstract The grade quality of flue-cured tobacco plays an important role in the formulation design and the stability of the cigarette industry. In this paper, 768 tobacco samples from 40 prefecture-level cities in China, 2018 are selected for the experiment. The samples were classified and graded by traditional industrial grading method, including 7 different grade grades of tobacco leaves. The way to establish a tobacco quality grade prediction model by near-infrared spectroscopy and the near-infrared absorption spectrum characteristics of chemical groups and related components in different grades of tobacco are studied. The results show that the national tobacco grades prediction model is established in the non-segregated area, and the prediction standard deviation between the modeling set and the test set is not more than 1.35. After the samples are divided into five major production areas, models of each production area are established, and the prediction standard deviation of the models built in each production area after the division is found to be lower than that of the national model. The model in the Southeast region, the Southwest region and the Huanghuai region increased greatly, and the standard deviation of the test set was no more than 1. 07. After the standard normal transform (SNV) pretreatment of the average spectrum of different quality grades tobacco samples, the analysis is performed based on the information of the organic groups and related substances absorbed by the near-infrared light in different frequency ranges. It is found that tobacco with better quality grades has the characteristics of lower cellulose content and higher sugar content such as starch. The tobacco with lower quality grades has the characteristics of higher cellulose content and lower sugar content such as starch. At the same time, the worst quality grade (the upper and lower) tobacco has the characteristics of higher protein content. The results show that the application of near-infrared spectroscopy can realize the rapid prediction of the quality level of tobacco leaves. The prediction deviation is basically between adjacent levels, which meets the actual application requirements, and the prediction accuracy can be further improved by establishing prediction models of different production areas. At the same time, different grades of tobacco have different absorption characteristics of groups mainly composed of cellulose, starch, sugars, and proteins, which is also the information basis for applying near-infrared spectroscopy to achieve rapid detection of tobacco quality grades. This has important practical significance for improving the tobacco leaf grading evaluation system, further optimizing the grading scheme, and providing more scientific method guidance and technical support for product quality and maintenance.

Key words：Near infrared spectroscopy; Classification of tobacco industry; Quality classification of tobacco; Tobacco production areas

Received: 2019-10-27 Accepted: 2020-04-06

ZTFLH:

O657.33

Corresponding Authors: LI Jun-hui E-mail: caunir@cau.edu.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)03-0943-05 OR https://www.gpxygpfx.com/EN/Y2021/V41/I03/943

[1] DING Ying(丁莹) . Infrared(红外), 2012, 33(7): 1.
[2] YAN Yan-lu(严衍禄). Principle, Technology and Application of NIR Spectra Analysis(近红外光谱分析的原理、技术与应用). Beijing: China Light Industry Press(北京：中国轻工业出版社), 2013.
[3] CHU Xiao-li, LIU Hui-ying, YAN Ze-cheng, et al(褚小立，刘慧颖，燕泽程，等). Practical Handbook for Near Infrared Spectroscopy(近红外光谱分析技术实用手册). Beijing: China Machine Press(北京：机械工业出版社), 2016. 3.
[4] Swarbrick Brad. NIR News, 2016, 27(1): 39.
[5] Lin C, Chen X, Jian L, et al. Food Chemistry, 2014, 162: 10.
[6] ZHAO An-xin, TANG Xiao-jun, ZHANG Zhong-hua, et al(赵安新, 汤晓君, 张钟华, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(5): 1340.
[7] Liudmil Antonov. Journal of Near Infrared Spectroscopy, 2017, 25(2): 145.
[8] FU Yi, ZHANG Yong-jun, CHEN Hua-cai, et al(傅谊，张拥军，陈华才, 等). Food Science and Technology(食品科技), 2012, 37(5): 42.
[9] ElMasry G, Sun D W, Allen P, et al. Journal of Food Engineering, 2012, 100(1): 127.
[10] CAI Jian-hua, XIAO Yong-liang, LI Xiao-qin(蔡剑华, 肖永良, 黎小琴). China Tobacco(中国烟草学报), 2017, 23(4): 9.
[11] ZHOU Ji-yue, YANG Pan-pan, LIU Lei, et al(周继月, 杨盼盼, 刘磊, 等). China Tobacco(中国烟草学报), 2018, 24(1): 6.
[12] XIA Jun, LU Yang, SU Yan, et al(夏骏, 陆扬, 苏燕, 等). China Tobacco(中国烟草学报), 2015, 2: 19.
[13] WANG Jian-wei, LIU Hai-lun, DUAN Wei-dong, et al(王建伟, 刘海轮, 段卫东, 等). Tobacco Science & Technology(烟草科技), 2016, 49(2): 7.
[14] GB/T 37969—2019, Standard Guidelines for Near Infrared Qualitative Analysis(近红外光谱定性分析通则)，2019. 15.
[15] Luan Lili，Wang Yuheng，Li Xueying，et al. Journal of Near Infrared Spectroscopy, 2016, 24(4): 363.
[16] Yang Yuqing, Ma li, Du Guorong, et al. Spectroscopy, 2018, 33(6): 27.