Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2021-12-02 DOI:10.1155/2021/8807199

Mengyao Lu, Qiang Zhou, Tian’en Chen, Junhui Li, Shuwen Jiang, Qin Gao, Cong Wang, Dong Chen

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引用次数: 2

Abstract

To explore the application of near-infrared (NIR) technology to the quality analysis of raw intact tobacco leaves, a nondestructive discrimination method based on NIR spectroscopy is proposed. A “multiregion + multipoint” NIR spectrum acquisition method is developed, allowing 18 NIR diffuse reflectance spectra to be collected from an intact tobacco leaf. The spectral characteristics and spectral preprocessing methods of intact tobacco leaves are analyzed, and then different spectra (independent or average spectra) and different algorithms (discriminant partial least-squares (DPLS) and Fisher’s discriminant algorithms) are used to construct discriminant models for verifying the feasibility of intact leaf modeling and determining the optimal model conditions. Qualitative discrimination models based on the position, green-variegated (GV), and the grade of intact tobacco leaves are then constructed using the NIR spectra. In the application and verification stage, a multiclassification voting mechanism is used to fuse the results of multiple spectra from a single tobacco leaf to obtain the final discrimination result for that leaf. The results show that the position-GV discrimination model constructed using independent spectra and the DPLS algorithm and the grade discrimination model constructed using independent spectra and Fisher’s algorithm achieve optimal results with intact leaf NIR wavenumbers from 5006–8988 cm−1 and the first-derivative and standard normal variate transformation preprocessing method. Finally, when applied to new tobacco leaves, the position-GV model and the grade model achieve discrimination accuracies of 95.18% and 92.77%, respectively. This demonstrates that the two models have satisfactory qualitative discrimination ability for intact tobacco leaves. This study has established a feasible method for the nondestructive qualitative discrimination of the position, GV, and grade of intact tobacco leaves based on NIR technology.

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基于近红外技术的完整烟叶定性鉴别

为探索近红外(NIR)技术在烟叶原料质量分析中的应用，提出了一种基于近红外光谱的烟叶无损鉴别方法。提出了一种“多区域+多点”近红外光谱采集方法，可采集完整烟叶的18个近红外漫反射光谱。分析了完整烟叶的光谱特征和光谱预处理方法，利用不同光谱(独立光谱或平均光谱)和不同算法(判别偏最小二乘(DPLS)和Fisher判别算法)构建判别模型，验证完整烟叶建模的可行性，确定最优模型条件。然后利用近红外光谱构建了基于烟叶位置、绿斑(GV)和烟叶等级的定性判别模型。在应用和验证阶段，采用多分类投票机制对单个烟叶的多光谱结果进行融合，得到该烟叶的最终识别结果。结果表明，当完整叶片近红外波数在5006 ~ 8988 cm−1范围内，采用一阶导数和标准正态变量变换预处理方法时，采用独立光谱和DPLS算法构建的位置- gv判别模型和独立光谱和Fisher算法构建的等级判别模型获得了最优结果。最后，将位置- gv模型和等级模型应用于新烟叶，识别准确率分别达到95.18%和92.77%。这表明两种模型对完整烟叶具有满意的定性判别能力。本研究建立了一种基于近红外技术无损定性鉴别烟叶位置、GV和等级的可行方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.