烤烟等级质量对配方设计和卷烟工业生产的稳定起着重要的作用。 采用传统外观分级法对2018年全国40个地级市产地的768份烤烟烟叶样品进行分类定级, 包括7个不同烟叶质量等级。 应用近红外光谱建立烟叶质量等级预测模型, 分析不同等级烟叶化学基团及相关成分的近红外吸收光谱特征。 结果表明: 不分产区建立全国烟叶等级预测模型, 建模集与预测集的预测标准差不大于1.35。 将样品按五大产区分区后, 建立各产区模型, 发现较全国模型, 分区后各个产区所建模型的预测标准差有所降低, 其中东南、 西南、 黄淮烟区模型提高较大, 检验集与预测标准差均不大于1.07。 对不同质量等级烟叶的平均光谱进行标准正态变量变换预处理后, 依据近红外光在不同频率范围吸收的有机基团及相关物质成分信息, 发现质量等级较好的烟叶, 纤维素含量较低, 淀粉等糖类物质含量较高; 质量等级较差的烟叶, 纤维素含量较高, 淀粉等糖类物质含量较低; 质量等级最差(上部低等)烟叶, 同时具有蛋白质类物质含量较高的特点。 因此, 应用近红外光谱可实现烟叶质量等级的快速预测, 预测偏差基本在相邻等级之间, 满足实际应用要求, 通过建立不同产区预测模型可进一步提高预测准确度; 同时, 不同等级烟叶在以纤维素、 淀粉和糖类、 蛋白质类等物质为主产生的基团吸收特征不同, 这也是应用近红外光谱实现烟叶质量等级快速检测的信息基础。 该研究结果对完善烟叶分级评价体系, 进一步优化分级方案, 为产品质量和维护等方面可提供了更加科学的方法指导和技术支撑。

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.