As a product absorbed by human body, the safety of cigarettes highly depends on the quality of tobacco (Nicotiana tabacum L.) raw materials. The heavy metal is an important indicator of tobacco quality which attracts the widespread concerns. Current detection of heavy metals in tobacco mainly adopts the inductively coupled plasma mass spectrometry (ICP-MS) method, which contains the cumbersome and time-consuming procedures. This study attempted to establish the near-infrared spectroscopy (NIRS) models for the six heavy metals as Zn, As, Cd, Hg, Pb, and Cr in dark sun-cured tobacco based on chemometrics methods. After spectral pretreatments and variable selections, partial least squares (PLS) regression was used to establish the optimized models. Prediction of Pb by competitive adaptive reweighted sampling (CARS) combining PLS method was satisfactory with the R_cv² of 91.90 %, root mean square error of cross-validation (RMSECV) of 0.554, R_p² of 92.40 %, and root mean square error of prediction (RMSEP) of 1.120. Quantification of Cd was also positive, with R_p² of 89.10 % and RMSEP of 2.051. Besides, results of the other four heavy metals were also acceptable. The proposed non-destructive detection method can rapidly acquire the content of heavy metals in raw tobacco leaves from different regions, aiding to manage the tobacco resources reasonably and reduce the harmfulness of cigarettes from the source.

卷烟作为人体吸收的产物，其安全性很大程度上取决于烟草（Nicotiana tabacumL.) 原材料。重金属是烟草质量的重要指标，受到广泛关注。目前烟草中重金属的检测主要采用电感耦合等离子体质谱（ICP-MS）方法，步骤繁琐耗时。本研究试图基于化学计量学方法建立暗晒烟草中 Zn、As、Cd、Hg、Pb 和 Cr 六种重金属的近红外光谱 (NIRS) 模型。在光谱预处理和变量选择之后，使用偏最小二乘 (PLS) 回归建立优化模型。通过竞争自适应重加权采样 (CARS) 结合 PLS 方法对 Pb 的预测对于R _cv ²是令人满意的91.90 %，交叉验证的均方根误差 (RMSECV) 为 0.554，R _p ²为 92.40 %，预测的均方根误差 (RMSEP) 为 1.120。Cd 的定量也呈阳性，R _p ²为 89.10%，RMSEP 为 2.051。此外，其他四种重金属的结果也可以接受。提出的无损检测方法可以快速获取不同地区生烟叶中重金属的含量，有助于合理管理烟草资源，从源头上降低卷烟的危害性。