Predicting heavy metals in dark sun-cured tobacco by near-infrared spectroscopy modeling based on the optimized variable selections

Abstract

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 Rcv2 of 91.90 %, root mean square error of cross-validation (RMSECV) of 0.554, Rp2 of 92.40 %, and root mean square error of prediction (RMSEP) of 1.120. Quantification of Cd was also positive, with Rp2 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.