Evaluating MIR and NIR Spectroscopy Coupled with Multivariate Analysis for Detection and Quantification of Additives in Tobacco Products

被引:0
|
作者
Akhtar, Zeb [1 ,2 ]
Canfyn, Michael [1 ]
Vanhee, Celine [1 ]
Delporte, Cedric [3 ,4 ]
Adams, Erwin [2 ]
Deconinck, Eric [1 ]
机构
[1] Sciensano, Serv Med & Hlth Prod, Sci Direct Chem & Phys Hlth Risks, Rue Juliette Wytsmanstr 14, B-1050 Brussels, Belgium
[2] Katholieke Univ Leuven, Dept Pharmaceut & Pharmacol Sci, Pharmaceut Anal, Herestr 49,O&N2,PB 923, B-3000 Leuven, Belgium
[3] Univ Libre Bruxelles ULB, Fac Pharm, Pharmacognosy Bioanal & Drug Discovery Unit RD3, Bld Triomphe,Campus Plaine,CP 205-5, B-1050 Brussels, Belgium
[4] Univ Libre Bruxelles ULB, Analyt Platform, Fac Pharm, Bld Triomphe,Campus Plaine,CP 205-5, B-1050 Brussels, Belgium
关键词
tobacco products; MIR/NIR spectroscopy; multivariate calibration techniques; data analysis; DISCRIMINANT-ANALYSIS; PLS-DA; CHEMOMETRICS; INGREDIENTS; CIGARETTES; PYROLYSIS; SMOKING;
D O I
10.3390/s24217018
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The detection and quantification of additives in tobacco products are critical for ensuring consumer safety and compliance with regulatory standards. Traditional analytical techniques, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and others, although effective, suffer from drawbacks, including complex sample preparation, high costs, lengthy analysis times, and the requirement for skilled operators. This study addresses these challenges by evaluating the efficacy of mid-infrared (MIR) spectroscopy and near-IR (NIR) spectroscopy, coupled with multivariate analysis, as potential solutions for the detection and quantification of additives in tobacco products. So, a representative set of tobacco products was selected and spiked with the targeted additives, namely caffeine, menthol, glycerol, and cocoa. Multivariate analysis of MIR and NIR spectra consisted of principal component analysis (PCA), hierarchical clustering analysis (HCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) to classify samples based on targeted additives. Based on the unsupervised techniques (PCA and HCA), a distinction could be made between spiked and non-spiked samples for all four targeted additives based on both MIR and NIR spectral data. During supervised analysis, SIMCA achieved 87-100% classification accuracy for the different additives and for both spectroscopic techniques. PLS-DA models showed classification rates of 80% to 100%, also demonstrating robust performance. Regression studies, using PLS, showed that it is possible to effectively estimate the concentration levels of the targeted molecules. The results also highlight the necessity of optimizing data pretreatment for accurate quantification of the target additives. Overall, NIR spectroscopy combined with SIMCA provided the most accurate and robust classification models for all target molecules, indicating that it is the most effective single technique for this type of analysis. MIR, on the other hand, showed the overall best performance for quantitative estimation.
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页数:19
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