Analysis of chemosensory markers in cigarette smoke from different tobacco varieties by GC×GC-TOFMS and chemometrics.

Commercial cigarettes are made from a blend of different tobacco varieties, which in turn are the results of different agronomic practices and post-harvest curing processes. The highly complex mixture of smoke compounds reflects each tobacco variety and the levels of sensory-relevant markers. Therefore, the aim of this work was to identify potential relevant chemosensory markers in the mainstream smoke of four main types of commercial tobaccos and establish any possible relationship between them and the tobacco growing/curing practices. The tobacco samples were segregated into four segments: (1) three curing stages of flue-cured Virginia, (2) three curing stages of air-cured Burley, (3) three geo-regions of sun-cured Oriental and (4) three different process applied to tobacco. One hundred and twenty cigarettes (10 batches per flavour category) were produced and smoked under standard machine-smoking protocols. The mainstream smoke samples collected were extracted and analysed by GC × GC TOFMS. The processed data was analysed by partial least square discriminant analysis (PLS-DA) and the selectivity ratio was used to identify key chemosensory markers responsible for the four segments. All models had sensitivity and specificity equal to unity. Flue-cured Virginia (193 markers) and air-cured Burley (184 markers) showed a similar trend for O-heterocycles markers in the lighter leaf colours and N-heterocycles in the darker leaf colours post-processing, but they had compounds of different flavour descriptions, e. g. sweet and nutty. The three geo-regions of sun-cured Oriental (290 markers) also presented O-heterocycles markers in correlation with leaf sugar contents in addition of sucrose esters markers. The three unusually processed tobacco generated many chemical markers (436 markers), some derived from the so-called Cavendish fermentation process with sweet, spicy and peppery notes, whereas the dark fermented air-cured tobacco presented similar descriptors as air-cured Burley. In addition, some polycyclic aromatic hydrocarbons (PAH) were detected as markers from the fire-curing process. The PLS-DA with selectivity ratio evidenced total of 1098 chemosensory markers in cigarette smoke, in which 173 were tentatively identified.

High-throughput simultaneous quantitation of multi-analytes in tobacco by flow injection coupled to high-resolution mass spectrometry.

The high-throughput screening by flow injection coupled to high-resolution mass spectrometry (HTS-FIA-HRMS) is a powerful technique that enables the identification of several types of samples in a short period of time, either with qualitative or quantitative purposes. Sensory attributes of tobacco are affected by its chemical composition, and it is very important to quantify multi-analytes in a high-throughput methodology. HTS-FIA-HRMS coupled to multivariate analysis was used to create calibration models for 27 analytes, or group of compounds, of tobacco sensory interest. The models were validated by different approaches, including permutation test to avoid overfitting, evaluation of the equipment repeatability by control samples, reproducibility comparison of results from two different equipment and analysts, and with a blind test analysis. All tests demonstrated a good response to the proposed method. No statistical difference between the errors of both equipment was observed, with less than 7% error from the control samples, and a blind test error between 5.96% and 20.10%. The partial least squares (O-PLS) regression models were applied to 815 samples, and a principal component analysis (PCA) was performed from the predicted concentration values, aiming at the non-supervised classification based on tobacco type. We expect that this proposed methodology shows not only the applicability in tobacco samples, but also demonstrates a guideline to an efficient performance of multi-analytes target analysis using the flow injection mass spectrometry with reliable and robust validation steps.

Innovative Approaches for Estimating the Levels of Tobacco-Specific Nitrosamines in Cured Tobacco Samples.

Tobacco-specific nitrosamines (TSNAs), mainly the 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), are known carcinogens. Part of the NNK found in smoke is provided from matrix-bound NNK, and its determination is extremely relevant. However, the reference extraction procedure of matrix-bound NNK is time-consuming and labor-intensive and has a limited analytical capacity. Three different methodologies were proposed to predict matrix-bound NNK: simple linear regression (LR) with soluble NNK; multiple linear regression (MLR) considering soluble NNK and characteristic parameters of the samples; and orthogonal partial least-squares (O-PLS) regression using high-throughput screening by flow injection analysis coupled to high-resolution mass spectrometry (HTS-FIA-HRMS) data. Simple linear regression showed a high influence of matrix and leaf origin. Although an existing linearity trend has been observed ( R2 = 0.62) for the global model, higher correlation values were achieved for matrix and country segregation models. Multiple linear regression predicted matrix-bound NNK with more satisfactory efficiency than simple linear regression models. The coefficients of determination were 0.87 and 0.94 for flue-cured Virginia and air-cured Burley, respectively. However, this method has a limited application, since previous information about the sample is required. The proposed method based on HTS-FIA-HRMS and O-PLS has shown the most suitable performance in the prediction of matrix-bound NNK, with errors comparable to the reference method, and a higher throughput. In addition, this approach allows to determine other soluble nitrosamines, namely N'-nitrosoanatabine, N'-nitrosoanabasine, and N-nitrosonornicotine, with relative percentage errors between 5.25 and 11.98%. Therefore, the third approach is the best method for a large number of cured tobacco for accuracy in determination of TSNAs.