Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses.

The aim of this study is to improve the discrimination performance of electronic noses by introducing a new method for measuring the similarity of the signals obtained from the electronic nose. We constructed abstract odor factor maps (AOFMs) as the characteristic maps of odor samples by decomposition of three-way signal data array of an electronic nose. A similarity measure for two-way data was introduced to evaluate the similarities and differences of AOFMs from different samples. The method was assessed by three types of pipe and powder tobacco samples. Comparisons were made with other techniques based on PCA, SIMCA, PARAFAC and PARAFAC2. The results showed that our method had significant advantages in discriminating odor samples with similar flavors or with high VOCs release.

Characterization of particle size distribution of mainstream cigarette smoke generated by smoking machine with an electrical low pressure impactor.

Cigarette smoking is a particle-related exposure. Studying the characteristics of the particle size distribution of cigarette smoke can aid in providing knowledge of smoke aerosol attributes. We used an electrical low pressure impactor (ELPI) to measure the particle size distribution of mainstream cigarette smoke generated by a smoking machine and provided a continuum of particle sizes of cigarette smoke from a whole cigarette. The results showed that the aerodynamic diameters (D, geometric mean of a channel) of particles ranged from 0.021 to 1.956 µm, and the number concentrations were on the order of 10(5)-10(9) cm(-3) for different sizes of particles. The particle number of the size category below 0.1 µm approximated that of the category 0.1-2.0 µm, and the particles in the size category of 0.1-2.0 µm contributed extremely heavily to total particulate mass. In addition, the results with small samples indicated that the tar yields normalized per milligram of nicotine showed an approximately linear increase with increasing concentration of total particles.

Resolving batch chromatographic overlapping peaks of flavoring essence using stepwise key spectrum selection.

Stepwise key spectrum selection (SKSS) was introduced to resolve batch overlapping peaks from gas chromatography-mass spectrometry (GC-MS) analysis of ten batch tobacco flavoring samples in different storage times. Resolution was implemented on a software platform that embedded the SKSS method. The data from GC-MS analysis of the samples were saved and prepared in ASCII files and then were inputted into the software platform for visual inspections. The data segment with overlapping peaks was precut for subsequent analysis. Spectral background in the data was removed using a linear fitting of the baseline. Four components in the overlapping peaks were automatically detected by the SKSS method. The resolution of the concentration profiles and spectra of the four components was conducted by setting only one parameter, the negative area ratio, as 0.01. The fixed size moving window evolving factor analysis and evolving factor analysis were applied to validate the resolved concentration profiles. The resolved mass spectra were validated by the searched standard through library search at the pure component regions revealed by the resolved concentration profiles. The results showed that the SKSS method could be a simple but powerful tool in resolving batch chromatographic overlapping peaks.