Coupling gas chromatography and electronic nose detection for detailed cigarette smoke aroma characterization

Design and development of instrumentation for the measurement of sensor array responses

Indigenous instrumentation has been designed and developed for the measurement of the concentration of analytes from eight conductometric metal oxide sensors. The hardware scheme of instrumentation is based on the astable multivibrator configuration. The hardware measures the resistance output from the sensors, conditions, processes, and displays the data on the liquid crystal display. An 8051 based processor averages the data, converts them into engineering units, and sends them to remote PC through ethernet communication for post-data analysis. A graphical user interface (GUI) is developed to acquire, monitor, and display the eight channels' sensor output. GUI plots the online data and offline data as a popup window. The hardware and software of the instrument were tested with standard resistors for calibration and found that in-house developed instrumentation is able to measure with an accuracy of ±0.5% with a resolution of 500 Ω. The instrument has been tested with a semiconductor metal oxide sensor, viz., chromium niobate (CrNbO₄).

Classification and Identification of Industrial Gases Based on Electronic Nose Technology

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.

Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.

InnOscent system: Advancing flavor analysis using an original gas chromatographic analytical device

Despite continuous advances in analytical and physiological knowledge, the comprehension of an aroma is still a challenge. Gas chromatography coupled to olfactometry (GC-O) is an efficient method to identify and estimate individual potential of odorants, but there is a gap between this individual characterization and the effective contribution of compounds in the mixture, which is due to complex chemical and perceptual interactions. Therefore, recombination and omission experiments are often performed to achieve an understanding of food aromas. In this study, a chromatographic device, developed to facilitate aroma analysis, is presented. It was configured to perform both (1) conventional analyses by GC coupled with a mass spectrometer, olfactometric port(s), and a flame ionization detector (FID), and (2) omission or recombination experiments. This dual capability is due to the singular configuration of the system using an ingenious combination of splitter and Deans switch microfluidics transfer modules, and the existence of multiple outlets. The operational status of the system was tested using a purposely simple mixture of compounds. The similarity of retention times (RT) and FID peak areas obtained for each outlet demonstrates that the multiple outlets of the system are equivalent. The reproducibility of retention times (RT) and FID peak areas obtained in switching and non-switching conditions, also demonstrates the efficiency of switching operations. The validation of the system enables multiple detectors to be connected to the outlets and complementary information can be obtained from the eluate. The connection of recovery disposals to the outlets provides fraction collection and recombination possibilities, which contribute much to the understanding of aroma-aroma interactions. As an illustration of the InnOscent system relevance for the comprehension of more complex aromas, the device was used to study the aroma of a wine made from Cabernet Franc grape variety. An olfactometric profile was efficiently produced with the device configured as a GC-MS coupled to a dual olfactometric port. The main odorant active compounds were identified. The omission approach, carried out with the system on isopropyl- and isobutyl-methoxypyrazines, demonstrates the significant contribution of these compounds to the aroma of the wine studied, despite an individual perception among the weakest of the aromagram. A similar approach can be used to evaluate the contribution of any compound to any aroma. This approach overcomes constraints of current methodologies associated to reconstituted model solutions and paves the way for a better understanding of aroma construction.