Determination of Odour Interactions of Three-Component Gas Mixtures Using an Electronic Nose

Abstract

The paper presents an application of an electronic nose prototype comprised of six TGS-type sensors and one PID-type sensor to identify odour interaction phenomena in odorous three-component mixtures. The investigation encompassed eight odorous mixtures—toluene-acetone-triethylamine and formaldehyde-butyric acid-pinene—characterized by different odour intensity and hedonic tone. A principal component regression (PCR) calibration model was used for evaluation of predicted odour intensity and hedonic tone. Correctness of identification of odour interactions in the odorous three-component mixtures was determined based on the results obtained with the electronic nose. The results indicated a level of 75–80% for odour intensity and 57–73% for hedonic tone. The average root mean square error of prediction amounted to 0.03–0.06 for odour intensity determination and 0.07–0.34 for hedonic tone evaluation of the odorous three-component mixtures.

A Smart Rig for Calibration of Gas Sensor Nodes

“…where u s (t) = (u(t − 1)u(t) + r(t + 1))/3; t = 1, 2, N. u s (t) is defined to give smoother behavior of u(t). We can see in the calculation of the polynom coefficients by LabView, that the order of the polynom will not exceed 2 which confirms the models given in (9).…”

“…Oxygen and other types of sensors are used in medical applications, for example, for analyzing human breath [4][5][6]. Gas sensors may also be used in electronic olfaction schemes for odor detection and identification [7][8][9][10]. Gas sensors may even be used to evaluate fruit ripening [11,12].…”

Application of fuzzy logic to determine the odour intensity of model gas mixtures using electronic nose

“…Observed differences in odour intensity determination may be due to the presence of odour interactions between the gas mixture components [12]. Conducted studies have shown that the greatest differences were observed for samples with high α-pinene content.…”

“…Then the signal is sent to the recognition system, which in the case of human is the brain, and in the case of e-nose is the appropriate mathematical algorithm. The most commonly used data processing methods are: principal component analysis (PCA), principal component regression (PCR), partial least square regression (PLS), fuzzy logic and artificial neural networks (ANN) [10][11][12][13][14]. The electronic nose consists of four basic and independent elements: − sampling system -provide reproducible and stable measurement conditions (gas flow velocity, humidity, temperature) and eliminates all undesirable factors that can affect the sensor response, − detection system -a set of chemical sensors located in the measuring chamber which exhibit different selectivity and sensitivity to the individual components of the sample, but as a whole they generate a characteristic chemical pattern of the gas mixture (so-called "fingerprint"), − data processing system -responsible for signal processing, − pattern recognition system -assigns the received set of signals to one of the pattern classes.…”

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

“…The e‐nose sensor in conjunction with fuzzy logic pattern recognition system was successfully employed to determine the odor intensity of gas mixtures by comparing the results with the values obtained by sensory analysis, and multiple linear regression model. In their earlier studies, Szulczyński et al determined the odor interaction in formaldehyde‐butyric acid‐pinene, and toluene‐acetone‐triethylamine characterized by various odor intensity, and hedonic tone using e‐nose prototype.…”