X. Correig

Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array

Quantitative analysis of gases, by means of semiconductor sensor arrays and pattern-recognition techniques such as artificial neural networks, has been the goal of a great deal of work over the last few years. However, the lack of selectivity, repeatability and drifts of the sensors, have limited the applications of these systems to qualitative or semi-quantitative gas analysis. While the steady-state response of the sensors is usually the signal to be processed in such analysis systems, our method consists of processing both, transient and steady-state information. The sensor transient behaviour is characterised through the measure of its conductance rise time (Tr), when there is a step change in the gas concentration. Tr is characteristic of each gas/sensor pair, concentration-independent and shows higher repeatability than the steady state measurements. An array of four thick-film tin oxide gas sensors and pattern-recognition techniques are used to discriminate and quantify among ethanol, toluene and o-xylene [concentration range: 25, 50 and 100 ppm]. A principal component analysis is carried out to show qualitatively that selectivity improves when the sensor behaviour is dynamically characterised. The steady-state and transient conductance of the array components are processed with artificial neural networks. In a first stage, a feed-forward back-propagation-trained ANN discriminates among the studied compounds. Afterwards, three separate ANN (one for each vapour) are used to quantify the previously identified compound. Processing data from the dynamic characterisation of the sensor array, considerably improves its identification performance, rising the discrimination success rate from a 66% when only steady-state signals are used up to 100%.

Fruit ripeness monitoring using an Electronic Nose

Abstract In this work, the use of an Electronic Nose for non-destructively monitoring the fruit ripening process is presented. Based on a tin oxide chemical sensor array and neural network-based pattern recognition techniques, the olfactory system designed is able to classify fruit samples into three different states of ripeness (green, ripe and overripe) with very good accuracy. Measures done with peaches and pears show a success rate above 92%, while a slightly worse accuracy is reached with apples. An additional feature of the system is its ability to accurately predict the number of days the fruit has been in storage since harvest. Measures done with peaches show a maximum error of 1 day.

Fabrication of Highly Selective Tungsten Oxide Ammonia Sensors

Most of the chemical sensors described in the literature are based on inorganic semiconducting oxides. In some cases, the main oxide is modified by doping with small amounts of additives such as other oxides and/or metals. The sensing principle is based on the change in the conductance undergone by the oxide film when gases are adsorbed and react on its surface. A survey of typical sensor materials for detecting different gases in characteristic temperature ranges can be found elsewhere. 1,2 In recent years, some gas-sensitivity studies with tungsten trioxide (WO 3 ) based semiconductors have been reported. Pure or doped tungsten oxide is a promising material for the detection of nitrogen oxides (NO and NO 2 ) 3-6 and sulfur dioxide, 7 two substances which are considered to be responsible for ambient degradation together with carbon oxides and hydrocarbons. The gas-sensing properties of WO 3 are highly dependent on the deposition method. Tungsten oxide is generally deposited by reactive rf sputtering. 8-10 However, other techniques such as thermal evaporation, 11,12 sol-gel methods, 13 screen printing, 14 or photochemical production 6 have been reported to grow thin or thick active

Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples

Abstract In this work, an electronic nose is used to assess the ripeness state of pinklady apples through their shelf-life. In order to evaluate the electronic nose performance, fruit quality indicators are also obtained to compare results from both techniques. Pinklady apples were harvested at their optimal date so that electronic nose measurements and fruit quality measurements could be performed on the fruit samples during their ripening process. A PCA analysis, a well-known linear classification technique, does not show any clustering behaviour that might be attributed to ripening. On the other hand, Fuzzy art, an unsupervised neural network classification algorithm, shows a tendency to classify measurements regarding to their shelf-life period. Finally, electronic nose signals are correlated with classical fruit quality parameters such as firmness, starch index and acidity. Good correlation coefficients are obtained, a clear indication that electronic nose signals are related to the ripening process of apples.

Metabolomic assessment of the effect of dietary cholesterol in the progressive development of fatty liver disease.

Nonalcoholic fatty liver disease is considered to be the hepatic manifestation of metabolic syndrome and is usually related to high-fat, high-cholesterol diets. With the rationale that the identification and quantification of metabolites in different metabolic pathways may facilitate the discovery of clinically accessible biomarkers, we report the use of (1)H NMR metabolomics for quantitative profiling of liver extracts from LDLr(-/-) mice, a well-documented mouse model of fatty liver disease. A total of 55 metabolites were identified, and multivariate analyses in a diet- and time-comparative strategy were performed. Dietary cholesterol increased the hepatic concentrations of cholesterol, triglycerides, and oleic acid but also decreased the [PUFA/MUFA] ratio as well as the relative amount of long-chain polyunsaturated fatty acids in the liver. This was also accompanied by variations of the hepatic concentration of taurine, glutathione, methionine, and carnitine. Heat-map correlation analyses demonstrated that hepatic inflammation and development of steatosis correlated with cholesterol and triglyceride NMR derived signals, respectively. We conclude that dietary cholesterol is a causal factor in the development of both liver steatosis and hepatic inflammation.

Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition

A new method, which is based on the discrete wavelet transform, is presented for extracting important features from the response transients of a micromachined, tin oxide-based gas sensor. It is shown that two components in a mixture can be simultaneously and accurately quantified by processing the response dynamics of a single sensor operated in a temperature-modulated mode. The discrete wavelet transform outperforms the fast Fourier transform (classical approach) because it is more appropriate for the non- linear frequency-time problem encountered here.

Wavelet transform and fuzzy ARTMAP-based pattern recognition for fast gas identification using a micro-hotplate gas sensor

Abstract It is shown that a single thermally-modulated tin oxide-based resistive microsensor can discriminate between two different pollutant gases (CO and NO2) and their mixtures. The method employs a novel feature-extraction and pattern classification method, which is based on a 1-D discrete wavelet transform and a Fuzzy adaptive resonant theory map (ARTMAP) neural network. The wavelet technique is more effective than FFT in terms of data compression and is highly tolerant to the presence of additive noise and drift in the sensor responses. Furthermore, Fuzzy ARTMAP networks lead to a 100% success rate in gas recognition in just two training epochs, which is significantly lower than the number of epochs required to train the back-propagation network.

Evaluation of an electronic nose to assess fruit ripeness

The main goal of our study was to see whether an artificial olfactory system can be used as a nondestructive instrument to measure fruit maturity. In order to make an objective comparison, samples measured with our electronic nose prototype were later characterized using fruit quality techniques. The cultivars chosen for the study were peaches, nectarines, apples, and pears. With peaches and nectarines, a PCA analysis on the electronic nose measurements helped to guess optimal harvest dates that were in good agreement with the ones obtained with fruit quality techniques. A good correlation between sensor signals and some fruit quality indicators was also found. With pears, the study addressed the possibility of classifying samples regarding their ripeness state after different cold storage and shelf-life periods. A PCA analysis showed good separation between samples measured after a shelf-life period of seven days and samples with four or less days. Finally, the electronic nose monitored the shelf-life ripening of apples. A good correlation between electronic nose signals and firmness, starch index, and acidity parameters was found. These results prove that electronic noses have the potential of becoming a reliable instrument to assess fruit ripeness.

Analysis of the conductance transient in thick-film tin oxide gas sensors

Abstract In this paper, we analyse the conductance transient of a Taguchi TGS-822 sensor under a step change in the vapour concentration. A diffusion-limited range in the conductance transient is observed. Adjustments between the theoretical calculations based on a non-linear diffusion-reaction model and the experimental results alloy a constant, τ′, to be estimated, which is independent of the final conductance value and depends, among other parameters, on the effective diffusion coefficients of vapours in the porous tin oxide sensor. From transient measurements of organic solvents (benzene and o-xylene) we have obtained τ′ values that are independent of concentration and characteristic for each vapour. This new parameter can give useful information for gas/vapour recognition.

Assessment of compatibility between extraction methods for NMR- and LC/MS-based metabolomics.

Because of the wide range of chemically and structurally diverse metabolites, efforts to survey the complete metabolome rely on the implementation of multiplatform approaches based on nuclear magnetic resonance (NMR) and mass spectrometry (MS). Sample preparation disparities between NMR and MS, however, may limit the analysis of the same samples by both platforms. Specifically, deuterated solvents used in NMR strategies can complicate LC/MS analysis as a result of potential mass shifts, whereas acidic solutions typically used in LC/MS methods to enhance ionization of metabolites can severely affect reproducibility of NMR measurements. These intrinsically different sample preparation requirements result in the application of different procedures for metabolite extraction, which involve additional sample and unwanted variability. To address this issue, we investigated 12 extraction protocols in liver tissue involving different aqueous/organic solvents and temperatures that may satisfy the requirements for both NMR and LC/MS simultaneously. We found that deuterium exchange did not affect LC/MS results, enabling the measurement of metabolites by NMR and, subsequently, the direct analysis of the same samples by using LC/MS with no need for solvent exchange. Moreover, our results show that the choice of solvents rather than the temperature determined the extraction efficiencies of metabolites, a combination of methanol/chloroform/water and methanol/water being the extraction methods that best complement NMR and LC/MS analysis for metabolomic studies.