Aroma Sensor Research Articles

3D Urchin-Like WO3-x based nanostructures with abundant oxygen vacancies for ppb-Level tea aroma sensing

The development of high-performance tea aroma sensors has great significance for evaluating tea quality and monitoring tea plant pests and diseases. Hence, we fabricate a tea aroma sensor based on tungsten oxide with abundant oxygen vacancies for the rapid response of tea aroma in ppb-level. The tea aroma sensor exhibits a high sensitivity with a value of about 25 and the corresponding response and recovery time are 19 s and 5 s at 275 ℃ for 5 ppm linalool, respectively. It also shows impressive low detection limit with a response value of 2.5 for 150 ppb linalool at 275 ℃. In addition, the WO3-x based tea aroma sensor also shows excellent selectivity, cyclic and long-term stability. The outstanding sensing performance of WO3-x based tea aroma sensor for linalool is mainly attributed to the abundant oxygen vacancies. Theoretical calculations further demonstrate that the oxygen vacancies contribute to adsorb linalool molecules and enhance electronic interaction between linalool molecules and WO3-x, resulting in a remarkable sensing performance for linalool. Furthermore, a portable wireless prototype based on the tea aroma sensor is developed to achieve the real-time online remote monitoring of linalool. This work paves the way for designing high-performance tea aroma sensor and provides the methods for applying tea aroma sensor to assess tea quality and monitor infestation of tea plant pests and diseases.

Aroma profiling analysis of grape berries based on electronic nose detection

Aroma analysis of grape berries can provide support for the breeding of new varieties and increase economic value. In order to preliminarily explore and screen the berry aroma characteristics of 191 grape germplasm resources, the electronic nose detection method was used to analyze the berry aroma characteristics. The clustering results of 10 sensors showed that the aroma types of grape berries could be identified. The first category (I) contains 139 germplasm; The second group (II) contains 32 germplasms; The third group (III) contains four germplasms; The fourth group (IV) and the fifth group (V)include 8 germplasm. The berry aroma of cluster I, II, IV and V was significantly different from that of cluster III, mainly manifested by the significant difference in the response values of W1S (methane), W2S (alcohols) and W5S (nitrogen oxides). The W1S, W2S and W5S sensors, as well as the W2W (aromatic components and organic sulfides) aroma sensors show high response values for 'Ayaneskeal' and 'Urbana', indicating a strong aroma. The results of this study preliminarily elucidates the aroma differences among different grape germplasms, providing a data basis for the selection of grape germplasms with distinct aroma, which will be used as a source to obtain new varieties with aromatic value for the production of grapes for fresh consumption and beverages, such as wine. It also provided data basis for the establishment of grape germplasm resource bank.

Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network

Coffee is one of the famous plants’ commodities in the world. There are some coffee powders such as Arabica dan Robusta. This study aimed to identify two various coffee powders, Arabica and Robusta based on the blended aroma profiles, employing the backpropagation Artificial Neural Network (ANN). Four taste sensors were employed, namely TGS 2602, 2610, 2611, and 2620, to capture the diverse coffee aroma. These detectors were combined with the aroma sensors having transducers integrated with signal amplifiers or processors, which featured a load of 10 KΩ resistance. Three aroma types were investigated, namely Arabica coffee, Robusta coffee, and without coffee beans. The neural network architecture consisted of four inputs from all sensors, with one hidden layer housing eight neurons. Two neuron outputs were employed for classification, with 70 samples used for training ANN for each type. During the training phase, the developed neural network showed an impressive accuracy rate of 91.90%. TGS 2602 and 2611 sensors showed the most significant differences among the three aroma types. When analyzing ground Robusta coffee, TGS 2602 and 2611 sensors recorded 2.967 volts and 1.263 volts, with a gas concentration of 17.92 ppm and 2441.8 ppm. Similarly, the sensors for ground Arabica coffee displayed 3.384 volts and 1.582 volts with a gas concentration of 20.445 ppm and 3058.5 ppm in both TGS 2602 and 2611, respectively. The implemented ANN with aroma sensor as input successfully identify the coffee powders.

Gas Detection and Classification Using Neural Network Based Gas Sensors

Alcoholic beverages, apart from being haram, also cause loss of consciousness. The influence of alcohol while driving is very dangerous and can result in an accident. For this reason, it is necessary to detect the alcohol content in beverages so that their halal status is known and to avoid the dangers of consuming alcohol. This research is to detect the aroma of alcohol using the MQ-3 gas sensor, which consists of an aroma sensor in general with an Artificial Neuron Network (ANN), such as the number of neurons, layers, and epoch. Most of the learning schemes require testing to optimize the model structure. For this experiment, ANN is used as a liquid classification in grouping alcoholic and non-alcoholic liquids. The MQ-3 gas sensor successfully reads liquid vapor in alcohol with levels of 30%, 50%, 70%, and other water-based liquids. An artificial neural network with 2 hidden layers, 10 neurons, and 1000 iterations with the sigmoid activation function can approach a regression score of 1.1545 and sq error score of 0.5781.

Fuzzy Logic System to Determine Fruit Quality

Nowadays the production and consumption of food has increased very vast. The buyers and sellers are getting the food products without any proper checking their quality. So because of this there are many lot of health issues has been rising up daily. The farmers are using many chemicals and fertilizers in order to ripen the fruits. Those fruits will be very harmful for any purpose. So these days a new technology came to existence in order to check the quality of the fruit before it reaches the market. This module comprises of various sensors like aroma sensor, water level sensor and aroma sensor to check the quality of the fruit. All the sensors are impressed on a WSN which consists of IOT. This works by a wifi module in order to get the output for the personal mobile or laptop.

Aroma Detection of Ethanol Content in Fruits Using ZnO Doped Nano Thin Films

The nano materials of Zinc Oxide (ZnO) were studied to exhibit excellent properties of semi conductors and are also used as nano wires and nano rods in varied applications. In the fields of aroma sensors, Zinc Oxide acts as a very good gas sensing agent. The Zinc Oxide has properties of changing electrical conductivity and resistance values when exposed to gas sensing atmosphere. This experimental research is to identify the changes in the resistivity values when exposed to ethanol vapors at different vapor pressures. The Zinc Oxide nano films are designed by dip coating process, which results in different resistance values when exposed on the ethanol vapors. The prepared thin film of Zinc Oxide is capable of detecting the presence of ethanol vapors when doped with PVA (poly vinyl alcohol) at 3% concentration. This produces efficient results and detects the presence of ethanol even at high and low vapor pressure.

In situ cocoa beans quality grading by near-infrared-chemodyes systems

Cocoa beans were quality graded innovatively using a near-infrared chemo-dyes system as aroma sensor to capture and detect their volatiles.

Detection of 3-Carene in mango using a quartz crystal microbalance sensor

The exacting technology of piezoelectric aroma sensor has engendered a great hope for determining the ripening aroma of mango (Mangifera indica L.). 3-Carene is one of the most important ripeness indicating aromas of mango. Detection of 3-Carene content in mango is very important because fruit aroma and flavor characteristics are of key importance in determining consumer acceptance in commercial fruit markets. quartz crystal microbalance (QCM) transduces changes in mass on the crystal surface into measurable shifts in resonant frequency due to its sensitive solution-surface interface measurement capability. In this exploration, poly-dimethylsiloxane (PDMS) coated QCM sensor has been developed to sense the aroma of 3-Carene volatile in Indian mango varieties namely Langda and Chausa cultivars of mango. Empirically the performance of the sensor has been validated by characterizing the sensor through different studies viz. sensitivity, selectivity, repeatability, reproducibility, period of reusability, etc. The sensor was found to be selective to 3-Carene mostly, unlike other dominant aromas present in mango. Validation of the proposed sensor with real mangoes are carried out by correlating the frequency deviation of the sensor with the estimations of 3-Carene obtained from gas chromatography mass spectrometry (GC–MS).

Characterization and classification of the aroma of beer samples by means of an MS e-nose and chemometric tools

An electronic nose based on coupling of headspace (HS) with a mass spectrometer (MS) has been used in this study to classify and characterize a series of beers according to their production site and chemical composition. With this objective, we analyzed 67 beers of the same brand and preparation process but produced in different factories. The samples were also subjected to sensory evaluation by a panel of experts. Linear discriminant analysis (LDA) was used as the classification technique and stepwise LDA based on Wilk's lambda criterion was used to select the most discriminating variables. To interpret the aroma characteristics of the beers from the m/z ions obtained, score and loading bi-plots were obtained by applying canonical variables. Because the beers analyzed were marketed with the same name and brand, we expected to be working with the same product irrespective of its origin. However, results from both sensory evaluation and use of the e-nose revealed differences between factories. With the e-nose it was possible to relate these differences to the presence (and abundance) of characteristic ions of different compounds typically found in beer. These results demonstrate that the HS-MS e-nose is not only an aroma sensor capable to classify and/or differentiate samples but it can also provide information about the compounds responsible for this differentiation.

Distinction and identification of lignins based on their volatile headspace composition

A new non-degradation methodology is proposed for the distinction and identification of lignins, according to their biological origin and/or isolation methodology, using an electronic aroma sensing system. The system, once trained with representative lignins, could quickly and objectively be used as a simple tool by providing, in real-time, information about the lignin origin, based on their headspace volatile composition. In order to understand which kind of volatile compounds are responsible to the different sensors response present in the headspace, lignins were also analysed by headspace-solid phase microextraction followed by gas chromatography–mass spectrometry detection mode (HS-SPME/GC–MS). Among volatile compounds in the lignin headspace were identified a series of aromatic compounds, C 14 to C 18 fatty acids and an isoprenoid, squalene. It was proposed that a small proportion of volatile degradation products emerged during the lignin partial degradation during the technogenic process (i.e. delignification) play a key role regarding the aroma sensor response.

Optimization of an aroma sensor for assessing grape quality for wine making

Control of the raw material has considerable influence on final wine quality. The objective of this work was to develop a sensor capable of assessing grape juice quality in the cellar. Sensor response was compared with conventional chemical analysis using multivariate data techniques. The aroma sensor array is composed of 14 metal oxide sensors (MOS) gas transducers plus a temperature and a relative humidity sensor. This work showed that the sensor could detect rotten flavours in grape juice.

Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry

This paper aims at assessing the capability of high-speed analytical devices, such as aroma sensors (“electronic noses”), Fourier Transform InfraRed (FT-IR) and ultraviolet spectrometers to classify white grape musts (grape juices before fermentation) in variety categories. Due to the complexity of the signal generated, specific data processing techniques have been developed and are described here. First, a pre-processing technique, based on Genetic Algorithms, is applied to spectra to improve spectrometer efficiency without expert knowledge in spectrometry; by selecting the most discriminant subsets of wavelengths, this stochastic method tends to reduce over-fitting and improves classification results. Secondly, the Partial Least Squares Regression technique is adapted to a pattern recognition problem, using Partial Least Squares-Discriminant Analysis, a multivariate classification technique. These devices and data processing techniques are applied to more than 100 must samples. FT-IR spectrometry is the most satisfactory technique with a 9.6% classification error level. Finally, outputs of the three individual sensors are combined in a “low-level” fusion method, by concatenating the individual sensor signals. This straightforward fusion method does not significantly improve results.

Free-grown polypyrrole thin films as aroma sensors

We describe the use of free-grown thin polypyrrole films as fast response sensors of volatile compounds. Using a variety of dopants, the polymeric films were prepared directly on conducting glass substrates. Ultraviolet-visible and near infrared regions absorbance measurements were used to investigate the deposition process and the doping level of the films. We identify the polymerization time as the fundamental parameter for determining the characteristics of the sensor, since it affects not only the final level of doping but also the kinetics of adsorption and the homogeneity of the films. Electrical measurements confirmed the sensitivity and selectivity of the sensors to the presence of vapors of volatile organic solvents. A fast response time was observed in all cases, although the intensity of the corresponding signal was in general larger for the polar than for the nonpolar compounds. The sensitiveness of the sensors towards simple organic solvents is analyzed as well as their reproducibility and the aging effect upon the specific responses.

Fusion of aroma, FT-IR and UV sensor data based on the Bayesian inference. Application to the discrimination of white grape varieties

The objective of this study is to present a fusion method based on the Bayesian inference to combine the outputs of various sensors. The sensors studied here are aroma sensors, FT-IR and UV spectrometers. The application deals with classifying musts of white grapes according to their variety. The fusion procedure is not based on the combination of the signals, but of the class assignments provided individually by each sensor. Two methods have been developed based on the Bayesian inference: the Bayesian minimum error fusion rule and the minimum risk rule. The latter involves both experimental knowledge, in computing error probability values, and expert knowledge, through the level of error costs. The paper presents the mathematical theory concerning the Bayesian approach and the results obtained on white grape classification. This effective fusion method leads to a significant improvement in the grape variety discrimination: the final misclassification error is 4.7%, whereas the best individual sensor (FT-IR) gave a misclassification error twice as high, i.e. 9.6%. Bayesian fusion proved to be very well suited to the combination of all kinds of analytical measurements or sensors (curves or single value outputs), as long as they provide individual classification outputs. Furthermore, Bayesian fusion is able to cope with sensors providing large, noisy and redundant data as well as sensors showing very dissimilar efficiency levels.

Characterization of Royal Gala apple aroma using electronic nose technology-potential maturity indicator.

Changes in aroma of apple harvested at four different maturities were measured at harvest and after short-term storage using electronic aroma sensors ("electronic nose") and classical headspace/gas chromatography methods. Stored fruits were also evaluated by a trained sensory panel. Compared with headspace/gas chromatography, the electronic nose was found to be more sensitive ( approximately 40 times) in terms of sample size. The sampling procedure for the electronic nose was much less complex. Using discriminant function analysis, both methods classified the apples tested into groups according to harvest date. After storage, the groupings were more diffuse. Results from sensory testing showed partial separation along the first linear discriminant but did not classify the apple into distinct groups. Important differences between treatments were found for "overall flavor", "acid flavor" intensity, "crispness", "cider/fermented aroma", "vegetative aroma", and "canned pear aroma".

Polypyrrole based aroma sensor

The change in conductivity of conducting polymers when exposed to volatile gases can be exploited for the construction of aroma sensors. In this communication we report the preliminary tests of two versions of an aroma sensor based on arrays of doped polypyrrole films. We have used these prototypes for the identification of simple organic vapors and complex aromas. The input signal was the fractional change in the resistance of the films and the data acquisition was performed automatically. The pattern recognition process was implemented by a multivariate analysis and by neural network processing. The discriminating power among different odorants was better than 90%.

An Aroma Sensor for Assessing Peach Quality

Abstract A gas concentration transducer has been used to build an aroma sensor whose signals are related to concentration levels of delta and gamma decalactones, which are the main volatiles that compose peach aroma. In order to estimate the efficiency of assessing peach quality with the sensor, its readings were compared with peach quality classes determined by experts depending on fruit external appearance (colour, firmness, and estimated maturity). The sensor was able to reproduce this classification with a 75–85% success rate. No clear relationship was found between the signal of the sensor and other quality parameters, such as sugar content or firmness. However, preliminary tests have succesfully demonstrated the possibility of using these transducers to detect rotten fruit in cool chambers.

Volatile flavor release from foods during eating.

Classical analyses for volatile flavors (headspace or distillation/extract methods) give information on either the volatiles present in the air above a food before eating or the total volatile composition of the food. When foods are eaten, however, many changes take place (such as hydration/dilution with saliva, increase in surface area, etc.) that affect the release of volatiles from the food and therefore the profile of volatiles that are sensed in the nose. If we wish to study the relationship between flavor volatiles and the sensory properties of a food, it seems logical to measure the volatile profile that exists during eating. Although volatile flavor release during eating has been measured using a variety of sensory and psychophysiological analyses, only recently have instrumental methods been developed to measure the release of volatile compounds in humans as they eat. Whereas the sensory data give an overall measure of flavor perception, instrumental analyses can potentially follow the release of each and every flavor volatile and thus give a full picture of the aroma profiles generated during eating. From these instrumental measurements, a number of key factors have been identified. First, it has been shown that the volatile profile measured during eating is indeed different from the headspace profile of whole foods. Second, it is clear that the volatile profile in-mouth changes with time as the state of the food changes with chewing. Third, the volatile release from low-water foods is affected by the rate and extent of hydration in-mouth. The ability to measure aroma before, during, and after eating may lead to an understanding of the links between aroma release, interaction of volatiles with aroma sensors in the nose, and the overall perception of food flavor.