Electronic Nose

Studies suggest that breath and urine analysis can be viable non-invasive methods for diabetes management, with the potential for disease diagnosis. In the present work, we employed two sensing strategies. The first strategy involved analyzing volatile organic compounds (VOCs) in biological matrices, such as exhaled breath and urine samples collected from patients with diabetes mellitus (DM) and healthy controls (HC). The second strategy focused on discriminating between two types of DM, related to type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), by using a data fusion method. For this purpose, an electronic nose (e-nose) based on five tin oxide (SnO2) gas sensors was employed to characterize the overall composition of the collected breath samples. Furthermore, a voltametric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. To evaluate the diagnost...

The current diagnostic work-up and monitoring of pulmonary infections may be perceived as invasive, is time consuming and expensive. In this explorative study, we investigated whether or not a non-invasive exhaled breath analysis using an electronic nose would discriminate between cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) with or without various well characterized chronic pulmonary infections. We recruited 64 patients with CF and 21 with PCD based on known chronic infection status. 21 healthy volunteers served as controls. An electronic nose was employed to analyze exhaled breath samples. Principal component reduction and discriminant analysis were used to construct internally cross-validated receiver operator characteristic (ROC) curves. Breath profiles of CF and PCD patients differed significantly from healthy controls p = 0.001 and p = 0.005, respectively. Profiles of CF patients having a chronic P. aeruginosa infection differed significantly from to non-chronical...

In this article a method to increase the capability of an electronic nose to discriminate between a priori similar odours is presented. Olive oil is analyzed because it is well known that the characteristics of its aroma impair in many cases the discrimination between different kinds of olive oils especially when they are from similar geographic regions. In the present work the improvement of the electronic nose performance for the above-mentioned discrimination by a non-destructive method of IR laser vaporization of the sample is studied. Laser vaporization has also been performed at two different IR wavelengths to analyze the effect of laser radiation absorption of the sample on the electronic nose selectivity.

GC-MS Analysis of Volatile Compounds the Peel and Pulp of Citrus Medica L. Var. Sarcodactylis Swingle (Foshou Fruit) Amer Ali Mahdi, Waleed Al-Ansi, Qais Ali Al-Maqtari, Aqsa Ahmed, Mohamed Ismael Ahmed, Jalaleldeen Khaleel Mohammed and Hongxin Wang* 1 State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China. 2 National Engineering Research Center for Functional Food, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. 3 Department of Food Science and Technology, Faculty of Agriculture, Sana’a University, Sana’a, Yemen. 4 Department of Biology, Faculty of Science, Sana’a University, Sana'a, Yemen. 5 School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China.

A prototype of an electronic nose (e-nose) system integrating a set of general-purpose gas sensors, an electronic module, and signal processing and classification methods has been designed and implemented to detect certain environmental odors that might pose a risk to human health. The proposed device explores the filter diagonalization method (FDM), an advanced signal processing technique for accurate spectral estimation, to detect the presence of odors together with random forest (RF), a popular machine learning algorithm, to classify the features of such spectra. Experimental results show that the proposed FDM-RF approach can recognize the targeted odors with an accuracy of 96.4%.

Metal oxides are ideal for the fabrication of gas sensors: they are sensitive to many gases while allowing the device to be simple, tiny, and inexpensive. Nonetheless, their lack of selectivity remains a limitation. In order to achieve good selectivity in applications with many possible interfering gases, the sensors are inserted into an electronic nose that combines the signals from nonselective sensors and analyzes them with multivariate statistical algorithms in order to obtain selectivity. This review analyzes the scientific articles published in the last decade regarding electronic noses based on metal oxide nanowires. After a general introduction, Section 2 discusses the issues related to poor intrinsic selectivity. Section 3 briefly reviews the main algorithms that have hitherto been used and the results they can provide. Section 4 classifies the recent literature into fundamental research, agrifood, health, security. In Section 5, the literature is analyzed regarding the met...

Recently, low-dimensional (1D, 2D) nanostructured materials have been attracting more and more interest as building blocks for innovative systems. Metal oxide nanowires are one of the most widely used materials for solid-state gas sensors, as they are simple to make, inexpensive, and sensitive to a wide range of gases and volatiles. Unfortunately, their broad sensitivity has a price to pay, which is very low selectivity. Fortunately, this flaw is not a problem for all applications. Where the boundary conditions are defined and “simple” (only the presence of a target gas is expected, without any interfering gases), a single traditional chemiresistor may be the best choice, while in cases where the variables are many, it is better to use an intelligent system. In this paper, we will show a resistive sensor based on a single SnO2 nanowire which, working at three temperatures (200, 250, and 300 °C), is able to detect tens of ppb of ammonia (30 ppb at 300 °C). The limit of detection (LoD...

Aroma teh merupakan sesuatu yang sulit diukur karena membutuhkan kejelian dalam indra penciuman sehingga untuk mengukur aroma dalam rangka mempertahankan kualitas teh yaitu menggunakan alat Hidung elektronik/enose yang terdiri dari sensor-sensor yang digunakan untuk mendeteksi aroma atau bau. Hidung elektronik/Enose terbuat dari beberapa larik sensor gas dan memiliki sistem kerja seperti indra penciuman manusia. Melalui e-nose aroma teh hijau dapat diidentifikasi dan diklasifikasi menggunakan metode LDA (linear discriminant analysis). Tujuan penelitian ini untuk mengetahui klasifikasi beberapa merek teh hijau menggunakan hidung elektronik dan analisis hasil data keluaran dari hidung elektronik menggunakan metode linear diskriminan analisis (LDA) berdasarkan aromanya. Hasil pengukuran hidung elektronik (e-nose) yang berupa kurva sensing dan flessing, dicari luasannya dengan menggunakan metode integral trapesium. Data berupa luasan diolah dengan menggunakan metode LDA. Dilakukan pengu...

The testing system based on the electronic nose has 10 sensor array that was used for pattern classification smell of various cooking oils. The sample were a fortune-branded cooking oil palm, palm oil brands horse, palm oil rose brand, oil olive oil, canola flower oil, lard. The sensor array was formed of 10 gas sensors MQ and TGS. Testing was be done by taking a random sample of cooking oil from traditional markets and modern markets. The sensor response was evaluated using a method of pattern recognition Linear Discriminant Analysis (LDA). The LDA data processing was using stepwise method for selecting discriminator variables that have contributed in the prediction model discriminant function. Based on the first discriminant function have a value of 94.8%, which means a variant to the first discriminant function was sufficient to classify and distinguish the smell of all six samples of cooking oil. The second discriminant function was showed the variance 5.2% which was less able t...

A chemometric-based electronic nose has designed for analyzing pork oil and olive oil  using the odor pattern classifications. The electronic nose (e-nose) built from a combination of several chemical sensors derived from a semiconductor. The data retrieval was done by vaporizing the sample, then being captured by the sensor and identified by the electronic nose (e-nose). The output data from the electronic nose is the voltage released by each sensor. The analyzed samples were 100% olive oil, 100% pork oil and a combination of olive oil and pork oil with a ratio of 50%: 50%. The result of pattern classification using linear discriminant analysis (LDA) method shows that each sample is clustered well with the percentage of first discriminant function value is 87,9% and second discriminant function is 12,1%.

The classification of the aroma pattern of pork oil and canola oil on the chemometric-based electronic nose has been classified. The electronic nose used is a series of chemical sensors combined in parallel. Sensors are made of semiconductor material that can detect changes in gas in the air. Each sample measured by an electronic nose provides output in the form of different voltages on each sensor. The data processing method used is Linear Discriminant Analysis (LDA) which is able to classify based on patterns. The samples used were canola oil, pork oil and a mixture of pork oil and canola oil with a percentage of 50%: 50%. The results of the classification of electronic nose patterns with samples of pork oil and canola oil show that each sample is fairly well clustered with the value of the first disk function is 99.9% and the second discriminant function value is 0.1%.

In this study, the electronic nose (E-Nose) is based on array gas sensor. This tool consists of 10 MQ gas sensors related to Volatile Organic Compound (VOC) gas components. The working principle of the tool is to imitate the biological system of the nose which is useful to smell both bad and pleasant smell. E-Nose will detect samples of breath or bad breath on Tuberculosis (TBC) and Diabetes Mellitus patients and healthy people. Then, the data responses will be evaluated by using 2 patterns recognition methods called Principle Component Analysis (PCA) and Cluster Analysis (CA). The classification results can be explained by the value of the first 3 PCs from the score plot on the PCA of the data. PC1 accounts for 52.6% of the variance, while PC2 accounts for 17.8% of the variance, and PC3 accounts for 9.5% of the variance. The cumulative value of the first 3 PCs is 79.9%. The score plot graph shows a perfect classification of 3 data groups between healthy people, tuberculosis and diabetes mellitus patients. The results of this study indicate that the electronic nose (E-Nose). The instrument can identify the pattern of bad breath of healthy people and TBC and diabetes mellitus patients.

There are several testing processes for consuming meat products. Organoleptic evaluation is an evaluation based on color, texture, smell, and taste. This research aims to find out the response pattern of 10 gas sensor array contained in the electronic nose against the odor pattern of beef and pork base on a smell. The classification method used is using the Principal Component Analysis (PCA) method. This method is expected to simplify the test of differences in beef and pork based on the aroma. The meat used is standard beef and pork consumption that has been sold in supermarkets. Samples of beef and pork are then ground until smooth. After that, it is weighed until it reaches 1 ounce. The meat samples were tested using an electronic nose consisting of 10 gas sensors. The multivariate analysis method was used to classify the aroma of beef and pork. The results of the data processing showed that the aroma classification of beef and pork which was indexed by the electronic nose was pe...

This multidisciplinary study focuses on the application and comparison of several topology preserving mapping models upgraded with some classifier ensemble and boosting techniques in order to improve those visualization capabilities. The aim is to test their suitability for classification purposes in the field of food industry and more in particular in the case of dry cured ham. The data is obtained from an electronic device able to emulate a sensory olfative taste of ham samples. Then the data is classified using the previously mentioned techniques in order to detect which batches have an anomalous smelt (acidity, rancidity and different type of taints) in an automated way.

Fusarium graminearum and F. culmorum are considered some of the most dangerous pathogens of plant diseases. They are also considerably dangerous to humans as they contaminate stored grain, causing a reduction in yield and deterioration in grain quality by producing mycotoxins. Detecting Fusarium fungi is possible using various diagnostic methods. In the manuscript, qPCR tests were used to determine the level of wheat grain spoilage by estimating the amount of DNA present. High-performance liquid chromatography was performed to determine the concentration of DON and ZEA mycotoxins produced by the fungi. GC-MS analysis was used to identify volatile organic components produced by two studied species of Fusarium. A custom-made, low-cost, electronic nose was used for measurements of three categories of samples, and Random Forests machine learning models were trained for classification between healthy and infected samples. A detection performance with recall in the range of 88-94%, precision in the range of 90-96%, and accuracy in the range of 85-93% was achieved for various models. Two methods of data collection during electronic nose measurements were tested and compared: sensor response to immersion in the odor and response to sensor temperature modulation. An improvement in the detection performance was achieved when the temperature modulation profile with short rectangular steps of heater voltage change was applied.

Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be important for selecting effective preventive measures against their spread. A low-cost electronic nose applying six commercially available TGS-series gas sensors from Figaro Inc. was used in our research. Different modes of operation of the electronic nose were applied and compared, namely, gas adsorption and desorption, as well as modulation of the sensor's heating voltage. Classification models using the random forest technique were applied to differentiate between measured sample categories of four species: F. avenaceum, F. culmorum, F. greaminarum, and F. oxysporum. In our research, it was found that the mode of operation with modulation of the heating voltage had the advantage of collecting data from which features can be extracted, leading to the training of machine learning classification models with better performance compared to cases where the sensor's response to the change in composition of the measured gas was exploited. The optimization of the data collection time was investigated and led to the conclusion that the response of the sensor at the beginning of the heating voltage modulation provides the most useful information. For sensor operation in the mode of gas desorption/absorption (i.e., modulation of the gas composition), the optimal time of data collection was found to be longer.

Electronic nose and SPME-GCMS were used to monitor the autoxidation in long chain polyunsaturated fatty acid (LCPUFA)-enriched lipid microparticles produced by spray congealing with ultrasonic nebulization, during storage at 20 • C up to 6 weeks with sufficient air supply and limited air supply. Conjugated dienes and peroxide value as well as secondary lipid oxidation products were analysed to follow the course of autoxidation. Principal Component Analysis evidenced that only MOS sensors but not MOS-FET sensors contributed to the discrimination of the samples and facilitated the ability of the electronic nose to distinguish the LCPUFA-enriched lipid microparticles into two groups according to the different oxidative status. The selected MOS sensor responses correlated well with quantitative dominating volatile compounds (propanal and hexanal) and with volatile compounds which have been associated with fishy and rancid off flavour (1-penten-3-one, 1-penten-3-ol, 2,4-heptadienal and 2,6-nonadienal). Bread mix supplemented with the LCPUFA-enriched microparticles was analysed as an example for a LCPUFA supplemented food. Data from the present study indicate that the electronic nose can be used as a sensitive tool to evaluate the lipid oxidative status of LCPUFA-enriched microparticles. In supplemented foods like bread mix, matrix-related changes, which occur in supplemented and non-supplemented samples, make a clear distinction more difficult.

A combined acousticemechanical technique was employed to simultaneously characterise in uniaxial compression mode the texture properties of commercial toasted sliced breads and laboratory-made ones that were made by modifying the amount of sucrose in the recipe. Also an Image Analysis technique was used to describe the morphology of the gas cells. The analysis of the flavour release was performed by means of a commercial electronic nose system. Advanced techniques of multivariate statistical analysis were employed to get the maximum information from the proposed instrumental techniques. Some selected mechanical parameters and flavour release responses are dominant in discriminating sliced bread samples and both express similar information. The Regularised Discriminant Analysis output for the acoustic, mechanical and flavour release data was: Non Error Rate Cross Validated equal to 80.1% in the cross-validation mode. The NER CV % index is an estimation of the predictive potential of the model that was validated according to the leave one out (LOO) method. The LOO procedure removes each sample from the data set, one at a time. The classification model is rebuilt and the class of the removed sample is predicted by using the obtained model. By combining these instrumental characterisation methods, the result of the texture/aroma description of these cellular solids was improved.

A lipophilic aroma widely used in the food field, R-(+)-limonene, was entrapped in a solid emulsion in absence of emulsifiers using agar gel as encapsulating medium. The perception of the flavour release was evaluated by means of an electronic nose. The relation between structural organization of the agar gels and flavour release and perception was studied. Different viscoelastic properties of the continuous phase of the solid emulsion were achieved by varying the gelling polysaccharide concentration from 0.2% up to 1% (w/v) and the consequent aroma trapping effect was correlated with the perception of the flavour release from the solid emulsion during aging up to 15 days storage. Release phenomena were finally accelerated by increasing temperature of the gel from 30 to 77 °C. Structure assessment of the gelled phase take place within 24 h from the gel setting time and during this transient period most of the aroma release happens.

This paper describes progress that has been made towards realizing an artificial nose based on arrays of conducting polymers. Electrically conducting organic polymers based on heterocyclic molecules display reversible changes in conductivity when exposed to polar volatile chemicals . In the sensor described, the polymers are interrogated for resistance changes by means of an application-specific integrated circuit (ASIC) realized in BiCMOS technology . The ASIC and the polymer array are housed on a single thick-film ceramic substrate .

Suspended graphene (SUS-G) has long been hailed as a potential ‘true graphene’ as its conductive properties are much closer to those of theoretical graphene. However, substantial issues with yield during any device fabrication process have severely limited its use to date. We report the successful fabrication of a fully operational prototype of a miniature 9 mm2 suspended graphene array sensor chip, incorporating 64 graphene sensor devices, each comprising of 180 SUS-G membranes with ever reported 56% fully intact graphene membranes for sensitive and selective gas sensing applications. While a bare sensor chip can operate as a sensitive gas sensor for a variety of gasses such as ammonia, nitrogen dioxide and carbon monoxide, down to ppm/ppb concentrations, a tetrafluorohydroquinone functionalized sensor acquires specificity to formaldehyde gas molecules with limited cross-sensitivity for ethanol, toluene and humidity. Unlike an equivalent device with fully supported functionalized g...

Odorant binding proteins (OBPs) have been considered one of the best candidates for conceiving a biosensor to detect biochemical molecules and for odor sensor applications. In the same way, MEMS sensors for chemical and biological applications proved to be a good option for applications requiring high sensitivity. In this context, micro-cantilever sensor is one of the most promising micromechanical sensor. In this paper a grafting method, compatible with MEMS technology, was used for grafting porcine OBPs on polycrystalline diamond cantilevers. These sensors have been tested, for the first time, in combination with an autonomous dedicated electronic system developed to automatically detect sensors response signals. Electronic detection of binding 2-isobutyl-3-methoxypyrazine (IBMP) to OBP was demonstrated.

Recent advances in the sample handling techniques as part of analytical workflow of analysis with electronic nose systems are reviewed. Classic approaches of headspace analysis as well as advanced methods of sample enrichment and dynamic separation are considered and summarized. The efficiency of application of different sampling methods for specific analytical tasks is demonstrated. Special attention is paid to the analysis of present trends of searching the approaches aimed to replace time-consuming and expensive sample preparation procedures with alternative techniques, including development of sensors arrays with adaptive selectivity profiles, creation of sensor interfacial architectures with tunable adsorption properties, advanced experimental data coding and use of classic spectroscopic methods for "virtual" sensor array formation.

In this study, a prototype electronic nose was developed for monitoring the quality of cocoa beans. The system comprises an array of metal-oxide semiconductor sensors and an artificial neural network pattern recognition unit. The results obtained from assessment ...

Electronic nose methodology was used for discriminating between samples of n-primary alcohols and phenol in water. An electronic nose device developed at our laboratory was applied to detect the vapors from the samples. The device consists of 10 nonspecific gas sensors based on tindioxide, whose electrical conductivity change when exposed to the vapors. The whole set of responses constitutes a fingerprint associated with the sample for the particular array of sensors. Using this method, we could differentiate among n-primary alcohols, pure phenol, and aqueous dilutions of phenol. Dilutions of primary alcohols and phenol of 1, 3, 5, and 10% (v/v) were assayed. The fingerprints of the dilutions are similar to that of the respective pure (not diluted) alcohol, but with lower signal intensities. Principal component analysis and cluster analysis were performed in order to explore the feasibility of discriminating among the samples. Dilutions of phenol were successfully discriminated from those of primary alcohols. Discrimination of n-octanol and n-decanol from the other alcohols, including phenol, was also achieved. The results suggest that it is possible to discriminate among dilutions of phenol in water and to give an approximate gross estimation of phenol concentration in water.

The qualities of a mango can be viewed from its characteristics such as color, size, sweetness, acidity, firmness and texture. Detection techniques can also use microwaves to determine the characteristics of the dielectric value. This technique is non-destructive and noninvasive. This dielectric value measurement technique uses a coaxial cavity resonator. The emitted microwave works in the frequency range from 100 MHz to 3 GHz and the resonator is affixed to the surface of the object. The mangoes that were sampled were honey mangoes. The values of S 21 parameter and impedance have been measured to determine the resonance characteristics of the mango. The measurement of glucose levels is also done as a comparison data, so that the correlation is obtained between measurements using microwaves with sugar content. The measurement results show there is a relationship between the resonance value and sweetness level, which is in the frequency range from 1.44 to 1.66 GHz. While the attenuation value range from -11dB to -29dB. Furthermore, the average value of the sweetness of 21.18% ripe tree mangoes has a trend above 20.53% once ripe mangoes, 14.76% early ripe mangoes, and 8.93% unripe mangoes. So, the results of these measurements show the same relationship with the comparison value.

Various teas, a tea extract, and a catechin mixture reagent were measured in transmittancereflectance mode using visible and NIR (Vis-NIR) spectroscopy. Total polyphenol (TPP) content in the tea samples was determined using Vis-NIR spectra. Then, reference TPP content was determined by the colorimetric ferrous tartrate method. Partial least squares (PLS) regression, a method of multivariate analysis, was performed in combination with various wavelength ranges and spectral pretreatments. Usually, multivariate analyses in which many independent variables are applied have been used for NIR data to obtain high accuracy for the determination. Nevertheless, surprisingly, only 2142 nm, which has high sensitivity and is in the NIR region, was identified for the determination of TPP content in various teas and tea extracts based on regression coefficient values, selectivity ratio (SR) values of the best PLS model [correlation coefficient (R = 0.98, n = 69)], correlation coefficients (a correlation spectrum) between the reference TPP content and Savitzky-Golay smoothing with the second derivatives (SG2D) with standard normal variate (SNV) preprocessing of raw spectra, and the peaks of the raw spectra and their SG2D with SNV. The R value between SG2D with SNV at 2142 nm and reference TPP content was 0.96 (n = 69).

Background: Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that may be used as non-invasive markers of lung disease. The electronic nose analyzes VOCs by composite nanosensor arrays with learning algorithms. It has been shown that an electronic nose can distinguish the VOCs pattern in exhaled breath of lung cancer patients from healthy controls. We hypothesized that an electronic nose can discriminate patients with lung cancer from COPD patients and healthy controls by analyzing the VOC-profile in exhaled breath. Methods: 30 subjects participated in a cross-sectional study: 10 patients with non-small cell lung cancer (NSCLC, [age 66.4 ± 9.0, FEV 1 86.3 ± 20.7]), 10 patients with COPD (age 61.4 ± 5.5, FEV 1 70.0 ± 14.8) and 10 healthy controls (age 58.3 ± 8.1, FEV 1 108.9 ± 14.6). After 5 min tidal breathing through a non-rebreathing valve with inspiratory VOC-filter, subjects performed a single vital capacity maneuver to collect dried exhaled air into a Tedlar bag. The bag was connected to the electronic nose (Cyranose 320) within 10 min, with VOC-filtered room air as baseline. The smellprints were analyzed by onboard statistical software. Results: Smellprints from NSCLC patients clustered distinctly from those of COPD subjects (cross validation value [CVV]: 85%; M-distance: 3.73). NSCLC patients could also be discriminated from healthy controls in duplicate measurements (CVV: 90% and 80%, respectively; M-distance: 2.96 and 2.26). Conclusion: VOC-patterns of exhaled breath discriminates patients with lung cancer from COPD patients as well as healthy controls. The electronic nose may qualify as a non-invasive diagnostic tool for lung cancer in the future.

Background: Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that may be used as non-invasive markers of lung disease. The electronic nose analyzes VOCs by composite nanosensor arrays with learning algorithms. It has been shown that an electronic nose can distinguish the VOCs pattern in exhaled breath of lung cancer patients from healthy controls. We hypothesized that an electronic nose can discriminate patients with lung cancer from COPD patients and healthy controls by analyzing the VOC-profile in exhaled breath. Methods: 30 subjects participated in a cross-sectional study: 10 patients with non-small cell lung cancer (NSCLC, [age 66.4 ± 9.0, FEV 1 86.3 ± 20.7]), 10 patients with COPD (age 61.4 ± 5.5, FEV 1 70.0 ± 14.8) and 10 healthy controls (age 58.3 ± 8.1, FEV 1 108.9 ± 14.6). After 5 min tidal breathing through a non-rebreathing valve with inspiratory VOC-filter, subjects performed a single vital capacity maneuver to collect dried exhaled air into a Tedlar bag. The bag was connected to the electronic nose (Cyranose 320) within 10 min, with VOC-filtered room air as baseline. The smellprints were analyzed by onboard statistical software. Results: Smellprints from NSCLC patients clustered distinctly from those of COPD subjects (cross validation value [CVV]: 85%; M-distance: 3.73). NSCLC patients could also be discriminated from healthy controls in duplicate measurements (CVV: 90% and 80%, respectively; M-distance: 2.96 and 2.26). Conclusion: VOC-patterns of exhaled breath discriminates patients with lung cancer from COPD patients as well as healthy controls. The electronic nose may qualify as a non-invasive diagnostic tool for lung cancer in the future.

Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25–100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography–mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82–91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines ...

The potentiometric electronic tongue system has been tested as a potential analytical tool for brand uniformity control of monoculture Apulian red wines (Primitivo and Negroamaro). The sensor array was composed of eight porphyrin coatings obtained by electrochemical polymerization process and was employed for both wines discrimination and quantitative detection of wine defect compounds: “off-odour” 3-(methylthio)-propanol; isoamyl alcohol fusel oil; benzaldehyde (marker of the yeast activity) and acetic acid (marker of vinegar formation). PLS-DA applied to Electronic tongue output data has permitted a correct discrimination of more than 70% of analysed wines in respect to the original brand affiliation. Satisfactory PLS1 predictions were obtained in real wine samples; with R2 = 0.989 for isoamyl alcohol and R2 = 0.732 for acetic acid. Moreover; the possibility to distinguish wine samples on the base of permitted levels of fault compounds content was shown.

The ability to avoid superparasitism provides a selective advantage to parasitoid females, allowing them to avoid depositing eggs in lower quality host. We observed in a Yolfactometer that generalist aphid parasitoids, Aphidius ervi and Aphidius rhopalosiphi (Hymenoptera, Braconidae), were more attracted toward non-parasitized than parasitized Sitobion avenae (Hemiptera, Aphididae) colonies. We collected the odors released from healthy aphids and aphids parasitized for 2 and 6 days using an electronic nose. Sitobion avenae alarm pheromone, (E)-ß-farnesene (EßF), was the only chemical identified, and was found in lower quantities in parasitized aphids. Both parasitoid species provided pronounced electrical depolarizations to EßF in electroantennography (EAG), and both were attracted to the latter compound in the Y-olfactometer. Parasitoid attraction was known to be guided by a variety of odorant cues released by plants and hosts, and our results support the hypothesis that the aphid alarm pheromone acts as a kairomone for A. ervi and A. rhopalosiphi.

BACKGROUND: Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. RESEARCH QUESTION: This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? STUDY DESIGN AND METHODS: In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. RESULTS: A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. INTERPRETATION: Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer.

To apply an e-nose system for monitoring headspace volatiles in biological samples from Egyptian patients with active pulmonary tuberculosis (TB) and healthy controls (HCs) and compare them with standard sputum analysis. The study population comprised 260 (140 males, 120 females) newly diagnosed TB patients and 240 (120 males, 120 females) HCs matched by age and socio-economic level admitted to hospitals specialising in chest diseases in Alexandria, Behera, Giza and Damietta Governorates, Egypt. Participants provided a history of TB and underwent clinical examinations, chest X-ray, and microbiological and e-nose analyses. Biological samples (blood, breath, sputum and urine) were collected.

Lung cancer (LC) incidence represents 11.5% of all new cancers, resulting in 1.72 million deaths worldwide in 2015. With the aim to investigate the capability of the electronic nose (e-nose) technology for detecting and differentiating complex mixtures of volatile organic compounds in biofluids ex-vivo, we enrolled 50 patients with suspected LC and 50 matching controls. Tissue biopsy was taken from suspicious lung mass for histopathological evaluation and blood, exhaled breath, and urine samples were collected from all participants and qualitatively processed using e-nose. Odor-print patterns were further analysed using the principal component analysis (PCA) and artificial neural network (ANN) analysis. Adenocarcinoma, non-small cell LC and squamous cell carcinoma were the predominant pathological types among LC patients. PCA cluster-plots showed a clear distinction between LC patients and controls for all biological samples; where the overall success ratios of classification for principal components #1 and #2 were: 95.46, 82.01, and 91.66% for blood, breath and urine samples, respectively. Moreover, ANN showed a better discrimination between LC patients and controls with success ratios of 95.74, 91.67 and 100% for blood, breath and urine samples, respectively. The e-nose is an easy noninvasive tool, capable of identifying LC patients from controls with great precision.

To apply an e-nose system for monitoring headspace volatiles in biological samples from Egyptian patients with active pulmonary tuberculosis (TB) and healthy controls (HCs) and compare them with standard sputum analysis. The study population comprised 260 (140 males, 120 females) newly diagnosed TB patients and 240 (120 males, 120 females) HCs matched by age and socio-economic level admitted to hospitals specialising in chest diseases in Alexandria, Behera, Giza and Damietta Governorates, Egypt. Participants provided a history of TB and underwent clinical examinations, chest X-ray, and microbiological and e-nose analyses. Biological samples (blood, breath, sputum and urine) were collected. Being a confirmed TB patient was directly proportional to e-nose 10-sensor responses. Principal component analysis clusters showed a clear distinction between TB and HC groups, with variances of 93%, 85%, 75% and 95% for blood, breath, sputum and urine samples, respectively. Overall accuracy, sens...

Objectives: Benzene is commonly emitted in several industries, leading to widespread environmental and occupational exposure hazards. While less toxic solvents have been substituted for benzene, it is still a component of petroleum products and is a trace impurity in industrial products resulting in continued higher occupational exposures in industrial settings in developing countries. Materials and Methods: We investigated the potential use of an electronic nose (e-nose) to monitor the headspace volatiles in biological samples from benzene-exposed Egyptian workers and non-exposed controls. The study population comprised 150 non-smoking male workers exposed to benzene and an equal number of matching non-exposed controls. We determined biomarkers of benzene used to estimate exposure and risk including: benzene in exhaled air and blood; and its urinary metabolites such as phenol and muconic acid using gas chromatography technique and a portable e-nose. Results: The average benzene concentration measured in the ambient air of the workplace of all studied industrial settings in Alexandria, Egypt; was 97.56±88.12 μg/m 3 (range: 4.69-260.86 μg/m 3 ). Levels of phenol and muconic acid were significantly (p < 0.001) higher in both blood and urine of benzene-exposed workers as compared to non-exposed controls. Conclusions: The e-nose technology has successfully classified and distinguished benzene-exposed workers from non-exposed controls for all measured samples of blood, urine and the exhaled air with a very high degree of precision. Thus, it will be a very useful tool for the low-cost mass screening and early detection of health hazards associated with the exposure to benzene in the industry.

There are 12 known different types of leukaemia, depending on the type of stem cell they develop from and on their progression rates, of which only four have the greatest prevalence . Exposure to benzene at high concentrations may cause decreases in the numbers of erythrocytes, leucocytes and/or thrombocytes in workers blood, leading to the development of myelodysplasia and eventually to one of the four types: acute lymphocytic leukaemia (ALL), acute myeloid leukaemia (AML), chronic lymphocytic leukaemia (CLL) or chronic myeloid leukaemia (CML) . We have recently employed the electronic nose (e-nose) technology for monitoring workers occupational exposure to benzene in their blood, urine and the exhaled air with a very high degree of precision . This preliminary study comprised 500 free volunteers, who were selected consecutively into five equal groups (n = 100) as follows: ALL, AML, CLL, CML and healthy controls (HC). Blood samples were collected from all participants into heparinized vacutainer tubes in doublicates used for standard haematological examinations and e-nose experiments. All participants were provided with written informed consents for blood sampling and collection and review of their medical records prior to their enrollment, and the ethics committee of the Medical Research Institute, Alexandria University approved the study protocol. Diagnosis of leukaemia was based on clinical findings, complete blood counts and peripheral blood morphology (Table ). We investigated qualitative odour differences for all fresh blood samples randomly, without any prior knowledge of participant identity and/or diagnosis, using a commercially available e-nose (PEN3, Airsense Analytics GmbH, Schwerin, Germany). The e-nose has an array of 10 different metal-oxide sensors that measure independently and register continuously the relative changes in electric conductance due to stereochemical characteristics of odorant molecules, particularly volatile organic compounds (VOCs), in the headspace over each blood sample in a vacutainer tube. Odours in the headspace were carried by the carrier gas (i.e. dry air) to the array of sensors through the e-nose inlet at a rate of 400 ml/min. Solenoid valves alternately switched the pure carrier gas and the headspace sample vapour, and the differences in the sensor output were

This paper investigates the incorporation of machine learning (ML) techniques in Very Large-Scale Integration (VLSI) design, concentrating on pruning methods to boost circuit efficiency. By utilizing ML algorithms for feature extraction, optimization, and decisionmaking while applying pruning methods to eliminate redundancy, our study introduces a unique strategy for achieving superior circuit performance. Key contributions include a thorough methodology that describes MLbased pruning in hardware design along with an assessment of its effects on design metrics like area, power, and speed. The findings indicate substantial advancements, implying that ML-based pruning could transform VLSI design processes.

The volatile and olfactory profiles of three Portuguese olive oils with protected designations of origin (PDO) were studied: "Azeite do Alentejo Interior", "Azeites da Beira Interior", and "Azeite de Trás-os-Montes". Seven classes of volatiles were identified, with aldehydes, followed by hydrocarbons and alcohols, the most prevalent (5.63, 2.92, and 2.79 mg/kg olive oil, respectively). The "Azeites da Beira Interior" oils exhibited the highest amount of volatiles (18.2 ± 4.6 mg/kg olive oil) compared to the oils from the other two PDOs. Ten positive olfactory sensations were detected, and a significant effect of the PDO on the intensities of fruity, apple, cabbage, tomato, dry and fresh herbs was observed. Specifically, "Azeite do Alentejo Interior" PDO oils were characterized as fruity-ripe, while "Azeite de Trás-os-Montes" PDO oils were labelled as fruity-green. Conversely, "Azeites da Beira Interior" PDO oils encompassed both fruity-ripe and fruity-green oils. Unique volatile and olfactory fingerprints were established for each PDO, allowing the linear discrimination of the oils according to the PDO, with a predictive sensitivity of 98.0 ± 4.2% (repeated K-fold-CV). Furthermore, a lab-made electronic nose successfully discriminated the studied oils based on the PDO, with a predictive accuracy of 99.7 ± 2.0% (repeated K-fold-CV). This device also allowed predicting the concentrations of the three main volatile classes found in the oils through multiple linear regression models (R 2 ≥ 0.923 ± 0.101 and RMSE ≤ 1.32 ± 0.72 mg/kg oil; repeated K-fold-CV). These findings underscore * Nuno Rodrigues

Nerve agents are often used at the military warfront, where diesel is a very common interferant. In the present work, a group of surface acoustic wave (SAW) sensors, called E-Nose with dissimilar sensing layers is developed for the recognition of the mixture of diesel and dimethyl methylphosphonate (DMMP) vapors. The exposure of DMMP and diesel vapors is kept at ppb and ppm levels respectively. Varied response patterns of DMMP and diesel vapors were obtained by SAW E-nose. Principal component analysis (PCA) has been used to extract features from the response curves of SAW sensors. Artificial Neural Network pattern recognition has been implemented to identify the precise detection of DMMP vapors in the binary mixture of DMMP and diesel. The effect of pre-processing (using PCA) the raw data before feeding it to artificial neural network is also studied.

A functionalized polymer (SXFA: fluoroalcoholpolysiloxane) coated single surface acoustic wave (SAW) sensor E-Nose is proposed for the detection of harmful vapors by operating at different temperature. The polymer coated SAW sensor is used along with a reference SAW device in dual Colpitt's oscillator configuration. The sensor is tested with four different classes of vapors at different concentrations in an environmental chamber whose temperature can be varied in the range -20 to +70 • C. The sensor showed variation in response with exposure to benzene, methanol, diesel and DMMP vapors at different temperatures. The polymer coated SAW sensor was giving frequency shifts of 0.21-1.12 kHz for methanol (1000-4000 ppm), 0.3-2.69 kHz for benzene (400-1600 ppm), 0.81-7.39 kHz for diesel (12-48 ppm) and 4.27-8.07 kHz for DMMP (1-10 ppm) vapors at 30 • C operating temperature. Principal component analysis and artificial neural network algorithms are successfully implemented to classify and detect the target vapors.

Chemical warfare agents (CWA) are deadly chemicals even at low levels of concentration. In the present work, an electronic-nose (E-nose) comprising of an array of four surface acoustic wave (SAW) sensors having different chemical sensitive coatings of ZnO, TeO 2 , SnO 2 and TiO 2 is prepared for the detection of CWA. The oxide thin films are coated on the surface of SAW devices by rf sputtering technique. ZnO and SnO 2 thin films are poly-crystalline with fine surface morphology whereas TeO 2 and TiO 2 thin films are amorphous. The E-nose shows good sensitivity toward the four simulants (dimethyl methylphosphonate (DMMP), dibutyl sulfide (DBS), chloroethyl phenyl sulfide (CEPS) and diethyl chlorophosphate (DECP)) of CWA allowing sub ppm level detection possible. Different simulants give distinct characteristic pattern when passed through the surface of E-nose. Using principal component analysis (PCA), the four different simulants of CWA are well separated from each other. Even with the incorporation of interferants (petrol, diesel, kerosene, VOCs and water vapors) in the PCA study, all four simulants are well separated showing the capability of the prepared SAW E-nose to classify simulants of chemical warfare agents efficiently.

An Electronic nose (E-nose) made up of an array of metal oxide thin film based SAW sensors is proposed as a detection system for the binary mixture of DMMP (simulant of nerve agent) and methanol at room temperature. The SAW devices are one port surface acoustic wave (SAW) resonators having center frequency of 433.9 MHz. The E-nose sensor array has SAW sensors coated with four different chemical sensitive coatings of ZnO, TeO 2 , SnO 2 and TiO 2 in order to recognize the individual components in a binary mixture of DMMP (in ppb levels) and methanol (in sub ppm levels). Principal Component Analysis (PCA) as a data pre-processing technique and Artificial Neural Network (ANN) as a pattern classification technique have been applied for obtaining a clear discrimination and correct classification.

Studies suggest that breath and urine analysis can be viable non-invasive methods for diabetes management, with the potential for disease diagnosis. In the present work, we employed two sensing strategies. The first strategy involved analyzing volatile organic compounds (VOCs) in biological matrices, such as exhaled breath and urine samples collected from patients with diabetes mellitus (DM) and healthy controls (HC). The second strategy focused on discriminating between two types of DM, related to type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), by using a data fusion method. For this purpose, an electronic nose (e-nose) based on five tin oxide (SnO2) gas sensors was employed to characterize the overall composition of the collected breath samples. Furthermore, a voltametric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. To evaluate the diagnost...

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Moroccan and French honeys from different geographical areas were classified and characterized by applying a voltammetric electronic tongue (VE-tongue) coupled to analytical methods. The studied parameters include color intensity, free lactonic and total acidity, proteins, phenols, hydroxymethylfurfural content (HMF), sucrose, reducing and total sugars. The geographical classification of different honeys was developed through three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cluster analysis (HCA). Honey characterization was achieved by partial least squares modeling (PLS). All the PLS models developed were able to accurately estimate the correct values of the parameters analyzed using as input the voltammetric experimental data (i.e. r>0.9). This confirms the potential ability of the VE-tongue for performing a rapid characterization of honeys via PLS in which an uncomplicated, costeffective sample preparation process that does not require the use of additional chemicals is implemented.

In recent decades, remarkable progress in the development of electronic nose (EN) technologies, particularly for disease detection, has been accomplished through the disclosure of novel methods and associated devices, mainly for the detection of volatile organic compounds (VOCs). Herein, we assessed the ability of a novel EN technology (MENT-EGAS prototype) to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. Principal Component Analyses (PCA) evidenced the presence in the analyzed samples of sufficient information to consent the discrimination of different environmental backgrounds, feed headspaces and exhalated breath between two groups of cows fed with two different types of feed. Moreover, discrimination was also observed within the same group between exhalated breaths sampled before and after feed intake. Based on these findings, we provided evidence that the MENT-EGAS prototype can identify error sources with accuracy. Livestock precision farming technologies are powerful tools for monitoring animal health and welfare parameters in a continuous and automated way.

Native starch derivatization with octenyl succinic anhydride (OSA) is a chemical modification designed to enhance flavor microencapsulation performance. Hi Cap 100 and Capsul are two OSA starches derived from waxy maize base, which are especially suited for encapsulation processes. This work performs for the first time the encapsulation of vanilla extract with Capsul and Hi Cap 100 using both spray and freeze drying procedures. The encapsulation efficiency was studied correlating the starch texture with the aroma retention. Texture analysis was accomplished by means of grey level co-occurrence matrix feature extraction (GLCM), yielding image parameters that clearly differ in function of the type of starch and the drying method used for the encapsulation of the flavor. In parallel, the data recorded with a gas sensor array (e-nose) and analyzed by unsupervised multivariate methods allowed to follow up the evolution of the aroma through the whole process. The joint analysis of the GLC...