Metal oxide sensors for electronic noses and their application to food analysis

A Wearable IoT Aldehyde Sensor for Pediatric Asthma Research and Management

A cloud-based wearable IoT aldehyde sensor system for asthma research and management.

Electronic Noses in Medical Diagnostics

BACKGROUND

Electronic nose technology is being developed in order to analyse complex mixtures of volatiles in a way parallel to biologic olfaction. When applied in the field of medicine, the use of such devices should enable the identification and discrimination between different diseases. In this review, a comprehensive summary of research in medical diagnostics using electronic noses is presented. A special attention has been paid to the application of these devices and sensor technologies, in response to current trends in medicine.

METHODS

Peer-reviewed research literature pertaining to the subject matter was identified based on a search of bibliographic databases. The quality and relevance of retrieved papers was assessed using standard tools. Their content was critically reviewed and certain information contained therein was compiled in tabularized form.

RESULTS

The majority of reviewed studies show promising results, often surpassing the accuracy and sensitivity of established diagnostic methods. However, only a relatively small number of devices have been field tested. The methods used for sample collection and data processing in various studies were listed in a table, together with electronic nose models used in these investigations.

CONCLUSION

Despite the fact that devices equipped with arrays of chemical sensors are not routinely used in everyday medical practice, their prospective use would solve some established issues in medical diagnostics, as well as lead to developments in prophylactics by facilitating a widespread use of non-invasive screening tests.

Prediction of the Biogenic Amines Index of Poultry Meat Using an Electronic Nose

The biogenic amines index of fresh chicken meat samples during refrigerated storage was predicted based on the headspace analysis using an electronic nose equipped with an array of electrochemical sensors. The reference biogenic amines index values were obtained using dispersive liquid–liquid microextraction–gas chromatography–mass spectrometry. A prototype electronic nose with modular construction and a dedicated sample chamber was used to rapidly analyze the volatile fraction of chicken meat samples, with a single measurement time of five minutes. Back-propagation artificial neural network was used to estimate the biogenic amines index of the samples with a determination coefficient of 0.954 based on ten-fold stratified cross-validation. The results indicate that the determination of the biogenic amines index is a good reference method for studies in which the freshness of meat products is assessed based on headspace analysis and fingerprinting, and that the described electronic device can be used to assess poultry meat freshness based on this value with high accuracy.

Multipurpose sensing applications of biocompatible radish-derived carbon dots as Cu2+ and acetic acid vapor sensors

A recent trend in the preparation of carbon dots, optically unique nanomaterials, revolves around the use of readily-available, low-cost natural resources as precursors and their multipurpose applications. In this work, a hydrothermal method for preparing biocompatible carbon dots from radish was developed. The carbon dots were then tested for sensing of Cu²⁺ and acetic acid vapor. The carbon dots exhibited blue emission under UV illumination with, a quantum yield of 15%. The fluorescence emission was selectively quenched when Cu²⁺ ions were added, giving a detection limit of 0.16 μM. A paper-based fluorescent sensor was fabricated and shown to sense Cu²⁺ with a limit of detection of 6.8 μM. The carbon dots were able to determine the Cu²⁺ concentration in real water samples, with excellent recovery and reliability. The carbon dots were also used as the sensing material in an optical electronic nose, and tested for real-time detection of acetic acid vapor. Using principal component analysis, different ratios of acetic acid to methanol in solution were successfully identified with a detection limit of 15.5%. The acetic acid concentration in a real vinegar sample was also accurately determined. Our results demonstrated that label-free carbon dots derived from readily available radish can be simply used as versatile probes, giving them potential uses in multipurpose sensing applications.

Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection

In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.

Rapid prediction of deoxynivalenol contamination in wheat bran by MOS-based electronic nose and characterization of the relevant pattern of volatile compounds

BACKGROUND

Deoxynivalenol (DON) is a mycotoxin, mainly produced by Fusarium sp., most frequently occurring in cereals and cereal-based products. Wheat bran refers to the outer layers of the kernel, which has a high risk of damage due to chemical hazards, including mycotoxins. Rapid methods for DON detection in wheat bran are required.

RESULTS

A rapid screening method using an electronic nose (e-nose), based on metal oxide semiconductor sensors, has been developed to distinguish wheat bran samples with different levels of DON contamination. A total of 470 naturally contaminated wheat bran samples were analyzed by e-nose analysis. Wheat bran samples were divided in two contamination classes: class A ([DON] ≤ 400 µg kg^-1 , 225 samples) and class B ([DON] > 400 µg kg^-1 , 245 samples). Discriminant function analysis (DFA) classified wheat bran samples with good mean recognizability in terms of both calibration (92%) and validation (89%). A pattern of 17 volatile compounds of wheat bran samples that were associated (positively or negatively) with DON content was also characterized by HS-SPME/GC-MS.

CONCLUSIONS

These results indicate that the e-nose method could be a useful tool for high-throughput screening of DON-contaminated wheat bran samples for their classification as acceptable / rejectable at contamination levels close to the EU maximum limit for DON, reducing the number of samples to be analyzed with a confirmatory method. © 2018 Society of Chemical Industry.

The Smell of Synthetic Biology: Engineering Strategies for Aroma Compound Production in Yeast

Yeast—especially Saccharomyces cerevisiae—have long been a preferred workhorse for the production of numerous recombinant proteins and other metabolites. S. cerevisiae is a noteworthy aroma compound producer and has also been exploited to produce foreign bioflavour compounds. In the past few years, important strides have been made in unlocking the key elements in the biochemical pathways involved in the production of many aroma compounds. The expression of these biochemical pathways in yeast often involves the manipulation of the host strain to direct the flux towards certain precursors needed for the production of the given aroma compound. This review highlights recent advances in the bioengineering of yeast—including S. cerevisiae—to produce aroma compounds and bioflavours. To capitalise on recent advances in synthetic yeast genomics, this review presents yeast as a significant producer of bioflavours in a fresh context and proposes new directions for combining engineering and biology principles to improve the yield of targeted aroma compounds.

Rapid detection of adulterated peony seed oil by electronic nose

Peony seed oil has recently been introduced as a high-quality food oil. Because the high price of peony seed oil may tempt unscrupulous merchants to dilute it with cheaper substitutes, a rapid detection method for likely adulterants is required. In this study, the fatty acid composition of peony seed oil and four less expensive edible oils (soybean oil, corn oil, sunflower oil, and rapeseed oil) were measured by gas chromatography mass spectrometry. Peony oil adulterated by other edible oils was assessed using iodine values to estimate the extent of adulteration. Adulteration was also measured using an electronic nose (E-nose) combined with principal component analysis (PCA) or linear discriminant analysis (LDA). Results indicated that peony seed oil was highly enriched in α-linolenic acid. Although the iodine value can be used to detect some adulterants by measuring unsaturation, it was not able to detect all four potential adulterants. In contrast, the E-nose can rapidly identify adulterated peony seed oil by sampling vapor. Data analyses using PCA and LDA show that LDA more effectively clusters the data, discriminates between pure and adulterated oil, and can detect adulteration at the 10% level. E-nose combined with LDA suitable for detection of peony seed oil adulteration.

Canonical discriminant analysis and meat quality analysis as complementary tools to detect the illicit use of dexamethasone as a growth promoter in Friesian bulls

A screening method based on meat quality parameters and production traits for detecting the effects of illegal administration of dexamethasone in Friesian bulls was assessed. Twenty finishing bulls were divided into an untreated control group (n=8) and two treatment groups receiving dexamethasone orally at dosages of 1.4 (n=6) or 0.7 (n=6)mg per head per day for 60 days. The animals were slaughtered 26days after cessation of treatment. Thirty-six parameters were measured on live animals, carcasses and samples of the longissimus thoracis muscle. The production traits were similar between groups, but there were significant differences in meat quality between treatment groups. The higher dosage of dexamethasone improved meat tenderness, while the lower dosage resulted in more saturated red meat, with increased meat cooking shrinkage and cooking loss. The use of a portable 'electronic nose' as a screening tool was not successful in discriminating between treated and untreated meat. These results indicate that a multivariable approach using canonical discriminant analysis may be a complementary tool to identify meat from animals illegally treated with dexamethasone, based on several parameters (meat flavour, cooking and thawing loss, tenderness, colour and live weight gain), which are part of the normal analysis of meat quality.

Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform

In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R² = 0.9942) worked much better than PLSR.

Role of vacancy sites and UV-ozone treatment on few layered MoS2 nanoflakes for toxic gas detection

Various issues like global warming and environmental pollutions have led to the research of toxic gas detection worldwide. In this work, we have tried to develop a molybdenum disulfide (MoS₂) based gas sensor to detect toxic gases like ammonia and NO. MoS₂, an inorganic analog of graphene, has attracted lots of attention for many different applications recently. This paper reports the use of liquid exfoliated MoS₂ nanoflakes as the sensing layer in a handheld, resistive toxic gas sensor. The nanoflakes were exfoliated from MoS₂ bulk powder using a sonication based exfoliation technique at room temperature. The successful exfoliation of the nanoflakes was characterized using different techniques e.g., optical microscopy, atomic force microscopy, field emission scanning electron microscopy, high resolution transmission electron microscopy, x-ray diffraction, Raman spectroscopy, x-ray photoelectron spectroscopy and ultraviolet-visible spectrophotometry. The characterization results showed that few-layered nanoflakes have successfully been exfoliated. The MoS₂ nanoflakes showed reasonable sensing towards ammonia and NO. In order to explore the effect of particle size on ammonia sensing, the MoS₂ flakes were also exfoliated using different sonication times. We also observed that various factors like presence of vacancy sites, ambient oxygen, humidity, different contact electrodes have significant effect on the sensing characteristics. In fact, the response of the sensing layer against 400 ppm of ammonia increased from 54.1% to ∼80% when it was UV-ozone treated. This work holds promises to developing cost-effective, reliable and highly sensitive MoS₂ based ammonia sensors.

A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms.

Metal Oxide Gas Sensors, a Survey of Selectivity Issues Addressed at the SENSOR Lab, Brescia (Italy)

This work reports the recent results achieved at the SENSOR Lab, Brescia (Italy) to address the selectivity of metal oxide based gas sensors. In particular, two main strategies are being developed for this purpose: (i) investigating different sensing mechanisms featuring different response spectra that may be potentially integrated in a single device; (ii) exploiting the electronic nose (EN) approach. The former has been addressed only recently and activities are mainly focused on determining the most suitable configuration and measurements to exploit the novel mechanism. Devices suitable to exploit optical (photoluminescence), magnetic (magneto-optical Kerr effect) and surface ionization in addition to the traditional chemiresistor device are here discussed together with the sensing performance measured so far. The electronic nose is a much more consolidated technology, and results are shown concerning its suitability to respond to industrial and societal needs in the fields of food quality control and detection of microbial activity in human sweat.

Preservation Mechanism of Chitosan-Based Coating with Cinnamon Oil for Fruits Storage Based on Sensor Data

The chitosan-based coating with antimicrobial agent has been developed recently to control the decay of fruits. However, its fresh keeping and antimicrobial mechanism is still not very clear. The preservation mechanism of chitosan coating with cinnamon oil for fruits storage is investigated in this paper. Results in the atomic force microscopy sensor images show that many micropores exist in the chitosan coating film. The roughness of coating film is affected by the concentration of chitosan. The antifungal activity of cinnamon oil should be mainly due to its main consistent trans-cinnamaldehyde, which is proportional to the trans-cinnamaldehyde concentration and improves with increasing the attachment time of oil. The exosmosis ratios of Penicillium citrinum and Aspergillus flavus could be enhanced by increasing the concentration of cinnamon oil. Morphological observation indicates that, compared to the normal cell, the wizened mycelium of A. flavus is observed around the inhibition zone, and the growth of spores is also inhibited. Moreover, the analysis of gas sensors indicate that the chitosan-oil coating could decrease the level of O₂ and increase the level of CO₂ in the package of cherry fruits, which also control the fruit decay. These results indicate that its preservation mechanism might be partly due to the micropores structure of coating film as a barrier for gas and a carrier for oil, and partly due to the activity of cinnamon oil on the cell disruption.

Identification of protein markers for the occurrence of defrosted material in milk through a MALDI-TOF-MS profiling approach

Mozzarella di Bufala Campana is a soft, stretched curd Italian cheese made from fresh buffalo milk that obtained the Protected Designation of Origin (PDO) registration in EU legislation. Seasonality of buffalo milk production, rapid cheese decay and transport of its preserving liquid have relevant practical/economic consequences for mozzarella production; consequently, a progressive diffusion of cheese products realized with frozen curd or frozen milk has recently been observed. In order to meet the demand of the dairy producers and consumers for a reduction of starting material adulterations and for the certification of the raw milk used for cheese manufacturing, we have developed a rapid/robust MALDI-TOF-MS polypeptide profiling procedure that assays material quality through the identification of specific markers of its freshness. Massive analysis of fresh and frozen buffalo milks (stored for different times) was realized to this purpose; a tough statistical evaluation of the resulting data ultimately permitted the typing of milk samples. We identified 28 polypeptide markers of the milk freezing storage, among which 13 and 15 showed down- and over-representation, respectively. Quantitative data were confirmed by an independent analytical approach on selected markers. GLYCAM1-derived phosphopeptides (1-53), β-casein-derived phosphopeptides (1-68), β-casein-derived γ2-, γ3- and γ4-fragments, α-lactalbumin and β-lactoglobulin were components showing the highest significance. The occurrence of the first compounds in buffalo milk is here described for the first time; their formation in the frozen material was ascribed to the activity of plasmin or of unknown bacterial proteases/peptidases stable at low temperatures. In conclusion, data reported here suggest the application of this MALDI-TOF-MS polypeptide profiling platform to other high-quality dairy productions, in which milk freshness has important consequences on final product organoleptic properties.

BIOLOGICAL SIGNIFICANCE

In the last decades, several studies have provided the molecular basis underlying the relation between food quality and human wellness/health. In this context, Foodomics emerged as a novel scientific discipline studying food and nutrition domains through the application of advanced omics technologies, including genomics, transcriptomics, proteomics and/or metabolomics. Above-mentioned technologies have been used in an integrated, holistic way to study foods for: i) compound profiling, authenticity, and/or biomarker-detection related to product quality or safety; ii) contaminants and their whole toxicity; iii) bioactivity and general effects on human health; iv) their digestion and assumption in human body; v) development of new transgenic products; and vi) evaluation of their modifications within the digestive tract. In the first context, a highly reproducible MALDI-TOF-MS polypeptide profiling procedure is here presented, which provides information on buffalo milk quality through the identification of specific markers of its freshness. Among identified markers, some were indicative of the action of various proteolytic enzymes and the resulting occurrence of specific defense components in buffalo milk having the physiological role to limit bacterial/virus content in this biological fluid. Data suggest the possible application of similar MALDI-TOF-based platforms to other high-quality food productions, where storage conditions of the starting materials may have important consequences on final product characteristics.

Selectively enhanced sensing performance for oxidizing gases based on ZnO nanoparticle-loaded electrospun SnO2 nanotube heterostructures

In this work, we present gas sensors based on ZnO nanoparticle-loaded electrospun SnO₂ nanotube (ZnO/SnO₂) n–n heterostructures (HSs) synthesized by electrospinning combined with facile thermal decomposition.

Detection of Zygosaccharomyces rouxii and Candida tropicalis in a High-Sugar Medium by a Metal Oxide Sensor-Based Electronic Nose and Comparison with Test Panel Evaluation

Osmotolerant yeasts are primarily responsible for spoilage of sugar-rich foods. In this work, an electronic nose (e-nose) was used to diagnose contamination caused by two osmotolerant yeast strains (Zygosaccharomyces rouxii and Candida tropicalis) in a high-sugar medium using test panel evaluation as the reference method. Solid-phase microextraction gas chromatography with mass spectrometry (GC-MS) was used to determine the evolution of the volatile organic compound fingerprint in the contaminated samples during yeast growth. Principal component analysis and linear discriminant analysis revealed that the e-nose could identify contamination after 48 h, corresponding to the total yeast levels of 3.68 (Z. rouxii) and 3.09 (C. tropicalis) log CFU/ml. At these levels, the test panel could not yet diagnose the spoilage, indicating that the e-nose approach was more sensitive than the test panel evaluation. Loading analysis indicated that sensors 8 and 6 were the most important for detection of these two yeasts. Based on the result obtained with the e-nose, the incubation time and total yeast levels could be accurately predicted by established multiple regression models with a correlation of greater than 0.97. In the sensory evaluation, spoilage was diagnosed after 72 h in samples contaminated with C. tropicalis and after 48 to 72 h for samples contaminated with Z. rouxii. GC-MS revealed that compounds such as acetaldehyde, acetone, ethyl acetate, alcohol, and 3-methyl-1-butanol contributed to spoilage detection by the e-nose after 48 h. In the high-sugar medium, the e-nose was more sensitive than the test panel evaluation for detecting contamination with these test yeast strains. This information could be useful for developing instruments and techniques for rapid scanning of sugar-rich foods for contamination with osmotolerant yeasts before such spoilage could be detected by the consumer.

Early detection of Zygosaccharomyces rouxii--spawned spoilage in apple juice by electronic nose combined with chemometrics

Spoilage spawned by Zygosaccharomyces rouxii can cause sensory defect in apple juice, which could hardly be perceived in the early stage and therefore would lead to the serious economic loss. Thus, it is essential to detect the contamination in early stage to avoid costly waste of products or recalls. In this work the performance of an electronic nose (e-nose) coupled with chemometric analysis was evaluated for diagnosis of the contamination in apple juice, using test panel evaluation as reference. The feasibility of using e-nose responses to predict the spoilage level of apple juice was also evaluated. Coupled with linear discriminant analysis (LDA), detection of the contamination was achieved after 12h, corresponding to the cell concentration of less than 2.0 log 10 CFU/mL, the level at which the test panelists could not yet identify the contamination, indicating that the signals of e-nose could be utilized as early indicators for the onset of contamination. Loading analysis indicated that sensors 2, 6, 7 and 8 were the most important in the detection of Z. rouxii-contaminated apple juice. Moreover, Z. rouxii counts in unknown samples could be well predicted by the established models using partial least squares (PLS) algorithm with high correlation coefficient (R) of 0.98 (Z. rouxii strain ATCC 2623 and ATCC 8383) and 0.97 (Z. rouxii strain B-WHX-12-53). Based on these results, e-nose appears to be promising for rapid analysis of the odor in apple juice during processing or on the shelf to realize the early detection of potential contamination caused by Z. rouxii strains.

Effect of hot air drying on volatile compounds of Flammulina velutipes detected by HS-SPME-GC-MS and electronic nose

Volatile compounds are important factors that affect the flavor quality of Flammulina velutipes, but the changes occurring during hot air drying is still unclear. To clarify the dynamic changes of flavor components during hot air drying, comprehensive flavor characterization and volatile compounds of F. velutipes were evaluated using electronic nose technology and headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS), respectively. Results showed that volatile components in F. velutipes significantly changed during hot air drying according to the principal component analysis and radar fingerprint chart of electronic nose. Volatile compounds of fresh F. velutipes consisted mainly of ketones, aldehydes and alcohols, and 3-octanone was the dominant compound. Drying process could significantly decrease the relative content of ketones and promoted the generation of alcohols, acids, and esters, which became the main volatile compounds of dried F. velutipes. These may provide a theoretical basis for the formation mechanism of flavor substances in dried F. velutipes.

Fruit and Vegetable Quality Assessment via Dielectric Sensing

The demand for improved food quality has been accompanied by a technological boost. This fact enhances the possibility of improving the quality of horticultural products, leading towards healthier consumption of fruits and vegetables. A better electrical characterization of the dielectric properties of fruits and vegetables is required for this purpose. Moreover, a focused study of dielectric spectroscopy and advanced dielectric sensing is a highly interesting topic. This review explains the dielectric property basics and classifies the dielectric spectroscopy measurement techniques. It comprehensively and chronologically covers the dielectric experiments explored for fruits and vegetables, along with their appropriate sensing instrumentation, analytical modelling methods and conclusions. An in-depth definition of dielectric spectroscopy and its usefulness in the electric characterization of food materials is presented, along with the various sensor techniques used for dielectric measurements. The collective data are tabulated in a summary of the dielectric findings in horticultural field investigations, which will facilitate more advanced and focused explorations in the future.

Sensor Array Design for Complex Sensing Tasks

Chemical detection in complex environments presents numerous challenges for successful implementation. Arrays of sensors are often implemented for complex chemical sensing tasks, but systematic understanding of how individual sensor response characteristics contribute overall detection system performance remains elusive, with generalized strategies for design and optimization of these arrays rarely reported and even less commonly adopted by practitioners. This review focuses on the literature of nonspecific sensor array design and optimization strategies as well as related work that may inform future efforts in complex sensing with arrays.

A low cost instrumentation system to analyze different types of milk adulteration

In this paper, the design of a complete instrumentation system to detect different types of milk adulteration has been reported. A simple to use indicator type readout device is reported which can be used by milk community people. A low cost microcontroller based automatic sensing system is also reported to detect 'synthetic milk', which has been reconstructed after adulterating the milk with 'liquid-whey'.

Changes in the aromatic profile of espresso coffee as a function of the grinding grade and extraction time: a study by the electronic nose system

The changes in chemical attributes and aromatic profile of espresso coffee (EC) were studied taking into account the extraction time and grinding level as independent variables. Particularly, using an electronic nose system, the changes of the global aromatic profile of EC were highlighted. The results shown as the major amounts of organic acids, solids, and caffeine were extracted in the first 8 s of percolation. The grinding grade significantly affected the quality of EC probably as an effect of the particle size distribution and the percolation pathways of water through the coffee cake. The use of an electronic nose system allowed us to discriminate the fractions of the brew as a function of the percolation time and also the regular coffee obtained from different grinding grades. Particularly, the aromatic profile of a regular coffee (25 mL) was significantly affected by the grinding level of the coffee grounds and percolation time, which are two variables under the control of the bar operator.

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

Aroma characterization of whole cigarette smoke samples using sensory panels or electronic nose (E-nose) devices is difficult due to the masking effect of major constituents and solvent used for the extraction step. On the other hand, GC in combination with olfactometry detection does not allow to study the delicate balance and synergetic effect of aroma solutes. To overcome these limitations a new instrumental set-up consisting of heart-cutting gas chromatography using a capillary flow technology based Deans switch and low thermal mass GC in combination with an electronic nose device is presented as an alternative to GC-olfactometry. This new hyphenated GC-E-nose configuration is used for the characterization of cigarette smoke aroma. The system allows the transfer, combination or omission of selected GC fractions before injection in the E-nose. Principal component analysis (PCA) and discriminant factor analysis (DFA) allowed clear visualizing of the differences among cigarette brands and classifying them independently of their nicotine content. Omission and perceptual interaction tests could also be carried out using this configuration. The results are promising and suggest that the GC-E-nose hyphenation is a good approach to measure the contribution level of individual compounds to the whole cigarette smoke.

Application of an electronic nose instrument to fast classification of Polish honey types

The paper presents practical utilization of an electronic nose prototype, based on the FIGARO semiconductor sensors, in fast classification of Polish honey types-acacia flower, linden flower, rape, buckwheat and honeydew ones. A set of thermostating modules of the prototype provided gradient temperature characteristics of barbotage-prepared gas mixtures and stable measurement conditions. Three chemometric data analysis methods were employed for the honey samples classification: principal component analysis (PCA), linear discriminant analysis (LDA) and cluster analysis (CA) with the furthest neighbour method. The investigation confirmed usefulness of this type of instrument in correct classification of all aforementioned honey types. In order to provide optimum measurement conditions during honey samples classification the following parameters were selected: volumetric flow rate of carrier gas-15 L/h, barbotage temperature-35 °C, time of sensor signal acquisition since barbotage process onset-60 s. Chemometric analysis allowed discrimination of three honey types using PCA and CA and all five honey types with LDA. The reproducibility of 96% of the results was within the range 4.9%-8.6% CV.

The potential of e-nose aroma profiling for identifying the geographical origin of licorice (Glycyrrhiza glabra L.) roots

Licorice roots cultivated commercially in distinct geographical areas such as China, Iran, Italy (Abruzzo, Basilicata, Calabria and Sicily) and Turkey were classified using an artificial olfactive system (e-nose) based on metal oxide semiconductor sensors (MOS). The resultant instrumental data were processed using a multivariate statistical analysis method in order to classify the raw samples according to its origin. The e-nose odourprintings were obtained by a canonical discriminant analysis carried out with the aim of relating the specific data-sets corresponding to whole licorice roots aroma with the e-nose reference dataset. E-nose results were compared to those obtained by SPME/GC-MS. The HS-SPME/GC/MS analysis was used as a control system to check for the actual existence of differences in the chemical composition of sample headspace. These results imply the possibility to use an electronic nose as a tool for a quick, effective and non-destructive authentication of licorice roots.

Nondestructive measurement of fruit and vegetable quality

We review nondestructive techniques for measuring internal and external quality attributes of fruit and vegetables, such as color, size and shape, flavor, texture, and absence of defects. The different techniques are organized according to their physical measurement principle. We first describe each technique and then list some examples. As many of these techniques rely on mathematical models and particular data processing methods, we discuss these where needed. We pay particular attention to techniques that can be implemented online in grading lines.

The detection of foodborne bacteria on beef: the application of the electronic nose

This study aims to investigate the application of a fast electronic nose system (Cyranose 320) for detecting foodborne bacteria. The system proved to be very efficient in detecting microbes in beef and sausage samples. In the first part of the study, the total viable counts (TVC) from fresh and frozen beef samples were determined using the standard microbiological method and by the application of the electronic nose. The second part applied the electronic nose to beef before and after contamination with different bacterial pathogens separately: E. coli O157: H7, Salmonellatyphimurium 857, Staphylococcus aureus 29213 and Pseudomonas aeruginosa 27853. The results revealed that the Cyranose 320 can detect the TVC in different beef and sausage samples and quantify the volatile organic compounds produced at concentrations from 50 ppb to > 350 ppb. The concentrations of gases collected from the samples before and after separate contamination with these pathogenic bacteria were highly significantly correlated (P < 0.005). From this study one can conclude that the electronic nose system is a rapid way for detecting volatile organic compounds produced by foodborne bacteria that contaminate beef.

Towards a chemiresistive sensor-integrated electronic nose: a review

Electronic noses have potential applications in daily life, but are restricted by their bulky size and high price. This review focuses on the use of chemiresistive gas sensors, metal-oxide semiconductor gas sensors and conductive polymer gas sensors in an electronic nose for system integration to reduce size and cost. The review covers the system design considerations and the complementary metal-oxide-semiconductor integrated technology for a chemiresistive gas sensor electronic nose, including the integrated sensor array, its readout interface, and pattern recognition hardware. In addition, the state-of-the-art technology integrated in the electronic nose is also presented, such as the sensing front-end chip, electronic nose signal processing chip, and the electronic nose system-on-chip.