Invited review article: an odor-sensing system--powerful technique for foodstuff studies

Assessment of fruity aroma intensity in olive oils from different Spanish regions using a portable electronic nose

BACKGROUND

The organoleptic profile of an olive oil is a fundamental quality parameter obtained by human sensory panels. In this work, a portable electronic nose was employed to predict the fruity aroma intensity of 199 olive oil samples from different Spanish regions and cultivar varieties ('Picual', 'Arbequina', and 'Cornicabra'), with special emphasis in testing the robustness of the predictions versus cultivar variety variability. The primary data given by the electronic nose were used to obtain two different feature vectors that were employed to fit ridge and lasso regressions models to two datasets: one consisting of all the samples and another just the cv. Picual samples.

RESULTS

The results obtained showed mean average error (MAE) values below 0.88 in all cases, with an MAE of 0.67 for the 'Picual' model. These MAE values and the similarities in the model parameters fitted for the different data folds are in agreement with the results obtained in previous studies.

CONCLUSION

The large number of samples analyzed and the results obtained show the robustness of the approach and the applicability of the methods. Also, the results suggest that better performance can be obtained when specific models are fitted for particular cultivars. Overall, the proposed methods are capable of providing useful information for a fast screening of the fruity aroma intensity of olive oils. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Transparent, flexible, and eco-friendly starch-based films for reversible optoelectronic noses for food spoilage monitoring in smart packaging

A reversible optoelectronic nose is presented consisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pH range. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork). A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics.

Enhancing Odor Analysis with Gas Chromatography-Olfactometry (GC-O): Recent Breakthroughs and Challenges

Gas chromatography-olfactometry (GC-O) has made significant advancements in recent years, with breakthroughs in its applications and the identification of its limitations. This technology is widely used for analyzing complex odor patterns. The review begins by explaining the principles of GC-O, including sample preparation, separation methods, and olfactory evaluation techniques. It then explores the diverse range of applications where GC-O has found success, such as food and beverage industries, environmental monitoring, perfume and aroma development, and forensic analysis. One of the major breakthroughs in GC-O analysis is the improvement in separation power and resolution of odorants. Techniques like rapid GC, comprehensive two-dimensional GC, and multidimensional GC have enhanced the identification and quantification of odor-active chemicals. However, GC-O also has limitations. These include the challenges in detecting and quantifying trace odorants, dealing with matrix effects, and ensuring the repeatability and consistency of results across laboratories. The review examines these limitations closely and discusses potential solutions and future directions for improvement in GC-O analysis. Overall, this review presents a comprehensive overview of the recent advances in GC-O, covering breakthroughs, applications, and limitations. It aims to promote the wider usage of GC-O analysis in odor analysis and related industries. Researchers, practitioners, and anyone interested in leveraging the capabilities of GC-O in analyzing complex odor patterns will find this review a valuable resource. The article highlights the potential of GC-O and encourages further research and development in the field.

Electronic Tongues and Noses: A General Overview

As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

A Machine Learning Method for the Quality Detection of Base Liquor and Commercial Liquor Using Multidimensional Signals from an Electronic Nose

Chinese liquor is a world-famous beverage with a long history. Base liquor, a product of liquor brewing, significantly affects the flavor and quality of commercial liquor. In this study, a machine learning method consisting of a deep residual network (ResNet)18 backbone with a light gradient boosting machine (LightGBM) classifier (ResNet-GBM) is proposed for the quality identification of base liquor and commercial liquor using multidimensional signals from an electronic nose (E-Nose). Ablation experiments are conducted to analyze the contribution of the framework’s components. Five evaluation metrics (accuracy, sensitivity, precision, F1 score, and kappa score) are used to verify the performance of the proposed method, and six other frameworks (support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), multidimensional scaling-support vector machine (MDS-SVM), and back-propagation neural network (BPNN)) on three datasets (base liquor, commercial liquor, and mixed base and commercial liquor datasets). The experimental results demonstrate that the proposed ResNet-GBM model achieves the best performance for identifying base liquor and commercial liquors with different qualities. The proposed framework has the highest F1 score for the identification of commercial liquor in the mixed dataset due to the contribution of similar microconstituents from the base liquor. The proposed method can be used for the quality control of Chinese liquor and promotes the practical application of E-nose devices.

Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants-A Review

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Embedded gas sensing setup for air samples analysis

This paper describes a measurement setup (eNose) designed to analyze air samples containing various volatile organic compounds (VOCs). The setup utilizes a set of resistive gas sensors of divergent gas selectivity and sensitivity. Some of the applied sensors are commercially available and were proposed recently to reduce their consumed energy. The sensors detect various VOCs at sensitivities determined by metal oxide sensors' technology and operating conditions. The setup can utilize prototype gas sensors, made of resistive layers of different compositions, as well. Their properties can be modulated by selecting operating temperature or using UV light irradiation. The unit is controlled by an embedded system M5Stack Core2 ESP32 IoT. We used this development kit to program the measurement procedure and data recording fastly. The setup utilizes an aluminum gas chamber of a volume of 220 ml, a set of electrical valves to introduce there an air sample with the help of an electrical micropump. The handling of the setup was simplified to a selection of a few operations by touch screen only without a necessity of extra training. The recorded data are saved in a memory card for further processing. The evolved setup can be upgraded to apply more advanced data processing by utilizing WiFi or Bluetooth connection. The control program was prepared using the Arduino IDE software environment and can be further advanced with ease. The applied materials and the established measurement procedure can use various air samples, including exhaled breath samples for patients' screening check-ups. We applied the same time of 10 min for response and recovery, acceptable for practical use.

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Electronic nose is an electronic olfactory system that simulates the biological olfactory mechanism, which mainly includes gas sensor, data pre-processing, and pattern recognition. In recent years, the proposals of electronic nose have been widely developed, which proves that electronic nose is a considerably important tool. However, the most recent studies concentrate on the applications of electronic nose, which gradually neglects the inherent technique improvement of electronic nose. Although there are some proposals on the technique improvement, they usually pay attention to the modification of gas sensor module and barely consider the improvement of the last two modules. Therefore, this paper optimizes the electronic nose system from the perspective of data pre-processing and pattern recognition. Recurrent neural network (RNN) is used to do pattern recognition and guarantee accuracy rate and stability. Regarding the high-dimensional data pre-processing, the method of locally linear embedding (LLE) is used to do dimensionality reduction. The experiments are made based on the real sensor drift dataset, and the results show that the proposed optimization mechanism not only has higher accuracy rate and stability, but also has lower response time than the three baselines. In addition, regarding the usage of RNN model, the experimental results also show its efficiency in terms of recall ratio, precision ratio, and F1 value.

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.

Effects of different drying methods on physicochemical properties, volatile profile, and sensory characteristics of kimchi powder

The effects of five different drying conditions on kimchi powder quality were determined by comparatively analyzing their physicochemical characteristics, volatile profile, and sensory evaluations. The moisture content of the kimchi powder obtained by each method was < 10%, and the yield after drying differed among methods ranging from 9.50 to 10.38% (p < 0.05). Electronic nose and tongue analyses demonstrated significant differences (p < 0.05) between samples based on the drying temperature. The particle size distribution did not differ considerably between drying methods, except for the ground kimchi (p < 0.05). The sensory evaluation test revealed that the flavor and taste were rated the highest for the kimchi powder prepared using HADHT. Therefore, hot-air drying at a high temperature was the most effective method for kimchi powder production owing to have a good flavor and taste and the shorter drying time.

Design of a portable electronic nose for real-fake detection of liquors

Portability is a major issue that influences the practical application of electronic noses (e-noses). For liquors detection, an e-nose must preprocess the liquid samples (e.g., using evaporation and thermal desorption), which makes the portable design even more difficult. To realize convenient and rapid detection of liquors, we designed a portable e-nose platform that consists of hardware and software systems. The hardware system contains an evaporation/sampling module, a reaction module, a control/data acquisition and analysis module, and a power module. The software system provides a user-friendly interface and can achieve automatic sampling and data processing. This e-nose platform has been applied to the real-fake recognition of Chinese liquors. Through parameter optimization of a one-class support vector machine classifier, the error rate of the negative samples is greatly reduced, and the overall recognition accuracy is improved. The results validated the feasibility of the designed portable e-nose platform.

Electronic noses: Powerful tools in meat quality assessment

Main factors that are considered by consumers when choosing meat products are colour and aroma, of which the latter is a more reliable indicator of quality. However, a simple sensory evaluation of hedonistic qualities is often not sufficient to determine whether protein is past its shelf life, and consumption of spoiled meat can lead to serious health hazards. Some volatile compounds can be used as spoilage indicators, and so a device equipped with a sensor sensitive to particular odorants would prove useful. Unfortunately, no such single compound has yet been identified, as the changes taking place in a sample of meat during storage are contingent on numerous factors. On the other hand, a combination of volatile compounds may form a unique 'fingerprint' which can be analysed pattern recognition algorithms with an electronic nose. It can supplement established techniques of meat quality assessment by providing results that correlate well with hedonic perception in a short time and at a low cost.

The Verification of the Usefulness of Electronic Nose Based on Ultra-Fast Gas Chromatography and Four Different Chemometric Methods for Rapid Analysis of Spirit Beverages

Spirit beverages are a diverse group of foodstuffs. They are very often counterfeited which cause the appearance of low quality products or wrongly labelled products on the market. It is important to find a proper quality control and botanical origin method enabling the same time preliminary check of the composition of investigated samples, which was the main goal of this work. For this purpose, the usefulness of electronic nose based on ultra-fast gas chromatography (fast GC e-nose) was verified. A set of 24 samples of raw spirits, 33 samples of vodkas, and 8 samples of whisky were analysed by fast GC e-nose. Four data analysis methods were used. The PCA was applied for the visualization of dataset, observation of the variation inside groups of samples, and selection of variables for the other three statistical methods. The SQC method was utilized to compare the quality of the samples. Both the DFA and SIMCA data analysis methods were used for discrimination of vodka, whisky, and spirits samples. The fast GC e-nose combined with four statistical methods can be used for rapid discrimination of raw spirits, vodkas, and whisky and in the same for preliminary determination of the composition of investigated samples.

Measurement techniques for assessing the olfactory impact of municipal sewage treatment plants

The study presents information about the measurement techniques used for the assessment of air quality in terms of the olfactory intensity resulting from the operation of municipal sewage treatment plants. Advantages and disadvantages of the measurement techniques used are presented. Sources of malodourous substance emission from sewage treatment plants were described, and the malodourous substances emitted were characterised. Trends in development of analysis and monitoring of the malodourous substances in the air were also presented.

Chemical composition analysis and authentication of whisky

Whisky (whiskey) is one of the most popular spirit-based drinks made from malted or saccharified grains, which should mature for at least 3 years in wooden barrels. High popularity of products usually causes a potential risk of adulteration. Thus authenticity assessment is one of the key elements of food product marketing. Authentication of whisky is based on comparing the composition of this alcohol with other spirit drinks. The present review summarizes all information about the comparison of whisky and other alcoholic beverages, the identification of type of whisky or the assessment of its quality and finally the authentication of whisky. The article also presents the various techniques used for analyzing whisky, such as gas and liquid chromatography with different types of detectors (FID, AED, UV-Vis), electronic nose, atomic absorption spectroscopy and mass spectrometry. In some cases the application of chemometric methods is also described, namely PCA, DFA, LDA, ANOVA, SIMCA, PNN, k-NN and CA, as well as preparation techniques such SPME or SPE.

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.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

Food analysis using artificial senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.

Quality evaluation of agricultural distillates using an electronic nose

The paper presents the application of an electronic nose instrument to fast evaluation of agricultural distillates differing in quality. The investigations were carried out using a prototype of electronic nose equipped with a set of six semiconductor sensors by FIGARO Co., an electronic circuit converting signal into digital form and a set of thermostats able to provide gradient temperature characteristics to a gas mixture. A volatile fraction of the agricultural distillate samples differing in quality was obtained by barbotage. Interpretation of the results involved three data analysis techniques: principal component analysis, single-linkage cluster analysis and cluster analysis with spheres method. The investigations prove the usefulness of the presented technique in the quality control of agricultural distillates. Optimum measurements conditions were also defined, including volumetric flow rate of carrier gas (15 L/h), thermostat temperature during the barbotage process (15 °C) and time of sensor signal acquisition from the onset of the barbotage process (60 s).

Volatile emissions from compressed tissue

Since almost every fifth patient treated in hospital care develops pressure ulcers, early identification of risk is important. A non-invasive method for the elucidation of endogenous biomarkers related to pressure ulcers could be an excellent tool for this purpose. We therefore found it of interest to determine if there is a difference in the emissions of volatiles from compressed and uncompressed tissue. The ultimate goal is to find a non-invasive method to obtain an early warning for the risk of developing pressure ulcers for bed-ridden persons. Chemical analysis of the emissions, collected in compresses, was made with gas-chromatography – mass spectrometry and with a chemical sensor array, the so called electronic nose. It was found that the emissions from healthy and hospitalized persons differed significantly irrespective of the site. Within each group there was a clear difference between the compressed and uncompressed site. Peaks that could be certainly deemed as markers of the compression were, however, not identified. Nonetheless, different compounds connected to the application of local mechanical pressure were found. The results obtained with GC-MS reveal the complexity of VOC composition, thus an array of non-selective chemical sensors seems to be a suitable choice for the analysis of skin emission from compressed tissues; it may represent a practical instrument for bed side diagnostics. Results show that the adopted electronic noses are likely sensitive to the total amount of the emission rather than to its composition. The development of a gas sensor-based device requires then the design of sensor receptors adequate to detect the VOCs bouquet typical of pressure. This preliminary experiment evidences the necessity of studies where each given person is followed for a long time in a ward in order to detect the insurgence of specific VOCs pattern changes signalling the occurrence of ulcers.

Highly selective gas sensor arrays based on thermally reduced graphene oxide

The electrical properties of reduced graphene oxide (rGO) have been previously shown to be very sensitive to surface adsorbates, thus making rGO a very promising platform for highly sensitive gas sensors. However, poor selectivity of rGO-based gas sensors remains a major problem for their practical use. In this paper, we address the selectivity problem by employing an array of rGO-based integrated sensors instead of focusing on the performance of a single sensing element. Each rGO-based device in such an array has a unique sensor response due to the irregular structure of rGO films at different levels of organization, ranging from nanoscale to macroscale. The resulting rGO-based gas sensing system could reliably recognize analytes of nearly the same chemical nature. In our experiments rGO-based sensor arrays demonstrated a high selectivity that was sufficient to discriminate between different alcohols, such as methanol, ethanol and isopropanol, at a 100% success rate. We also discuss a possible sensing mechanism that provides the basis for analyte differentiation.

Associations of volatile compounds with sensory aroma and flavor: the complex nature of flavor

Attempts to relate sensory analysis data to specific chemicals such as volatile compounds have been frequent. Often these associations are difficult to interpret or are weak in nature. Although some difficulties may relate to the methods used, the difficulties also result from the complex nature of flavor. For example, there are multiple volatiles responsible for a flavor sensation, combinations of volatiles yield different flavors than those expected from individual compounds, and the differences in perception of volatiles in different matrices. This review identifies some of the reasons sensory analysis and instrumental measurements result in poor associations and suggests issues that need to be addressed in future research for better understanding of the relationships of flavor/aroma phenomena and chemical composition.

Application of the electronic nose technique to differentiation between model mixtures with COPD markers

The paper presents the potential of an electronic nose technique in the field of fast diagnostics of patients suspected of Chronic Obstructive Pulmonary Disease (COPD). The investigations were performed using a simple electronic nose prototype equipped with a set of six semiconductor sensors manufactured by FIGARO Co. They were aimed at verification of a possibility of differentiation between model reference mixtures with potential COPD markers (N,N-dimethylformamide and N,N-dimethylacetamide). These mixtures contained volatile organic compounds (VOCs) such as acetone, isoprene, carbon disulphide, propan-2-ol, formamide, benzene, toluene, acetonitrile, acetic acid, dimethyl ether, dimethyl sulphide, acrolein, furan, propanol and pyridine, recognized as the components of exhaled air. The model reference mixtures were prepared at three concentration levels-10 ppb, 25 ppb, 50 ppb v/v-of each component, except for the COPD markers. Concentration of the COPD markers in the mixtures was from 0 ppb to 100 ppb v/v. Interpretation of the obtained data employed principal component analysis (PCA). The investigations revealed the usefulness of the electronic device only in the case when the concentration of the COPD markers was twice as high as the concentration of the remaining components of the mixture and for a limited number of basic mixture components.

A conductive polymer based electronic nose for early detection of Penicillium digitatum in post-harvest oranges

We describe the construction of an electronic nose, comprising four chemiresistive sensors formed by the deposition of thin conductive polymer films onto interdigitated electrodes, attached to a personal computer via a data acquisition board. This e-nose was used to detect biodeterioration of oranges colonized by Penicillium digitatum. Significant responses were obtained after only 24 h of incubation i.e. at an early stage of biodeterioration, enabling remedial measures to be taken in storage facilities and efficiently distinguishing between good and poor quality fruits. The instrument has a very low analysis time of 40 s.

State of the art in feedstuff analysis: a technique-oriented perspective

The need for global feed supply traceability, the high-throughput testing demands of feed industry, and regulatory enforcement drive the need for feed analysis and make extremely complex the issue of the control and evaluation of feed quality, safety, and functional properties, all of which contribute to the very high number of analyses that must be performed. Feed analysis, with respect to animal nutritional requirements, health, reproduction, and production, should be multianalytically approached. In addition to standard methods of chemical analysis, new methods for evaluation of feed composition and functional properties, authenticity, and safety have been developed. Requirements for new analytical methods emphasize performance, sensitivity, reliability, speed, simplified use, low cost for high volume, and routine assays. This review provides an overview of the most used and promising methods for feed analysis. The review is intentionally focused on the following techniques: classical chemical analysis; in situ and in vitro methods; analytical techniques coupled with chemometric tools (NIR and sensors); and cell-based bioassays. This review describes both the potential and limitations of each technique and discusses the challenges that need to be overcome to obtain validated and standardized methods of analysis for a complete and global feed evaluation and characterization.

Characterization of a clinical olfactory test with an artificial nose

Clinical olfactory tests are used to address hyposmia/anosmia levels in patients with different types of olfactory impairments. Typically, a given test is employed clinically and then replaced by a new one after a certain period of use which can range from days to several months. There is a need to assess control quality of these tests and also for a procedure to quantify their degradation over time. In this paper we propose a protocol to employ low-cost artificial noses for the quantitative characterization of olfactory tests used in clinical studies. In particular, we discuss a preliminary study on the Connecticut Chemosensorial Clinical Research Center Test kit which shows that some odorants, as sensed by an artificial nose, seem to degrade while others are potentiated as the test ages. We also discuss the need to establish a map of correspondence between human and machine olfaction when artificial noses are used to characterize or compare human smell performance in research and clinical studies.