Synergistic approaches for odor active compounds monitoring and identification: State of the art, integration, limits and potentialities of analytical and sensorial techniques

Metal-Organic Frameworks in Surface Enhanced Raman Spectroscopy-Based Analysis of Volatile Organic Compounds

Volatile Organic Compounds (VOC) are a major class of environmental pollutants hazardous to human health, but also highly relevant in other fields including early disease diagnostics and organoleptic perception of aliments. Therefore, accurate analysis of VOC is essential, and a need for new analytical methods is witnessed for rapid on-site detection without complex sample preparation. Surface-Enhanced Raman Spectroscopy (SERS) offers a rapidly developing versatile analytical platform for the portable detection of chemical species. Nonetheless, the need for efficient docking of target analytes at the metallic surface significantly narrows the applicability of SERS. This limitation can be circumvented by interfacing the sensor surface with Metal-Organic Frameworks (MOF). These materials featuring chemical and structural versatility can efficiently pre-concentrate low molecular weight species such as VOC through their ordered porous structure. This review presents recent trends in the development of MOF-based SERS substrates with a focus on elucidating respective design rules for maximizing analytical performance. An overview of the status of the detection of harmful VOC is discussed in the context of industrial and environmental monitoring. In addition, a survey of the analysis of VOC biomarkers for medical diagnosis and emerging applications in aroma and flavor profiling is included.

State-of-art of the legislation on odour emissions with a focus on the Italian studies

This review would like to point out the state-of-art of the European legislation for the odour pollution determination and management. Odour is generated by a mixture of more or less volatile and persistent compounds that surround us in daily life. European directives impose the use of corresponding technical standards for the application of the limits imposed. The different approaches (chemicals and/or olfactometries) and integrated evaluation methods for measuring and characterizing odour, even if in a very different way in the European territories, will be reviewed and commented. Specifically, the authors will describe and comment the main procedures for odour concentration determination (e.g., multigas sensors, electronic sensors for odour monitoring). It is important to note that the definition of odour does not take into account if an odour is "good" or "bad", but only if it exists. The limit value must guarantee a total equivalent level of environmental protection and does not involve a greater polluting load in the environment. Further, a deep revision of the Italian situation in terms of legislation and studies, will complete the paper.

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.

Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry

Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.

Comprehensively assessing priority odorants emitted from swine slurry combining nontarget screening with olfactory threshold prediction

The lack of one-to-one olfactory thresholds (OTs) poses an obstacle to the comprehensive assessment of priority odorants emitted from swine slurry using mass spectrometric nontarget screening. This study screened out highly performing quantitative structure-activity relationship (QSAR) models of OT prediction to complement nontarget screening in olfactory perception evaluation. A total of 27 compounds emitted at different slurry removal frequencies were identified and quantified using gas chromatography-mass spectrometry (GC-MS), including thiirane, dimethyl trisulfide (DMTS), and dimethyl tetrasulfide (DMQS) without OT records. Ridge regression (RR, R2 = 0.77, RMSE = 0.93, MAE = 0.73) and random forest regression (RFR, R2 = 0.76, RMSE = 0.97, MAE = 0.69) rather than the commonly used principal component regression (PCR) and partial least squares regression (PLSR) were used to assign OTs and assess the contributions of emerging volatile sulfur compounds (VSCs) to the sum of odor activity value (SOAV). Priority odorants were p-cresol (25.0-58.9 %) > valeric acid (8.3-31.7 %) > isovaleric acid (6.7-19.0 %) > dimethyl disulfide (4.7-15.7 %) > methanethiol (0-13.6 %) > isobutyric acid (0-8.6 %), whereas the contributions of three emerging VSCs were below 10 %. Vital olfactory active structures were identified by QSAR models as having high molecular polarity, high hydrophilicity, high charge quantity, flexible structure, high reactivity, and a high number of sulfur atoms. This protocol can be further extended to evaluate odor pollution levels for distinct odor sources and guide the development of pertinent deodorization technologies.

Analysis of Bioactive Aroma Compounds in Essential Oils from Algerian Plants: Implications for Potential Antioxidant Applications

In samples of Artemisia campestris (AC), Artemisia herba-alba (AHA) and Salvia jordanii (SJ) essential oils, up to 200 distinct volatile compounds were identified. Using headspace solid-phase microextraction combined with gas chromatography-olfactometry-mass spectrometry (HS-SPME-GC-O-MS), different panelists detected 52 of these compounds. This study offers the most detailed analysis of bioactive compound profiles conducted so far. The most abundant compounds identified were curcumene, making up 12.96% of AC, and camphor, constituting 21.67% of AHA and 19.15% of SJ. The compounds with the highest odor activity value (OAV) were (E,Z)-2,4-nonadienal (geranium, pungent), 3-nonenal (cucumber) and 2-undecenal (sweet) in AC, AHA and SJ, respectively. AHA essential oil showed significant antioxidant activity (IC50 = 41.73 ± 4.14 mg/g) and hydroxyl radical generation (hydroxylation percentage = 29.62 ± 3.14), as assessed by the diphenylpicrylhydrazyl (DPPH) method. In terms of oxygen radical absorbance capacity (ORAC), the strongest antioxidant activity was obtained for SJ essential oil (antioxidant activity of the essential oils, AOX = 337.49 ± 9.87).

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Application of Field Olfactometry to Monitor the Odour Impact of a Municipal Sewage System

Odorant emissions are associated with, among other things, wastewater transport in sewer networks; they contribute to air pollution and result in complaints from residents living close to emission sources. The critical location in terms of the formation of unpleasant odour compounds is the pressure line that connects the pumping station and the expansion well; this is where they are released into the atmosphere. This paper presents comprehensive results of olfactometric and chromatographic tests in the Polish city of Białystok using portable devices that allow for multiple determinations and instant results. The study attempts to investigate the relationship between odour and odorant concentrations and check the suitability of field olfactometry as a tool for the ongoing monitoring of the emission of noxious odours and for verifying complaints submitted by residents. Statistical analysis shows a very high correlation coefficient between cod and the concentrations of individual odorants, ranging from 0.82 to 0.91. This olfactometric research, mainly conducted in situ, can be an appropriate method for the ad hoc monitoring of processes in sewage networks. This method allows the detection of unwanted emissions of odours at individual points in the network in concentrations that are not detected by standard sensors but that nevertheless cause odour nuisances, complaints, and social conflict. The research results provide evidence in favour of the energetic usage of wastewater, which is in line with circular economy conception, since odour nuisance is one of its indicators.

Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC

The human senses shape the life in several aspects, namely well-being, socialization, health status, and diet, among others. However, only recently, the understanding of this highly sophisticated sensory neuronal pathway has gained new advances. Also, it is known that each olfactory receptor cell expresses only one type of odorant receptor, and each receptor can detect a limited number of odorant substances. Odorant substances are typically volatile or semi-volatile in nature, exhibit low relative molecular weight, and represent a wide variety of chemical families. These molecules may be released from foods, constituting clouds surrounding them, and are responsible for their aroma properties. A single natural aroma may contain a huge number of volatile components, and some of them are present in trace amounts, which make their study especially difficult. Understanding the components of food aromas has become more important than ever with the transformation of food systems and the increased innovation in the food industry. Two-dimensional gas chromatography and time-of-flight mass spectrometry (GC × GC-ToFMS) seems to be a powerful technique for the analytical coverage of the food aromas. Thus, the main purpose of this review is to critically discuss the potential of the GC × GC-based methodologies, combined with a headspace solvent-free microextraction technique, in tandem with data processing and data analysis, as a useful tool to the analysis of the chemical aroma clouds of foods. Due to the broad and complex nature of the aroma chemistry subject, some concepts and challenges related to the characterization of volatile molecules and the perception of aromas will be presented in advance. All topics covered in this review will be elucidated, as much as possible, with examples reported in recent publications, to make the interpretation of the fascinating world of food aroma chemistry more attractive and perceptive.

A sensing network involving citizens for high spatio-temporal resolution monitoring of fugitive emissions from a petroleum pre-treatment plant

In this study an innovative sensing network consisting of eight photoionization detectors, meteorological sensors, a video camera and a telephonic system able to systematize the population complaints was developed for the monitoring of odor emissions. The development of monitoring approaches with high temporal and spatial resolution and actively involving citizens, is strategic in areas where relevant and also short-term emissive events frequently occur and the conventional approaches fail due to the high variability of fugitive emissions. Moreover, even if unpleasant odors are not necessarily direct triggers of health effects, they could be associated with the release of other harmful compounds. Monitoring approaches also involving citizens are thus strategic tools because odors annoyance perceived by population may be a potential health risk warning. Therefore, the developed sensing network was set up in Val d'Agri (Basilicata, Italy) where a petroleum pre-treatment plant (COVA) rises in a rural and inhabited area. The data collected during the monitoring campaign from the 16th February to the 30th July 2017, showed Total Volatile Organic Compounds (TVOCs) concentrations decreasing moving away from the plant and up to five times higher than levels registered in the closest municipality (Viggiano). Moreover, recurrent short-term critical events characterized by concentration values far above the average of the period and with maximum values ranging from 0.92 to 1.89 ppm, were registered in correspondence with high levels of benzene (up to 23.9 μg/m³) and anemometric conditions able to transport pollutants from COVA to each receptor site. The spatial and temporal distribution of TVOC concentrations proved to be affected by the distance from COVA, wind direction and industrial activities verified using video reportage and citizen claims. Therefore, the developed approach has proven to be a useful tool to credit people's perception of odors and also to quantify citizen exposure to VOCs during short-term events.

Development of Gas Sensor Array for Methane Reforming Process Monitoring

The article presents a new method of monitoring and assessing the course of the dry methane reforming process with the use of a gas sensor array. Nine commercially available TGS chemical gas sensors were used to construct the array (seven metal oxide sensors and two electrochemical ones). Principal Component Regression (PCR) was used as a calibration method. The developed PCR models were used to determine the quantitative parameters of the methane reforming process: Inlet Molar Ratio (IMR) in the range 0.6-1.5, Outlet Molar Ratio (OMR) in the range 0.6-1.0, and Methane Conversion Level (MCL) in the range 80-95%. The tests were performed on model gas mixtures. The mean error in determining the IMR is 0.096 for the range of molar ratios 0.6-1.5. However, in the case of the process range (0.9-1.1), this error is 0.065, which is about 6.5% of the measured value. For the OMR, an average error of 0.008 was obtained (which gives about 0.8% of the measured value), while for the MCL, the average error was 0.8%. Obtained results are very promising. They show that the use of an array of non-selective chemical sensors together with an appropriately selected mathematical model can be used in the monitoring of commonly used industrial processes.

Assessment of odour emissions by the use of a dispersion model in the context of the proposed new law in Chile

Chile is looking to define a regulatory framework for the odour emissions of various critical industrial activities. One of these is the sanitary sector, with 300 wastewater treatment plants (WWTP). The basis currently used by the Chilean environmental authority to assess odours is the set of odour emission factors (OEF) taken from the Dutch standard. The aim of this study was to compare these, used as a national reference, with our own OEF calculated from measurements using dynamic olfactometry of 41 WWTP. The dependence of OEF on operational variables such as flow rate and BOD5 was analysed in different plant processes. The current regulations were assessed under the two OEF scenarios for the 95th, 98th and 99.9th percentiles in the Temuco WWTP, using the WRF-CALPUFF modelling protocol. The OEF values of the emission sources showed no strong correlation with operating variables like BOD5 and wastewater flow rates in all plant sections. Our OEF values based on real measurements presented significant differences from the Dutch reference OEF, of the order of 6 UOe/m2/s. The odour emitting-units with the largest differences were the pre-treatment units, flow-splitting chamber and most units of the sludge processing sections. These new OEF offer an alternative paradigm for measuring emissions and an incentive to more accurate calculation of the emissions in critical units such as sludge treatment lines. When the WWTP studied in Temuco was assessed using the OEF calculated in this study, a difference of 1041 OUe/s was found above the odours emissions calculated using the Dutch reference database. Using the Dutch OEF, the odour immission concentrations at nearby receptors were not exceeded for the 95th and 98th percentiles; this might result in deficient environmental assessment under current Chilean laws. We therefore recommend that Chilean institutions should assess projects using the OEF calculated in this study.

Optimization of Classification Prediction Performances of an Instrumental Odour Monitoring System by Using Temperature Correction Approach

Odour emissions generated by industrial and environmental protection plants are often a cause of nuisances and consequent conflicts in exposed populations. Their control is a key action to avoid complaints. Among the odour measurement techniques, the sensory-instrumental method with the application of Instrumental Odour Monitoring Systems (IOMSs) currently represents an effective solution to allow a continuous classification and quantification of odours in real time, combining the advantages of conventional analytical and sensorial techniques. However, some aspects still need to be improved. The study presents and discusses the investigation and optimization of the operational phases of an advanced IOMS, applied for monitoring of environmental odours, with the aim of increasing their performances and reliability of the measures. Accuracy rates of over 98% were reached in terms of classification performances. The implementation of automatic correction systems for the resistance values of the measurement sensors, by considering the influence of the temperature, has been proven to be a solution to further improve the reliability of IOMS. The proposed approach was based on the application of corrective coefficients experimentally determined by analyzing the correlation between resistance values and operating conditions. The paper provides useful information for the implementation of real-time management activities by using a tailor-made software, able to increase and enlarge the IOMS fields of application.

Identifying the location of odour nuisance emitters using spatial GIS analyses

The primary objective of the article was to establish the location of odour emitters with the use of spatial GIS analyses. The odour emitter location analysis based on measurements was carried out using QGIS software tools. The algorithm of the procedure was developed by analysing vector elements, including simulated odour streaks (in the form of wedge buffer surfaces) and a regular grid of points needed to determine the presence of a given buffer in a given location. Statistical analysis was performed in ArcGIS software. The detailed aim of the conducted study was to analyse the nuisance of odours from the measurement data on the basis of IDW interpolation and the arrangement of buildings. An innovative element of the developed analysis is the application of the inversed method of fragrance streak propagation. Identifying a streak from the measuring point towards the blowing wind is very likely to determine the approximate location of the odour emitter measured. The described method should provide better results at the location of emitters than the above mentioned methods using interpolation, because of taking into account wind direction and speed. In addition, it is a method that can be applied over a large open area where methods based on simulation and particle propagation would not be efficient or highly impossible to apply due to extensive and complex analysis. It was necessary to conduct field studies in order to meet the main goal, which resulted in sensory evaluation of the intensity of odour nuisance in an urbanised area. Odour concentration tests using field olfactometry with NasalRanger olfactometer were taken.

Advancements in Microfabricated Gas Sensors and Microanalytical Tools for the Sensitive and Selective Detection of Odors

In recent years, advancements in micromachining techniques and nanomaterials have enabled the fabrication of highly sensitive devices for the detection of odorous species. Recent efforts done in the miniaturization of gas sensors have contributed to obtain increasingly compact and portable devices. Besides, the implementation of new nanomaterials in the active layer of these devices is helping to optimize their performance and increase their sensitivity close to humans' olfactory system. Nonetheless, a common concern of general-purpose gas sensors is their lack of selectivity towards multiple analytes. In recent years, advancements in microfabrication techniques and microfluidics have contributed to create new microanalytical tools, which represent a very good alternative to conventional analytical devices and sensor-array systems for the selective detection of odors. Hence, this paper presents a general overview of the recent advancements in microfabricated gas sensors and microanalytical devices for the sensitive and selective detection of volatile organic compounds (VOCs). The working principle of these devices, design requirements, implementation techniques, and the key parameters to optimize their performance are evaluated in this paper. The authors of this work intend to show the potential of combining both solutions in the creation of highly compact, low-cost, and easy-to-deploy platforms for odor monitoring.

Identification of key odorants in complex mixtures occurring in nature

Covering: up to 2019Soon after the birth of gas chromatography, mass spectrometry and olfactometry were used as detectors, which allowed impressive development to be achieved in the area of odorant determinations. Since the mid-80s, structured methods of gas chromatography-olfactometry have appeared, allowing the determination of which odor constituents play a key role in materials. Progressively, numerous strategies have been proposed for sample preparation from raw materials, the representativeness evaluation of extracts, the identification of odor constituents, their quantification, and subsequently, the recombination of the key odorants to mimic the initial odor. However, the multiplicity of options at each stage of the analysis leads to a confusing landscape in this field, and thus, the present review aims at critically presenting the available options. For each step, the most frequently used alternatives are described, together with their strengths and weaknesses based on theoretical and experimental justifications according to the literature. These techniques are exemplified by many applications in the literature on aromas, fragrances and essential oils, with the initial focus on wine odorants, followed by a short overview on the molecular diversity of key odorants, which illustrates most of the facets and complexities of odor studies, including the issues raised by odorant interactions such as synergies.

Chemical Characterization of Electronic Cigarette (e-cigs) Refill Liquids Prior to EU Tobacco Product Directive Adoption: Evaluation of BTEX Contamination by HS-SPME-GC-MS and Identification of Flavoring Additives by GC-MS-O

The present study focused on the determination of benzene, toluene, ethylbenzene and xylenes (BTEX) concentration levels in 97 refill liquids for e-cigs selected by the Italian National Institute of Health as representative of the EU market between 2013 and 2015 prior to the implementation of the European Union (EU) Tobacco Product Directive (TPD). Most of the e-liquids investigated (85/97) were affected by BTEX contamination, with few exceptions observed (levels below the limit of quantification (LOQ) of headspace-solid phase micro extraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) methodology). Across brands, concentration levels ranged from 2.7 to 30,200.0 µg/L for benzene, from 1.9 to 447.8 µg/L for ethylbenzene, from 1.9 to 1,648.4 µg/L for toluene and from 1.7 to 574.2 µg/L for m,p,o-xylenes. The variability observed in BTEX levels is likely to be related to the variability in contamination level of both propylene glycol and glycerol and flavoring additives included. No correlation was found with nicotine content. Moreover, on a limited number of e-liquids, gas chromatography-mass spectrometry-olfactometry (GC-MS-O) analysis was performed, allowing the identification of key flavoring additives responsible of specific flavor notes. Among them, diacetyl is a flavoring additive of concern for potential toxicity when directly inhaled into human airways. The data reported are eligible to be included in the pre-TPD database and may represent a reference for the ongoing evaluation on e-liquids safety and quality under the current EU Legislation.

Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.

Surfactant-free and rinsing-resistant biodegradable nanoparticles with high adsorption on natural fibers for the long-lasting release of fragrances

Synthetic polymers are attracting growing attention as additives for laundry and personal care products. In particular, the high volatility of many common fragrances requires the development of polymeric particles for their encapsulation and controlled release. Unfortunately, the vast majority of these carriers is made from polymers that are not biodegradable. This poses severe concerns about the accumulation of nano- and microplastics. Hence, such particles are expected to be banned from the market in the coming years. Therefore, biodegradable particles enabling a long-lasting release of the fragrances are urgently needed. In this work, we produced biodegradable nanoparticles (NPs) that are structurally composed of lactones, i.e. well known perfumes that occur naturally and that are already considered safe by regulatory agencies. We polymerized these lactones via ring opening polymerization (ROP) using an ionizable tertiary amine as initiator to produce in a single step amphiphilic oligoesters able to directly self-assemble into NPs once nanoprecipitated in water. In this way, we can produce biodegradable NPs with a perfume loading up to 85 % w/w without the need for additional surfactants. Subsequently we show that the ionizable group is able to confer a positive charge to our nanoparticles and, in turn, a high adsorption capacity on natural fibers (i.e. hairs and cotton fabric). Finally, we demonstrate the nanoparticle resistance to rinsing and their ability to confer a long-lasting fragrance perception to treated hair swatches for at least 3 weeks.

Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review

Food safety issues across the global food supply chain have become paramount in promoting public health safety and commercial success of global food industries. As food regulations and consumer expectations continue to advance around the world, notwithstanding the latest technology, detection tools, regulations and consumer education on food safety and quality, there is still an upsurge of foodborne disease outbreaks across the globe. The development of the Electronic nose as a noninvasive technique suitable for detecting volatile compounds have been applied for food safety and quality analysis. Application of E-nose for pathogen detection has been successful and superior to conventional methods. E-nose offers a method that is noninvasive, fast and requires little or no sample preparation, thus making it ideal for use as an online monitoring tool. This manuscript presents an in-depth review of the application of electronic nose (E-nose) for food safety, with emphasis on classification and detection of foodborne pathogens. We summarise recent data and publications on foodborne pathogen detection (2006-2018) and by E-nose together with their methodologies and pattern recognition tools employed. E-nose instrumentation, sensing technologies and pattern recognition models are also summarised and future trends and challenges, as well as research perspectives, are discussed.

Development of a Piezoelectric-Based Odor Reproduction System

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

Breath Analysis: A Systematic Review of Volatile Organic Compounds (VOCs) in Diagnostic and Therapeutic Management of Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a rare neoplasm related to asbestos exposure and with high mortality rate. The management of patients with MPM is complex and controversial, particularly with regard to early diagnosis. In the last few years, breath analysis has been greatly implemented with this aim. In this review the strengths of breath analysis and preliminary results in searching breath biomarkers of MPM are highlighted and discussed, respectively. Through a systematic electronic literature search, collecting papers published from 2000 until December 2018, fifteen relevant scientific papers were selected. All papers considered were prospective, comparative, observational case-control studies although every single one pilot and based on a relatively small number of samples. The identification of diagnostic VOCs pattern, through breath sample characterization and the statistical data treatment, allows to obtain a strategic information for clinical diagnostics. To date the collected data provide just preliminary information and, despite the promising results and diagnostic accuracy, conclusions cannot be generalized due to the limited number of individuals included in each cohort study. Furthermore none of studies was externally validated, although validation process is a necessary step towards clinical implementation. Breathomics-based biomarker approach should be further explored to confirm and validate preliminary findings and to evaluate its potential role in monitoring the therapeutic response.

A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks-VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50-were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.