Odour detection methods: olfactometry and chemical sensors

Augmenting insect olfaction performance through nano-neuromodulation

Biological olfactory systems are highly sensitive and selective, often outperforming engineered chemical sensors in highly complex and dynamic environments. As a result, there is much interest in using biological systems to build sensors. However, approaches to read-out information from biological systems, especially neural signals, tend to be suboptimal due to the number of electrodes that can be used and where these can be placed. Here we aim to overcome this suboptimality in neural information read-out by using a nano-enabled neuromodulation strategy to augment insect olfaction-based chemical sensors. By harnessing the photothermal properties of nanostructures and releasing a select neuromodulator on demand, we show that the odour-evoked response from the interrogated regions of the insect olfactory system can not only be enhanced but can also improve odour identification.

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.

A comparative assessment of biomethane potential of fresh fecal matter and fecal sludge and its correlation with malodor

Comprehensive and proper management of fecal sludge (FS) is an ongoing concern in many nations. Decentralized fecal sludge treatment plants (FSTPs) are effective in this regard; however, many have experienced strong public opposition based partly on suspicion of malodor. Fecal sludge and freshly generated fecal matter (FM) samples from various FSTPs were collected, characterized, and investigated for biomethane potential. The homogenized samples were anaerobically digested for 28 days. Digestion successfully reduced total suspended solids, biochemical oxygen demand, and threshold odor number values of 97,350-97,420 mg/l, 43,230-43,260 mg/l, and 130-150 for FM, to 49,500-49,650 mg/l, 23,760-23,850 mg/l, and 3338, respectively, for FS samples. The comprehensive gas yield from Bhongir, Boduppal, and Shadnagar FS samples was 40, 55, and 31 ml, respectively. In contrast, cumulative gas generation from the FM was 26,361 ml. Digestion of FS samples also reduced concentrations of volatile solids and coliforms by 66-72% and 99%, respectively. Characterization of gas samples revealed methane and carbon dioxide concentrations as 56% and 22% for FM, and 0.4% and 61% for FS samples, respectively. Hydrogen sulfide and ammonia gas were absent in FS samples, dispelling common societal misconceptions of FSTPs being associated with malodor.

Effect of industrial wastewater treatment system upgrade on the composition of emitted odorants and volatile organic compounds from a cheese production facility

This study investigates the rarely studied volatile organic compound emissions from a cheese production facility and the impact of its wastewater treatment system upgrade on the composition of emitted odorants. Wastewater grab samples were collected from six separate wastewater channels before (2019) and after (2021) the system upgrade and analyzed for volatile organic compounds, pH, total dissolved solids, and electrical conductivity. Results showed that the channel from hard cheese production in 2021 had the highest number of volatile organic compounds (35), followed by the fresh cheese production channel (22). Following the industrial wastewater treatment system upgrade, a mineral oil contamination occurred; however, the number of odorants with nasal impact frequency (NIF) ≥ 0.5 in the effluent decreased from 11 to 5. 2-Propenoic acid butyl ester (NIF 0.75) stood out as the most prominent compound, described as fruity, waxy, or green. After the industrial wastewater treatment system upgrades, we observed a decrease in the number of odorants. However other measures must be taken to ensure proper wastewater processing. PRACTITIONER POINTS: More than 60 VOCs were identified in 6 channels from the cheese production facility.15 odorants in cheese production wastewater were detected by SPME-GC-MS/O. The most potent odorants before and after the system upgrade were 1-octen-3-ol and 2-propenoic acid butyl ester, respectively. The upgrades of the industrial wastewater treatment system had a positive impact on reducing the number of odorants and their odor intensity.

Assessment of dimethyl sulphide odorous emissions during coal extraction process in Coal Mine Velenje

Underground coal extraction at Coal Mine Velenje occasionally gives rise to odour complaints from local residents. This manuscript describes a robust quantification of odorous emissions of mine sources and a model-based analysis aimed to establish a better understanding of the sources, concentrations, dispersion, and possible control of odorous compounds during coal extraction process. Major odour sources during underground mining are released volatile sulphur compounds from coal seam that have characteristic malodours at extremely low concentrations at micrograms per cubic metre (μg/m3) levels. Analysis of 1028 gas samples taken over a 6-year period (2008-2013) reveals that dimethyl sulphide ((CH3)2S) is the major odour active compound present in the mine, being detected on 679 occasions throughout the mine, while hydrogen sulphide (H2S) and sulphur dioxide (SO2) were detected 5 and 26 times. Analysis of gas samples has shown that main DMS sources in the mine are coal extraction locations at longwall faces and development headings and that DMS is releasing during transport from main coal transport system. The dispersion simulations of odour sources in the mine have shown that the concentrations of DMS at median levels can represent relatively modest odour nuisance. While at peak levels, the concentration of DMS remained sufficiently high to create an odour problem both in the mine and on the surface. Overall, dispersion simulations have shown that ventilation regulation on its own is not sufficient as an odour abatement measure.

The use of gas chromatography combined with chemical and sensory analysis to evaluate nuisance odours in the air and water environment

Varieties of gas chromatography (GC) combined with chemical detection (CD) and sensory analysis at the odour detection port (ODP) for the evaluation of environmental odorants has steadily increased in application and sophistication; this has given rise to a plethora of techniques that cater to specific tasks. With this diversity of approaches in mind, there is a need to assess the critical points at which these approaches differ, as well as likely risks and factors that may affect them. These critical points explained within this review include sample preparation, GC separation techniques (with associated co-elution risks), how the elute is separated between CD and sensory analysis, the type of CD, the type of sensory analysis (with particular attention paid to its factors and guidelines), integrative data techniques, as well as how that data may be used. Additionally, this review provides commentary on the current state of the research space and makes recommendations based on how these analyses should be reported, the standardisation of nomenclature, as well as the impediments to the future goals of this research area. By careful consideration of the critical points of varying analytical processes and how best to communicate these findings, the quality of output within this area will improve. This review provides a benchmark for how GC-CD/sensory analysis should be undertaken and reported.

Sweat and odor in sportswear - A review

Sportswear worn next to the skin is easily soaked by sweat and may become a breeding ground for the microbiome, thus a source of malodor. Malodor can cause social embarrassment and discomfort to both wearer and others. Given the risks current deodorant products pose to nature and human life, the development of sustainable textiles for odor control comes to the forefront. This review introduces the odor-generating mechanism in clothing from the perspectives of perspiration composition and cutaneous microbiome. With the knowledge of the significant role of sweat in odor formation, the sweat distribution of the human body, measurement techniques, and advanced technologies developed for quick-dry function are presented in the second part. Lastly, odor management in sportswear is evaluated, covering the odor-assessing techniques, the effects of various textile materials, and emerging solutions in terms of antibacterial treatment, adsorbent materials, and photocatalytic degradations of odorous compounds. Overall, it is of both personal and social value to develop novel textile materials with odor-control functions by making use of natural materials and fabric designs.

Post-traumatic olfactory dysfunction: a scoping review of assessment and rehabilitation approaches

Post-traumatic Olfactory Dysfunction (PTOD) consists of a complete or partial loss of olfactory function that may occur after a traumatic brain injury (TBI). PTOD may be linked to some neuropsychiatric features, such as social, cognitive and executive dysfunction, as well as behavioral symptoms, especially when TBI involves the orbito-frontal cortex. The diagnosis of PTOD is based on medical history and clinical data and it is supported by psychometric tests (i.e., subjective tools) as well as electrophysiological and neuroimaging measures (i.e., objective methods). The assessment methods allow monitoring the changes in olfactory function over time and help to establish the right therapeutic and rehabilitative approach. In this context, the use of the olfactory training (OT), which is a non-pharmacological and non-invasive treatment option, could promote olfactory function through top-down (central) and bottom-up (peripheral) processes. To better manage patients with TBI, PTOD should be detected early and properly treated using the various therapeutic rehabilitative possibilities, both conventional and advanced, also taking into consideration the emerging neuromodulation approach.

Effect of Synthetic Low-Odor Thiol-Based Hardeners Containing Hydroxyl and Methyl Groups on the Curing Behavior, Thermal, and Mechanical Properties of Epoxy Resins

A novel thiol-functionalized polysilsesqioxane containing hydroxyl and methyl groups was synthesized using a simple acid-catalyzed sol-gel method to develop an epoxy hardener with low odor, low volatile organic compound (VOC) emissions, and fast curing at low temperatures. The synthesized thiol-based hardeners were characterized using Fourier transform infrared spectroscopy, nuclear magnetic resonance, thermogravimetric analysis (TGA), and gel permeation chromatography and compared with commercially available hardeners in terms of odor intensity and VOC emissions using the air dilution olfaction method and VOC analysis. The curing behavior and thermal and mechanical properties of the epoxy compounds prepared with the synthesized thiol-based hardeners were also evaluated. The results showed that synthetic thiol-based hardeners containing methyl and hydroxyl groups initiated the curing reaction of epoxy compounds at 53 °C and 45 °C, respectively. In contrast, commercial thiol-based hardeners initiated the curing reaction at 67 °C. Additionally, epoxy compounds with methyl-containing synthetic thiol-based hardeners exhibited higher TGA at a 5% weight loss temperature (>50 °C) and lap shear strength (20%) than those of the epoxy compounds with commercial thiol-based hardeners.

Indoor Air Quality-An Overview of Methods for Measuring Odor Detection Thresholds of Single Substances

Odor evaluation is an important topic in the field of indoor air quality. Odor detection threshold (ODT) values are used to derive limit values like odor guide values or odor activity value. However, ODT values for the same substance available in compilations or published before 2003 rarely have an accuracy of less than three orders of magnitude. Stimulus preparation, including analytical verification, stimulus presentation, as well as selection and training of test subjects have been identified as major sources of variability. ODT values obtained by validated standardized methods are now considered objective, reliable, and reproducible. They exhibit a variability of one or two orders of magnitude and are lower than traditionally assumed and reported. This review presents the essential criteria for a well-performed ODT determination. It is intended to assist health and safety professionals in assessing whether the methodological approach of a study was appropriate for determining a valid and reliable ODT value.

Model of Neuromorphic Odorant-Recognition Network

We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of "decoder" neurons that recognize these odors. It is demonstrated that connecting these layers with memristive synapses enables the training of the "decoder" layer to recognize two types of odorants of varying concentrations. In the absence of such synapses, the layer of "decoder" neurons does not exhibit specificity in recognizing odorants. The recognition of the 'odorant' occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model showcases the potential use of a memristive synapse in practical odorant recognition applications.

Volatile Organic Compounds: A Promising Tool for Bed Bug Detection

The recent decades' resurgence of bed bugs as a public health concern in industrialized countries has driven an increased interest on new sustainable insecticide-free methods to monitor and control these ectoparasites. Current methods of detection rely mainly on visual inspection or canine scent detection, which are methods that are time-consuming, require experience, are non-specific or require costly mission repetitions. Volatile organic compounds (VOCs) are considered an environmentally friendly alternative and a promising approach for bed bug detection. An overview of the released literature on VOCs, their chemical characteristics and their role in bed bugs' intra- and inter-species communications allowed us to highlight the identification of 49 VOCs in Cimex lectularius (23 molecules) and C. hemipterus (26), which are emitted by both sexes during diverse compartments including aggregation (46), mating (11), defense (4), etc., and all life stages including exuviae or dead bed bugs as a principal indicator of infestation. The latter has a great importance for application of these semiochemicals in successful detection and control management of bed bugs and to prevent their further dispersion. This approach has the advantage of more reliability compared to conventional detection methods with no need for repeated inspections, household furniture moving or resident rehousing for bed bugs' VOC detection, which are commonly performed by active or passive sampling with absorbing tubes and analyzed by gas chromatography-based analytical platforms.

Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour

Cannabis flower odour is an important aspect of product quality as it impacts the sensory experience when administered, which can affect therapeutic outcomes in paediatric patient populations who may reject unpalatable products. However, the cannabis industry has a reputation for having products with inconsistent odour descriptions and misattributed strain names due to the costly and laborious nature of sensory testing. Herein, we evaluate the potential of using odour vector modelling for predicting the odour intensity of cannabis products. Odour vector modelling is proposed as a process for transforming routinely produced volatile profiles into odour intensity (OI) profiles which are hypothesised to be more informative to the overall product odour (sensory descriptor; SD). However, the calculation of OI requires compound odour detection thresholds (ODT), which are not available for many of the compounds present in natural volatile profiles. Accordingly, to apply the odour vector modelling process to cannabis, a QSPR statistical model was first produced to predict ODT from physicochemical properties. The model presented herein was produced by polynomial regression with 10-fold cross-validation from 1,274 median ODT values to produce a model with R2 = 0.6892 and a 10-fold R2 = 0.6484. This model was then applied to terpenes which lacked experimentally determined ODT values to facilitate vector modelling of cannabis OI profiles. Logistic regression and k-means unsupervised cluster analysis was applied to both the raw terpene data and the transformed OI profiles to predict the SD of 265 cannabis samples and the accuracy of the predictions across the two datasets was compared. Out of the 13 SD categories modelled, OI profiles performed equally well or better than the volatile profiles for 11 of the SD, and across all SD the OI data was on average 21.9% more accurate (p = 0.031). The work herein is the first example of the application of odour vector modelling to complex volatile profiles of natural products and demonstrates the utility of OI profiles for the prediction of cannabis odour. These findings advance both the understanding of the odour modelling process which has previously only been applied to simple mixtures, and the cannabis industry which can utilise this process for more accurate prediction of cannabis odour and thereby reduce unpleasant patient experiences.

Odour impact assessment using kinetics and optimization: case studies on removal of multiple volatile organo-sulphur compounds from sewage wastewater using porous functional materials

Expanding industries and booming population have led to the increase in the installation of wastewater and sewer systems, even in close proximity to residential areas. Emissions from these installations particularly volatile organo-sulphur compounds (VOSCs) such as methyl mercaptan (CH₃SH), ethyl mercaptan (C₂H₅SH), dimethyl sulphide (CH₃SCH₃) and carbon disulphide (CS₂) are a nuisance to people even when present in small concentration. Strategies for removal involve addition of chemicals or other chemical processes which are generally expensive. Biofilters, on the other hand, consume large amount of energy and wash waters. Hence keeping commercialization in mind, it is important to develop a strategy which would be cost-effective and at the same time be effective to remove most of the odorous compounds present in these systems. In the present research work, granular activated carbons (GAC) are functionalized with alkali solution to improve the adsorption capacity. Liquid phase batch adsorption is performed with GAC and various functionalized activated carbons (FACs) with the help of raw sewage water from a local sewage water treatment plant. Concentration of odour was evaluated by two methods-olfactometry-based analysis for sensory measurement and GCMS-based analysis for analytical estimation of a specific odorous compound. The adsorption capacities of the functionalized GACs are higher primarily because of complex formation at the surface of modified GACs. Pseudo-second-order kinetic model agreed well with experimental results with the rate constant being 0.0191 mg/l min and 0.0153 mg/l min for methyl and ethyl mercaptan adsorption onto FAC-NH₃. Boyd's film diffusion along with rate kinetic model supported that chemical adsorption forms the rate-limiting step. Response surface methodology (RSM) was used to optimize the removal of VOSCs with respect to different process parameters like adsorbent amount and time. The olfactometry removal of overall odour was also optimized taking 6 factors in the Box Behnken design. Variance of analysis results indicated that all the models displayed considerable goodness of fit with R² values close to 1. Methyl mercaptan turned out to be the highest contributor to the overall odour as confirmed both from experimental and optimization study. The optimized olfactometry odour removal (77.4%) along with CH₃SH removal (80.34%), C₂H₅SH removal (59.16%), CH₃SCH₃ removal (63.21%) and CS₂ removal (71.95%) was found at optimum process conditions, with amount of adsorbent of 10.29 g, adsorption time of 2.92 h. This result indicates that methyl mercaptan (CH₃SH) is the highest odour contributing component out of the studied VOSCs. The results show promising potential for the use of activated carbon as an adsorbent for removal of odorous compounds from STPs.

Identification of agricultural quarantine materials in passenger's luggage using ion mobility spectroscopy combined with a convolutional neural network

As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine materials (AQMs) in travelers' baggage. Thus, it is meaningful to develop efficient methods for early detection and prompt action against AQMs. In this study, a method based on the combination of odor detection of AQMs using ion mobility spectroscopy (IMS) and convolutional neural network (CNN) analysis for the identification of AQM species in luggage was developed. Two different ways were investigated to feed the IMS data of AQMs into the CNN, either as one-dimensional data (1D) (as a spectrum) or as two-dimensional data (2D) (as an IMS topographic map). The performances of CNN models were also compared to those of the commonly used classification algorithms: partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). By doing gradient-weighted class activation mapping (Grad-CAM), the essential IMS feature regions from the CNN models to predict different AQM species were also identified. The results of this research demonstrated that the application of the CNN to the IMS data of AQMs yielded superior classification performance compared to PLS-DA and SIMCA. Especially, the CNN-2D model which utilized the IMS topographic map as input achieved the best classification accuracy both on the calibration and validation sets. In addition, the Grad-CAM method had an ability to detect critical discriminating spectral regions for different types of AQM samples, and could provide explanation for the CNNs' decision-making. Despite the inherent limitations of the present analytical protocol, the results showed that the method of IMS in combination with a CNN has great potential to be a complement for sniffer dogs and X-ray imaging techniques to detect AQMs.

Multipoint characterization of the emission of odour, volatile organic compounds and greenhouse gases from a full-scale membrane-based municipal WWTP

Different environmental and social concerns can arise due to the generation of gaseous emissions during the treatment of urban wastewater. However, there is not an extensive knowledge about which are the main potential odour and greenhouse gas (GHG) emission sources in a wastewater treatment plant (WWTP) and their variability. In this study, a multipoint characterization of the gaseous emissions generated in a full-scale municipal WWTP located in Barcelona was conducted, aiming at identifying the main odour and GHG emission sources. The WWTP under study treats an average inlet flow of 33,000 m³ d^-1 using a Ludzack-Ettinger system with Membrane BioReactor (MBR) technology, and it has installed a gas caption and treatment system consisting of a biotrickling filter followed by a conventional biofilter to treat part of the off-gases produced during the wastewater treatment. For this work, gaseous emissions characterization campaigns were conducted to assess the proper performance of the gas treatment unit and to estimate the emission factors referred to odorants and GHGs for the different emission sources and to assess the proper performance of the gas treatment system. Besides, a chemical characterization of the different volatile organic compounds (VOC) present in the gaseous emissions was performed through TD-GC/MS. The main potential odour sources were the reception tank, the barscreens building and the primary settler, where odour concentrations were in the range of 1300 and 2600 ou·m^-3. Moreover, GHG emissions were found during the primary treatment and in the MBR units, ranging from 2.21 to 68,217.13 mg CO_2eq·m^-3. Different VOCs such as aromatic hydrocarbons, alkanes and ketones were found in the gaseous emissions with a high variability among all the emission sources. The results obtained are valuable indicators that can be used to develop odour and GHG mitigation strategies in WWTPs and to estimate the environmental impact of these facilities.

Determination of Odor Air Quality Index (OAQII) Using Gas Sensor Matrix

This article presents a new way to determine odor nuisance based on the proposed odor air quality index (OAQII), using an instrumental method. This indicator relates the most important odor features, such as intensity, hedonic tone and odor concentration. The research was conducted at the compost screening yard of the municipal treatment plant in Central Poland, on which a self-constructed gas sensor array was placed. It consisted of five commercially available gas sensors: three metal oxide semiconductor (MOS) chemical sensors and two electrochemical ones. To calibrate and validate the matrix, odor concentrations were determined within the composting yard using the field olfactometry technique. Five mathematical models (e.g., multiple linear regression and principal component regression) were used as calibration methods. Two methods were used to extract signals from the matrix: maximum signal values from individual sensors and the logarithm of the ratio of the maximum signal to the sensor baseline. The developed models were used to determine the predicted odor concentrations. The selection of the optimal model was based on the compatibility with olfactometric measurements, taking the mean square error as a criterion and their accordance with the proposed OAQII. For the first method of extracting signals from the matrix, the best model was characterized by RMSE equal to 8.092 and consistency in indices at the level of 0.85. In the case of the logarithmic approach, these values were 4.220 and 0.98, respectively. The obtained results allow to conclude that gas sensor arrays can be successfully used for air quality monitoring; however, the key issues are data processing and the selection of an appropriate mathematical model.

Simulated Forest Immersion Therapy: Methods Development

Shinrin-yoku, forest bathing, may provide relief from chronic and breakthrough pain in patients with axial spondyloarthritis and improve immune function through increasing NK cell numbers and activity and their downstream effectors, perforin and granulysin, after chemo- or radiation therapy in breast and prostate cancer patients. The aim of this paper is to describe the study protocol for a simulated forest immersion therapy using virtual reality and atomized phytoncides, volatile organic compounds found in forested areas designed to effect positive change for these two patient populations. The setting, including the room set up and samples with inclusion/exclusion specific to this type of intervention, is outlined. Measures and calibration procedures pertinent to determining the feasibility of simulated forest immersion therapy are presented and include: ambient and surface room temperatures and relative humidity in real time, ambient ultrafine particulate matter, ambient droplet measurement that coincides with volatile organic compounds, specific phytoncides, and virtual reality and atomization of phytoncide set up. Particular lessons learned while training and setting up the equipment are presented. Simulated forest immersion therapy is possible with attention to detail during this early phase when development of methods, equipment testing, and feasibility in deploying the intervention become operational. The expected outcome of the development of the methods for this study is the creation of a standardized approach to simulating forest therapy in a controlled laboratory space.

Volatile Aroma Surfactants: The Evaluation of the Adsorption-Evaporation Behavior under Dynamic and Equilibrium Conditions

Multicomponent heterogeneous systems containing volatile amphiphiles are relevant to the fields ranging from drug delivery to atmospheric science. Research presented here discloses the individual interfacial activity and adsorption-evaporation behavior of amphiphilic aroma molecules at the liquid-vapor interface. The surface tension of solutions of nonmicellar volatile surfactants linalool and benzyl acetate, fragrances as such, was compared with that of the conventional surfactant sodium dodecyl sulfate (SDS) under equilibrium as well as under no instantaneous equilibrium, including a fast-adsorbing regime. In open systems, the increase in the surface tension on a time scale of ∼10 min is evaluated using a phenomenological model. The derived characteristic mass transfer constant is shown to be specific to both the desorption mechanism and the chemistry of the volatile amphiphile. Fast-adsorbing behavior disclosed here, as well as the synergetic effect in the mixtures with conventional micellar surfactants, justifies the advantages of volatile amphiphiles as cosurfactants in dynamic interfacial processes. The demonstrated approach to derive specific material parameters of fragrance molecules can be used for an application-targeted selection of volatile cosurfactants, e.g., in emulsification and foaming, inkjet printing, microfluidics, spraying, and coating technologies.

Impact of Ageing on Pea Protein Volatile Compounds and Correlation with Odor

Vegetal proteins are of high interest for their many positive aspects, but their ‘beany’ off-flavor is still limiting the consumer’s acceptance. The aim of this work was to investigate the conservation of pea protein isolate (PPI) during time and especially the evolution of their organoleptic quality under two storage conditions. The evolution of the volatile compounds, the odor and the color of a PPI has been investigated during one year of storage. PPI was exposed to two treatments mimicking a lack of control of storage conditions: treatment A with light exposition at ambient temperature (A—Light 20 °C) and treatment B in the dark but with a higher temperature (B—Dark 30 °C). For each sampling time (0, 3, 6, 9, 12 months), the volatile compounds were determined using HS-SPME-GC-MS, the odor using direct sniffing, and the color using the measurement of L*, a*, b* parameters. Treatment A was the most deteriorating and led to a strong increase in the total volatile compounds amount, an odor deterioration, and a color change. Furthermore, a tentative correlation between instrumental data on volatile compounds and the perceived odor was proposed. By the representation of volatile compounds sorted by their sensory descriptor, it could be possible to predict an odor change with analytical data.

An Automated Canine Line-Up for Detection Dog Research

Currently, there is a need to develop technology that facilitates and improves detection dog research. The aim of this research was to develop an automated computer-driven olfactory line-up task. The apparatus consisted of three olfactometers. Each olfactometer was equipped with flow meters to regulate air flow and dilution and six solenoid valves connected to odor jars. Each olfactometer generated an odor which was carried to an odor port where the dogs sample it. The olfactometer's valves were activated by a microcontroller, and a Python program was built to control each olfactometer and randomize and balance the odor presentation. Dogs (N = 12) received one or two 40-trial training sessions in a day where they progressed through a series of training phases where they learned to detect and alert to double-base smokeless powder (SP). An “alert” consisted of a 4-s nose hold. This was measured by infrared sensors in the ports. For each trial, the apparatus recorded dogs' search latency, sniff time, port entries, and response. All this information was automatically recorded in a csv file. A photoionization detector (PID) and solid-phase microextraction followed by gas chromatography-mass spectrometry (SPME-GC/MS) were used to evaluate the odor dynamics and to instrumentally verify odor presence and clearance. A control test was conducted at the end of the training to ensure dogs were alerting exclusively to the odorant. All 12 dogs readily learned to operate the apparatus within 23 days, and all exceeded 85% accuracy. Control tests indicated dogs were leveraging only olfactory cues and not unintentional cues such as auditory cues from the apparatus. Analytical data showed that odor was detected in the port immediately after the activation of a valve and that odor clearance occurred immediately after the valve was closed. The apparatus developed was easy to operate by the dogs and allowed substantial data collection using double-blind testing procedures in a very short period at an affordable cost point for research equipment (~$5,000 USD). The apparatus may prove to be a useful research tool to provide optimal odor stimuli control, ensure double-blind conditions, reduce labor, and significantly increase the amount of data collected.

Tensiometry as a Simple Analytical Method for Quantification of Solubility and Release of Aroma Molecules in Aqueous Media

Dynamic tensiometry is shown to be a high-potential analytical tool in assessing physico-chemical characteristics of fragrance molecules, such as solubility limit, volatility as well as much rarely assessed interfacial activity of these amphiphilic molecules. Surface tension of aqueous solutions of selected essential oils has been measured as a function of time and fragrance concentration using maximum bubble pressure method. The effect of the temperature and saline solution on the rate of dissolution in water was assessed. Dynamic surface tension turned to be sensitive to the composition of fragrances, as demonstrated on examples of natural and synthetic mixtures. Furthermore, presented work reveals the possibility of maximum bubble pressure tensiometry method to quantify the amount of fragrance compositions in flavored salts, including the artificially aged carrier samples. Suggested here analytical approach can be used for the detection of the purity of essential oils, for the optimization of compositions and of the manufacturing processes of fragrances-containing products, as well as for the assessment of the release/evaporation of fragrances from carrier systems.

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

One of the biggest global challenges for our societies is to provide natural resources to the rapidly expanding population while maintaining sustainable and ecologically friendly products. The increasing public concern about toxic insecticides has resulted in the rapid development of alternative techniques based on natural infochemicals (ICs). ICs (e.g., pheromones, allelochemicals, volatile organic compounds) are secondary metabolites produced by plants and animals and used as information vectors governing their interactions. Such chemical language is the primary focus of chemical ecology, where behavior-modifying chemicals are used as tools for green pest management. The success of ecological programs highly depends on several factors, including the amount of ICs that enclose the crop, the range of their diffusion, and the uniformity of their application, which makes precise detection and quantification of ICs essential for efficient and profitable pest control. However, the sensing of such molecules remains challenging, and the number of devices able to detect ICs in air is so far limited. In this review, we will present the advances in sensing of ICs including biochemical sensors mimicking the olfactory system, chemical sensors, and sensor arrays (e-noses). We will also present several mathematical models used in integrated pest management to describe how ICs diffuse in the ambient air and how the structure of the odor plume affects the pest dynamics.

Industrial odour pollution and human health: a systematic review and meta-analysis

OBJECTIVE

To conduct a systematic review to evaluate the association between residential or occupational short- and long-term exposure to odour pollution from industrial sources and the health status of the exposed population.

METHODS

The searches were conducted in Medline, EMBASE and Scopus in April 2021. Exposure to an environmental odour from industrial sources in population resident near the source or in workers was considered. We considered outcomes for which there was a biological plausibility, such as wheezing and asthma, cough, headache, nausea and vomiting (primary outcomes). We also included stress-related symptoms and novel outcomes (e.g. mood states). Risk of bias was evaluated using the OHAT tool. For primary outcomes, when at least 3 studies provided effect estimates by comparing exposed subjects versus not exposed, we pooled the study-specific estimates of odour-related effect using random effects models. Heterogeneity was evaluated with Higgins I2.

RESULTS

Thirty studies were eligible for this review, mainly cross-sectional (n = 23). Only one study involved school-age children and two studies involved workers. Only five studies reported odour effects on objective laboratory or clinical outcomes. Animal Feeding Operations and waste were the most common industrial sources. The overall odds ratios in exposed versus not exposed population were 1.15 (95% CI 1.01 to 1.29) for headache (7 studies), 1.09 (95% CI 0.88 to 1.30) for nausea/vomiting (7 studies), and 1.27 (95% CI 1.10 to 1.44) for cough/phlegm (5 studies). Heterogeneity was a moderate concern. Overall, the body of evidence was affected by a definitely high risk of bias in exposure and outcome assessment since most studies used self-reported information.

CONCLUSIONS

Findings underline the public health importance of odour pollution for population living nearby industrial odour sources. The limited evidence for most outcomes supports the need for high quality epidemiological studies on the association between odour pollution and its effects on human health.

Application of Olfactometry to Assess the Anti-Odor Properties of Filtering Facepiece Respirators Containing Activated Carbon Nonwovens

Filtering facepiece respirators (FFR) with anti-odor properties are used to reduce odor nuisance occurring both in everyday life and at workplaces. Unfortunately, there are no standardized methods to measure the efficiency of odor reduction of such personal protective devices. This paper aims to determine whether olfactometric-based methods, commonly used in environmental studies, can be employed for this purpose. The proposed procedure is based on the detection of n-butanol by study participants, and it consists of three subsequent stages: (i) defining the individual levels of odor sensitivity of each study participant; (ii) determining THE odor detection level while using FFRs with varying anti-odor properties; and (iii) completing a questionnaire concerning the subjective perceptions of study participants. As a measure of odor reduction efficiency, a coefficient W, defined as a quotient of the degree of odor reduction by the FFR, and the individual odor sensitivity of the subject, was proposed. The experimental results showed the ability of our measure to differentiate the effectiveness of odor reduction of tested FFRs. This indicates that it can be potentially employed as the assessment tool to confirm the effectiveness of such respiratory protective devices as a control measure mitigating the adverse effects of malodors on workers’ health, cognition, and behavior.

Challenges and Control Strategies of Odor Emission from Composting Operation

Composting is a biological decomposition process that occurs from microbial progression, which brings about the degradation and stabilization of various organic waste into compost. During composting, the emission of undesirable odor adversely affects compost quality and causes environmental deterioration. Also, odor emission from composting adversely affects human health and well-being. Ammonia (NH₃), volatile organic compounds (VOCs), and hydrogen sulfide (H₂S) are major components of odorous gases responsible for unpleasant odor. Physiological parameters such as pH, temperature, and aeration affect the pattern of odor emission during the composting process. The lack of techniques for the accurate identification and estimation of odor and control are some major challenges associated with composting. Therefore, the present review article concentrates on challenges and solutions to odor control. Biotrickling filter, optimization of process parameters, usage of additives, microbial inoculation, and pre-treatment techniques are practiced to lower odor emission during the process. The application of metagenomics may provide insight into the various biogeochemical pathways that can be explored in the future for odor control.

The importance of smell and taste in everyday life: Dysfunction in COVID-19 patients

Human-to-human transmission of coronavirus (SARS-CoV-2) - COVID-19 (corona virus disease 2019) - is characterized by a pandemic exponential rate and the patients with mild to moderate infection have odor and taste problems that represent a new atypical disease. A new viral syndrome of acute anosmia or "new loss of taste or smell" without rhinitis and nasal obstruction or rhinorrhea has been placed on the list of symptoms that may occur 2 to 14 days after exposure to the COVID-19 virus. Two months after declaring the COVID-19 pandemic in May 2020, the World Health Organization (WHO) has recognized changes in the perception of smell and taste as symptoms of this disease. The described cardinal symptoms are more common in the population of young patients and able-bodied people which facilitates the spread of disease. Significantly higher prevalence of patients with COVID-19 who have lost their taste and smell is treated at home (rare hospitalization), lung damage is rare, as well as oxygen therapy with mild lymphopenia. Different scenarios of SARS-CoV-2 viral infection can be assumed: it is probable that the virus does not enter directly into olfactory sensory neurons (they do not have ACE2 and TMPRSS2 receptors), but it is localized to vascular pericytes and causes inflammatory processes and vasculopathies. On the other hand, direct infection of non-neuronal cells which contain said receptors is possible. Those are specific cell types in the olfactory epithelium such as sustentacular, horizontal basal cells, as well as Bowman's glands, which leads to massive degeneration and loss of olfactory neurons. The sense of taste is a complex sensation that is the result of the interaction of smell, taste, temperature and texture of food. The virus damages cranial nerves, epithelial receptors and blood vessels leading to taste damage (ageusia or dysgeusia). A multidisciplinary approach with epidemiological, clinical and basic research is needed to elucidate the mechanism of sensorineural odor and taste loss caused by coronavirus.

Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

3D-Printed Bubble-Free Perfusion Cartridge System for Live-Cell Imaging

The advent of 3D-printing technologies has had a significant effect on the development of medical and biological devices. Perfusion chambers are widely used for live-cell imaging in cell biology research; however, air-bubble invasion is a pervasive problem in perfusion systems. Although 3D printing allows the rapid fabrication of millifluidic and microfluidic devices with high resolution, little has been reported on 3D-printed fluidic devices with bubble trapping systems. Herein, we present a 3D-printed millifluidic cartridge system with bent and flat tapered flow channels for preventing air-bubble invasion, irrespective of bubble volume and without the need for additional bubble-removing devices. This system realizes bubble-free perfusion with a user-friendly interface and no-time-penalty manufacturing processes. We demonstrated the bubble removal capability of the cartridge by continually introducing air bubbles with different volumes during the calcium imaging of Sf21 cells expressing insect odorant receptors. Calcium imaging was conducted using a low-magnification objective lens to show the versatility of the cartridge for wide-area observation. We verified that the cartridge could be used as a chemical reaction chamber by conducting protein staining experiments. Our cartridge system is advantageous for a wide range of cell-based bioassays and bioanalytical studies, and can be easily integrated into portable biosensors.

Highly effective volatile organic compound dissolving strategy based on mist atomization for odorant biosensors

The detection of volatile organic compound (VOC) mixtures is crucial in the medical and security fields. Receptor-based odorant biosensors sensitively and selectively detect odorant molecules in a solution; however, odorant molecules generally exist as VOCs in the air and exhibit poor water solubility. Therefore, techniques that enable the dissolution of poorly water-soluble VOCs using portable systems are essential for practical biosensors' applications. We previously proposed a VOC dissolution method based on water atomization to increase the surface area via the generation of fine bubbles, as a proof-of-concept; however, the system was lab-based (non-mobile) and the dissolution was limited to one VOC. In this study, we established a highly effective VOC dissolution method based on mist atomization that can be used in the field. This new method demonstrated a rapid dissolution potential of a sparsely-soluble VOC mixture with various functional groups in distilled water (DW) within 1 min, without the use of any organic solvents. Calcium imaging revealed that odorant receptor 13a-expressing Sf21 cells (Or13a cells) responded to 1-octen-3-ol in the mixture. Further, we successfully developed a field-deployable prototype vacuum and dissolution system with a simple configuration that efficiently captured and rapidly dissolved airborne 1-octen-3-ol in DW. This study proposes a field-deployable system that is appropriate for solubilizing various airborne odorant molecules and therefore is a practical strategy to use in the context of odorant biosensors.

Wastewater monitoring by means of e-nose, VE-tongue, TD-GC-MS, and SPME-GC-MS

The presence of wastewater and air pollution has become an important risk factor for citizens, not only in terms of problems related to health risks, but also because of its negative impact on the country's image. For this reason, malodorous emission monitoring and control techniques are in high demand in urban areas and industries. The aim of this work is first to build an electronic nose (e-nose) and a Voltammetric Electronic tongue (VE-tongue) in order to study their ability to discriminate between polluted and clean environmental samples. Secondly, Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS), and Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) are utilized to explain this discrimination by identifying specific compounds from these samples. Indeed, the e-nose, consisted of metal oxide semiconductor gas sensors, is used for the assessment of the studied odorous air and headspace samples from water and wastewater sites. Moreover, the VE-tongue, based on metal electrodes, is utilized to determine the patterns of the sensor array responses, which serve as fingerprints profiles of the analyzed liquid samples. Chemometric tools, such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vector Machines (SVMs) are operated for the processing of data from the e-nose and the VE-tongue. By using the both systems, the analyses of headspace and liquid samples from the seven sites allow better discrimination. To explain the cause of the obtained discrimination, TD-GC-MS and SPME-GC-MS analyses are well performed to identify compounds related sites. According to these outcomes, the proposed e-nose and VE-tongue are proved to be rapid and valuable tools for analysis of environmental polluted matrices.

The Impact of Technological Processes on Odorant Emissions at Municipal Waste Biogas Plants

Municipal waste treatment is inherently associated with odour emissions. The compounds characteristic of the processes used for this purpose, and at the same time causing a negative olfactory sensation, are organic and inorganic sulphur and nitrogen compounds. The tests were carried out at the waste management plant, which in the biological part, uses the methane fermentation process and is also equipped with an installation for the collection, treatment, and energetic use of biogas. The tests include measurements of the four odorant concentrations and emissions, i.e., volatile organic compounds (VOCs), ammonia (NH3), hydrogen sulphide (H2S), and methanethiol (CH3SH). Measurements were made using a MultiRae Pro portable gas detector sensor. The tests were carried out in ten series for twenty measurement points in each series. The results show a significant impact of technological processes on odorant emissions. The types of waste going to the plant are also important in shaping this emission. On the one hand, it relates to the waste collection system and, on the other hand, the season of year. In addition, it has been proved that the detector used during the research is a valuable tool enabling the control of technological processes in municipal waste processing plants.

Potentials of truffles in nutritional and medicinal applications: a review

Truffles, the symbiotic hypogeous edible fungi, have been worldwide regarded as a great delicacy because of their unique flavor and high nutritional value. By identifying their bioactive components such as phenolics, terpenoids, polysaccharides, anandamide, fatty acids, and ergosterols, researchers have paid attention to their biological activities including antitumor, antioxidant, antibacterial, anti-inflammatory, and hepatoprotective activities. In addition, numerous factors have been investigating that can affect the quality and productivity of truffles to overcome their difficulty in culturing and preserving. To provide the information for their potential applications in medicine as well as in functional food, this review summarizes the relevant literature about the biochemical composition, aromatic and nutritional benefits, and biological properties of truffles. Besides, various factors affecting their productivity and quality as well as the preservation methods are also highlighted.

Application of Low-Cost Electrochemical Sensors to Aqueous Systems to Allow Automated Determination of NH3 and H2S in Water

Usage of commercially available electrochemical gas sensors is currently limited by both the working range of the sensor with respect to temperature and humidity and the spikes in sensor response caused by sudden changes in temperature or humidity. Using a thermostatically controlled chamber, the sensor response of ammonia and hydrogen sulfide sensors was studied under extreme, rapidly changing levels of humidity with the aim of analyzing nebulized water samples. To protect the sensors from damage, the gas stream was alternated between a saturated gas stream from a Flow Blurring® nebulizer and a dry air stream. When switching between high and low humidity gas streams, the expected current spike was observed and mathematically described. Using this mathematical model, the signal response due to the change in humidity could be subtracted from the measured signal and the sensor response to the target molecule recorded. As the sensor response is determined by the model while the sensor is acclimatizing to the new humid conditions, a result is calculated faster than that by systems that rely on stable humidity. The use of the proposed mathematical model thus widens the scope of electrochemical gas sensors to include saturated gas streams, for example, from nebulized water samples, and gas streams with variable humidity.

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.

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components’ mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches.

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.

Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach

Odorous compound emissions and odor complaints from the public are rising concerns for agricultural, industrial, and water resource recovery facilities (WRRFs) near urban areas. Many facilities are deploying sensors that measure malodorous compounds and other factors related to odor creation and dispersion. Focusing on the Metropolitan Water Reclamation District of Greater Chicago's (MWRDGCs) Thornton Composite Reservoir (7.9 billion gallon capacity), we used meteorological, operational, and H2S sensor data to train a 3-day advance-warning predictor of local odor complaints, so as to implement targeted odor prevention measures. Using a machine learning approach, we bypassed difficulties in modeling both physical dispersion and human perception of odors. Utilizing random forest algorithms with varied settings and input attributes, we find that a small network of H2S sensors, meteorological data, and operational data are able to predict odor complaints three days in advance with greater than 60% accuracy and less than 25% false-positive rates, exceeding MWRDGC's standards required for full-scale deployment. PRACTITIONER POINTS: A random forest algorithm trained on H₂ S, weather, and operations data successfully predicted odor complaints surrounding a large composite reservoir. Thirty-two data attribute combinations were tested. It was found that H₂ S sensor data alone are insufficient for predicting odor complaints. The best predictor was a Random Forest Classifier trained on weather, operational, and H₂ S readings from the reservoir corner locations. This study demonstrates odor complaint prediction capability utilizing a limited set of data sources and open-source machine learning techniques. Given a small network of H₂ S sensors and organized data management, WRRFs and similar facilities can conduct advance-warning odor complaint prediction.

Evaluation of ammonia and odour emissions from animal slurry and digestate storage in the Po Valley (Italy)

Ammonia and odour emissions from one lagoon (Lagoon 1: pig slurry) and three tanks (Tank 2: cow slurry; Tank 3: digestate from pig slurry and energy crops; Tank 4: digestate from pig and cow slurries plus energy crop) used for slurry storage were sampled for two years (2015-2017) in livestock farms that differed for animal breeding and manure management (anaerobic digestion). On average, the ammonia emission rate (AER) was higher for Tank 3 (AER of 30.68 ± 28.1 g N-NH₃ m^-2 d^-1) than for Lagoon 1 and Tank 2 and 4, i.e. 9.29 ± 14.89 gN-NH₃ m^-2 d^-1, 9.38 ± 13.75 g N-NH₃ m^-2 d^-1, 15.74 ± 21.91 g N-NH₃ m^-2 d^-1, respectively. PLS regression analysis (R² = 0.544; R²_Adj. = 0.484) indicated that temperature was the main predictor of ammonia emitted, followed by concentration in the slurry of total ammonia and the relative percentage of volatile solids (VS). On the other hand, PLS analysis (R² = 0.529, R²adj. = 0.417) indicated that odour emissions from animal slurry storages depended similarly upon total solids and VS (both referred to fresh weight) slurry contents, TAN/TKN ratio and degrees of biological stability (measured by anaerobic biogas potential - ABP), resulting in the Specific Odours Emission Rates (SOER) of 12,124 ± 7,914 and 35,207 ± 41,706 OUE m^-2 h^-1, 65,430 ± 45,360 and 43,971 ± 53,350 OUE m^-2 h^-1, for Lagoon 1 and Tanks 2, 3 and 4. These results suggest covering the tanks to limit both ammonia and odour emissions.

Integrated model for estimating odor emissions from civil wastewater treatment plants

The objective of this research project was the design and development of an integrated model for odor emission estimation in wastewater treatment plants. The SMAT's plant, the largest wastewater treatment facility in Italy, was used as a case study. This article reports the results of the characterization phase that led to the definition and design of the proposed conceptual model for odor emission estimation. In this phase, concentrations of odor chemical tracers (VOC, H₂S, NH₃) and odor concentrations were monitored repeatedly. VOC screening with GC-MS analysis was also performed. VOC concentrations showed significant variability in space and magnitude. NH₃ and H₂S were also detected at considerable concentrations. Results were elaborated to define a spatially variable linear relationship between the sum of odor activity values (SOAV) and odor concentrations. Based on the results, a conceptual operational model was presented and discussed. The proposed system is composed by a network of continuous measurement stations, a set of algorithms for data elaboration and synchronization, and emission dispersion modeling with the application of Lagrangian atmospheric models.

Measurements techniques and models to assess odor annoyance: A review

Odors have received increasing attention among atmospheric pollutants. Indeed, odor emissions are a common source of complaints, affecting the quality of life of humans and animals. The odor is a property of a mixture of different volatile chemical species (sulfur, nitrogen, and volatile organic compounds) capable of stimulating the olfaction sense sufficiently to trigger a sensation of odor. The impact of odors on the surrounding areas depends on different factors, such as the amount of odors emitted from the site, the distance from the site, weather conditions, topography, other than odors sensitivity and tolerance of the neighborhood. Due to the complexity of the odor issue, the aim of this review was to give an overview of: (i) techniques (sensorial and analytical) that can be used to determine a quantitative and qualitative characterization; (ii) air dispersion models applied for the evaluation of the spatial and temporal distribution of atmospheric pollutants in terms of concentration in air and/or deposition in the studied domain; (iii) major sources of odor nuisance (waste and livestock); (iv) mitigation actions against odor impact. Among sensorial techniques dynamic olfactometry, field inspection, and recording from residents were considered; whereas, for analytical methodologies: gas chromatography-mass spectrometry, identification of specific compounds, and electronic nose. Both kinds of techniques evaluate the odor concentration. Instead, to account for the effective impact of odors on the population, air dispersion models are used. They can provide estimates of odor levels in both current and future emission scenarios. Moreover, they can be useful to estimate the efficiency of mitigation strategies. Most of the odor control strategies involve measures oriented to prevent, control dispersion, minimize the nuisance or remove the odorants from emissions, such as adequate process design, buffer zones, odor covers, and treatment technologies.

GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction

Food flavor quality evaluation is attracting continuous attention, but a suitable evaluation system is severely lacking. Gas chromatography-mass spectrometry/olfactometry (GC-MS/O) is widely used to solve the food flavor evaluation problem, but the olfactometry evaluation is unfeasible to be carried out in large batches and is unreliable due to potential issue of an operator or systematic laboratory effect. Thus, a novel fingerprint modeling and profiling process was proposed based on several machine learning models including convolutional neural network (CNN). The fingerprint template was created by the data analysis of existing GC-MS spectrum dataset. Then the fingerprint image generation program was applied for structuring the complex instrumental data. Food olfactometry result was obtained by a machine learning method based on CNN using fingerprint image as the input. The case study on peanut oil samples demonstrated the model accuracy of around 93%. By structure optimization and further dataset expansion, the whole process has the potential to be utilized by sensory laboratories for aroma analysis instead of humans.

Assessment of the Effects of Wastewater Treatment Plant Modernization by Means of the Field Olfactometry Method

Methodological aspects of odor studies in ex-post analyses for Polish wastewater management facilities were analyzed based on the example of a modernized and enlarged wastewater treatment plant (WWTP) in Mazovia, in the vicinity of the Warsaw agglomeration. It is a mechanical–biological treatment plant with increased efficiency of biogen removal, using activated sludge in the treatment process, with a maximum hydraulic capacity of 60,000 m3/day. Olfactometric research was carried out by means of a method based on identification and characterization of the odor plume emitted from the examined source. This paper presents the results of odor intensity assessment (in sensory examinations according to a 6-stage scale) and odor concentration measurement (using portable field olfactometers) after the completion of the project, and compares them with similar studies conducted before the commencement of the investment. A total of 10 measurement series were carried out before modernization, and 12 after modernization of the WWTP. Odor concentration and intensity were determined, and the current meteorological situation was assessed at the measurement and observation points (receptors) located within the premises (in total 462 points) and around the WWTP (342 points). In each series of measurements on the windward side of the treatment plant, the background of air pollution with odorous substances was marked. The research showed that air flowing into the area of the sewage treatment plant is clean in terms of odor. During the research, basic sources of odor nuisance were identified, and their impact before and after modernization was characterized. The results presented in radar diagrams show changes in the percentage distribution of frequency of occurrence of individual intensity values at receptor points within and outside the area of the treatment plant. After modernization, a significant decrease in the concentration of odor emitted from the sludge dewatering building and sludge containers was determined. The air-tightness of the sewage channel (covered with concrete slabs and sealed) resulted in a significant decrease in the concentration of odor emitted from this source. Waste (in particular, sewage sludge) collected in the emergency waste storage yard was identified as the main source of odor nuisance. The waste, even after modernization, was an emitter of odorous compounds spreading outside the area of the WWTP. Nevertheless, as a result of the investment, the desired effect of reduction of the degree of odor nuisance was achieved.

Aroma Molecules as Dynamic Volatile Surfactants: Functionality beyond the Scent

Understanding of nonequilibrium processes at dynamic interfaces is indispensable for advancing design and fabrication of solid-state and soft materials. The research presented here unveils specific interfacial behavior of aroma molecules and justifies their usage as multifunctional volatile surfactants. As nonconventional volatile amphiphiles, we study commercially available poorly water-soluble compounds from the classes of synthetic and essential flavor oils. Their disclosed distinctive feature is a high dynamic interfacial activity, so that they decrease the surface tension of aqueous solutions on a time scale of milliseconds. Another potentially useful property of such amphiphiles is their volatility, so that they notably evaporate from interfaces on a time scale of seconds. This behavior allows for control of wetting and spreading processes. A revealed synergetic interfacial behavior of mixtures of conventional and volatile surfactants is attributed to a decrease of the activation barrier as a result of high statistical availability of new sites at the surface upon evaporation of the volatile component. Our results offer promising advantages in manufacturing technologies which involve newly creating interfaces, such as spraying, coating technologies, ink-jet printing, microfluidics, laundry, and stabilization of emulsions in cosmetic and food industry, as well as in geosciences for controlling aerosol formation.

Compounds responsible for off-odors in several samples composed by polypropylene, polyethylene, paper and cardboard used as food packaging materials

Seven commercial samples, consisted of plastic bags, tetrabrik and box, were evaluated by gas chromatography-olfactometry-mass spectrometry (GC-O-MS) to find the compounds responsible for off-odors in different PP, PE, multilayer cardboard and paper materials used for food contact. Migration assays were carried out with Tenax as food simulant to analyze the food safety as well as to evaluate the odor intensity after migration assay. Forty six compounds with characteristic odors were directly found in the materials studied. The strongest odors identified were acetic, propanoic and butyric with vinegar and rancid odors and octanal, nonanal and decanal with fat/soup odors, all of them found in PP and PE samples. Trimethylbenzenes with solvent and oily odors as well as terpenes with weakly woody odors were found in cardboard and paper materials. After migration, all compounds were below the European Legislation limits and maximum migration values recommended by Cramer. However propanoic, acetic and butyric acid as well as aldehydes compounds, phenol and 1-octanol were detected by sniffers, after migration assay, with high modified frequency (between 50 and 78%), what could change the organoleptic properties of packaged food.

Low-Power Detection of Food Preservatives by a Novel Nanowire-Based Sensor Array

Food preservatives are compounds that are used for the treatment of food to improve the shelf life. In the food industry, it is necessary to monitor all processes for both safety and quality of the product. An electronic nose (or e-nose) is a biomimetic olfactory system that could find numerous industrial applications, including food quality control. Commercial electronic noses are based on sensor arrays composed by a combination of different sensors, which include conductometric metal oxide devices. Metal oxide nanowires are considered among the most promising materials for the fabrication of novel sensing devices, which can enhance the overall performances of e-noses in food applications. The present work reports the fabrication of a novel sensor array based on SnO₂, CuO, and WO₃ nanowires deposited on top of μHPs provided by ams Sensor Solutions Germany GmbH. The array was tested for the discrimination of four typical compounds added to food products or used for their treatment to increase the shelf life: ethanol, acetone, nitrogen dioxide, and ozone. Results are very promising; the sensors array was able to operate for a long time, consuming less than 50 mW for each single sensor, and principal component analysis (PCA) confirmed that the device was able to discriminate between different compounds.

Portable biohybrid odorant sensors using cell-laden collagen micropillars

Biohybrid odorant sensors (BOSs) composed of biological materials and artificial detectors have recently attracted much attention due to their high degree of sensitivity and selectivity. Although portability is crucial for the practical use of BOSs on site, the currently used artificial detectors for biological signals are unportable. In this study, we propose a portable cell-based odorant sensor, which uses cell-laden collagen micropillars to compensate the low optical abilities of portable artificial detectors. The micropillars were composed of HEK293T cells expressing olfactory receptors, which emit a fluorescence signal based on the extent of odorant stimulation using a calcium fluorescent indicator. By stacking cells vertically in the micropillars, we achieved different levels of amplification of the fluorescence signals by varying the height of the micropillars. As a working demonstration of the portable BOS, we successfully detected different concentrations of odorants using an inexpensive web camera. The BOS was also able to distinguish the slight differences between an agonist and an antagonist. We believe that the portability of our BOS would facilitate its applications in point-of-care testing and on-site detection of hazardous materials.

Fast and Furious: Ethylene-Triggered Changes in the Metabolism of Papaya Fruit During Ripening

Papaya is a climacteric fleshy fruit characterized by fast ripening after harvest. During the relatively short postharvest period, papaya fruit undergoes several changes in metabolism that result in pulp softening and sweetening, as well as the development of a characteristic aroma. Since papaya is one of the most cultivated and appreciated tropical fruit crops worldwide, extensive research has been conducted to not only understand the formation of the quality and nutritional attributes of ripe fruit but also to develop methods for controlling the ripening process. However, most strategies to postpone papaya ripening, and therefore to increase shelf life, have failed to maintain fruit quality. Ethylene blockage precludes carotenoid biosynthesis, while cold storage can induce chilling injury and negatively affect the volatile profile of papaya. As a climacteric fruit, the fast ripening of papaya is triggered by ethylene biosynthesis. The generation of the climacteric ethylene positive feedback loop is elicited by the expression of a specific transcription factor that leads to an up-regulation of 1-aminocyclopropane-1-carboxylic acid (ACC) synthase (ACS) and ACC-oxidase (ACO) expression, triggering the system II ethylene biosynthesis. The ethylene burst occurs about 3 to 4 days after harvest and induces pectinase expression. The disassembling of the papaya cell wall appears to help in fruit sweetness, while glucose and fructose are also produced by acidic invertases. The increase in ethylene production also results in carotenoid accumulation due to the induction of cyclases and hydroxylases, leading to yellow and red/orange-colored pulp phenotypes. Moreover, the production of volatile terpene linalool, an important biological marker for papaya's sensorial quality, is also induced by ethylene. All these mentioned processes are related to papaya's sensorial and nutritional quality. We describe the understanding of ethylene-triggered events that influence papaya quality and nutritional traits, as these characteristics are a consequence of an accelerated metabolism during fruit ripening.