Search Results (27)

Cognitive Odor Source Localization (OSL) strategies are reliable search strategies for turbulent environments, where chemical cues are sparse and intermittent. These methods estimate a probabilistic belief over the source location using Bayesian inference and guide the searching movement by evaluating expected entropy reduction at candidate new positions. By maximizing expected information gain, agents make informed decisions rather than simply reacting to sensor readings. However, computing entropy reductions is computationally expensive, making real-time implementation challenging for resource-constrained platforms. Interestingly, search trajectories produced by cognitive algorithms often resemble those of small insects, suggesting that informative movement patterns might be replicated using simpler, bio-inspired searching strategies. This work investigates that possibility by analysing spatial distribution of entropy reductions across the entire search area. Rather than focusing on searching algorithms and local decisions, the analysis maps information gain over the full environment, identifying consistent high-gain regions that may serve as navigational cues. Results show that these regions often emerge near the source and along plume borders and that expected entropy reduction is strongly influenced by prior belief shape and sensor observations. This global perspective enables identification of spatial patterns and high-gain regions that remain hidden when analysis is restricted to local neighborhoods. These insights enable synthesis of hybrid search strategies that preserve cognitive effectiveness while significantly reducing computational cost. Full article

The foreign grain beetle, Ahasverus advena (Waltl) (Coleoptera: Silvanidae), has been reported from 110 countries on more than 162 commodities, more than 35 types of facilities, and 14 other habitats such as compost heaps and haystacks or manure. Compost heaps, haystacks, and manure heated by fermentation may allow overwintering in cold climates, making them important sources of infestation. From these sources the A. advena can fly and infest grain storage and processing facilities. A. advena has been found in empty grain storage bins, is often found in wheat immediately after harvest, and is most abundant early in wheat storage. Larvae and adults of A. advena are well adapted to feeding on several species of fungi and have higher chitinase levels and greater tolerance for fungal aflatoxins than other species. A. advena lay more eggs on the fungal species on which their offspring can develop most successfully. They are attracted to fungal odors and high moisture commodities and have the capability to disseminate grain fungi that cause hot spots within the grain mass. The presence of fungus beetles is indicative of poor storage conditions. A. advena is capable of feeding on some commodities and is a predator that may have a potential role in biological control. They are strong fliers but are distributed extensively with the movement of commodities in the marketing system. In countries with a zero tolerance for insects, their presence is sufficient for rejection of a load and associated economic losses. In other countries, contamination by A. advena is a problem, and in India, it is listed as a quarantine pest. Extension agents have had many requests for the identification of this species, and two other species of the same genus have been found in stored products. Some information is available for the effectiveness of nine pest management methods for A. advena. Full article

The need for odor measurement and pollution source identification in various sectors (aeronautic, automobile, healthcare…) has increased in the last decade. Multisensor modules, such as electronic noses, seem to be a promising and inexpensive alternative to traditional sensors that were only sensitive to one gas at a time. However, the selectivity, the non-repetitiveness of their manufacture, and their drift remain major obstacles to the use of electronic noses. In this first work, we show how the mathematical modeling of the sensor response can be used to find new selectivity characteristics, different from those classically used in the literature. We identified new specific characteristics that have no physical meaning that can be used to find criteria for the presence of formaldehyde and nitrogen dioxyde alone or in a mixture. We discuss the limitations of the methodology presented and suggest avenues for improvement, with more precise modeling techniques involving symbolic regression. Full article

The visualization of the spatial distributions of gases from various sources is essential to understanding the composition, localization, and behavior of these gases. In this study, an inkjet-printed localized surface plasmon resonance (LSPR) subpixel gas sensor array was developed to visualize the spatial distributions of gases and to differentiate between acetic acid, geraniol, pentadecane, and cis-jasmone. The sensor array, which integrates gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), and fluorescent pigments, was positioned 3 cm above the gas source. Hyperspectral imaging was used to capture the LSPR spectra across the sensor array, and these spectra were then used to construct gas information matrices. Principal component analysis (PCA) enabled effective classification of the gases and localization of their sources based on observed spectral differences. Heat maps that visualized the gas concentrations were generated using the mean squared error (MSE) between the sensor responses and reference spectra. The array identified and visualized the four gas sources successfully, thus demonstrating its potential for gas localization and detection applications. The study highlights a straightforward, cost-effective approach to gas sensing and visualization, and in future work, we intend to refine the sensor fabrication process and enhance the detection of complex gas mixtures. Full article

Gardenia fruit is a popular functional food and raw material for natural pigments. It comes from a wide range of sources, and different products sharing the same name are very common. Volatile organic compounds (VOCs) are important factors that affect the flavor and quality of gardenia fruit. This study used the Heracles NEO ultra-fast gas phase electronic nose with advanced odor analysis performance and high sensitivity to analyze six batches of gardenia fruit from different sources. This study analyzed the VOCs to find a way to quickly identify gardenia fruit. The results show that this method can accurately distinguish the odor characteristics of various gardenia fruit samples. The VOCs in gardenia fruit are mainly organic acid esters, ketones, and aldehyde compounds. By combining principal component analysis (PCA) and discriminant factor analysis (DFA), this study found that the hexanal content varied the most in different gardenia fruit samples. The VOCs allowed for the fruit samples to be grouped into two main categories. One fruit sample was quite different from the fruits of other origins. The results provide theoretical support for feasibility of rapid identification and quality control of gardenia fruit and related products in the future. Full article

Chitosan samples were prepared from the shells of marine animals (crab and shrimp) and the cell walls of fungi (agaricus bisporus and aspergillus niger). Fourier-transform infrared spectroscopy (FT-IR) was used to detect their molecular structures, while headspace-gas chromatography–ion mobility spectrometry (HS-GC-IMS) was employed to analyze their odor composition. A total of 220 volatile organic compounds (VOCs), including esters, ketones, aldehydes, etc., were identified as the odor fingerprinting components of chitosan for the first time. A principal component analysis (PCA) revealed that chitosan could be effectively identified and classified based on its characteristic VOCs. The sum of the first three principal components explained 87% of the total variance in original information. An orthogonal partial least squares discrimination analysis (OPLS-DA) model was established for tracing and source identification purposes, demonstrating excellent performance with fitting indices R²X = 0.866, R²Y = 0.996, Q² = 0.989 for independent variable fitting and model prediction accuracy, respectively. By utilizing OPLS-DA modeling along with a heatmap-based tracing path study, it was found that 29 VOCs significantly contributed to marine chitosan at a significance level of VIP > 1.00 (p < 0.05), whereas another set of 20 VOCs specifically associated with fungi chitosan exhibited notable contributions to its odor profile. These findings present a novel method for identifying commercial chitosan sources, which can be applied to ensure biological safety in practical applications. Full article

Herbal spices are an agricultural commodity, economically very important and beneficial in primary healthcare in the food and medicine sectors. Herbal spices are used as food flavoring agents as well as in phytotherapies throughout the world and have nutritive benefits. The food and medicine industries widely employ artificial or natural adulteration to retard the deterioration and utilization of these adulterants in food and medicine products has given rise to significant apprehension among consumers, primarily stemming from the potential health risks that they pose. Thus, their characterization for the purpose of identification, origin, and quality assurance is mandatory for safe human consumption. Here, we studied 22 samples of commonly traded herbal spices that belong to 20 different genera and 21 species comprising 14 families, investigated macroscopically or organoleptically as well as histologically under microscopic examination. In this study, we provide details on organoleptic features including appearance, taste, odor, color, shape, size, fractures, types of trichomes, and the presence of lenticels among the examined herbal spices and these features have great significance in the detection of both natural as well as artificial deterioration. In terms of microscopic characterization, each examined plant part comprising different anatomical characteristics has taxonomic importance and also provides useful information for authentication from natural adulterants. Furthermore, the studied taxa were also described with nutritive and therapeutic properties. For condiments, herbal beverages and medicinal purposes, different herbal parts such as leaves, floral buds, seeds, fruit, and accessory parts like mericarp, rhizome, bulbs, and bark were used and commercially traded. Similarly, in this study, the leaves of Cinnamomum tamala and Mentha spicata, the floral buds of Syzygium aromaticum, the seeds of Amomum subulatum, Brassica nigra, Punica granatum, Myristica fragrans, Phyllanthus emblica, and Elettaria cardamomum, the mericarp of Coriandrum sativum, and Cuminum cyminum were observed. As a result, we show the potential of herbal spices as a source of many valuable phytochemicals and essential nutrients for food, nutraceutical, and homoeopathic medicine. Full article

Odorous sediments containing volatile organic sulfur compounds (VOSCs) are a common issue in shallow water reservoirs globally. Volatile organic sulfur compounds are a typical class of malodorous substances that have attracted widespread attention due to their pungent odors and extremely low odor thresholds. The insufficient hydrodynamic conditions in the reservoir area lead to the accumulation of pollutants in the sediment, where biochemical reactions occur at the sediment–water interface, serving as a significant source of foul-smelling substances in the water body. This study analyzed sediment samples from 10 locations across a shallow water reservoir using flavor profile analysis, an electronic nose, and gas chromatography-mass spectrometry. The predominant odor types were earthy/musty and putrid/septic, with key odorants being VOSCs, 2-methylisoborneol, and geosmin. The results revealed VOSCs from organic matter account for up to 96.7% of odor activity. More importantly, concentrations and release fluxes of VOSCs consistently decrease along the water flow direction from dam regions to tail regions. This trend matches organic matter accumulation patterns in shallow reservoirs and highlights dam areas as hotspots for malodorous sediment. The generalized spatial distribution pattern and identification of key malodorous compounds establish a basis for understanding and managing odor issues in shallow freshwater reservoir sediments. Full article

Odor is one of the most intuitive indicators for assessing drinking water quality in waterworks. Removing odors is of great importance to improve the quality of tap water, ensure people’s health, and address public perception. The effective identification of odors in drinking water and the exploration of the source of the odor are the prerequisites for eliminating odors. Therefore, this article first discusses the sources and types of odors that are typical in current drinking water, focuses on reviewing the research progress of odor removal technologies in water treatment plants, including adsorption technology, chemical oxidation technology, biodegradation technology and combined technology, and explains the advantages, disadvantages, principles, research progress, practical application scenarios, considerations and application prospects of each odor-removal technology. It is expected to provide a reference for controlling odor pollution in drinking water. Full article

The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor gas concentrations in environments where these substances are manipulated. Gas sensor technology has evolved rapidly in recent years, offering increasingly precise and reliable solutions. However, there are still challenges to be overcome, especially when sensors are deployed on unmanned aerial vehicles (UAVs). This article discusses the use of UAVs to locate gas sources and presents real test results using the SGP40 metal oxide semiconductor gas sensor onboard the Crazyflie 2.1 nano quadcopter. The solution proposed in this article uses an odor source identification strategy, employing a gas distribution mapping approach in a three-dimensional environment. The aim of the study was to investigate the feasibility and effectiveness of this approach for detecting gases in areas that are difficult to access or dangerous for humans. The results obtained show that the use of drones equipped with gas sensors is a promising alternative for the detection and monitoring of gas leaks in closed environments. Full article

Actinidia arguta, known for its distinctive flavor and high nutritional value, has seen an increase in cultivation and variety identification. However, the characterization of its volatile aroma compounds remains limited. This study aimed to understand the flavor quality and key volatile aroma compounds of different A. arguta fruits. We examined 35 A. arguta resource fruits for soluble sugars, titratable acids, and sugar–acid ratios. Their organic acids and volatile aroma compounds were analyzed using high-performance liquid chromatography (HPLC) and headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS). The study found that among the 35 samples tested, S12 had a higher sugar–acid ratio due to its higher sugar content despite having a high titratable acid content, making its fruit flavor superior to other sources. The A. arguta resource fruits can be classified into two types: those dominated by citric acid and those dominated by quinic acid. The analysis identified a total of 76 volatile aroma substances in 35 A. arguta resource fruits. These included 18 esters, 14 alcohols, 16 ketones, 12 aldehydes, seven terpenes, three pyrazines, two furans, two acids, and two other compounds. Aldehydes had the highest relative content of total volatile compounds. Using the orthogonal partial least squares discriminant method (OPLS-DA) analysis, with the 76 volatile aroma substances as dependent variables and different soft date kiwifruit resources as independent variables, 33 volatile aroma substances with variable importance in projection (VIP) greater than 1 were identified as the main aroma substances of A. arguta resource fruits. The volatile aroma compounds with VIP values greater than 1 were analyzed for odor activity value (OAV). The OAV values of isoamyl acetate, 3-methyl-1-butanol, 1-hexanol, and butanal were significantly higher than those of the other compounds. This suggests that these four volatile compounds contribute more to the overall aroma of A. arguta. This study is significant for understanding the differences between the fruit aromas of different A. arguta resources and for scientifically recognizing the characteristic compounds of the fruit aromas of different A. arguta resources. Full article

In order to improve the precision of gas detection and develop valid search strategies, the improved quantitative identification algorithm in odor source searching was researched based on the gas sensor array. The gas sensor array was devised corresponding to the artificial olfactory system, and the one-to-one response mode to the measured gas was set up with its inherent cross-sensitive properties. The quantitative identification algorithms were researched, and the improved Back Propagation algorithm was proposed combining cuckoo algorithm and simulated annealing algorithm. The test results prove that using the improved algorithm to obtain the optimal solution −1 at the 424th iteration of the Schaffer function with 0% error. The gas detection system designed with MATLAB was used to obtain the detected gas concentration information, then the concentration change curve may be achieved. The results show that the gas sensor array can detect the concentration of alcohol and methane in the corresponding concentration detection range and show a good detection performance. The test plan was designed, and the test platform in a simulated environment in the laboratory was found. The concentration prediction of experimental data selected randomly was made by the neural network, and the evaluation indices were defined. The search algorithm and strategy were developed, and the experimental verification was carried out. It is testified that the zigzag searching stage with an initial angle of 45° is with fewer steps, faster searching speed, and a more exact position to discover the highest concentration point. Full article

Sour meat is a highly appreciated traditional fermented product, mainly from the Guizhou, Yunnan, and Hunan provinces. The flavor profiles of sour meat from goose and pork were evaluated using gas chromatography–ion mobility spectrometry (GC–IMS) combined with an electronic nose (E-nose) and tongue (E-tongue). A total of 94 volatile compounds were characterized in fermented sour meat from both pork and goose using GC–IMS. A data-mining protocol based on univariate and multivariate analyses revealed that the source of the raw meat plays a crucial role in the formation of flavor compounds during the fermentation process. In detail, sour meat from pork contained higher levels of hexyl acetate, sotolon, heptyl acetate, butyl propanoate, hexanal, and 2-acetylpyrrole than sour goose meat. In parallel, sour meat from goose showed higher levels of 4-methyl-3-penten-2-one, n-butyl lactate, 2-butanol, (E)-2-nonenal, and decalin than sour pork. In terms of the odor and taste response values obtained by the E-nose and E-tongue, a robust principal component model (RPCA) could effectively differentiate sour meat from the two sources. The present work could provide references to investigate the flavor profiles of traditional sour meat products fermented from different raw meats and offer opportunities for a rapid identification method based on flavor profiles. Full article

Odor management plans indicate the need to identify odor sources in waste management facilities. Finding the right tool for this type of task is a key element. This article covers a new approach for odor quantification and source identification at a selected waste management facility by coupling field olfactometry and the spatial interpolation method, such as inverse weighted distance. As the results show, this approach works only partially. Field olfactometry seems to be a suitable tool for odor identification that could be an instrument incorporated into odor management plans as it allowed for recognition of most odor-generating places at the selected facility, i.e., waste stabilization area, green waste storage area, and bioreactors. However, spatial distributions obtained by the selected interpolation method are characterized by high errors during cross-validation, and they tend to overestimate odor concentrations. The substantial weakness of the selected interpolation method is that it cannot handle points where the odor concentration is below the detection threshold. Therefore, the usefulness of such a method is questionable when it comes to odor management plans. Since field olfactometry is a reliable tool for odor measurements, further research into computational methods is needed, including advanced interpolation methods or dispersion modeling based on field olfactometry data. Full article

Although clothes washing machines remove dirt, microorganisms are not reliably removed by modern cold-water machine-washing practices. Microbial bioburden on clothing originates from the wearer’s skin, the environment (indoor and outdoor), and the washing machine itself. While most clothing microbes are commensals, microbes causing odors and opportunistic pathogens may also be present. Understanding the extent of microbial transfer from washing machines to clothes may inform strategies for odor control and for mitigating the transmission of microbes through the laundering process. This study was designed to quantify and identify bacteria/fungi transferred from laundromat machines to sentinel cotton washcloths under standard cold-water conditions. Bacterial 16S rRNA and fungal ITS sequencing enabled identification of microorganisms in the washcloths following laundering. Total plate-based enumeration of viable microorganisms also was performed, using growth media appropriate for bacteria and fungi. Opportunistic human bacterial pathogens, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., were recovered. The fungal bioburden was ~two-fold lower than the bacterial bioburden. Most sequences recovered were assigned to non-pathogenic fungi, such as those from genera Malassezia and Ascomycota. These results suggest that public washing machines represent a source of non-pathogenic and pathogenic microbial contamination of laundered garments. Full article