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Summary: Ensuring the freshness of fish is essential to guaranteeing safer products for consumers. A classification model was developed using the PEN3 electronic nose to predict fish freshness with its storage day. Frigate tuna (Auxis thazard) was used for odour sampling along with the total volatile base nitrogen (TVB‐N), at 4 °C for 14 days every other day. Random forest and support vector machine (SVM) were performed to develop freshness classification models based on PEN3 data and 100% and 99.8% accuracies were obtained, respectively, in distinguishing the eight storage day classes. The PEN3 sensors including W2S, W1S, W1W, W3S, and W6S were identified as key sensors for fish freshness. TVB‐N analysis advances the freshness prediction of e‐nose and three freshness levels were identified as 'Fresh', applicable from the 0th to the 6th day; 'Moderately fresh', lasting from the 7th to the 9th day; and 'Unsuitable for consumption', applicable from the 10th day onwards. [ABSTRACT FROM AUTHOR]

Summary: In this study, the effect of short‐term storage at different temperatures (0, 4 and −8 °C) on the quality of Antarctic krill was investigated using the main quality indicators as a reference, combined with electronic nose and untargeted metabolomics. Antarctic krill stored at lower temperatures (−8 °C) were of better quality and had a flavour similar to that of the original Antarctic krill. Metabolomics analyses showed that differential metabolites (DMS) were more abundant in Antarctic krill at higher temperatures (4 °C), with sixty‐five metabolites having reduced expression and forty‐five metabolites having increased expression; Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that amino acid metabolism and nucleotide metabolism pathways were the Five DMS, including α‐ketoglutarate, glutamate, succinate, sucrose and glutarate, can be used as potential biomarkers to monitor quality changes during short‐term storage of Antarctic krill. In addition, the association between quality indicators and differential metabolites remains to be explored. [ABSTRACT FROM AUTHOR]

BACKGROUND: Adding ZaoShao liquor (high‐concentration liquor) is one of the most important steps in the brewing process of Shaoxing Jiafan wine, a product protected by Chinese geographical indications. The focus of this study is the effect of different additive amounts of liquor on the flavor of end products. RESULTS: In this study, four kinds of Shaoxing Jiafan wine were brewed by changing the amount of ZaoShao liquor. Headspace solid‐phase microextraction and gas chromatography–mass spectrometry were used to detect the flavor substances of four kinds of Jiafan wine. The difference in flavor of four kinds of Jiafan wine was evaluated by electronic nose analysis technology and verified by sensory evaluation. Finally, the reliability of the experimental results was verified through an aroma reconstruction experiment of rice wine. In this study, the differences in flavoring substances under different amounts of ZaoShao liquor were verified from various angles. The results showed that the flavors of the four kinds of wines were significantly different. CONCLUSION: The composition of flavor substances in Shaoxing rice wine varies with the amount of ZaoShao liquor. This study provided a scientific basis for the improvement of production technology of Shaoxing wine. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Portable and personalized artificial intelligence (AI)‐driven sensors mimicking human olfactory and gustatory systems have immense potential for the large‐scale deployment and autonomous monitoring systems of Internet of Things (IoT) devices. In this study, an artificial Q‐grader comprising surface‐engineered zinc oxide (ZnO) thin films is developed as the artificial nose, tongue, and AI‐based statistical data analysis as the artificial brain for identifying both aroma and flavor chemicals in coffee beans. A poly(vinylidene fluoride‐co‐hexafluoropropylene)/ZnO thin film transistor (TFT)‐based liquid sensor is the artificial tongue, and an Au, Ag, or Pd nanoparticles/ZnO nanohybrid gas sensor is the artificial nose. In order to classify the flavor of coffee beans (acetic acid (sourness), ethyl butyrate and 2‐furanmethanol (sweetness), caffeine (bitterness)) and the origin of coffee beans (Papua New Guinea, Brazil, Ethiopia, and Colombia‐decaffeine), rational combination of TFT transfer and dynamic response curves capture the liquids and gases‐dependent electrical transport behavior and principal component analysis (PCA)‐assisted machine learning (ML) is implemented. A PCA‐assisted ML model distinguished the four target flavors with >92% prediction accuracy. ML‐based regression model predicts the flavor chemical concentrations with >99% accuracy. Also, the classification model successfully distinguished four different types of coffee‐bean with 100% accuracy. [ABSTRACT FROM AUTHOR]

Disease is a serious problem in tomato plants, causing huge economic losses. Disease detection, as the premise of protection, is important. This paper employed Electronic nose (E-nose) and Gas Chromatography–Mass Spectrometer (GC–MS), as an auxiliary technique, to predict disease type and its severity in the tomato plant. Twenty-five volatile constituents were identified using GC–MS. Their concentrations were calculated and showed the difference in different groups. Furthermore, the results of E-nose and GC–MS were compared and showed a good correlation. Moreover, the possibility of E-nose in classifying tomato plants infected with different types and severities of disease either respectively or together was proved based on either Principal Component Analysis (PCA) or Discriminant Functions Analysis (DFA). Then, Backpropagation neural network (BPNN) was introduced and showed that the correct classification rates were 98.3% for the training set and 97.5% for the testing set for predicting disease type and severity. Moreover, 100% correct classification rate was obtained for the diseased groups, which was very meaningful for the prevention of disease spread and met actual application needs. This study demonstrates the feasibility of E-nose in predicting tomato plants infected with disease in different types and severities. [ABSTRACT FROM AUTHOR]

Beer is a popular alcoholic beverage worldwide. However, limited research has been conducted on identifying key odor-active components in lager-type draft beers for the Chinese market. Therefore, this study aims to elucidate the odor characteristics of the four most popular draft beer brands through a sensory evaluation and an electronic nose. Subsequently, the four draft beers were analyzed through solid-phase microextraction and liquid–liquid extraction using a two-dimensional comprehensive gas chromatography–olfactometry–mass spectrometry analysis (GC×GC–O–MS). Fifty-five volatile odor compounds were detected through GC×GC–O–MS. Through an Aroma Extract Dilution Analysis, 22 key odor-active compounds with flavor dilution factors ≥ 16 were identified, with 11 compounds having odor activity values > one. An electronic nose analysis revealed significant disparities in the odor characteristics of the four samples, enabling their distinct identification. These findings help us to better understand the flavor characteristics of draft beer and the stylistic differences between different brands of products and provide a theoretical basis for objectively evaluating the quality differences between different brands of draft beer. [ABSTRACT FROM AUTHOR]

Summary: Aroma is a vital attribute of dairy products. This study employed a combination of human sensory evaluation and instrumental analysis to study the effect of two different pre‐sterilisation processes (A and B) on the odour characteristics of milk and long shelf‐life yoghurt. The results from the sensory evaluation and electronic nose analysis indicated that the pre‐sterilisation process significantly influences the aroma profile and compounds of the samples. Stir bar sorptive extraction coupled with comprehensive two‐dimensional gas chromatography−olfactometry−mass spectrometry (SBSE‐GC × GC−O−MS) was applied to identify the aroma‐active compounds present. Aldehydes and ketones emerged as the most abundant aroma‐active compounds. Quantitative analysis showed variations in the concentrations of aroma‐active compounds. For example, higher concentrations of hexanal, butanoic acid and hexanoic acid were observed in long shelf‐life yoghurt and milk produced by process B than process A, while higher concentrations of nonanal, 2,3‐butanedione and 3‐hydroxy‐2‐butanone were detected in products from process A than process B. Initial odour activity values provided insight into the contribution of aroma‐active compounds to the overall odour perception. Based on the results of aroma recombination, omission and spiking experiments, 13 and 11 key aroma‐active compounds were identified in long shelf‐life yoghurt and milk, respectively. These compounds, including 2,3‐butanedione, 2,3‐pentanedione, 2‐heptanone, 2‐undecanone, hexanal, octanal, nonanal, decanal, furfural, butanoic acid, hexanoic acid, 3‐hydroxy‐2‐butanone and styrene, can serve as potential indicators for determining odour differences between long shelf‐life yoghurt (milk) samples. Consequently, they offer a chemical basis for manufacturers to make informed decisions regarding production processes. [ABSTRACT FROM AUTHOR]

An electronic nose prototype based on the novel temperature‐modulation technique is developed, where the heater voltage changes its amplitude and frequency periodically. The system comprises a sensor analog front end and a digital processing part including a heater voltage generator and a sensor data transmitter, where the digital part was implemented by a field programmable gate array. The fabricated prototype successfully generated the modulated heater waveform and synchronously acquired sensor responses at a sampling rate of 10 kHz. Whereby, sensor responses to the test gases of volatile organic compounds were collected. Gas classification was achieved with an accuracy of 99.6% based on the data obtained by the prototype, demonstrating the promising properties of the prototype as a compact and highly precise electronic nose. [ABSTRACT FROM AUTHOR]

Jujube has various functional properties and is a promising source of bioactive compounds and flavors. This study investigated the functional properties and flavor characteristics of milk from cows supplemented with jujube powder (JP). Here, milk volatile profiles and taste properties were analyzed by using an electronic nose and headspace solid-phase microextraction GC–MS. Compared with the control group, the total antioxidant capacity, 2,2′-azino-bis-3-ethylbenzothiazoline-6-sulphonic free-radical-scavenging activity, lactoferrin, and IgG levels increased significantly in the JP group. Volatile flavor analysis indicated that ketone levels increased, acid abundance decreased, and toluene and dimethyl sulfone significantly increased in the JP group. Taste-profile analyses demonstrated that jujube supplementation altered the taste of the milk. In summary, dietary JP supplementation affects the volatile flavor composition and aroma of milk, as well as the bioactive components and antioxidant properties. These findings enhance our understanding of milk production using direct dietary supplementation to produce sustainable dairy products. [ABSTRACT FROM AUTHOR]

This paper combines artificial intelligence deep learning technology to optimize the wine making process of mango wine. Moreover, in view of the shortcomings of traditional electronic nose data processing methods, a deep learning method based on SSAE‐BPNN is proposed for electronic nose data processing. In addition, according to the characteristics of automatic learning features, this paper uses a deep learning method based on SSAE‐BPNN to simplify the process of traditional data processing methods. Finally, this paper constructs an electronic nose system that can be used to identify mango wine making characteristics, and enhances the effect of electronic nose recognition through deep learning. Through the analysis, it can be seen that the mango wine making process optimization method based on artificial intelligence deep learning technology proposed in this paper has a certain effect, and it has optimized the traditional mango wine making process. [ABSTRACT FROM AUTHOR]

Milk and dairy products are included in the list of the Food Security Doctrine and are of paramount importance in the diet of the human population. At the same time, the presence of many macro- and microcomponents in milk, as available sources of carbon and energy, as well as the high activity of water, cause the rapid development of native and pathogen microorganisms in it. The goal of the work was to assess the possibility of using an array of gas chemical sensors based on piezoquartz microbalances with polycomposite coatings to assess the microbiological indicators of milk quality and to compare the microflora of milk samples. Piezosensors with polycomposite coatings with high sensitivity to volatile compounds were obtained. The gas phase of raw milk was analyzed using the sensors; in parallel, the physicochemical and microbiological parameters were determined for these samples, and species identification of the microorganisms was carried out for the isolated microorganisms in milk. The most informative output data of the sensor array for the assessment of microbiological indicators were established. Regression models were constructed to predict the quantity of microorganisms in milk samples based on the informative sensors' data with an error of no more than 17%. The limit of determination of QMAFAnM in milk was 243 ± 174 CFU/cm3. Ways to improve the accuracy and specificity of the determination of microorganisms in milk samples were proposed. [ABSTRACT FROM AUTHOR]

To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose. [ABSTRACT FROM AUTHOR]

Providing employees with proper work conditions should be one of the main concerns of any employer. Even so, in many cases, work shifts chronically expose the workers to a wide range of potentially harmful compounds, such as ammonia. Ammonia has been present in the composition of products commonly used in a wide range of industries, namely production in lines, and also laboratories, schools, hospitals, and others. Chronic exposure to ammonia can yield several diseases, such as irritation and pruritus, as well as inflammation of ocular, cutaneous, and respiratory tissues. In more extreme cases, exposure to ammonia is also related to dyspnea, progressive cyanosis, and pulmonary edema. As such, the use of ammonia needs to be properly regulated and monitored to ensure safer work environments. The Occupational Safety and Health Administration and the European Agency for Safety and Health at Work have already commissioned regulations on the acceptable limits of exposure to ammonia. Nevertheless, the monitoring of ammonia gas is still not normalized because appropriate sensors can be difficult to find as commercially available products. To help promote promising methods of developing ammonia sensors, this work will compile and compare the results published so far. [ABSTRACT FROM AUTHOR]

To compare apple aroma intensities, apples were analyzed from the calyx side (on the opposite side of the stem) using an electronic nose (e-nose) sensor device and direct mass spectrometry. The results indicated that the sensor value tended to increase in accordance with the total intensity of apple aroma components measured by direct mass spectrometry. In addition, the e-nose sensor values for apple aroma did not correlate with the sugar content and ripeness measurements using optical sensors. Moreover, the relative standard deviations of repeatability and intermediate precision in the measurement of apple flavor (apple lip balm) were within 1.36–9.96%. Similar to the utilization of sugar content and ripeness values, the aroma measured from the calyx side can be potentially used for apple evaluation. [ABSTRACT FROM AUTHOR]

The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases. [ABSTRACT FROM AUTHOR]

Fermented foods are sensitive to the production conditions because of microbial and enzymatic activities, which requires intelligent flavor sensing system (IFSS) to monitor and optimize the production process based on the flavor properties. As the simulation system of human olfaction and gustation, IFSS has been widely used in the field of food with the characteristics of nondestructive, pollution-free, and real-time detection. This paper reviews the application of IFSS in the control of fermentation, ripening, and shelf life, and the potential in the identification of quality differences and flavor-producing microbes in fermented foods. The survey found that electronic nose (tongue) is suitable to monitor fermentation process and identify food authenticity in real time based on the changes of flavor profile. Gas chromatography-ion mobility spectrometry and nuclear magnetic resonance technology can be used to analyze the flavor metabolism of fermented foods at various production stages and explore the correlation between flavor substances and microorganisms. [ABSTRACT FROM AUTHOR]

Background: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. Methods: Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. Results: In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). Conclusion: Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. Clinical trial registration: This study is not a clinical trial and was therefore not registered. [ABSTRACT FROM AUTHOR]

Enhancing the sensing properties of resistive sensors holds pivotal importance as a non-invasive technology for identifying lung cancer biomarkers. This study focused on the development of a robust sensing platform to effectively detect low-concentration biomarkers with low concentration. Ti 3 C 2 T x MXene was prepared by HF etching and ZnO was successfully grown on the MXene nano-multilayers via hydrothermal method subsequently. TiO 2 –ZnO samples were obtained after the calcination step. Various characterization methods were applied to validate the structural integrity and formation of the TiO 2 –ZnO n-n heterojunction. The TiO 2 –ZnO sensor with a Ti:Zn atom ratio of 1:1 (TZ1) exhibited special sensing characteristics, which can be attributed to the higher activation energy (89.2 kJ/mol) and the more substantial interfacial contact areas between the two metal oxides. This architectural arrangement gives rise to the creation of numerous energy barriers, effectively hindering electron migration. Despite exhibiting the acceptable response, TZ1 can be applied in electronic nose systems to effectively detect lung cancer indicators with low concentrations (i.e., acetone, formaldehyde, NH 3) in light of its high stability and steady signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

This paper reports a novel additive manufacturing technique for MEMS devices using newly developed APSLD (atmospheric pressure sputtering layer deposition) technology. It uses a microplasma at atmospheric pressure to deposit microstructures with defined properties, such as conducting or isolating directly at different surfaces additively without complex and time‐consuming manufacturing routes like planar technology. Feasibility tests have been successfully done to deposit metal lines, isolating and metal oxide layers on 3D surfaces to manufacture high stable MEMS sensor system for harsh environments. This is a promising technology in the field of MEMS manufacturing, as it can simplify the manufacturing process and reduce the cost. The ability to deposit different materials can expand the design possibilities for MEMS devices. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Fructus Aurantii (FA) is an edible and medicinal functional food used worldwide that enhances digestion. Since raw FA (RFA) possesses certain side effects for some patients, processed FA (PFA) is commonly used in clinical practice. This study aimed to establish an objective and comprehensive quality evaluation of the PFA that employed the technique of steaming and fermentation. Combined with the volatile and non‐volatile components, as well as the regulation of gut microbiota, the differentiation between RFA and PFA was analyzed. The results showed that the PFA considerably reduced the contents of flavonoid glycosides while increasing hesperidin‐7‐O‐glucoside and flavonoid aglycones. The electronic nose and GC‐MS (Gas chromatography/mass spectrometry) effectively detected the variation in flavor between RFA and PFA. Correlation analysis revealed that eight volatile components (relative odor activity value [ROAV] ≥ 0.1) played a key role in inducing odor modifications. The original floral and woody notes were subdued due to decreased levels of linalool, sabinene, α‐terpineol, and terpinen‐4‐ol. After processing, more delightful flavors such as lemon and fruity aromas were acquired. Furthermore, gut microbiota analysis indicated a significant increase in beneficial microbial taxa. Particularly, Lactobacillus, Akkermansia, and Blautia exhibited higher abundance following PFA treatment. Conversely, a lower presence of pathogenic bacteria, including Proteobacteria, Flexispira, and Clostridium. This strategy contributes to a comprehensive analysis technique for the quality assessment of FA, providing scientific justifications for processing FA into high‐value products with enhanced health benefits. Practical Application: This study provided an efficient approach to Fructus Aurantii quality evaluation. The methods of fermentation and steaming showed improved quality and safety. [ABSTRACT FROM AUTHOR]

This study performed a comparative analysis of the sensory and microbial profiles of light‐flavor Bijou (LFD) from Taiyuan (Shanxi Province) and Suizhou (Hubei Province) in China. The results of the electronic nose showed that the aromatic substances of the LFD from Taiyuan (TLFD) were significantly higher (p <.05), while alcohol and aldehyde substances were significantly lower (p <.05) compared with the LFD from Suizhou (SLFD). The average response values of sensors W1C (sensitive to aromatic hydrocarbons), W3C (sensitive to amine and aromatic components), W5C (sensitive to olefins, aromatics, and polar molecules), and W2S (sensitive to alcohol and aldehyde compounds) to TLFD were 0.26, 0.33, 0.34, and 7.72, whereas the response values to SLFD were 0.25, 0.32, 0.33, and 8.04, respectively. The electronic tongue results showed that the aftertaste A (bitter aftertaste) and aftertaste B (astringent aftertaste) of the TLFD were significantly higher (p <.05) and umami was significantly lower (p <.05) as compared to the SLFD. The relative intensities of the aftertaste A, aftertaste B, and umami indicators of TLFD were 0.10, −0.008, and −0.22, respectively, while those of SLFD were −0.23, −0.36, and 0.835, respectively. MiSeq high‐throughput sequencing results showed that TLFD exhibited lower fungal richness and diversity compared to SLFD. The dominant bacterial genera were mainly Bacillus (58.12%), Kroppenstedtia (10.11%), and Weissella (6.26%), and the dominant fungal genera were Saccharomycopsis (67.53%), Rasamsonia (9.90%), and Thermoascus (7.10%). Streptomyces and Staphylococcus were identified as the key characteristic microorganisms in TLFD, while Kroppenstedtia, Rasamsonia, and Thermoascus were the key characteristic microorganisms in SLFD. Correlation analysis indicated a stronger correlation between microorganisms and sensory characteristics in SLFD samples. This study provides valuable insights into the sensory and microbiological characteristics of LFD from different regions and offers a new perspective for understanding the production of differently flavored light‐flavor Baijiu. [ABSTRACT FROM AUTHOR]

Purpose: Artificial fruit ripening is hazardous to mankind. In the recent past, artificial fruit ripening is increasing gradually due to its commercial benefits. To discriminate the type of fruit ripening involved at the vendors' side, there is a great demand for on-sight ethylene detection in a nondestructive manner. Therefore, this study aims to deal with a comparison of various laboratory and portable methods developed so far with high-performance metrics to identify the ethylene detection at fruit ripening site. Design/methodology/approach: This paper focuses on various types of technologies proposed up to date in ethylene detection, fabrication methods and signal conditioning circuits for ethylene detection in parts per million and parts per billion levels. The authors have already developed an infrared (IR) sensor to detect ethylene and also developed a lab-based setup belonging to the electrochemical sensing methods to detect ethylene for the fruit ripening application. Findings: The authors have developed an electrochemical sensor based on multi-walled carbon nanotubes whose performance is relatively higher than the sensors that were previously reported in terms of material, sensitivity and selectivity. For identifying the best sensing technology for optimization of ethylene detection for fruit ripening discrimination process, authors have developed an IR-based ethylene sensor and also semiconducting metal-oxide ethylene sensor which are all compared with literature-based comparable parameters. This review paper mainly focuses on the potential possibilities for developing portable ethylene sensing devices for investigation applications. Originality/value: The authors have elaborately discussed the new chemical and physical methods of ethylene detection and quantification from their own developed methods and also the key findings of the methods proposed by fellow researchers working on this field. The authors would like to declare that the extensive analysis carried out in this technical survey could be used for developing a cost-effective and high-performance portable ethylene sensing device for fruit ripening and discrimination applications. [ABSTRACT FROM AUTHOR]

This study proposes an optimization method for temperature modulation in chemiresistor-type gas sensors based on Bayesian optimization (BO), and its applicability was investigated. As voltage for a sensor heater, our previously proposed waveform was employed, and the parameters determining the voltage range were optimized. Employing the Bouldin–Davies index (DBI) as an objective function (OBJ), BO was utilized to minimize the DBI calculated from a feature matrix built from the collected data followed by pre-processing. The sensor responses were measured using five test gases with five concentrations, amounting to 2500 data points per parameter set. After seven trials with four initial parameter sets (ten parameter sets were tested in total), the DBI was successfully reduced from 2.1 to 1.5. The classification accuracy for the test gases based on the support vector machine tends to increase with decreasing the DBI, indicating that the DBI acts as a good OBJ. Additionally, the accuracy itself increased from 85.4% to 93.2% through optimization. The deviation from the tendency that the accuracy increases with decreasing the DBI for some parameter sets was also discussed. Consequently, it was demonstrated that the proposed optimization method based on BO is promising for temperature modulation. [ABSTRACT FROM AUTHOR]

BACKGROUND: Roasting is an important process in the formation of coffee flavor characteristics, which determines the quality of coffee and consumer acceptance. However, the influence of roasting degree on the flavor characteristics of cold brew coffee has not been fully described. RESULTS: In the present study, the flavor characteristics of cold brew coffee with different roasting degrees were compared in detail by using chromatographic and electronic sensory approaches, and the flavor changes induced by freeze‐drying were investigated. Pyrazine and heterocyclic compounds were the main aroma compounds in coffee, and gradually dominated with the increase of roasting. Pyridine was consistently present in cold brew coffees of different roasting degrees and showed significant gradient of quantity accumulation. Aroma compounds such as pyrazine, linalool and furfuryl acetate were the main contributors to coffee roasting, floral and fruity flavor. Freeze‐drying preserved the fruity and floral aromas of medium‐roasted cold brew coffee, whereas reducing the bitterness, astringency and acidity properties that are off‐putting to consumers. CONCLUSION: The higher consumer acceptance and enjoyment in medium roast cold brew coffee may be related to its stronger floral and fruity aroma. The aroma profile qualities of freeze‐drying processed medium roasted cold brewed coffee were more dominant and more suitable for freeze‐drying processing than medium dark roasting. Application of freeze‐drying for cold brew coffee will promote the convenience of drinking. The present study provides valuable technical guidance in improving the flavor and quality of cold brew coffee, and also promotes its commercialization process. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Optical cross-correlation is a technique that can achieve both high specificity and high sensitivity when deployed as the basis for a sensing technology. Offering significant gains in cost, size and complexity, it can also deliver significantly higher signal-to-noise ratios than traditional approaches such as absorption methodologies. In this paper, we present an optical cross-correlation technology constructed around a bespoke customised Fiber Bragg Grating (FBG). Exploiting the remarkable flexibility in design enabled by multiple aperiodic Bragg gratings, optical filters are devised that exactly mimic the absorption features of a target gas species (for this paper, acetylene C 2 H 2 ) over some waveband of interest. This grating forms the heart of the sensor architecture described here that employs modulated optical cross-correlation for gas detection. An experimental demonstration of this approach is presented, and shown to be capable of differentiating between different concentrations of the C 2 H 2 target gas. Furthermore these measurements are shown to be robust against interloper species, with minimal impact on the detection signal-to-noise arising from the introduction of contaminant gases. This represents is a significant step toward the use of customised FBGs as low-cost, compact, and highly customisable photonic devices for deployment in gas detection. [ABSTRACT FROM AUTHOR]

In order to predict the freshness of grass carp, a novel data preprocessing method was proposed for electronic nose (E-nose) signals. The signal sequences from six sensors were selected and subsequently normalized. The direct signal sequence merging (DSSM) and reversed signal sequence merging (RSSM) modes were used for signal sequence merging. Subsequently, the genetic algorithm (GA) was used to evaluate the contribution of diverse sensors, and the merged data sequence was compressed using wavelet transform (WT). Using approximation coefficient and detail coefficient based on different scales and different signal sequence merging modes, principal component analysis (PCA) discriminated successfully storage time of chilled fish fillet. The PCA plots clearly demonstrated that all extracted feature data fully retain the signal characters. The partial least squares (PLS) and artificial neural network (ANN) models were used to establish prediction models for the freshness of grass carp during storage. The DSSM-ANN- A 5 and DSSM-PLS- D 4 models were chosen as the TVB-N content prediction models, while the DSSM-ANN- D 5 and RSSM-PLS- A 0 models were selected as the K value prediction models. The R 2 values of these models are higher than 0.9, and they have a good coefficient of determination. The results of this study suggest that it using E-nose signals to predict TVB-N content and K value is an effective method for assessing the freshness of grass carp during storage. [ABSTRACT FROM AUTHOR]

The electronic nose is a non-invasive technology suitable for the analysis of edible oils. One of the practical applications in the olive oil industry is the classification of virgin oils based on their sensory characteristics. Notwithstanding that this technology, at this stage, cannot realistically replace the currently used methods, it is fruitful for a preliminary analysis of the oil quality. This work makes use of this technology to develop a methodology for the detection of the threshold by which an extra-virgin olive oil (EVOO) drops into the virgin olive oil (VOO) category. With this aim, two features were studied: the level of fruitiness level and the type of defect. The results showed a greater influence of the level of fruitiness than the type of defect in the determination of the detection threshold. Furthermore, three of the sensors (S2, S7 and S9) of the commercial e-nose PEN3 were identified as the most discriminating in the classification between EVOO and VOO oils. [ABSTRACT FROM AUTHOR]

As the most widespread juice produced and consumed globally, citrus juice (mandarin juice, orange juice, and grapefruit juice) is appreciated for its attractive and distinct aroma. While the decrease of characteristic aroma-active compounds and the formation of off-flavor compounds are easy to occur in processing and storage conditions. This review provides a comprehensive literature of recent research and discovery on citrus juice off-flavor, primarily focusing on off-flavor compounds induced during processing and storage (i.e., thermal, storage, light, oxygen, package, fruit maturity, diseases, centrifugal pretreatment, and debittering process), formation pathways (i.e., terpene acid-catalyzed hydration, caramelization reaction, Maillard reaction, Strecker degradation, and other oxidative degradation) of the off-flavor compounds, effective inhibitor pathway to off-flavor (i.e., electrical treatments, high pressure processing, microwave processing, ultrasound processing, and chemical treatment), as well as odor assessment techniques based on molecular sensory science. The possible precursors (terpenes, sulfur-containing amino acids, carbohydrates, carotenoids, vitamins, and phenolic acids) of citrus juice off-flavor are listed and are also proposed. This review intends to unravel the regularities of aroma variations and even off-flavor formation of citrus juice during processing and storage. Future aroma analysis techniques will evolve toward a colorimetric sensor array for odor visualization to obtain a "marker" of off-flavor in citrus juice. (1) Processing and storage can cause the degradation of nutrients in citrus juice and the formation of off-flavor compounds. (2) Terpene degradation products, sulfur-containing compounds, phenols, acids, and furans are contributed to citrus juice off-flavor. (3) Nonthermal techniques such as electrical treatments, high pressure, microwave, and ultrasound processing is beneficial to preservation of the original aroma and sensory qualities of citrus juice. (4) Potential off-flavor compounds (especially trace level) explored by molecular sensory science also significantly impact the aroma of citrus juice. [ABSTRACT FROM AUTHOR]

Transferring the concept of chemical‐driven responses into artificial intelligence technology holds the key to mimicking olfactory for neuromorphic computing of chemical recognition. Currently, artificial olfactory systems are designed based on chemical sensor arrays. Time‐dependent responses of the sensor arrays are processed by artificial neural networks for recognition. However, the sensors generate instantly volatile responses, and algorithms for the processing of the time‐dependent responses have not been involved. The recognition accuracy and speed are severely impeded. A sensor array can only achieve an accuracy of 90% after at least 5 training epochs. Herein an artificial olfactory chemical‐resistant synapse consisting of 3D hierarchical WO3@WO3 nanofibers are demonstrated. The nanofibers exhibit persistent resistance responses through chemical exposures due to the strong chemisorption of water molecules. Typical synaptic behaviors including paired‐pulse facilitation, long‐term −1 short‐term memory, and learning experience have been achieved. Next, a recurrent neural network that is committed to processing the time‐dependent data is used to identify gas‐phase chemicals of 3‐hydroxy‐2‐butanone, triethylamine, and trimethylamine. Training‐free gas recognition has been realized by a WO3@WO3 nanofiber synapse only, in which the accuracy is above 90% at the first epoch. The results have great potential to satisfy stringent performance requirements on artificial perception systems. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the volatile flavor compounds and tastes of six kinds of sauced pork from the southwest and eastern coastal areas of China using gas chromatography–ion mobility spectroscopy (GC-IMS) combined with an electronic nose (E-nose) and electronic tongue (E-tongue). The results showed that the combined use of the E-nose and E-tongue could effectively identify different kinds of sauced pork. A total of 52 volatile flavor compounds were identified, with aldehydes being the main flavor compounds in sauced pork. The relative odor activity value (ROAV) showed that seven key volatile compounds, including 2-methylbutanal, 2-ethyl-3, 5-dimethylpyrazine, 3-octanone, ethyl 3-methylbutanoate, dimethyl disulfide, 2,3-butanedione, and heptane, contributed the most to the flavor of sauced pork (ROAV ≥1). Multivariate data analysis showed that 13 volatile compounds with the variable importance in projection (VIP) values > 1 could be used as flavor markers to distinguish six kinds of sauced pork. Pearson correlation analysis revealed a significant link between the E-nose sensor and alcohols, aldehydes, terpenes, esters, and hetero-cycle compounds. The results of the current study provide insights into the volatile flavor compounds and tastes of sauced pork. Additionally, intelligent sensory technologies can be a promising tool for discriminating different types of sauced pork. [ABSTRACT FROM AUTHOR]

Summary: Recently, there has been growing interest in intentionally incorporating fermentation starters into the traditional brewing process. Our study examined the impact of Saccharomyces cerevisiae and Lactobacillus rhamnosus on the flavour profiles of sweet fermented glutinous rice (SFGR). Saccharomyces cerevisiae obviously increased the ethanol content, whereas Lactobacillus rhamnosus notably raised the total acidity. Lactic acid became the dominant organic acid after the addition of Lactobacillus rhamnosus. The volatiles were analysed using electronic nose (E‐nose) and gas chromatography‐ion mobility spectrometry (GC‐IMS). S4 sensor of E‐nose exhibited the most extensive response to all samples. Saccharomyces cerevisiae increased ethanol and 3‐methylbutan‐1‐ol levels, while Lactobacillus rhamnosus led to a significant increase in acetone. Principal component analysis effectively discriminated the aroma of SFGR with different starters. Additionally, a strong correlation was observed between the E‐nose and GC‐IMS results. Our study shows that Saccharomyces cerevisiae and Lactobacillus rhamnosus can enhance the flavour of fermented rice products. [ABSTRACT FROM AUTHOR]

Fresh-cut fruits were convenient for consumers but vulnerable to quality deterioration, including browning, softening, and volatile aroma loss. In this study, the whole nectarines were pretreated with 100% O2 (pure oxygen, PO) for 2 h and stored at near-freezing temperature (NFT, -1.5 ± 0.1°C) for 9 days after cutting. We investigated the changes of samples in browning degree, enzyme activity, cell membrane permeability, antioxidant activity, and aroma components. Specifically, PO pretreatment increased the peroxidase (POD) and phenylalanine ammonia-lyase (PAL) activities, which endows the whole nectarines with resistance to environmental stresses before fresh-cut. NFT storage effectively inhibited the activity of the polyphenol oxidase (PPO) and the increase of the total phenolic content (TPC). Higher antioxidant levels and anti-browning effects were observed in fresh-cut nectarines treated with PO + NFT, which demonstrated by higher levels of 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging capacity, ferricion reducing antioxidant power (FRAP), and enzyme activity ratios (ratio 1 and ratio 2). During the storage of 9 days, the firmness and membrane permeability were preserved, malondialdehyde (MDA) content was suppressed, which delayed the softening of nectarine tissue. Furthermore, soluble solids content (SSC) and titratable acid (TA) were also well preserved by PO + NFT, which maintained the physiological and metabolic qualities. Electronic nose tests revealed that PO + NFT postponed the volatile aroma deterioration. In conclusion, PO + NFT effectively maintains the storage quality of fresh-cut nectarines. [ABSTRACT FROM AUTHOR]

At present, the electronic nose has became a new technology for the rapid detection of pesticides. However, the technique may misidentify them for samples that have not been involved in training. Therefore, a hybrid model based on unsupervised and supervised learning was proposed for the first time in this paper. The model divided the detection process of soil pesticide residues into two steps: (1) an unsupervised machine learning method was used to identify whether the soil was contaminated with pesticides; (2) when the soil was contaminated with pesticides, a supervised classifier was further used to predict the types of pesticides in the soil. The experimental results showed that the model had a recognition accuracy of 99.3% and 99.27% for whether the soil was contaminated with pesticides and the pesticide type of the contaminated soil, respectively, with a detection time of 0.03 s. The results revealed that the proposed hybrid model can quickly and comprehensively reflect the soil information's status. [ABSTRACT FROM AUTHOR]

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

This study was focused on the analysis of the emission of volatile compounds as an indicator of changes in the quality degradation of corn groats with 14% and 17% moisture content (wet basis) using an electronic nose (Agrinose) at changing vertical pressure values. The corn groats were used in this study in an unconsolidated state of 0 kPa (the upper free layer of bulk material in the silo) and under a consolidation pressure of 40 kPa (approximately 3 m from the upper layer towards the bottom of the silo) and 80 kPa (approximately 6 m from the upper layer towards the bottom of the silo). The consolidation pressures corresponded to the vertical pressures acting on the layers of the bulk material bed in medium-slender and low silos. Chromatographic determinations of volatile organic compounds were performed as reference tests. The investigations confirmed the correlation of the electronic nose response with the quality degradation of the groats as a function of storage time. An important conclusion supported by the research results is that, based on the determined levels of intensity of volatile compound emission, the electronic nose is able to distinguish the individual layers of the bulk material bed undergoing different degrees of quality degradation. [ABSTRACT FROM AUTHOR]

BACKGROUND: Lactic acid bacteria (LABs) are widely present in foods and affect the flavour of fermented cultures. This study investigates the effects of fermentation with Lactobacillus acidophilus JYLA-16 (La), Lactobacillus plantarum JYLP-375 (Lp), and Lactobacillus rhamnosus JYLR-005 (Lr) on the flavour profile of blueberry juice. RESULTS: This study showed that all LABs strains preferentially used glucose rather than fructose as the carbon source during fermentation. Lactic acid was the main fermentation product, reaching 7.76 g L-1 in La-fermented blueberry juice, 5.86 g L-1 in Lp-fermented blueberry juice, and 6.41 g L-1 in Lr-fermented blueberry juice. These strains extensively metabolized quinic acid, whereas oxalic acid metabolism was almost unaffected. Sixty-four volatile compounds were identified using gas chromatography--ion mobility spectrometry (GC--IMS). All fermented blueberry juices exhibited decreased aldehyde levels. Furthermore, fermentation with La was dominated by alcohols, Lp was dominated by esters, and Lr was dominated by ketones. Linear discriminant analysis of the electronic nose and principal component analysis of the GC--IMS data effectively differentiated between unfermented and fermented blueberry juices. CONCLUSION: This study informs LABs selection for producing desirable flavours in fermented blueberry juice and provides a theoretical framework for flavour detection. [ABSTRACT FROM AUTHOR]

Gas detection plays different roles in different environments. Traditional algorithms implemented on electronic nose for gas detection and recognition have high complexity and cannot resist device drift. In response to the above issues, we propose a convolutional neural network based on memristive Stochastic Computing (SC), which combines the characteristics of small devices and low power consumption of memristor devices, as well as the fast and fault-tolerant random calculation speed. It can effectively utilize hardware advantages, recognizing gases by electronic nose. The experimental results show that for two different gas sensor array datasets, the accuracy of the proposed method can achieve the level of 99%. When using memristive SC for deduction, the accuracy decreases by less than 1%, but in drift data, the accuracy can be improved by about 3%. Finally, the improvement in area, power, and energy compared to inference in GPU (NVIDIA Geforce RTX 3060 Laptop) is 1104X, 48X, and 9X, respectively. [ABSTRACT FROM AUTHOR]

BACKGROUND: Botrytis cinerea (Bc) is the causative agent of gray mold disease in wine grape bunches. Under particular climatic and edaphic conditions, typical of some wine regions, the grapes infected by this fungus can develop noble rot, the basic phenomenon for the production of sweet botrytized wines or some high‐quality dry wines, such as Amarone. The possibility of early detection of noble rot on plants and at postharvest is an interesting option for managing botrytized wines. RESULTS: The present work aimed at early detection of noble rot and monitoring its development, at postharvest, on Trebbiano wine grapes by means of destructive and non‐destructive analytical approaches (e.g., electronic nose and near‐infrared spectroscopy). The development of Bc led to substantial modifications in grape composition, including dehydration, biosynthesis, and accumulation of different compounds due to Bc metabolism, grape stress responses, or both. However, these modifications are appreciable, notably at advanced stages of infection. Consequently, a specific focus was to monitor the infection in the first 72 h post inoculation for testing, potentially through non‐destructive technologies, and to identify the real early stages of Bc development. CONCLUSION: The destructive chemical analyses performed over the 16 monitored days confirmed what is widely reported in the literature regarding the metabolic/compositional changes that occur following the development of Bc. Moreover, non‐destructive technologies allowed us to identify the evolution of Bc, even at early stages of its presence. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Death is a multifaceted process wherein each individual cell and tissue has a metabolic homeostasis and a time of functional cessation defined by the dying process as well as by intrinsic and extrinsic factors. Decomposition is physiologically associated with the release of different types of volatile organic compounds (VOCs), and these form volaboloma mortis. The main purpose of this study was to record the volabolomic fingerprint produced by volatile molecules during the physiological decomposition process of human tissue and muscle cells. The volatile chemical signature has important implications for an open issue in forensics and pathology, namely the estimation of the postmortem interval (PMI), which decreases in accuracy with the passage of time. Volatile metabolites emitted from human tissues and muscle cells at 0, 24, 48, and 72 h were recorded in real time with an electronic nose sensor device. The key findings were the continuous sampling of VOCs emitted from tissues and cells. These showed a common behavior as time progressed; particularly, after 48 h the distributions became dispersed, and after 72 h they became more variable. Volabolomic fingerprinting associated with time progression relevant to the study of PMIs was reconstructed. Additionally, there may be broader applications, such as in dog training procedures for detecting human remains, and perhaps even for studying scavenger and insect attractants. [ABSTRACT FROM AUTHOR]

Milk fat is a premium nutritional health product, yet there is a lack of high-fat dairy products for daily consumption in the current market. This study investigated the influence of different milk fat contents on the physicochemical and textural properties of fermented milk. The research revealed that an increase in milkfat content significantly improved the water-holding capacity, syneresis, color, hardness, springiness, gumminess, and chewiness of fermented milk, while showing minimal changes in pH and total titratable acidity. Response surface analysis indicated that fermented milk with 25% milk fat, 2.5% inoculum, a fermentation time of 16 h, and a fermentation temperature of 30 °C exhibited the highest overall acceptability. Using GC-IMS technology, 36 volatile compounds were identified, with an increase in milk fat content leading to elevated levels of ketone compounds, and 14 compounds were defined as key aroma compounds (ROAV > 1). Electronic nose distinguished samples with different milk fat contents. The results demonstrate that an increase in milk fat content enhances the physicochemical and flavor attributes of fermented milk. This work provides theoretical references for the production and development of high-fat fermented milk. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is the eighth most common cancer in women. Since screening programs do not exist, it is often diagnosed in advanced stages. Today, the detection of OC is based on clinical examination, transvaginal ultrasound (US), and serum biomarker (Carbohydrate Antigen 125 (CA 125) and Human Epididymis Protein 4 (HE4)) dosage, with a sensitivity of 88% and 95%, respectively, and a specificity of 84% for US and 76% for biomarkers. These methods are clearly not enough, and OC in its early stages is often missed. Many scientists have recently focused their attention on volatile organic compounds (VOCs). These are gaseous molecules, found in the breath, that could provide interesting information on several diseases, including solid tumors. To detect VOCs, an electronic nose was invented by a group of researchers. A similar device, the e-tongue, was later created to detect specific molecules in liquids. For the first time in the literature, we investigated the potential use of the electronic nose and the electronic tongue to detect ovarian cancer not just from breath but also from urine, blood, and plasma samples. [ABSTRACT FROM AUTHOR]

As a traditional Chinese medicine with the dual use of medicine and food, vine tea has been utilized for centuries as a daily health beverage due to its various health benefits. The primary component responsible for its biological activity is its total flavonoid content. While numerous studies have been conducted on the extracts of vine tea, research has primarily centered on its ingredients and effects, with limited focus on its sensory characteristics. As it has the problems of the undesirable flavor of bitterness and astringency, the development of the vine tea industry is limited. Furthermore, there is currently no standardized brewing process for vine tea. Therefore, it is essential to find a brewing method that is universally applicable and balances a high level of active ingredients with a rich sensory experience. In this study, we conducted a comprehensive investigation into the impact of various water qualities, tea-water ratios, brewing temperatures, steeping times, and frequencies on the total flavonoid content. To investigate the differences between samples from different brewing methods, one-factor experiments (n = 20) and orthogonal experiments were conducted. The total flavonoid content was measured using UV spectrophotometry. To thoroughly examine the flavor compounds, we employed mathematical relationship analysis, radar-gram analysis, principal component analysis (PCA), and odor fingerprinting. By integrating these methods with the electronic tongue and electronic nose, we were able to compare the flavor variances between samples and offer a concise analysis of the flavor compounds. The brewing process is influenced by several factors, with the tea-to-water ratio having the most significant impact, followed by brewing temperature and then brewing time. Electronic tongues and electronic noses can distinguish vine tea samples with different brewing processes to a certain extent. The optimal brewing conditions determined in this study were pure water, a 1 : 50 tea-to-water ratio, an 85°C brewing temperature, an 8-minute brewing time, and a single brew cycle. These conditions produced the highest total flavonoid content in the vine tea, at 326.8 mg/g, and the best flavor quality. This study offers novel approaches to standardize the brewing process of vine tea, along with valuable perspectives for enhancing the sensory attributes of the tea. It can serve as a benchmark for the industry's future growth in the canning of vine tea, facilitating the creation of diverse vine tea drinks. [ABSTRACT FROM AUTHOR]

Food is one of the most necessary needs since human civilization. For Muslims, it is mandatory to consume halal food. From a halal authentication perspective, adulteration of food products is an emerging challenge worldwide. The demand for halal food consumption has resulted in an ever‐increasing need for halal product validity. In the market, there are several food products in which actual ingredients and their source are not mentioned on the label and cannot be observed by the naked eye. Commonly nonhalal items include pig derivatives like lard, pork, and gelatin derivatives, dead meats, alcohol, blood, and prohibited animals. Purposely, various conventional and modern methods offer precise approaches to ensure the halalness and wholesomeness of food products. Conventional methods are physiochemical (dielectric) and electrophoresis. At the same time, modern techniques include high‐pressure liquid chromatography (HPLC), gas chromatography (GC), electronic nose (E‐Nose), polymerase chain reaction (PCR), enzyme‐linked immunosorbent assay (ELISA), differential scanning calorimetry (DSC), nuclear magnetic resonance (NMR), near‐infrared (NIR) spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. This review intends to give an extensive and updated overview of conventional and modern analytical methods for ensuring food halal authenticity. [ABSTRACT FROM AUTHOR]

The rise in global travel has led to an increased need for heightened security measures at airports. Despite the best efforts of airport security officers, in the past year, hundreds of kilograms of illegal drugs and thousands of agricultural invasive species have found their way into the country, posing a severe threat to public safety and the environment. Moreover, human threats pose a significant risk to civil aviation, reinforcing the need for advanced security technology. In response to these challenges, NOSI (Novel Odor Sensing Intelligence) and ROSI (Reconnaissance Operations Security Intelligence), intelligence surveillance systems consisting of semi-autonomous controller-responder robots, were developed as a proof of concept to supplement the efforts of security and K-9 (police dogs) operators at airports. NOSI is equipped with multi-channel gas sensors for odor detection, enabling it to identify illegal drugs and invasive species in the baggage handling process, while ROSI is equipped with computer vision to identify individuals already in the government's database of persons of interest. These coordinated robots also provide travelers with important information pertaining to their journey and allow them to trigger emergency alerts. The robots were tested in a custom-designed test bed that replicated both the behind-the-scenes baggage handling and front-office customer service operations of an airport, thus simulating a realistic airport-like setting. Based on design criteria, NOSI and ROSI demonstrated success rates of 73.4 percent and 69.8 percent, respectively. Improvements in areas of robot stability, sensor accuracy, and feature expansion were documented for further development. In conclusion, the NOSI and ROSI framework can enhance the efficiency and accuracy of airport infrastructure monitoring and supplement the capabilities of human and K9 operators. Overall, this approach can potentially revolutionize operations in various infrastructures and represents the future of human-robot collaboration. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine the differences of aroma and taste in three black sesame originsbefore and after processing via flavor and widely metabolomics. By analyzing the sensory characteristics and metabolites of raw and treated black sesame from China, Vietnam, and Myanmar, treated Chinese sesame have the most significant change in hardness after thermal processing, low viscosity and was easy to chew. The electronic nose could distinguish between raw and treated sesame due to the aroma distribution. The reason of treated sesame from China was "fragrant" is due to the highest content (2545.50 µg/kg) of total pyrazines including 2,5-dimethylpyrazine, 2-ethyl-5-methylpyrazine, 2,3,5-trimethylpyrazine, 3-ethyl-2,5-dimethylpyrazine. 933 metabolites were detected via a wide targeted metabolomics in the taste of raw and treated sesame. Based on the analysis of metabolites related to bitterness, 145 substances were selected. The main bitter contributors may be amino acids, dipeptides and organic acids. [ABSTRACT FROM AUTHOR]

In areas where livestock are bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. However, existing electronic nose systems have slow response times and limited detection accuracy. In this study, we introduce a novel solution: the bionic chamber construction of the electronic nose is optimized, and the sensor response data in the chamber are analyzed using an intelligent algorithm. We analyze the structure of the biomimetic chamber and the surface airflow of the sensor array to determine the sensing units of the system. The system employs an electronic nose to detect ammonia and ethanol gases in a circulating airflow within a closed box. The captured signals are processed, followed by the application of classification and regression models for data prediction. Our results suggest that the system, leveraging the biomimetic chamber, offers rapid gas detection response times. A high classification prediction accuracy, with a determination coefficient R2 value of 0.99 for single-output regression and over 0.98 for multi-output regression predictions, is achieved by incorporating a backpropagation (BP) neural network algorithm. These outcomes demonstrate the effectiveness of the electronic nose, based on an optimized bionic chamber combined with a BP neural network algorithm, in accurately detecting ammonia emitted during livestock excreta fermentation, satisfying the ammonia detection requirements of breeding farms. [ABSTRACT FROM AUTHOR]

As highly promising devices for odor recognition, current electronic noses are still not comparable to human olfaction due to the significant disparity in the number of gas sensors versus human olfactory receptors. Inspired by the sniffing skills of wine tasters to achieve better odor perception, a multiple overlapping sniffs (MOSS) strategy is proposed in this study. The MOSS strategy involves rapid and continuous inhalation of odorants to stimulate the sensor array to generate feature‐rich temporal signals. Computational fluid dynamics simulations are performed to reveal the mechanism of complex dynamic flows affecting transient responses. The proposed strategy shows over 95% accuracy in the recognition experiments of three gaseous alkanes and six liquors. Results demonstrate that the MOSS strategy can accurately and easily recognize odors with a limited sensor number. The proposed strategy has potential applications in various odor recognition scenarios, such as medical diagnosis, food quality assessment, and environmental surveillance. [ABSTRACT FROM AUTHOR]

Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the original classification algorithm. In order to make the probability distributions of the drifted data and the regular data consistent, we introduce the Conditional Adversarial Domain Adaptation Network (CDAN)+ Sharpness Aware Minimization (SAM) optimizer—a state-of-the-art deep transfer learning method.The core approach involves the construction of feature extractors and domain discriminators designed to extract shared features from both drift and clean data. These extracted features are subsequently input into a classifier, thereby amplifying the overall model's generalization capabilities. The method boasts three key advantages: (1) Implementation of semi-supervised learning, thereby negating the necessity for labels on drift data. (2) Unlike conventional deep transfer learning methods such as the Domain-adversarial Neural Network (DANN) and Wasserstein Domain-adversarial Neural Network (WDANN), it accommodates inter-class correlations. (3) It exhibits enhanced ease of training and convergence compared to traditional deep transfer learning networks. Through rigorous experimentation on two publicly available datasets, we substantiate the efficiency and effectiveness of our proposed anti-drift methodology when juxtaposed with state-of-the-art techniques. [ABSTRACT FROM AUTHOR]