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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]

Deep learning algorithms are widely used for pattern recognition in electronic noses, which are sensor arrays for gas mixtures. One of the challenges of using electronic noses is sensor drift, which can degrade the accuracy of the system over time, even if it is initially trained to accurately estimate concentrations from sensor data. In this paper, an effective drift compensation method is introduced that adds sensor drift information during training of a neural network that estimates gas concentrations. This is achieved by concatenating a calibration feature vector with sensor data and using this as an input to the neural network. The calibration feature vector is generated via a masked-autoencoder-based feature extractor trained with transfer samples, and acts as a prompt to convey sensor drift information. Our method is tested on a 3-year gas sensor array drift dataset, showing that a neural network using our method performs better than other models, including a network with additional fine tuning, demonstrating that our method is efficient at compensating for sensor drift. In this study, the effectiveness of using prompts for network training is confirmed, which better compensates for drifts in new sensor signals than network fine-tuning. [ABSTRACT FROM AUTHOR]

This study aims to investigate the relationship between the grades of Tuo tea and the quality of compounds. A combination of artificial sensory evaluation, intelligent sensory technologies (electronic nose and electronic tongue), gas chromatography–mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), chemical–physical analysis, and multivariate statistical analysis were employed to examine the differences among three grades of Tuo tea (SG, 1G, and 2G). The results of artificial sensory evaluation, electronic tongue, and electronic nose revealed that the aroma and taste of different grades of Tuo tea varied greatly. A total of 112 volatile compounds and 44 non-volatile compounds were identified. In order to elucidate the key components that cause differences in the quality of Tuo tea, 2 partial least squares discriminant analysis (PLS-DA) models with excellent parameters (volatile, R2Y = 0.999 and Q2 = 0.996; non-volatile, R2Y = 0.992 and Q2 = 0.972) were established. A total of 80 key differential volatile compounds were identified with the double selection criterion of variable importance in projection (VIP) greater than 1 and p < 0.05. Among these, 43 compounds with OAV > 1 were further identified as the odor-active compounds in all three grades of Tuo. Moreover, 22 key non-volatile compounds that contribute to the quality differences have been screened out. This investigation implied that the volatile and non-volatile compounds of Tuo tea could serve as indicators of its quality. The results provided a new approach to distinguish the grades of Tuo tea. [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]

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 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]

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]

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]

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]

In this paper, we extend the concept of a generalized Parikh vector of the partial word known as e--generalized Parikh vector, and its related properties are studied. We also introduce the e--generalized Parikh matrix of the partial word and provide its characterization theorem. Further, we discuss the algebraic properties of partial words in terms of e--generalized Parikh matrix. In addition, we define partial line languages and confer their properties concerning e--generalized Parikh vector of partial words. [ABSTRACT FROM AUTHOR]

This paper presents the high performance based one dimension image filter. Image convolution has been widely used in applications followed by image de-nosing filters, feature extraction and computer vision. Realization of two dimensional(2D) convolution based image filter requires massive computational requirements. FPGA based 2D image filter implementation is one of the most challenging task, more computationally exhaustive due to requirement of large number of multiplications and addition operations. The 2D-filter implementation requires extra memory bandwidth for eliminating Gaussian noise. The proposed one dimension (1D) image filter technique reduces the memory access rate by using pixel reuse. Therefore, it improves the performance as well as flexibility. The novelty of the proposed image de-noising filter is hardware reusability, pixels reuse mechanism in accordance with intermediate data shared for consecutive Multiply and Accumulate (MAC) operations. The optimized Reconfigurable Common Subexpression Elimination (RCSE) based MAC diminishes the area in terms of number of multiplications and adder operations of the filter. The 1D-filter approach uses less number of clock cycles and eliminates the data dependencies among neighborhood pixels. In comparison of state-of the art MAC structures, the proposed latency-hiding MAC design achieves optimal delay of 3.396 ns. An evaluation results show that, the design can achieve 9 times better performance and reduces the 40% of area. The power dissipation of the proposed 1D convolution based image filter architecture is 158mw. [ABSTRACT FROM AUTHOR]

The effect of irradiation sterilization on the quality of low-salt sliced bacon was investigated by subjecting the samples to high-energy electron gas pedal treatment at 2, 3 and 4 kGy at room temperature. The samples were stored at 4 °C and the unsterilized group was used as the control group. The total number of colonies of the samples, flavor substances and the physicochemical indexes were determined when the storage time was 0, 60, 120 and 180 days, respectively. The results showed that the nitrite content of low-salt sliced bacon decreased significantly (P<0.05), the a* and peroxide value (POV) values increased with the increase of irradiation dose under the same conditions. At 180 d, the moisture content and b* values of the irradiated group were significantly higher than those of the control group (P<0.05), the L* and a* values were not significantly different from those of the control group (P>0.05), and the POV values were significantly lower than those of the control group (P<0.05). During the storage stage, the total bacterial colony values of the irradiated group increased slowly and were always lower than those of the control group, and the content of unpleasant substances such as dimethyl disulfide increased, and the flavor differences with the control group increased, resulting in a decrease in aroma scores. The experimental results of gas chromatography-mass spectrometry (GC-MS) and electronic nose showed that the irradiated offflavors decreased with the increase of storage time, the content of major flavor substances increased, and the flavor differences with the control group decreased. Overall, 2, 3 kGy-treated bacon samples had lower fat oxidation and the flavor difference between irradiated and control was smaller and the off-flavor was relatively weak. The moisture content was higher and the flavor was better after storage. However, in terms of bactericidal effect and texture, the 3 kGy group was slightly better than the 2 kGy group, with stronger bactericidal effect and slightly higher tissue structure score. Therefore the optimal irradiation was determined to be 3 kGy in this study. [ABSTRACT FROM AUTHOR]

This study evaluated the potential of electronic nose (e-nose) and electrochemical fingerprinting for identifying and discriminating between four commercial braising seasoning packets (Huachang, Jideli, Chuanzhen, Gusong) applied to chicken meat samples. E-nose analysis of marinated chicken aromas showed divergent sensor resistance profiles reflecting variations in volatile compounds between seasoning formulations. Principal component analysis of e-nose data differentiated samples into two distinct groups, with Chuanzhen and Gusong clustered separately from Huachang and Jideli. Differential pulse voltammetry also discriminated samples based on phytochemical fingerprints. Chuanzhen and Gusong chicken exhibited higher oxidation peaks indicating greater transfer of phenolic antioxidants from spices. Gas chromatography-mass spectrometry quantified key aromatic compounds in seasoning packets, revealing higher levels of sulfur, nitrogen, and phenolic volatiles in Chuanzhen and Gusong. Multivariate models accurately predicted seasoning identities from analytical data. This demonstrates the utility of rapid global aroma and electrochemical profiling combined with targeted GC-MS analysis to characterize complex food formulations and detect differences based on spice compositions. [ABSTRACT FROM AUTHOR]

Consumer purchasing of beef is often driven by the trinity of flavor, palatability, and convenience. Currently, beef patties in the United States are manufactured with fat and lean trimmings derived from skeletal muscles. A reduction in total beef supply may require the use of animal by-product utilization such as variety meats to achieve patty formulations. The current study aimed to assess textural, color, and flavor characteristics in addition to volatile compounds through electronic technology, e-nose and e-tongue, of ground beef patties formulated with beef heart. Ground beef patties were manufactured with 0%, 6%, 12%, or 18% beef heart, with the remainder of the meat block being shoulder clod-derived ground beef. Patties (n = 65/batch/treatment) within each batch (n = 3) with each treatment were randomly allocated to cooked color (n = 17/batch/treatment), Allo–Kramer shear force (AKSF; n = 17/batch/treatment), texture profile analysis (TPA; n = 6/batch/treatment), cooking loss (n = 17/batch/treatment), consumer panel (n = 3/batch/treatment), e-nose (n = 1/batch/treatment), and e-tongue (n = 1/batch/treatment) analysis groups. Patties containing beef heart did not require additional cooking time (p = 0.1325) nor exhibit greater cooking loss (p = 0.0803). Additionally, inclusion rates of beef heart increased hardness (p = 0.0030) and chewiness values (p = 0.0316) in TPA, were internally redder (p = 0.0001), and reduced overall liking by consumer panelists (p = 0.0367). Lastly, patties containing beef heart exhibited greater red-to-brown (p = 0.0003) and hue angle (p = 0.0001) values than control patties. The results suggest that beef heart inclusion does alter ground beef quality characteristics and consumer acceptability. [ABSTRACT FROM AUTHOR]

Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography–mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection. [ABSTRACT FROM AUTHOR]

The aim of this research was to apply an electronic device as indirect predictive technology to evaluate toxic chemical compounds in roasted espresso coffee. Fresh coffee beans were subjected to different thermal treatments and analyzed to determine volatile organic compounds, content of acrylamide and 5-hydroxymethylfurfural, sensory characteristics and electronic nose data. In total, 70 different volatile compounds were detected and grouped into 15 chemical families. The greatest percentage of these compounds were furans, pyrazines, pyridines and aldehydes. The positive aroma detected had the intensity of coffee odor and a roasted aroma, whereas the negative aroma was related to a burnt smell. A linear relationship between the toxic substances and the sensory defect was established. A high sensory defect implied a lower content of acrylamide and a higher content of 5-hydroxymethylfurfural. Finally, electronic signals were also correlated with the sensory defect. This relationship allowed us to predict the presence of these contaminants in the roasted coffee beverage with an indirect method by using this electronic device. Thus, this device may be useful to indirectly evaluate the chemical contaminants in coffee beverages according to their sensory characteristics. [ABSTRACT FROM AUTHOR]

Aroma is an indispensable factor that substantially impacts the quality assessment of black tea. This study aims to uncover the dynamic alterations in the sweet and floral aroma black tea (SFABT) throughout various manufacturing stages using a comprehensive analytical approach integrating gas chromatography electronic nose, gas chromatography–ion mobility spectrometry (GC-IMS), and gas chromatography–mass spectrometry (GC-MS). Notable alterations in volatile components were discerned during processing, predominantly during the rolling stage. A total of 59 typical volatile compounds were identified through GC-IMS, whereas 106 volatile components were recognized via GC-MS throughout the entire manufacturing process. Among them, 14 volatile compounds, such as linalool, β-ionone, dimethyl sulfide, and 1-octen-3-ol, stood out as characteristic components responsible for SFABT with relative odor activity values exceeding one. This study serves as an invaluable theoretical platform for strategic controllable processing of superior-quality black tea. [ABSTRACT FROM AUTHOR]

To investigate the impact of various cooking methods on the volatile aroma compounds of button mushroom, gas chromatography-mass spectrometry (GC-MS) and electronic nose (E-nose) were utilized for aroma analysis. The results indicated that the E-nose was able to effectively distinguish between the samples prepared using different cooking methods. In the raw, steamed, boiled and baked samples, 37, 23, 33 and 35 volatiles were detected, respectively. The roasting process significantly contributed to the production of flavor compounds, giving button mushroom its distinctive flavor. Sixteen differential aromas were identified based on the p-value and VIP value. Additionally, the cluster analysis of differential aroma substances revealed a stronger odor similarity between the steamed and raw groups, consistent with the results of the OPLS-DA analysis of overall aroma components. Seven key aromas were identified through OAV analysis and omission experiments. In addition, 1-octen-3-one was identified as the main aroma component of cooked button mushroom. The findings of the study can be valuable for enhancing the flavor of cooked button mushroom. [ABSTRACT FROM AUTHOR]

This paper aims to provide a large coverage of recent developments regarding environmental monitoring using metal oxide-based sensors. Particular attention is given to the detection of gases such as H2, COx, SOx, NOx, and CH4. The developments and analyses of the design of sensors and types of metal oxide sensing materials are emphasized. The sensing mechanisms and peculiarities of metal oxides used in chemoresistive sensors are provided. The main parameters that affect the sensitivity and selectivity of metal oxide sensors are indicated and their significance to the sensor signal is analyzed. Modern data processing algorithms, employed to optimize the measurement process and processing of the sensor signal, are considered. The existing sensor arrays/e-nose systems for environmental monitoring are summarized, and future prospects and challenges encountered with metal oxide-based sensor arrays are highlighted. [ABSTRACT FROM AUTHOR]

China is a large consumer of meat and meat products. People's daily diets include a variety of meat, but meat food adulteration problems are common. This paper discusses the research progress of the electronic nose and near-infrared spectroscopy in the field of meat adulteration detection. Through the study of dozens of related papers in recent years, it has been found that the use of the electronic nose and near-infrared spectroscopy for meat detection has the advantages of speed, a nondestructive nature, high sensitivity, strong quantitative analysis, high automation, a wide applicability, an improved product quality, and cost reduction over the traditional detection, but it may be limited in detecting the adulteration of a specific meat, and there are issues with the life and stability of the sensors of the electronic nose in the process of detection, along with the problems of the high requirements for the modeling of the data of near-infrared spectroscopy. This paper takes adulterated meat as the research object and briefly summarizes the detection principles of the electronic nose and near-infrared spectroscopy, as well as the types of sensors applied in the electronic nose. The research progress of the electronic nose and near-infrared detection technology in meat adulteration assessment is reviewed, the advantages and disadvantages of the two in practical application are analyzed, the classification of pattern recognition methods and their applications in meat identification are described, and the feasibility and practical significance of the joint application of the two in meat adulteration detection are envisioned. Meanwhile, the challenges faced by the two in meat detection are pointed out. [ABSTRACT FROM AUTHOR]

A rapid and efficient technique was applied, which used an electronic nose based on a mass detector (MS-based e-nose) combined with headspace solid-phase microextraction sampling and chemometric tools to classify beer samples between fresh and aged and between samples contained in aluminium cans or glass bottles, and to predict the shelf life of beer. The mass spectra obtained from the MS-based e-nose contained details about volatile compounds and were recorded as the abundance of each ion at different mass-to-charge (m/z) ratios. The analysis was performed on 53 naturally aged samples for eleven months without light and with a controlled temperature of around 14 °C ± 0.5 °C. Principal component analysis (PCA) was performed on the data and showed a grouping of samples between fresh and aged. Partial least square discriminant analysis (PLS-DA) allowed the discrimination of fresh from aged beers but could not discriminate between the samples according to the type of packaging. Finally, partial least squares regression (PLSR) proved to be an effective method for predicting beer shelf life. [ABSTRACT FROM AUTHOR]

Fresh-cut apples are commonly known as minimally processed agricultural products because of their convenience and ease of consumption. However, during storage, the quality of the apple rapidly changes after cutting due to enzymatic and non-enzymatic processes. This study aimed to monitor the quality changes and volatile compounds in fresh-cut apples at various temperatures using an electronic nose. The quality relationships of the product during distribution and storage using cold chain systems were also evaluated. The results showed that the total viable count initially differed between fresh-cut apples (2.59 Log CFU/g) and mixed fruits (apple 'Hongro', kumquat, and cherry tomatoes) (3.2 Log CFU/g) during the storage period (p < 0.05). There were no significant differences (p > 0.05) in the physicochemical properties except for the firmness, color values, browning index, whiteness index, and titratable acidity. The volatile compounds found in fresh-cut apples indicating apple fruit aroma were propyl propanoate and ethyl isovalerate, hexanol (freshness), and methanethiol and ethyl acetate (unpleasant off-odor), and these compounds could be used as markers for the deterioration process in fresh-cut apples during storage. Methanethiol and ethyl acetate were correlated with microbial growth (Pearson correlation of 0.81–0.98 for total viable microbe and 0.49–0.90 for coliform count). The limonene level was higher in the mixed fruits than in other treatments and gradually increased during storage due to the kumquat. [ABSTRACT FROM AUTHOR]

The aim of this research was to investigate the influence of flow pattern of the gas carrier inside the electronic nose chamber on the response rate of the signal from an array of gas sensors. The gas sensors in the electronic nose chamber were tested with laminar flow and turbulent flow. The principles of Reynolds number and the Navier-Stokes equation were employed to calculate and model the airflow in Computational Fluid Dynamics (CFD) simulations. The simulated airflow was compared to the actual airflow using smoke as a visual indicator to indicate the type of flow. Variables and conditions derived from the flow patterns were used in an actual experiment of electronic nose to detect specific odor compounds in brown rice (cv. KDML105). The six sensors were installed in an electronic nose chamber. The signals from the experiment were then used to determine the most effective sensor response between laminar and turbulent flow. Significantly different results were observed between the two flow patterns with a p-value < 0.05 for five out of six sensors. Additionally, the analysis of the rate of signal change indicated that the laminar flow pattern had higher values compared to the turbulent flow pattern. [ABSTRACT FROM AUTHOR]

Exhaled breath analysis comprises chemical compounds that can be utilized for diagnostic purposes, including asthma detection. An electronic nose can be offered as a means of monitoring patient circumstances. A significant problem often occurs when determining the appropriate number of gas sensors while maintaining high accuracy. The firefly algorithm (FA) is very effective because of its exploratory capabilities, presents theories that are easy to understand and has relatively fewer parameters. This study aims to reduce and determine the appropriate number of gas sensors for an electronic nose in differentiating healthy and asthmatic subjects using the FA and exhaled breath analysis. The experimental results indicate that the FA provides only four gas sensors that still maintain high performance. The convolutional neural network model was favored for its ability to classify the entire asthma dataset, making it the best machine learning model for the electronic nose, with an accuracy of 97.8%. [ABSTRACT FROM AUTHOR]

Forest ecosystems are long-term exposed to dry periods in Europe, which leads to a significant loss of vitality and higher mortality, especially in coniferous forests. Identifying stress in the early stages when measures can be taken to protect the forest and living trees is crucial. Current detection methods are based on field surveys by forest workers or remote sensing methods to cover larger areas, which use changes in spectral reflectance of the forest canopy. In some cases, the attacked trees do not change their appearance, and based on calculations of vegetation indices from remote sensing data, the attack cannot be mapped. We present an innovative methodology based on non-optical analysis, namely identifying a group of volatile compounds and microclimate signs in forest stands that indicate stress factors in forest stands. An attacked tree by a bark beetle produces increased amounts of biogenic volatile organic compounds associated with defense, and the microclimate changes due to interrupted transpiration. In addition, the bark beetle uses the aggregation pheromone to attract more individuals and to attack the tree massively. In this study, we tested three electronic noses (Miniature Bosch sensor device with 25,419 samples, Sensory device for environmental applications with 193 samples, Handheld VOC Detector Tiger with 170 samples) in a freshly infested spruce stand. The measurement was conducted at ground level with the help of a human operator and was repeated six times to verify the detection capability of the electronic noses. To verify the capability of electronic noses to predict tree infestation, we used machine learning Random Forest. The results demonstrated that electronic noses can detect bark beetle infestation start (within 1 week of the first attack). The Miniature Bosch sensor device achieved the highest accuracy with a value of 95%, in distinguishing forest sections that are healthy and infested; the second most accurate electronic nose is the Sensory device for environmental applications, with an accuracy of 89%. Our proposed methodology could be used to detect bark beetle presence.

The article presents a study which evaluated whether exhaled breath analysis using eNose technology can differentiate between patients who have systemic sclerosis (SSc) with and without interstitial lung disease (ILD). Topics discussed include baseline characteristics of the patients, a major challenge in patients with SSc, and weaknesses of the study.

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

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]

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection. [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]

The development of mobile technology has facilitated the widespread adoption of digital currency worldwide. An important application of mobile technology is digital currency, which has gained global popularity due to its blockchain and peer-to-peer mobile payment mechanisms. The emergence of private digital currencies, such as Bitcoin, poses a challenge to the monetary policy of governments. To address this threat, many central banks are developing their own central bank digital currencies (CBDCs) with legal support. China's CBDC, e-CNY (electronic China Yuan), is one of the most advanced examples of this innovation. However, the adoption and usage of e-CNY by residents is still limited and requires improvement. This paper aims to identify and analyze the factors that influence residents' willingness to use e-CNY, based on a literature review and a logistic regression model. The results indicate that residents' awareness, convenience, cost, promotional activities, and the involvement of relevant parties (merchants, commercial banks, payment institutions, and the People's Bank of China (PBC)) significantly influence their willingness to use e-CNY. This paper presents implications and suggestions for promoting and implementing e-CNY in China. [ABSTRACT FROM AUTHOR]

The odor of alfalfa (Medicago sativa L.), the most extensively cultivated forage, can interfere with livestock feeding. This study used an electronic nose in conjunction with gas chromatography–mass spectrometry (GC–MS) to examine the quality, volatile organic compounds (VOCs), and metabolic pathways of alfalfa at budding (X), early flowering (C), and full flowering (S) stages. Results showed that terpenoids increased first and then decreased with growth and development, heterocyclic substances decreased continuously, and alcohols and ketones increased. The crude protein and ether extract decreased and were positively correlated with terpenoids, heterocycles, and nitrogen and sulfur compounds, while the dry matter content, soluble carbohydrates, and neutral detergent fiber increased and were positively correlated with alcohols and ketones. The VOCs were most accumulated in the early flowering stages, which had more sweet and fruity flavors, and the main substances that differed from the budding stage and the full flowering stage were methyl heptanoate, butyl butyrate, β-ionone, and other esters and terpenoids. The monoterpene, sesquiterpene, and triterpene pathways were up-regulated in the early flowering stage, and the phenyl-propylene synthesis pathway was up-regulated in the full flowering stage. These substances and pathways were key to further improving alfalfa odor, grade and utilization. [ABSTRACT FROM AUTHOR]

Summary: This study aimed to investigate aroma compound transformation and precursor relation in dry‐shredded beef using gas chromatography–ion mobility spectrometry, gas chromatography–mass spectrometry, high‐performance liquid chromatography, and electronic nose technologies. Forty‐eight aroma compounds were identified, of which twelve (pentanal, hexanal, heptanal, 3‐methylbutanal, nonanal, phenylacetaldehyde, trans‐2‐decenal, acetoin, 3‐octenol, 3‐octanol, ethyl hexanoate, and 2‐acetylthiazol) exhibited odour activity values (OAVs) >1. The concentration of fatty acids (FAs) decreased progressively, while that of 19 free amino acids (FAAs) increased with prolonged cooking. Pearson's correlation analysis revealed correlations between aroma components (OAV >1), FAAs, and FAs. Specifically, 2‐acetylthiazol, acetoin, heptanal, 3‐methylbutanal, and hexanal exhibited positive correlations, while phenylacetaldehyde and trans‐2‐decenal showed inverse correlations with most FAAs. Notably, phenylacetaldehyde positively correlated with C16:0, C18:1, n9c, and C18:2, n6c. This research presents a practical framework for the application of analytical and computational techniques in the commercial production of cooked beef and meat products. [ABSTRACT FROM AUTHOR]

The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources. [ABSTRACT FROM AUTHOR]

BACKGROUND: Excessive sugar consumption has been linked to type 2 diabetes, obesity and other diseases. Therefore, it is indispensable to reduce sugar of food. However, the sensory characteristics of food are affected after sugar reduction (SR). Currently, SR has been reported in drinks, jams, candies, and other fruit related or sweet foods; but salty or protein related foods have not been explored, therefore there is a big gap that needs to be filled. RESULTS: Sensory scores of initial sweetness and sweetness reduced by 0.26 and 0.12 in 10% SR dried fish mince product compared with control, and there was a small difference between 25% SR (3.33) and 40% SR (3.09) samples. It also showed that 10% SR sample had a small reduction in sweetness value and free sugar content by 3.5% (0.42/11.9) and 7.8% (2.12/27.06) compared with control; while values in 25% SR sample decreased sharply but were not much different from 40% SR sample. Electronic nose results showed that SR had a small effect on odor. Texture analysis showed that texture properties of 25% SR sample were significantly different from control. CONCLUSIONS: Dried fish mince product with below 10% SR had a small difference on the sensory characteristics and there was a big change when SR was more than 20%. Dried fish mince product with 25% SR and 40% SR had no significant difference. SR had a small effect on odor, but had a great effect on texture properties, especially over 20% SR. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Volatile components are important indicators for banana flavor evaluation. In this study, the aroma composition characteristics of three disease-resistant banana varieties (Nantianhuang, Guijiao No. 2 and Zhongjiao No. 8) were investigated using SuperNose electronic nose combined with headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC–MS). The relative odor activity value (ROAV) analysis revealed that isoamyl butyrate and isoamyl acetate were the key aroma components contributing to the overall flavor composition of the three banana varieties. Hexyl acetate and isopentyl isopentanoate were identified as important aroma components for the overall banana flavor composition. Through the analysis of the radar chart of aroma types, it was found that the aroma types of the three varieties were mainly fruity and green, and covered 11, 10 and 13 different aroma types in NTH, GJ2 and ZJ8, respectively, which constituted the unique composite flavors of the different varieties. The electronic nose analysis revealed that GJ2 and ZJ8 had a high similarity, but both were easily distinguishable from NTH. Sensors S1, S6, and S9 showed a strong correlation with differential aroma components among the varieties. This study highlights the variations in aroma components and characteristics of disease-resistant banana varieties, and provides new approaches and fundamental data for selecting varieties and evaluating their quality. [ABSTRACT FROM AUTHOR]

The research investigates the variations in floral scent composition among different species and developmental stages of Iris plants: Iris uniflora, Iris typhifolia, and Iris sanguinea. The study analyzes the fragrance components by utilizing electronic nose technology in tandem with headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Principal component analysis (PCA), linear discriminant analysis (LDA), and loading analysis are applied to discern whether floral scents of the same Iris species at distinct stages could be differentiated. The results show that the electronic nose significantly distinguishes the aromas from different stages and that there are differences in aroma composition. Gas Chromatography-Mass Spectrometry confirms significant differences in volatile components regarding the three Iris species, with common compounds like alcohols, aromatics, and aldehydes present throughout stages. Notably, nonyl aldehyde, capric aldehyde, 2,4-di-tert-butylphenol, and n-heptadecane are consistently found. Cluster analysis reveals a grouping of decay stage samples of Iris typhifolia and Iris sanguinea due to terpene and ester abundance. Nonyl aldehyde significantly contributes to the aroma profiles of all species, owing to its high odor activity value. The significant content of volatile compounds in these Iris varieties suggests economic and medicinal potential beyond ornamental value, providing references for the development of Iris-scented products, aromatherapy, and the extraction of pharmacologically active substances from Iris. [ABSTRACT FROM AUTHOR]

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The combination of electronic nose and headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was used to analyse the differences in volatiles in soybean oil from three processing methods, which was extracted oil, hot pressed oil and cold pressed oil, respectively. The results showed that 86 volatile com¬pounds were detected in the three soybean oil samples, 53 of them in the extracted oil, 54 of them in the hot pressed oil and 45 in the cold pressed oil. The main volatile components included aldehydes, alcohols, ketones, acids, esters, terpenes, heterocycles (furans and pyrazines) and hydrocarbons and so on. 24 volatile components were identical in the three types of soybean oil. The principal component analysis and hierarchical cluster analysis indicated that the processing method had a big impact on the flavour of soybean oil. By odour activity value (OAV) technique, there were 15 key flavour compounds detected in the extracted oil, dominated by substances such as 3-nonen-2-one, resulting in a berry flavour in the extracted oil. Moreover, 16 key flavour compounds detected in hot pressed oil, which reflected distinctive baking flavour due to heterocyclic compounds such as 2, 5 -dimethylpyrazine. However, 12 key flavour compounds were detected in the cold pressed oil, dominated by aldehydes such as (A,A)-2,4-heptadienal, which gave natural oil flavour of the cold pressed oil. The results of the partial least squares regression analysis showed a good correlation between the electronic nose and the GC-MS results. [ABSTRACT FROM AUTHOR]

Identifying relevant machine learning features for multi-sensing platforms is both an applicative limitation to recognize environments and a necessity to interpret the physical relevance of transducers' complementarity in their information processing. Particularly for long acquisitions, feature extraction must be fully automatized without human intervention and resilient to perturbations without significantly increasing the computational cost of a classifier. In this study, we investigate the relative resistance and current modulation of a 24-dimensional conductimetric electronic nose, which uses the exponential moving average as a floating reference in a low-cost information descriptor for environment recognition. In particular, we identified that depending on the structure of a linear classifier, the 'modema' descriptor is optimized for different material sensing elements' contributions to classify information patterns. The low-pass filtering optimization leads to opposite behaviors between unsupervised and supervised learning: the latter favors longer integration of the reference, allowing the recognition of five different classes over 90%, while the first one prefers using the latest events as its reference to cluster patterns by environment nature. Its electronic implementation shall greatly diminish the computational requirements of conductimetric electronic noses for on-board environment recognition without human supervision. [ABSTRACT FROM AUTHOR]

Driven by economic interests, adding adulterations in chili powder is a problem which threatens people's health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed. [ABSTRACT FROM AUTHOR]

Salt is a key ingredient that can both enhance the taste and extend the shelf life of fermented vegetables. However, it is important to note that excessive salt levels can have adverse effects on consumer health. This study aimed to investigate the impact of various salt additions (2%, 4%, 6%, 8%, and 10% wt/wt) on the flavor profile of fermented ciba pepper, a traditional Chinese fermented chili sauce, using gas chromatography–ion mobility spectrometry (GC-IMS) in combination with an electronic nose (E-nose). Fermented ciba pepper samples were prepared with different salt additions: 2% (LJA), 4% (LJB), 6% (LJC), 8% (LJD), and 10% (LJE) (wt/wt). The physicochemical and sensory properties of the fermented ciba pepper samples were evaluated. Sensory evaluation indicated that LJC and LJD received higher scores compared to the other groups. The total acid and amino acid nitrogen contents displayed contrasting trends with the salt additions (p < 0.05). The E-nose analysis successfully differentiated the flavor profiles of the ciba pepper samples fermented with varying salt additions. Additionally, the GC-IMS analysis identified a total of 72 volatile compounds, including 14 alcohols, 21 esters, nine aldehydes, four acids, eight ketones, three terpenes, and eight other substances. Notably, the ciba pepper samples with lower salt additions exhibited higher levels of alcohols, aldehydes, and esters. In conclusion, the addition of salt during the fermentation process significantly influenced the formation of flavor compounds in ciba pepper. This study provides valuable insights into ciba pepper fermentation with different salt additions and offers prospects for the development of low-salt fermented ciba pepper products. [ABSTRACT FROM AUTHOR]

To enable a wider utilization of co–products from beer processing and minimize the negative effect of added grain on bread quality, flavor, and other attributes, brewer's spent grains (BSG) are processed through microwave pretreatment, and then the microwave–treated BSG (MW–BSG) is added to bread. So far, there has been no investigation on the effect of microwave–pretreated BSG on bread quality and flavor. In this study, we examined the effects of diverse microwave treatment variables on the physicochemical structure of BSG and explored the consequences of MW–BSG on the quality and flavor of bread. The results showed that soluble dietary fiber and water–soluble protein levels in MW–BSG increased significantly (144.88% and 23.35%) at a 540 W microwave power, 3 min processing time, and 1:5 material–liquid ratio of BSG to water. The proper addition of MW–BSG positively affected the bread texture properties and color, but excessive amounts led to an irregular size and distribution of the bread crumbs. The result of electronic nose and HS–SPME–GC–MS analyses showed that the addition of MW–BSG modified the odor profile of the bread. A sensory evaluation showed mean scores ranging from 6.81 to 4.41 for bread containing 0–10% MW–BSG. Consumers found a maximum level of 6% MW–BSG acceptable. This study endeavors to decrease environmental contamination caused by brewing waste by broadening the methods by which beer co–products can be utilized through an innovative approach. [ABSTRACT FROM AUTHOR]

The assessment of microbial spoilage in fresh fish is a major concern for the fish industry. This study aimed to evaluate the efficiency and reliability of an electronic nose (E-nose) to detect microbial spoilage of fresh sardines (Sardinella longiceps) by comparing its measurements with Total Bacterial Count (TBC), Hydrogen Sulfide (H2S) producing bacterial count and Trimethylamine Oxide (TMAO) reducing bacterial count after variable storage conditions. The samples were stored at 0 °C (0, 2, 4, 6, and 8 days) and 25 °C (0, 3, 6, and 9 h), while day 0 was used as a control. The E-nose measurements were analyzed by Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN). Microbial counts increased significantly and simultaneously with the changes in E-nose measurements during storage. The LDA and ANN showed a good classification of E-nose data for different storage times at two storage temperatures (0 °C and 25 °C) compared to PCA. It is expected as PCA is based on linear relationships between the factors, while ANN is based on non-linear relationships. Correlation coefficients between E-nose and TBC, TMAO-reducing bacterial and H2S-producing bacterial counts at 0 °C were 0.919, 0.960 and 0.915, respectively, whereas at 25 °C, the correlation coefficients were 0.859, 0.945 and 0.849, respectively. These positive correlations qualify the E-nose as an efficient and reliable device for detecting microbial spoilage of fish during storage. [ABSTRACT FROM AUTHOR]

In recent years, green and healthy foods have attracted much attention. Plant-based foods have become an alternative to animal-derived foods. In this study, we used walnut and purple rice as the primary raw materials to produce a fermented plant drink. The process included boiling, mixing, grinding, inoculation, fermentation, and sterilization. We then analyzed the similarities and differences between the resulting walnut and purple rice fermented plant drink and an unfermented walnut and purple rice plant drink, as well as dairy-based yoghurt, in terms of physical chemistry, flavor, and sensory characteristics. We also examined the similarities and differences between the walnut and purple rice fermented plant drink and room-temperature yoghurt. The study results revealed that the walnut and purple rice fermented plant drink exhibited greater viscosity than the walnut and purple rice unfermented plant drink and room-temperature yoghurt. Additionally, the former displayed enhanced stability and recovery ability. Notably, distinguishable differences were observed between the three samples in terms of the presence of unknown volatiles and the umami signal, as indicated by electronic nose/tongue and GC-IMS analyses. The umami flavor of the walnut and purple rice fermented plant drink surpasses that of room-temperature yoghurt, while its taste is less salty than that of the walnut and purple rice plant drink. Despite possessing a weaker aroma than dairy-based yogurt, it is more potent than the walnut and purple rice plant drink. Additionally, its relative abundance of olefins, ketones, and alcohols enhances its unique flavor profile, surpassing both other options. Based on sensory analysis, it can be deduced that walnut and purple rice fermented plant drink has the highest overall acceptance rate. [ABSTRACT FROM AUTHOR]

The cooking method is extremely important for the production of low-salt, wet-marinated, fermented golden pomfret because it strongly influences its flavor components and organoleptic quality. There are also significant differences in flavor preferences in different populations. The present study analyzed differences in the aroma characteristics of wet-marinated fermented golden pomfret after boiling, steaming, microwaving, air-frying, and baking using a combination of an electronic nose, GC-IMS, and SPME-GC-MS. Electronic nose PCA showed that the flavors of the boiled (A), steamed (B), and microwaved (C) treatment groups were similar, and the flavors of the baking (D) and air-frying (E) groups were similar. A total of 72 flavor compounds were detected in the GC-IMS analysis, and the comparative analysis of the cooked wet-marinated and fermented golden pomfret yielded a greater abundance of flavor compounds. SPME-GC-MS analysis detected 108 flavor compounds, and the results were similar for baking and air-frying. Twelve key flavor substances, including hexanal, isovaleraldehyde, and (E)-2-dodecenal, were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) and VIP analysis. These results showed that the cooking method could be a key factor in the flavor distribution of wet-marinated fermented golden pomfret, and consumers can choose the appropriate cooking method accordingly. The results can provide theoretical guidance for the more effective processing of fish products and the development of subsequent food products. [ABSTRACT FROM AUTHOR]

Effects of hot air circulation roasting temperature on cashew nut sensory and aroma profiles were assessed in this study. Optimal roasting at 130°C for 20 min yielded a uniform, glossy appearance and rapid water loss. Sensory and electronic nose (E‐nose) analyses indicated enhanced sweet, milky, and fatty aromas. Gas chromatography–mass spectrometry identified more than 180 volatile compounds. Roasting at 150°C for 20 min produced a burnt aroma, with a decrease in alkenes and aromatic hydrocarbons and an increase in alkanes, acids, ketones, and pyrazines. Principal component analysis distinguished between 130 and 150°C roasting samples, and key volatile compounds included phenylethanol, 1‐octen‐3‐ol, naphthalene, (−)‐alpha pinene, trans caryophyllene, 2,5‐dimethylpyrazine, 2‐methylbutyr‐aldehyde, heptaldehyde, 2,3,5‐trimethylpyrazine, and n‐octanol. Compared to traditional roasting, hot air circulation roasting offers efficiency, convenience, energy savings, and environmental benefits. Practical applications: Cashew nuts are esteemed as a nutritious treat mainly due to their profound taste and elevated nutrient content. This research explored the influence of applying hot air circulation during the roasting procedure on various aspects of physical and chemical properties, flavor, and energy consumption. By establishing a systematic theoretical model of the process, this work can effectively manipulate the roasting parameters to attain an optimal flavor profile desirable for cashew nuts. Moreover, the efficiency of this novel approach outperforms conventional techniques, offering substantial energy cost savings, and showcasing its virtues of high efficacy, eco‐friendliness, and environmental preservation. The investigation revealed that utilizing hot air circulation leads to cashew nuts possessing a superior gustatory sensation and nutritional profile. Therefore, it can safely be stated that hot air circulation roasting could serve as an exemplary methodology for household nut manufacturing, thereby warranting further consideration of this innovative technique in private nut production. [ABSTRACT FROM AUTHOR]

The artificial olfactory image was proposed by Lundström et al. in 1991 as a new strategy for an electronic nose system which generated a two-dimensional mapping to be interpreted as a fingerprint of the detected gas species. The potential distribution generated by the catalytic metals integrated into a semiconductor field-effect structure was read as a photocurrent signal generated by scanning light pulses. The impact of the proposed technology spread beyond gas sensing, inspiring the development of various imaging modalities based on the light addressing of field-effect structures to obtain spatial maps of pH distribution, ions, molecules, and impedance, and these modalities have been applied in both biological and non-biological systems. These light-addressing technologies have been further developed to realize the position control of a faradaic current on the electrode surface for localized electrochemical reactions and amperometric measurements, as well as the actuation of liquids in microfluidic devices. [ABSTRACT FROM AUTHOR]