Search Results (926)

The growing demand for alternative sources of non-animal proteins has stimulated research in this area. Mushrooms show potential in the innovation of plant-based food products. In this study, the aim was to develop prototype fish fillets analogues from Pleurotus ostreatus mushrooms applying enzymatic treatment (β-glucanase and transglutaminase-TG). A Plackett–Burman 20 experimental design was used to optimize forty variables. Oat flour (OF) exerted a positive effect on the hardness and gumminess texture parameters but a negative effect on cohesiveness and resilience. Soy protein isolate (SPI) exhibited a positive effect on elasticity, gumminess and chewiness, while acacia gum had a negative effect on elasticity, cohesiveness and resilience. After sensory analysis the assay with 1% cassava starch, 5% OF, 5% SPI, 0.1% transglutaminase (240 min/5 °C), 1% coconut oil, 1% soybean oil, 0.2% sodium tripolyphosphate, 0.6% β-glucanase (80 °C/10 min) and without β-glucanase inactivation was found to exhibit greater similarity to fish fillet. The classes hydrocarbons, alcohols and aldehydes are the predominant ones in aromatic profile analysis by chromatography and electronic nose. It is concluded that a mushroom-based analogue of fish fillet can be prepared using enzymatic treatment with TG. Full article

Volatile organic compounds (VOCs) are secondary metabolites emitted by all living carbon-based organisms. These VOCs are of great importance in the agricultural sector due to their use as biofungicides and biopesticides. In addition, they can also be used as indicators of microbial contamination. The latter has rarely been studied; however, such a role is very relevant because it allows the timely application of corrective treatments that avoid food waste, the development of toxins dangerous to humans, and the design of biosensors. Gas chromatography–mass spectrometry (GC-MS), electronic nose (e-nose), and proton transfer reaction mass spectrometry (PTR-MS) are some of the techniques used to detect VOCs in fruits and vegetables contaminated by microorganisms. Therefore, the objective of this work is to deepen our knowledge of VOCs emitted by microorganisms in terms of their use as an indicator of microbial contamination of fresh agricultural products, as well as the analytical techniques used for their detection. Full article

Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field. Full article

Odors existing in natural environment are typically mixtures of a large variety of chemical compounds in specific proportions. It is a challenging task for an electronic nose to recognize the gas mixtures. Most current research is based on the overall response of sensors and uses relatively simple datasets, which cannot be used for complex mixtures or rapid monitoring scenarios. In this study, a novel electronic nose (E-nose) using a spiking neural network (SNN) model was proposed for the detection and recognition of gas mixtures. The electronic nose integrates six commercial metal oxide sensors for automated gas acquisition. SNN with a simple three-layer structure was introduced to extract transient dynamic information and estimate concentration rapidly. Then, a dataset of mixed gases with different orders of magnitude was established by the E-nose to verify the model’s performance. Additionally, random forests and the decision tree regression model were used for comparison with the SNN-based model. Results show that the model utilizes the dynamic characteristics of the sensors, achieving smaller mean squared error (MSE < 0.01) and mean absolute error (MAE) with less data compared to random forest and decision tree algorithms. In conclusion, the electronic nose system combined with the bionic model shows a high performance in identifying gas mixtures, which has a great potential to be used for indoor air quality monitoring in practical applications. Full article

Cold plasma (CP) is an alternative to traditional thermal sterilization techniques. This study aimed to investigate the preservation effects of CP treatment at 120 kV and 130 Hz for 150 s on fresh-cut Hami melons during storage at 4 °C for 8 d. There was no significant difference in the pH, color, firmness, and soluble solids content of the two groups during 0–4 days of storage. After CP treatment, the enzyme activities, total viable count (TVC), and values of the electronic nose (E-nose) changed. During storage, the increase in polyphenol oxidase (PPO) and peroxidase (POD) activities was inhibited by CP treatment. Initially, CP treatment yielded a 1.06 log reduction in total viable count (TVC). During storage, the TVC of the CP-treated group was significantly lower than that of the untreated group. CP treatment affected the E-nose values related to ketones, terpenes, polar, aromatic, and sulphur compounds. This study indicated that high-voltage and short-time CP treatment can extend the shelf-life of fresh-cut Hami melons by inhibiting oxidation and reducing microbial contamination without negative effects on physical quality. Full article

Most studies in astrobotany employ soil as the primary crop-growing medium, which is being researched and innovated. However, utilizing soil for planting in microgravity conditions may be impractical due to its weight, the issue of particles suspended in microgravity, and its propensity to harbor pathogenic microorganisms that pose health risks. Hence, soilless irrigation and fertigation systems such as fogponics possess a high potential for space farming. Fogponics is a promising variation of aeroponics, which involves the delivery of nutrient-rich water as a fine fog to plant roots. However, evaluating the strengths and weaknesses of fogponics compared to other soilless cultivation methods is essential. Additionally, optimizing fogponics systems for effective crop cultivation in microgravity environments is crucial. This study investigated the interaction of fogponics and artificial intelligence for crop cultivation in microgravity environments, aiming to replace soil-based methods, filling a significant research gap as the first comprehensive examination of this interplay in the literature. A comparative assessment of soilless fertigation and irrigation techniques to identify strengths and weaknesses was conducted, providing an overview through a literature review. This highlights key concepts, methodologies, and findings, emphasizing fogponics’ relevance in space exploration and identifying gaps in current understanding. Insights suggest that developing adaptive fogponics systems for microgravity faces challenges due to uncharacterized fog behavior and optimization complexities without gravity. Fogponics shows promise for sustainable space agriculture, yet it lags in technological integration compared with hydroponics and aeroponics. Future research should focus on microgravity fog behavior analysis, the development of an effective and optimized space mission-compatible fogponics system, and system improvements such as an electronic nose for an adaptive system fog chemical composition. This study recommends integrating advanced technologies like AI-driven closed-loop systems to advance fogponics applications in space farming. Full article

Dendrobium stem is a valuable food with medicinal and edible properties. Due to its high medicinal value and price, closely related Dendrobium varieties are often sold as imitations on the market. Therefore, there is an urgent need to develop new methods that can quickly identify Dendrobium stem and its closely related species. The Heracles Neo ultra-fast gas phase electronic nose was used in this study to determine and analyze the composition and contents of volatile organic compounds (VOCs) in Dendrobium stem and samples of five other species closely related to it. A total of 20 VOCs were identified, and a fingerprint map of the VOCs was constructed. Principal component analysis (PCA), Euclidean distance, and other methods were used to comprehensively process and analyze the obtained VOC information. The AroChemBase database was also used for qualitative analysis of the compounds. The results showed that there are significant differences in the odor fingerprint spectra of Dendrobium stem and the five other closely related species. The main types of compounds in Dendrobium stem and its five closely related species were organic esters, aldehydes, ketones, and olefins. Among them, 3-methylbutanal and n-butanol were characteristic compounds of the Dendrobium stem sample, while the VOCs acetonitrile and trometamol were present in the five related Dendrobium species samples. The Heracles Neo ultra-fast gas phase electronic nose can quickly and accurately identify Dendrobium stem and its five closely related species. It can also be used for the quality evaluation of Dendrobium stem, providing a theoretical reference for reducing the phenomenon of medicinal confusion in the Dendrobium stem market. Full article

Baijiu, one of the world’s six major distilled spirits, has an extremely rich flavor profile, which increases the complexity of its flavor quality evaluation. This study employed an electronic nose (E-nose) and electronic tongue (E-tongue) to detect 42 types of strong-aroma Baijiu. Linear discriminant analysis (LDA) was performed based on the different production origins, alcohol content, and grades. Twelve trained Baijiu evaluators participated in the quantitative descriptive analysis (QDA) of the Baijiu samples. By integrating characteristic values from the intelligent sensory detection data and combining them with the human sensory evaluation results, machine learning was used to establish a multi-submodel-based flavor quality prediction model and classification model for Baijiu. The results showed that different Baijiu samples could be well distinguished, with a prediction model R² of 0.9994 and classification model accuracy of 100%. This study provides support for the establishment of a flavor quality evaluation system for Baijiu. Full article

An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium: F. avenaceum, F. langsethiae, F. poae, and F. sporotrichioides. The electronic nose prototype is a low-cost device based on commercially available TGS series sensors from Figaro Corp. Two types of gas sensors that respond to the perturbation are used to collect signals useful for discriminating between the samples under study. First, an electronic nose detects the transient response of the sensors to a change in operating conditions from clean air to the presence of the gas being measured. A simple gas chamber was used to create a sudden change in gas composition near the sensors. An inexpensive pneumatic system consisting of a pump and a carbon filter was used to supply the system with clean air. It was also used to clean the sensors between measurement cycles. The second function of the electronic nose is to detect the response of the sensor to temperature disturbances of the sensor heater in the presence of the gas to be measured. It has been shown that features extracted from the transient response of the sensor to perturbations by modulating the temperature of the sensor heater resulted in better classification performance than when the machine learning model was built from features extracted from the response of the sensor in the gas adsorption phase. By combining features from both phases of the sensor response, a further improvement in classification performance was achieved. The E-nose enabled the differentiation of F. poae from the other fungal species tested with excellent performance. The overall classification rate using the Support Vector Machine model reached 70 per cent between the four fungal categories tested. Full article

The massive import of uncontrolled technical grain from the East into the European Community poses a risk to public health when it ends up in the mills to be used as flour for food purposes instead of being burnt (biofuel). In fungal infections of wheat, the most dangerous species belong to the genus Fusarium. F. poae is a pathogen that is most commonly isolated from cereals worldwide and causes various types of diseases in animals and humans due to the numerous toxins it produces. The manuscript reports an attempt to distinguish between four species of Fusarium, F. avanceum, F. langsethiae, F. poae, and F. sporotrichioides, in wheat grains by measuring the volatiles emitted. The patterns obtained from the signals captured by the electronic nose PEN3 were used to build the Random Forests classification model. The recall and precision of the classification performance for F. poae reached 91 and 87%, respectively. The overall classification accuracy reached 70%. Gas chromatography coupled with mass spectrometry (GC–MS) was used to analyze the chemical composition of the emitted volatiles. The patterns found in the GC–MS results allowed an explanation of the main patterns observed when analyzing the electronic nose data. The mycotoxins produced by the Fusarium species analyzed were detected. The results of the reported experiment confirm the potential of the electronic nose as a technology that can be useful for screening the condition of the grain and distinguishing between different pathogenic infestations. Full article

Heat stress has received growing concerns regarding the impact on seafood quality. However, the effects of heat stress on the sensory properties of seafood remain unknown. In this study, the sensory properties of fresh oyster (Crassostrea ariakensis) treated with chronic heat stress (30 °C) for 8 weeks were characterized using electronic nose, electronic tongue, sensory evaluation, HS–SPME–GC–MS, LC–MS and transcriptomics. Overall, chronic heat stress reduced the overall sensory properties of oysters. The metabolic network constructed. based on enrichment results of 423 differential metabolites and 166 differentially expressed genes, showed that the negative effects of chronic heat stress on the sensory properties of oysters were related to oxidative stress, protein degradation, lipid oxidation, and nucleotide metabolism. The results of the study provide valuable insights into the effects of heat stress on the sensory properties of oysters, which are important for ensuring a sustainable supply of high-quality seafood and maintaining food safety. Full article

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time–frequency attention convolutional neural network (TFA-CNN). A time–frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model’s robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R²) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time–frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system. Full article

Volatile organic compounds (VOCs), as a biological element released from plants, have been correlated with disease status. Although analysis of VOCs using GC-MS is a routine procedure, it has limitations, including being time-consuming, laboratory-based, and requiring specialist training. Electronic nose devices (E-nose) provide a portable and rapid alternative. This is the first pilot study exploring three types of commercially available E-nose to assess how accurately they could detect mango twig tip dieback and Panama disease in bananas. The devices were initially trained and validated on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated using inbuilt data analysis methods, mainly by principal component analysis (PCA) and cross-validation. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3/MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-noses are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS. The study was conducted in controlled laboratory conditions, as E-noses are highly sensitive to surrounding volatiles. Full article

In this study, the flavor characteristics and physicochemical properties of salted egg yolk (SEY) under different cooking methods (steaming/baking/microwaving) were investigated. The microwave-treated SEY exhibited the highest levels of salt content, cooking loss, lightness, and b* value, as well as the highest content of flavor amino acids. A total of 31, 27, and 29 volatile compounds were detected after steaming, baking, and microwave treatments, respectively, covering 10 chemical families. The partial least squares discriminant analysis confirmed that 21 compounds, including octanol, pyrazine, 2-pentyl-furan, and 1-octen-3-ol, were the key volatile compounds affecting the classification of SEY aroma. The electronic nose revealed a sharp distinction in the overall flavor profile of SEY with varying heat treatments. However, no dramatic differences were observed in terms of fatty acid composition. Microwave treatment was identified as presenting a promising approach for enhancing the aroma profile of SEY. These findings contribute novel insights into flavor evaluation and the development of egg products as ingredients for thermal processing. Full article

In the global food industry, fermented dairy products are valued for their unique flavors and nutrients. Lactococcus lactis is crucial in developing these flavors during fermentation. Meeting diverse consumer flavor preferences requires the careful selection of fermentation agents. Traditional assessment methods are slow, costly, and subjective. Although electronic-nose and -tongue technologies provide objective assessments, they are mostly limited to laboratory environments. Therefore, this study developed a model to predict the electronic sensory characteristics of fermented milk. This model is based on the genomic data of Lactococcus lactis, using the DBO (Dung Beetle Optimizer) optimization algorithm combined with 10 different machine learning methods. The research results show that the combination of the DBO optimization algorithm and multi-round feature selection with a ridge regression model significantly improved the performance of the model. In the 10-fold cross-validation, the R² values of all the electronic sensory phenotypes exceeded 0.895, indicating an excellent performance. In addition, a deep analysis of the electronic sensory data revealed an important phenomenon: the correlation between the electronic sensory phenotypes is positively related to the number of features jointly selected. Generally, a higher correlation among the electronic sensory phenotypes corresponds to a greater number of features being jointly selected. Specifically, phenotypes with high correlations exhibit from 2 to 60 times more jointly selected features than those with low correlations. This suggests that our feature selection strategy effectively identifies the key features impacting multiple phenotypes, likely originating from their regulation by similar biological pathways or metabolic processes. Overall, this study proposes a more efficient and cost-effective method for predicting the electronic sensory characteristics of milk fermented by Lactococcus lactis. It helps to screen and optimize fermenting agents with desirable flavor characteristics, thereby driving innovation and development in the dairy industry and enhancing the product quality and market competitiveness. Full article