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ABSTRACT Coffee, a popular beverage worldwide, requires thorough quality assessment to ensure its authenticity and meet consumer demands. Traditional methods in the industry are often subjective, expensive, and time‐consuming. This study used a compact, portable electronic nose (e‐nose) with machine learning models to classify and distinguish between civet and non‐civet roasted beans. The polynomial feature extraction method was used to extract important parameters from the sensor response and improve system performance. Classification models like linear discriminant analysis (LDA), logistic regression (LR), quadratic discriminant analysis (QDA), and support vector machines (SVM) were applied to classify the samples. Among these, the LDA model with polynomial features yielded the highest validation and test accuracies, with values of 0.89 ± 0.04 and 0.93, respectively. This was higher than the statistical feature methods, which obtained validation and test accuracies of 0.80 ± 0.07 and 0.87, respectively. The acquired e‐nose results were correlated with compound concentrations in roasted coffee beans measured by gas chromatography–mass spectrometry (GC–MS). These findings demonstrate the e‐nose system's promising potential to effectively distinguish civet from non‐civet roasted coffee beans based on their aroma profiles using polynomial feature extraction methods. [ABSTRACT FROM AUTHOR]

The efficient, economical, and sustainable production of biodiesel from waste cooking oils (WCOs) depends on the availability of simple, rapid, and low-cost methods to test the quality of potential feedstocks. The aim of this study was to establish the applicability of stochastic modeling of e-nose profiles in the evaluation of recycled WCO characteristics. Olfactory profiles of 10 WCOs were determined using a Sensigent Cyranose® 320 chemical vapor-sensing device with a 32 sensor-array, and a stepwise multiple linear regression (MLR) analysis was performed to select stochastic parameters (explanatory variables) for inclusion in the final predictive models of the physicochemical properties of the WCOs. The most important model parameters for the characterization of WCOs were those relating to the time of inception of the e-nose signal "plateau" and to the concentration of volatile organic compounds (VOCs) in the sensor region. A comparison of acid values, peroxide values, water contents, and kinematic viscosities predicted by the MLR models with those determined by conventional laboratory methods revealed that goodness of fit and predictor accuracy varied from good to excellent, with all metric values >90%. Combining e-nose profiling with stochastic modeling was successful in predicting the physicochemical characteristics of WCOs and could be used to select suitable raw materials for efficient and sustainable biodiesel production. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate detection of stale beef on the market is important for protecting the legitimate rights and interests of consumers. To this end, we combined electronic nose measurements with machine learning technology to classify beef samples. We used an electronic nose to collect information about the odor characteristics of different beef samples and used linear discriminant analysis to reduce data dimensionality. We then classified samples using the following algorithms: extreme gradient boosting, logistic regression, K‐nearest neighbor, random forest, support vector machine, and neural networks for pattern recognition. We assessed model performance using a 10‐fold cross‐validation technique. All these methods reached an accuracy of 95% or above, with F1 scores and AUC values above 0.96. The support vector machine algorithm outperformed all other models, achieving perfect recognition with 100% accuracy and F1/AUC scores of 1.0. Our study demonstrates that electronic nose data combined with support vector machine can be used to successfully discriminate between stale and fresh beef, paving the way for novel research directions in the detection of stale beef. [ABSTRACT FROM AUTHOR]

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications. [ABSTRACT FROM AUTHOR]

Atractylodis Macrocephalae Rhizoma (AMR) is a traditional Chinese medicine used for gastrointestinal diseases. With increased demand, there are more and more places of cultivation for AMR. However, the quality of AMR varies from place to place, and there is no good way to distinguish AMR from different origins at present. In this paper, we determined the content of eight chemical components including 60% ethanol extracts, essential oil, polysaccharides, atractylenolides, and atractylone, obtained the color parameters of AMR powder by colorimetry, and odor information was captured by the electronic nose, all of which were combined with machine learning to establish a rapid origin traceability method. The results of the principal component analysis of the chemical components revealed that Zhejiang AMR has a high comprehensive score and overall better quality. The Kruskal–Wallis test demonstrated that there are varying degrees of differences in chemical composition and color parameters across the different origin. However, the accuracy of the classification model is low (less than 80%), making it difficult to distinguish between different origins of AMR. The electronic nose demonstrated excellent classification performance in the traceability of AMR from different origins, with accuracy reaching more than 90% (PLS-DA: 96.88%, BPNN: 96.88%, PSO-SVM: 100%). Overall, this study clarified the quality differences of AMR among different origins, and a rapid and precise method combining machine learning was developed to trace the origin of AMR. [ABSTRACT FROM AUTHOR]

Evaluation concerning the presence of bacteria in meat products is mandatory for commercializing these goods. Although food bacteria detection is based on microbiological methods, these assays are usually laborious and time-consuming. In this paper, an electronic nose is used to differentiate Salmonella spp. (SA), Escherichia coli (EC), and Pseudomonas fluorescens (PF) inoculated in raw meat (beef, chicken, and pork) and incubated at 22 °C for 3 days. The obtained data were evaluated by principal component analysis (PCA) and different machine learning algorithms. From the graphical analysis of the PCA, on day 1, the clusters were close to each other for beef, chicken, and pork, while on days 2 and 3, more separated bacteria clusters were obtained regardless of the meat type, allowing for the discrimination of the samples for the latter days. To estimate the growth rates of the microorganisms, the distance between clusters was calculated and provided a pattern for the three bacteria, with the slowest-, moderate-, and fastest-growing being EC, SA, and PF, respectively. Concerning the machine learning algorithms, the accuracy varied from 93.8 to 100% for beef and chicken, while for pork, it varied from 75% to 100%. Thus, these results suggest that the proposed methodology based on electronic nose has the potential for the direct discrimination of bacteria in raw meat, with reduced analysis time, costs, and manipulating steps. [ABSTRACT FROM AUTHOR]

Methanol, commonly used to cut costs in the production of counterfeit alcohol, is extremely harmful to human health, potentially leading to severe outcomes, including death. In this study, an electronic nose system was designed using 11 inexpensive gas sensors to detect the proportion of methanol in an alcohol mixture. A total of 168 odor samples were taken and analyzed from eight types of ethanol–methanol mixtures prepared at different concentrations. Only 4 features out of 264 were selected using the feature selection method based on feature importance. These four features were extracted from the data of MQ-3, MQ-4, and MQ-137 sensors, and the classification process was carried out using the data of these sensors. A Voting Classifier, an ensemble model, was used with Linear Discriminant Analysis, Support Vector Machines, and Extra Trees algorithms. The Voting Classifier achieved 85.88% classification accuracy before and 81.85% after feature selection. With its cost effectiveness, fast processing time, and practicality, the recommended system shows great potential for detecting methanol, which threatens human health in counterfeit drink production. [ABSTRACT FROM AUTHOR]

Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H2 and a mixture of the two, which are characteristic gases released during the thermal runaway of lithium batteries. In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. In addition, we also explored the influence of the selection of the response time length on the classification and regression tasks, achieving the best results in a short time through the optimum algorithm. To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. The results demonstrated that MLP achieved 99.23% classification accuracy during the 20 s response period. In the concentration regression phase, we combined the classification results with the raw features to create a new feature set, which was then input into a multi-output MLP regression model. The root mean square error (RMSE) employing the new feature set was used to measure the prediction error. Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Condiment is the property of China Condiment and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tea standard samples are the benchmark for tea product quality control. Understanding the inherent differences in Chinese national standards for Lapsang Souchong black tea of different grades is crucial for the scientific development of tea standardization work. In this study, Lapsang Souchong black tea of different grades that meet Chinese national standards was selected as the research object. The aroma characteristics were comprehensively analyzed through sensory evaluation, electronic nose, and HS-SPME-GC-MS (headspace solid-phase microextraction gas chromatography–mass spectrometry). The findings indicate that the higher-grade Lapsang Souchong has a higher evaluation score. The results of electronic nose analysis indicate that the volatiles with differences in tea of different grades were mainly terpenoids and nitrogen oxides. The results of HS-SPME-GC-MS analysis show that the odor characteristics of the super-grade samples are mainly floral and fruity, and these substances mainly include D-Limonene, 3,7-dimethyl-1,6-octadien-3-ol and 3-Hydroxymandelic acid, and ethyl ester. The primary aroma characteristics of the first-grade samples are floral, sweet, woody, and green, with key contributing compounds including 2-Furanmethanol, 1-Octen-3-ol, and 5-ethenyltetrahydro-α,α,5-trimethyl-cis-, 4,5-di-epi-aristolochene. The main aroma characteristics of the second-grade samples are green, herbal scent, and fruity, and the main substances include 3,7-dimethyl-1,6-octadien-3-ol, 2,3-dimethylthiophene, Dihydroactinidiolide, and Naphthalene-1-methyl-7-(1-methylethyl)-. It is worth noting that the second-grade samples contain a large amount of phenolic substances, which are related to the smoking process during processing. This study lays a solid foundation for the preparation of tea standard samples and the construction of the tea standard system. [ABSTRACT FROM AUTHOR]

In this study, ginger slices were dehydrated through a combination of far-infrared radiation and hot-air drying (FIR-HAD). The changes in volatile composition were subsequently identified using gas chromatography-mass spectrometry (GC-MS) and metal oxide semiconductor electronic nose (MOS e-nose). The GC-MS analysis revealed that terpenes comprised over 75% of volatile compounds in all examined samples, with zingiberene, β-sesquiphellandrene, and (E, E)-α-farnesene being the dominant constituents. Fresh ginger contained a higher relative proportion compared to dried samples. After FIR-HAD treatment, the terpene levels decreased by 11.9%, 8.9%, 13.7%, and 18.2% at 50 °C, 60 °C, 70 °C, and 80 °C, respectively. Among the various drying conditions, ginger subjected to FIR-HAD at 60 °C displayed the highest concentrations of volatile compounds and exhibited flavor characteristics closest to those of fresh samples. The flavor profiles were further evaluated using MOS e-nose analysis. The terpene compounds demonstrated a strong correlation with the response values of W1W and W2W sensors according to partial least squares regression models. In addition, the concentrations of 6-gingerol, 8-gingerol, and 10-gingerol in ginger exposed to FIR-HAD at 60 °C were also higher compared to other drying conditions. This indicated that the FIR-HAD at 60 °C is more suitable for producing dried ginger with remaining flavor attributes. These findings offer theoretical insights into the changes in volatile compounds of ginger slices during the FIR-HAD procedure. They also highlight the potential for improving drying efficiency through aroma online control techniques in future research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the last decade, significant efforts have been made to predict sensory characteristics using electronic senses, such as the electronic nose (e-nose) and the electronic tongue (e-tongue), and discuss their relationship to the eating quality evaluated by human panels. This study was conducted (1) to characterize the aroma and taste profiles of strawberries over a 5-day storage period (4 °C) using both electronic senses and human panels and (2) to correlate the electronic sense data with human panel data. A total of 10 sensory attributes of strawberries, including 7 aroma and 3 taste attributes, were analyzed by a descriptive sensory panel (n = 16) over the five days. Although the human panel did not find significant differences in the intensities of the strawberry attributes over the five days, the intensity ratings showed an increasing or decreasing trend over the storage period. However, the e-nose and the e-tongue discriminated each of the storage days of the strawberry samples. Furthermore, the partial least square regression coefficients of determination (R2) indicated that the e-nose and the e-tongue were highly predictive in their evaluation of the intensities of all the descriptive sensory attributes. Lastly, the concentrations of furaneol, one of the key volatiles imparting a distinct ripe strawberry aroma, were determined using an e-nose to correlate with the intensities of aroma attributes evaluated by the panel. A significant positive Pearson's correlation coefficient was found with the intensities of overripe aroma. The findings indicate the potential of electronic senses to determine sensory characteristics and their excellent capability to predict the eating quality of strawberries. [ABSTRACT FROM AUTHOR]

The detection of inert gases presents a challenging task because these molecules do not react with most known sensor-based units aimed at environment monitoring. Here, we ionize these molecules in a low-potential discharge mode to force their interaction with semiconducting SnO2 single-crystal nanobelts (NBs) deposited as a mat over a multielectrode chip and show the ion-induced resistivity changes similar to a conventional operation of the metal oxide sensor. In particular, the conductance of the percolating NB mats is significantly enhanced under irradiation with He+, Ne+, and Ar+ ions at a low-pressure oxygen background. The phenomenon is well explained via ab initio calculations performed in the framework of density functional theory (DFT), which clarify the interaction of various ions with SnO2 nanocrystals related primarily to the appearance of electronic states and redistribution of their density dependent on the kind of ions. The differences in ion interaction with the SnO2 surface is the background for options to distinguish the inert gases with a SnO2 NB-based multisensor array as a low-cost selective detector via transferring the variations in a charge exchange at the adsorbate/adsorbent interface into a varied chemiresistive vector signal like a (mass) spectrometer performs. [ABSTRACT FROM AUTHOR]

In this study, shrimp paste was prepared using Antarctic krill and fermented Antarctic krill shrimp paste as raw materials. Two commonly used heating methods, stir‐fried and steaming, were analyzed, the main difference between the two methods being that stir‐frying involves putting the shrimp paste into a wok and stir‐frying it for different periods of time, while steaming involves putting the shrimp paste into a steamer and steaming it for different periods of time. The effects of different salt concentrations and processing techniques on the volatile flavor compounds of shrimp paste were also observed. Electronic nose and gas chromatography–ion mobility spectrometry (GC‐IMS) were employed to analyze the volatile flavor compounds. A total of 52 volatile flavor compounds were detected by GC‐IMS, of which 38 were identified (including monomers, dimers, and polymers). The identified compounds included 11 aldehydes, 6 ketones, 14 alcohols, 2 esters, 2 acids, 1 pyridine compound, and 2 sulfur compounds. In addition, 14 compounds were identifiable. Using the results of the electronic nose analysis, we were also able to differentiate between the volatile flavor compounds in shrimp pastes produced by different processing methods. [ABSTRACT FROM AUTHOR]

The study aimed to develop a portable electronic nose system for detecting adulteration with soybean protein isolate (SPI) in chicken meat. The system mainly consisted of three parts: the gas sensor array, the DSP28335 control board, and the upper computer. The DSP28335 control board, developed using C language, included analog to digital converter (ADC) module, digital output (DO) module, pulse width modulation (PWM) module, controller area network (CAN) module, power module, drive circuit, and so forth. The upper computer, developed using LabVIEW, facilitated user interaction with the user by primarily handling CAN configuration and monitoring, displaying and storing sensor data, temperature and flow data, and sending and monitoring electronic nose commands. The feasibility of the proposed electronic nose for characterizing adulterated chicken meat was tested on six classes of chicken meat that had been adulterated with varied quantities of SPI. The mass fractions of SPI were 0%, 5%, 10%, 15%, 20%, and 25%, respectively. On the basis of odor data from the electronic nose, K‐nearest neighbor (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) were applied to qualitatively distinguish minced chicken meat with different adulteration ratios. The results showed that the SVM model had the best recognition effect. When the best parameters (c, g) were c = 16 and g = 1, the accuracy of SVM model was 97.22% and 93.75% in the training and testing sets, respectively. These results demonstrated that the portable electronic nose designed in this paper effectively identifies minced chicken meat under various adulteration conditions, enabling rapid and nondestructive detection of chicken meat adulteration. [ABSTRACT FROM AUTHOR]

After a brief review of electronic nose technology, the use of an Australian electronic nose to identify an unknown odour out of a set of known odours is described. Multivariate supervised learning is accomplished by applying Bayes' theorem to data from metal oxide semiconductor sensors responding to each of a number of target odours. An odour from an unknown source is then assigned a probability of membership of each of the training sets by applying either a Naïve Bayes algorithm to the deemed independent data from each sensor, or to a multinormal distribution of the data. A flat prior (equal probabilities of each outcome) is usually adopted, but for particular situations where one odour is known to predominate, then suitably weighted priors can be used. A source 'none of the above', which has a small likelihood covering the space of the possible sensor responses, is included for completeness. This also avoids the assignment to a source that has an extremely small probability but which is greater than that of any other source. Examples are given of a single source (detecting diabetes from a patient's breath), and three sources of unpleasant odours in a meat processing plant. The 18th century cleric Thomas Bayes gave his name to an elegant statement of the probability of an event – in this case identification of an odour – given some evidence: output from a number of metal oxide semiconductor sensors. Knowing the distributions of outputs for target odours, we assign the probabilities of an unknown odour. The greatest probability wins! (Image credit: E-nose Pty Ltd and D. B. Hibbert.) This article belongs to the 10th Anniversary Collection of RACI and AAS Award papers. [ABSTRACT FROM AUTHOR]

Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the human body, possibly leading to significant health effects. At the same time, the toxins amassing in the organism can lead to significant changes in breath composition, resulting in halitosis with peculiar features like the popular ammonia breath. Starting from this evidence, scientists have started to work on systems that can detect the presence of kidney diseases using a minimally invasive approach, minimizing the burden to the individuals, albeit providing clinicians with useful information about the disease's presence or its main related features. The electronic nose (e-nose) is one of such tools, and its applications in this specific domain represent the core of the present review, performed on articles published in the last 20 years on humans to stay updated with the latest technological advancements, and conducted under the PRISMA guidelines. This review focuses not only on the chemical and physical principles of detection of such compounds (mainly ammonia), but also on the most popular data processing approaches adopted by the research community (mainly those relying on Machine Learning), to draw exhaustive conclusions about the state of the art and to figure out possible cues for future developments in the field. [ABSTRACT FROM AUTHOR]

Bronchopulmonary dysplasia (BPD) is the most common respiratory disease in preterm and is still associated with increased mortality and morbidity. The great interest lies in identifying early biomarkers that can predict the development of BPD. This pilot study explores the potential of e-nose for the early identification of BPD risk in premature infants by analyzing volatile organic compounds (VOCs) in the exhaled breath condensate (EBC). Fourteen mechanically ventilated very preterm infants were included in this study. The clinical parameters and EBC were collected within the first 24 h of life. The discriminative ability of breath prints between preterms who did and did not develop BPD was investigated using pattern recognition, a machine learning algorithm, and standard statistical methods. We found that e-nose probes can significantly predict the outcome of "no-BPD" vs. "BPD". Specifically, a subset of probes (S18, S24, S14, and S6) were found to be significantly predictive, with an AUC of 0.87, 0.89, 0.82, 0.8, and p = 0.019, 0.009, 0.043, 0.047, respectively. The e-nose is an easy-to-use, handheld, non-invasive electronic device that quickly samples breath. Our preliminary study has shown that it has the potential for early prediction of BPD in preterms. [ABSTRACT FROM AUTHOR]

Introduction: A watch-and-wait strategy for patients with rectal cancer who achieve a clinical complete response after neoadjuvant (chemo) radiotherapy is a valuable alternative to rectal resection. In this pilot study, we explored the use of an electronic nose to predict response to neoadjuvant therapy by analyzing breath-derived volatile organic compounds. Materials and Methods: A pilot study was performed between 2020 and 2022 on patients diagnosed with intermediate- or high-risk rectal cancer who were scheduled for neoadjuvant therapy. Breath samples were collected before and after (chemo) radiotherapy. A machine-learning model was developed to predict clinical response using curatively treated rectal cancer patients as controls. Results: For developing the machine-learning model, a total of 99 patients were included: 45 patients with rectal cancer and 54 controls. In the training set, the model successfully discriminated between patients with and without rectal cancer, with a sensitivity and specificity of 0.80 and 0.65, respectively, and an accuracy of 0.72. In the test set, the model predicted partial or (near) complete response with a sensitivity and specificity of 0.64 and 0.47, respectively, and an accuracy of 0.58. The AUC of the ROC curve was 0.63. Conclusions: The prediction model developed in this pilot study lacks the ability to accurately differentiate between partial and (near) complete responders with an electronic nose. Machine-learning studies demand a substantial number of patients and operate in a rapidly evolving field. Therefore, the prevalence of disease and duration of a study are crucial considerations for future research. [ABSTRACT FROM AUTHOR]

To compare the physicochemical properties and flavour differences of ghee in different pastoral areas, the moisture, fat, protein and volatile components of ghee were analysed. The results showed that yak ghee's moisture content was lower than cow ghee's moisture content, but the protein and fat content was higher than cow ghee's moisture content. Electronic nose (E‐nose) analysis showed that the flavour characteristics of ghee from different pastoral areas were similar, but the flavours of cow ghee and yak ghee were different. A total of 50 volatile components were identified by gas chromatography–mass spectrometry (GC–MS), mainly acids, aldehydes and alcohols. In addition, there were 13, 9, 4 and 13 characteristic volatile components in ghee from Gansu, Qinghai, Tibet and Xinjiang pastoral areas, respectively. This study distinguished the flavour differences of ghee in different pastoral areas and provided a theoretical basis for the traceability of ghee origin. [ABSTRACT FROM AUTHOR]

BACKGROUND: The total volatile basic nitrogen (TVB‐N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB‐N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB‐N, in this article, a self‐developed electronic nose (e‐nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB‐N value was established to complete the freshness detection. RESULTS: The TVB‐N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short‐term memory (LSTM) models, and finally, the final TVB‐N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD‐LSTM model optimized by SSA in predicting TVB‐N. The coefficient of determination (R2), the root‐mean‐squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%. CONCLUSIONS: This method improves the performance of e‐nose in detecting the freshness of fish meal and provides a reference for the quality detection of e‐nose in other materials. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Summary: The recent global cocoa supply decline calls for investigating disease‐resistant and single‐origin cocoa varieties in Asia. While genetic analysis has identified promising clones, information on bean quality and flavour remains limited. This research aims to analyse quality attributes of single‐origin cocoa beans and key volatile organic compounds in cocoa liquors and to determine consumer acceptability of indigenous cocoa beverage (Tablea) from single‐origin cocoa in the Philippines. Aroma fingerprinting of cocoa clones (UF18, BR25, W10) using fast GC electronic nose revealed distinctive profiles. Cocoa clone UF18 exhibited high‐fat content (52.1%), surpassing African cocoa clones. Even more important is the discovery of elevated levels of desirable volatile compounds in cocoa clone W10 such as methyl decanoate (fruity) and phenylacetaldehyde (honey, floral), which marks the first study that identified fine aroma components in single‐origin cocoa beans in Asia. Harnessing potential of these cocoa clones ensures consumer acceptability of the cocoa beverage Tablea. [ABSTRACT FROM AUTHOR]

The present research sought to evaluate the detection capacity of the electronic nose for the hazelnut aroma in a filled chocolate wafer, measuring its release and physical–chemical, thermal, and microbiological properties during 60 days where the product was stored at 25 °C and 40 °C. It was found that moisture, Aw, pH, thermal resistivity, and diffusivity remained stable during storage at both temperatures, with no significant difference (p > 0.05). While there was variation in color (L*, a*, b*, and ΔE) and in thermal conductivity. The microbiological analyses of mesophilic bacteria, Escherichia coli, Salmonella spp., and coagulase-positive staphylococci were maintained in accordance with the standards established by current legislation. The results obtained with the sensors array showed good sensitivity and a high limit of detection (> 0.001 mg/mL) for hazelnut aroma. The electronic nose was able to discriminate between the release of volatiles from chocolates stored at 25 and 40 °C, with a greater response observed at high temperature (40 °C) at the beginning of storage. Through the application of the Arrhenius model, we successfully determined the activation energy for key color components (L*, a*, b*) and thermal conductivity. Additionally, our findings revealed a good fit of volatile component release to the zero-order model, characterized by a high correlation coefficient (> 0.9). These findings underscore the importance of considering storage temperature when preserving the quality of products such as chocolates. Furthermore, the study validates the use of the electronic nose as a viable technique for analyzing volatiles in food, providing valuable insights for the food industry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Condiment is the property of China Condiment and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Huangjinya has recently seen widespread adoption in key tea-producing areas of China, celebrated for its unique varietal traits. Its leaves are also used to produce black tea with distinctive sensory characteristics. The fragrance-enhancing (EF) process is essential in crafting Huangjinya black tea (HJYBT) and is significant in flavor development. However, the impact of EF on non-volatile metabolites (NVMs), volatile metabolites (VMs), and their interactions remains poorly understood. This study aims to investigate how EF temperatures (60 °C, 70 °C, 80 °C, 90 °C, and 110 °C) influence HJYBT flavor transformation. Quantitative descriptive analysis revealed that EF improved the color, aroma, and appearance of tea leaves. Moreover, after an EF temperature of 80 °C, the HJYBT exhibited lower bitterness and astringency, whereas floral, sweet, and fruity aromas became stronger. However, when EF temperatures exceeded 90 °C, a pronounced burnt aroma developed, with HJYBT at 100 °C exhibiting caramel and roasted notes. Partial least squares discriminant analysis indicated that geraniol and linalool contribute to floral and fruity aromas, while 2-ethyl-6-methyl-pyrazine, furfural, and myrcene are key volatiles for caramel and roast aromas. Heptanal, methyl salicylate, α-citral, 1-hexanol, and (E)-3-hexen-1-ol were found to modify the green and grassy odor. Overall, HJYBT treated at 80 °C EF exhibited the highest umami, sweetness, floral and fruity aromas, and overall taste, exhibiting the least astringency, bitterness, and green and grassy notes. These results provide a significant theoretical basis for enhancing HJYBT quality and selecting the optimal EF method. [ABSTRACT FROM AUTHOR]

In this experiment, five different parts of beef were selected to make boletus beef sauce, namely beef hind leg (BHL), beef shoulder (BS), beef knuckle (BK), beef brisket (BB), and beef tendon (BT), and the effects of different beef parts on the quality and volatile aroma components of spicy beef boletus sauce were investigated. The boletus beef sauce made from BB reported the highest sensory score of 87.4. In terms of texture, beef sauce made from BB exhibited the best quality, with hardness, elasticity, gumminess, and chewiness values of 14.01 N, 4.71 mm, 3.22 N, and 15.30 mJ, respectively. Significant differences (P<0.05) in color of beef sauce were observed among different beef parts based on the L* and a* values. Electronic nose could effectively distinguish the spicy beef boletus sauce made from different parts of beef. In terms of flavor substances, 84 volatile flavor substances were detected in BHL, 36 in BS, 52 in BT, 102 in BK, and 87 in BB. The main aroma components were consistent with the types of sensitive substances identified by the electronic nose. Among them, hexanal, D-limonene, α-pinene, diallyl disulfide, and anethole were the common aroma components in the five parts, with ROAV>1. In summary, the BB was proven to be the best raw material in making spicy beef boletus sauce. These research findings provide a theoretical basis and data reference for the future cooking process and flavor characteristics of beef sauce. [ABSTRACT FROM AUTHOR]

The quality of oysters is reflected by volatile organic components. To rapidly assess the freshness level of oysters and elucidate the changes in flavor substances during storage, the volatile compounds of oysters stored at 4, 12, 20, and 28 °C over varying durations were analyzed using GC-MS and an electronic nose. Data from both GC-MS and electronic nose analyses revealed that alcohols, acids, and aldehydes are the primary contributors to the rancidity of oysters. Notably, the relative and absolute contents of Cis-2-(2-Pentenyl) furan and other heterocyclic compounds exhibited an upward trend. This observation suggests the potential for developing a simpler test for oyster freshness based on these compounds. Linear Discriminant Analysis (LDA) demonstrated superior performance compared to Principal Component Analysis (PCA) in differentiating oyster samples at various storage times. At 4 °C, the classification accuracy of the optimal support vector machine (SVM) and random forest (RF) models exceeded 90%. At 12 °C, 20 °C, and 28 °C, the classification accuracy of the best SVM and RF models surpassed 95%. Pearson correlation analysis of the concentrations of various volatile compounds and characteristic markers with the sensor response values indicated that the selected sensors were more aligned with the volatiles emitted by oysters. Consequently, the volatile compounds in oysters during storage can be predicted based on the response information from the sensors in the detection system. This study also demonstrates that the detection system serves as a viable alternative to GC-MS for evaluating oysters of varying freshness grades. [ABSTRACT FROM AUTHOR]

Monitoring chicken meat is a crucial process for food safety and consumer health, as it helps prevent the growth of harmful bacteria, minimizing the risk of foodborne illnesses. Currently, electronic nose (E-nose) technology plays a significant role in food quality assessment as it can detect changes in volatile compounds associated with food freshness. In this study, a self-designed, cost-efficient E-nose system was introduced to evaluate the freshness and bacterial growth of vacuum-packaged chicken meat stored at room temperature and refrigerator at 4 °C. Polynomial feature extraction with varying degrees was employed to extract important information from the sensor responses. Principal component analysis (PCA) and linear discriminant analysis (LDA) were implemented for data dimensionality reduction and classification. A support vector regression (SVR) model was built and employed to evaluate the bacteria population based on response patterns from the E-nose device. The LDA results clearly showed the classification of chicken meat freshness corresponding to different storage days and temperatures. The E-nose device with the SVR model combined with extracted parameters using a 2-degree polynomial provided good prediction results for the bacteria population with high scores of 0.99 and 0.99, scores of 0.97 and 0.93, and minimum scores of 0.08 and 0.11 log , and scores of 0.24 and 0.30 log , at room and refrigerated temperatures, respectively. The results indicate that the developed E-nose system could be used as a fast, portable, low-cost, and non-destructive measurement tool in evaluating the bacterial growth of chicken meat with high relative accuracy. Highlights: Electronic nose (E-nose) was used to predict the bacteria population of chicken meat. Enhanced performance was achieved using a second-degree polynomial feature method. values > 0.93 and were obtained using SVR model. The method is nondestructive and could be applied in a real-world environment. [ABSTRACT FROM AUTHOR]

A common theme of food authentication studies is the requirement to which the raw apple juice samples can be compared to establish its authenticity. In order to discriminate eight varieties of apple juices using electronic nose (EN), we tried to extract the most relevant information from EN response signals. Experiment parameters were optimized to ensure the response curve fully characterizing sample information. The optimal conditions were 10 mL of volume and 90 min equilibration. Sensor optimization was conducted to eliminate redundant information. Sensors W1C, W5C, W3S, W2S, W5S and W1W were chosen for pattern recognition. This process improved the PCA-based pattern performance. Best discrimination performance was obtained utilizing response signals of stationary phase according to multivariate analysis. Linear discriminant analysis (LDA) and Support Vector Machine (SVM) were carried out to develop discrimination models. Both LDA and SVM achieved satisfactory variety-based classification performance, with both 100% accuracy classification rates in terms of recognition ability and prediction ability. The perfect performance indicated that EN can be successfully applied in apple juices discrimination. And the results obtained will play a positive role in successful commercialization application of EN to apple juice industry. [ABSTRACT FROM AUTHOR]

Aspergillus spp. are common dominant fungi in the moldy agricultural products. Different Aspergillus species exhibit high morphological and genetic sequence similarity, making identification challenging. The measurement of mycelial growth in these Aspergillus species often requires a relatively long time. Therefore, there is a need to develop a new rapid method for identifying Aspergillus species and monitoring their growth. The volatile compounds released by five Aspergillus species on culture media were detected by gas chromatography-mass spectrometer (GC–MS), electronic nose and headspace-gas chromatography-ion migration spectrometry (HS-GC-IMS), their characteristic volatile signals were captured, the models for identifying Aspergillus species were established, and prediction models for mycelial growth of five Aspergillus species were developed. The results showed that the LDA classification training set model constructed using the fusion of GC-IMS and electronic nose data achieved a 100% accuracy rate for identifying Aspergillus species in the training set and an 83.3% accuracy rate in the test set, significantly outperforming models based on HS-GC-IMS or electronic nose data alone. When the result combined with morphological analysis, a 100% accuracy rate in identifying Aspergillus species could be achieved. The partial least squares regression (PLSR) prediction models for mycelial growth in Aspergillus species, constructed using both fusion electronic nose data and HS-GC-IMS data, demonstrated high accuracy with prediction accuracies greater than 0.972 for all five species. The prediction model based solely on electronic nose data had lower accuracy, while models using HS-GC-IMS data alone or its combined with electronic nose data achieved good results. This fusion approach of HS-GC-IMS and electronic nose might have the potential to serve as a preliminary screening tool for fungi identification. [ABSTRACT FROM AUTHOR]

Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry. [ABSTRACT FROM AUTHOR]

In order to study the effects of lychee wood chips with different toasting degrees used in the aging process on the flavor of lychee brandy, the quality and volatile flavor components of lychee brandy aged with lightly toasted (LT), moderately toasted (MT) or heavily toasted (HT) lychee wood chips were evaluated and analyzed using sensory evaluation, electronic nose and gas chromatography-mass spectrometry. Raw and aged lychee brandy with heavily toasted oak chips were used as controls. The results of electronic nose analysis were in good agreement with those of sensory evaluation, and the brandy aged with heavily roasted lychee wood chip wood had the highest overall sensory score. A total of 44 volatile aroma compounds were identified in the five brandy samples, and the types and contents of volatile components varied greatly among these samples, each with their unique olefinic flavor substances. The major volatile components of aged lychee brandy were alcohols, esters, olefins, along with lesser amounts of aldehydes and ketones, alkanes, and aromatic hydrocarbons. Ethyl decanoate, ethyl acetate, ethyl caprylate, 2-methylbutanol, isoamyl alcohol, isobutanol, alpha stigmasterol and beta stigmasterol together constituted the major flavor profile of the aged lychee brandy. Therefore, aging with a moderate amount of heavily roasted lychee wood chips resulted in better improvement in the quality of lychee brandy compared to heavily toasted oak chips.

To evaluate the effects of long-term frozen storage on the flavor quality of Rosa roxburghii juice, sensory evaluation, electronic nose, and headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to analyze the dynamic changes of sensory and aroma characteristics of Rosa roxburghii juice at −20 ℃ for various storage times(0, 2, 4, 6, 8 M). The results revealed the identification of a total of 127 volatile compounds, the majority of which were alcohols (31), hydrocarbons (34), and esters (24). The contents of 1-hexanol, (Z)-3-hexen-1-ol, 1-octene-3-ol, linalool, 1-octanol, α-copaene, ethyl hexanoate, ethyl octanoate, and methyl eugenol decreased over time, indicating that the fragrance of green grass, fruit, and flowers may diminish over time. The concentration of nonanal increased substantially (P

In this experiment, five different parts of beef were selected to make boletus beef sauce, namely beef hind leg (BHL), beef shoulder (BS), beef knuckle (BK), beef brisket (BB), and beef tendon (BT), and the effects of different beef parts on the quality and volatile aroma components of spicy beef boletus sauce were investigated. The boletus beef sauce made from BB reported the highest sensory score of 87.4. In terms of texture, beef sauce made from BB exhibited the best quality, with hardness, elasticity, gumminess, and chewiness values of 14.01 N, 4.71 mm, 3.22 N, and 15.30 mJ, respectively. Significant differences (P1. In summary, the BB was proven to be the best raw material in making spicy beef boletus sauce. These research findings provide a theoretical basis and data reference for the future cooking process and flavor characteristics of beef sauce.

Thinning is an expensive and time-consuming management practice used in peach orchards to improve resource distribution among plants and improve production quality. Determining the right time and intensity for thinning is challenging and involves expertise. Furthermore, it generally consumes many hours of work, which makes, in some cases, unfeasible to analyze an entire orchard. For this reason, information that can assist in making making decisions about thinning can improve the cost–benefit ratio of the technique. To mitigate these problems, an electronic nose system, the e-nose, that explores the relationship between the smell of peach trees outdoors and the different growth phases was developed. Twenty-two composed volatile samples were collected from around peach trees (open environment) during its reproductive period (around 39 days) and five supervised machine learning classification algorithms (k-nearest neighbors (KNN), multilayer perceptron (MLP), random forest (RF), logistic regression (LR), and support vector machine (SMV)) were used to analyze the data, to evaluate the possibility of estimating phenological stages from odor environment. The result showed that all models achieved a balanced accuracy greater than 97.5%. As a secondary contribution, the importance of sensors was also analyzed for this application, and a combination of three sensors achieved a classification rate of 100% with the KNN classifier. The e-nose system was successful in distinguishing between petal drop, initial fruit formation, advanced fruit formation, and formed fruit. These results demonstrate the potential of using an electronic nose in a remote system to assist in decision-making in orchard practices as thinning.

To study the effect of different drying methods on volatile components of jasmine tea, headspace solid phase microextraction combined with gas chromatography-mass spectrometry and electronic nose technology were used for quantitative and qualitative analysis of the volatile compounds in jasmine tea dried by four methods, including heat pump drying, hot air drying, microwave drying and vacuum-freeze drying. The results showed that, the top three response values of electronic nose sensors were W1W, W2W and W5S. Under different drying treatments, it can better reflect the differences in volatile substances in jasmine tea. The results of HS-SPME-GC-MS showed that a total of 96 volatile components (alcohol esters, aldehydes, ketones, olefins and other classes) were identified in jasmine tea by four different drying methods. Among these components, the species of olefin compounds were the most with the amount of 37. The total amounts of the volatile components in jasmine tea by freeze-drying were the most abundant with a relative content of 47.382 μg/g, followed by heat pump drying with a relative content of 44.429 μg/g. And the relative contents of olefin and alcohols were higher than other components. The JTF (jasmine tea fragrance) index of jasmine tea was the highest with a amount of 4.766 by heat pump drying. Therefore, HS-SPME-GC-MS combined with electronic nose technology could be used to distinguish the quality of jasmine tea by different drying methods, and would provide theoretical reference for the selection of drying methods in jasmine tea production.

To investigate the impact of liquid nitrogen quick freezing on the quality of prepared chicken chops during frozen storage, this study compared −18 ℃ air freezing with −80 ℃ and −100 ℃ liquid nitrogen quick freezing. Then the frozen samples were stored in a −18 ℃ refrigerator for 0~180 days. The evaluation criteria included sensory scores, color, water holding capacity, texture characteristics, electronic nose analysis, pH, oxidation indicators, and total bacterial count. The results revealed a significant decrease in sensory scores for all groups with the extension of frozen time (P0.05), and the count remained below 5 lg (CFU/g) during frozen storage. Measurement of relevant oxidation indicators revealed that liquid nitrogen quick freezing effectively delayed protein and lipid oxidation. On the 180th day of frozen storage, the malondialdehyde (MDA) content in the −18 ℃ air freezing group was 21.95% and 37.43% higher than that in the −80 ℃ and −100 ℃ liquid nitrogen quick freezing groups, respectively. The total sulfhydryl group (-SH) content was reduced by 26.58% and 44.09% compared to the other two groups, respectively. In summary, the prepared chicken chops by all three freezing methods remained safe for consumption after being frozen at −18 ℃ for 180 days. But the quality characteristics of the prepared chicken chops treated with liquid nitrogen quick freezing were significantly improved during frozen storage.

Abstract A set of nine unique tobacco extract samples was analyzed using a self-developed electronic nose (E-nose) system, a commercial E-nose, and gas chromatography-mass spectrometry (GC–MS). The evaluation employed principal component analysis, statistical quality control, and soft independent modeling of class analogies (SIMCA). These multifaceted statistical methods scrutinized the collected data. Subsequently, a quality control model was devised to assess the stability of the sample quality. The results showed that the custom E-nose system could successfully distinguish between tobacco extracts with similar odors. After further training and the development of a quality control model for accepted tobacco extracts, it was possible to identify samples with normal and abnormal quality. To further validate our E-nose and extend its use within the tobacco industry, we collected and accurately classified the flavors of different tobacco leaf positions, with a remarkable accuracy rate of 0.9744. This finding facilitates the practical application of our E-nose system for the efficient identification of tobacco leaf positions.

Five kinds of commercially available sour fish sauce were taken as the research object to clarify their quality and flavor characteristics. The physicochemical and nutritional indexes, free amino acids, and volatile components were analyzed. The results showed that the moisture, ash, total acid, amino acid nitrogen, reducing sugar, protein, vitamin C and fat contents in the five kinds of sour fish sauce were significantly different (P

'Black pearl' fresh corns from different regions were analyzed using an electronic nose to capture the aroma profile. Principal component analysis (PCA) and discriminant function analysis (DFA) were used for multivariate statistical analysis of 200 data from two regions. Based on this, the judgment model of samples from Heilongjiang production area was built using a soft independent modeling class analysis (SIMCA) algorithm, and a back propagation neural network model was established by Pytorch software to identify and differentiate samples from different regions. The results illustrated that, although the volatile flavor of 'black pearl' fresh corns from different origins were similar, it also showed obvious origin characteristics. SIMCA model could effectively distinguish whether unknown samples come from Heilongjiang (the accuracy rate was 97%), while BP neural network model could predict and identify the origin of 'black pearl' fresh corns from unknown production areas, and the average accuracy rate was 99.44%. The combination of electronic nose technology and BP neural network model could accurately distinguish and identify the origin of 'black pearl' fresh corns.

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates volatile organic compound (VOC) profiles in the exhaled breath of normal subjects under different oxygenation conditions—normoxia (FiO2 21%), hypoxia (FiO2 11%), and hyperoxia (FiO2 35%)—using an electronic nose (e-nose). We aim to identify significant differences in VOC profiles among the three conditions utilizing principal component analysis (PCA) and canonical discriminant analysis (CDA). Our results indicate distinct VOC patterns corresponding to each oxygenation state, demonstrating the potential of e-nose technology in detecting physiological changes in breath composition (cross-validated accuracy values: FiO2 21% vs. FiO2 11% = 63%, FiO2 11% vs. FiO2 35% = 65%, FiO2 21% vs. FiO2 35% = 71%, and p < 0.05 for all). This research underscores the viability of breathomics in the non-invasive monitoring and diagnostics of various respiratory and systemic conditions. [ABSTRACT FROM AUTHOR]

In this study, we present an electronic nose (e-nose) humidity compensation system based on rapid detection to solve the issue of humidity drift's potential negative impact on the performance of electronic noses. First, we chose the first ten seconds of non-steady state (rapid detection mode) sensor data as the dataset, rather than waiting for the electronic nose to stabilize during the detection process. This was carried out in the hope of improving the detection efficiency of the e-nose and to demonstrate that the e-nose can collect gasses efficiently in rapid detection mode. The random forest approach is then used to optimize and reduce the dataset's dimensionality, filtering critical features and improving the electronic nose's classification capacity. Finally, this study builds an electronic nose humidity compensation system to compensate for the datasets generated via rapid real-time detection, efficiently correcting the deviation of the sensor response caused by humidity variations. This method enhanced the average resolution of the electronic nose in this trial from 87.7% to 99.3%, a 12.4% improvement, demonstrating the efficacy of the humidity compensation system based on rapid detection for the electronic nose. This strategy not only improves the electronic nose's anti-drift and classification capabilities but also extends its service life, presenting a new solution for the electronic nose in practical detecting applications. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Condiment is the property of China Condiment and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)