Data set for Gambung green tea aroma using on electronic nose.

OBJECTIVES: In recent years, there has been much discussion and research on electronic nose (e-nose). This topic has developed mainly in the medical and food fields. Typically, e-nose is combined with machine learning algorithms to predict or detect multiple sensory classes in each tea sample. Therefore, in e-nose systems, e-nose signal processing is an important part. In many situations, a comprehensive set of experiments is required to ensure the prediction model can be generalized well. This data set specifically focuses on two main goals such as classification of green tea quality and prediction of organoleptic score. In this experiment, Gambung dry green tea samples were used. The challenge is that dry tea does not emit as strong an aroma as tea infusions, making it more difficult for the e-nose system to detect and identify the aromas. This data set offers a valuable resource for researchers and developers to conduct investigations and experiments by classifying and detecting organoleptic scores that aim to categorize and identify organoleptic ratings. This enables a deeper understanding of the quality of dry green tea and encourages further integration of e-nose technology in the tea industry. DATA DESCRIPTION: This experiment focused on analyzing green tea aroma using six gas sensors. Seventy-eight green tea samples were tested, each observed three times, using a tea chamber connected to a sensor chamber via a hose and an intake micro air pump. Air flowed from the tea chamber to the sensor chamber for 60 s, followed by 60 s of aroma data recording. This data was saved into CSV files and labeled according to the Indonesian National Standard (SNI) 3945:2016, which includes special and general requirements for green tea quality. An organoleptic test by a tea tester further labeled the data set into "good" or "quality defect" for classification and provided organoleptic scores based on dry appearance, brew color, taste, aroma, and dregs of brewing for continuous label.

Dynamic changes in volatile profiles and bacterial communities during natural fermentation of Mei yu, traditional Chinese fermented fish pieces.

Microbial metabolism is important for the unique flavor formation of Mei yu, a kind of traditional Chinese fermented fish pieces. However, the interactive relationship between microorganisms and flavor components during fermentation is still unclear. In this study, electronic nose and headspace-solid-phase microextraction-gas chromatography-mass spectrometry analysis were performed to identify flavor components in Mei yu during the fermentation, and the absolute microbial quantification was conducted to identify the diversity and succession of microbial communities. During fermentation, there was an increase in the types of volatile compounds. Alcohols, aldehydes, aromatics and esters were the main flavor compounds and significantly increased in Mei yu, while hydrocarbon and aldehydes significantly decreased. The absolute abundances of Lactobacillus, Lactococcus and Weissella increased significantly after 3 days' fermentation, which were closely associated with the productions of 1-nonanol, 2-methoxy-4-vinylphenol, guaiacol, ethyl palmitate and ethyl caprylate that might though pathways related to fatty acid biosynthesis and amino acid metabolism. However, these genera were negatively correlated with the production of indole. Additionally, the total volatile basic nitrogen (TVB-N) levels of Mei yu fermented during 3 days were within the limits of 25 mg TVB-N/100 g fish, with the contents of free amino acids and lipoxygenase activities were significant lower than that of 4 days' fermentation. In view of food safety and flavor, it suggested that the natural fermented Mei yu at room temperature should be controlled within 3 days. This study highlights the application of absolute quantification to microbiome analysis in traditional fermented Mei yu and provides new insights into the roles of microorganisms in flavor formation during fermentation.

Change in volatile profiles of wheat flour during maturation.

The volatile profiles of wheat flour during maturation were examined through headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) combined with electronic nose (E-nose) and electronic tongue (E-tongue) analyses. The wheat flour underwent maturation under three distinct conditions for predetermined durations. While GC/MS coupled with E-tongue exhibited discernment capability among wheat flour samples subjected to varying maturation conditions, E-nose analysis solely relying on principal component analysis failed to achieve discrimination. 83 volatile compounds were identified in wheat flour, with the highest abundance observed in samples matured for 50 d at 25 °C. Notably, trans-2-Nonenal, decanal, and nonanal were the main contributors to the characteristic flavor profile of wheat flour. Integration of HS-SPME-GC/MS with E-tongue indicated superior flavor development and practical viability in wheat flour matured for 50 d at 25 °C. This study furnishes a theoretical groundwork for enhancing the flavor profiles of wheat flour and its derivative products.

Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography-mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

Characterization of gelatin-oxidized riclin cryogels and their applications as reusable ice cubes in shrimp preservation.

Traditional ice is usually employed to preserve food freshness and extend shelf life. However, ice cannot bear repeated freeze - thaw cycles during the transportation and retailing process, resulting in microbial cross-contamination and spoilage of foods. Herein, succinoglycan riclin was oxidated (RO) and crosslinked with gelatin (Ge), the Ge-RO cryogels were prepared via Schiff base reaction and three freeze - thaw cycles. The Ge-RO cryogels showed improved storage modulus (G') and thermal stability compared with pure gelatin hydrogel. The polymer framework of Ge-RO gels exhibited stable properties against ice crystals destructions during nine freeze - thaw treatments. During the storage and repeated freeze - thaw treatments of shrimps, Ge-RO cryogels exhibited a remarkable preservation effect on shrimps, and their freshness was evaluated using an electronic nose technique equipped with ten sensors. The results demonstrated that the shrimp muscle preserved in ice generated off-odors and resulted in high sensor responses. The sensor responses were reduced sharply of shrimps preserved in cryogels. Moreover, 1H NMR-based metabolomics analysis revealed that shrimps in Ge-RO cryogels group reversed the metabolic perturbations compared with the traditional ice group, the metabolic pathways were related to energy metabolism, nucleotide metabolism, and amino acid metabolism, which provide new clues to the freshness of shrimps. Furthermore, RO exhibited superior antimicrobial activity against E. coli and S. aureus microorganisms. Thus, the crosslinked cryogels are potentially applicable to food preservation, offering sustainable and reusable solutions against traditional ice.

Characterization of the flavor profile and dynamic changes in Chinese traditional fish sauce (Yu-lu) based on electronic nose, SPME-GC-MS and HS-GC-IMS.

In this study, flavor characteristics and dynamic change of Chinese traditional fermented fish sauce (Yu-lu) with different fermentation time (2, 4, 6, 8, and 12 months) were analyzed. The electronic nose analyses confirmed a notable flavor change in fish sauce samples from different stages. During the 12-months fermentation, the total volatile compounds in fish sauce increased from 3.9 mg/L to 13.53 mg/L. Acids, aldehydes, esters and phenols were the main aroma substances and their contents gradually increased during the fermentation process. The PCA of GC-MS and GC-IMS showed that fish sauce samples from different fermentation periods can be well distinguished. A total of 110 volatile compounds identified by GC-MS, and 102 volatile compounds were detected by GC-IMS. Among them, 13 compounds were identified by both GC-MS and GC-IMS. The most varieties (49) of volatiles appeared after 8 months of fermentation. The odor activity value (OAV) analysis showed that 10 volatile compounds were considered as characteristic flavor in traditional fish sauce. The variable influence on projections (VIPs) in PLS-DA models constructed by GC-MS and GC-IMS identified 5 and 10 volatile compounds as biomarkers, respectively. Our results revealed the dynamic changes of characteristic flavor in fish sauce in combination of GC-MS and GC-IMS, which provides theoretical basis for the production and flavor regulation of fish sauce.

Firewood as a tool to valorize meat: A sensory and instrumental approach to grilled veal, lamb, and pork meat.

Two firewood species (beech and olive) were used for grilling three meat types (lamb, pork, and veal) to assess their influence on the sensorial properties of meat. A multimethod approach was adopted, including sensory evaluation with consumers and two analytical techniques to characterize the volatile fraction (Solid-Phase Micro-Extraction Gas Chromatography-Mass Spectrometry [SPME-GC/MS] and electronic nose [e-nose]). The sensory session included three pairwise preference tests (one for each type of meat), an overall liking test, a Rate-All-That-Apply test, and a questionnaire on the interest and perceived value of using sustainably certified firewood in food preparation. The firewood species significantly affected the perception of a few crucial attributes. In particular, olive wood increased the roasted meat flavor perception in lamb and veal, while beech wood increased the perceived intensity of a vegetable/herbaceous flavor in veal. No effect of firewood was observed on preference within each pair of meat samples. Lamb was the significantly most liked meat by consumers, followed by pork; veal was the least liked meat type. Positive and negative drivers of preference were discussed. 36 volatile organic compounds were identified from SPME-GC/MS in meats. Congruently with sensory data, the two veal samples showed a greater distance in terms of volatile composition. Relative distances among samples on maps obtained from SPME-GC/MS and the e-nose were similar. This multi-method approach innovatively showed the potential of using firewood as a 'gastronomic' tool to sensorially characterize and valorize cooked meat.

Establishing Healthy Breath Baselines With Tin Oxide Sensors: Fundamental Building Blocks for Noninvasive Health Monitoring.

INTRODUCTION: Volatile organic compounds (VOCs) in breath serve as a source of biomarkers for medical conditions relevant to warfighter health including Corona Virus Disease and other potential biological threats. Electronic noses are integrated arrays of gas sensors that are cost-effective and miniaturized devices that rapidly respond to VOCs in exhaled breath. The current study seeks to qualify healthy breath baselines of exhaled VOC profiles through analysis using a commercialized array of metal oxide (MOX) sensors. MATERIALS AND METHODS: Subjects were recruited/consented through word of mouth and using posters. For each sample, breath was analyzed using an array of MOX sensors with parameters that were previously established. Data were also collected using a lifestyle questionnaire and from a blood test to assess markers of general health. Sensor data were processed using a feature extraction algorithm, which were analyzed through statistical approaches to identify correlations with confounding factors. Reproducibility was also assessed through relative standard deviation values of sensor features within a single subject and between different volunteers. RESULTS: A total of 164 breath samples were collected from different individuals, and 10 of these volunteers provided an additional 9 samples over 6 months for the longitudinal study. First, data from different subjects were analyzed, and the trends of the 17 extracted features were elucidated. This revealed not only a high degree of correlation between sensors within the array but also between some of the features extracted within a single sensor. This helped guide the removal of multicollinear features for multivariate statistical analyses. No correlations were identified between sensor features and confounding factors of interest (age, body mass index, smoking, and sex) after P-value adjustment, indicating that these variables have an insignificant impact on the observed sensor signal. Finally, the longitudinal replicates were analyzed, and reproducibility assessment showed that the variability between subjects was significantly higher than within replicates of a single volunteer (P-value = .002). Multivariate analyses within the longitudinal data displayed that subjects could not be distinguished from one another, indicating that there may be a universal healthy breath baseline that is not specific to particular individuals. CONCLUSIONS: The current study sought to qualify healthy baselines of VOCs in exhaled breath using a MOX sensor array that can be leveraged in the future to detect medical conditions relevant to warfighter health. For example, the results of the study will be useful, as the healthy breath VOC data from the sensor array can be cross-referenced in future studies aiming to use the device to distinguish disease states. Ultimately, the sensors may be integrated into a portable breathalyzer or current military gear to increase warfighter readiness through rapid and noninvasive health monitoring.

Quality characterization of tobacco flavor and tobacco leaf position identification based on homemade electronic nose.

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.

Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing.

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.

Monitoring of meat quality and change-point detection by a sensor array and profiling of bacterial communities.

BACKGROUND: Real-time monitoring of food consumer quality remains challenging due to diverse bio-chemical processes taking place in the food matrices, and hence it requires accurate analytical methods. Thresholds to determine spoiled food are often difficult to set. The existing analytical methods are too complicated for rapid in situ screening of foodstuff. RESULTS: We have studied the dynamics of meat spoilage by electronic nose (e-nose) for digitizing the smell associated with volatile spoilage markers of meat, comparing the results with changes in the microbiome composition of the spoiling meat samples. We apply the time series analysis to follow dynamic changes in the gas profile extracted from the e-nose responses and to identify the change-point window of the meat state. The obtained e-nose features correlate with changes in the microbiome composition such as increase in the proportion of Brochothrix and Pseudomonas spp. and disappearance of Mycoplasma spp., and with representative gas sensors towards hydrogen, ammonia, and alcohol vapors with R2 values of 0.98, 0.93, and 0.91, respectively. Integration of e-nose and computer vision into a single analytical panel improved the meat state identification accuracy up to 0.85, allowing for more reliable meat state assessment. SIGNIFICANCE: Accurate identification of the change-point in the meat state achieved by digitalizing volatile spoilage markers from the e-nose unit holds promises for application of smart miniaturized devices in food industry.

Sweat-based stress screening with gas chromatography-ion mobility spectrometry and electronic nose.

BACKGROUND: Diagnosis of stress generally involves uses of questionnaires which can provide biased results. The more reliable approach relies on observation of individual symptoms by psychiatrists which is time consuming and could not be applicable for massive scale screening tests. This research established alternative approaches with gas chromatography-ion mobility spectrometry (GC-IMS) and electronic nose (e-nose) to perform fast stress screening based on fingerprinting of highly volatile compounds in headspaces of sweat. The investigated samples were obtained from 154 female nurse volunteers who also provided the data of questionnaire-based mental health scores with the high stress cases confirmed by psychiatrists. RESULTS: The interviews by psychiatrists revealed 14 volunteers with high stress. Their axillary sweat samples and that from 32 nurses with low/moderate stress (controls) were collected onto cotton rods and analysed with GC-IMS. The possible marker peaks were selected based on the accuracy data. They were tentatively identified as ammonia, diethyl ether, methanol, octane, pentane, acetone and dimethylamine which could involve different endogenous mechanisms or the relationships with the local microbiomes. The data were further analysed using partial least squares discriminant analysis with the receiver operating characteristic curves showing the optimum accuracy, sensitivity and selectivity of 87%, 86% and 88%, respectively. Providing that the samples were obtained from the nurses without deodorant uses, the high stress cases could be screened using e-nose sensors with the accuracy of 89%. The sensor responses could be correlated with the marker peak area data in GC-IMS with the coefficients ranging from -0.70 to 0.80. SIGNIFICANCE: This represents the first investigation of highly volatile compound markers in sweat for high stress screening. The established methods were simple, reliable, rapid and non-invasive, which could be further adapted into the portable platform of e-nose sensors with the practical application to perform the screening tests for nurses in Phra Nakorn Si Ayutthaya hospital, Thailand.

Nanozyme-based colorimetric sensor arrays coupling with smartphone for discrimination and "segmentation-extraction-regression" deep learning assisted quantification of flavonoids.

Achieving rapid, cost effective, and intelligent identification and quantification of flavonoids is challenging. For fast and uncomplicated flavonoid determination, a sensing platform of smartphone-coupled colorimetric sensor arrays (electronic noses) was developed, relying on the differential competitive inhibition of hesperidin, nobiletin, and tangeretin on the oxidation reactions of nanozymes with a 3,3',5,5'-tetramethylbenzidine substrate. First, density functional theory calculations predicted the enhanced peroxidase-like activities of CeO2 nanozymes after doping with Mn, Co, and Fe, which was then confirmed by experiments. The self-designed mobile application, Quick Viewer, enabled a rapid evaluation of the red, green, and blue values of colorimetric images using a multi-hole parallel acquisition strategy. The sensor array based on three channels of CeMn, CeFe, and CeCo was able to discriminate between different flavonoids from various categories, concentrations, mixtures, and the various storage durations of flavonoid-rich Citri Reticulatae Pericarpium through a linear discriminant analysis. Furthermore, the integration of a "segmentation-extraction-regression" deep learning algorithm enabled single-hole images to be obtained by segmenting from a 3 × 4 sensing array to augment the featured information of array images. The MobileNetV3-small neural network was trained on 37,488 single-well images and achieved an excellent predictive capability for flavonoid concentrations (R2 = 0.97). Finally, MobileNetV3-small was integrated into a smartphone as an application (Intelligent Analysis Master), to achieve the one-click output of three concentrations. This study developed an innovative approach for the qualitative and simultaneous multi-ingredient quantitative analysis of flavonoids.

A machine learning-based electronic nose system using numerous low-cost gas sensors for real-time alcoholic beverage classification.

This study introduces numerous low-cost gas sensors and a real-time alcoholic beverage classification system based on machine learning. Dogs possess a superior sense of smell compared to humans due to having 30 times more olfactory receptors and three times more olfactory receptor types than humans. Thus, in odor classification, the number of olfactory receptors is a more influential factor than the number of receptor types. From this perspective, this study proposes a system that utilizes distinctive data patterns resulting from heterogeneous responses among numerous low-cost homogeneous MOS-based sensors with poor gas selectivity. To evaluate the performance of the proposed system, learning data were gathered using three alcoholic beverage groups including different aged whiskeys, Korean soju with 99% same compositions, and white wines made from the Sauvignon blanc variety, sourced from various countries. The electronic nose system was developed to classify alcoholic samples measured using 30 gas sensors in real time. The samples were injected into a gas chamber for 60 seconds, followed by a 60-second injection of clean air. After preprocessing the time-series data into four distinct datasets, the data were analyzed using a machine learning algorithm, and the classification results were compared. The results showed a high classification accuracy of over 99%, and it was observed that classification performance varied depending on data preprocessing. As the number of gas sensors increased, the prediction accuracy improved, reaching up to 99.83 ± 0.21%. These experimental results indicated that the proposed electronic nose system's classification performance was comparable to that of commercial electronic nose systems. Additionally, the implementation of an alcoholic beverage classification system based on a pretrained LDA model demonstrated the feasibility of real-time classification using the proposed system.

Application of electronic nose technology in the diagnosis of gastrointestinal diseases: a review.

Electronic noses (eNoses) are electronic bionic olfactory systems that use sensor arrays to produce response patterns to different odors, thereby enabling the identification of various scents. Gastrointestinal diseases have a high incidence rate and occur in 9 out of 10 people in China. Gastrointestinal diseases are characterized by a long course of symptoms and are associated with treatment difficulties and recurrence. This review offers a comprehensive overview of volatile organic compounds, with a specific emphasis on those detected via the eNose system. Furthermore, this review describes the application of bionic eNose technology in the diagnosis and screening of gastrointestinal diseases based on recent local and international research progress and advancements. Moreover, the prospects of bionic eNose technology in the field of gastrointestinal disease diagnostics are discussed.

Effect of glycine and Pediococcus pentosaceus R1 combined application on the physicochemical properties, oxidative stability, and taste profile of Harbin dry sausages.

This study investigated the effects of the application of glycine (Gly) and Pediococcus pentosaceus R1(Pp), alone or in combination, on the physicochemical properties, oxidative stability, and taste quality of Harbin dry sausages. The results demonstrated that after nine days of fermentation, the Gly + Pp group exhibited significantly (P < 0.05) lower moisture content (19.04%), water activity (0.686), and pH (4.78) values, alongside notably (P < 0.05) higher lactic acid bacteria count (8.11 log CFU/g sausage) and redness value (17.2), compared to the other three groups (P < 0.05). In addition, the dry sausages in the Gly + Pp group exhibited the lowest peroxide value (0.34 meq/kg sausage), thiobarbituric acid reactive substances (0.46 MAD/kg sausage), and protein carbonyl content (1.26 nmol/kg protein) during fermentation, followed by the Gly group, Pp group, and control group. Electronic tongue (e-tongue) and sensory evaluations revealed that the combined treatment with P. pentosaceus R1 and Gly resulted in superior taste characteristics. Besides, partial least squares regression (PLSR) analysis illustrated that the taste qualities characterized using the e-tongue were accordant with the sensory evaluation consequences, and total free amino acids (FAAs) and organic acids contributed to the dry sausages' taste properties. In conclusion, the combined application of Gly and P. pentosaceus R1 enhanced the physicochemical properties, oxidative stability, and taste profile of Harbin dry sausages.

A comprehensive analysis of aroma quality and perception mechanism in ginger-infused stewed beef using instrumental analysis, sensory evaluation and molecular docking.

The ginger-infused stewed beef exhibited a satisfactory odor in Chinese cooking meat. This study aimed to reveal its aroma quality and perception mechanism through electronic nose, sensory evaluation and gas chromatography-mass spectrometry (GC-MS), gas chromatography-ion mobility spectrometry (GC-IMS) coupled with chemometric methods and molecular docking. Sensory evaluation and electronic nose analysis indicated ginger could greatly modify aroma profile of beef. Most C6-C10 aldehydes significantly decreased and terpenes increased in ginger-infused stewed beef. Orthogonal partial least squares-discriminant analysis (OPLS-DA) found 7 key markers for distinguishing stewed beef with or without ginger. Ginger additions could reduce fatty acids consumption. Moreover, the key contributors of fatty, bloody, meaty, ginger and mint aroma attributes, namely (E)-2-octenal, 1-octen-3-ol, 2-acetylthiazole, zingiberene and γ-elemene, respectively, selected by partial least squares regression (PLSR) analysis were docked with the olfactory receptor. Hydrogen bonds and hydrophobic interactions were the main interaction forces between olfactory receptor and the five compounds.

Portable Sensing Probe for Real-Time Quantification of Ammonia in Blood Samples.

The detection of ammonia levels in blood is critical for diagnosing and monitoring various medical conditions, including liver dysfunction and metabolic disorders. However, traditional diagnostic methods are slow and cumbersome, often involving multiple contact-based steps such as ammonia separation in alkali conditions followed by distillation or microdiffusion, leading to delays in diagnosis and treatment. Herein, we developed a colorimetric assay capable of rapid detection of ammonia in whole blood or plasma samples, utilizing 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO)-oxidized cellulose nanocrystals (TCNC) coupled with gold nanoparticles (AuNPs). The basis of our assay relies on either (i) the interaction between the carboxylate group (-COO) of TEMPO and ammonium ions or (ii) the manipulation of AuNPs surface plasmon resonance (SPR) through the formation of Au(NH3)43+, which displaces a redox mediator, resazurin, resulting in observable multicolor displays at various concentrations of ammonia. The colorimetric assay exhibits a wide linear detection range for dissolved NH4+ (0.1-37 µM) with a low limit of detection (LOD) of 0.1 µM. Additionally, it effectively measures NH3(g) concentrations in the range of 0.5-144 µM. The fabricated electrochemical nose (E-nose) device demonstrates excellent analytical performance for plasma ammonia sensing (0.05-256 µM). Experimental results demonstrate a linear detection range suitable for clinical applications, with excellent correlation to standard laboratory methods, offering a practical solution for point-of-care (PoC) testing. We anticipate that this approach can be applied broadly to improve patient monitoring and treatment by providing immediate and accurate ammonia measurements in a clinical setting.

Discriminative analysis of aroma profiles in diverse cigar products varieties through integrated sensory evaluation, GC-IMS and E-nose.

Cigars, treasured for their rich aromatic profiles, occupy a notable segment in the global consumer market. The objective of this study was to characterize the volatile aroma compounds that shape the flavor profiles of six distinct varieties of Great Wall cigars, contributing to the understanding of cigar aroma analysis. Utilizing HS-GC-IMS and sensory evaluation, the study discerned the aroma profiles of GJ No. 6 (GJ), Animal from the Chinese zodiac (SX), Range Rover No. 3 Classic (JD), Miracle 132 (QJ), Sheng Shi No. 5 (SS), and Red 132 (HS) cigars. The analysis uncovered a spectrum of characteristic aromas, including tobacco, creaminess, cocoa, leather, baking, herbaceous, leathery, woodsy, and fruity notes. A total of 88 compounds were identified, categorized into 11 chemical classes, with their quantities varying among the cigars in a descending order of QJ, JD, GJ, SS, HS, and SX. 24 compounds, such as 2-heptanone, n-butanol, 2,6-dimethylpyrazine and 2-furfuryl methyl sulfide were considered as key differential components. The volatile components were effectively differentiated using principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and cluster analysis, revealing correlations between sensory attributes, key components, and electronic nose (E-nose). This research introduces a novel method for analyzing volatile aroma components in cigars, offering insights to enhance cigar quality and to foster the development of new products with unique aroma profiles.

Phase-Engineered MoSe2/CeO2 Composites for Room-Temperature Gas Sensing with a Drastic Discrimination of NH3 and TEA Gases.

Detecting and distinguishing between hazardous gases with similar odors by using conventional sensor technology for safeguarding human health and ensuring food safety are significant challenges. Bulky, costly, and power-hungry devices, such as that used for gas chromatography-mass spectrometry (GC-MS), are widely employed for gas sensing. Using a single chemiresistive semiconductor or electric nose (e-nose) gas sensor to achieve this objective is difficult, mainly because of its selectivity issue. Thus, there is a need to develop new materials with tunable and versatile sensing characteristics. Phase engineering of two-dimensional materials to better utilize their physiochemical properties has attracted considerable attention. Here, we show that MoSe2 phase-transition/CeO2 composites can be effectively used to distinguish ammonia (NH3) and triethylamine (TEA) at room temperature. The phase transition of nanocomposite samples from semimetallic (1T) to semiconducting (2H) prepared at different synthesis temperatures is confirmed via X-ray photoelectron spectroscopy (XPS). A composite sensor in which the 2H phase of MoSe2 is predominant lacks discrimination capability and is less responsive to NH3 and TEA. An MoSe2/CeO2 composite sensor with a higher 1T phase content exhibits high selectivity for NH3, whereas one with a higher 2H phase content (2H > 1T) shows more selective behavior toward TEA. For example, for 50% relative humidity, the MoSe2/CeO2 sensor's signal changes from the baseline by 45% and 58% for 1 ppm of NH3 and TEA, respectively, indicating a low limit of detection (LOD) of 70 and 160 ppb, respectively. The composites' superior sensing characteristics are mainly attributed to their large specific surface area, their numerous active sites, presence of defects, and the n-n type heterojunction between MoSe2 and CeO2. The sensing mechanism is elucidated using Raman spectroscopy, XPS, and GC-MS results. Their phase-transition characteristics render MoSe2/CeO2 sensors promising for use in distributed, low-cost, and room-temperature sensor networks, and they offer new opportunities for the development of integrated advanced smart sensing technologies for environmental and healthcare.