Search Results (1,242)

It is well known that gas sensor responses are affected by the presence of humidity in the analyzed gas. This is particularly true when dealing with biological fluid samples, whose high moisture content interferes with the adsorption of the trace volatile organic compounds (VOCs) on the sensors’ active layer. To address this challenge, this study focuses on designing and testing a novel sampling system for the dehumidification of biological fluid headspace to be characterized by an electronic nose (e-Nose). Such a system, based on the use of disposable polymeric sampling bags purged with dry air, exploits the polymers’ permeability to water vapor to reduce sample humidity. Tested materials included Nalophan^TM (20 μm), high-density polyethylene (HDPE, 8, 9, 10 and 11 μm), low-density polyethylene (LDPE, 12 and 50 μm), and biodegradable polyester (Bio-PS, 15 μm). First, dehumidification performance was characterized as a function of dry air flow rate and film type. A purge of 1 L/min accelerated the sample humidity removal compared to passive storage of bags from >2 h to <1 h (from 80% to 20% RH). Second, a mass-balance model was applied to dedicated experiments to decouple water losses due to diffusion and adsorption, showing that diffusion through the polymer wall dominates, while adsorption occurs in the early stages of conditioning. Third, because these materials are not selectively permeable to water, potential loss of water-soluble VOCs during dehumidification was investigated. Pooled urine headspace samples—both raw and spiked with a metabolite mix of VOCs—were dried using each material and analyzed using a photo-ionization detector (PID) and an e-Nose. Results were compared against a Nafion^TM dryer. Comparison was based on the e-Nose’s ability to discriminate between pooled vs. spiked samples and reveal real-life metabolomic changes. Nalophan^TM bags and Nafion^TM dryer provided the highest VOC fingerprint to support discrimination by the e-Nose, while Bio-PS provided the fastest sample dehumidification. The proposed bag-based system offers a cost-effective, disposable, and contamination-free solution to humidity interference in e-Noses. Full article

Idiopathic pulmonary fibrosis (IPF) and autoimmune usual interstitial pneumonia (aUIP) share overlapping clinico-radiological features, complicating differential diagnosis. Electronic nose (eNose) technology characterizes exhaled breath profiles (“breathprints”) and may offer a non-invasive diagnostic approach in fibrotic interstitial lung diseases. To evaluate whether eNose breathprint analysis can discriminate between IPF and aUIP. In this cross-sectional study of 60 patients (34 IPF, 26 aUIP), breathprints were analyzed using principal component analysis (PCA, retaining eigenvalues > 1). Group differences were assessed via independent t-tests. Linear discriminant analysis (LDA) with leave-one-out cross-validation evaluated the discriminatory performance of PC combinations. PCA identified four principal components, with PC1 explaining 96% of the total variance. PC1 scores were significantly higher in aUIP compared to IPF (mean difference −0.53; 95% CI −1.04 to −0.02; p = 0.04); PC2-PC4 showed no significant differences (p > 0.3). LDA utilizing PC1 and PC3 achieved a cross-validated classification accuracy of 73.3% (95% CI 60.7–84.4, p < 0.05). eNose-derived breathprints showed preliminary discriminatory potential between IPF and autoimmune UIP, supporting further validation of this non-invasive adjunctive approach. Breathomics represents a promising non-invasive adjunctive tool for phenotyping fibrotic interstitial lung diseases, though larger validation studies integrating clinical and biological data are warranted. Full article

Four sweet cherry cultivars (FuChen, Redlight, Huangmi, and Samituo) grown in northern China were used to produce sweet cherry wines with two alcohol levels. Physicochemical properties, antioxidant capacity, and volatile aroma compounds of the wines were systematically investigated. The results showed that wine from the Redlight cultivar with an alcohol content of 11.22 ± 0.17% contained the highest phenolic content and also exhibited the strongest antioxidant capacity as measured by DPPH and ABTS•⁺ assays. Meanwhile, wine from the FuChen cultivar with an alcohol content of 11.45 ± 0.03% had the highest anthocyanin content and showed the strongest FRAP antioxidant activity. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on electronic nose data clearly distinguished the eight sweet cherry wine samples from different cultivars. A total of 58 volatile compounds were identified by headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS). Both principal component analysis (PCA) and OPLS-DA revealed clear differences among the sweet cherry wines based on their volatile composition. Using variable importance in projection (VIP) scores > 1 and relative odor activity values (ROAVs), the key aroma compounds contributing to the characteristic aroma profiles of the eight sweet cherry wines were identified as ethyl butanoate, isoamyl acetate, isoamyl hexanoate, methyl decanoate, ethyl decanoate, ethyl benzoate, methyl salicylate, citronellol, and eugenol. These findings provide important guidance for the selection of raw materials to improve the production of sweet cherry wines with targeted alcohol levels. Full article

Coconut milk-based Filipino foods provide a favorable environment for microbial growth and are highly susceptible to spoilage. Traditionally, spoilage in such foods has been assessed through subjective sensory evaluation, a method that often lacks consistency and accuracy. The present study introduces an electronic nose system employing Support Vector Machine (SVM) algorithms to objectively and quantitatively determine spoilage in coconut milk-based Filipino foods, including Bicol Express, Ginataang Langka, Laing, Bilo-bilo, Maja Blanca, and Ginumis. The developed system integrates six MQ gas sensors connected to an Arduino Nano and a Raspberry Pi 4B to detect and process volatile organic compounds emitted from the foods. The SVM algorithm was selected for its effectiveness in high-dimensional spaces and its ability to construct a binary classifier capable of distinguishing between spoiled and fresh samples. Dimensionality reduction in sensor data was achieved using Principal Component Analysis, which further enhanced classifier performance. System evaluation results demonstrated a high classification accuracy of approximately 98.95%, indicating the robustness of the proposed approach. The utilization of this technology offers significant benefits, not only for individuals with impaired olfactory function but also for the food industry, providing a reliable tool for food quality control and safety. Moreover, the outcomes suggest broader applicability to other perishable food products, with potential contributions to improved global food safety and storage practices. Full article

Background: Sensors and mass spectrometry (MS) are frequently used in combination for food safety and quality assessment, yet their functional integration lacks a formal methodological framework. This review categorizes the synergies between these technologies into distinct Relational Connections. Methodology: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 155 original research articles published between 2015 and 2025 were systematically analyzed. Records were identified via the Scopus database within the food science domain. Experimental meta-data, including extraction protocols, instrumental configurations (ionization source, mass analyzer, cost tier), and chemometric strategies, were extracted to identify core methodological patterns. Statistical associations were quantified using chi-squared tests with Cramer’s V effect sizes. Results: Five Relational Connections were identified: (1) MS as reference for sensor validation (25.2%); (2) MS-sensor correlative analysis (10.3%); (3) MS quantifying data to train predictive sensor models (6.5%); (4) MS identifying targets for sensor detection (7.1%); and (5) MS enabling sensor classification models (51.0%). Technology pairing is governed by a three-level hierarchy: analyte polarity determines the ionization source (V = 0.69), required precision determines the mass analyzer (V = 0.64), and cost/availability constraints shape the practical integration strategy. Gas Chromatography (GC)-MS is predominantly coupled with Electronic Noses for volatile profiling (86% of classification studies), while Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) pairs with biosensors for contaminant analysis (74% of reference validation studies). Systematic analysis of the full pairing matrix reveals that 75% of theoretically possible MS-sensor combinations remain unexplored or underrepresented, identifying both technical boundaries and innovation frontiers. Discussion: The findings clarify the strategic logic behind technology pairings, demonstrating that MS provides the quantitative molecular data required for sensor training. The hierarchical decision framework and identification of underexplored pairings provide an evidence-based guide for designing future integrated food analysis systems. Full article

This study systematically evaluated the potential of five native starches, including corn (CS), potato (PS), tapioca (TS), rice (RS), and sweet potato (SPS), as fat replacers in low-fat pork sausages. The obtained results showed that amylose content varied significantly, with PS and SPS having the highest levels (30.06% and 28.60%, respectively), which were beneficial for forming starch gels. Correspondingly, PS and SPS demonstrated the highest solubility and swelling power. In sausage applications, PS and SPS exhibited superior water-retention capacities, with drying losses of 6.75% and 7.03%, and cooking losses of 2.23% and 2.52%, which were lower than those of the normal control (NC) and low-fat control (LFC) groups. Moreover, the results of texture profile analysis revealed that PS and SPS enabled the sausages to achieve the highest levels of hardness and springiness, contributing to maintaining the moisture retention and toughness of the sausages. Electronic tongue and nose analyses indicated that incorporating these starches did not adversely affect the taste and odor profiles of the sausages, except for RS, which showed distinct flavor encapsulation properties. Overall, PS and SPS served as excellent fat replacers in the meat industry, offering healthier alternatives without compromising product quality. Full article

Identifying pest infestations in fresh fruits is a crucial aspect of international trade. Currently, inspections rely on visual observations and destructive sampling, which are, in most cases, quite demanding. The detection of oviposition signs or early larval development is largely not feasible. Therefore, new methods that are sensitive and non-destructive are urgently needed to detect fruit fly infestation during inspections of fresh produce before their introduction and spread into pest-free areas. Portable electronic olfactory systems, or electronic noses (e-noses), are used in various scientific fields and industries. In this study, we evaluated the potential of a portable PEN3 electronic nose to discriminate between non-infested and infested fruits for three fruit fly species: Ceratitis capitata (Wiedemann), Bactrocera dorsalis (Hendel), and Bactrocera zonata (Saunders) (Diptera: Tephritidae). E-nose datasets were generated from samples of each combination of fruit, fruit fly species, infestation status, and storage condition. These datasets were used to develop classification models. The classification accuracy of the models ranged from 50 to 99% during calibration and cross-validation conditions. However, their performance decreased substantially when applied to independent datasets, highlighting limitations in robustness. These findings indicate that although the PEN3 system shows promise as a non-destructive detection tool, its performance is strongly influenced by seasonal and experimental variability. Further work is needed to incorporate multi-season and multi-variety datasets, improve calibration, and robust validation before practical implementation in field inspection systems. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

Rapid and reliable tea classification is valuable for routine product screening, yet conventional sensory or physicochemical methods are subjective or time-consuming. Electronic nose (E-nose) sensing provides a fast alternative, but performance often degrades under domain shifts caused by different tea types, commercial categories, or acquisition conditions. This study proposes MGDA-Net, a multi-granularity domain adversarial network for cross-domain tea classification using E-nose time-series signals. MGDA-Net learns local temporal dynamics via a CNN branch and global contextual dependencies via a self-attention branch, and fuses them through an adaptive gating module. A branch-level adversarial alignment strategy is introduced to reduce source–target discrepancy at both local and global feature levels. A three-stage training procedure, consisting of source pretraining, adversarial alignment, and target fine-tuning, enables knowledge transfer from a labeled green tea source-domain to two target tasks. Experiments on oolong tea commercial-category classification (6 classes) and jasmine tea retail price-level classification (8 classes) show that MGDA-Net achieves mean accuracies of 99.31 ± 0.69% and 99.38 ± 0.51% over 10 independent runs, substantially outperforming all compared baseline methods. Ablation studies, feature-space analyses, and label-efficiency experiments further confirm the contribution of each component and show that MGDA-Net maintains mean accuracies above 87% when only 40% of the target-domain labels are used for fine-tuning. These findings suggest that MGDA-Net is a promising approach for cross-domain tea classification using E-nose data. Full article

Honey is one of the most valued natural food products, yet it remains highly vulnerable to fraud through mislabeling and adulteration, practices that mislead consumers and compromise food safety. We develop a low-cost and portable electronic nose (e-nose) system for classifying locally available honey brands in the Philippines. The system integrates an array of eight MQ gas sensors to detect volatile organic compounds (VOCs), with an Arduino Mega 2560 handling data acquisition and a Raspberry Pi 5 executing data processing and classification. An Extreme Gradient-Boosted Decision Tree (XGBoost) algorithm was applied to analyze the VOC profiles of three honey brands, each with 38 samples, resulting in a total dataset of 114 samples. The dataset was divided into training, testing, and validation sets to assess the system’s classifying and predictive performance, with accuracy evaluated using a 3 × 3 confusion matrix. The results showed that the system effectively distinguished between honey brands, achieving a validation accuracy of 87.50%, corresponding to 21 out of 24 correctly identified validation trials. Full article

The increasing global demand for plant-based meat alternatives, driven by concerns for environmental sustainability, animal welfare, and health, has led to a growing need for reliable food authentication methods. Animal-based and plant-based meat products are visually similar, which poses a challenge for consumers to distinguish them. We developed an electronic nose (e-nose) system with an array of MQ gas sensors (MQ-2, MQ-3, MQ-7, MQ-135, MQ-136, MQ-138), an Arduino MEGA microcontroller, and an LCD for displaying results. A fuzzy logic algorithm was implemented to process sensor data and enable decision-making through membership functions and IF-THEN rule evaluation to classify meat products as either animal meat or plant-based meat. The system performance was validated with 20 independent test samples. Determination accuracy for both categories, as well as the overall accuracy, was assessed using a confusion matrix. The findings demonstrate that the e-nose system can reliably distinguish between animal-based and plant-based meat products. Full article

Thamnaconus modestus (black scraper) is an economically important fish species in Chinese coastal fisheries, yet its pronounced fishy off-odor, primarily attributed to sulfur-containing compounds and trimethylamine (TMA), severely limits consumer acceptance and product diversification. However, a systematic investigation into how different thermal processing methods affect its volatile flavor profile is lacking. This study employed an integrated multi-instrumental flavoromics platform combining sensory evaluation, electronic nose (E-nose), electronic tongue (E-tongue), gas chromatography–ion mobility spectrometry (GC-IMS), and headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC-MS), coupled with chemometric analysis, to systematically characterize the aroma variations of T. modestus subjected to steaming, boiling, deep-frying, and roasting treatments compared with raw samples. A total of 62 (GC-IMS) and 129 (GC-MS) volatile compounds were identified, from which 78 characteristic markers (VIP > 1) and 45 key odorants (OAV ≥ 1) were screened. Thermal processing markedly reduced sulfur-containing compounds and TMA concentrations (raw >> steamed ≈ boiled >> deep-fried > roasted) while promoting lipid oxidation- and Maillard reaction-derived aldehydes and furans. Two distinct flavor modulation patterns were revealed: moist-heat methods (steaming, boiling) generated grassy/fatty notes through moderate lipid oxidation, whereas dry-heat methods (deep-frying, roasting) produced characteristic roasted/nutty notes via synergistic activation of Strecker degradation and Maillard reaction. These findings provide scientific evidence for precise flavor quality control and diversified processing optimization of T. modestus products. Full article

Milkfish (Chanos chanos), a widely consumed fish in the Philippines, is highly perishable, and conventional freshness assessments based on physical and olfactory inspection are often subjective and unreliable. To address this, we introduce an electronic nose system for the accurate classification of milkfish freshness based on spoilage-related gas emissions, namely methane, ammonia, hydrogen sulfide, and trimethylamine. The system integrates the MQ-series sensors and Taguchi gas sensor with Arduino Nano and Raspberry Pi 5 for data acquisition and signal processing. The k-nearest neighbor algorithm was used for classification, and its performance was evaluated using a confusion matrix. The data was gathered from 100 samples, consisting of 50 fresh and 50 spoiled fish. The evaluation demonstrated a peak classification accuracy of 92% for k-values between 1 and 15, confirming the system’s reliability. These findings indicate the system’s potential as a practical, low-cost, and efficient tool for enhancing consumer safety and quality assurance in the fish supply chain. Full article

Pyropia haitanensis, an economically significant cultivated seaweed in China, exhibits substantial geographical variations in nutritional and sensory qualities that influence its market value. The nutritional quality of the samples, including total sugar, total protein, and amino acid content, as well as color quality, assessed through phycobiliprotein and chlorophyll content, and sensory quality evaluated using an electronic nose and electronic tongue, were determined. To elucidate these quality variations, this study employed an integrated metabolomics and chemometrics approach to analyze samples from five major cultivation regions. Principal component analysis (PCA) effectively differentiated the samples; orthogonal partial least squares discriminant analysis (OPLS-DA) validated this classification with robust model parameters (R²X = 0.791, R²Y = 0.995, Q² = 0.984) and identified key discriminatory metabolites. Weighted gene co-expression network analysis (WGCNA) identified origin-specific metabolic modules correlated with quality traits, revealing that pathways such as cysteine and methionine metabolism underpin the observed differences in flavor profiles across cultivation regions. Furthermore, mediation analysis quantitatively confirmed that inorganic nitrogen primarily influences key flavor attributes by regulating sulfur-containing amino acid and nucleotide metabolism. This study systematically elucidates the metabolic mechanisms governing quality formation in P. haitanensis, providing a scientific foundation for quality control and geographical origin traceability. Full article

In this study, vegetable oil-based high-internal-phase Pickering emulsions (HIPPEs) were formulated from soy protein isolate and sodium alginate, and the effects of different replacement ratios (20–100%) of pork back fat on the quality of emulsified sausages were investigated. With the increase in the fat replacement ratio, cooking loss, released fat, and lipid oxidation significantly decreased (p < 0.05). Similarly, as the replacement ratio rose, L*-values, pH and springiness increased, while a*-values, hardness, cohesiveness, and chewiness showed a significant decrease. The reformulated sausages exhibited superior slice compactness, a macroscopic trait corroborated by the dense network structure observed via microstructural analysis. Electronic nose and electronic tongue measurements indicated that the inclusion of HIPPEs modulated both the aroma profiles and taste attributes of the emulsified sausages. Moreover, although differences were observed in some sensory attributes and flavor characteristics, all formulations with HIPPEs remained within an acceptable sensory range. Full article