Search Results (581)

Consumer preferences for pork are increasingly prioritizing quality traits such as flavor and tenderness, which are often superior in Chinese indigenous pig breeds. The primary objective of this study was to explore the molecular basis of flavor traits using Rongchang (RR), Yorkshire (YY), and RR × YY (YR) breeds. The investigation focused on meat quality traits, along with untargeted metabolomics, lipidomics, and volatile flavor compound (VOC) profiling of the longissimus dorsi muscle. The results indicated that RR pork exhibited higher pH levels and overall acceptability. Analyses using electronic nose and tongue demonstrated that RR pork elicited stronger responses for W2S, W1S, and W1C sensors, as well as for umami and sourness. A total of 15 VOCs were identified as differing among the breeds. RR pork contained higher levels of benzothiazole and dimethyl sulfoxide, but lower levels of nonane, 2-methylheptane, and 2,4-dimethylheptane. Metabolomic analysis revealed 45 distinct metabolites, with a greater abundance of flavor precursors such as α-ketoglutaric acid in RR pork. Lipidomic analysis identified 22 different lipids, with triglycerides being more enriched in RR pork. Phospholipids, such as phosphatidylcholine (PC) and phosphatidylethanolamine (PE), varied by breed, with PC (e) being lowest and cardiolipin highest in RR pork. Correlation network analysis revealed that nonane, 2-methylheptane was the most connected flavor compound, positively correlating with certain lipids and metabolites, such as PC (18:1_18:1), PE (18:2e_22:6), PC (36:4) and 2-phenylglycine, and negatively correlating with PC (32:0e), SM (d41:1), N-hydroxy-2-acetamidofluorene, and histamine. This multi-omics approach provides a comprehensive view of the molecular signatures associated with pork preference, identifying potential biomarkers for meat quality that can be leveraged for future breeding strategies. Full article

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

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

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

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

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

Alcoholic fermentation in winemaking is a complex bioprocess governed by physicochemical parameters such as temperature, density, pH, CO₂ and redox potential, which critically affect yeast metabolism and wine quality. This review provides an integrated analysis of fermentative dynamics and emerging sensorization technologies, highlighting how their combined implementation enables real-time monitoring and advanced control in precision enology. Advances in conventional physicochemical sensors, spectroscopic techniques (NIR/MIR/UV-Vis) and non-conventional devices (e-noses, electronic tongues) integrated into IoT platforms enable continuous data acquisition, overcoming traditional manual sampling limitations. Predictive modeling, including kinetic models, machine learning approaches (e.g., Random Forest, XGBoost) and model predictive control (MPC/NMPC), supports anomaly detection, optimization of enological interventions and energy-efficient thermal management, while virtual sensors based on Kalman filters improve the estimation of non-measurable states (e.g., biomass, ethanol kinetics). Despite current challenges in calibration and interoperability, these innovations foster sustainable and reproducible winemaking under climate variability and pave the way for digital twins and semi-autonomous fermentation systems. Full article

MOS gas sensors have proven to be useful in electronic noses, which utilize these sensors to detect volatile organic compounds in human breath to detect various lung diseases. Unfortunately, the long settling time of MOS gas sensors is ill-suited to measuring human breath, where complete breathing cycles are often shorter than 5 s. Existing studies circumvent this limitation by collecting gas samples and injecting them into a sealed chamber to react with the sensors. However, it would be convenient if breath-by-breath analysis could be conducted without the need to store breath samples. To accomplish this, we present a novel forecasting methodology to predict the final value $t_{\infty }$ of a gas sensor’s response based on its initial transient behavior. To do this, we present and validate a second-order mathematical model of the sensors’ response characteristics, which we then use in our preliminary work using neural networks to predict the final sensor value. Although some challenges were encountered, the initial results are encouraging, and we plan to extend our study in the future to collect a more expansive dataset and explore the use of other types of machine learning algorithms for this application. Full article

Condiment sauces such as soy sauce, fish sauce, oyster sauce, and Worcestershire sauce play a vital role in culinary practices and cultural identity, particularly in the Philippines. These sauces are distinguished by their unique volatile organic compound profiles, which define their aroma and flavor. With the growing demand for these condiment products, there is an increasing need for accurate and efficient methods to classify them, ensuring product authenticity and strengthening quality control. However, conventional approaches such as sensory evaluation and laboratory-based chemical analysis are often expensive, time-consuming, and subjective. To address this limitation, we used an electronic nose (e-nose) system integrated with a Support Vector Machine (SVM) classifier for the classification of dark condiment sauces. The system consists of an array of MQ-series gas sensors connected to an Arduino Mega 2560 for analog-to-digital conversion, with Raspberry Pi 5 serving as the primary processing unit. Sensor data undergo preprocessing steps, including standardization and dimensionality reduction through principal component analysis, before being classified using SVM. A total of 120 samples, consisting of 40 readings per condiment type, were used for training and testing, while 60 additional samples—15 per class—were reserved for validation. The e-nose system achieved a 95% classification performance, as evaluated using a confusion matrix and overall accuracy metrics. These results demonstrate the potential of the e-nose combined with SVM as a reliable tool for condiment classification. The system offers practical applications in quality control and product authentication. Future work may extend its capabilities toward spoilage detection, the integration of different gas sensors, and the classification of a wider variety of condiment sauces. Full article

Electronic nose systems are advanced technological tools that enable the objective evaluation of odors through sensor arrays mimicking the human olfactory mechanism and sophisticated data processing algorithms. These systems facilitate rapid, reproducible, and standardized measurement of chemical components in applications such as food safety, environmental monitoring, medical diagnostics, and industrial quality control. In this study, measurements obtained from electronic nose sensors were compared with olfactometry panelist assessments using n-butanol as a reference substance in accordance with the TS EN 13725 standard. Furthermore, machine learning algorithms, including Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), were applied to model the sensor data and evaluate their predictive accuracy. The results demonstrated the reliability and applicability of the electronic nose system, achieving training mean absolute percentage error (MAPE) values of 6.53% for PLS, 10.89% for SVR, and 0.15% for GPR. This study presents an innovative approach that systematically assesses the performance of electronic nose technology using a standardized reference odor and highlights the effectiveness of the modeling approach. Full article

Traditional sensory evaluation of traditional Chinese medicine (TCM) and medicinal and food homologous products has long relied on human observation of appearance, color, aroma, and taste. However, this approach is highly subjective, difficult to quantify, and often lacks reproducibility across evaluators. Intelligent sensory systems, including the electronic nose, electronic tongue, and machine vision, provide objective and digitized sensory information for TCM quality evaluation. Nevertheless, these platforms generate high-dimensional and heterogeneous datasets, creating a strong demand for efficient artificial intelligence (AI)-based analytical tools. This review summarizes recent advances in the application of machine learning and deep learning methods, such as support vector machine, random forest, convolutional neural network, and long short-term memory networks, for intelligent sensory evaluation of TCM. Particular emphasis is placed on how AI supports feature extraction, pattern recognition, classification, regression, and multisource data fusion across electronic nose, electronic tongue, and machine vision systems. Representative applications in raw material authentication, geographical origin discrimination, processing monitoring, and quality grading are also discussed. In addition, the current challenges related to data standardization, sensor drift, model robustness, and interpretability are highlighted. Overall, this review provides an integrated overview of AI-enabled intelligent sensory technologies and clarifies their potential to advance TCM quality evaluation toward a more objective, efficient, and holistic framework. Full article

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

In vitro fecal inoculation coupled with gas chromatography–mass spectrometry (GC-MS) has been used for evaluating fecal deodorants. However, high cost and complex data interpretation limit its routine application. An electronic nose (eNose) offers a rapid, cost-effective alternative. This study aimed to evaluate the eNose as a screening tool for fecal odor compared with solid-phase microextraction gas chromatography–mass spectrometry (SPME GC-MS) and to examine the in vitro effects of fecal deodorant supplements on fecal odor profiles. Feces from ten healthy cats were serially diluted (1:1 to 1:8) and analyzed using both instruments. Four dietary supplements—Yucca schidigera extract (YSE), Quillaja saponaria extract (QSE), fructooligosaccharides (FOS), and oat beta-glucans (OBG)—were tested at concentrations of 0.0, 0.2, 0.4, and 0.8 g/100 mL. The eNose showed comparable performance to GC-MS in discriminating among sample dilutions. In vitro fermentation showed that FOS and OBG significantly increased volatile fatty acid (VFA)-related sensor responses while signals linked to ammonia and sulfur compounds were reduced. QSE had minimal effect, whereas YSE produced moderate changes. The total sensor response intensities did not differ between treatments. These findings indicate that prebiotic supplements exert stronger effects than saponin-based supplements and highlight the potential of eNoses with in vitro fermentation for rapid screening of fecal deodorants. Full article