Search Results (147)

This study evaluated the effects of partial replacement of fishmeal with 11% defatted Hermetia illucens meal (corresponding to approximately 35% replacement of the fishmeal-derived animal protein fraction) on growth performance, fillet proximate composition, and instrumental sensory responses of gilthead seabream (Sparus aurata) reared under commercial offshore farming conditions. A total of 60,000 fish were distributed into four sea cages and fed either a control diet (FM) or an insect-based diet (HIM) for 181 days. No significant differences were observed between dietary treatments in final body weight, weight gain, specific growth rate, feed conversion ratio, protein efficiency ratio, or somatic indices, indicating that insect meal inclusion did not impair productive performance under farm-scale conditions. Fillet proximate composition was largely preserved. Fillet sensory characteristics were assessed using an integrated artificial sensing platform including an electronic eye (E-eye), electronic nose (E-nose), and electronic tongue (E-tongue) coupled with multivariate analysis. E-eye and E-nose analyses showed no clear discrimination between dietary groups, indicating that dietary insect meal inclusion had limited effects on fillet visual appearance and volatile compound profiles. In contrast, E-tongue analysis revealed a clear separation between treatments, suggesting selective modulation of taste-related attributes associated with dietary inclusion of insect meal. Overall, the results demonstrate that defatted H. illucens meal can be incorporated into practical seabream diets under commercial farming conditions without compromising productive performance or major fillet quality traits. Furthermore, this study provides farm-scale evidence that artificial sensing technologies can effectively detect subtle diet-related changes in sensory characteristics, particularly those associated with taste perception. Full article

Conventional chemical analysis relies on costly laboratory instrumentation, while current e-nose systems are expensive for widespread deployment. New opportunities for low-cost, accessible e-nose applications are emerging for diverse fields due to the rapid evolution of inexpensive sensor technologies. We developed a framework that enables rapid integration of newly available low-cost gas sensors into functional e-nose systems, continuously evaluating them as they become commercially available. By characterizing their performance in multi-sensor arrays that mimic biological olfaction, the framework demonstrates effective odor discrimination in a low-cost e-nose system through coordinated behavior of a heterogeneous sensor array. Our testing approach includes sensor sensitivity, selectivity, and stability, which are to be combined with appropriate pattern recognition and AI algorithms in the future for effective chemical discrimination. This work provides a pathway for continuously updating e-nose technology with the latest available sensors in a cost-effective manner, thereby making advanced chemical sensing accessible for resource-limited settings and enabling large-scale deployment in real-world applications with future potential applications such as food quality monitoring, environmental sensing, smart agriculture, etc. Full article

Precision in estimating the ripeness of fruits is critical in quality control and minimizing losses in supply chains of agricultural produce following harvesting. Conventional ripeness assessment techniques tend to be destructive, time-consuming and unsuited to monitoring in real-time. In order to avoid these drawbacks, this research suggests a cloud-integrated smart electronic nose (E-nose) system to predict fruit ripeness in a non-destructive and real-time manner. The system uses a low-priced, non-selective gas sensor array with an ESP8266-based Internet of Things (IoT) board to record volatile organic compound (VOC) signatures released at various maturation phases of fruits. The obtained sensor data will be sent to a cloud server to be preprocessed centrally and classified using machine learning, thus reducing the computational needs at the edge. There is a collection of 953 samples of the unripe, ripe, and rotten stages of banana under controlled conditions. Several supervised machine learning algorithms are tested, and methods of ensemble boosting proved to be more effective. The Light Gradient Boosting Machine (LightGBM) is the most accurate in terms of classification of 96.50% and weighted F1-score of 96.49%. The confusion matrix analysis shows that the majority of misclassifications are observed among the neighboring stages of ripeness, indicating the gradual biochemical changes. The system is practically applicable as visualization of the predicted ripeness levels occurs in real time via a mobile application. The suggested model provides a scalable, low-cost, and smart solution to precision agriculture, which can allow efficient, automated, and non-destructive measurement of fruit quality. Full article

Prostate cancer (PCa) is one of the leading causes of cancer-related morbidity and mortality in men worldwide, and early detection remains crucial for ensuring effective treatment and improving patient outcomes. In this context, the development of non-invasive, accurate, and cost-effective screening strategies is of paramount importance. One particularly promising and innovative approach is the analysis of volatile organic compounds (VOCs), a field known as volatolomics. VOCs, which are metabolic by products released by the body, reflect underlying biochemical processes and offer a valuable, non-invasive source of diagnostic information. Recent advances have highlighted the potential of VOC profiling in PCa detection. A variety of biological systems have demonstrated remarkable sensitivity and specificity in recognizing disease-associated VOC signatures. Notably, trained dogs, selected invertebrates, and artificial sensing platforms have all shown the ability to identify PCa-related olfactory patterns. Among technological approaches, electronic noses (eNoses), which combine chemical sensor arrays with pattern recognition algorithms such as neural networks, represent a rapidly evolving diagnostic tool. Together, these biologically inspired and technology-driven strategies are reshaping the landscape of cancer diagnostics. They offer a compelling foundation for the development of rapid, non-invasive, and clinically translatable methods for PCa detection. This narrative review summarizes recent advances in using VOCs for PCa diagnosis and evaluates the reproducibility and clinical robustness of these approaches, focusing on challenges such as standardizing sampling, storage, and analysis, small cohort sizes, and the need for external validation and regulatory integration. Full article

Tea quality evaluation is increasingly moving from subjective sensory assessment and destructive laboratory analysis toward rapid, non-destructive, and data-driven approaches. This review summarizes recent advances in multimodal sensing integrated with deep learning for tea quality evaluation, with emphasis on sensor complementarity, data-fusion strategies, representative applications, and deployment-related limitations. Major sensing modalities, including machine vision, near- and mid-infrared spectroscopy, Raman and fluorescence spectroscopy, hyperspectral imaging, and electronic nose/electronic tongue systems, are discussed in relation to their ability to characterize appearance, chemical composition, aroma, flavor, processing status, and safety-related attributes. Applications are examined for quality grading, chemical composition prediction, aroma and flavor characterization, fermentation monitoring, and safety-related extensions across representative tea products, including green tea, black tea, dark tea, matcha, and jasmine tea. Overall, multimodal approaches can outperform single-sensor systems only when the selected modalities provide complementary, rather than redundant, information layers. However, practical translation remains constrained by small and weakly standardized datasets, insufficient external validation, sensor instability, limited model transferability, high computational cost, and insufficient interpretability. Future research should prioritize standardized datasets, leakage-free validation protocols, interpretable multimodal modeling, truly independent external validation, interoperable multi-sensor platforms, and lightweight deployable models. Full article

Electronic nose platforms based on metal-oxide (MOX) sensors offer potential for low-power gas classification under dynamic operating conditions. This study evaluates a BME688-based digital nose configured with a temperature-modulated heater profile (HP-354) and reduced duty cycle (RDC-5-10) for binary ethylene presence classification in fruit headspace. Seven climacteric fruit types were sealed in bags to allow natural ethylene accumulation and were sampled across multiple sessions over a two-week period. A structured alternating protocol between fruit headspace (Class A) and neutral air (Class B) generated 21 ethylene sessions and 23 neutral-air sessions, comprising 38,882 individual thermal scan cycles (~10 s per cycle). Each full heater cycle was treated as a training instance within BME AI-Studio. A supervised neural-network classifier trained on 70% of cycle-level data achieved 92.9% overall accuracy with a macro F1 score of 91.9% on validation data. Results demonstrate that temperature-modulated MOX signatures enable robust discrimination of biologically generated ethylene from baseline air under realistic headspace variability. This study demonstrated classification feasibility under naturally accumulated fruit emissions while highlighting the need for future concentration-resolved calibration studies. 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

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

This work presents an enhanced sensing framework for MEMS gas sensors based on tunable-amplitude periodic modulation, enabling multi-state excitation and feature enrichment without increasing the number of sensing elements. A multi-level periodic driving scheme is introduced to realize sensor virtualization, and the resulting multi-state responses are processed using a short-term baseline-tracking algorithm and a dislocated sparse-sampling strategy to improve feature discrimination. A lightweight multilayer perceptron (MLP) classifier is subsequently optimized and deployed on a field-programmable gate array (FPGA)-based accelerator to enable gas recognition under constrained hardware resources. Experimental results obtained from ternary mixtures of CH₄, CO, and H₂ demonstrate a classification accuracy of 98.5%, accompanied by a 60% reduction in model size and a fivefold improvement in computational speed on the FPGA accelerator. Full article

Two-dimensional carbide crystals (MXenes) are emerging as a promising platform for the development of novel gas sensors, offering advantages in energy efficiency and tunable analyte selectivity. One of the most effective strategies to enhance and tailor their functional performance involves forming hetero-structured composites with metal oxides. In this work, we explore a chemiresistive effect in double-metal MXene of Ti_0.2V_1.8C and its composites with 2 mol. % SnO₂ and Co₃O₄ nanocrystalline oxides toward feasibility tests with alcohol and ammonia vapor probes. The materials were characterized by simultaneous thermal analysis, X-ray diffraction analysis, Raman spectroscopy, and scanning/transmission electron microscopy. Gas-sensing experiments were carried out on composite layers deposited on multi-electrode substrates to be exposed to the test gases, 200–2000 ppm concentrations, at an operating temperature of 370 °C. The developed sensor array demonstrated clear analyte discrimination. The distinct sensor responses enabled a selective identification of vapors through linear discriminant analysis, demonstrating the further potential of MXene-based materials for integrated electronic nose applications. Full article

Volatile organic compounds (VOCs) released from automotive interior materials and exchanged with external air seriously compromise cabin air quality and pose health risks to occupants. Electronic noses (E-noses) based on metal oxide semiconductor (MOS) micro-electro-mechanical system (MEMS) sensor arrays provide an efficient, real-time solution for in-vehicle gas monitoring. This review examines the use of SnO₂-, ZnO-, and TiO₂-based MEMS sensor arrays for this purpose. The sensing mechanisms, performance characteristics, and current limitations of these core materials are critically analyzed. Key MEMS fabrication techniques, including magnetron sputtering, chemical vapor deposition, and atomic layer deposition, are presented. Commonly employed pattern recognition algorithms—principal component analysis (PCA), support vector machines (SVM), and artificial neural networks (ANN)—are evaluated in terms of principle and effectiveness. Recent advances in low-power, portable E-nose systems for detecting formaldehyde, benzene, toluene, and other target analytes inside vehicles are highlighted. Future directions, including circuit–algorithm co-optimization, enhanced portability, and neuromorphic computing integration, are discussed. MOS MEMS E-noses effectively overcome the drawbacks of conventional analytical methods and are poised for widespread adoption in automotive air-quality management. Full article

Exhaled breath analysis has gained considerable interest as a noninvasive diagnostic tool capable of detecting volatile organic compounds (VOCs) and inorganic gases that serve as biomarkers for various diseases. Polymer-based gas sensors have garnered significant attention due to their high sensitivity, room-temperature operation, excellent flexibility, and tunable chemical properties. This review comprehensively summarized recent advancements in polymer-based gas sensors for the detection of disease biomarkers in exhaled breath. The gas-sensing mechanism of polymers, along with novel gas-sensitive materials such as conductive polymers, polymer composites, and functionalized polymers was examined in detail. Moreover, key applications in diagnosing diseases, including asthma, chronic kidney disease, lung cancer, and diabetes, were highlighted through detecting specific biomarkers. Furthermore, current challenges related to sensor selectivity, stability, and interference from environmental humidity were discussed, and potential solutions were proposed. Future perspectives were offered on the development of next-generation polymer-based sensors, including the integration of machine learning for data analysis and the design of electronic-nose (e-nose) sensor arrays. Full article

Accurate, real-time monitoring of meat freshness is essential for reducing food waste and safeguarding consumer health, yet conventional methods rely on costly, laboratory-grade spectroscopy or destructive analyses. This work presents a low-cost electronic-nose platform that integrates a compact array of metal-oxide gas sensors (Figaro TGS2602, TGS2603, and Sensirion SGP30) with a Gaussian Process Regression (GPR) model to estimate a continuous freshness index under refrigerated storage. The pipeline includes headspace sensing, baseline normalization and smoothing, history-window feature construction, and probabilistic prediction with uncertainty. Using factorial analysis and response-surface optimization, we identify history length and sampling interval as key design variables; longer temporal windows and faster sampling consistently improve accuracy and stability. The optimized configuration (≈143-min history, ≈3-min sampling) reduces mean absolute error from ~0.51 to ~0.05 on the normalized freshness scale and shifts the error distribution within specification limits, with marked gains in process capability and yield. Although it does not match the analytical precision or long-term robustness of spectrometric approaches, the proposed system offers an interpretable and energy-efficient option for short-term, laboratory-scale monitoring under controlled refrigeration conditions. By enabling probabilistic freshness estimation from low-cost sensors, this GPR-driven e-nose demonstrates a proof-of-concept pathway that could, after further validation under realistic cyclic loads and operational disturbances, support more sustainable meat management in future smart refrigeration and cold-chain applications. This study should be regarded as a methodological, laboratory-scale proof-of-concept that does not demonstrate real-world performance or operational deployment. The technical implications described herein are hypothetical and require extensive validation under realistic refrigeration conditions. Full article

Roasting intensity and cultivar shape the physicochemical composition and sensory characteristics of date seed-based coffee alternatives. This study evaluated quality traits among eight date seed cultivars (Zahidi, Medjool, Deglet nour, Thoory, Halawi, Barhee, Khadrawy, Bau Strami) roasted at three intensities (light: 180 °C; medium: 200 °C; dark: 220 °C) using digital technologies, including near-infrared spectroscopy (NIR), electronic nose (e-nose), and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), supported by machine learning (ML) modeling. NIR spectra showed distinct chemical fingerprints for date seed powders and beverages, with key absorption bands from 1673–2396 nm and 1720–1927/2238–2396 nm, respectively. E-nose outputs showed higher volatile emissions in dark-roasted samples, particularly for ethanol and NH₃. GC-MS identified 25 volatile compounds, mainly pyrazines and furanic compounds. Pyrazine concentration was greatest in Bau Strami and Medjool cultivars, whereas Halawi and Thoory cultivars had greater content of furfural. Two ML classification models achieved high accuracy in classifying cultivars (NIR inputs: 99%; e-nose inputs: 98%) and roasting levels, while regression models (NIR inputs: R = 0.88; e-nose inputs: R = 0.90) effectively predicted volatile aromatic compounds obtained using GC-MS. Dark roasting resulted in a significant pH reduction and intensified browning, with furfural persisting as a stable aroma contributor. These findings highlight the potential of date seeds as a coffee alternative, with roasting level and cultivar selection influencing flavor profiles. The findings also demonstrate the utility of digital sensing technologies as an efficient, low-cost tool for rapid quality assessment and process optimization in the development of novel beverages. Full article

The meat industry faces significant economic losses and environmental impacts due to spoilage and waste, much of which results from inadequate, delayed, or inefficient quality assessment. Traditional methods used for assessing meat quality are often time-consuming, labor-intensive, and lack the ability to provide real-time information, making them insufficient for modern supply chains that demand safety, freshness, and minimal waste. Recent advances in nanotechnology position nanofibers (NFs) as promising materials for addressing these challenges through smart sensing and active packaging. NFs, characterized by their high surface-to-volume ratio, tunable porosity, and small diameter, enable superior encapsulation and immobilization of sensing agents. These features improve the efficiency of colorimetric indicators, electronic noses, biosensors and time–temperature indicators. Electrospun NFs functionalized with metallic nanoparticles can detect contaminants such as antibiotics and hormones, while polymeric NFs embedded with reduced graphene oxide act as electrodes for advanced biosensing. Freshness indicators based on pH and nitrogenous compounds demonstrate real-time spoilage detection through visible color changes. This review explores nanofiber fabrication methods, their integration into sensing systems, and their potential to advance rapid, sustainable, and cost-effective meat quality monitoring. Full article