The electronic nose concept was formalised by Gardner and Bartlett in 1988 as an instrument comprising multiple chemical sensors capable of detecting and classifying volatile compounds. Its logic mirrors mammalian olfaction: rather than identifying an odour with a single highly specific receptor, the device derives odour identity from the collective, multi-dimensional response of an array of ten to fifty partially overlapping sensors. Each sensor responds preferentially to a different subset of volatile organic compounds, so the ensemble produces a chemical fingerprint unique to a given aroma. Metal-oxide-semiconductor sensors dominate commercial platforms because they are cheap, broadly sensitive and reasonably stable, while organic field-effect transistors are gaining ground for miniaturised and wearable applications. A sampling system delivers the odour reproducibly, analogue-to-digital converters capture the signals, and a pattern-recognition engine compares the resulting vector against a trained reference database.

The analytical core of any e-nose is its data pipeline, and the review devotes careful attention to it. Raw sensor signals pass through pre-processing stages of normalisation, baseline correction, denoising and transient compression, steps that materially improve reproducibility and reduce variance from sensor drift. Classical statistical methods remain the workhorses: principal component analysis for exploratory visualisation, linear discriminant analysis for supervised class separation, partial least squares for quantifying concentrations, and SIMCA for flagging novel, out-of-class samples. When response surfaces are non-linear, artificial neural networks take over, and recent architectures have pushed performance further. Convolutional neural networks learn hierarchical temporal features directly from raw time-series sensor data without manual feature engineering, while hybrid discrete wavelet transform and long short-term memory models combine wavelet denoising with sequential modelling, outperforming conventional classifiers in beef quality monitoring.

Sensor array design itself has become an optimisation problem. Redundant or uninformative sensors waste computation, raise cost and inject noise into the feature space, so researchers increasingly apply feature selection criteria such as Fisher’s discriminant, t-statistics and minimum redundancy maximum relevance to identify the smallest sensor subset that retains maximum class separability. A tea-aroma detection system was optimised by combining correlation coefficient analysis with hierarchical clustering, and a multi-criteria evaluation framework using the Entropy Weight Method with TOPSIS lifted recognition accuracy for a carbon monoxide and methane gas mixture from 78.3 percent to 96.5 percent. The lesson generalises: a heterogeneous array in which every sensor contributes non-redundant chemical information beats a bigger, blunter one.

The application catalogue is broad and, in places, striking. An organic field-effect transistor e-nose detected spoilage-associated gases at bacterial loads of four times ten to the fourth colony-forming units per gram, far below consumption thresholds, enabling intervention before food becomes hazardous. Miniaturised systems built around MEMS metal-oxide arrays and CMOS circuitry now fit inside domestic refrigerators, monitoring odour changes continuously without sample removal. In meat, fusing e-nose data with Fourier-transform near-infrared spectroscopy improved identification of duck meat and beef combinations beyond the 83.3 percent achieved by the nose alone, and genetic-algorithm feature selection enabled detection of Salmonella typhimurium and Escherichia coli in fresh pork with a coefficient of determination of 0.989. In brewing, a thirteen-sensor prototype tracked alcohol content with a correlation of 0.888, and vertically stacked sensor arrays paired with two-dimensional convolutional networks authenticated beers at 99.3 percent accuracy, a direct answer to product counterfeiting.

Adulteration detection may be the technology’s most commercially compelling use case. A smart e-nose applying sample-slicing, normalisation and pattern recognition achieved a reported classification accuracy of 99.996 percent with a root mean square error of 0.02864 under laboratory conditions. Combined with support vector machines, e-nose systems identified pork adulteration in beef in forty seconds, a third of the time of conventional methods, and have exposed vegetable oil in raw milk, soybean and corn oil in sesame oil, and margarine in cow ghee. In edible oils more broadly, artificial neural network classifiers exceeded 97 percent efficiency, in-situ detection of aldehydes and ketones tracks secondary oxidation, and sensor responses correlated with peroxide values allow degradation phases to be identified without destructive chemical testing.

Seafood, plantation crops and dairy round out the portfolio. A portable seafood e-nose combining four metal-oxide sensors, two electrochemical cells and a photoionisation detector, paired with k-nearest-neighbour classification, achieved perfect accuracy in controlled species and quality grading, while a dedicated Shrimp-Nose device graded Pacific white shrimp with 95.73 percent validation accuracy. Low-cost e-noses have reached the field for fruit crops: a multilayer perceptron predicted kiwifruit soft rot with roughly 90 percent training accuracy before visible symptoms, and a grid-search-optimised support vector machine detected minor mechanical damage in strawberries at 0.84 accuracy. In coffee and tea, systems integrating principal component analysis, radial basis function networks and self-organising maps have classified fermentation and firing conditions almost perfectly, and artificial neural networks distinguish coffee roast levels and geographic origins above 90 percent while catching aroma deviations from mould or poor post-harvest handling.

But the review’s central argument is that headline accuracies are a poor measure of industrial readiness. The highest figures, above 99 percent, come almost exclusively from tightly controlled problems such as laboratory-prepared adulteration models and beer authentication, where classes, matrices and environments are fixed. Performance becomes far less certain when a system meets new production batches, different sensor units, seasonal volatile shifts, fluctuating humidity or heterogeneous supply chains. Sensor drift, the temporal fluctuation of response as sensors age and foul, undermines reproducibility and forces recalibration; metal-oxide sensors are acutely humidity-sensitive; and models trained on narrow datasets overfit, excelling in the lab and failing in the plant. Calibration transfer, adaptive learning, domain adaptation and online model updating are the emerging remedies, but the authors note a shortage of public long-term drift datasets and standardised reporting protocols, making it hard to compare strategies or set realistic expectations. Regulatory acceptance adds another hurdle, since food-quality decisions demand traceability and validated performance across independent batches, sensors and sites, not merely high accuracy from random train-test splits.

The realistic near-term role, the review concludes, is hybrid: e-noses as rapid, non-destructive, low-cost screening tools that triage samples toward confirmatory techniques such as gas chromatography-mass spectrometry, high-performance liquid chromatography or trained sensory panels, which remain the regulatory reference standard. Four converging trajectories could shift that balance. Deep learning, including domain-adversarial networks, active learning and few-shot meta-learning, promises models that adapt to new sensor conditions without full recalibration, though explainable AI and uncertainty estimation will be essential for regulatory trust. Internet-of-things architectures are turning standalone instruments into continuous monitoring nodes, with WiFi-connected brewery units and refrigerator-embedded sensors as early demonstrations and edge-AI chips poised to cut latency. Nanomaterial sensing elements based on graphene oxide, carbon nanotubes and metal-organic frameworks offer detection limits orders of magnitude below conventional metal-oxide sensors. And multi-sensor fusion, combining e-nose smell with e-tongue taste and e-eye vision, exploits physically distinct transduction mechanisms that are unlikely to drift or fail simultaneously, yielding fingerprints that generalise better across batches and sites. If those pieces mature together, the machine nose may finally earn its place as a cornerstone of intelligent, automated food quality assurance, extending eventually into clinical diagnostics and environmental monitoring.

Subject of Research: Electronic nose technology for food and agricultural quality assessment

Article Title: Recent advances and challenges in electronic nose technology for agricultural commodities and food quality assessment

Article References: Maneysha, R. V., Sanjani, V., & Suresh, K. (2026). Recent advances and challenges in electronic nose technology for agricultural commodities and food quality assessment. Discover Agriculture, 4(1), Article 307. https://doi.org/10.1007/s44279-026-00789-9

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00789-9

Keywords: electronic nose, food quality, food safety, volatile organic compounds, machine learning, sensor drift, food adulteration, spoilage detection, metal-oxide semiconductors, deep learning, sensor fusion, IoT

Researchers led by Chengkai Yu, Fengling Zhang, and Francesco Marinello have built a framework that embeds data trustworthiness directly into the life cycle assessment of what they call the Digitization Footprint, the total resources consumed as data are acquired, transmitted, processed, stored, and reused. Their central insight is deceptively simple: when data quality degrades, through sensor drift, calibration failure, or environmental interference, the system does not merely produce bad numbers. It fights back. Verification routines, retransmissions, and rollback recomputations kick in, and these defensive behaviors generate no additional agricultural output while consuming enormous amounts of energy. Under the worst conditions simulated in the study, this hidden overhead consumed up to 48.13 percent of total information and communication technology energy on a modeled mid-scale farm.

The scale of the problem emerges from the architecture of modern agricultural IoT. Data from a field sensor typically traverse five to ten hops, moving through gateways and edge computing units before reaching cloud platforms where irrigation schedules and pest warnings are computed. Along the way, data are frequently processed, retransmitted, and replicated. Conventional life cycle assessment models attribute energy fluctuations to workload variation and hardware efficiency, assuming data remain trustworthy throughout their journey. The authors call the prevailing regime a blind forwarding, delayed correction paradigm: a data flow that passes through a faulty or compromised node continues to propagate contaminated values downstream, and only when terminal validation finally triggers does the system perform a full-path rollback and recomputation, with penalty costs that can reach ten times the baseline per hop.

To break that cascade, the team decomposed the entire farm infrastructure into what they term Data Trust Units, or DTUs. Each node, whether a soil probe, a gateway, an edge server, or a cloud platform, is abstracted as a unit whose trust state can be independently measured and dynamically updated across four dimensions: reliability, security, recoverability, and integrity. Reliability is quantified through a context-aware Markov chain in which nodes move among operational, degraded, failed, and repair states, with transition probabilities that shift as system load rises, capturing phenomena such as increased sensor failure during extreme weather. A complementary non-homogeneous Poisson process models how failure rates spike under event-driven triggers, environmental complexity, and peak loads. Security is scored through behavioral entropy, which flags dispersed and uncertain access patterns, and through Bayesian attack graphs that estimate how risk propagates across multi-step intrusion paths. Recoverability is measured by the shape of the recovery trajectory itself, not just the time to repair, while integrity relies on structural hash comparisons across multiple data versions and replicas.

What makes the framework distinctive is that trust is treated not as a passive score but as an active control variable. Before each forwarding operation, the system evaluates the instantaneous trust of candidate nodes. If trust falls below a threshold, the data flow is terminated through path pruning, a pre-emptive stop-loss that incurs a small evaluation cost but eliminates the far larger cascading penalty of letting contaminated data complete its journey. The energy model captures this elasticity explicitly: at high trust, flows proceed along their original paths, while at low trust, pruning blocks high-risk propagation and allows energy consumption to converge topologically. Crucially, the framework is built on classical probabilistic tools, including Markov chains, Poisson processes, Bayesian networks, and structural hashing, rather than opaque machine learning, keeping the trust evaluation interpretable.

The experimental results reveal a striking threshold-governed transition. Simulating a 500-node directed acyclic graph calibrated against published agricultural IoT parameters, with 20 data flows per step traversing 5 to 10 hops, the researchers generated four trust environments ranging from roughly 0.85 down to 0.40. Baseline energy stayed within a narrow band of 7.9 to 8.2 kilowatt-hours, yet traditional-mode energy climbed nonlinearly from 11.10 to 15.61 kilowatt-hours as trust fell, an added increment of 7.63 kilowatt-hours, nearly equivalent to one full baseline workload cycle. The DTU mechanism, by contrast, delivered savings that grew from a negligible 1.07 percent at high trust to 6.83 percent, then 43.93 percent, and finally 48.13 percent under crisis conditions, executing more than 17,000 path prunings in the lowest-trust scenarios to intercept contaminated flows before they could trigger downstream recomputation.

A full-factorial analysis of the four trust dimensions uncovered effects that linear models would miss entirely. Reliability degradation proved the steepest single driver, raising traditional-mode energy by roughly 1,820 watt-hours, followed by security at about 860 watt-hours, with integrity and recoverability contributing milder increments. But the real surprise lay in the interactions: when reliability and recoverability degraded simultaneously, the combined energy increment far exceeded the sum of their individual contributions. The mechanism is intuitive once stated, simultaneous sensor failures across a monitoring zone combined with delayed field repairs, a common reality on geographically dispersed farms, push the system into a high-trigger-probability region where expected end-point recomputations multiply. Under the most severe multi-dimensional degradation, traditional energy climbed to 17.4 kilowatt-hours while the DTU framework held it to approximately 12.0.

Threshold tuning emerged as the decisive operational question. Scanning the trust cutoff from 0.50 to 0.75 revealed three governance zones: below roughly 0.57, stop-loss interventions rarely fire and the governance overhead goes unrecovered; between 0.58 and 0.70 lies a critical benefit zone yielding stable 5 to 7 percent net savings; and above 0.73, the system over-prunes, cutting nearly all flows so that nominal energy reductions come from shrinking the business itself rather than eliminating abnormal consumption. Sensitivity tests showed the structure is robust: with cascading penalties set at five times baseline, savings moderated to about 35 percent, and at twenty times they rose to roughly 58 percent, while the critical zone shifted only marginally. The per-hop cost of the trust evaluation itself is two orders of magnitude smaller than baseline forwarding energy, meaning the framework operates on a principle of small fixed cost and large variable gain.

To test the trust-scoring logic against real measurements, the team applied the reliability and integrity dimensions to the Intel Berkeley Research Lab sensor dataset, a public deployment of more than 50 wireless motes. Composite trust scores across the 20 most data-rich motes averaged 0.490 with a standard deviation of 0.077, spanning from 0.672 at the top to 0.365 at the bottom, and the lowest-trust mote exhibited visibly more erratic temperature readings, more frequent dropouts, and larger deviations from peer medians. Ninety percent of motes fell below the 0.60 mark, suggesting that even in a stable indoor environment a substantial fraction of nodes would sit inside the critical benefit zone. The authors are careful to note, however, that this validates the internal logic of the scoring method, not performance under real agricultural conditions, where soil moisture drift under variable rainfall, UAV sensing noise, and livestock tag irregularities introduce degradation patterns an indoor dataset cannot capture.

The implications extend well beyond accounting. As the researchers emphasize, trustworthiness is not merely a descriptive quality metric but an endogenous variable that shapes execution paths and energy consumption across agricultural IoT. Optimal thresholds and dimension weights will differ by production context: open-field cropping may tolerate lower thresholds because spatial averaging corrects occasional bad readings, greenhouse climate control demands stricter integrity safeguards, and livestock monitoring elevates security and recoverability given privacy concerns and frequent device disconnections. Before field deployment, practical barriers remain, from protocol heterogeneity across LoRaWAN, NB-IoT, and Zigbee systems to the need for adaptive thresholds that track pest outbreaks, harvest peaks, and extreme weather. But the core message stands: the energy cost of distrusting your own data may be the largest hidden line item in the digital farm’s ledger, and now, for the first time, it can be measured, located, and cut off at the source.

Subject of Research: Embedding data trustworthiness into digitization footprint life cycle assessment for smart agriculture IoT systems

Article Title: Integrating data trustworthiness into digitization footprint life cycle assessment for smart agriculture: Quantifying data credibility through data trust units

Article References: Yu, C., Zhang, F., Dan, Y., Chen, Q., Huang, Q., & Marinello, F. (2026). Integrating data trustworthiness into digitization footprint life cycle assessment for smart agriculture: Quantifying data credibility through data trust units. Smart Agricultural Technology, 15, Article 102520. https://doi.org/10.1016/j.atech.2026.102520

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102520

Keywords: smart agriculture, data trustworthiness, digitization footprint, life cycle assessment, agricultural IoT, energy consumption, Data Trust Units, sensor drift, edge computing, path pruning, Markov chains, sustainability

The field’s foundation rests on a remarkable class of substances known as two-dimensional materials: crystals that are only one or a few atoms thick yet can span vast lateral areas. Graphene, the honeycomb lattice of carbon atoms that pioneered the field, remains the most famous example, but the modern catalogue is far larger. Hundreds of such materials have already been experimentally validated and thousands more have been theoretically predicted, ranging from transition-metal dichalcogenides such as molybdenum disulfide to hexagonal boron nitride, elemental X-enes, black phosphorus, MXenes and more. Each brings a different combination of electrical, mechanical, geometrical and chemical properties that can be matched to specific sensing tasks.

The technical appeal of these atomically thin channels for biosensing stems from their geometry and their physics. Because every atom in a two-dimensional material lies at the surface, the entire conduction path is exposed to the surrounding environment. When a biomolecule binds to the surface of a transistor built from such a material, it directly perturbs the flow of charge carriers through the channel, shifting the device’s transfer characteristics in a measurable way. This direct coupling between molecular recognition and charge transport is what enables amplification-free, label-free detection at extraordinary sensitivity, in some devices reaching femtomolar detection limits without enzymes, fluorophores or polymerase chain reaction.

Graphene has led the way. Its exceptional carrier mobility, quantum capacitance, mechanical flexibility and chemical stability have produced electrolyte-gated field-effect transistors capable of detecting pH changes, protein adsorption, DNA, viruses and biomarkers in physiological fluids. Antibody-functionalized graphene transistors have detected SARS-CoV-2 directly in clinical nasopharyngeal swabs without amplification, and flexible graphene electronic tattoos have continuously tracked arterial blood pressure and mapped cortical brain activity wirelessly. Operating graphene transistors near the charge neutrality point, where conductivity is at a minimum and low-frequency noise is favorable, maximizes sensitivity and has clarified how ionic Debye screening constrains signal transduction in salt-rich environments such as blood and sweat.

Yet the authors argue that graphene is only the beginning. Transition-metal dichalcogenides offer a distinct trade-off: unlike gapless graphene, their semiconducting bandgaps allow transistor channels to be fully switched off, reducing background current and enabling different operating regimes. Molybdenum disulfide transistors have detected nucleic acids and proteins with femtomolar sensitivity, and the metallic 1T phase of these materials supports fast heterogeneous electron transfer for enzyme-based electrochemical sensing. Phase engineering, in which the atomic arrangement of the material is switched between semiconducting and metallic forms, adds a further design lever. Beyond the dichalcogenides, black phosphorus brings a tunable gap that varies with layer thickness, indium selenide field-effect transistors have rapidly identified SARS-CoV-2 variants, platinum dichalcogenides have yielded reusable ultrathin electronic tattoos, and MXenes contribute metallic conductivity and rich surface chemistry for biosensing composites.

A recurring theme of the Perspective is that in real devices, structural and chemical variability often outweighs intrinsic material properties in determining performance. Defects, grain boundaries, surface terminations, adsorbed contamination and variations in layer thickness all scatter charge carriers, shift the charge neutrality point and introduce noise and hysteresis. Two nominally identical devices can behave quite differently because of fabrication-related differences invisible to the naked eye. The authors contend that further progress will depend on linking biosensing performance more directly to the intrinsic structures and properties of individual materials, so that sensitivity and stability become predictable design outcomes rather than empirical surprises from batch to batch.

Equally decisive is the biofunctionalization strategy, the chemistry that connects the sensor surface to the biological world. Covalent attachment of receptors provides durable immobilization but can degrade the electronic properties of the underlying channel; non-covalent approaches based on pyrene linkers or supramolecular interactions preserve the lattice while anchoring antibodies, aptamers or enzymes, but may be less stable over time. The choice of linker chemistry and receptor controls the sensor’s stability, drift and usable lifetime. Structure-switching aptamers have even allowed small-molecule detection to overcome the Debye length limitation, by transducing binding-induced charge redistribution close to the channel, and wearable aptamer transistors have achieved non-invasive, continuous cortisol monitoring from sweat. Emerging tools such as CRISPR-Cas9 immobilized on graphene transistors, epitope-imprinted surfaces and DNA origami architectures extend molecular selectivity still further.

The authors are candid about the field’s persistent weaknesses. Sensitivity alone is no longer sufficient; devices must also deliver selectivity against interfering species, reproducibility across laboratories, minimal baseline drift during long-term operation and stable performance under physiological conditions, including high ionic strength, temperature and protein-rich media. Noise sources such as charge traps and adsorbed water, hysteresis in transfer characteristics and gradual surface fouling all degrade real-world reliability. The diversity of two-dimensional materials complicates comparison, since results obtained on different materials, geometries and test protocols cannot be directly juxtaposed. The Perspective therefore calls for standardized benchmarks and testing workflows as a central requirement for comparing biosensing performance across materials, devices and laboratories, a step the authors regard as essential for the field’s credibility and maturation.

Biocompatibility and scalability round out the remaining challenges. Graphene and several other two-dimensional materials have shown encouraging compatibility with neurons and other cells, with purified neurons surviving on peptide-free graphene layers and graphene-based interfaces leaving target nerve cells unaltered, but toxicity depends strongly on material type, surface reactivity, dose and impurities, and other families such as graphene oxide or certain MXenes require careful assessment. Meanwhile, translating atomically thin sensors from individual hand-built devices to manufacturable technology demands wafer-scale growth, clean transfer processes and quality control, areas where machine learning is increasingly being deployed to optimize fabrication, screen candidate materials and even interpret complex chemical sensor arrays. Technology readiness levels differ widely across applications, with wearable and environmental sensing nearer to market and implantable diagnostics further behind.

If those obstacles can be cleared, the destination is compelling: flexible, portable point-of-care devices and conformal wearables that support continuous health monitoring and feed the Internet of Medical Things, an interconnected ecosystem of bioelectronic devices that collect, transmit and intelligently analyze health data at population scale. The authors frame the moment as a critical inflection point. The sensitivity problem has largely been solved; the selectivity, stability and reproducibility problems are now the frontier. By matching the unique properties of specific two-dimensional materials to specific sensing functions, and by grounding device design in material physics rather than empirical trial and error, the field aims to convert two decades of laboratory brilliance into reliable translational sensing technologies that patients and consumers can actually depend on.

Some of the physical underpinnings of these sensors deserve closer attention. In an electrolyte-gated configuration, the physiological liquid itself acts as the gate, so the electric double layer that forms at the material surface can capacitively modulate the channel with exceptional efficiency. This liquid gating, combined with the sizable quantum capacitance of graphene, means that even minute surface charge changes from adsorbed proteins or nucleic acids produce measurable shifts in device characteristics. At the same time, the ultimate detection floor is set by noise: low-frequency 1/f noise and charge fluctuations from trapped impurities and adsorbed water often dominate the signal, which is why operating points, electrolyte composition and surface cleanliness matter as much as the receptor chemistry itself.

The mechanical and chemical robustness of atomically thin films also underpins their wearable ambitions. Monolayer graphene can endure extreme bending and repeated tension-compression cycles without fracture, remains nearly transparent to visible light, and even acts as a corrosion-inhibiting coating on underlying metals. These attributes allow conformal patches to follow skin topology during motion while maintaining stable electrical contact, a prerequisite for long-term bioimpedance and electrophysiological recordings. Graphene multielectrode arrays have likewise served as versatile tools for extracellular measurements, and graphene oxide electrodes have been shown to elicit distinct calcium signaling in brain astrocytes, illustrating how material form and surface chemistry shape biological response.

Finally, the sheer breadth of the two-dimensional catalogue, documented in comprehensive material atlases, suggests that many sensing niches remain unclaimed. Systematic pairing of bandgap, phase, defect density and surface termination with a given analyte and operating environment offers a rational path forward, provided that cross-laboratory benchmarking becomes routine practice.

Subject of Research: Two-dimensional electronic biosensors based on graphene and related atomically thin materials

Article Title: Engineering two-dimensional electronic biosensors

Article References: Hasan, M. M., Bao, H., Montes-García, V., Zhang, Y., Ping, J., Samorì, P., Duan, X., & Kireev, D. (2026). Engineering two-dimensional electronic biosensors. Nature Reviews Electrical Engineering. https://doi.org/10.1038/s44287-026-00326-2

Image Credits: AI Generated

DOI: 10.1038/s44287-026-00326-2

Keywords: two-dimensional materials, graphene, biosensors, field-effect transistors, transition metal dichalcogenides, biofunctionalization, wearable biosensors, point-of-care diagnostics, Internet of Medical Things, sensor selectivity, sensor drift, MXenes