While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines. One key bottleneck is the lack of diverse, multimodal olfactory training data collected in natural settings. We present New York Smells, a large dataset of paired image and olfactory signals captured ``in the wild.'' Our dataset contains 7,000 smell-image pairs from 3,500 distinct objects across indoor and outdoor environments, with approximately 70$\times$ more objects than existing olfactory datasets. Our benchmark has three tasks: cross-modal smell-to-image retrieval, recognizing scenes, objects, and materials from smell alone, and fine-grained discrimination between grass species. Through experiments on our dataset, we find that visual data enables cross-modal olfactory representation learning, and that our learned olfactory representations outperform widely-used hand-crafted features.

Due to environmental changes and sensor aging, sensor drift challenges the performance of electronic nose systems in gas classification during real-world deployment. Previous studies using the UCI Gas Sensor Array Drift Dataset reported promising drift compensation results but lacked robust statistical experimental validation and may overcompensate for sensor drift, losing class-related variance.To address these limitations and improve sensor drift compensation with statistical rigor, we first designed two domain adaptation tasks based on the same electronic nose dataset: using the first batch to predict the remaining batches, simulating a controlled laboratory setting; and predicting the next batch using all prior batches, simulating continuous training data updates for online training. We then systematically tested three methods: our proposed novel Knowledge Distillation (KD) method, the benchmark method Domain Regularized Component Analysis (DRCA), and a hybrid method KD-DRCA, across 30 random test set partitions on the UCI dataset. We showed that KD consistently outperformed both DRCA and KD-DRCA, achieving up to an 18% improvement in accuracy and 15% in F1-score, demonstrating KD's superior effectiveness in drift compensation. This is the first application of KD for electronic nose drift mitigation, significantly outperforming the previous state-of-the-art DRCA method and enhancing the reliability of sensor drift compensation in real-world environments.

Animals have evolved to rapidly detect and recognise brief and intermittent encounters with odour packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results -- existing solutions are either slow; or bulky, expensive, and power-intensive -- limiting applicability in real-world scenarios for mobile robotics. Here we introduce a miniaturised high-speed electronic nose; characterised by high-bandwidth sensor readouts, tightly controlled sensing parameters and powerful algorithms. The system is evaluated on a high-fidelity odour delivery benchmark. We showcase successful classification of tens-of-millisecond odour pulses, and demonstrate temporal pattern encoding of stimuli switching with up to 60 Hz. Those timescales are unprecedented in miniaturised low-power settings, and demonstrably exceed the performance observed in mice. For the first time, it is possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond.

Graphene's 2D nature, single molecule sensitivity, low noise, and high carrier concentration have generated a lot of interest in its application in gas sensors. However, due to its inherent non-selectivity, and huge p-doping in atmospheric air, its applications in gas sensing are often limited to controlled environments such as nitrogen, dry air, or synthetic humid air. While humidity conditions in synthetic air could be used to achieve controlled hole doping of the graphene channel, this does not adequately mirror the situation in atmospheric air. Moreover, atmospheric air contains several gases with concentrations similar to or larger than the analytic gas. Such shortcomings of graphene-based sensors hinder selective gas detection and molecular species identification in atmospheric air, which is required for applications in environmental monitoring, and non-invasive medical diagnosis of ailments.

Artificial intelligence is likely to emerge as the most transformative technological force of this decade--or maybe this century! From automated investments to gravitational wave detection in space, AI is breaking boundaries in many areas. For instance, an AI application built by Micron Technology allows the company to invest its funds and generate higher returns in the current low-interest-rate environment. There are many more such use cases of AI solutions emerging across industries. With tech gadgets dominating our lives, AI is gearing up for a more tech-driven future for businesses and consumers alike.

You are free to share this article under the Attribution 4.0 International license. An odor-based test that sniffs out vapors emanating from blood samples was able to distinguish between benign and pancreatic and ovarian cancer cells with up to 95% accuracy, according to a new study. The findings suggest that the tool--which uses artificial intelligence and machine learning to decipher the mixture of volatile organic compounds (VOCs) emitting off cells in blood plasma samples--could serve as a noninvasive approach to screen for harder-to-detect cancers, such as pancreatic and ovarian. "It's an early study but the results are very promising," says A.T. Charlie Johnson, a professor of physics and astronomy at the University of Pennsylvania. "The data shows we can identify these tumors at both advanced and the earliest stages, which is exciting. If developed appropriately for the clinical setting, this could potentially be a test that's done on a standard blood draw that may be part of your annual physical."

A new sensor may be able to detect cancer by'sniffing' blood samples. An odor based test that detects vapors from human blood plasma samples was able to tell the difference between benign and cancerous cells with up to 95% accuracy, according to work presented this week at the American Society of Clinical Oncology meeting in Philadelphia. The study was led by scientists at the University of Pennsylvania and Penn Perelman School of Medicine and utilizes artificial intelligence (AI) and machine learning to analyze molecules called'volatile organic compounds,' (VOCs). These are released from cells in blood and tissues and the'electronic nose' contains nanosensors which are calibrated to detect VOCs. The researchers took samples from 20 patients with ovarian cancer, 20 with non-cancerous ovarian tumors and 20 people who had no tumors at all and found that the electronic nose could tell apart the ovarian cancer samples with a 95% accuracy.

Machine olfaction is usually crystallized as electronic noses (e-noses) which consist of an array of gas sensors mimicking biological noses to'smell' and'sense' odors [1]. Gas sensors in the array should be carefully selected based on several specifications (sensitivity, selectivity, response time, recovery time, etc.) for specific detecting purposes. On the other side, some general-purpose e-noses may have an array of gas sensors that are sensitive to a variety of odorous materials so that such e-noses can be applied to many fields. An increasing number of researches and applications utilized machine olfaction in recent years. In the early 20th century, some studies applied e-noses to the analysis of products along with gas chromatography-mass spectrometers (GC-MS) [2]. Some linear methods such as principal component analysis (PCA), linear discriminant analysis (LDA), support vector machines (SVM), etc. were used in the analysis [3].

Scientists from the Nanyang Technological University of Singapore have developed an AI-powered "electronic nose" that accurately assesses the freshness of meat. The system uses a barcode inserted in food packaging that changes color when it senses gasses emitted from rotting meat. A smartphone app then scans the barcode pattern to measure the freshness of the meat within 30 seconds. In tests on commercially-packaged chicken, beef, and fish samples that were left to age, the system predicted the meats' freshness with 98.5% accuracy. Co-lead author Professor Chen Xiaodong said the app could help consumers decide whether meat is fit for consumption better than a "best before" label: These barcodes help consumers to save money by ensuring that they do not discard products that are still fit for consumption, which also helps the environment.

A team of scientists led by Nanyang Technological University, Singapore (NTU Singapore) has invented an artificial olfactory system that mimics the mammalian nose to assess the freshness of meat accurately. The'electronic nose' (e-nose) comprises a'barcode' that changes colour over time in reaction to the gases produced by meat as it decays, and a barcode'reader' in the form of a smartphone app powered by artificial intelligence (AI). The e-nose has been trained to recognise and predict meat freshness from a large library of barcode colours. When tested on commercially packaged chicken, fish and beef meat samples that were left to age, the team found that their deep convolutional neural network AI algorithm that powers the e-nose predicted the freshness of the meats with a 98.5 per cent accuracy. As a comparison, the research team assessed the prediction accuracy of a commonly used algorithm to measure the response of sensors like the barcode used in this e-nose.