Programme > Tutorials
Tutorial 1
Title: Bioinspired Electronic Nose and Electronic Tongue and Recognition Algorithms
Presenter: Ping Wang, Hao Wan(Zhejiang University), Chunsheng Wu(Xi’an Jiaotong University)
Date: 17.05.2026
Abstract: Olfaction and gustation are of vital importance to all living beings, including humans. They can sense various chemical signals to fulfill multiple purposes such as survival, feeding and reproduction etc. Bioinspired electronic nose and electronic tongue are odor and taste sensors and instruments developed by imitating the olfactory and gustatory systems of living organisms. These instruments can detect chemical and biological signals presented by various odor molecules and taste substances, and convert them into electrical signals, quantitatively displaying the types and concentrations of the substances being tested. Bio-inspired odor and taste sensors have high performance, high sensitivity, rapid response and excellent selectivity in chemical perception and biochemical analysis.
Pattern recognition technology involves the signals detected by olfactory and gustatory sensors, which are identified and classified based on their common characteristics or attributes. Cluster analysis is a branch of pattern recognition, and it is based on dividing a data set into several subgroups, where the objects within each subgroup have a certain degree of similarity, that is, the categories are determined by the proximity or distance of each pattern in the feature space. The rise of artificial neural networks and bioinspired intelligent recognition algorithms (ANN, CNN, DNN and SNN) information processing technology has injected vitality into the development of electronic nose and electronic tongue for detection and evaluation of food, drugs, medicine and health diagnosis.
This Toturial Lecture will focus on research related to the design, modeling, and application of Bioinspired Electronic Nose and Electronic Tongue and Recognition Algorithms:
- The Development of Electronic Nose and the Introduction of Bioinspired Electronic Nose;
- The Introduction of Bioinspired Electronic Nose and Applications in the Detection and Evaluation of Food, Drugs, Medicine and Health Diagnosis;
- The Development of Electronic tongueand the Introduction of Bioinspired Electronic Tongue;
- The Introduction of Bioinspired Electronic Tongue and Applications in the Detection and Evaluation of Food, Drugs, Medicine and Health Diagnosis;
- The Development of Recognition Algorithms and Introduction of PCA, LDA, PLS, ANN, CNN, DNN and SNN Algorithms etc;
- The Applications of Intelligent Algorithms in the Bioinspired Electronic Nose and Electronic Tongue
Tutorial 2
Title: Instrumental Odour Monitoring Systems (IOMS) in Practice: From Experimental Design to Hands-on Exploration of Industrial Field Monitoring Data
Presenter: Carmen Bax(Politecnico di Milano)
Date: 17.05.2026
Abstract: Instrumental Odour Monitoring Systems (IOMS), commonly referred to as electronic noses, are increasingly adopted for the continuous monitoring of odour emissions in environmental contexts, directly at industrial emission sources, plant fencelines, or sensitive receptors. Their effective application, however, requires the rigorous implementation ofstandardized experimental procedures to ensure reliability, traceability, and regulatory relevance of the results.
This tutorial lecture provides a comprehensive and practice-oriented overview of the use of IOMS for environmental odour monitoring, combining methodological foundations with hands-on analysis based on field-acquired monitoring data. The lecture is structured to guide participants through the complete workflow of an IOMS application, with a specific focus on best practices at each operational stage.
The first part of the tutorial addresses the design and implementation of a robust training phase, including the identification of relevant odour sources, construction of representative training datasets, and development of qualitative and quantitative models using multivariate and machine-learning techniques. Particular attention is given to the role of emission samples collection, olfactometric characterization and preparation strategies of representative samples to reproduce ambient odour conditions at the monitoring location.
The second part focuses on the installation of IOMS at the monitoring site and the continuous acquisition of environmental data. Key aspects such as representativeness of the monitoring location, temporal resolution of outputs, and management of operational and environmental interferences are discussed.
Finally, the lecture addresses signal processing and interpretation of results, covering odour classification and quantification, performance verification, and critical evaluation of outputs in view of regulatoryn compliance and decision-making. These concepts are consolidated through hands-on exercises based on real IOMS signals acquired at an industrial plant, allowing participants to directly apply the presented methodologies and critically interpret monitoring outcomes.
Tutorial 3
Title: Odour Environmental Monitoring and E‑nose Classification: Standards and Laboratory Experimental Procedures
Presenter: Ettore Massera(ENEA)
Date: 17.05.2026
Abstract: The monitoring and classification of environmental odours are emerging as essential components of air‑quality management and industrial impact assessment. This tutorial provides an integrated overview of scientific odour characterization, controlled laboratory methodologies for testing environmental sensors, and the evolving landscape of international standards for instrumental odour monitoring systems.
The first part introduces the scientific definition of odour through its physico‑chemical descriptors and perceptual dimensions. Controlled laboratory equipment—dynamic dilution systems, gas‑mixing units, olfactometric tools, and analytical platforms—forms the basis for generating reproducible odour references. Particular attention is given to the use of Gas Chromatography–Ion Mobility Spectrometry (GC‑IMS), which enables rapid chemical fingerprinting of odorous mixtures and supports the interpretation of e‑nose responses under well‑defined exposure conditions.
The tutorial then focuses on calibration and validation procedures carried out in controlled laboratory environments, including concentration‑controlled testing, sensor stability, repeatability, and cross‑sensitivity assessment. These methods serve as the foundation for future outdoor deployment, where calibration models must cope with environmental variability, long‑term drift, and complex interferents.
A distinctive contribution of this tutorial is the discussion of current standardization efforts. The speaker’s direct involvement in several technical working groups offers a unique perspective: the revision of the UNI 11761:2023 standard; participation in the IEEE SA P2520.1 project on baseline performance requirements for odour analysis devices—now entering the ballot phase—and the preliminary full laboratory procedure executed on a GC‑IMS system to validate the applicability of the draft standard; and active contribution to the CEN Working Group 41, which is approaching the first near‑final draft of the European standard for Instrumental Odour Monitoring Systems. These initiatives collectively demonstrate how laboratory methodologies, performance criteria, and harmonized procedures are converging toward robust, comparable, and regulatory‑ready odour monitoring tools.
By combining scientific odour characterization, rigorous laboratory practices, and an insider’s view of ongoing standardization efforts, this tutorial provides a comprehensive framework for advancing odour sensing technologies from controlled experiments to real‑world environmental applications.
Tutorial 4
Title: Chemical and Environmental Sensing with Aerial Robots: Concepts, Constraints, and Applications
Presenter: Patrick P. Neumann(Bundesanstalt für Materialforschung und -prüfung (BAM))
Date: 17.05.2026
Abstract: Environmental monitoring increasingly requires measurements in hazardous, hard-to-access, or highly dynamic places (e.g., industrial plants, confined spaces, emergency scenarios). Aerial Robot Olfaction (ARO) addresses this gap by combining aerial robots (drones) with chemical sensing and complementary environmental modalities (e.g., airflow, temperature, humidity) to detect, localize, and map gaseous compounds and their sources in real time. The core difficulty is that gas dispersion is turbulent and intermittent, while rotor-induced airflow disturbs the local plume—
meaning that “higher concentration” is often not a reliable or stable indicator on its own. Therefore, successful ARO systems must be designed as an integrated stack, encompassing sensing principles, platform integration, calibration strategies, and data interpretation methods that explicitly account for plume structure rather than assuming smooth gradients.
This tutorial introduces key concepts and terminology progressively, starting from Mobile Robot Olfaction tasks (gas detection, distribution mapping, source localization) and advancing to practical UAV implementation choices. We contrast in-situ sensing (e.g., MOX/IAQ sensors) with remote or open-path approaches, discussing when each is appropriate and their limitations in the presence of rotor-induced mixing. Practical examples are drawn from nanoUAV platforms (e.g., Crazyflie-class systems) and controlled experiments that reveal rotor downwash effects, sensor response characteristics, and mapping strategies. A central element is a guided, intuitive introduction to plume-aware features that exploit the temporal and spatial structure of gas signals, and how such features support gas source localization with multiple aerial platforms, illustrated through simulation and experimental case studies. Selected concepts are illustrated through guided examples and interactive discussion.
Planned topics:
• Motivation and use-cases for environmental sensing with aerial robots
• ARO/MRO basics: tasks, constraints, and terminology
• Gas sensing principles: MOX/IAQ vs open-path/remote sensing trade-offs
• Platform integration: payload, localization, calibration, and rotor–plume interactions
• Gas distribution mapping with nano-UAVs: experimental pitfalls and trajectory implications
• Plume-aware gas source localization concepts and multi-robot strategies
Tutorial 5
Title: Aerial Monitoring of Pollution and Odours in Industrial Plants
Presenter: Agustín Gutierrez-Galvez(University of Barcelona)
Date: 17.05.2026
Abstract:
Industrial facilities are importatant sources of environmental pollution and odour emissions that affect the well-being of neighbouring populations. Odour nuisance, beyond chemical toxicity, is a major source of public complaints and regulatory pressure, often generating conflict between industries and neighbours. Conventional monitoring, based on fixed stations and periodic sampling, provides limited spatial resolution and often misses transient releases or complex dispersion patterns. Aerial sensing platforms equipped with custome-made IOMS (Instrumental Odour Monitoring System) offer a perfect alternative to perform fast measurements around facilities and within emission plumes.
This tutorial will present the basic principles and technologies underlying aerial pollution and odour monitoring. It will begin with a structured review of the range of sensing technologies currently employed in drone-based platforms for chemical and environmental monitoring, covering electrochemical, metal-oxide, photoionization, and spectroscopic approaches. The tutorial will discuss how these sensing modalities are integrated addressing payload constraints, power management, communication architectures, and real-time data acquisition. The design of custom-made IOMS for aerial deployment will then be analysed in detail, with attention to sensor selection criteria based on sensitivity, selectivity, and response time. Particular focus will be placed on engineering solutions to counteract rotor downwash effects that can distort sampling, as well as on sensor chamber design to ensure sensor measurements with minimum distorsion. Furthermore, methods for odour estimation from chemical sensor arrays will be discussed, emphasizing modelling strategies that link multidimensional sensor responses to odour concentration metrics, particularly European Odour Units, through machine learning approaches. Calibration transfer of measurements between different industrial plants will also be addressed. Participants will gain a good understanding of how aerial artificial olfaction systems can support industrial monitoring.
Tutorial 6
Title: Electrochemical biosensor fundamentals for artificial olfaction and taste applications
Presenter: Hadar Ben-Yoav(Ben-Gurion University of the Negev)
Date: 17.05.2026
Abstract:
Electrochemical sensor arrays generate rich, multivariate response patterns that require sophisticated computational methods to extract meaningful analytical information. While the electronic nose community has developed extensive pattern recognition expertise for gas-phase sensing, and electronic tongue research has relied heavily on electrochemical transduction, these fields have evolved with remarkably little algorithmic cross-pollination. This creates a critical gap: researchers developing electrochemical sensor arrays often lack systematic knowledge of the recognition algorithms essential for achieving selectivity, maintaining calibration, compensating drift, and detecting biofouling in real-world applications.
This tutorial addresses the computational intelligence layer that transforms raw electrochemical signals into reliable chemical information. We focus on four algorithmic pillars where electrochemical sensor arrays face unique challenges. First, selectivity algorithms—from classical PCA and PLS-DA through modern transformer architectures—that distinguish target analytes from interferents in complex samples. Second, calibration strategies including multivariate methods, standardization techniques, and deep transfer learning that reduce recalibration time when deploying sensors across different units or environments. Third, drift compensation mechanisms spanning explicit correction, drift modeling, and domain adaptation methods. Fourth, biofouling detection and correction algorithms, an emerging frontier where impedance-based monitoring approaches offer practical solutions.
Participants will gain working knowledge of algorithm selection criteria matched to dataset characteristics, implementation strategies using open-source tools, and validation approaches. Case studies from water quality monitoring, food authentication, and biomedical diagnostics demonstrate algorithm performance under realistic conditions. The tutorial includes troubleshooting guidance for common failure modes: overfitting in small datasets, poor generalization across drift conditions, and calibration transfer breakdown. No prior electrochemistry or machine learning expertise is required.