Nexisense AI-Nose Intelligent Electronic Nose System: 6-Channel MEMS Array Solving Complex Gas Recognition Challenges

Engineering Challenges of Complex Gas Detection in Embedded Systems

In fields such as the Internet of Things (IoT), smart home appliances, and automotive electronics, single gas sensors struggle to cope with the multi-component, cross-interference nature of real-world environments. Traditional solutions often face issues like insufficient selectivity, severe drift, or high power consumption, leading to increased false alarm rates or maintenance costs. The Nexisense AI-Nose intelligent electronic nose system integrates 6 independent MEMS sensing units to form a high-dimensional response "fingerprint." Combined with onboard pre-processing and open algorithm interfaces, it provides a scalable multi-gas classification platform for system integrators.

Optimized for dynamic and mixed gas scenarios, the system supports pattern recognition ranging from Volatile Organic Compounds (VOCs) and inorganic gases to bio-sourced odors, meeting the requirements of GB/T 18883, ISO 16000 series, and automotive industry AQS standards. Integrators can use it as a front-end sensing module to build a complete chain from raw signals to decision output, enhancing the intelligence and reliability of terminal devices.

System Hardware Architecture and Signal Processing Workflow

The core of AI-Nose is a modular 6-channel MEMS array. Each channel is equipped with specific sensing materials (such as metal oxides or polymer composite films) that generate resistance/conductance changes in response to specific gas molecules. Channels can be independently selected (any combination of 1-6 channels), allowing users to flexibly configure the system based on the target gas spectrum and budget.

Key Hardware Features:

• Micro-heater Control: Independent temperature control for each unit (typical 200-400°C) to optimize power consumption and selectivity.

• Onboard Signal Chain: Includes low-noise amplification, ADC (12-16 bit), digital filtering, and temperature/humidity compensation (integrated DHT or similar sensor).

• Output Interfaces: UART (default 115200 bps), I2C, or SPI; supports Modbus RTU expansion for easy connection to MCU/edge processors.

Raw response data is output after standardized processing (baseline subtraction, normalization, temperature compensation) to form a high-dimensional feature vector. This vector is fed directly into upper-level models for qualitative and quantitative gas analysis.

Algorithm Framework and Model Integration Support

The system provides an open algorithm interface, supporting the deployment of traditional machine learning (SVM, Random Forest, KNN) and deep learning (CNN, RNN, Transformer variants). The Nexisense SDK includes:

• Feature Extraction Tools: PCA, LDA dimensionality reduction, time-domain/frequency-domain features.

• Training Pipeline Examples: Offline training and edge inference based on Python/TensorFlow Lite.

• Model Deployment Framework: Supports ONNX format conversion and MCU-side inference (STM32, ESP32 series).

Integrators can use their own datasets to fine-tune models, achieving customized precision for specific applications (e.g., food spoilage stage identification, in-car odor classification).

Typical Application Scenarios and Project Integration Cases

The AI-Nose system has been verified and deployed in multiple vertical fields. Key scenarios are detailed below:

Smart Home Appliances: Food Preservation and Fume Control

High-end refrigerators use the electronic nose to monitor ethylene (C₂H₄), amines (NH₃ derivatives), and sulfides to judge fruit/vegetable ripeness and meat spoilage processes, linked to temperature/humidity adjustment.

Project Case: A home appliance OEM integrated a 4-channel AI-Nose configuration into a preservation drawer module. Response fingerprints are collected via UART, and an edge model classifies three stages: fresh, slightly spoiled, and severely spoiled. Result: Preservation period extended by 15-25%, reducing food waste. The solution is compatible with main-control RTOS and supports OTA model updates.

In range hoods, the system distinguishes between cooking fumes (high VOCs, accompanied by particles) and water vapor (dominated by humidity) to realize adaptive adjustment of public flue pressure.

Automotive Electronics: Interior Air Quality and Safety Monitoring

Automotive AQS needs to detect CO₂, VOCs, PM, and odors in real-time to automatically switch between internal/external circulation or activate purifiers. It also supports left-behind life detection (based on CO₂/humidity/odor patterns).

Case: In a New Energy Vehicle (NEV) air management system upgrade, an integrator adopted a 6-channel AI-Nose array connected to the CAN bus. The model identifies formaldehyde, benzene series, and body odors to trigger A/C mode switching. Project Verification: Interior VOCs reduced by 30% on average, passenger comfort improved, and OTA firmware/model iterations are supported. The solution complies with ISO 12219 interior air standards.

Industrial Safety and Environmental Monitoring

Sewage treatment plants and chemical plants need to monitor multi-component leaks of H₂S, NH₃, SO₂, etc. AI-Nose serves as a fixed or mobile node, providing redundant alarms and source localization.

Case: An engineering company deployed a distributed AI-Nose array in a chemical plant, transmitting feature data to the cloud via LoRaWAN. The model distinguishes leak types and concentration gradients to link ventilation/isolation valves. Result: Response time < 20 s, false alarm rate < 5%, significantly improving safety compliance.

Medical and Laboratory Air Monitoring

ICUs and cleanrooms require continuous monitoring of CO₂, O₃, and disinfection by-products. The system connects to the BMS to achieve environmental parameter linkage.

Selection Guide: Matching Project Needs and Conditions

Selection requires evaluating target gases, number of channels, power consumption, and interfaces.

Integration Precautions: Ensuring Reliable System Operation

• Installation and Airflow Optimization: Place the module in a stable airflow zone, avoiding direct wind or dead corners. Heater operation requires preheating stabilization (typical 30-60 s).

• Calibration and Model Maintenance: Factory multi-gas calibration provided; field support for zero/span adjustment. Model lifecycle management: regular retraining to cope with sensor aging.

• System Integration and Data Security: SDK includes drivers and sample code. Data encryption (optional AES) complies with GDPR. Redundant design: dual-module configuration for critical applications.

• Challenge Mitigation Strategies: Environmental interference handled by temp/humidity compensation + algorithm filtering. Drift addressed by periodic background correction. EMC compliance with IEC 61000 standards.

Nexisense Advantages: OEM Customization and Bulk Supply

Nexisense focuses on B2B electronic nose partnerships, offering channel material customization, algorithm framework adjustments, housing/interface optimization, and private labeling. Annual capacity exceeds 100,000 sets, lead time is 6-10 weeks, and ISO 9001 certification ensures consistency. We support the transition from small-batch prototypes to mass production, reducing development cycles and risks.

FAQ

Conclusion: Empowering Integrators to Solve Complex Gas Sensing Problems

The Nexisense AI-Nose intelligent electronic nose system, centered on a 6-channel MEMS array and combined with an open algorithm framework and flexible configuration, provides a reliable multi-gas recognition solution for smart homes, automotive electronics, industrial safety, and medical environmental monitoring. Facing the engineering challenges of mixed gas scenarios, system integrators, IoT providers, and project contractors can leverage our hardware platform and technical support to achieve precise perception and intelligent decision-making. Contact the Nexisense team to discuss specific application needs and customized integration solutions, and together we will advance innovative applications in the field of intelligent gas detection.