Sensor
Room-temperature electronic nose arrays
LIG-based sensing channels respond differently to odor, VOC, and gas patterns without relying on bulky optical equipment.
Olfactory intelligence, made tangible.
NeuroSense builds electronic nose sensor modules and scent AI systems that help products understand odor, gas, and air signals as structured data.
A compact sensing stack for invisible chemical signals.
Conventional air sensors often report a total number. NeuroSense focuses on pattern recognition: sensor response, signal cleanup, scent data, and AI interpretation working together as one product layer.
A signal path built for product use.
The stack starts with sensor response, then turns chemical variation into repeatable data that can guide real-world product decisions.
Data
Chemical patterns become usable fingerprints
Noise reduction, labeling, and signal processing turn raw response curves into repeatable scent data.
AI
Context-aware decisions for products
Lightweight models help products distinguish causes such as smoke, pet odor, freshness loss, or breath ketone signals.
Sensor hardware, porous material, and AI signal handling.
The source materials emphasize a room-temperature, multi-channel LIG sensor approach, paired with algorithms that reduce noise from humidity, temperature, and real-world interference.
- Gas fingerprints instead of one-dimensional TVOC totals
- Room-temperature, low-power sensing architecture
- Dataset-led classification for complex real-world odors
Designed for products that need to read smell, not decorate it.
The same electronic nose stack can support air-care products, health signal monitoring, food freshness, and machine safety scenarios.
Air care
Detect why indoor air has changed, not only that it changed.
Health signalsRead breath or waste-related VOC patterns without invasive sensors.
Food freshnessRecognize freshness, spoilage, and fermentation states before they are visible.
RoboticsGive machines a way to sense leaks, smoke, contamination, and unseen risk.
Operating logic
From "air is bad" to "this is what changed."
NeuroSense positions olfactory AI as a way to identify causes and context: smoke versus cooking, spoilage before visual change, breath ketone patterns, or unseen gas risks in spaces and machines.