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US 2026/0118304 A1Published application
Odor signature identification system
OwnerShubham Chandrainventor
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Abstract
This invention introduces an advanced odor detection and classification system that integrates optical and conductive sensors with artificial intelligence for precise, real-time identification of odor profiles across diverse fields. The system employs a colorimetric sensor to capture CIELAB values and a conductive sensor to measure voltage, with data processed through a Neural Network model. This model is trained on an extensive dataset to recognize and predict odors, adapting to variations in odor intensity for enhanced accuracy. Designed for high selectivity, stability, and resilience to environmental factors, the system is ideal for applications in medical diagnostics, food quality control, environmental monitoring, and industrial safety.
Claims21
1:
- An odor detection and classification system comprising:
- a colorimetric sensor configured to capture color data in response to odorant interactions;
- a conductive sensor configured to detect odorant interactions by measuring electrical conductivity, represented as a voltage response;
- a data processing unit configured to receive, preprocess, and normalize data from both the colorimetric and conductive sensors, with the processed data formatted to match the input requirements of a convolutional neural network (CNN) model;
- a CNN model trained on a dataset comprising both colorimetric and conductivity data associated with various odor profiles to classify the odor in real-time;
- an output display configured to present the classified odor based on predictions from the CNN model.
3 more elements
2:
- The odor detection and classification system of claim 1, wherein the colorimetric sensor captures color data that creates a unique profile for each odorant.
3:
- The odor detection and classification system of claim 1, wherein the CNN model is trained using a dataset that includes variations in odorant intensity for both colorimetric and conductive data, allowing adaptive classification for different odor intensities.
4:
- The odor detection and classification system of claim 1, further comprising a calibration unit configured to perform periodic drift compensation on both colorimetric and conductive sensors to ensure consistent performance over time.
5:
- The odor detection and classification system of claim 1, wherein the CNN model is trained using a dataset with odorant intensity variations for both colorimetric and conductive data, allowing adaptive classification based on odor intensity.
6:
- The odor detection and classification system of claim 1, wherein the data processing unit performs baseline correction on both colorimetric and conductivity data prior to CNN input to enhance prediction accuracy.
7:
- The odor detection and classification system of claim 1, wherein the CNN model outputs a probability distribution across odor classes, identifying the class with the highest probability as the detected odor.
8:
- The odor detection and classification system of claim 1, wherein the CNN model provides a probability distribution for each odor class, selecting the class with the highest probability as the identified odor.
9:
- The odor detection and classification system of claim 1, further comprising memory storage for saving the CNN model in Keras format, enabling deployment on external processing devices.
10:
- The odor detection and classification system of claim 1, wherein the output display is configured to show real-time odor classification, intensity levels, and sensor status, facilitating user interaction and monitoring.
11:
- An odor detection and classification system comprising:
- a colorimetric sensor configured to detect odorant interactions by capturing color data;
- a data processing unit configured to receive and preprocess the color data from the colorimetric sensor;
- a CNN model trained on a dataset of odor profiles to classify the odor based on the color data;
- and
- an output display configured to present the classified odor in real-time based on the CNN model prediction.
3 more elements
12:
- An odor detection and classification system comprising:
- a conductive sensor configured to detect odorant interactions by measuring electrical conductivity in terms of voltage response;
- a data processing unit configured to receive and preprocess voltage data from the conductive sensor;
- a convolutional neural network (CNN) model trained on a dataset of odor profiles to classify the odor based on the conductivity data;
- and
- an output display configured to present the classified odor in real-time based on the CNN model prediction.
3 more elements
13:
- The odor detection and classification system of claim 11, wherein the CNN model is trained on a dataset that includes varying odorant intensity levels for enhanced adaptive classification.
14:
- The odor detection and classification system of claim 11, further comprising a calibration unit to periodically calibrate the colorimetric sensor, ensuring accuracy under different environmental conditions.
15:
- The odor detection and classification system of claim 11, wherein the data processing unit is configured to normalize the color data before inputting it into the CNN model, thereby enhancing consistency in predictions.
16:
- The odor detection and classification system of claim 12, wherein the conductive sensor includes materials sensitive to a range of volatile organic compounds (VOCs) to measure conductivity response specific to each odorant.
17:
- The odor detection and classification system of claim 12, wherein the CNN model is trained on a dataset that includes conductivity data for various odorants at different intensity levels, allowing for adaptive classification based on odor intensity.
18:
- The odor detection and classification system of claim 12, wherein the data processing unit performs baseline correction on the voltage data to enhance accuracy in sensor response.
19:
- The odor detection and classification system of claim 12, further comprising a calibration unit configured to adjust the conductivity baseline for environmental drift compensation.
20:
- The odor detection and classification system of claim 12, wherein the conductive sensor includes a range of conductive materials selected for high sensitivity to sulfur-containing compounds, amines, and other specific odorant groups.
21:
- The odor detection and classification system of claim 12, wherein the CNN model is trained using a dataset of conductivity measurements from the conductive sensor, processed and labeled to ensure robust odor classification across a variety of compounds.