Photonic Nose: Spectral Analysis and HITRAN Data Processing

Photonic Nose: Spectral Analysis and HITRAN Data Processing

Project Overview

The Photonic Nose project explores non-invasive blood glucose estimation through breath analysis using fiber optic gas absorption spectroscopy. This research focuses on identifying viable spectral regions for detecting glucose-related biomarkers in breath.

Disclaimer: This is exploratory research only. No clinical testing has been performed, and no medical claims are made.

Time Period: 2024–2025 Status: Data processing complete, interaction prototype in development Data Source: HITRAN 2020 Database (27.8MB, 171,626 lines)

Spectroscopy Fundamentals

Basic Principle

Gas absorption spectroscopy relies on the principle that molecules absorb specific wavelengths of light based on their molecular structure. The Beer-Lambert law describes this relationship:

I(λ) = I₀(λ) × exp(-α(λ) × c × l)

Where:

  • I(λ) = transmitted intensity at wavelength λ
  • I₀(λ) = incident intensity at wavelength λ
  • α(λ) = absorption coefficient at wavelength λ
  • c = concentration of absorbing species
  • l = path length

Target Biomarkers

For breath-based glucose estimation, we focus on:

  1. Acetone (CH₃COCH₃) - Primary ketone body correlated with glucose
  2. Isoprene (C₅H₈) - Associated with cholesterol synthesis
  3. Methane (CH₄) - Gut microbiome activity indicator
  4. Water (H₂O) - Background absorption (needs compensation)

Data Processing Methodology

HITRAN Database Parsing

The HITRAN (High-Resolution Transmission) database contains spectroscopic parameters for atmospheric molecules. We processed 171,626 lines of data for relevant molecules.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path

# Load HITRAN data
hitran_file = '/Users/wujiajun/Downloads/66f40145.par.txt'
columns = ['molecule_id', 'isotope', 'transition', 'nu', 'line_intensity',
           'air_broadened', 'self_broadened', 'lower_state_energy',
           'temperature_dependence', 'pressure_shift']

df = pd.read_csv(hitran_file, sep='\s+', header=None, names=columns)

# Filter for target molecules and spectral regions
target_molecules = {
    1: 'H2O',      # Water
    2: 'CO2',      # Carbon dioxide
    6: 'CH4',      # Methane
    26: 'C2H2',    # Acetylene
    27: 'C2H6',    # Ethane
    35: 'H2CO',    # Formaldehyde
    41: 'CH3OH',   # Methanol
    45: 'CH3Cl',   # Methyl chloride
}

# Filter for relevant spectral regions (1.5-1.6 μm region)
target_wavelengths = (15600, 15900)  # in wavenumbers (cm⁻¹)

Spectral Region Selection

We identified two promising spectral regions:

Region 1: 1.56 μm (6410 cm⁻¹)

  • Primary absorber: Water vapor
  • Secondary: Methane, acetylene
  • Advantage: Strong water absorption for baseline
  • Challenge: High water interference

Region 2: 1.59 μm (6289 cm⁻¹)

  • Primary absorber: Methane
  • Secondary: Acetylene, water
  • Advantage: Less water interference
  • Challenge: Weaker overall absorption

Absorption Line Analysis

def calculate_absorption_profile(wavelengths, molecule_data, temperature=298, pressure=1):
    """
    Calculate absorption profile for given molecule data
    """
    absorption = np.zeros_like(wavelengths)

    for _, row in molecule_data.iterrows():
        # Voigt profile parameters
        gamma_L = row['air_broadened'] * (pressure / 1.0) * (298 / temperature) ** 0.5
        gamma_D = row['nu'] * np.sqrt(8 * k_B * T * np.log(2) / (m * c**2)) / c

        # Voigt profile calculation
        for i, wavelength in enumerate(wavelengths):
            x = (wavelength - row['nu']) / gamma_L
            absorption[i] += row['line_intensity'] * voigt_profile(x, gamma_L, gamma_D)

    return absorption

# Calculate absorption profiles for each molecule
wavelengths = np.linspace(15600, 15900, 1000)  # cm⁻¹
ch4_absorption = calculate_absorption_profile(wavelengths, ch4_data)
h2o_absorption = calculate_absorption_profile(wavelengths, h2o_data)
c2h2_absorption = calculate_absorption_profile(wavelengths, c2h2_data)

Data Visualization Results

Single Molecule Absorption

Single molecule absorption spectra

Key Observations:

  • Water shows strong, broad absorption throughout the region
  • Methane has distinct narrow absorption lines
  • Acetylene shows moderate absorption with specific peaks

Multi-Gas Overlay Analysis

Multi-gas absorption overlay

Interference Analysis:

  • Water vapor dominates baseline absorption
  • Methane lines at 1.59 μm show minimal water interference
  • Acetylene provides additional spectral features for confirmation

Concentration Sensitivity Simulation

def simulate_concentration_effects():
    concentrations = {
        'H2O': 0.01,    # ~1% water vapor
        'CH4': 1e-6,     # 1 ppm methane
        'C2H2': 1e-9,    # 1 ppb acetylene
        'CH3COCH3': 1e-9  # 1 ppb acetone
    }

    # Calculate combined absorption
    total_absorption = np.zeros_like(wavelengths)

    for molecule, conc in concentrations.items():
        molecule_data = get_molecule_data(molecule)
        absorption = calculate_absorption_profile(wavelengths, molecule_data)
        total_absorption += absorption * conc

    return total_absorption

Technical Challenges and Solutions

1. Water Vapor Interference

Challenge: Water vapor absorption overwhelms target biomarkers

Solutions:

  • Differential measurement techniques
  • Reference channel compensation
  • Multi-wavelength ratiometric approaches

2. Low Concentration Detection

Challenge: Target biomarkers in ppb-ppm range

Solutions:

  • Multi-pass absorption cells (10-100m effective path)
  • Cavity-enhanced absorption spectroscopy
  • Wavelength modulation spectroscopy

3. Temperature and Pressure Effects

Challenge: Environmental variables affect absorption lines

Solutions:

  • Real-time environmental monitoring
  • Algorithmic compensation
  • Temperature-controlled optical path

System Architecture

Hardware Components

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│    Laser Source │───▶│  Gas Cell (Sample)│───▶   Photodetector │
│   1.56-1.59 μm  │    │   Multi-pass Cell │    │   InGaAs        │
└─────────────────┘    └──────────────────┘    └─────────────────┘
         │                        │                       │
         ▼                        ▼                       ▼
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│ Temperature     │    │ Pressure Monitor │    │  Signal Processor│
│ Controller      │    │   (MPX Series)   │    │    (STM32)       │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Signal Processing Pipeline

class BreathAnalyzer:
    def __init__(self):
        self.calibration_data = load_calibration_data()
        self.baseline_spectrum = None
        self.noise_filter = KalmanFilter(dim_state=1, dim_obs=1)

    def analyze_breath_sample(self, raw_spectrum):
        # Preprocessing
        filtered_spectrum = self.noise_filter.filter(raw_spectrum)

        # Baseline correction
        if self.baseline_spectrum is None:
            self.baseline_spectrum = filtered_spectrum

        corrected_spectrum = filtered_spectrum - self.baseline_spectrum

        # Concentration estimation
        concentrations = self.estimate_concentrations(corrected_spectrum)

        # Glucose correlation
        glucose_estimate = self.correlate_to_glucose(concentrations)

        return {
            'concentrations': concentrations,
            'glucose_estimate': glucose_estimate,
            'confidence': self.calculate_confidence(corrected_spectrum)
        }

Ethical and Safety Considerations

Privacy and Data Handling

  • All processing performed locally on device
  • No cloud data transmission for health data
  • User-controlled data retention policies
  • Compliance with health data regulations

Safety Limitations

  • No diagnostic capabilities
  • Not for medical decision making
  • Clear user communication about limitations
  • Recommendations for medical consultation

Research Ethics

  • Institutional review board approval needed for clinical studies
  • Informed consent procedures for human testing
  • Data anonymization protocols
  • Transparent reporting of limitations

Current Status and Next Steps

Completed Work

  • ✅ HITRAN database processing and parsing
  • ✅ Spectral region identification
  • ✅ Multi-gas interference analysis
  • ✅ Basic system architecture design

In Progress

  • 🔄 Hardware prototype development
  • 🔄 Signal processing algorithm optimization
  • 🔄 User interface design for home monitoring
  • 🔄 Preliminary testing with simulated samples

Future Development

  1. Laboratory Validation (6 months)

    • Controlled gas mixture testing
    • Sensor calibration and validation
    • Accuracy assessment across ranges

  2. Pilot Study (12 months)

    • Small-scale human testing
    • Correlation with blood glucose measurements
    • User experience evaluation

  3. Regulatory Pathway

    • Medical device classification assessment
    • FDA/CE marking requirements
    • Clinical trial protocols

Collaboration Opportunities

I’m seeking collaboration in the following areas:

  • Clinical Research: Partnerships for human studies
  • Sensor Development: Hardware optimization
  • Data Science: Advanced analysis techniques
  • Medical Expertise: Clinical validation guidance

Technical Specifications

Target Performance Metrics

  • Detection Limit: Sub-ppb for target biomarkers
  • Response Time: <5 seconds per measurement
  • Accuracy: ±15% compared to reference methods
  • Size: Portable device (<500g)

Environmental Requirements

  • Temperature Range: 15-30°C operational
  • Humidity Range: 20-80% RH
  • Power Consumption: <5W average
  • Battery Life: 8+ hours continuous operation

Conclusion

The Photonic Nose project demonstrates the feasibility of using absorption spectroscopy for breath-based biomarker detection. While significant technical challenges remain, the spectral analysis shows promising regions for detecting glucose-related compounds.

The research provides a foundation for further development in non-invasive health monitoring, with potential applications beyond glucose monitoring to include metabolic health monitoring and disease detection.

Data Source: HITRAN 2020 Database Analysis Scripts: Available upon request Contact: hi@wujiajun.space Institution: Shenzhen Tech University, Industrial Design Program

Note: This research is exploratory and not intended for clinical use. Any health-related decisions should be made in consultation with medical professionals.