Smartphone-based quantitative measurements on holographic sensors

Area of Science:

• Analytical Chemistry
• Biomedical Engineering
• Computer Science

Background:

• Standardizing analyte concentration determination is crucial for accurate diagnostics.
• Current methods can be complex and require specialized equipment.
• Holographic sensors offer potential for sensitive detection but require robust quantification.

Purpose of the Study:

• To develop and validate a smartphone-based color quantification algorithm for holographic sensors.
• To standardize and improve the determination of analyte concentrations.
• To create a cost-effective platform for remote, real-time analyte monitoring.

Main Methods:

• Integration of a generic holographic sensor platform with a smartphone color quantification algorithm.
• Image processing including encryption, compression, hologram recognition, and color space conversion (RGB to CIEXYZ, CIEL*a*b*).
• Machine learning algorithms to correlate color coordinates with analyte concentration.

Main Results:

• Demonstrated utility by analyzing holographic pH sensor color in near real-time.
• Successfully recognized holograms in complex backgrounds and segmented images.
• Correlated color coordinates to analyte concentration using machine learning.

Conclusions:

• The integrated holographic sensor and smartphone algorithm offers a standardized and improved method for analyte quantification.
• This approach facilitates cost-effective, real-time remote monitoring of analytes in accessible body fluids.
• Potential for use by minimally trained individuals in diverse settings.