Inline contaminant monitoring based on holographic imaging

Overview

This solution offers a compact, label-free microbial monitoring platform based on digital holographic imaging. It enables real-time detection and differentiation of bacteria, fungi, and other microbes commonly found in aqueous solutions, while simultaneously assessing cell viability and gram positivity. By eliminating the need for reagents or staining, the technology dramatically reduces per-test costs and integrates directly into existing fluid lines or medical devices for continuous inline monitoring.

The platform targets applications where rapid and reliable contaminant detection is essential for product safety and regulatory compliance, including pharmaceutical manufacturing, medical device production, and bioprocessing.

Technical specifications

Key features:

• Digital holographic imaging using low-power laser sources at multiple wavelengths to capture morphological and biochemical signatures of individual cells
• Machine learning-driven analysis for real-time classification of microbes, viability, and gram positivity without labels or reagents
• Single-cell sensitivity capable of detecting individual bacteria and fungi
• Compact, integratable hardware designed for embedding directly into medical devices or production lines
• Cloud-based data computing and management for scalable deployment and remote monitoring
• Near-zero cost per test since no consumable reagents are required; only hardware setup and maintenance

Underlying approach: The biochemical composition of a cell influences how it absorbs and transmits light at different wavelengths. When laser light passes through a cell, the interference between scattered and reference light produces holograms encoding this information. Machine learning models trained on these holographic datasets extract morphological and biochemical features to classify microbial contaminants in real time.

Patented technologies: Four patented techniques support the platform, including real-time holography for particle analysis using machine learning and multi-wavelength holography for cell analysis.

Technology readiness level

The technology has been validated in laboratory settings, demonstrating accurate detection and differentiation of five common microbes as well as viability and metabolic state assessment of yeast cells. Current efforts focus on systematic holographic imaging of additional microbes relevant to industrial use cases, with the goal of building expanded machine learning models for real-time deployment. The development of both hardware and software packages, including cloud integration, is underway. The projected timeline for industrial deployment is one to two years, depending on partner-specific requirements.