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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Published on: February 8, 2014

Slice-Inference-Assisted Lightweight Small Object Detection Model for Holographic Digital Immunoassay Quantification.

Minjie Han^1,2, Junpeng Zhao², Weiqi Zhao²

¹State Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian 116034, Liaoning, China.

Analytical Chemistry

|June 20, 2025

Summary

A new holographic biosensor with a lightweight deep learning model (SIALSO) offers sensitive, cost-effective detection of chloramphenicol in food. This portable device improves accuracy and reduces computational load for food safety applications.

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Last Updated: Sep 18, 2025

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Area of Science:

Biosensing
Deep Learning
Holographic Imaging

Background:

Portable, cost-effective detection is vital for food safety, environmental monitoring, and clinical diagnostics.
Existing methods like ELISA can be limited in sensitivity and detection range for certain analytes.

Purpose of the Study:

To develop a portable, sensitive, and cost-effective holographic biosensor for chloramphenicol quantification in food samples.
To integrate a sliced inference-assisted lightweight small object detection model (SIALSO) for enhanced detection accuracy and efficiency.

Main Methods:

Lens-free holographic imaging system combined with a lightweight deep learning model (SIALSO).
SIALSO model utilizes sliced inference to improve small object detection and reduce computational complexity.
Digital immunoassay for quantifying chloramphenicol using microsphere probes.

Main Results:

SIALSO biosensor demonstrated a linear detection range of 50 pg/mL to 100 ng/mL (R² = 0.986).
Achieved higher sensitivity and broader detection range compared to ELISA.
SIALSO model reduced computational parameters by 29% versus YOLOv5s, with 98.2% precision and 95.7% recall.

Conclusions:

The SIALSO holographic biosensor provides a robust platform for sensitive and efficient food safety analysis.
This technology lays the groundwork for developing advanced portable detection devices for various monitoring applications.
The integration of holographic imaging and deep learning offers a promising approach for digital immunoassays.