# Preliminary Study of Real-Time Detection of Chicken Embryo Viability Using Photoplethysmography

## TL;DR

This study introduces a new system using photoplethysmography to detect chicken embryo viability in real-time, improving vaccine production quality control.

## Contribution

A novel real-time photoplethysmography-based system for non-invasive chicken embryo viability detection is developed and tested.

## Key findings

- The system achieved 100% accuracy in distinguishing viable and non-viable embryos.
- It successfully captured PPG signals for 15 viable embryos over days 10–14 with consistent performance.
- The system offers a non-invasive alternative to traditional methods with high accuracy and reliability.

## Abstract

Currently, in influenza vaccine production via the chicken embryo splitting method, embryo viability detection is a pivotal quality control step—non-viable embryos are prone to microbial contamination, directly endangering the vaccine batch quality. However, the predominant manual candling method suffers from unstable accuracy and occupational visual health risks. To address this challenge, we developed a novel real-time embryo viability detection system based on photoplethysmography (PPG) technology, comprising a hardware circuit for chicken embryo PPG signal collection and customized software for real-time signal filtering and time–frequency-domain analysis. Based on this system, we conducted three pivotal experiments: (1) impact of the source–detector spatial arrangement on PPG signal acquisition, (2) viable/non-viable embryo discrimination, and (3) embryo PPG signal detection performance for days 10–14. The experimental results show that within the sample size (15 viable, 5 non-viable embryos), the system achieved a 100% discrimination accuracy; meanwhile, it realized 100% successful multi-day (days 10–14) PPG signal capture for the 15 viable embryos, with consistent performance across the developmental stages. This PPG-based system overcomes limitations of traditional and existing automated methods, provides a non-invasive alternative for embryo viability detection, and presents significant implications for standardizing vaccine production quality control and advancing optical biosensing for biological viability detection.