Predicting Boar Sperm Survival during Liquid Storage Using Vibrational Spectroscopic Techniques

Area of Science:

• Reproductive Biology
• Biotechnology
• Spectroscopy

Background:

• Artificial insemination (AI) is vital for swine reproduction, relying heavily on semen quality.
• Current semen analysis methods show limitations, leading to fertility inconsistencies.
• Enhanced evaluation of cool-stored boar semen is crucial for optimizing AI success.

Purpose of the Study:

• To assess boar semen quality during 10-day storage at 17 °C.
• To investigate the utility of Raman and near-infrared (NIR) spectroscopy with machine learning for semen evaluation.
• To correlate spectroscopic data with sperm parameters and predict semen viability.

Main Methods:

• Boar semen samples were stored for 10 days at 17 °C.
• Analysis included motility, morphology, membrane integrity, apoptosis, and oxidative stress.
• Raman and NIR spectroscopy were employed alongside machine learning algorithms.
• Computer-assisted sperm analysis was used for quantitative assessment.

Main Results:

• Sperm motility and morphology declined significantly during storage.
• Poor quality semen samples exhibited higher apoptosis, membrane damage, and reactive oxygen species on Day 0.
• Raman spectroscopy provided distinct spectral profiles correlating with semen quality and predicted storage survival effectively.
• Raman spectroscopy demonstrated superior performance over NIR spectroscopy.

Conclusions:

• Raman spectroscopy coupled with machine learning offers a promising approach for objective boar semen evaluation.
• This method can predict semen quality and survival during storage, aiding AI programs.
• Spectroscopic analysis enhances the prognosis of semen viability, improving reproductive efficiency in swine.