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Related Concept Videos

Sperm Structure and Semen Composition01:22

Sperm Structure and Semen Composition

During ejaculation, males release around 2-5 milliliters of semen, which is a complex mixture of mature sperm and various fluids produced by accessory glands. The mature sperm cells measure approximately 60 micrometers in length and consist of a head, neck, midpiece, and tail. The head is flattened and tapered, measuring about 4 to 5 micrometers in length. It contains a nucleus with condensed chromosomes and an acrosome, a cap-like structure filled with enzymes essential for penetrating the...

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Predicting Boar Sperm Survival during Liquid Storage Using Vibrational Spectroscopic Techniques.

Serge L Kameni1, Bryan Semon2, Li-Dunn Chen3

  • 1Department of Animal and Dairy Sciences, Mississippi State University, Starkville, MS 39759, USA.

Biology
|October 25, 2024
PubMed
Summary

Artificial insemination (AI) in swine benefits from improved semen evaluation. Raman spectroscopy combined with machine learning accurately predicts boar semen quality and storage survival, enhancing fertility outcomes.

Keywords:
Raman spectroscopyextended semenhogmachine learningnear-infrared spectroscopysemen preservationsperm parameters

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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.