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利用红外血清光谱和机器学习技术提高高产奶牛雌性个体的早期识别。

Enhancing early identification of high-fertile cattle females using infrared blood serum spectra and machine learning.

机构信息

Veterinary Science Graduate Program, Universidade Federal de Mato Grosso do Sul - UFMS, Campo Grande, MS, 79070-900, Brazil.

Optics and Photonics Laboratory - SISFOTON/UFMS, Universidade Federal de Mato Grosso do Sul - UFMS, Campo Grande, MS, 79070-900, Brazil.

出版信息

Sci Rep. 2024 Aug 21;14(1):19446. doi: 10.1038/s41598-024-70211-1.

DOI:10.1038/s41598-024-70211-1
PMID:39169105
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11339307/
Abstract

Artificial insemination (AI) success in bovine reproduction is vital for the cattle industry's economic sustainability and for advancing the understanding of reproductive physiology. Identify high-fertile animals' fertility is a complex task due to multifactorial traits, including hormonal, age-related, and body condition factors. Early high-fertility identification is crucial for timely interventions and enhancing AI success. In this study, we present the potential use of Fourier-transform infrared (FTIR) spectroscopy on blood serum for early identification of high-fertile Nellore female cows for AI protocols. Blood serum FTIR spectra were obtained from Nellore female cows before AI. FTIR spectra underwent data analysis and the results demonstrated successful discrimination between animals that exhibit pregnant and non-pregnant diagnoses 30 days after AI. FTIR spectra revealed consistent vibrational modes, emphasizing Amide I and II bands. Principal Component Analysis (PCA) effectively segregated groups based on molecular information. Linear SVM with C = 10 and 4 PCs achieved 100% accuracy in the group classification. This innovative approach using FTIR spectroscopy and ML algorithms offers a promising means of high-fertile cow identification, potentially improving AI outcomes in Nellore cattle. The study presents valuable insights into advancements in reproductive management practices for this economically significant breed.

摘要

人工授精（AI）在牛繁殖中的成功对牛养殖业的经济可持续性以及对生殖生理学的理解至关重要。由于激素、年龄相关和身体状况等多因素特征，识别高繁殖力动物的繁殖力是一项复杂的任务。早期识别高繁殖力对于及时干预和提高 AI 成功率至关重要。在这项研究中，我们提出了在血液血清中使用傅里叶变换红外（FTIR）光谱技术，以早期识别用于 AI 方案的高繁殖力内罗尔母牛。在 AI 之前，从内罗尔母牛采集了血液血清 FTIR 光谱。对 FTIR 光谱进行了数据分析，结果表明，在 AI 后 30 天，成功区分了表现出妊娠和非妊娠诊断的动物。FTIR 光谱揭示了一致的振动模式，强调了酰胺 I 和 II 带。主成分分析（PCA）有效地根据分子信息对组进行了分类。使用 C=10 和 4 个 PCs 的线性 SVM 实现了组分类的 100%准确性。这种使用 FTIR 光谱和 ML 算法的创新方法为识别高繁殖力的奶牛提供了一种有前途的手段，可能会提高内罗尔牛的 AI 结果。该研究为这一具有经济重要性的品种的生殖管理实践的进步提供了有价值的见解。

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