不同建模策略下过渡乳谱对荷斯坦奶牛子宫炎和乳腺炎的预测和分类

IF 4.4 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
D Lin, J A A McArt
{"title":"不同建模策略下过渡乳谱对荷斯坦奶牛子宫炎和乳腺炎的预测和分类","authors":"D Lin, J A A McArt","doi":"10.3168/jds.2024-26217","DOIUrl":null,"url":null,"abstract":"<p><p>Metritis and mastitis are common early-lactation diseases of dairy cows that reduce milk production. Early prediction enables timely intervention and management, yet no studies have investigated the ability of milk Fourier-transform infrared (FTIR) spectroscopy for predicting the onset and development of metritis or mastitis within the first 2 wk postpartum. Our study aimed to assess the potential of milk FTIR spectra for early detection of postpartum metritis and clinical mastitis and to describe their spectral variations as lactation advances and diseases progress. Holstein cows (n = 1,103) from a commercial dairy farm in Cayuga County, New York, were monitored through 14 DIM and classified as healthy (n = 784; no adverse health events) or as diagnosed with metritis (n = 57; diagnosis of metritis but not ketosis, displaced abomasum, or clinical mastitis within 14 DIM) or clinical mastitis (n = 72; diagnosis of clinical mastitis but not ketosis, displaced abomasum, or metritis within 14 DIM). We constructed models for predicting and classifying postpartum metritis and mastitis using pooled, multiblock, and single-day partial least squares discriminant analysis (PLS-DA) strategies, assessed with repeated leave-one-out cross-validation and permutation tests. Across all modeling strategies, metritis was more distinguishable than mastitis, a pattern that corresponded with increasing fat and decreasing protein and lactose absorbance in transition milk from cows developing metritis. In the pooled strategy, models using spectra from DIM 1 to 7 achieved average area under the receiver operating characteristic curve of 79.4% for identifying metritis from healthy cows and 79.0% for distinguishing metritis from mastitis, whereas mastitis prediction reached only 60.7%. The multiblock and single-day PLS-DA models showed similarly strong performance for metritis (up to 79.2%) but failed to detect mastitis reliably. Furthermore, the added value of FTIR spectra for metritis prediction appeared contingent on sufficient sample size, as demonstrated by down-sampling experiments in the pooled strategy (with the down-sampled ratios of 80%, 60%, 40%, 20%, 10%, 5%), where models with spectral data outperformed those without only at or above 40% sampling. We conclude that transition milk FTIR spectra within the first 7 DIM showed disease-related signatures that may support early identification, although performance varied with sample size and modeling strategy, and multiherd validation is required to confirm generality and practical value.</p>","PeriodicalId":354,"journal":{"name":"Journal of Dairy Science","volume":" ","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction and classification of metritis and mastitis in Holstein cows using transition milk spectra under different modeling strategies.\",\"authors\":\"D Lin, J A A McArt\",\"doi\":\"10.3168/jds.2024-26217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Metritis and mastitis are common early-lactation diseases of dairy cows that reduce milk production. Early prediction enables timely intervention and management, yet no studies have investigated the ability of milk Fourier-transform infrared (FTIR) spectroscopy for predicting the onset and development of metritis or mastitis within the first 2 wk postpartum. Our study aimed to assess the potential of milk FTIR spectra for early detection of postpartum metritis and clinical mastitis and to describe their spectral variations as lactation advances and diseases progress. Holstein cows (n = 1,103) from a commercial dairy farm in Cayuga County, New York, were monitored through 14 DIM and classified as healthy (n = 784; no adverse health events) or as diagnosed with metritis (n = 57; diagnosis of metritis but not ketosis, displaced abomasum, or clinical mastitis within 14 DIM) or clinical mastitis (n = 72; diagnosis of clinical mastitis but not ketosis, displaced abomasum, or metritis within 14 DIM). We constructed models for predicting and classifying postpartum metritis and mastitis using pooled, multiblock, and single-day partial least squares discriminant analysis (PLS-DA) strategies, assessed with repeated leave-one-out cross-validation and permutation tests. Across all modeling strategies, metritis was more distinguishable than mastitis, a pattern that corresponded with increasing fat and decreasing protein and lactose absorbance in transition milk from cows developing metritis. In the pooled strategy, models using spectra from DIM 1 to 7 achieved average area under the receiver operating characteristic curve of 79.4% for identifying metritis from healthy cows and 79.0% for distinguishing metritis from mastitis, whereas mastitis prediction reached only 60.7%. The multiblock and single-day PLS-DA models showed similarly strong performance for metritis (up to 79.2%) but failed to detect mastitis reliably. Furthermore, the added value of FTIR spectra for metritis prediction appeared contingent on sufficient sample size, as demonstrated by down-sampling experiments in the pooled strategy (with the down-sampled ratios of 80%, 60%, 40%, 20%, 10%, 5%), where models with spectral data outperformed those without only at or above 40% sampling. We conclude that transition milk FTIR spectra within the first 7 DIM showed disease-related signatures that may support early identification, although performance varied with sample size and modeling strategy, and multiherd validation is required to confirm generality and practical value.</p>\",\"PeriodicalId\":354,\"journal\":{\"name\":\"Journal of Dairy Science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Dairy Science\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.3168/jds.2024-26217\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, DAIRY & ANIMAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Dairy Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.3168/jds.2024-26217","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0

摘要

子宫炎和乳腺炎是奶牛常见的泌乳早期疾病,会降低产奶量。早期预测可以及时干预和管理,但没有研究调查牛奶傅里叶变换红外(FTIR)光谱预测产后2周内子宫炎或乳腺炎的发生和发展的能力。我们的研究旨在评估牛奶FTIR光谱在产后子宫炎和临床乳腺炎早期检测中的潜力,并描述其光谱随哺乳进展和疾病进展的变化。来自纽约州卡尤加县一家商业奶牛场的荷斯坦奶牛(n = 1103)通过14个DIM进行监测,并将其分类为健康(n = 784,无不良健康事件)或诊断为子宫炎(n = 57,诊断为子宫炎,但未诊断为酮症、移位性皱胃或临床乳腺炎)或临床乳腺炎(n = 72,诊断为临床乳腺炎,但未诊断为酮症、移位性皱胃或子宫炎)。我们使用合并、多区块和单日偏最小二乘判别分析(PLS-DA)策略构建了预测和分类产后子宫炎和乳腺炎的模型,并使用重复的留一交叉验证和排列检验进行评估。在所有建模策略中,子宫炎比乳腺炎更容易区分,这种模式与发生子宫炎的奶牛的过渡乳中脂肪增加、蛋白质和乳糖吸收减少相对应。在合并策略中,使用DIM 1 ~ 7谱线的模型对健康奶牛的指标炎的平均识别面积为79.4%,对乳腺炎和指标炎的平均识别面积为79.0%,而乳腺炎的预测面积仅为60.7%。多块和单日PLS-DA模型对乳腺炎的检测效果相似(高达79.2%),但不能可靠地检测乳腺炎。此外,FTIR光谱对指标预测的附加值似乎取决于足够的样品量,正如在混合策略中的下采样实验(下采样率为80%,60%,40%,20%,10%,5%)所证明的那样,其中具有光谱数据的模型优于仅在40%或以上采样的模型。我们的结论是,前7个DIM内的过渡牛奶FTIR光谱显示了与疾病相关的特征,可能支持早期识别,尽管性能因样本量和建模策略而异,并且需要多群验证以确认普遍性和实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and classification of metritis and mastitis in Holstein cows using transition milk spectra under different modeling strategies.

Metritis and mastitis are common early-lactation diseases of dairy cows that reduce milk production. Early prediction enables timely intervention and management, yet no studies have investigated the ability of milk Fourier-transform infrared (FTIR) spectroscopy for predicting the onset and development of metritis or mastitis within the first 2 wk postpartum. Our study aimed to assess the potential of milk FTIR spectra for early detection of postpartum metritis and clinical mastitis and to describe their spectral variations as lactation advances and diseases progress. Holstein cows (n = 1,103) from a commercial dairy farm in Cayuga County, New York, were monitored through 14 DIM and classified as healthy (n = 784; no adverse health events) or as diagnosed with metritis (n = 57; diagnosis of metritis but not ketosis, displaced abomasum, or clinical mastitis within 14 DIM) or clinical mastitis (n = 72; diagnosis of clinical mastitis but not ketosis, displaced abomasum, or metritis within 14 DIM). We constructed models for predicting and classifying postpartum metritis and mastitis using pooled, multiblock, and single-day partial least squares discriminant analysis (PLS-DA) strategies, assessed with repeated leave-one-out cross-validation and permutation tests. Across all modeling strategies, metritis was more distinguishable than mastitis, a pattern that corresponded with increasing fat and decreasing protein and lactose absorbance in transition milk from cows developing metritis. In the pooled strategy, models using spectra from DIM 1 to 7 achieved average area under the receiver operating characteristic curve of 79.4% for identifying metritis from healthy cows and 79.0% for distinguishing metritis from mastitis, whereas mastitis prediction reached only 60.7%. The multiblock and single-day PLS-DA models showed similarly strong performance for metritis (up to 79.2%) but failed to detect mastitis reliably. Furthermore, the added value of FTIR spectra for metritis prediction appeared contingent on sufficient sample size, as demonstrated by down-sampling experiments in the pooled strategy (with the down-sampled ratios of 80%, 60%, 40%, 20%, 10%, 5%), where models with spectral data outperformed those without only at or above 40% sampling. We conclude that transition milk FTIR spectra within the first 7 DIM showed disease-related signatures that may support early identification, although performance varied with sample size and modeling strategy, and multiherd validation is required to confirm generality and practical value.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Dairy Science
Journal of Dairy Science 农林科学-奶制品与动物科学
CiteScore
7.90
自引率
17.10%
发文量
784
审稿时长
4.2 months
期刊介绍: The official journal of the American Dairy Science Association®, Journal of Dairy Science® (JDS) is the leading peer-reviewed general dairy research journal in the world. JDS readers represent education, industry, and government agencies in more than 70 countries with interests in biochemistry, breeding, economics, engineering, environment, food science, genetics, microbiology, nutrition, pathology, physiology, processing, public health, quality assurance, and sanitation.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信