ArticleVeterinary sciences2022
Estrus Detection in a Dairy Herd Using an Electronic Nose by Direct Sampling on the Perineal Region.
Article in Veterinary sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Estrus detection in dairy cattle: an updated review on strategies and technologies.Frontiers in veterinary science · 2026Review
- Automatic cattle identification system based on color point cloud using hybrid PointNet++ Siamese network.Scientific reports · 2025Article
- Interpretable Artificial Intelligence for Analysing Changes in Gases in the Uterine Environment of Cows According to Physiological Structures in the Ovary.Veterinary medicine and science · 2025Article
- Recent Advances in Biosensor Technologies for Meat Production Chain.Foods (Basel, Switzerland) · 2025Review
- Profile of uterine flush lipid mediators in cows with subclinical endometritis: pilot study.The Journal of veterinary medical science · 2024Article
- Differences in Milk Proteomic Profiles between Estrous and Non-Estrous Dairy Cows.Animals : an open access journal from MDPI · 2023Article
- Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases.Animals : an open access journal from MDPI · 2023Review
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Authors and funding
11 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estrus detection is very important for the profitability of dairy herds. Different automatic systems for estrus detection have been developed over the last decades. Our study aimed to assess the ability of the electronic nose (EN) MENT-EGAS prototype to detect estrus, based on odor release from the perineal headspace in dairy cattle by direct sampling. The study was performed in an Italian dairy farm using 35 multiparous Holstein-Friesian cows. The cows were divided into three groups: group I included 10 lactating 5-month pregnant cows, group II included 19 lactating cycling cows, and group III included 6 cows that were artificially inseminated 18 days before the trial. Odors from the perineal headspace were collected using the MENT-EGAS prototype. In group I, odors were collected once a day for 5 consecutive days. In group II, odors were collected twice daily from day 18 until day 1 of the reproductive cycle. In group III, odors were also collected twice daily from the presumable day 18 of gestation until day 22. Principal component analyses (PCA) of the perineal headspace samples were performed. PCA in group I revealed no significant discrimination. PCA in group II revealed clear discrimination between proestrus and estrus, and between estrus and metestrus but no significant discrimination was obtained between proestrus and metestrus. PCA in group III revealed that in four cows the results were similar to group I and in two cows the results were similar to group II. On day 40 of the presumable pregnancy, the ultrasound examination revealed that only the four cows were pregnant and the other two cows were regularly cycling. On the basis of our findings, we conclude that it is possible to accurately detect estrus in dairy cattle from directly collected odor samples using the MENT-EGAS prototype. This represents the first study of estrus detection using an EN detection by direct sampling. EN technologies, such as MENT-EGAS, could be applied in the future in dairy cattle farms as a precise, non-invasive method for estrus detection.
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