Microwave Dielectric Response of Bovine Milk as Pregnancy Detection Tool in Dairy Cows

Cindy Galindo, Guy Levy, Yuri Feldman, Zvi Roth, Jonathan Shalev, Chen Raz, Edo Mor, Nurit Argov-Argaman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The most reliable methods for pregnancy diagnosis in dairy herds include rectal palpation, ultrasound examination, and evaluation of plasma progesterone concentrations. However, these methods are expensive, labor-intensive, and invasive. Thus, there is a need to develop a practical, non-invasive, cost-effective method that can be implemented on the farm to detect pregnancy. This study suggests employing microwave dielectric spectroscopy (MDS, 0.5–40 GHz) as a method to evaluate reproduction events in dairy cows. The approach involves the integration of MDS data with information on milk solids to detect pregnancy and identify early embryonic loss in dairy cows. To test the ability to predict pregnancy according to these measurements, milk samples were collected from (i) pregnant and non-pregnant randomly selected cows, (ii) weekly from selected cows (n = 12) before insemination until a positive pregnancy test, and (iii) daily from selected cows (n = 10) prior to insemination until a positive pregnancy test. The results indicated that the dielectric strength of Δε and the relaxation time, τ, exhibited reduced variability in the case of a positive pregnancy diagnosis. Using principal component analysis (PCA), a clear distinction between pregnancy and nonpregnancy status was observed, with improved differentiation upon a higher sampling frequency. Additionally, a neural network machine learning technique was employed to develop a prediction algorithm with an accuracy of 73%. These findings demonstrate that MDS can be used to detect changes in milk upon pregnancy. The developed machine learning provides a broad classification that could be further enhanced with additional data.

Original languageAmerican English
Article number2742
JournalSensors
Volume24
Issue number9
DOIs
StatePublished - May 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • bovine milk
  • microwave dielectric spectroscopy
  • non-invasive pregnancy detection
  • water

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