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Visible-near infrared spectroscopy for detection of blood in sheep faeces

E. Kho,J. Fernandes,A. Kotze,G. Fox,M. Lord,A. Beasley,Stephen S. Moore,P. James

Published 2020 in Journal of Near Infrared Spectroscopy

ABSTRACT

Heavy infestations of the blood-sucking gastrointestinal nematodes, Haemonchus contortus can cause severe anaemia in sheep and leakage of blood into the faeces, leading to morbidity and mortality. Early and accurate diagnosis of infections is critical for timely treatment of sheep, minimizing production and sheep welfare impacts. In pursuit of a quick and easy measure of H. contortus infections, we investigated the use of portable visible near infrared spectrometers for detecting the presence of haemoglobin in sheep faeces as an indicator of H. contortus infection. Calibration models built within the 400–600 nm region by partial least square regression resulted in acceptable prediction accuracies (r 2 p > 0.70 and root mean squared error of prediction <2.64 µg Hb mg−1 faeces) for haemoglobin quantification using two spectrometers. The prediction results from support vector machine regression further improved the prediction of haemoglobin in moist sheep faeces (r 2 p > 0.87 and root mean squared error of prediction <2.00 µg haemoglobin mg−1 faeces). Based on a threshold for anthelmintic treatment of 3 µg Hb mg−1 faeces, both the partial least square and support vector machine models showed high sensitivity (89%) and high specificity (>77%). The specificity of the prediction model for detecting haemoglobin in sheep faeces may be improved by adding more variations in faecal composition into the calibration model. Our success in detecting haemoglobin in sheep faeces, following minimal sample preparation, suggests that with further development, vis–near infrared spectroscopy can provide a sensitive and convenient method for on-farm diagnosis of H. contortus infections.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Journal of Near Infrared Spectroscopy

  • Publication date

    2020-06-09

  • Fields of study

    Agricultural and Food Sciences, Medicine, Chemistry, Environmental Science

  • Identifiers
    DOI 10.1177/0967033520927519
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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