2016
DOI: 10.1016/j.idairyj.2015.08.004
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A novel method for the quantification of bovine colostral immunoglobulin G using infrared spectroscopy

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Cited by 12 publications
(26 citation statements)
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“…For the IR spectroscopy, the cut‐point of 50 g/L provided strong specificity and moderate sensitivity values across the unheated and heat‐treated colostrum (Table ). The accuracy of the IR spectroscopy (91.7%) to differentiate between unheated poor and good quality colostrum was higher than that reported in previous studies . However, the accuracy of the IR instrument to correctly determine quality of heat‐treated colostrum was lower with more samples with poor IgG levels categorized as adequate.…”
Section: Discussionmentioning
confidence: 56%
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“…For the IR spectroscopy, the cut‐point of 50 g/L provided strong specificity and moderate sensitivity values across the unheated and heat‐treated colostrum (Table ). The accuracy of the IR spectroscopy (91.7%) to differentiate between unheated poor and good quality colostrum was higher than that reported in previous studies . However, the accuracy of the IR instrument to correctly determine quality of heat‐treated colostrum was lower with more samples with poor IgG levels categorized as adequate.…”
Section: Discussionmentioning
confidence: 56%
“…Collected spectra were converted into a printable format (PRN) in GRAMS software . A previously developed PLS model built for the prediction of bovine colostral IgG concentrations by IR spectra was used to predict colostral IgG concentrations by MATLAB software . The IgG concentration was predicted from each spectrum, and subsequently, the IgG concentration for each colostrum sample was calculated as the average of the 6 replicate values.…”
Section: Methodsmentioning
confidence: 99%
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“…Current research also deals with the risk factors associated with insufficient passive transfer, mainly poor colostrum quality (Beam et al, 2009;Abdel-Salam et al, 2014;Cabral et al, 2016;Elsohaby et al, 2016;Reschke et al, 2017), inconvenient management factors (e.g. Vogels et al, 2013, Cuttance et al, 2017 and additional risk factors, for example breed, herd size, housing system, month of calving, duration of gestation, dry period length, parity of the dam or individual farm influence (McGuirk and Collins, 2004;MacFarlane et al, 2015;Reschke et al, 2017).…”
Section: Introductionmentioning
confidence: 99%