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Prediction of sheep milk chemical composition using milk yield, pH, electrical conductivity and refractive index

Published online by Cambridge University Press:  22 February 2018

Athanasios I Gelasakis ,
Rebecca Giannakou ,
Georgios E Valergakis ,
Paschalis Fortomaris ,
Antonios Kominakis  and
Georgios Arsenos
Athanasios I Gelasakis*
Affiliation:
Veterinary Research Institute, ELGO-Demeter, Thermi, Thessaloniki, GR 57001, Greece
Rebecca Giannakou
Affiliation:
Laboratory of Animal Husbandry, School of Veterinary Medicine, Aristotle University of Thessaloniki, Box 393, Thessaloniki, GR 54124, Greece
Georgios E Valergakis
Affiliation:
Laboratory of Animal Husbandry, School of Veterinary Medicine, Aristotle University of Thessaloniki, Box 393, Thessaloniki, GR 54124, Greece
Paschalis Fortomaris
Affiliation:
Laboratory of Animal Husbandry, School of Veterinary Medicine, Aristotle University of Thessaloniki, Box 393, Thessaloniki, GR 54124, Greece
Antonios Kominakis
Affiliation:
Department of Animal Science and Aquaculture, Agricultural University of Athens, 75 Iera Odos str. Athens, GR 11855, Greece
Georgios Arsenos
Affiliation:
Laboratory of Animal Husbandry, School of Veterinary Medicine, Aristotle University of Thessaloniki, Box 393, Thessaloniki, GR 54124, Greece
*
*For correspondence; e-mail: [email protected]
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Abstract

This Research Communication addresses the hypothesis that fat, protein, lactose and total solids content can be predicted using daily milk yield (DMY), pH, electrical conductivity (MEC) and refractive index (RI) of milk as predictors. It also addresses the possibility of these measurements being used for on-farm benchmarking activities towards selecting the highest yielding animals and flocks regarding milk quality traits (MQT). A total of 308 purebred Frizarta ewes were used for the study. From each individual ewe, a composite milk sample was collected. pH, MEC and RI of milk were measured and the samples were assayed for fat, protein, lactose and total solids content, using an automatic infrared milk analyser. The predictive value of DMY, pH, MEC and RI of milk on its MQT was assessed using multiple linear regression analysis. Significant regression equations were produced for all of the studied traits. RI and MEC were significant and reliable predictors for all studied MQT, whereas DMY was a significant predictor for most MQT with the exception of protein content. pH was a marginally significant predictor for some of the MQTs at the initial development of the equations but proved unreliable after bootstraping. Using these equations a number of ewes varying from 75 (for fat) to 97 (for protein) out of the 100 highest MQT yielders were correctly predicted, whereas, none of the ewes out of the 100 lowest MQT yielders was mispredicted as a high yielder for protein-, lactose- and total solids- content. Three out of 100 lowest fat-yielders were mispredicted as high fat-yielders. Similar equations can be used for benchmarking activities towards selecting the highest protein-, fat-, lactose- and total solids- yielding animals and flocks in cases where laboratories for MQT analyses are not readily available or the cost of chemical analyses is high. The method can be regarded as a handy tool for the dairy industry to readily assess milk quality at the farm level.

Keywords

Sheep milk qualityprediction modelpHelectrical conductivityrefractive index
Type
Research Article
Information
Journal of Dairy Research , Volume 85 , Issue 1 , February 2018 , pp. 78 - 82
Copyright
Copyright © Hannah Dairy Research Foundation 2018 

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