Indian Journal of Animal Research

  • Chief EditorK.M.L. Pathak

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Indian Journal of Animal Research, volume 50 issue 6 (december 2016) : 969-973

Receiver operating characteristic analysis of milk lactose for identification of mastitis in buffaloes 

T.K. Patbandha*, K. Ravikala, B.R. Maharana, Rupal Pathak1, S. Marandi, P.U. Gajbhiye2, T.K. Mohanty3, R. Malhotra3
1<p>College of Veterinary Science and A.H.,&nbsp;Junagadh Agricultural University (JAU), Junagadh-362 001, India.</p>
Cite article:- Patbandha* T.K., Ravikala K., Maharana B.R., Pathak1 Rupal, Marandi S., Gajbhiye2 P.U., Mohanty3 T.K., Malhotra3 R. (2016). Receiver operating characteristic analysis of milk lactosefor identification of mastitis in buffaloes . Indian Journal of Animal Research. 50(6): 969-973. doi: 10.18805/ijar.11176.

Receiver operating characteristic (ROC) analysis is a simple statistical tool used to classify a diagnostic indicator in terms of area under a ROC curve (AUC) and to develop potential threshold values of a diagnostic indicator. Milk lactose was analyzed by ROC analysis to see its accuracy to discriminate infected and healthy udder quarters, and to develope an optimum threshold value along with corresponding sensitivity (Se), specificity (Sp) and positive likelihood ratio (LR+) value. Data for the present study comprised of 1516 milk samples collected from Jaffrabadi buffaloes. Milk lactose was estimated by milk analyzer ‘LACTOSCAN’ and further samples were checked for sub-clinical mastitis by California mastitis test (CMT). The threshold values of milk lactose for identification of moderate and severe infection were found to be 5.31g% (Se, 58.82%; Sp, 58.28%) and 5.23g% (Se, 70.97%; Sp, 64.41%), respectively by ROC analysis. Milk samples with lactose content below 5.31g% were 1.41 times more likely come from moderately infected quarters (LR+ = 1.41); whereas, below 5.23g% were 1.99 times more likely come from severely infected quarters (LR+ = 1.99). The overall accuracy of milk lactose for discrimination of normal quarters from moderately infected quarters was 64% (AUC=0.64) and from severely infected quarters was 72% (AUC=0.72) (P<0.001). Thus, the present study indicated that milk lactose classified mastitic and healthy udder quarters in Jaffrabadi buffaloes with moderate accuracy.


  1. Bansal, B.K., Hamann, J., Grabowski, N. and Singh, K.B. (2005). Variation in the composition of selected milk fraction samples from healthy and mastitis quarters, and its significance for mastitis diagnosis. J. Dairy Res., 72:144-152.

  2. Bansal, B.K., Hamann, J., Lind, O., Singh, S.T. and Dhaliwal, P.S. (2007). Somatic cell count and biochemical components of milk related to udder health in buffaloes. Ital. J. Anim. Sci., 6:1035-1038.

  3. Fan, J., Upadhye, S. and Worster, A. (2006). Understanding receiver operating characteristic (ROC) curves. Can J Emerg Med., 8:19-20.

  4. Hortet, P. and Seegers, H. (1998). Loss in milk yield and related composition changes resulting from clinical mastitis in dairy cows. Prev. Vet. Med., 37:1-20.

  5. Hughes, G. and Bhattacharya, B. (2013). Symmetry properties of bi-normal and bi-gamma receiver operating characteristic curves are described by Kullback-Leibler divergences. Entropy, 15:1342-1356.

  6. Hussain, R., Javed M.T. and Khan, A. (2012). Changes in some biochemical parameters and somatic cell counts in the milk of buffalo and cattle suffering from mastitis. Pak. Vet. J., 32:418-421.

  7. Reis, C.B.M., Barreiro, J.R., Mestieri, L., Porcionato, M.A. and dos Santos, M.V. (2013). Effect of somatic cell count and mastitis pathogens on milk composition in Gyr cows. BMC Vet. Res., 9:67. doi: 10.1186/1746-6148-9-67.

  8. Patbandha, T.K., Mohanty, T.K., Layek, S.S., Kumaresan, A. and Behera, K. (2012). Application of pre-partum feeding and social behaviour in predicting risk of developing metritis in crossbred cows. Appl. Anim. Behav. Sci., 139:10-17.

  9. Patbandha, T.K., Mohanty, T.K., Layek, S.S., Kumaresan, A., Kantwa, S.C., Malhotra, R., Ruhil, A.P. and Prasad, S. (2013). ROC analysis of pre-partum feeding time can accurately predict post-partum metritis development in Holstein Friesian (HF) crossbred cows. J. Vet. Behav., 8:362-366.

  10. Peterson, A.T., Papes, M. and Soberon, J. (2008). Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecol. Model., 213:63-72.

  11. Pyorala, S. (2003). Indicators of inflammation in the diagnosis of mastitis. Vet. Res., 34:565-578.

  12. Ravikala, K., Patbandha T. K. and Vataliya, P.H. (2014). Nutritional management of dairy animals through milk yield and its component evaluation. Proc. of 21st annual convention of Indian Society of Animal Production and Management, January 28-30, AAU, Anand, Gujarat, India. pp. 137-144.

  13. Sharif, A., Ahmad, T., Bilal1, M.Q., Yousaf, A. and Muhammad, G. (2007). Effect of severity of sub-clinical mastitis on somatic cell count and lactose contents of buffalo milk. Pak. Vet. J., 27:142-144.

  14. Kumari, S., Prasad, S., Patbandha, T.K., Pathak, R., Kumaresan, A., Boro, P., Manimaran A. and Mohanty, T.K. (2014). Metabolic indicators for retention of fetal membranes in Zebu and crossbred dairy cattle. Anim Prod. Sci., doi: 10.1071/AN14941

  15. Tripaldi, C., Palocci, G., Miarelli, M., Catta, M., Orlandini, S., Amatiste, S., Di Bernardini, R. and Catillo, G. (2010). Effects of mastitis on buffalo milk quality. Asian-Aust. J. Anim. Sci., 23:1319-1324.

  16. Zou, K.H., O’Malley, A.J. and Mauri, L. (2007). Receiver Operating Characteristic analysis for evaluating diagnostic tests and predictive models. Circulation, 115:654-657.

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