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Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods

Published online by Cambridge University Press:  22 March 2017

Serap Durakli Velioglu*
Affiliation:
Department of Food Engineering, Faculty of Agriculture, Namik Kemal University, 59030 Tekirdag, Turkey
Elif Ercioglu
Affiliation:
Department of Food Engineering, Faculty of Engineering, Hacettepe University, Beytepe, 06800 Ankara, Turkey
Ismail Hakki Boyaci
Affiliation:
Department of Food Engineering, Faculty of Engineering, Hacettepe University, Beytepe, 06800 Ankara, Turkey
*
*For correspondence; e-mail: [email protected]

Abstract

This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400–550 nm excitation range with Δλ of 10–100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.

Type
Research Article
Copyright
Copyright © Proprietors of Journal of Dairy Research 2017 

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