Department of Food Engineering, Hacettepe University, Beytepe 06800, Ankara, Turkey.
NANOSENS Industry and Trade Inc., Ankara University Technology Development Zone, 06830, Golbasi, Ankara, Turkey.
Food Chem. 2019 Jun 30;284:60-66. doi: 10.1016/j.foodchem.2019.01.093. Epub 2019 Jan 22.
In the dairy industry, substitution of high priced milk species with low priced ones is a common practice, and determination of milk species is critical. In this study, synchronous fluorescence spectroscopy (SFS) method was developed for identification of milk species in fermented dairy products (yoghurt and cheese). Three partial least square-discriminant analysis models were developed in order to identify pure-mixed samples, milk species and binary mixture type, and partial least square (PLS) model was utilized to quantify the mixing ratio in binary mixtures. PLS models used for yoghurt and cheese samples showed that detection limits of adulteration were below 3.3%. Apart from the buffalo-cow yoghurt and goat-cow cheese, precision of the measurements was found to be below 6.2. It can be said that SFS technique is applicable on yoghurt and cheese samples as it's a less destructive and a less costly method compared to DNA and protein based conventional methods.
在乳制品行业,用低价牛奶品种替代高价牛奶品种是一种常见做法,因此确定牛奶品种至关重要。本研究采用同步荧光光谱法(SFS)鉴定发酵乳制品(酸奶和奶酪)中的牛奶品种。为了鉴定纯混合样品、牛奶品种和二元混合物类型,建立了三个偏最小二乘判别分析模型,并利用偏最小二乘(PLS)模型对二元混合物的混合比例进行定量。用于酸奶和奶酪样品的 PLS 模型表明,掺假检测限低于 3.3%。除了水牛-奶牛酸奶和山羊-奶牛奶酪之外,测量精度低于 6.2。可以说,与基于 DNA 和蛋白质的传统方法相比,SFS 技术适用于酸奶和奶酪样品,因为它的破坏性和成本都较低。