Detection of adulteration with cow milk of milk from other species through an immersible photonic immunosensor

Milk and dairy products have high nutritional value and their consumption contributes to a balanced and nutritious diet. Cow milk is more allergenic than the milk from other species and therefore adulteration of these milks with cow milk can pose a serious threat to consumers [1]. To detect milk adulteration, several methods have been employed which, however, cannot be performed at the point of need. On the other hand, biosensors can provide fast and quantitative on-site determinations [2]. In this work, a silicon-based photonic dip-stick immunosensor, which includes two U-shaped Mach-Zehnder Interferometers (MZIs), was employed for the detection of ewe, goat and donkey milk adulteration with cow milk. The sensing windows of the two MZIs are located to one end of the chip allowing its immersion directly into the sample and the light input and output ports are located at the other end of the chip which is connected through a bifurcated optical fiber to white light source and a spectrophotometer. The transmission spectrum of both MZIs is subjected to Fourier transform to distinguish and monitor in real-time the phase shifts due to bioreactions taking place onto the sensing windows of the two MZIs. For detection of milk adulteration, the sensing arm of one of the MZIs is modified with bovine k-casein and the other with blocking protein to serve as reference. A competitive immunoassay format was followed where the chip was first immersed in a mixture of 50-times diluted milk with a rabbit anti-k-casein antibody solution for 5 min, followed by 5-min immersion in a secondary antibody solution. Limit of detections of 0.05 and 0.1% cow milk in ewe/goat and donkey milk, respectively, were achieved for a total assay time of 12 min. Thus, this fast, sensitive, and simple assay is ideal for on-site detection of milk adulteration.

References:

1. H. Montgomery, S.A. Haughey, C.T. Elliott, Glob. Food Sec. 26 (2020) 100447.
2. M. Nehra, M. Lettieri, N. Dilbaghi, S. Kumar, G. Marrazza, Sensors 20 (2020) 32.

Acknowledgment: This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: Τ2ΕΔΚ-01934 FOODSENS).