“…For example, (Dave et al, 2016) used two parameters, the attenuation coefficient and time of flight to analyze the fat content and the amount of water adulteration present in raw milk samples obtained from different dairies and skimmed milk samples with different fat content from popular Indian brands. Also, (Nazário et al, 2008) applied neural networks techniques to classify milk samples as a function of the amount of water and serum added to milk, relating these properties to acoustic parameters (Ultrasonic propagation velocity and attenuation coefficient) as a function of temperature. Finally, (Elvira and Rodriguez, 2009) showed that acoustical characterization of tainted liquid milk, through density and/or sound speed measurements, can be used to detect gross melamine contamination.…”