A New Acoustic-Based Approach for Assessing Induced Adulteration in Bovine Milk

Area of Science:

• Food Science
• Analytical Chemistry
• Instrumentation Engineering

Background:

• Milk's nutritional importance necessitates robust quality control measures.
• Increased demand for milk makes it vulnerable to fraudulent adulteration.
• Existing quality control methods can be costly, complex, or lack portability.

Purpose of the Study:

• To develop a simple, low-cost, and portable system for identifying industrial contaminants in milk.
• To utilize acoustic wave propagation and advanced signal processing for milk adulteration detection.
• To assess the effectiveness of specific signal processing techniques in contaminant classification.

Main Methods:

• Employed a pitch-catch mode system with low-cost piezoelectric transducers to propagate acoustic waves.
• Tested bovine milk samples adulterated with sodium bicarbonate, urea, and hydrogen peroxide (0.5%).
• Applied signal processing techniques including chromatic technique, correlation coefficient, Euclidean norm, and cross-correlation square difference (CCSD).

Main Results:

• Cross-correlation square difference (CCSD) and Root Mean Square Difference (RMSD) metrics showed high effectiveness in identifying milk contaminants.
• CCSD demonstrated superior sensitivity (2.28 × 10^5 times greater) than RMSD for distinguishing adulteration.
• Chromatic clustering technique exhibited significant selectivity, particularly between sodium bicarbonate and urea contamination.

Conclusions:

• The proposed acoustic instrumentation system offers an effective and rapid method for assessing milk quality.
• The system can successfully classify common industrial contaminants in milk.
• This approach provides a viable alternative for on-site milk quality control in industrial and farm settings.