Advanced Analytical Strategies for Detecting Non-Milk Fat Adulteration and Species Mixing to Ensure Dairy Authenticity: Current Status and Future Trends

Milk and dairy products are highly vulnerable to Economically Motivated Adulteration
(EMA), particularly through the substitution of milk fat with cheaper non-milk fats. This
paper presents a comprehensive review of analytical approaches used for detecting veg-
etable oils (e.g., palm, coconut, sunflower) and animal-origin fats such as pork lard and
bovine tallow, as well as the fraudulent mixing of milk from different species. Methods
for lipid extraction are examined, including traditional gravimetric procedures such as
the Röse–Gottlieb method and high-efficiency alternatives such as Accelerated Solvent
Extraction and supercritical fluid extraction. Analytical strategies for fraud detection are
evaluated, demonstrating that while fatty acid profiling is widely applied, its sensitivity is
limited by natural variability. Greater discriminatory power can often be achieved through
triacylglycerol analysis combined with mathematical models such as the Precht formu-
lae (which generate S-values), although performance varies depending on the adulterant
matrix, adulteration level, reference population, and analytical protocol. Similarly, sterol
profiling, particularly the detection of phytosterols like β-sitosterol, is a valuable marker
for vegetable oil adulteration but does not provide an equivalent solution for detecting
animal fat adulteration. The potential of rapid, non-destructive screening tools, including
Fourier-transform infrared and Raman spectroscopy supported by chemometrics, is also
assessed. A central analytical challenge in detecting such fraud lies in the complexity
and variability of milk fat composition, which hinders any single analytical method from
universally identifying all forms of non-milk fat adulteration; therefore, a tiered strategy
combining rapid screening tools with high-resolution confirmatory methods is preferable.
Future perspectives highlight the increasing importance of green analytical approaches,
artificial intelligence, and portable detection systems for enhancing verification within the
global dairy supply chain. However, the effectiveness of AI and chemometric methods
depends heavily on the availability of representative training datasets and rigorous external
validation to avoid overfitting and ensure reliable application across diverse samples.