From matrix complexity to fraud detection: Mechanistic challenges and integrated authentication frameworks for camel milk

Milk adulteration remains one of the most persistent challenges in dairy authentication because fraudulent practices can alter product identity, nutritional quality, consumer safety, and market trust. This problem is particularly important for high-value non-bovine milks, where premium pricing and limited production increase the incentive for economically motivated adulteration. Camel milk is especially vulnerable because it is commonly adulterated through substitution with cheaper milks such as cow, goat, or buffalo milk, dilution with water, or addition of non-milk constituents including whey proteins, nitrogen-rich compounds, stabilizers, and non-milk fats. Detecting these practices is complicated by the intrinsic variability of camel milk composition, which is influenced by breed, feeding system, geography, season, lactation stage, and processing history. Such natural matrix variability can overlap with adulteration-induced changes, making fixed thresholds and single-marker approaches unreliable. This review critically synthesises current analytical strategies for camel milk adulteration detection, with emphasis on sensitivity, specificity, robustness, processing compatibility, calibration transfer, and practical limitations under real matrix conditions, offering a comparative performance evaluation based on target specificity and processing compatibility rather than a descriptive overview of available tools. The approaches include DNA-based methods like PCR, qPCR, multiplex PCR, and LAMP; protein-based assays targeting β-lactoglobulin; spectroscopic techniques such as NIR, MIR/FT-IR, Raman, fluorescence spectroscopy, and multispectral imaging with chemometrics; chromatographic methods including UPLC, RP-HPLC, GC-MS, and LC-MS; and emerging omics, sensor, and AI systems. Although these platforms have improved detection capacity, each is affected differently by matrix variability, processing-induced marker instability, calibration robustness, and validation design. Therefore, this review highlights the need for a tiered authentication framework integrating rapid screening, targeted confirmation, omics-supported biomarker validation, and AI-assisted data fusion structured around camel-milk-specific matrix failure modes.