Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity.

The aim of this study is to reveal the relationship between changes in the physical and chemical characterization of eggs during storage and to provide a new way to assess egg freshness at the molecular chemical level. In this study, the Fourier transform infrared (FTIR) spectra of egg albumin from 60 fresh eggs (ISA Brown layer) stored for 1, 10, 20, 30, and 40 days at 4°C and 10°C were measured ( ), and the amide I band of the egg albumin spectra was quantified by Fourier deconvolution and curve fitting to obtain the composition of the protein molecular structure. The results showed that different storage temperatures and storage times led to protein denaturation, resulting in changes in the conformation of the protein secondary structure. With increasing storage temperature and storage time, the content of the ordered structures β-sheet and α-helix in the egg protein secondary structure decreased significantly ( ), and the content of the disordered structure β-turn and random coil increased significantly ( ). Based on the pattern of protein secondary structure changes during storage, it was found that fresh eggs were better stored at 4°C for up to 15 days. Spearman’s correlation analysis of egg protein height, Haugh unit, egg weight, and protein secondary structure in eggs stored at 4°C showed a significant positive correlation between β-sheet and α-helix and egg weight, protein height, and Haugh unit. And a significant negative correlation between random coil and egg weight, protein height, and Haugh unit. Therefore, a curve fitting method based on Fourier transform infrared spectroscopy of deconvoluted spectra is an effective method for evaluating egg quality and freshness.