Computational and Statistical Tools in Fatty Acid Profiling of Goat Milk: a Systematic Review and Meta-Analysis

This review evaluates the analytical steps in GC-based fatty acid profiling in goat milk, with a focus on improving accuracy, reproducibility, and comparability across studies. Fatty acids, the fundamental building blocks of lipids, play critical roles in biological systems. Whether in free or bound form, their intake must be regulated, as excessive consumption has been linked to various diseases. Numerous analytical strategies have been developed to quantify fatty acids in a wide range of matrices, including food and biological samples. However, no single method has yet been optimized for comprehensive analysis of all known fatty acids in milk. Investigating fatty acid composition using robust and reliable methodologies is especially important for goat milk, which is increasingly promoted as a nutritional alternative to cow milk. This systematic review and meta-analysis critically assess the three core stages of gas chromatography (GC)-based analysis, extraction, derivatization, and GC quantification, that define the analytical workflow. Emphasis is placed on computational tools and data processing strategies, including meta-analysis and forest plots, to enhance data integration and interpretation across literature.