An interactive R-based custom quantification program for semi-quantitative analysis of triacylglycerols in bovine milk.

Abstract

The R programming language, RStudio, and open-source software solutions for analysis of liquid chromatography-mass spectrometry (LC-MS) data have been used with user-written R-based custom quantification programs (CQP) for semi-quantification of triacylglycerols (TAGs) in bovine milk lipid extracts. Using the peak-finding capabilities of the package "xcms" in RStudio, peaks were integrated, and retention times aligned, normalized, and then used for semi-quantitative analysis of a custom set of four extraction internal standards (EISs) and 29 TAG regioisomers using the choice of four analytical internal standards (AISs). Alternating stereospecific numbering (sn) 1,3 TAG regioisomers (standards 1, 3, and 5 of six calibration standards) and sn-1,2 TAG regioisomers (standards 2, 4, and 6 of six standards) were used to make a set of six calibration standards, which were used for quantification using a linear fit model, polynomial fit model, power fit model, level-bracketed linear fit, replicate-bracketed polynomial fit, replicate-bracketed power fit, and replicate- and level-bracketed linear fit and response factors. For example, the linear fit for EIS1 gave an unacceptable coefficient of determination (CoD), r2 = 0.9616, whereas the polynomial fit gave r2 = 0.9908 and the power fit gave r2 = 0.9928, while the double-bracketed linear fit gave CoDs of r2 = 0.9960, 0.9848, and 0.9781 for the three brackets, yet gave the least % difference to known calibration concentrations. For unparalleled transparency, the CQP produced webpages that allowed every step in the data processing and quantification sequence to be verified and reproduced, and contained interactive figures. The data are publicly available using a digital object identifier (DOI). The R code can be downloaded and used with the downloadable data to reproduce the results, to modify the code and further customize the results, or to copy and paste and adapt the code to other quantification applications.