Introducing an Experimental Design Approach for Efficient Optimization of Chiral Derivatization Conditions for D- and L-Glyceric Acids

Experimental design of a stable isotope labeling derivatized UHPLC-MS/MS method for the detection/quantification of primary/secondary bile acids in biofluids

An efficient analytical platform is required to characterize the human metabolome in pathology. For this purpose, ultra-high performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) combined with chemical derivatization stands out as one of the most powerful techniques. A targeted metabolomics platform for 11 bile acids (BAs) profiling in human serum and bile samples using a stable isotope labeling derivatization (SILD) was applied. For SILD, the design of experiments (DoE) was employed to optimize the reaction conditions such five factors in three levels. The sample preparation built upon a liquid-liquid extraction requiring small volumes (20 μL). In application, the relation between the BA and short-chain fatty acid levels in human serum and bile samples from patients with bile duct diseases were investigated. The proposed method offers significant utility in the large-scale biological analyses of hepato-biliary-pancreatic-related diseases.

Chemometrics and Related Fields in Python

The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.