Rapid Identification of Polypeptide from Carbapenem‐Resistant and SusceptibleEscherichia colivia Orbitrap‐MS and Pattern Recognition Analyses

Abstract

A rapid and accurate analytical method was established to identify CREC and CSEC. Orbitrap-MS was used to detect the polypeptide of CREC and CSEC strains, and MS data were analyzed by pattern recognition analyses such as hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). HCA based on the farthest distance method could well distinguish the two types of E. coli, and the cophenetic correlation coefficient of the farthest distance method was 0.901. Comparing the results of PCA, PLS-DA, and OPLS-DA, OPLS-DA exhibited the highest accuracy in predicting the CREC and CSEC strains. A total of 26 compounds were identified, and six of the compounds were the highly significant difference between the two types of strains. MS combined with pattern recognition can achieve a more comprehensive and efficient statistical analysis of complex biological samples.

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

“…In recent years, multivariate statistical methods for analyzing metabolomics data have gained widespread popularity, encompassing techniques such as PCA, PLS-DA, and OPLS-DA (Liland et al, 2011). These methods effectively address the challenges of data analysis with limited samples, numerous independent variables, and high correlation by employing dimensionality reduction techniques (Sun et al, 2022). Volatile compounds in food coupled with multivariate data statistical methods have emerged as a pivotal research focus for verifying their geographical origin and detecting adulteration (Sadgrove et al, 2022).…”