Antibacterial evaluation of Salvia miltiorrhizae on Escherichia coli by microcalorimetry coupled with chemometrics

Abstract

For seeking novel antibacterial agents with high efficacy and low toxicity to deal with drug resistance, the effects of Salvia miltiorrhizae from various sources on Escherichia coli were evaluated by microcalorimetry coupled with chemometrics. Firstly, the heat-flow power-time curves of E. coli growth affected by different S. miltiorrhizae samples were recorded. Then, some crucial quantitative thermo-kinetic parameters including growth rate constant, heat-flow power and heat output, etc. were obtained from theses curves and were further investigated by some powerful chemometric techniques including similarity analysis, multivariate analysis of variance, hierarchical clustering analysis and principle component analysis. By analyzing the principle parameters, growth rate constant of the second exponential phase (k 2) and the heat-flow output powers of the second highest peak (P 2), together with the derived parameter inhibitory ratio (I, %), it could be quickly concluded that the tested S. miltiorrhizae samples from different sources in China exhibited strong antibacterial effects on E. coli and the samples from Beijing city exhibited the strongest anti-E. coli effects, which might be used as novel and underlying antibacterial candidates for the resistance of E. coli to the existing drugs in practice. This study provides a useful tool and helpful idea to accurately and rapidly evaluate the antibacterial effects of some complex matrices, offering some references for exploring new antibacterial agents.

Multi‐element determination in Schisandra chinensis fructus by inductively coupled plasma mass spectrometry and the study of element transfer rules from herbs to decoctions

“…Principal Component Analysis (PCA) is a sophisticated technique in applied data analysis work and has a satisfactory ability to simply multivariate variation and confused data set, and only the important or main characteristics of the original data were retained . In this study, PCA was operated on mean‐normalized data of the 12 quantitative parameters including Pb, Cd, Cr, As, Hg, Cu, Fe, Ni, Al, Mn, Ba and Co. Hierarchical clustering analysis (HCA) is one of the most commonly used approaches for multivariate analysis, which can classify the objects (samples) into classes (clusters) by means of measuring either the distance or the similarity between the objects.…”