A new strategy based on PCA for inter-batches quality consistency evaluation

Abstract

Due to cultivation position, climate, harvest times, storage conditions and processing method, the evaluation of intra- and inter- batches quality consistency of botanical drugs has always been a thorny problem since it concerns safety and efficacy. The combination of fingerprint based on instrumental analysis and chemometrics is a common evaluation method in recent years. The differences between groups can be judged intuitively and superficially through principal component analysis (PCA) multi-dimensional score plots, but there is a lack of scientific and quantitative index to quantify the differences between groups. How to quantify the difference between groups is basically a blank area of research. Based on traditional F-statistic, we proposed a new F*-statistic to quantify the difference between groups in PCA score plots from the perspective of statistics. As the results revealed, the calculated F*-statistic was 2.58, smaller than the critical value 3.17 (α = 0.05), which indicated that there was no significant difference between groups. Our study add another dimension for PCA application, which offers a new strategy to quantify differences between groups by a new perspective, namely, a combination of fingerprint, chemometrics and statistics to evaluate inter-batches quality consistency quantitatively and objectively. Therefore, this manuscript could provide new ideas and technical references for the quality consistency evaluation of natural drugs, thus better guarantee their clinical efficacy and safety, and better promote industrial development.