A New Method to Evaluate the Similarity of Chromatographic Fingerprints: Weighted Pearson Product-Moment Correlation Coefficient
Ying Liu, Qiong Meng, Rui Chen, Jingjing Wang, S. Jiang, Yongmei Hu
Abstract
Ying Liu, Qiong Meng, Rui Chen, Jingjing Wang, S. Jiang, Yongmei Hu
Abstract
The Pearson product-moment correlation coefficient is being used to evaluate the similarity of the high-performance liquid chromatographic fingerprints of traditional Chinese medicine (TCM) in China. It is confirmed that a large range of peak areas produced the wrong results. A new algorithm concerning weighted Pearson product-moment correlation coefficient is proposed in this article. The results for both real cases and simulated data sets show that the weighted Pearson product-moment correlation coefficients allow relatively larger differences for large values, smaller differences for small values, and more reliable results than the unweighted Pearson product-moment correlation coefficients. Weight selection depends on the specific scientific problem.
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The Pearson product-moment correlation coefficient is being used to evaluate the similarity of the high-performance liquid chromatographic fingerprints of traditional Chinese medicine (TCM) in China. It is confirmed that a large range of peak areas produced the wrong results. A new algorithm concerning weighted Pearson product-moment correlation coefficient is proposed in this article. The results for both real cases and simulated data sets show that the weighted Pearson product-moment correlation coefficients allow relatively larger differences for large values, smaller differences for small values, and more reliable results than the unweighted Pearson product-moment correlation coefficients. Weight selection depends on the specific scientific problem.
Key concepts: Pearson product-moment correlation coefficient, Correlation coefficient, Similarity (geometry), Moment (physics), Correlation, Statistics, Mathematics, Product (mathematics)