Comparison of two exploratory data analysis methods for classification of Phyllanthus chemical fingerprint: unsupervised vs. supervised pattern recognition technologies

Abstract

In this study, unsupervised and supervised classification methods were compared for comprehensive analysis of the fingerprints of 26 Phyllanthus samples from different geographical regions and species. A total of 63 compounds were identified and tentatively assigned structures for the establishment of fingerprints using high-performance liquid chromatography time-of-flight mass spectrometry (HPLC/TOFMS). Unsupervised and supervised pattern recognition technologies including principal component analysis (PCA), nearest neighbors algorithm (NN), partial least squares discriminant analysis (PLS-DA), and artificial neural network (ANN) were employed. Results showed that Phyllanthus could be correctly classified according to their geographical locations and species through ANN and PLS-DA. Important variables for clusters discrimination were also identified by PCA. Although unsupervised and supervised pattern recognitions have their own disadvantage and application scope, they are effective and reliable for studying fingerprints of traditional Chinese medicines (TCM). These two technologies are complementary and can be superimposed. Our study is the first holistic comparison of supervised and unsupervised pattern recognition technologies in the TCM chemical fingerprinting. They showed advantages in sample classification and data mining, respectively.

Zebrafish as a Model for Epilepsy-Induced Cognitive Dysfunction: A Pharmacological, Biochemical and Behavioral Approach

“…For studying various brain disorders, zebrafish ( Danio rerio ) are gaining importance and emerging as a promising model organism ( Guo et al, 2015 ). Seizure-like behavioral responses can be induced in adult zebrafish by several pharmacological approaches and there is an increasing trend of utilizing zebrafish model in epilepsy research ( Kalueff et al, 2014 ).…”

Development of a UHPLC–MS/MS method for the quantitation of bioactive compounds in Phyllanthus species and its herbal formulations

“…Curtain gas, GS 1 and GS 2 and ion source temperature were also optimized to obtain highest abundance of precursor and product ions. The different column temperatures (20,25,30…”

“…The quantitative analysis of bioactive lignans has been carried out in Phyllanthus species by traditional methods such as high-performance thin-layer chromatography (HPTLC) and HPLC [13][14][15][16][17][18][19][20][21][22][23][24][25]. Several researchers have reported fingerprinting, identification and characterization of bioactive compounds in crude extracts of Phyllanthus species using LC-MS [24][25][26][27][28][29][30][31][32][33][34]. But there was only one report of quantitation by LC-ESI-MS/MS [33].…”

Identification and characterization of phenolics and terpenoids from ethanolic extracts of Phyllanthus species by HPLC-ESI-QTOF-MS/MS

“…Most of the qualitative and quantitative analyses of phenolics are commonly reported by traditional methods such as high performance thin layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) in Phyllanthus species [17] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] . There are few reports on the comparative identification and characterization of compounds in crude extracts of Phyllanthus species by liquid chromatography–mass spectrometry (LC–MS) [16] , [17] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , gas chromatography–mass spectrometry (GC–MS) [36] and high performance liquid chromatography-solid phase extraction-nuclear magnetic resonance (HPLC-SPE-NMR) [37] , [38] , [39] . Published LC–MS methods either had very long run time [30] , [35] and identified few compounds with unit mass [29] , or targeted or studied only one species [31] , [32] , [33] .…”