An Integrated FTIR-TLC-PCA Approach for an Accurate Classification Model of Kratom Venation

Kratom (M. speciosa), has a long history of traditional use for various ailments as well as for recreational purposes due to its opioid and psychoactive effects. Nowadays kratom is easily accessible via online markets, with leaf powders commonly categorized by vein color, suggesting different effects despite minimal variations in alkaloid content. To improve the identification and characterization of kratom samples, fingerprinting methods using chemometric tools are increasingly applied in food and pharmaceutical analysis. This study explores a combination of Fourier Transform Infrared Spectroscopy (FTIR) and Thin-Layer Chromatography (TLC) densitometry data, analyzed with Principal Component Analysis (PCA), to develop a model for distinguishing kratom venation and other alkaloid-containing plants.The TLC chromatogram revealed six consistent peaks (Rf values of 0.17, 0.27, 0.42, 0.73, 0.8, and 0.9), correlating with alkaloids found in kratom. Using PCA, we combined FTIR absorbance values at selected wavenumbers with TLC chromatogram data, resulting in four principal components (PC1, PC2, PC3, and PC4) that explained 84.1%, 9.7%, 2.7%, and 2.5% of the variance, respectively. The resulting score plot demonstrated distinct clustering of samples, which was then verified with cluster analysis. The resulting analysis indicated effective differentiation between kratom vein colors and plant species. The developed FTIR-TLC-PCA model offers a promising approach for sample classification, potentially aiding quality control and authenticity verification in pharmaceutical applications.