Direct FTIR and Chemometrics for Authentication of Kratom Powder and Other Alkaloids-containing Plant Matters

Penulis

  • Azka Muhammad Rusydan Prodi Farmasi (S-1), Fakultas Kesehatan, Universitas Jenderal Achmad Yani Yogyakarta
  • Rizqa Salsabila Firdausia Prodi Farmasi (S-1), Fakultas Kesehatan, Universitas Jenderal Achmad Yani Yogyakarta

DOI:

https://doi.org/10.30989/jop.v2i2,%20Special%20Edition.1482

Kata Kunci:

PCA, Cluster analysis, Plant characterization, FTIR, Kratom

Abstrak

Kratom (Mitragyna speciosa), has gained attention for its use as stimulant and opioid-like analgesic effects. In Indonesia, its legal status remains uncertain in many regions, and concerns about its safety have led to increasing regulation. Even with uncertainty with its legal status, kratom remains easily accessed via online market.  However with Indonesia’s Ministry of Trade is set to regulate its export regulation, a method to quickly distinguish kratom venations is needed. This study explores the potential of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy, combined with chemometric techniques like Principal Component Analysis (PCA), to differentiate between kratom venations and other alkaloid-containing plants. Direct ATR-FTIR analysis of ground kratom, tea, and coffee leaves revealed characteristic functional groups, with distinct spectral variations observed across the plant samples. Cluster variable analysis reduced the dimensionality of the spectral data by 98.6%, while maintaining 98% similarity level.  PCA highlighting key principal components (PC1 and PC2) responsible for 93.9% of the variance. The model successfully grouped the samples into five clusters with a similarity level of 88.3% and a cluster distance ratio > 1, confirming the method's ability to distinguish kratom venations and other plant materials. This study demonstrates that FTIR-PCA is an effective, rapid, and non-destructive tool for profiling plant materials, although further research with a larger and more diverse sample set is needed for more robust predictive modeling.

Diterbitkan

2024-11-30