DISCRIMINANT ANALYSIS OF SHODHANA (PROCESSING) ON BALIOSPERMUM MONTANUM MUELL (DANTI) ROOT SAMPLES BASED ON NEAR INFRARED SPECTROSCOPY AND MULTIVARIATE CHEMOMETRIC TECHNIQUE

Siba Prasad Rout; Rabinarayan Acharya; Jayanta Kumar Maji

doi:10.22159/ijpps.2017v9i7.18272

Authors

Siba Prasad Rout Department of Dravyaguna, IPGT & RA, Gujarat Ayurved University, Jamnagar, Gujarat, India- 361 008
Rabinarayan Acharya Head, Department of Dravyaguna, IPGT & RA, Gujarat Ayurved University, Jamnagar, Gujarat, India 361008
Jayanta Kumar Maji Department of Pharmaceutical laboratory, IPGT & RA, Gujarat Ayurved University, Jamnagar, Gujarat, India 361008

DOI:

https://doi.org/10.22159/ijpps.2017v9i7.18272

Keywords:

Danti, Multivariate, PCA, Discrimination Functional groups

Abstract

Objective: To establish a noticeable and a justifiable identification system to assess the impact of shodhana (processing) on various levels of Baliospermum montanum (Danti) root samples obtained through shodhana (processing technique) in quality agreement based on near-infrared-spectroscopy.

Methods: Authenticated raw Danti (R. D) root and various Danti root samples obtained after shodhana (processing) such as water processed Danti root (WPDR), Kusha processed Danti root (KPDR) and classical processed Danti root (CPDR), were dried, pulverized and shifted through eighty meshes. The samples were subjected to NIR spectral detection from 750 to 2500 nm at the interval of 1 nm. The multivariate analysis, principal component analysis (PCA) and hierarchical cluster analysis (HCA) analyzed with the help of Unscrambler and Matlab software.

Results: Direct spectral analysis indicated the existence of significant numerical and graphical differences between Danti root samples containing different treatments during processing in respect to CH, OH and NH functional groups. The multivariate PCA algorithom plot allowed a clear segregation of the Danti root samples after various data preprocessing technique onto the hotelling T2 95% confidence limit for principal component 1 and 2. The cluster analysis had shown the extra information on the metabolite profiling of the complex purificatory environment.

Conclusion: The present study demonstrates a generic, non-destructive solution to discriminate qualitatively in the sample matrix all the differently pretreated samples in favor of the NIR-sensitive functional group.

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