Open AccessDISCRIMINANT ANALYSIS OF SHODHANA (PROCESSING) ON BALIOSPERMUM MONTANUM MUELL (DANTI) ROOT SAMPLES BASED ON NEAR INFRARED SPECTROSCOPY AND MULTIVARIATE CHEMOMETRIC TECHNIQUE
Author(s) -
Siba Prasad Rout,
Rabinarayan Acharya,
Jayanta Kumar Maji
Publication year - 2017
Publication title -
international journal of pharmacy and pharmaceutical sciences/international journal of pharmacy and pharmaceutical sciences
Language(s) - English
Resource type - Journals
eISSN - 2656-0097
pISSN - 0975-1491
DOI - 10.22159/ijpps.2017v9i7.18272
Subject(s) - principal component analysis , multivariate statistics , multivariate analysis , linear discriminant analysis , mathematics , chemistry , pattern recognition (psychology) , statistics , computer science , artificial intelligence
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.