Open AccessSensory Analysis – A contemporary Quality Control tool for Asava-Arishta
Author(s) -
Mehul Mehta,
Saloni Ambasana,
VinayJ Shukla,
PK Prajapati
Publication year - 2020
Publication title -
international journal of ayurvedic medicine
Language(s) - English
Resource type - Journals
ISSN - 0976-5921
DOI - 10.47552/ijam.v11i3.1553
Subject(s) - sample (material) , sensory system , quality (philosophy) , control (management) , receiver operating characteristic , control sample , taste , set (abstract data type) , medicine , computer science , artificial intelligence , machine learning , psychology , food science , cognitive psychology , biology , philosophy , chemistry , epistemology , chromatography , programming language
Quality control of Ayurvedic medicines though being the need of the hour, is an arduous task. As Ayurveda encompasses the use of drug as a whole which leads to generation of a complex matrix. In such a scenario, selective and sensitive sophisticated analytical tools alone cannot serve the purpose. Besides, the ancient science of medicine is sensory driven. A number of sensory based quality standards in form of quality-attributes for drugs as well as critical end point for pharmaceutical operations are mentioned in the classics of Ayurveda. It is thus advisable to employ sensory based analytical methods for Quality control of Ayurvedic medicines. Henceforth, in the present study an attempt was made to develop a sensory based Quality control tool for discrimination of the market samples of Arjunaristha with regards to In-house prepared (Reference control) sample on the basis of smell and taste. Among the sensory methods available, Duo-Trio method was adopted. 40 pre-trained assessors were asked to identify the blindly coded sample analogous to Reference control. The results were analysed using Receiver Operating Characteristics (ROC) curve, Area Under Curve (AUC) and d-prime (d’) with the help of sensR package in R-studio ver. 1.0.143. In both the sample sets, Reference control sample was correctly identified with a significance level of P < 0.001 and Area Under Curve of 0.999 and 0.994 for each set respectively. Thus, it can be concluded that Sensory analysis method efficiently discriminated the Arjunaristha samples and thus can serve as a cost-effective routine quality control tool.