Optimization of salicylic acid and chitosan treatment for bitter secoiridoid and xanthone glycosides production in shoot cultures of Swertia paniculata using response surface methodology and artificial neural network

Abstract

Abstract Background In this study, response surface methodology (RSM) and artificial neural network (ANN) was used to construct the predicted models of linear, quadratic and interactive effects of two independent variables viz. salicylic acid (SA) and chitosan (CS) for the production of amarogentin (I), swertiamarin (II) and mangiferin (III) from shoot cultures of Swertia paniculata Wall. These compounds are the major therapeutic metabolites in the Swertia plant, which have significant role and demand in the pharmaceutical industries. Results Present study highlighted that different concentrations of SA and CS elicitors substantially influenced the % yield of (I), (II) and (III) compounds in the shoot culture established on modified ½ MS medium (supplemented with 2.22 mM each of BA and KN and 2.54 mM NAA). In RSM, different response variables with linear, quadratic and 2 way interaction model were computed with five-factor-three level full factorial CCD. In ANN modelling, 13 runs of CCD matrix was divided into 3 subsets, with approximate 8:1:1 ratios to train, validate and test. The optimal enhancement of (I) (0.435%), (II) (4.987%) and (III) (4.357%) production was achieved in 14 days treatment in shoot cultures of S. paniculata elicited by 9 mM and 12 mg L− 1 concentrations (SA) and (CS). Conclusions In optimization study, (I) show 0.170–0.435%; (II) display 1.020–4.987% and (III) upto 2.550–4.357% disparity with varied range of SA (1–20 mM) and CS (1–20 mg L− 1). Overall, optimization of elicitors to promote secoiridoid and xanthone glycoside production with ANN modeling (r 2 = 100%) offered more significant results as compared to RSM (r 2 = 99.8%). Graphical abstract

Keywords

• Swertia
• Elicitors
• Secoiridoids
• Mangiferin
• Response surface methodology
• Artificial neural network