Abstract
The drug yielding potential of turmeric (Curcuma longa L.) is largely due to the presence of phyto-constituent ‘curcumin.’ Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN) was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8) was the most suitable one with R2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.
Highlights
Turmeric (Curcuma longa L.) is a unique plant combining properties of a spice, colorant, cosmetic and a drug useful in a number of diseases
Extensive investigation over the last five decades has indicated that curcumin reduces blood cholesterol (Rao et al, 1970; Patil and Srinivasan, 1971), prevents LDL oxidation (Ramirez-Tortosa et al, 1999), inhibits platelet aggregation (Srivastava et al, 1986, 1995), suppresses thrombosis (Srivastava et al, 1985) and myocardial infarction (MI) (Dikshit et al, 1995), suppresses symptoms associated with type II diabetes (Srinivasan, 1972), rheumatoid arthritis (Deodhar et al, 1980), multiple sclerosis (Natarajan and Bright, 2002) and Alzheimer’s disease (Lim et al, 2001)
The results demonstrated that using a combination of soil and environmental data, we were able to successfully predict curcumin content with the developed artificial neural network (ANN) model
Summary
Turmeric (Curcuma longa L.) is a unique plant combining properties of a spice, colorant, cosmetic and a drug useful in a number of diseases. It is used as spice, herbal medicines, dyeing agents and cosmetics since vedic age (Salvi et al, 2000; Shirgurkar et al, 2001). The significance of turmeric in health and nutrition has greatly been recognized since the discovery of the pharmaceutical properties of naturally occurring phenolic compounds in it. It has been found that the dried rhizome of turmeric is a rich source of beneficial phenolic compounds known as the curcuminoids (Srinivasan, 1953; Lechtenberg et al, 2004). Extensive investigation over the last five decades has indicated that curcumin reduces blood cholesterol (Rao et al, 1970; Patil and Srinivasan, 1971), prevents LDL oxidation (Ramirez-Tortosa et al, 1999), inhibits platelet aggregation (Srivastava et al, 1986, 1995), suppresses thrombosis (Srivastava et al, 1985) and myocardial infarction (MI) (Dikshit et al, 1995), suppresses symptoms associated with type II diabetes (Srinivasan, 1972), rheumatoid arthritis (Deodhar et al, 1980), multiple sclerosis (Natarajan and Bright, 2002) and Alzheimer’s disease (Lim et al, 2001)
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