Phytochemical analysis of flower from Pandanus odorifer (Forssk.) Kuntze for industrial application.

Pandanus odorifer (Forssk.) Kuntze is an economically important aromatic plant. The essential oil from male flowers is widely used in aromatherapy, cosmetics and as food flavouring agent. Phenylethyl methyl ether (PEME), the major constituent of essential oil, gives the chief characteristic fragrance to the oil. In the present study, 180 samples from 12 different regions were collected and hydrodistilled for essential oil isolation. The oil was then subjected to GC and GC-MS analysis to find out the percentage of the constituents. The results revealed PEME as the major constituent ranging from 58.03 to 81.86% and terpinen-4-ol, the second major constituent ranging from 7.81 to 21.46%. Soil nitrogen was found to be the most influential factor for oil yield and PEME content. The flowers containing high essential oil yield and PEME content could be used as elite chemotypes with enough potential for large-scale commercial cultivation to meet the demand of kewda industries.

Differential expression of CURS gene during various growth stages, climatic condition and soil nutrients in turmeric (Curcuma longa): Towards site specific cultivation for high curcumin yield.

Curcuma longa L., accumulates substantial amount of curcumin and essential oil. Little is known about the differential expression of curcumin synthase (CURS) gene and consequent curcumin content variations at different agroclimatic zones. The present study aimed to evaluate the effect of climate, soil and harvesting phase on expression of CURS gene for curcumin yield in two high yielding turmeric cultivars. Expression of CURS gene at different experimental zones as well as at different harvesting phase was studied through transcriptional analysis by qRT-PCR. Curcumin varied from 1.5 to 5% and 1.4-5% in Surama and Roma respectively. The expression of CURS also varied from 0.402 to 5.584 fold in Surama and 0.856-5.217 fold in Roma. Difference in curcumin content at a particular zone varied among different harvesting period from 3.95 to 4.31% in Surama and 3.57-3.83% in Roma. Expression of CURS gene was also effected by harvesting time of the rhizome which varied from 7.389 to 16.882 fold in Surama and 4.41-8.342 fold in Roma. The CURS gene expression was found regardless of variations in curcumin content at different experimental zones. This may be due to the effects of soil and environmental variables. Expression was positively correlated with curcumin content with different harvesting time at a particular zone. This find indicates effect of soil and environment on molecular and biochemical dynamics of curcumin biosynthesis and could be useful in genetic improvement of turmeric.

Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.).

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.