Abstract
Jasmine is one of the important commercial traditional flower crops of India. It plays an important role in religious offerings, social and cultural activities of India. It is also used for the production of essential oils in the form of ‘concrete’ and ‘absolute’ which are used in cosmetics and perfumery industries. Fully open flowers contain maximum fragrance and needs to be collected for concrete extraction. India earns crores of foreign currency every year through export. Being an export commodity, ideal blossoming time and quality testing of jasmine flowers are extremely crucial matters in exportation of flowers and concrete. The quality evaluation is human dependent and their sensory organs may give different results at different points of time, as well as the result may vary from the one evaluator to another since no two person can perceive a thing identically. The results may also vary based on the physiological climatic and many other allied conditions. In a nutshell, the evaluation methodology is highly subjective and lacks the precision of an automated machine.
Hence, there is a need for an automated, portable, low-power, easy-to use and reliable machine, which can evaluate quality objectively and repetitively, so that it can assist the Floriculture Industry, research laboratories and flower exporters for final evaluation of jasmine flower and its concrete quality. A handheld Electronic Nose system has been developed which can be used to monitor the volatile emission pattern of jasmine flower and concrete on the basis of sensor outputs.
This paper presents a study where, first, the selection of appropriate sensors was carried out based on sensitivity with the major aroma-producing chemicals of jasmine flower and concrete. Then, this sensor array was exposed to flowers at its different stage of maturity that were collected from the farmer’s field at Sathyamangalam, Erode district, Tamil Nadu in India and to concrete of different species. The experimentation was conducted on the three species of Jasmine namely, Jasminum sambac, Jasminum auriculatum and Jasminum grandiflorum at the Tamil Nadu Agricultural University (TNAU), Coimbatore. The computational model has been developed based on statistical correlation methods to correlate the measurements with the human expert’s views. With unknown jasmine samples and concrete of different species of jasmine flowers, encouraging results have been obtained with more than 90% classification rate.
