The Effect of Post-harvest Conditions in Narcissus sp. Cut Flowers Scent Profile

Abstract

Narcissus flowers are used as cut flowers and to obtain high quality essential oils for the perfume industry. As a winter crop in the Mediterranean area, it flowers at temperatures ranging between 10 and 15°C during the day and 3-10°C during the night. Here we tested the impact of different light and temperature conditions on scent quality during post-harvest. These two types of thermoperiod and photoperiod. We also used constant darkness and constant temperatures. We found that under conditions of 12:12 Light Dark and 15-5°C, Narcissus emitted monoterpenes and phenylpropanoids. Increasing the temperature to 20°-10°C in a 12:12 LD cycle caused the loss of cinnamyl acetate and emission of indole. Under constant dark, there was a loss of scent complexity. Constant temperatures of 20°C caused a decrease of scent complexity that was more dramatic at 5°C, when the total number of compounds emitted decreased from thirteen to six. Distance analysis confirmed that 20°C constant temperature causes the most divergent scent profile. We found a set of four volatiles, benzyl acetate, eucalyptol, linalool, and ocimene that display a robust production under differing environmental conditions, while others were consistently dependent on light or thermoperiod. Scent emission changed significantly during the day and between different light and temperature treatments. Under a light:dark cycle and 15-5°C the maximum was detected during the light phase but this peak shifted toward night under 20-10°C. Moreover, under constant darkness the peak occurred at midnight and under constant temperature, at the end of night. Using Machine Learning we found that indole was the volatile with a highest ranking of discrimination followed by D-limonene. Our results indicate that light and temperature regimes play a critical role in scent quality. The richest scent profile is obtained by keeping flowers at 15°-5°C thermoperiod and a 12:12 Light Dark photoperiod.

Keywords: Random Forest; circadian rhythm; constitutive volatiles; floral scent; gcProfileMakeR; machine learning; non-constitutive volatile.