Revealing the mechanism of post-harvest processing on rose quality based on dynamic changes in water content, enzyme activity, volatile and non-volatile metabolites

Existing studies on post-harvest processing of edible roses have mainly focused on processing techniques and physicochemical properties of the final dried products, with limited studies on how changes in metabolites during processing affect the quality of these products. This study investigated changes in water content and status, enzyme activity, phenolic compounds, and volatile and non-volatile compounds during processing and revealed the mechanisms by which post-harvest processing (drying without blanching (WBD) and drying with blanching (BD)) affects the quality of dried roses by establishing their correlations. Results showed that the blanching reduced the relative content of free water and water activity, thus reducing the subsequent drying time and enzyme activity. The BD method caused higher levels of phenolic compounds than the WBD method in terms of gallic acid, ellagic acid, epicatechin, and quercetin. The OPLS-DA analysis identified 6 differential volatiles out of 72 detected volatiles, contributing to the unique aroma of dried roses by activating olfactory receptors through hydrogen bonding and hydrophobic interactions. 58 differential metabolites were screened from 964 non-volatile metabolites. KEGG pathway analysis revealed that the changes in volatile and non-volatile metabolites induced by different processing methods were due to the effect of blanching on glutathione and fatty acid metabolism. These findings provide a comprehensive understanding of how post-harvest processing affects the quality of dried roses.

Convenient use of near-infrared spectroscopy to indirectly predict the antioxidant activitiy of edible rose (Rose chinensis Jacq "Crimsin Glory" H.T.) petals during infrared drying

Infrared drying (IRD) was used for the dehydration process of rose petals for the purpose of improving drying efficiency as well as retaining product quality. A methodology to predict the antioxidant capacities of rose petals which include DPPH, ABTS radical scavenging capacities and ferric-ion reducing antioxidant power (FRAP) values during infrared drying (IRD) was established in this study. Partial least squares regression (PLSR) and back propagation-artificial neural network (BP-ANN) modelling were used to establish the relationships between the near infrared (NIR) spectrum and the antioxidant capacities. Results of model fitting showed that BP-ANN model displayed higher prediction accuracy than PLSR model for determining the DPPH, ABTS radical scavenging capacities and FRAP of rose petals during IRD based on NIR spectral data. The results obtained indicate that NIR spectroscopic parameters combined with multivariate calibration could be used reliably to predict the antioxidant capacities of IR-dried rose petals via appropriate mathematical models.