Background: Vacuum-packed broccoli stems and florets were subjected to heat treatment (60-99 °C) for various time intervals. The activity of peroxidase was measured after processing. Thermally processed samples were then stored at 4 °C for 35 days, and the color of the samples was measured every 7 days. Effects of parameters (heating temperature and duration, storage time) on the color of broccoli were modeled and simulated by an artificial neural network (ANN).
Results: Simulations confirmed that stems were predicted to be more prone to changes than florets. More color loss was observed with longer processing or storage combinations. The simulations also confirmed that higher temperatures during heat processing could retard color changes during storage. For stems treated at 80 °C for short durations, color loss was more predominant than both 65 and 99 °C, probably due to the incomplete inactivation of enzymes besides more tissue damage, with increased enzyme access to the substrate.
Conclusion: The greenness of both stems and florets during storage can be better preserved at higher temperatures (99 °C) and short times. The simulation results revealed that the ANN method could be used as an effective tool for predicting and analyzing the color values of heat-treated broccoli. © 2018 Society of Chemical Industry.
Keywords: artificial neural network (ANN); broccoli; color; optimization; thermal processing.
© 2018 Society of Chemical Industry.