The evaluation of peroxidase using multivariate regression models, neural networks and neuro-fuzzy in apple fruit treated with plant extract and Penicillium expansum

The use of plant extracts can consider as an alternative for pesticides, to control post-harvest diseases like apple blue mold. It was efficient method the measuring the enzymes activity involved in plant defense systems to evaluate the disease, but it was expensive method. At present research, the peroxidase activity in treated apple fruits with lavender extracts and pathogen during eight days was investigated. Then, we applied three common models (i.e. multiple linear regression models, neural networks and neuro-fuzzy) to predict the peroxidase activity based on the information of the previous test. The results showed the highest enzyme activity was observed in the infected apples treated with the extract in fifth days, as the days before and it was significant compared to other treatments. The most activity of peroxidase was observed in all treatments except control, was observed on the fifth day. Our constructed models revealed that neuro-fuzzy model had higher performance in the peroxidase activity prediction compared with the other models.