Development of computer vision system to predict peroxidase and polyphenol oxidase enzymes to evaluate the process of banana peel browning using genetic programming modeling

• Published in: Scientia horticulturae Vol. 231; pp. 201 - 209
• Main Authors: Nadafzadeh, Maryam, Abdanan Mehdizadeh, Saman, Soltanikazemi, Maryam
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 27.01.2018
• Subjects: Banana; Digital image processing; Enzymatic browning; Genetic programming; Peroxidase; Polyphenol oxidase; Peroxidase; Enzymatic browning; Genetic programming; Banana; Polyphenol oxidase; Digital image processing
• ISSN: 0304-4238; 1879-1018
• DOI: 10.1016/j.scienta.2017.12.047

•A computer vision system was designed and developed to evaluate the browning process of banana peel.•Method of digital image processing was effective to prediction and investigation of POD and PPO enzymes during browning.•Two equations were obtained using GP modeling to predict and detect the changes of the activity of the PPO and POD enzymes.•The accuracy of proposed method confirmed through comparisons with enzyme activity (PPO and POD).•The color features which were extracted from images of samples, play an important role in evaluation of browning process. The process of enzymatic browning is one of the most important chemical reactions, which effects on color, appearance and quality of fruits and vegetables. Polyphenol oxidase (PPO) and peroxidase (POD) enzymes are associated with enzymatic browning in the tissue of agricultural products. Quality of banana as a climacteric fruit is reduced by enzymatic browning during storage. Therefore, to evaluate enzymatic browning in banana, first, images of the fruits were taken at 25 °C for 9 days. Then, these images were investigated using digital image processing in order to predict and study POD and PPO enzymes during the browning process of banana peel. To this end, seventeen color parameters (R¯, G¯, B¯, VR, VG; VB, r, g, b, C1, C2, C3, C4, C5, C6, C7, C8) were extracted from each image as non-destructive parameters. In the following, PPO and POD both were measured through the laboratory methods Finally, using genetic programming (GP) modeling, two equations were obtained which can be used to predict and detect the changes of the activity of the PPO and POD enzymes during the storage period (9 days). The correlation coefficients between the measured values and the predicted values for PPO and POD enzymes were 0.98 and 0.97, respectively. Furthermore, there were no significant differences between predicted values with measured values of PPO and POD enzymes (p > 0.05); these results indicate the proper performance of the designed models.