Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia (Oreochromis niloticus) pupil and gill color during storage at 4 °C. (15th March 2018)

• Record Type: Journal Article
• Authors: Shi, Ce; Qian, Jianping; Han, Shuai; Fan, Beilei; Yang, Xinting; Wu, Xiaoming
• Is Part Of: Food chemistry. Volume 243(2018)
• Page Start: 134
• Page End: 140
• Publication Date: 2018-03-15
• Subjects: Machine vision -- Tilapia -- Pupil and gill -- Color parameters -- Freshness indicators -- Chilled storage; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2017.09.047
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Machine vision got color change in fish pupil and gill during storage automatically. Color regression models showed good results for predicting TVB-N, TBA and TVC. The distribution maps of freshness spoilage were generated. Pupil color parameters can used as a fast on-line recognition of tilapia freshness. Abstract: The study assessed the feasibility of developing a machine vision system based on pupil and gill color changes in tilapia for simultaneous prediction of total volatile basic nitrogen (TVB-N), thiobarbituric acid (TBA) and total viable counts (TVC) during storage at 4 °C. The pupils and gills were chosen and color space conversion among RGB, HSI and L ∗ a ∗ b ∗ color spaces was performed automatically by an image processing algorithm. Multiple regression models were established by correlating pupil and gill color parameters with TVB-N, TVC and TBA (R 2 = 0.989–0.999). However, assessment of freshness based on gill color is destructive and time-consuming because gill cover must be removed before images are captured. Finally, visualization maps of spoilage based on pupil color were achieved using image algorithms. The results show that assessment of tilapia pupil color parameters using machine vision can be used as a low-cost, on-line method for predicting freshness during 4 °C storage.