Color assessment by different techniques of gilthead seabream (Sparus aurata) during cold storage

Abstract Overall objective of the study was assessment for freshness parameters of gilthead seabream (Sparus aurata). The freshness was determined by two conventional method (i.e. Minolta color measurement method) was compared with a novel machine vision technology (i.e image color analysis method). The quality characteristics related to the freshness of the fresh fish are traditionally based on the color measurements of the whole fish by using Minolta color measurement method. This study aimed to compare the use of the image analysis method to see whether the freshness of seafood can be measured more accurately. Our analysis proved more exact and reliable results in the freshness evaluation of the Sparus aurata based on the color changes in the eyes, gill and skin of the fish. This study revealed that image analysis can be successfully used for the assessment of fish freshness by measuring the color parameters of images captured from skin, gill and eye of the fish during cold storage. It is easy and practical to use image color analysis method in the quality control evaluation of fish freshness measurement. This analysis method can be suitable for use in continuous process at the seafood processing factories (e.g. before packaging to sort out fresh vs old fish automatically).

摘要：本研究的总体目标为评估金头鲷（Sparus aurata）的新鲜度参数。本研究将传统检测方法（即美能达色彩测量法（Minolta color measurement method））与一种新型机器视觉技术（machine vision technology）衍生的图像色彩分析法（image color analysis method）进行对比，以验证其能否更精准地检测海产品新鲜度。传统上，鲜鱼新鲜度相关的品质特性检测，多基于美能达色彩测量法对整条鱼开展色彩测量。分析结果表明，基于鱼眼、鱼鳃与鱼皮的色彩变化，对金头鲷进行新鲜度评估可获得更准确可靠的结果。本研究证实，通过测量冷藏过程中采集的鱼皮、鱼鳃及鱼眼图像的色彩参数，图像色彩分析法可成功应用于鱼类新鲜度评估。该方法操作简便、实用性强，可用于鱼类新鲜度检测的质量控制评价，尤其适用于海产品加工厂的连续化生产流程（例如包装前自动分拣新鲜与不新鲜鱼类）。