Therefore, the analysis of the color and odor of the gills and eyes could be used as the main factors for identifying the freshness of the harvested fish. These in turn cause changes in the taste, quality and color during storage (Issac et al., 2017 Olafsdottir et al., 2004). On the other hand, changes in protein and fat, along with the changes in biogenic amines and hypoxanthine that significantly influence fish corruption, occur after the animal’s death. The color of the fish eye is another determinant index for evaluating its freshness. Gill is a red respiration organ in the fish body in which blood flows directly, and hence its odor and color patterns could be used as an indicator for evaluating the fish freshness. Color is one of the most important quality attributes of the fish having a direct relation with its freshness and consumer’s acceptance (Lawless and Heymann, 2010). The colorimetric technique is known as a determinative indicator for grading food products as fresh, moderately non-fresh or completely non-fresh (McCaig, 2002). To overcome these issues, machine vision, as a non-destructive method, has been used in external quality detection of aquatic products for its efficiency, objectiveness, consistency, and reliability (Hong et al., 2014). In addition, they cannot be used to determine the fish freshness in the early stages of storage, as they are sensitive to the latter phase of deterioration (Dowlati et al., 2013). However, most of these methods are either destructive or expensive.

There exist various sensory (quality index method and torry scheme), physical (tissue analysis, torrymeter, electronic nose, and near-infrared reflection spectroscopy), and chemical and biochemical (measurement of total volatile nitrogen, trimethylamine, pH, and adenosine triphosphate) methods for this purpose (Nollet and Toldra, 2009). These features can be used individually to assess the fish freshness (Issac et al., 2017). The changes during the storage of fish can be categorized based on apparent features including color, stiffness, odor, secretions, scales, skin, flesh, abdomen, eyes, and gills. Moreover, production, transportation, retail, home-keeping, and final food provision could have a negative impact on the fish quality and freshness (Dutta et al., 2016 Shi et al., 2018). Fish quality parameters such as safety, nutritional value, availability, and freshness are mainly influenced by storing and processing methods from harvesting until consumption. Recent changes in consumers’ lifestyle demand high-quality and immune fish products. Taste, health, quality, and freshness of fish are among the factors motivating its consumption (Tomić et al., 2016). The deficiency of this protein could cause nervous system problems, mental retardation, and physical weakness in human as well as reduction in body resistance to infectious diseases (Skuland, 2015). The fish protein, containing essential amino acids and growth promoters, has a positive effect on human health. Machine vision, as a non-destructive method, has been used in quality detection of aquatic products due to its efficiency, objectiveness, consistency, and reliability (Hong et al., 2014).įish is a valuable seafood in terms of nutritional value. Finally, it was concluded that the applied colorimetric system along with the developing models could be employed as a successful non-destructive approach for evaluation of fish freshness.Īquatic products are popular among consumers and their visual quality used to be detected manually for sorting, grading, species classification, and freshness assessment. Moreover, the gills’ features could describe the variance in the storage durations more efficiently than those extracted from the eyes. The overall accuracies of the developed models demonstrated that the ANN somewhat outperformed the SVM for both the extracted features from the eyes and gills. Feature extraction was fulfilled from the color spaces, and then artificial neural networks (ANNs) and support vector machines (SVMs) were applied for classification of the ice-storage durations.

Analysis of variance revealed that the RGB components of both eyes and gills had a significant change towards getting brighter during the ice-storage. The image data were collected from left and right eyes and gills in a 10-day ice-storage duration, and color components were extracted in RGB, HSV, and L*a*b* color spaces. In this research, digital image analysis was utilized to assess the freshness of rainbow trout fish by tracing the color attributes of its eyes and gills. Developing new techniques to determine fish freshness and quality can enhance nutritional value of the overall household food basket.

Color machine vision system fish uf