A study on freshness of fish by using fish freshness meter

Azeriee, Abang Ahmad; Hashim, Hadzli; Jarmin, Roziah; Ahmad, Anuar

doi:10.1109/cspa.2009.5069219

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…The fish's texture, appearance, color, and smell provide the necessary information about its freshness. However, the manual inspection would be time-consuming and error-prone due to the subjectivity of different inspectors (Venugopal, 2002;Azeriee et al, 2009). At that point, automation of fish freshness detection may be critical for human health in cases where large amounts of fish are stored or transferred (Vuori, 1992).…”

Section: Introductionmentioning

confidence: 99%

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Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

CENGİZLER

2023

Marine Science and Technology Bulletin

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Fish contains many nutrients beneficial to human health, which makes fish an essential component of a healthy diet. Omega-3 fatty acids, primarily found in fresh fish, can play a critical role in protecting heart and brain health. Freshness is one of the most important quality criteria of the fish to be selected for consumption. It is known that there may be pathogenic bacteria and toxins to human health in fish that are not stored in the right conditions and transferred by wrong logistics methods. One of the widely used approaches for evaluating the freshness of fish is sensory, which would be highly subjective and error-prone. Moreover, sensory analysis is widespread and one of the fastest approaches for evaluating large quantities of fish. At that point, a computer-aided diagnostic system can accelerate the evaluation of the degree of spoilage, reduce the human resources required for this task, and minimize the possibility of spoiled fish consumption. In this study, a fully automated freshness assessment mechanism based on the analysis of digital eye images of fish is proposed. Accordingly, the unsupervised clustering approach was used for feature extraction, and each image was divided into three regions according to their color distribution. The freshness was evaluated according to the intensity difference between these clusters. The results show that the proposed feature extraction approach is highly distinctive for the discrimination of spoilage and can be used to distinguish fresh fish from spoiled fish using machine learning methods without supervision.

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Section: Introductionmentioning

confidence: 99%

“…Several methods were previously presented for the automation of freshness estimation. For example, in one study, an electrode is used (applied to the fish muscle and skin) for detecting spoilage objectively (Azeriee et al, 2009). Raman spectroscopy is also one of the methods utilized for the assessment of fish quality (Herrero, 2008).…”

Section: Introductionmentioning

confidence: 99%

Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

CENGİZLER

2023

Marine Science and Technology Bulletin

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Consumable Fish Classification Using k-Nearest Neighbor

Winiarti

Indikawati

Oktaviana

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Fish is beneficial for the human body because it has high protein content. Consuming fish is necessary and expert knowledge is needed to identify fresh fish that are suitable for consumption. In this study, we developed a classification system to identify four classes of consumable fish by grouping fish images based on texture extraction and color features. We use fish meat and fish scale as identification parameters. Fish meat image is measured using the HSV colors model (Hue, Saturation, and Value) and GLCM (Gray Level Co-occurrence Matrix) method. We use these values for texture feature extraction of scales. Then we use k-Nearest Neighbor (kNN) as the classifier. The test results from 320 sample images show that the identification accuracy of tilapia meat is 90% and 97.5% for mackerel meat. Meanwhile for the scales, the accuracy up to 87.5% for tilapia scales and 95% for mackerel scales.

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Identification of Fish Freshness Through Gill Color Using CNN and LAB Image Segmentation

Martinez,

Escio,

Pellegrino

2023

2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering (ECICE)

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A study on freshness of fish by using fish freshness meter

Cited by 5 publications

References 3 publications

Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

Consumable Fish Classification Using k-Nearest Neighbor

Identification of Fish Freshness Through Gill Color Using CNN and LAB Image Segmentation

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A study on freshness of fish by using fish freshness meter

Cited by 5 publications

References 3 publications

Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

Consumable Fish Classification Using k-Nearest Neighbor

Identification of Fish Freshness Through Gill Color Using CNN and L*A*B Image Segmentation

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Identification of Fish Freshness Through Gill Color Using CNN and LAB Image Segmentation