A comparison on fish freshness determination method

Jarmin, Roziah; Khuan, Lee Yoot; Hashim, Hadzli; Rahman, Nur Hidayah Abdul

doi:10.1109/icsengt.2012.6339329

Cited by 11 publications

(4 citation statements)

References 1 publication

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“…Several studies have tested the significance of average RGB change for spoilage detection. They have revealed a correlation between average RGB indices variation and the deterioration process (Jarmin et al, 2012;Lalabadi et al, 2020). Moreover, Lalabadi et al (2020) extracted 54 features from the rainbow trout eye images in one study.…”

Section: Resultsmentioning

confidence: 98%

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: Resultsmentioning

confidence: 98%

Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

CENGİZLER

2023

Marine Science and Technology Bulletin

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“…Jarmin et al . () displayed the feasibility of freshness assessment using MVS, they also compared the Torrymeter and MVS for detecting fish freshness. They found better freshness determination by Torrymeter compared to MVS; however, MVS showed more accurate spoiling time in fish.…”

Section: Post‐harvestmentioning

confidence: 99%

“…Conventionally, chemical analysis has been used for determining the quality and freshness accurately, but they are time-consuming, expensive and destructive. Jarmin et al (2012) displayed the feasibility of freshness assessment using MVS, they also compared the Torrymeter and MVS for detecting fish freshness. They found better freshness determination by Torrymeter compared to MVS; however, MVS showed more accurate spoiling time in fish.…”

Section: Machine Vision Systemmentioning

confidence: 99%

Application of machine vision systems in aquaculture with emphasis on fish: state‐of‐the‐art and key issues

Saberioon

Gholizadeh

Císař

et al. 2016

Reviews in Aquaculture

180

109

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Demands of aquatic products are increasing dramatically during past decades. Also quality assurance has gradually received more attention by both producers and consumers. Thus, fish producers are exploring all possible approaches for improving the productivity and profitability. Monitoring of fish state and behaviour during cultivation may help to improve profitability for producers and also reduce the threat of severe loss because of disease and stress incidents. It is necessary to evaluate and measure quality of fish products in accurate, fast and objective way for meeting the different demands of the fish‐processing industry after harvesting. Traditional methods are usually time‐consuming, expensive, laborious and invasive. Using rapid, inexpensive and noninvasive methods is therefore important and desirable. Optical sensors and machine vision system provide the possibility of developing faster, cheaper and noninvasive methods for in situ and after harvesting monitoring of quality in aquaculture. This review describes the most recent technologies and the suitability of different optical sensors for the fish farming management and also assessment, measurement and prediction of fish products quality. Two major areas of optical sensors applications in aquaculture are discussed in this review: (i) preharvesting and during cultivation; and (ii) post‐harvesting. Finally, accuracy and uncertainty of optical sensors applications in aquaculture are discussed. This review showed that MVSs and optical sensors have found real‐world application based on tremendous possibility offered by digital camera development and increasing the speed of computer‐based processing; however, still new algorithms, methods and re‐engineered sensors need to be developed to meet real‐world requirements.

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“…The traditional meat freshness evaluation methods mainly include human sensory evaluation, as well as physical, chemical, and microbiological tests (Jarmin, Khuan, Hashim, & Hidayah, ; Özogul, Özyurt, Özogul, Kuley, & Polatet, ). Human sensory evaluation relies on the judgment of experimenters, which is greatly influenced by subjective factors; therefore, the freshness evaluation of chilled mutton was always determined by instruments in the present study (Mayr et al, ; Yu, Yu, & Li, ).…”

Section: Introductionmentioning

confidence: 99%

Correlation between physical properties and freshness of chilled mutton in China

Zhang

Huang

et al. 2019

J Food Process Preserv

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Chilled mutton is favored in the Chinese market for its rich nutrition and delicious taste. However, storage temperature can be higher than 4°C to reduce energy and transportation costs, which makes chilled mutton vulnerable to decomposition by microorganisms causing a high attrition rate of chilled mutton. The physical property method is a general widely applicable method to evaluate meat freshness and is simpler and faster than the complex chemical and microbiological methods. In this study, a prediction chilled model based on multiple linear regression was established to predict the change of freshness of chilled mutton using some physical indicators. The prediction model was further optimized with the coefficient of determination value (R2) and root mean square error (RMSE). Eight indicators were retained in the final prediction model (RMSE = 0.1850 and R2 = .6927). In the model, resilience was the best physical indicator to characterize the freshness of chilled mutton. Practical applications In this study, the combination of physical properties and freshness was used to study the change in freshness of chilled mutton under different storage temperatures over 6 days. A chilled mutton model based on multiple linear regression was established to determine the physical indicators that were highly correlated with freshness of chilled mutton. Eight indicators (springiness, cohesiveness, resilience, adhesiveness, PH, L*, a*, and b*) were retained in the final prediction model and resilience had the highest regression coefficient and was the best physical indicator to characterize the freshness of chilled mutton. These results provide a technical reference for detecting the physical properties of chilled mutton, and the prediction model provides an effective reference for detecting the freshness change in chilled mutton. The results provide a technical guarantee to ensure food quality, safety, and the rights of consumers.

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A comparison on fish freshness determination method

Cited by 11 publications

References 1 publication

Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

Fish Spoilage Classification Based on Color Distribution Analysis of Eye Images

Application of machine vision systems in aquaculture with emphasis on fish: state‐of‐the‐art and key issues

Correlation between physical properties and freshness of chilled mutton in China

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