Real-time nondestructive monitoring of Common Carp Fish freshness using robust vision-based intelligent modeling approaches

Taheri‐Garavand, Amin; Fatahi, Soodabeh; Banan, Ashkan; Makino, Yoshio

doi:10.1016/j.compag.2019.02.023

Cited by 48 publications

(14 citation statements)

References 32 publications

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“…Besides, many studies have shown that support vector machine (SVM) and support vector regression (SVR, based on statistical theory, SVR extends SVM to function fitting) can be used to determine the freshness of fish. Taheri‐Garavand et al 8 . built a computer vision method in combination with SVM as an intelligent technique for the evaluation of fish freshness with an accuracy of 91.52%.…”

Section: Introductionmentioning

confidence: 99%

Comparison of freshness prediction method for salmon fillet during different storage temperatures

et al. 2021

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BACKGROUND Many new forecasting models have been applied to fish freshness prediction like support vector regression (SVR) and radial basis function neural network (RBFNN). In this study, RBFNN, SVR, and Arrhenius models were established and compared for predicting and evaluating the quality of salmon fillets during storage at different temperatures, based on thiobarbituric acid (TBA), total volatile basic nitrogen (TVB‐N), total viable counts (TVCs), K value, and sensory assessment (SA). RESULTS The TBA, TVB‐N, TVC, and K values increased during storage whereas SA decreased. Residuals of the three models are random and irregular, indicating that these models were suitable for predicting the freshness of salmon fillets. The RBFNN predicted quality of salmon fillets stored at different temperatures with relative errors all within ±5% (except for the TVC value at day 6). Relative errors of the SVR model for predicting TVB‐N and K value were within 10%, while the relative errors of the Arrhenius model fluctuated greatly (ranging from ±0.46 to ±38.29%) and most of it exceeded 10%. RBFNN model had the best predictive performance by comparing the residual and relative errors of the three models. CONCLUSION RBFNN is a promising method for predicting the freshness of salmon fillets stored at −2 to 10 °C in the cold chain. © 2021 Society of Chemical Industry

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Section: Introductionmentioning

confidence: 99%

Comparison of freshness prediction method for salmon fillet during different storage temperatures

et al. 2021

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“…Similar studies were conducted on the gills of Indian rohu [ 60 ], pupil and gill of tilapia [ 61 ], and eye chromatic of European hake [ 62 ]. Finally, Taheri-Garavand et al [ 63 ] proposed a system based on the use different statistical algorithms to analyze changes in the color parameters of common carp during storage in ice. Results showed that the ANN classifier had the best performance for fish freshness classification (93.01% accuracy).…”

Section: Electronic Multi-sensory Techniquesmentioning

confidence: 99%

Sensing Technology for Fish Freshness and Safety: A Review

Franceschelli

Berardinelli

Dabbou

et al. 2021

Sensors

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Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. All these techniques require highly skilled operators. In the last decade, rapid advances in the development of novel techniques for evaluating food quality attributes have led to the development of non-invasive and non-destructive instrumental techniques, such as biosensors, e-sensors, and spectroscopic methods. The available scientific reports demonstrate that all these new techniques provide a great deal of information with only one test, making them suitable for on-line and/or at-line process control. Moreover, these techniques often require little or no sample preparation and allow sample destruction to be avoided.

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“…With the rapid development of computer science, spectroscopy, sensing technology, materials science, and other disciplines, researchers have developed three types of nondestructive testing techniques for freshness by detecting and analyzing the appearance characteristics (El Barbri, Llobet, El Bari, Correig, & Bouchikhi, 2008; Taheri‐Garavand, Fatahi, Banan, & Makino, 2019; W. Wang et al, 2013; Zaragozá et al, 2015), spectroscopic properties (Elmasry et al, 2015; Fengou et al, 2019; Herrero, Carmona, & Careche, 2004; Ren, Chai, Lu, Li, & Guo, 2014; Sivertsen, Kimiya, & Heia, 2011; H. Wang et al, 2017), and the types and contents of intrinsic chemicals during the spoilage of aquatic products (Shumilina, Ciampa, Capozzi, Rustad, & Dikiy, 2015; Tao et al, 2017; X. Yu, Wang, Wen, Yang, & Zhang, 2019): sensory evaluation, spectroscopy techniques, and electromagnetism techniques. Compared to traditional destructive testing, nondestructive testing technology offers a variety of advantages, such as avoiding sample damage, reducing time and equipment costs, enhancing automation, and meeting producer and consumer requirements for economic efficiency and safety (J. H. Cheng et al, 2013; Mathiassen, Misimi, Bondø, Veliyulin, & Østvik, 2011; X. Zhang, Zhang, Zhou, Tang, 2012).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research progress on nondestructive testing technology for aquatic products freshness

Wang

Xie

et al. 2022

J Food Process Engineering

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As one of the vital food, aquatic products help to improve diet structure and alleviate food pressure. Aquatics food has a tender taste, with protein, polyunsaturated fatty acid (PUFA), and other nutrients. At the same time, aquatic products are highly susceptible to spoilage due to their properties in processing, transportation, and storage, which damages the economic efficiency of producers and may pose a threat to the dietary safety of consumers. Therefore, researchers have been working to developing an efficient, nondestructive, and practical technology to detect the freshness of aquatic products in real-time and easily. This article describes the representative techniques applied in recent years for the nondestructive inspection of aquatic products' freshness in terms of working principles, application examples, and characteristics. It summarizes each technique's development prospects and challenges.

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Real-time nondestructive monitoring of Common Carp Fish freshness using robust vision-based intelligent modeling approaches

Cited by 48 publications

References 32 publications

Comparison of freshness prediction method for salmon fillet during different storage temperatures

Comparison of freshness prediction method for salmon fillet during different storage temperatures

Sensing Technology for Fish Freshness and Safety: A Review

Research progress on nondestructive testing technology for aquatic products freshness

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