Physicochemical, microbiological and sensory quality changes of tissues from Pacific oyster (Crassostrea gigas) during chilled storage

Min, Yue; Dong, Shiyuan; Su, Mingyue; Zhao, Yuanhui; Zeng, Mingyong

doi:10.1007/s13197-020-04280-1

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…The initial TVC content is 2.08, which gradually increases over time and reaches 5.25 on the 14th day, as shown in Figure 8 b. Therefore, proper hygienic handling procedures are important measures to control the microbiological population in oysters and to ensure food safety [ 38 ]. The initial pH value is 7, but it gradually decreases over time, reaching 6.07 on the 14th day, as shown in Figure 8 c. This is due to the dissolution of carbon dioxide produced by the metabolism of live oysters in the tissues leading to a decrease in pH.…”

Section: Resultsmentioning

confidence: 99%

Flexible Sensing Enabled Nondestructive Detection on Viability/Quality of Live Edible Oyster

Liu,

Qu,

Zhang

et al. 2024

Foods

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Environmental and physiological fluctuations in the live oyster cold chain can result in reduced survival and quality. In this study, a flexible wireless sensor network (F-WSN) monitoring system combined with knowledge engineering was designed and developed to monitor environmental information and physiological fluctuations in the live oyster cold chain. Based on the Hazard Analysis and Critical Control Point (HACCP) plan to identify the critical control points (CCPs) in the live oyster cold chain, the F-WSN was utilized to conduct tracking and collection experiments in real scenarios from Yantai, Shandong Province, to Beijing. The knowledge model for shelf-life and quality prediction based on environmental information and physiological fluctuations was established, and the prediction accuracies of TVB-N, TVC, and pH were 96%, 85%, and 97%, respectively, and the prediction accuracy of viability was 96%. Relevant managers, workers, and experts were invited to participate in the efficiency and applicability assessment of the established system. The results indicated that combining F-WSN monitoring with knowledge-based HACCP modeling is an effective approach to improving the transparency of cold chain management, reducing quality and safety risks in the oyster industry, and promoting the sharing and reuse of HACCP knowledge in the oyster cold chain.

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Section: Resultsmentioning

confidence: 99%

Flexible Sensing Enabled Nondestructive Detection on Viability/Quality of Live Edible Oyster

Liu,

Qu,

Zhang

et al. 2024

Foods

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“…In the case of incomplete metabolism, part of the glycogen in the body is converted to lactic acid, which is the main cause of the decrease in pH (Bindu et al, 2017). At the same time, with the decline of SCS, the ability of oysters to resist external intrusions was gradually weakened, and the mass reproduction of colonies led to the increase of TVC (Min et al, 2020). Microbial proliferation and metabolic activities in oyster bodies will produce volatile organic compounds, resulting in an increase in the content of TVB-N (Min et al, 2020).…”

Section: Physiological and Quality Information Correlation Analysismentioning

confidence: 99%

“…Oysters are filter feeders and contain a lot of plankton in their bodies. When these plankton die, their body tissue cells begin to break down and release nitrogen Over time, the number and reproduction of microorganisms in oysters gradually increased, which produced large amounts of metabolic and decomposition products, leading to increased levels of TVB-N (Min et al, 2020). In addition, the protein in oysters' body gradually degrades over time, producing amino acids and other organic substances.…”

Section: Tvb-nmentioning

confidence: 99%

Enhancing quality assessment of live Pacific oysters (Crassostrea gigas) using flexible sensors real‐time monitoring physiological signals

Liu,

Qu,

Glamuzina

et al. 2024

J Food Process Engineering

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Shell‐closing strength (SCS) is commonly used to assess oyster vigor and quality as an important physiological stress indicator of the oyster organism. In this study, a quality decision support system based on flexible wireless sensor network and web services was designed and developed to explore the application of SCS in the rapid assessment of vitality and quality. Based on physiological and environmental information collected by flexible wireless sensor networks, this study proposes a novel quantitative vitality assessment method to identify oyster life decline cycles and processes. Meanwhile, TVB‐N, TVC, and pH were selected as quality indicators, and a back‐propagation artificial neural network (BP‐ANN) was used to predict and evaluate oyster quality indicators. The results showed that the root mean square errors (RMSE) for TVB‐N, TVC, and pH were 0.4624, 0.8827, and 0.1941; the coefficients of determination (R2) were 0.8919, 0.8578, and 0.6249. Therefore, the rapid assessment of oyster vitality and quality based on a flexible wireless sensor network is a reliable and effective means.Practical applicationsThis study aims to explore the application of live oyster SCS for rapid quality evaluation. In this article, a quality decision support system based on a flexible wireless sensor network (WSN) and web services is developed to improve the quality and health of live oysters in the supply chain. The use of the multi‐sensor system enables the monitoring and collection of environmental and physiological signals of live oysters throughout the supply chain process. The evaluation of the quality of live oysters using physiological and environmental information collected by flexible wireless sensor networks is a reliable and efficient method. This will provide an effective and reliable quality evaluation and management for the oyster industry.

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“…As a popular edible shellfish in the world, oysters are rich in protein (39.1-53.1% on a dry basis) and have the reputation of "milk of the sea" [1,2]. Unfortunately, oysters are a short shelf-life product because they are easily perishable during storage and transportation, especially for the growing market of raw oysters on the half-shell or oyster meat, where rapid undesirable changes in external and internal properties significantly reduce their freshness and safety for consumption [3][4][5]. The commonly used methods to characterize the storage quality of oysters can be classified as sensory, chemical and microbial population evaluations, specifically including assessment of oyster appearance and texture properties, measurement of gross composition and pH changes, analysis of lipid oxidation and protein degradation and counting of bacterial colonies [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, oysters are a short shelf-life product because they are easily perishable during storage and transportation, especially for the growing market of raw oysters on the half-shell or oyster meat, where rapid undesirable changes in external and internal properties significantly reduce their freshness and safety for consumption [3][4][5]. The commonly used methods to characterize the storage quality of oysters can be classified as sensory, chemical and microbial population evaluations, specifically including assessment of oyster appearance and texture properties, measurement of gross composition and pH changes, analysis of lipid oxidation and protein degradation and counting of bacterial colonies [5][6][7]. In general, these quality evaluation indices are primarily determined by experiment-based laboratory methods that are highly dependent on experimenters' repetitive sample preparation and measurements [8].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Method for Predicting Pacific Oyster (Crassostrea gigas) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information

Lu,

Yu,

Han

et al. 2023

Foods

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To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an “MDA-SH-storage days” polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.72% and 94.06%, respectively. Next, the outputs of the two models were used as the inputs to “MDA-SH-storage days” model, which ultimately succeeded in predicting the corresponding MDA content, SH content and storage day for an oyster image within 0.03 ms. Furthermore, the interpretability of the two models for oyster meat image were also investigated by feature visualization and strongest activations techniques. Thus, this study brings new thoughts on oyster freshness prediction from the perspective of computer vision and artificial intelligence.

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Physicochemical, microbiological and sensory quality changes of tissues from Pacific oyster (Crassostrea gigas) during chilled storage

Cited by 9 publications

References 27 publications

Flexible Sensing Enabled Nondestructive Detection on Viability/Quality of Live Edible Oyster

Flexible Sensing Enabled Nondestructive Detection on Viability/Quality of Live Edible Oyster

Enhancing quality assessment of live Pacific oysters (Crassostrea gigas) using flexible sensors real‐time monitoring physiological signals

An Intelligent Method for Predicting Pacific Oyster (Crassostrea gigas) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information

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