Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China.
Yantai Institute, China Agricultural University, Yantai 264670, China.
Biosensors (Basel). 2024 Oct 14;14(10):502. doi: 10.3390/bios14100502.
Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.
牡蛎冷链中的生理和环境波动会导致质量恶化，突出了监测和评估牡蛎新鲜度的重要性。本研究使用十个基于部分选择性金属氧化物的气体传感器开发了一种电子鼻，用于快速评估新鲜度。同时进行了 GC-MS、TVB-N、微生物、质地和感官评估，以评估牡蛎的质量状况。在不同的储存温度（4°C、12°C、20°C、28°C）下进行实时电子鼻测量，以彻底研究不同储存条件下的质量变化。利用主成分分析将 10 维向量简化为 3 维向量，使样本聚类为新鲜、稍新鲜和腐烂类别。基于这三个类别的 GA-BP 神经网络模型的测试数据准确率超过 93%。征求专家意见进行性能分析和优化建议，提高了建立的预测系统的效率和适用性。结果表明，将电子鼻与质量指标相结合是诊断牡蛎腐败和降低牡蛎产业质量和安全风险的有效方法。
