Wu Cheng, Feng Yingjie, Cui Jiarui, Yao Zhang, Xu Hailong, Wang Songlei
School of Food Science and Engineering Ningxia University Yinchuan China.
Yinchuan Hi-Tech Industrial Development Zone Yinchuan China.
Food Sci Nutr. 2025 Mar 19;13(3):e70022. doi: 10.1002/fsn3.70022. eCollection 2025 Mar.
It is an important measure to ensure food quality and safety that real-time monitoring of the key quality indicators of fresh meat after packaging in the process of storage and transportation. The feasibility of combining hyperspectral imaging (HSI) technology with chemometrics and deep learning to detect the quality deterioration of polyethylene (PE)-packaged raw beef, especially for a key lipid oxidation indicator of malondialdehyde (MDA) content, was explored in this study. The feasibility of filtering to overcome the interference of packaging film on the spectral data was further investigated. Near-infrared HSI (400-1000 nm) was used to collect spectral and spatial data of beef samples during short-term storage. A least squares regression (PLSR) and echo-neural network optimized by vulture optimization algorithms (BES-ESN) models were developed by multivariate data processing methods. The results showed that the performance of models established by PE-packed beef samples was usually inferior to that established by unpacked beef samples. The changes of MDA content in beef were visualized according to the optimal model. In addition, Gaussian filtering was applied to reduce the interference effect caused by the packaging film. The results have demonstrated that HSI combined with Gaussian filter preprocessing and multivariate data processing provided an efficient and reliable tool for detecting the freshness of beef in PE packaging. The best model had a coefficient of determination ( ) of 0.8309 and a root mean squared error of prediction (RMSEP) of 0.2180, demonstrating the potential of hyperspectral techniques for real-time monitoring of packaged raw meat quality. The findings can provide some references for the meat industry to develop advanced non-invasive quality assurance systems in the meat industry.
在储存和运输过程中对包装后的鲜肉关键质量指标进行实时监测是确保食品质量安全的一项重要措施。本研究探讨了将高光谱成像（HSI）技术与化学计量学和深度学习相结合来检测聚乙烯（PE）包装的生牛肉质量劣化的可行性，特别是针对丙二醛（MDA）含量这一关键脂质氧化指标。进一步研究了通过滤波克服包装膜对光谱数据干扰的可行性。利用近红外高光谱成像（400 - 1000 nm）在短期储存期间收集牛肉样品的光谱和空间数据。通过多元数据处理方法建立了偏最小二乘回归（PLSR）和经秃鹰优化算法优化的回声神经网络（BES - ESN）模型。结果表明，用PE包装的牛肉样品建立的模型性能通常不如未包装的牛肉样品建立的模型。根据最优模型对牛肉中MDA含量的变化进行了可视化。此外，应用高斯滤波来减少包装膜引起的干扰效应。结果表明，HSI结合高斯滤波预处理和多元数据处理为检测PE包装牛肉的新鲜度提供了一种高效可靠的工具。最佳模型的决定系数（ ）为0.8309，预测均方根误差（RMSEP）为0.2180，证明了高光谱技术在实时监测包装生肉质量方面的潜力。这些发现可为肉类行业开发先进的非侵入性质量保证系统提供一些参考。
