State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China; College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang 843100, China.
State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China.
Food Res Int. 2024 Aug;189:114564. doi: 10.1016/j.foodres.2024.114564. Epub 2024 May 27.
Total volatile basic nitrogen (TVB-N) serves as a crucial indicator for evaluating the freshness of salmon. This study aimed to achieve accurate and non-destructive prediction of TVB-N content in salmon fillets stored in multiple temperature settings (-20, 0, -4, 20 °C, and dynamic temperature) using near-infrared (NIR) and Raman spectroscopy. A partial least square support vector machine (LSSVM) regression model was established through the integration of NIR and Raman spectral data using low-level data fusion (LLDF) and mid-level data fusion (MLDF) strategies. Notably, compared to a single spectrum analysis, the LLDF approach provided the most accurate prediction model, achieving an R of 0.910 and an RMSEP of 1.922 mg/100 g. Furthermore, MLDF models based on 2D-COS and VIP achieved R values of 0.885 and 0.906, respectively. These findings demonstrated the effectiveness of the proposed method for precise quantitative detection of salmon TVB-N, laying a technical foundation for the exploration of similar approaches in the study of other meat products. This approach has the potential to assess and monitor the freshness of seafood, ensuring consumer safety and enhancing product quality.
总挥发性碱性氮（TVB-N）可作为评价三文鱼新鲜度的重要指标。本研究旨在采用近红外（NIR）和拉曼光谱技术，实现对不同温度（-20、0、-4、20°C 和动态温度）下三文鱼鱼片 TVB-N 含量的准确、无损预测。通过低水平数据融合（LLDF）和中水平数据融合（MLDF）策略，整合 NIR 和拉曼光谱数据，建立偏最小二乘支持向量机（LSSVM）回归模型。与单一光谱分析相比，LLDF 方法提供了最准确的预测模型，R2 为 0.910，RMSEP 为 1.922mg/100g。此外，基于 2D-COS 和 VIP 的 MLDF 模型的 R2 值分别为 0.885 和 0.906。这些结果表明，所提出的方法可用于精确、定量检测三文鱼 TVB-N，为其他肉类产品研究中类似方法的探索奠定了技术基础。该方法有望用于评估和监测海产品的新鲜度，保障消费者安全，提高产品质量。
