College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China.
College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China; Hainan Engineering Research Center of Aquatic Resources Efficient Utilization in South China Sea, Hainan University, Haikou 570228, China.
Food Chem. 2021 Jun 30;348:129129. doi: 10.1016/j.foodchem.2021.129129. Epub 2021 Jan 19.
The potential of two different hyperspectral imaging systems (visible near infrared spectroscopy (Vis-NIR) and NIR) was investigated to determine TVB-N contents in tilapia fillets during cold storage. With Vis-NIR and NIR data, calibration models were established between the average spectra of tilapia fillets in the hyperspectral image and their corresponding TVB-N contents and optimized with various variable selection and data fusion methods. Superior models were obtained with variable selection methods based on low-level fusion data when compared with the corresponding methods based on single data blocks. Mid-level fusion data achieved the best model based on CARS, in comparison with all others. Finally, the respective optimized models of single Vis-NIR and NIR data were employed to visualize TVB-N contents distribution in tilapia fillets. In general, the results showed the great feasibility of hyperspectral imaging in combination with data fusion analysis in the nondestructive evaluation of tilapia fillet freshness.
研究了两种不同的高光谱成像系统（可见近红外光谱（Vis-NIR）和近红外（NIR））的潜力，以确定冷藏罗非鱼片的TVB-N 含量。使用 Vis-NIR 和 NIR 数据，通过高光谱图像中罗非鱼片的平均光谱与其相应的 TVB-N 含量之间建立了校准模型，并使用各种变量选择和数据融合方法对其进行了优化。与基于单个数据块的相应方法相比，基于低水平融合数据的变量选择方法获得了更好的模型。与其他所有方法相比，基于 CARS 的中水平融合数据获得了最佳模型。最后，分别使用优化后的单 Vis-NIR 和 NIR 数据模型可视化罗非鱼片的 TVB-N 含量分布。总的来说，结果表明高光谱成像与数据融合分析相结合在罗非鱼片新鲜度的无损评估中具有很大的可行性。
