School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
Food Chem. 2025 Feb 1;464(Pt 2):141701. doi: 10.1016/j.foodchem.2024.141701. Epub 2024 Oct 19.
Total volatile basic nitrogen (TVB-N) is one of the key indicators for assessing fish freshness. This research employed near-infrared (NIR) and Raman spectroscopy methods to detect the TVB-N content in snakehead fillets. We extracted feature variables associated with TVB-N from NIR and Raman spectroscopy using Variable Crossover Point Arithmetic - Improved Reduced-Input Vector (VCPA-IRIV). Using these features, we established partial least squares (PLS) and One-dimensional Convolutional Neural Network (1D-CNN) models. Subsequently, data fusion strategies were employed to predict the TVB-N content. Notably, feature-level fusion in conjunction with Bayesian-optimized 1D-CNN, reached the best results, as evidenced by calibration and predictive correlation coefficients of 0.9677 and 0.9676 for TVB-N. These findings underscore the effectiveness of both NIR and Raman spectroscopy in evaluating fish freshness. The fusion of these two vibrational spectroscopy techniques enables a more rapid, efficient and comprehensive quantification of fish freshness.
总挥发性碱性氮（TVB-N）是评估鱼类新鲜度的关键指标之一。本研究采用近红外（NIR）和拉曼光谱法检测蛇头鱼片的TVB-N 含量。我们使用变量交叉点算法-改进的输入向量减少（VCPA-IRIV）从 NIR 和拉曼光谱中提取与 TVB-N 相关的特征变量。使用这些特征，我们建立了偏最小二乘（PLS）和一维卷积神经网络（1D-CNN）模型。随后，采用数据融合策略预测 TVB-N 含量。值得注意的是，特征级融合结合贝叶斯优化的 1D-CNN 取得了最佳结果，TVB-N 的校准和预测相关系数分别为 0.9677 和 0.9676。这些发现表明 NIR 和拉曼光谱在评估鱼类新鲜度方面均具有有效性。这两种振动光谱技术的融合能够更快速、高效和全面地定量鱼类的新鲜度。
