利用荧光高光谱技术无损检测冷冻和冻融鲑鱼鱼片中蛋白质和 TVB-N 含量的变化

摘要：本研究提出了一种基于荧光高光谱成像技术的实用预测方法，用于无损检测冷藏和解冻冷冻三文鱼片中的蛋白质 和总挥发性盐基氮（（ TVB-N）含量。通过荧光高光谱成像技术（（ FHSI）和自动凯氏定氮仪分别获取了三文鱼片的光谱数 据和化学成分数据。研究表明，与传统机器学习模型相比，本研究开发的长短期记忆（（ LSTM）深度学习模型在光谱数据 分析方面表现更优。此外，本研究还开发了北方苍鹰优化算法（（NGO），用于为LSTM模型输入最优超参数（（NGO-LSTM）， 显著提升了预测精度。NGO-LSTM模型在多种预处理方法下的PLSR模型以及其他算法优化的LSTM模型中表现最佳， 其蛋白质和TVB-N的R²值分别达到0.910和0.987。该研究为未来肉类新鲜度检测研究奠定了理论基础。

关键词：三文鱼片；荧光高光谱成像；无损检测；蛋白质；总挥发性盐基氮（（ TVB-N）；基于北方苍鹰优化的LSTM模型

期刊：Journal of Food Composition and Analysis

DOI：https://doi.org/10.1016/j.jfca.2024.106435

设备：GaiaFluo (Pro)

A non-destructive detection method of protein and TVB-N content changes in refrigerated and frozen-thawed salmon ffllets using ffuorescence hyperspectral technology

Abstract：This study proposed a practical predictive method for non-destructive detection of protein and total volatile basic nitrogen (TVB-N) content in refrigerated and frozen-thawed salmon ffllets using ffuorescence hyperspectral imaging technology. The spectral and chemical data of salmon ffllets were obtained via FHSI and an automatic Kjeldahl nitrogen analyzer, respectively. The research demonstrated that the Long Short-Term Memory (LSTM) deep learning model developed in this study offered superior spectral data analysis compared to traditional machine learning models. Additionally, the Northern Goshawk Optimization algorithm was developed to input optimal hyperparameters into the LSTM model (NGO-LSTM), signiffcantly enhancing prediction accuracy. The NGO-LSTM model surpassed the PLSR model with various preprocessing methods and LSTM models optimized by other algorithms, achieving R2 values of 0.910 for protein and 0.987 for TVB-N. This research established a theoretical foundation for future studies on meat freshness detection.

Keywords：Salmon ffllets; Fluorescence hyperspectral imaging; Non-destructive detecting; Protein; TVB-N; Northern Goshawk Optimization-based LSTM model