通过贝叶斯优化的1D-CNN分析蛇头中的TVB-N,使用分子振动光谱技术:近红外和拉曼光谱
Qin Ouyang1, Zhenzhou Fan1, Huilin Chang1
1School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
Food chemistry
|October 23, 2024
概括
近红外 (NIR) 和拉曼光谱有效地通过测量总挥发性基本 (TVB-N) 来评估鱼的新鲜度. 将这些方法与先进的建模相结合,可以快速准确地评估鱼类的质量.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 总挥发性基本 (TVB-N) 是鱼的新鲜度的一个关键指标.
- 准确和快速的TVB-N评估对于海鲜行业至关重要.
研究的目的:
- 评估近红外 (NIR) 和拉曼光谱在蛇头片中检测TVB-N的有效性.
- 使用光谱数据和数据融合技术,开发和比较TVB-N内容的预测模型.
主要方法:
- 使用可变交叉点算法提取特征 - 从NIR和拉曼光谱中改进的减少输入向量 (VCPA-IRIV).
- 部分最小平方 (PLS) 和1D卷积神经网络 (1D-CNN) 模型的开发.
- 应用数据融合策略,包括特征级融合,与贝叶斯优化.
主要成果:
- 变量交叉点算法 - 改进的减少输入向量 (VCPA-IRIV) 成功提取了相关特征.
- 功能级数据融合与贝叶斯优化的1D-CNN模型相结合,实现了最高的准确性.
- 对TVB-N的校准和预测相关系数分别达到0.9677和0.9676.
结论:
- 尼尔射线和拉曼光谱是评估鱼的新鲜度的有效工具.
- NIR和拉曼光谱的融合提供了一种快速,高效和全面的方法来量化鱼的新鲜度.
- 先进的化学测量和机器学习方法提高了TVB-N的预测能力.
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