使用拉曼光谱学与深度学习模型相结合,在热氧化过程中定量确定棕油中的酸值
Shuxin Liang1, Guoqing Chen2, Chaoqun Ma1
1School of Science, Jiangnan University, Wuxi, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, China.
Food chemistry
|February 2, 2025
概括
包括CNN-LSTM在内的深度学习模型使用拉曼光谱显著改善了使用拉曼光谱的食用油中的酸值 (AV) 监测. 这种先进的技术在热氧化过程中为棕油提供了更准确,更有效的质量控制.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 准确的酸值 (AV) 监测对于食用油质量控制至关重要.
- 传统的化学测量方法在复杂的光谱数据上扎,用于AV量化.
- 热氧化显著影响食用油的质量,需要精确的监测.
研究的目的:
- 为了评估深度学习模型 (CNN,LSTM,变压器) 与拉曼光谱相结合的有效性,用于在热氧化棕油中的AV量化.
- 将深度学习模型的性能与传统的化学测量方法进行比较.
- 确定最佳的深度学习架构,以增强AV监控.
主要方法:
- 拉曼光谱法被用来收集热氧化棕油的光谱数据.
- 开发和应用了包括卷积神经网络 (CNN),长短期记忆 (LSTM) 和变压器在内的深度学习模型.
- 使用预测确定系数 (Rp2),根平均平方预测误差 (RMSEP) 和剩余预测偏差 (RPD) 等指标评估模型性能.
主要成果:
- 这三种深度学习模型都显示出与传统的化学测量方法相比,更高的预测准确度.
- 在CNN-LSTM模型实现了最高的性能,与Rp2=0.9978,RMSEP=0.0015,和RPD=21.21.
- 该研究证实了拉曼光谱学与深度学习相结合的潜力,用于准确的AV量化.
结论:
- 拉曼光谱与深度学习相结合,特别是CNN-LSTM,提供了一种高度准确和高效的方法来监测食用油中的酸值.
- 这种方法为食用油质量控制提供了一个新的技术参考,超越了传统方法.
- 建议对各种数据集进行进一步验证,以扩大这种先进技术的适用性.
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