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一个强大的深度学习模型用于预测绿茶在固定过程中的水分含量,使用近红外光谱学:集成多级特征融合和注意力机制

Yan Song¹, Wenqing Yi², Yu Liu²

¹School of Engineering, Anhui Agricultural University, No. 130, Changjiang West Road, Hefei 230036, China; State Key Laboratory of Tea Plant Biology and Utilization, No. 130, Changjiang West Road, Hefei 230036, China; Anhui Provincial Engineering Research Center of Intelligent Agricultural Machinery, No. 130, Changjiang West Road, Hefei 230036, China.

Food research international (Ottawa, Ont.)

|March 1, 2025

概括

一个新的深度学习网络DiSENet使用近红外光谱 (NIRS) 数据准确预测绿茶的水分含量,克服温度变化,改善茶叶加工的质量控制.

科学领域:

农业科学农业科学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

绿茶加工中的固定质量是通过叶子的水分含量进行批判性评估的.
近红外光谱 (NIRS) 广泛用于水分检测,但对温度波动敏感.
温度变化会扭曲NIRS数据,影响湿度预测模型的准确性.

研究的目的:

开发一种可靠的方法,用于准确预测绿茶固定中的水分含量.
解决和纠正因NIRS数据中的温度变化引起的光谱干扰.
提高茶叶加工过程中非破坏性水分检测的可靠性.

主要方法:

从各种固定阶段和温度的绿茶样本收集NIRS数据.
提出了一个新的深度学习网络 (DiSENet),结合了多级特征融合和注意力机制.
采用全球建模方法来预测水分含量.

主要成果:

在湿度预测方面,DiSENet实现了0.781的确定系数 (R_P2) 和1.720%的RMSE_P.
该模型与PLSR,SVR,EPO和GLS等传统方法相比,显示出更高的准确性.
实现了2.148的剩余预测偏差 (RPD),表明了良好的预测性能.

结论:

DiSENet有效地纠正温度变化带来的光谱干扰,提高湿度预测的准确性.
这种深度学习方法提供了一种可靠的解决方案,用于实时,在茶叶加工过程中进行非破坏性的水分监测.
这些发现支持先进的人工智能模型的整合,以改善食品加工行业的质量控制.

关键词:

注意力机制注意力机制预测水分含量的预测.多个尺度的特征融合.近红外光谱学近红外光谱学茶的固定方式温度的影响温度影响

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