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一种新的光谱校正方法,可以消除外干扰,提高Torreya grandis坚果内缺陷的NIR检测精度

Jiajun Zan¹, Binyan Hou¹, Yuan Du¹

¹College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China.

Food research international (Ottawa, Ont.)

|March 7, 2026

概括

使用完全连接的神经网络 (FNN) 和高斯过 (GS) 的新光谱校正方法有效地消除了Torreya grandis坚果的外干扰. 这提高了内部缺陷的非破坏性检测精度,在射频和SVM模型中达到100%的精度.

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:

坚果的光谱干扰妨碍了内部缺陷的准确检测.
近红外光谱 (NIRS) 是一种非破坏性质量评估的有前途的技术.

研究的目的:

开发和评估一种新的光谱校正方法,以改善Torreya grandis坚果中黑核缺陷检测.
为了比较完全连接的神经网络 (FNN) 与高斯过 (GS) 结合的有效性,与直接标准化 (DS) 和零碎直接标准化 (PDS) 等传统方法进行比较.

主要方法:

开发了一种近红外光谱校正方法,将完全连接的神经网络 (FNN) 与高斯过 (GS) 结合起来.
对 FNN-GS,DS 和 PDS 的校正性能进行了比较.
检测模型 (RF,SVM,XGBoost,LightGBM) 是使用修正和未修正的光谱数据构建的.

主要成果:

与DS和PDS相比,FNN-GS方法显著改善了光谱校正.
随机森林 (RF) 和支持矢量机器 (SVM) 模型在调整后在预测集上实现了100%的准确性,灵敏性和特异性.
拟议的方法增强了与缺陷相关的光谱特征,使得高精度检测成为可能.

结论:

在Torreya grandis坚果的NIRS分析中,FNN-GS光谱校正方法有效消除了外干扰.
这种方法可以非常准确地,非破坏性地检测内部黑内核缺陷.
该方法为使用NIRS的其他坚果品种的内部质量评估提供了有价值的参考.

关键词:

完全连接的神经网络和高斯平滑.在Torreya grandis的内部黑色缺陷.近红外光谱学近红外光谱学移除外干扰移除外干扰进行光谱校正.

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