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基于近红外光谱的玉米核中的蛋白质含量的定量预测

Chenlong Fan¹, Ying Liu¹, Tao Cui²

¹College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Foods (Basel, Switzerland)

|January 8, 2025

概括

这项研究开发了一种快速近红外光谱 (NIR) 方法,用于准确检测玉米粒粉中的蛋白质. 优化的部分最小平方回归 (PLSR) 模型实现了高预测准确性,改善了玉米质量控制.

科学领域:

农业科学农业科学
分析化学分析化学
频谱学是一种光谱学.

背景情况:

准确检测蛋白质含量对于玉米的质量保证至关重要.
传统的近红外光谱 (NIR) 方法用于全玉米粒的表面效应和样品均性受到限制.
分析玉米粒粉可以提高数据质量和蛋白质含量预测的准确性.

研究的目的:

开发一种快速而准确的方法,用近红外光谱 (NIR) 来确定玉米粒粉中的蛋白质含量.
评估不同光谱预处理技术和蛋白质预测化学模型的性能.
确定最佳特征波长,以提高模型准确度.

主要方法:

从各种玉米粒粉末品种收集近红外 (NIR) 反射频谱 (940-1660nm).
应用的光谱预处理技术:萨维茨基-戈莱 (S-G),乘数散射校正 (MSC),标准正常变量 (SNV) 和第一个导数 (1D).
使用化学测量模型,包括部分最小平方回归 (PLSR),支持向量机 (SVM) 和极端学习机 (ELM).
使用的特征选择算法:连续投影算法 (SPA) 和非信息变量消除 (UVE).

主要成果:

用1D+MSC预处理的部分最小平方回归 (PLSR) 模型表现出卓越的性能.
实现了0.3g/kg的预测根平均平方误差 (RMSEP),0.93的相关系数 (Rp) 和3.0的剩余预测偏差 (RPD).
选择的特征波长显著提高了模型的预测能力.

结论:

开发的NIR光谱法为量化玉米粒粉中的蛋白质含量提供了快速而准确的方法.
这些发现为改善玉米质量控制和加工提供了坚实的科学基础.
这种技术克服了与全谷物分析相关的局限性,使得蛋白质评估更可靠.

关键词:

玉米粉是一种玉米粉.机器学习是机器学习.接近红外的近红外线.预测模型的预测模型.频谱成像技术的使用.

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