一种具有成本效益和可扩展的机器学习方法,用于使用NIR光谱评估新鲜玉米核的质量
Jiang Shi1, Erkui Yue1, Xuejin Zhu2
1Institute of Crop and Ecology, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, P. R. China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|September 29, 2025
概括
一种新的机器学习方法,预测校正神经网络 (PCNN),使用小型数据集进行准确的近红外 (NIR) 质量分析. 这种方法大大减少了开发可靠校准模型的时间和成本.
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:
- 开发准确的近红外 (NIR) 校准模型来测量新鲜玉米的质量特征是耗时和昂贵的.
- 传统方法需要大量的样本集,这在育种计划中带来了挑战.
研究的目的:
- 引入一种新的机器学习方法,即预测校正神经网络 (PCNN),用于有效的NIR校准模型开发.
- 用合成数据增强的小样本集来实现准确的质量特征预测.
主要方法:
- 使用预测纠正神经网络 (PCNN) 模型与NIR光谱数据.
- 使用了小型校准样本集,并增加了合成生成的数据.
- 与部分最小平方 (PLS) 和人工神经网络 (ANN) 进行PCNN性能比较.
主要成果:
- 在使用32个样本时,PCNN仅对氨基百克丁和蛋白质等特征取得了高精度 (RPD 2.821-4.862,R2 0.869-0.951).
- 对糖 (果糖,葡萄糖,糖糖) 的准确预测得到了62个样本 (RPD>2,R2 ≥0.747).
- PCNN的表现优于PLS (38.99%-63.20%的RPD改善) 和ANN (7.07%-25.82%的RPD改善).
结论:
- PCNN提供了一个高效,准确,可扩展和具有成本效益的解决方案,用于快速评估新鲜玉米的质量.
- 该方法适用于NIR模型的开发,用于各种作物的小样本集,包括料玉米,大米,小麦和大麦.
- PCNN显著增强NIR校准模型的开发,解决时间,成本和劳动力方面的传统限制.
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