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Updated: Apr 14, 2026

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适应性混沌虫优化算法的应用在近红外光谱转移模型上
Shichuan Qian1, Zhi Wang1, Hui Chao1
1School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
概括
一种新方法改善了近红外 (NIR) 光谱模型的传输,用于在不同仪器上对六甲基二胺分析. 这种方法提高了准确性并扩大了适用性,因为不需要相同的样本进行校准.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 由于光谱变化,将近红外 (NIR) 光谱校准模型传输到不同的仪器是具有挑战性的.
- 在各种应用中,准确确定六甲基氨酸度至关重要.
- 现有的模型转移方法在准确性,稳定性和样本要求方面存在局限性.
研究的目的:
- 开发一种新且强大的方法,用于共享NIR校准模型,用于对六甲基二胺的分析.
- 为了提高在各种NIR光谱仪中模型转移的准确性和效率.
- 克服传统模型转移技术的局限性.
主要方法:
- 一种新的传输方法,结合了Savitzky-Golay第一个导数 (S_G_1) 和正交信号校正 (OSC) 预处理.
- 使用适应性混乱虫优化 (ACDBO) 算法与帐混乱映射和非线性递减策略进行特征变量优化.
- 用CEC-2017基准函数进行模型构建和验证的部分最小平方 (PLS) 回归.
主要成果:
- 与传统的优化方法相比,ACDBO算法显示出更高的融合,准确性和稳定性.
- 拟议的转移策略实现了优秀的校准和验证指标 (Rc2=0.99999,RMSEC=0.00195%,Rv2=0.99643,RMSEV=0.03818%,RPD=16.72574). 在此过程中,我们发现了
- 这种新方法产生了具有竞争力的预测性能 (Rp2=0.96228,RMSEP=0.12462%,RER=17.62331),与直接标准化 (DS) 和分片直接标准化 (PDS) 相当.
结论:
- 开发的转移方法有效地减少了不同NIR仪器之间的光谱差异.
- 这种方法显著提高了甲基胺度预测的准确性和稳定性.
- 该方法通过消除对不同仪器分析相同样本的需求,扩大了适用性,使其对特定测量样本转移非常有益.
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