基于BP-ANN算法建立紫石英中CaF拉曼光谱定量模型

陈龙; 明晶; 袁明洋; 黄必胜; 陈科力

doi:10.13422/j.cnki.syfjx.2016210077

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基于BP-ANN算法建立紫石英中CaF拉曼光谱定量模型

化学与分析 | 更新时间：2024-01-04

- 基于BP-ANN算法建立紫石英中CaF拉曼光谱定量模型
- Quantitative Model of Raman Spectra for CaF in Fluoritum Based on BP-ANN Algorithm
- 中国实验方剂学杂志 2016年22卷第21期页码：77-82
- 作者机构：
- 作者简介：
- 基金信息：
  
  武汉市高新技术产业发展行动计划生物技术与新医药专项（201260523193）
- DOI：10.13422/j.cnki.syfjx.2016210077
  中图分类号： R284
- 纸质出版日期：2016
- 稿件说明：
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陈龙, 明晶, 袁明洋, 等. 基于BP-ANN算法建立紫石英中CaF拉曼光谱定量模型[J]. 中国实验方剂学杂志, 2016,22(21):77-82.

CHEN Long, MING Jing, YUAN Ming-yang, et al. Quantitative Model of Raman Spectra for CaF in Fluoritum Based on BP-ANN Algorithm[J]. Chinese journal of experimental traditional medical formulae, 2016, 22(21): 77-82.
陈龙, 明晶, 袁明洋, 等. 基于BP-ANN算法建立紫石英中CaF拉曼光谱定量模型[J]. 中国实验方剂学杂志, 2016,22(21):77-82. DOI： 10.13422/j.cnki.syfjx.2016210077.

CHEN Long, MING Jing, YUAN Ming-yang, et al. Quantitative Model of Raman Spectra for CaF in Fluoritum Based on BP-ANN Algorithm[J]. Chinese journal of experimental traditional medical formulae, 2016, 22(21): 77-82. DOI： 10.13422/j.cnki.syfjx.2016210077.

摘要

目的：利用拉曼光谱技术建立误差反向传播人工神经网络（back-propagation artificial neural networks，BP-ANN）定量分析模型，用于紫石英中CaF2含量的快速检测。方法：采集紫石英及紫石英不同CaF2含量配比品共计54批样品的拉曼光谱，光谱在OPUS 7.0软件中作Fourier自去卷积（Fourier self-deconvolution，FSD），并以一阶导数（FD）+矢量归一化（VN），9点平滑的方法进行预处理，逐级组合区间偏最小二乘法（siPLS）优选建模谱段，以乙二胺四乙酸滴定法测定各批样品中CaF2含量结果为参考值，在Matlab 2014a软件中以BP-ANN算法建立CaF2快速定量模型。结果：建立了以1 675~1 625，1 525~1 475，850~800，750~700，650~600 cm-1为特征建模谱段的3层BP-ANN定量分析模型，模型预测均方根误差（RMSEP）2.73%，R2=85.64%，预测结果最大相对偏差5.55%，平均相对偏差2.30%，平均回收率99.74%，表明模型的预测效果良好。结论：在siPLS优选所得谱段基础上，建立了紫石英BP-ANN定量分析模型，该模型预测能力较好，可用于紫石英中CaF2含量的快速、准确测定。

Abstract

Objective: To establish back-propagation artificial neural networks quantitative models by Raman spectroscopy technique

the model can get a rapid determination for the content of CaF2 in Fluoritum. Method: Raman spectra of 54 batches of samples including Fluoritum and the mixture which had different content of CaF2 were collected

and were preprocessed with Fourier self-deconvolution (FSD)

first-order derivative (FD)

vector normalization (VN) and 9 point smoothing by OPUS 7.0 software.Modeling spectrum interval was optimized by siPLS

the quantitative models were established by BP-ANN algorithm in MATLAB 2014a software.The content of CaF2 was determined by EDTA titration method

which was used as a reference. Result: Three-layer BP-ANN quantitative models were established

the characteristic modeling spectrum intervals were 1 675-1 625

1 525-1 475

850-800

750-700

650-600 cm-1

RMSEP was 2.73%with R2 of 85.64%.The maximum relative deviation of prediction result was 5.55%

the average relative deviation was 2.30% and the average recovery was 99.74%. Conclusion: The three-layer BP-ANN quantitative model established on the basis of siPLS can get a rapid and accurate determination for the content of CaF2 in Fluoritum.

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