矿石拉曼光谱模型拟合分类方法

Model-Fitting Methods for Mineral Raman Spectra Classification

拉曼光谱因具有简单、快速及无损等特点,非常适合矿石的分类与鉴别。拉曼光谱模型拟合分类方法无需构建参考光谱库且避免了复杂的逐项光谱匹配,具有明显的优势。然而,已有的基于机器学习及深度学习的矿石拉曼光谱分类研究所采用的学习模型比较单一,缺乏具有参考意义的综合比较。对基于机器学习及深度学习的矿石拉曼光谱模型拟合分类方法进行综合评估验证,对比了KNN,XGBoost,SVM,RF四种传统机器学习方法和CNN,DNN,RNN三种深度学习模型在RRUFF矿物拉曼光谱数据集上的分类效果,验证了4种数据预处理方法和样本量对模型分类效果的影响。为提升机器学习模型的分类性能,本文还提出了一种拉曼光谱强度曲率的数据预处理方法,对经基线矫正后的拉曼光谱序列强度计算曲率作为构造特征,使模型更有效的提取出拉曼光谱的特征峰位置。实验结论:数据预处理对提升机器学习模型的分类性能效果明显,而对深度学习模型不敏感;样本量为影响模型分类效果的关键因素,当样本量较大时,深度学习模型的分类效果优于传统的机器学习模型;对于微小样本,深度学习模型难以发挥其优势,而辅以预处理的机器学习具有更优的分类性能。

Due to simplicity,rapidity and non-destructiveness,Raman spectroscopy is very suitable for mineral classification and identification.A Raman spectral model-fitting method does not need to build a reference spectral database and complex spectral matching,which is advantageous in mineral classification.However,there is a lack of comprehensive comparison of the existing model-fitting methods based on machine learning and deep learning since they use relatively single-learning models.To this end,this paper comprehensively evaluates the model-fitting classification methods of mineral Raman spectral using the RRUFF mineral Raman spectrum dataset.It compares the classification performance of four traditional machine learning methods of KNN,XGBoost,SVM,and RF,and three deep learning models of CNN,DNN,and RNN,as well as four data preprocessing methods and sample size on the classification effect.To improve the classification performance,we also propose a data preprocessing method of Raman spectral intensity curvature,which calculates the curvature of the baseline-corrected Raman spectral sequence intensity as a construction feature so that the model can extract the position of the spectra peaks more effectively.The experimental results showed that data preprocessing greatly improved the classification performance of machine learning models but had little effect on deep learning models.Additionally,the size of the sample is a key factor of the model performance.When the size is large,the deep learning models outperform the traditional machine learning models,whereas when the size is small,it is difficult for the deep learning models to exert their advantages,while the traditional machine learning models combined with data preprocessing work better.