|
|
|
|
|
|
| Model-Fitting Methods for Mineral Raman Spectra Classification |
| XIA Tong, LIU Yi-wei, GAO Yuan, CHENG Jie*, YIN Jian |
School of Mechanical, Electrical & Information Engineering, Shandong University (Weihai), Weihai 264209, China
|
|
|
|
|
Abstract 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.
|
|
Received: 2021-10-11
Accepted: 2022-04-30
|
|
|
|
Corresponding Authors:
CHENG Jie
E-mail: chjie@sdu.edu.cn
|
|
[1] ZHANG Yan-hui, WU Liang-ping, SUN Zhen-rong（张延会, 吴良平, 孙真荣）. Education in Chemistry（化学教学）, 2006, (4)：32.
[2] Sevetlidis V, Pavlidis G. Journal of Cultural Heritage, 2019, 37：121.
[3] Aggarwal R L, Di Cecca S, Farrar L W, et al. J. Raman Spectrosc., 2014, 45：677.
[4] Ishikawa S T, Gulick V C. Comput. Geosci., 2013, 54：259.
[5] Zhang R, Xie H M, Cai S N, et al. J. Raman Spectrosc., 2020, 51：176.
[6] Sattlecker M, Bessant C, Smith J, et al. Analyst, 2010, 135(5)：895.
[7] Jahoda P, Drozdovskiy I, Payler S J, et al. Analyst, 2021, 146：184.
[8] Downs R T. Proceedings of the 19th General Meeting of the International Mineralogical Association, 2006. 3.
[9] Liu J, Osadchy M, Ashton L, et al. Analyst, 2017, 142：4067.
[10] Howley T, Madden M G, O’Connell M L, et al. Knowledge—Based Systems, 2006, 19：363.
[11] Lafuente B, Downs R T, Yang H, et al. Roczniki Akademii Rolniczej W Poznaniu Rolnictwo, 2015.
[12] Pedregosa F, Varoquaux G, Gramfort A, et al. Journal of Machine Learning Research, 2011, 12：2825.
[13] Abadi M, Barham P, Chen Jian-Min, et al. Proceedings of OSDI’16: 12th USENIX, 2016：265.
[14] Buda M, Maki A, Mazurowski M A. Neural Netw, 2018, 106：249.
[15] BAI He-xuan, YANG Feng, LI Dan-yang, et al（白鹤轩, 杨 峰, 李丹阳, 等）. Acta Optica Sinica（光学学报）, 2021, 41(20)：2024001.
[16] Lecun Y, Bottou L, Bengio Y, et al. Proc. IEEE, 1998, 86：2278.
[17] Hochreiter S, Schmidhuber J. Neural Computation, 1997, 9(8)：1735.
|
| [1] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
| [2] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
| [3] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
| [4] |
WANG Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
| [5] |
WANG Lan-hua1, 2, CHEN Yi-lin1*, FU Xue-hai1, JIAN Kuo3, YANG Tian-yu1, 2, ZHANG Bo1, 4, HONG Yong1, WANG Wen-feng1. Comparative Study on Maceral Composition and Raman Spectroscopy of Jet From Fushun City, Liaoning Province and Jimsar County, Xinjiang Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 292-300. |
| [6] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [7] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
| [8] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
| [9] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1，ZHAO Yan-jie1*，NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
| [10] |
LU Wen-jing, FANG Ya-ping, LIN Tai-feng, WANG Hui-qin, ZHENG Da-wei, ZHANG Ping*. Rapid Identification of the Raman Phenotypes of Breast Cancer Cell
Derived Exosomes and the Relationship With Maternal Cells[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3840-3846. |
| [11] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
| [12] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [13] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
| [14] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [15] |
GUO He-yuanxi1, LI Li-jun1*, FENG Jun1, 2*, LIN Xin1, LI Rui1. A SERS-Aptsensor for Detection of Chloramphenicol Based on DNA Hybridization Indicator and Silver Nanorod Array Chip[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3445-3451. |
|
|
|
|
