A support vector machines framework for identification of infrared spectra
A support vector machines framework for identification of infrared spectra
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DOI：
10.1007/s00340-022-07879-8
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发表时间：
2022-08
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影响因子：
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通讯作者：
M. Chowdhury;T. Rice;M. Oehlschlaeger
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文献类型：
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作者：
M. Chowdhury;T. Rice;M. Oehlschlaeger
在先前的工作（Chowdhury，M.A.Z.，赖斯，T.E. & Oehlschlaeger，M.A.，Appl. Phys. B 127，34（2021）），我们发现支持向量机（SVM）擅长从太赫兹频率范围（7.33-11 cm-1）内的光谱数据中学习模式，用于气相物种形成。在这里，我们实现SVM，在一个对休息的框架，在一个广泛的频率范围（400-4000 cm− 1或2.5-25 μm）的红外光谱分类的34个气相化合物的压力范围从0.1到1大气压和吸收剂摩尔分数从1 ppm到1（纯气体）。在SVM框架内，分类器的超参数进行了优化，以选择一个最佳的核的SVM和可接受的软边缘常数，以尽量减少错误分类。该框架进行测试，使用交叉验证策略，以确定在压力和吸收剂浓度的变化的性能的依赖。通过考虑实验吸收光谱进行验证，从文献中，在三个随机试验，其中组合的实验分类精度大于90%。包含人工噪声的模拟光谱数据集用于在频率范围和分辨率变化的研究中进一步评估SVM分类器，以更好地询问SVM框架的能力。
In prior work (Chowdhury, M.A.Z., Rice, T.E. & Oehlschlaeger, M.A., Appl. Phys. B 127, 34 (2021)), we found support vector machines (SVM) to be adept at learning patterns from spectral data within a THz frequency range (7.33–11 cm−1) for the purposes of gas-phase speciation. Here, we implement SVM, in a one-versus-rest framework, for the classification of infrared spectra in a broad frequency range (400–4000 cm−1or 2.5–25 μm) for 34 gas-phase compounds at pressures ranging from 0.1 to 1 atm and for absorber mole fractions from 1 ppm to 1 (pure gases). Within the SVM framework, hyperparameters for the classifier were optimized to choose an optimum kernel for the SVM and acceptable soft margin constant to minimize misclassifications. The framework is tested using cross-validation strategies to determine the dependence of performance on variation in pressure and absorber concentration. Validation was carried out by considering experimental absorption spectra, from the literature, in three random trials, where the combined experimental classification accuracy was greater than 90%. A simulated spectral dataset containing artificial noise was used to further evaluate the SVM classifier in studies where the frequency range and resolution were varied, to better interrogate the capabilities of the SVM framework.