针对传统的反向匹配方法中存在的强弱峰权重差异和噪声峰的干扰问题, 提出了改进式反向匹配方法, 通过引入权重衰减函数来优化强峰和弱峰之间的权重占比关系, 使得谱图中各特征峰的权重分布在合理的范围内, 避免了强峰权重掩盖弱峰的情况; 通过概率分布函数动态滤噪的方法, 实现了噪声峰的自适应过滤, 从而提升了反向匹配方法的识别性能。 实验以大量的常规拉曼和表面增强拉曼的谱图为验证样本, 基于大型常规拉曼与表面增强拉曼数据库进行拉曼谱图识别验证。 实验表明该方法在大量数据测试下综合准确率达到91.52%, 相比于命中质量指数方法(51.08%)和传统的反向匹配方法(16.57%)有大幅度的提升。

Aiming at the weight difference between strong and weak peaks and the interference of noise peaks in the traditional reverse matching method (RMM), an improved reverse matching method (IRMM) is proposed in this paper. In this method, the weight attenuation function is introduced to optimize the weight proportion relationship between the strong peak and the weak so that the weight of each feature peak in the spectrum is distributed in a reasonable range, which avoids the situation that the weight of the strong peak masks the weak. Moreover, this method realizes the adaptive filtering of noise peaks by the method of dynamic noise filtering of the probability distribution function, which improves the recognition performance of the reverse matching method. In the experiment, many conventional Raman and surface-enhanced Raman spectra were used as verification samples, which were identified and verified based on a large database of conventional Ramanand surface-enhanced Raman. Experiments show that this method (IRMM) has a comprehensive accuracy rate of 91.52% under a large amount of data testing, which is greatly improved compared to the hit quality index method (HQI, 51.08%) and the traditional reverse matching method (RMM, 16.57%).