Zhai Jinglei, Wang Zilong, Chen Xin, Li Yunfeng, Wu Tengyu, Sun Biao, Liang Pei
School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China.
College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China.
Anal Chem. 2024 Aug 6;96(31):12883-12891. doi: 10.1021/acs.analchem.4c02659. Epub 2024 Jul 26.
Qualitative and quantitative analysis of Raman spectroscopy is a widely used nondestructive analytical technique in many fields. It utilizes the Raman scattering effect of lasers to obtain molecular vibration information on samples. By comparison with the Raman spectra of standard substances, qualitative and quantitative analyses can be achieved on unknown samples. However, current Raman spectroscopy analysis algorithms still have many drawbacks. They struggled to handle quantitative analysis between different instruments. Their prediction accuracy for concentration is generally low, with poor robustness. Therefore, this study addresses these deficiencies by designing the cross instrument-sparse Bayesian learning (CI-SBL) Raman spectroscopy analysis algorithm. CI-SBL can facilitate spectroscopic analysis between different instruments through the cross instrument module. CI-SBL converts data from portable instruments into data from scientific instruments, with high similarity between the converted spectrum and the spectrum from the scientific instruments reaching 98.6%. The similarity between the raw portable instrument spectrum and the scientific instrument spectrum is often lower than 90%. The cross instrument effect of the CI-SBL is remarkable. Moreover, CI-SBL employs sparse Bayesian learning (SBL) as the core module for analysis. Through multiple iterations, the SBL algorithm effectively identified various components within mixtures. In experiments, CI-SBL can achieve a qualitative accuracy of 100% for the majority of binary and multicomponent mixtures. On the other hand, the previous Raman spectroscopy analysis algorithms predominantly yield a qualitative accuracy below 80% for the same data. Additionally, CI-SBL incorporates a quantitative module to calculate the concentration of each component within the mixed samples. In the experiment, the quantification error for all substances was below 3%, with the majority of the substances exhibiting an error of approximately 1%. These experimental results illustrate that CI-SBL significantly enhances the accuracy of qualitative judgment of mixture spectra and the prediction of mixture concentrations compared with previous Raman spectroscopy analysis algorithms. Furthermore, the cross instrument module of CI-SBL allows for a flexible handling of data acquired from different instruments.
拉曼光谱的定性和定量分析是一种在许多领域广泛应用的无损分析技术。它利用激光的拉曼散射效应来获取样品的分子振动信息。通过与标准物质的拉曼光谱进行比较，可以对未知样品进行定性和定量分析。然而，当前的拉曼光谱分析算法仍然存在许多缺点。它们难以处理不同仪器之间的定量分析。它们对浓度的预测准确率普遍较低，稳健性较差。因此，本研究通过设计跨仪器稀疏贝叶斯学习（CI-SBL）拉曼光谱分析算法来解决这些不足。CI-SBL可以通过跨仪器模块促进不同仪器之间的光谱分析。CI-SBL将便携式仪器的数据转换为科学仪器的数据，转换后的光谱与科学仪器的光谱之间的相似度高达98.6%。原始便携式仪器光谱与科学仪器光谱之间的相似度通常低于90%。CI-SBL的跨仪器效果显著。此外，CI-SBL采用稀疏贝叶斯学习（SBL）作为核心分析模块。通过多次迭代，SBL算法有效地识别了混合物中的各种成分。在实验中，对于大多数二元和多组分混合物，CI-SBL可以实现100%的定性准确率。另一方面，对于相同的数据，以前的拉曼光谱分析算法的定性准确率主要低于80%。此外，CI-SBL还包含一个定量模块来计算混合样品中各成分的浓度。在实验中，所有物质的定量误差均低于3%，大多数物质的误差约为l%。这些实验结果表明，与以前的拉曼光谱分析算法相比，CI-SBL显著提高了混合物光谱定性判断的准确性和混合物浓度的预测能力。此外，CI-SBL的跨仪器模块可以灵活处理从不同仪器获取的数据。
