论文标题

RamanMetrix:一种令人愉快的分析拉曼光谱的方式

RAMANMETRIX: a delightful way to analyze Raman spectra

论文作者

Storozhuk, Darina, Ryabchykov, Oleg, Popp, Juergen, Bocklitz, Thomas

论文摘要

尽管拉曼光谱法广泛用于研究生物医学样品,并且在临床应用中具有很高的使用潜力,但在临床常规中并不常见。阻碍拉曼光谱工具整合到临床例程中的因素之一是数据处理工作流程的复杂性。简化光谱数据处理的软件工具可以通过使临床专家熟悉拉曼光谱的优势来促进这种整合。 在这里,RamanMetrix作为用户友好的软件引入,具有直观的基于Web的图形用户界面(GUI),该软件结合了一个完整的工作流程,用于对Raman Spectra的化学计量分析,从原始数据预处理到对机器学习模型的强大验证。该软件既可以用于模型培训,也可以用于将验证的模型应用于新数据集。用户可以在模型培训期间完全控制参数,但是测试数据流已冻结,不需要其他用户输入。 RamanMetrix有两个版本:作为独立软件和Web应用程序。由于现代软件体系结构,计算后端部分可以与GUI分开执行,并通过应用程序编程接口(API)访问,以将预构造的模型应用于测量数据。这为实时使用该软件作为测量设备的数据处理后端打开了可能性。 可以将经验丰富的用户预先构造的模型导出和重复使用,以简化单击数据预处理和预测,这需要用户与软件之间的互动最小。可以导出和保存不同数据处理步骤的此类预测和图形输出的结果。

Although Raman spectroscopy is widely used for the investigation of biomedical samples and has a high potential for use in clinical applications, it is not common in clinical routines. One of the factors that obstruct the integration of Raman spectroscopic tools into clinical routines is the complexity of the data processing workflow. Software tools that simplify spectroscopic data handling may facilitate such integration by familiarizing clinical experts with the advantages of Raman spectroscopy. Here, RAMANMETRIX is introduced as a user-friendly software with an intuitive web-based graphical user interface (GUI) that incorporates a complete workflow for chemometric analysis of Raman spectra, from raw data pretreatment to a robust validation of machine learning models. The software can be used both for model training and for the application of the pretrained models onto new data sets. Users have full control of the parameters during model training, but the testing data flow is frozen and does not require additional user input. RAMANMETRIX is available in two versions: as standalone software and web application. Due to the modern software architecture, the computational backend part can be executed separately from the GUI and accessed through an application programming interface (API) for applying a preconstructed model to the measured data. This opens up possibilities for using the software as a data processing backend for the measurement devices in real-time. The models preconstructed by more experienced users can be exported and reused for easy one-click data preprocessing and prediction, which requires minimal interaction between the user and the software. The results of such prediction and graphical outputs of the different data processing steps can be exported and saved.

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