拉曼光谱
谱线
规范化(社会学)
化学
Spike(软件开发)
算法
噪音(视频)
离群值
工作流程
计算机科学
模式识别(心理学)
人工智能
光学
物理
数据库
人类学
图像(数学)
软件工程
社会学
天文
标识
DOI:10.1016/j.aca.2024.342312
摘要
Our intuitive, open-source algorithms have been validated and allow automatic correction for a given set of samples. They do not require any pre-processing steps such as calibration or baseline subtraction, and their implementation with Python libraries is computationally efficient, allowing for immediate utilization within existing open-source packages for Raman spectra processing.
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