端元
高光谱成像
计算机科学
模式识别(心理学)
拉曼光谱
规范化(社会学)
人工智能
离群值
平滑的
数据处理
样品(材料)
生物系统
计算机视觉
光学
化学
物理
色谱法
生物
社会学
操作系统
人类学
作者
Robert W. Schmidt,Sander Woutersen,Freek Ariese
出处
期刊:Journal of Optics
[IOP Publishing]
日期:2022-04-20
卷期号:24 (6): 064011-064011
被引量:21
标识
DOI:10.1088/2040-8986/ac6883
摘要
Abstract Raman spectroscopy is a valuable tool for non-destructive vibrational analysis of chemical compounds in various samples. Through 2D scanning, it one can map the chemical surface distribution in a heterogeneous sample. These hyperspectral Raman images typically contain spectra of pure compounds that are hidden within thousands of sum spectra. Inspecting each spectrum to find the pure compounds in the dataset is impractical, and several algorithms have been described in the literature to help analyze such complex datasets. However, choosing the best approach(es) and optimizing the parameters is often difficult, and the necessary software was not yet combined in a single program. Therefore, we introduce RamanLIGHT, a fast and simple app to pre-process Raman mapping datasets and apply up to eight unsupervised unmixing algorithms to find endmember spectra of pure compounds. The user can select from six smoothing methods, four fluorescence baseline-removal methods, four normalization methods, and cosmic-ray and outlier removal to generate a uniform dataset prior to the unmixing. We included the most promising pre-processing methods, since there is no routine that perfectly fits all types of samples. Unmixed endmember spectra can be further used to visualize the distribution of compounds in a sample by creating abundance maps for each endmember separately, or a single labeled image containing all endmembers. It is also possible to create a mean spectrum for each endmember, which better describes the true compound spectrum. We tested RamanLIGHT on three samples: an aspirin-paracetamol-caffeine tablet, Alzheimer’s disease brain tissue and a phase-separated polymer coating. The datasets were pre-processed and unmixed within seconds to gain endmembers of known and unknown chemical compounds. The unmixing algorithms are sensitive to noisy spectra and strong fluorescence backgrounds, so it is important to apply pre-processing methods to a suitable degree. RamanLIGHT is freely available as an MATLAB and soon as standalone app.
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