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A novel multivariate curve resolution-alternating least squares (MCR-ALS) methodology for application in hyperspectral Raman imaging analysis
The Analyst Pub Date : 2019-08-05 , DOI: 10.1039/c9an00787c
Joseph P. Smith 1, 2, 3, 4, 5 , Erin C. Holahan 6, 7, 8, 9 , Frank C. Smith 7, 8, 9, 10 , Veronica Marrero 6, 7, 8, 9 , Karl S. Booksh 6, 7, 8, 9
Affiliation  

A new multivariate curve resolution-alternating least squares (MCR-ALS) methodology is presented that uses approximate reference spectra to determine optimal model complexity for identifying chemical constituents within hyperspectral imaging data.



中文翻译:

一种用于高光谱拉曼成像分析的新型多元曲线分辨率交替最小二乘法 (MCR-ALS) 方法

提出了一种新的多元曲线分辨率交替最小二乘 (MCR-ALS) 方法,该方法使用近似参考光谱来确定用于识别高光谱成像数据中的化学成分的最佳模型复杂性。

更新日期:2019-08-05
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