期刊
ANALYTICAL CHEMISTRY
卷 88, 期 19, 页码 9451-9458出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.6b01643
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资金
- EPSRC through studentships from the PSIBS Doctoral Training Centre [EP/F50053X/1]
- NPL strategic research programmes [116301, 117194]
- Engineering and Physical Sciences Research Council [1239350] Funding Source: researchfish
The amount of data produced by spectral imaging techniques, such as mass spectrometry imaging, is rapidly increasing as technology and instrumentation advances. This, combined with an increasingly multimodal approach to analytical science, presents a significant challenge in the handling, of large data from multiple sources. Here, we present software that can be used through the entire analysis workflow, from raw data through preprocessing (including a wide range of methods for smoothing, baseline correction, normalization, and image generation) to multivariate analysis (for example, :memory efficient principal component analysis (PCA), non-negative matrix factorization (NMF), maximum autocorrelation factor (MAF), and probabilistic latent semantic analysis (PLSA)), for data sets acquired from single experiments to large multi-instrument, multimodality, and multicenter studies. SpectralAnalysis was extensibility in mind to stimulate development comparisons, and evaluation of data analysis algorithms. also developed with
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