研究目的
To review recent progress and offer a perspective for multimodal chemical imaging in characterization of a rich sample spectrum in a correlated manner, discussing the use of data combinatorial hardware platforms, analysis, as well as machine learning and processing tools necessary for interpretation of the multidimensional data acquired from multimodal studies.
研究成果
The paper concludes that multimodal chemical imaging systems offer powerful capabilities by extracting additional information from cross correlating and combinatorial processing of the captured material signals. It suggests that future developments will include more complex processing capabilities and significant breakthroughs in multimodal hardware capable of capturing even more independent channels of information.
研究不足
The paper discusses the challenges associated with the use of data originated by the combinatorial hardware, analysis, and machine learning as well as processing tools necessary for interpretation of multidimensional data acquired from multimodal studies.