Lim, Mina Park, Kyu Ho Hwang, Jae Sung Choi, Mikyung Shin, Hui Youn Kim, Hong-Kyu 2024-02-07T05:13:02Z 2024-02-07T05:13:02Z 2024-02-02 2023-12 2045-2322 https://pubs.kist.re.kr/handle/201004/148549 Owing to the intrinsic signal noise in the characterization of chemical structures through Fourier transform infrared (FT-IR) spectroscopy, the determination of the signal-to-noise ratio (SNR) depends on the level of the concentration of the chemical structures. In situations characterized by limited concentrations of chemical structures, the traditional approach involves mitigating the resulting low SNR by superimposing repetitive measurements. In this study, we achieved comparable high-quality results to data scanned 64 times and superimposed by employing machine learning algorithms such as the principal component analysis and non-negative matrix factorization, which perform the dimensionality reduction, on FT-IR spectral image data that was only scanned once. Furthermore, the spatial resolution of the mapping images correlated to each chemical structure was enhanced by applying both the machine learning algorithms and the Gaussian fitting simultaneously. Significantly, our investigation demonstrated that the spatial resolution of the mapping images acquired through relative intensity is further improved by employing dimensionality reduction techniques. Collectively, our findings imply that by optimizing research data through noise reduction enhancing spatial resolution using the machine learning algorithms, research processes can be more efficient, for instance by reducing redundant physical measurements. English Nature Publishing Group Enhancing spatial resolution in Fourier transform infrared spectral image via machine learning algorithms Article 10.1038/s41598-023-50060-0 1 Scientific Reports, v.13, no.1 Scientific Reports 13 1 Y scie scopus 001131702800056 2-s2.0-85180246739 Multidisciplinary Sciences Science & Technology - Other Topics Article