Techniques of Artificial Intelligence Applied to Near-Infrared Spectra
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915548895379456 |
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| author | Sow, Aminata Diallo, Tidiane |
| author_facet | Sow, Aminata Diallo, Tidiane |
| contents | This article explores the application of various artificial intelligence techniques to the analysis of near-infrared (NIR) spectra of paracetamol, within the spectral range of 900 nm to 1800 nm. The main objective is to evaluate the performance of several dimensionality reduction algorithms; namely, Principal Component Analysis (PCA), Kernel PCA (KPCA), Sparse Kernel PCA, t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) in modeling and interpreting spectral features. These techniques, derived from data science and machine learning, are evaluated for their ability to simplify analysis and enhance the visualization of NIR spectra in pharmaceutical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Techniques of Artificial Intelligence Applied to Near-Infrared Spectra Sow, Aminata Diallo, Tidiane Optics This article explores the application of various artificial intelligence techniques to the analysis of near-infrared (NIR) spectra of paracetamol, within the spectral range of 900 nm to 1800 nm. The main objective is to evaluate the performance of several dimensionality reduction algorithms; namely, Principal Component Analysis (PCA), Kernel PCA (KPCA), Sparse Kernel PCA, t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) in modeling and interpreting spectral features. These techniques, derived from data science and machine learning, are evaluated for their ability to simplify analysis and enhance the visualization of NIR spectra in pharmaceutical applications. |
| title | Techniques of Artificial Intelligence Applied to Near-Infrared Spectra |
| topic | Optics |
| url | https://arxiv.org/abs/2510.10638 |