Signal fragmentation based feature vector generation in a model agnostic framework with application to glucose quantification using absorption spectroscopy.

Khadem, Heydar; Nemat, Hoda; Elliott, Jackie; Benaissa, Mohammed

Khadem, Heydar; Nemat, Hoda; Elliott, Jackie; Benaissa, Mohammed.

Afiliación

Khadem H; Department of Electronic and Electrical Engineering, University of Sheffield, UK. Electronic address: h.khadem@sheffield.ac.uk.
Nemat H; Department of Electronic and Electrical Engineering, University of Sheffield, UK. Electronic address: hoda.nemat@sheffield.ac.uk.
Elliott J; Department of Oncology and Metabolism, University of Sheffield, UK. Electronic address: j.elliott@sheffield.ac.uk.
Benaissa M; Department of Electronic and Electrical Engineering, University of Sheffield, UK. Electronic address: m.benaissa@sheffield.ac.uk.

Talanta ; 243: 123379, 2022 Jun 01.

Article en En | MEDLINE | ID: mdl-35306399

Asunto(s)

Glucosa; Espectroscopía Infrarroja Corta; Reproducibilidad de los Resultados; Espectroscopía Infrarroja Corta/métodos

Palabras clave

Glucose quantification; Machine learning; Mid-infrared spectroscopy; Near-infrared spectroscopy; SHAP

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectroscopía Infrarroja Corta / Glucosa Tipo de estudio: Prognostic_studies Idioma: En Revista: Talanta Año: 2022 Tipo del documento: Article Pais de publicación: Países Bajos

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