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oai:recercat.cat:20.500.12327/47902025-10-23T05:28:11Zcom_2072_4428com_2072_4427col_2072_487898

Transforming hyperspectral images into chemical maps: a novel End‐to‐End Deep Learning approach Engstrøm, Ole-Christian Galbo Albano-Gaglio, Michela Dreier, Erik Schou Bouzembrak, Yamine Font i Furnols, Maria Mishra, Puneet Pedersen, Kim Steenstrup Indústries Alimentàries Qualitat i Tecnologia Alimentària 663/664 Current approaches to chemical map generation from hyperspectral images are based on models such as partial least squares (PLS) regression, generating pixel-wise predictions that do not consider spatial context and suffer from a high degree of noise. This study proposes an end-to-end deep learning approach using a modified version of U-Net and a custom loss function to directly obtain chemical maps from hyperspectral images, skipping all intermediate steps required for traditional pixel-wise analysis. The U-Net is compared with the traditional PLS regression on a real dataset of pork belly samples with associated mean fat reference values. The U-Net obtains a test set root mean squared error of between 9% and 13% lower than that of PLS regression on the task of mean fat prediction. At the same time, U-Net generates fine detail chemical maps where 99.91% of the variance is spatially correlated. Conversely, only 2.53% of the variance in the PLS-generated chemical maps is spatially correlated, indicating that each pixel-wise prediction is largely independent of neighboring pixels. Additionally, while the PLS-generated chemical maps contain predictions far beyond the physically possible range of 0%–100%, U-Net learns to stay inside this range. Thus, the findings of this study indicate that U-Net is superior to PLS for chemical map generation. This work was supported by The Innovation Fund Denmark and FOSS Analytical A/S (grant number 1044-00108B); FEDER and MICIU/AEI/10.13039/501100011033/ (grant number RTI2018-096993-B-I00, 2019–2022); and the Spanish National Institute of Agricultural Research (INIA) (grant number PRE2019-089669, 2020–2024). info:eu-repo/semantics/publishedVersion 2025-07-16 info:eu-repo/semantics/article Engstrøm, Ole-Christian Galbo, Michela Albano‐Gaglio, Erik Schou Dreier, Yamine Bouzembrak, Maria Font‐i‐Furnols, Puneet Mishra, and Kim Steenstrup Pedersen. 2025. “Transforming hyperspectral images into chemical maps: a novel End‐to‐End Deep Learning approach”. Journal of Chemometrics, 39(8): e70041. doi:10.1002/cem.70041. 0886-9383 http://hdl.handle.net/20.500.12327/4790 https://doi.org/10.1002/cem.70041 eng Journal of Chemometrics MICIU/Programa Estatal de I+D+I orientada a los retos de la sociedad/RTI2018-096993-B-I00/ES/CLASIFICACION Y EVALUACION DE LA CALIDAD GLOBAL DE LA PANCETA DE CERDO MEDIANTE TECNOLOGIAS NO DESTRUCTIVAS Y PERCEPCION POR PARTE DE LOS CONSUMIDORES/ Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess 17 Wiley