Peraza Alemán, Carlos MiguelLópez Maestresalas, AinaraJarén Ceballos, CarmenRuiz de Galarreta, José IgnacioBarandalla, LeireArazuri Garín, Silvia2025-03-252025-03-14Peraza-Alemán, C. M., López-Maestresalas, A., Jarén, C., Ruiz de Galarreta, J. I., Barandalla, L., Arazuri, S. (2025). Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics. Food Chemistry, 479, 1-11. https://doi.org/10.1016/j.foodchem.2025.143794.0308-814610.1016/j.foodchem.2025.143794https://academica-e.unavarra.es/handle/2454/53805This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed under the same frying conditions were analysed. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR), combined with a logarithmic transformation of the acrylamide levels, were applied to develop predictive models. The most optimal outcomes for PLSR yielded R2 p: 0.85, RMSEP: 201 μg/kg and RPD: 2.53, while for SVMR yielded R2 p: 0.80, RMSEP: 229 μg/kg and RPD: 2.22. Furthermore, the selection of significant wavelengths enabled an 87.95 % reduction in variables without affecting the model’s accuracy. Finally, spatial mapping of acrylamide content was conducted on all chips in the external validation set. This method provides both quantification and visualization capabilities, thus enhancing quality control for acrylamide identification in processed potatoes.application/pdfeng© 2025 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0.Spatial distributionMachine learningNIR-HSISolanum tuberosum L.PLSRinfo:eu-repo/semantics/article2025-03-25info:eu-repo/semantics/embargoedAccess