Replication Data for: NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
Reflectance data for different samples of maize kernels are provided. Concentration of deoxynivalenol and fumonisins determined by HPLC-DAD and HPLC-fluorescence, and raw reflectance data acquired from maize samples using NIR-HSI (893-1730 nm) equipment. From these raw data regression models were de...
| Autores: | , , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat de Lleida (UdL) |
| Repositorio: | Repositori Obert UdL |
| OAI Identifier: | oai:repositori.udl.cat:10459.1/467376 |
| Acceso en línea: | https://doi.org/10.34810/data2012 |
| Access Level: | acceso abierto |
| Palabra clave: | Agricultural Sciences vomitoxin fumonisins maize infrared spectrophotometry image analysis mycotoxins |
| Sumario: | Reflectance data for different samples of maize kernels are provided. Concentration of deoxynivalenol and fumonisins determined by HPLC-DAD and HPLC-fluorescence, and raw reflectance data acquired from maize samples using NIR-HSI (893-1730 nm) equipment. From these raw data regression models were developed for predicting DON, FB1, FB2 and FB1+FB2 contamination in samples of maize kernels. Equally, classification models were developed for classifying samples of maize kernels according to its DON, FB1+FB2 or DON+FB1+FB2 contamination. Unscrambler software was used to develop the regression models, while Quasar was used to develop the classification models. |
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