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...

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Bibliographic Details
Authors: Borràs Vallverdú, Bernat, Marín Sillué, Sònia, Sanchís Almenar, Vicente, Gatius Cortiella, Ferran, Ramos Girona, Antonio J.
Format: conjunto de datos
Publication Date:2025
Country:España
Institution:Consorci de Serveis Universitaris de Catalunya (CSUC)
Repository:CORA.Repositori de Dades de Recerca
OAI Identifier:oai:dnet:cora.rdr____::787e66af2e1819d6c005dfe838aa8c1b
Online Access:https://doi.org/10.34810/DATA2012
Access Level:Open access
Keyword:Agricultural Sciences
vomitoxin
fumonisins
maize
infrared spectrophotometry
image analysis
mycotoxins
Description
Summary: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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