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Statistical analysis of wheat under different seed treatments: development of a discriminative model based on
Luiz Antonio Molotto1, Daniele Scheres Firak2, Priscila Lagner da Silveira Estevão2
1Campos Gerais Higher Education Center (CESCAGE), School Farm, Ponta Grossa, PR, Brazil.
Background:
Quality control in the wheat industry comprises numerous analyses that are time-consuming and demand numerous procedures and specific apparatus. The application of multivariate calibration techniques contributes to the interpretation of the data generated during these analyses. The present study aimed to correlate a representative number of wheat properties with the treatment applied to the wheat seeds using multivariate calibration techniques.
Results:
In the present study, a wheat pilot planting experiment applying different fungicides combination as a seed treatment (carbendazim, carbendazim + thiram, carboxin + thiram, and triadimenol) was conducted. The resulting wheat grains were subjected to 33 analyses routinely performed in industry. A principal components analysis indicated all analyses were relevant for the different seed treatment discrimination. Afterwards, a k-nearest neighbors discriminative model was developed and was able to classify the seed treatments. In accordance with this model, the most relevant variables for the seed treatment discrimination were the rheological properties of the dough.
Conclusion:
It was possible to develop a discriminative model that directly correlated the wheat seed treatment with the properties of the resulting grains and flours. © 2017 Society of Chemical Industry.
