Relationship between color and tannin content in sorghum grain: application of image analysis and artificial neural network

• Autor(a) principal: Sedghi,M
• Data de Publicação: 2012
• Outros Autores: Golian,A, Soleimani-Roodi,P, Ahmadi,A, Aami-Azghadi,M
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Poultry Science (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2012000100010
• topic: Image analysis; neural network model; Sorghum grain; tannin

The relationship between sorghum grain color and tannin content was reported in several references. In this study, 33 phenotypes of sorghum grain differing in seed characteristics were collected and analyzed by Folin-Ciocalteu method. A computer image analysis method was used to determine the color characteristics of all 33 sorghum phenotypes. Two methods of multiple linear regression and artificial neural network (ANN) models were developed to describe tannin content in sorghum grain from three input parameters of color characteristics. The goodness of fit of the models was tested using R², MS error, and bias. The computer image analysis technique was a suitable method to estimate tannin through sorghum grain color strength. Therefore, the color quality of the samples was described according three color parameters: L* (lightness), a* (redness - from green to red) and b* (blueness - from blue to yellow. The developed regression and ANN models showed a strong relationship between color and tannin content of samples. The goodness of fit (in terms of R²), which corresponds to training the ANN model, showed higher accuracy of prediction of ANN compared with the equation established by the regression method (0.96 vs. 0.88). The ANN models in term of MS error showed lower residuals distribution than that of regression model (0.002 vs. 0.006). The platform of computer image analysis technique and ANN-based model may be used to estimate the tannin content of sorghum.