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

Abstract

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

Predicting In vitro Culture Medium Macro-Nutrients Composition for Pear Rootstocks Using Regression Analysis and Neural Network Models

“…Response surface method (RSM) is one of the software that has been repeatedly used to optimize growth medium for in vitro culture of pear genotypes (Reed et al, 2013a,b; Wada et al, 2013, 2015). Previous studies detected that ANN-GA models had a substantially higher accuracy of prediction than RSM models (Sedghi et al, 2012). Moghri et al (2015) indicated that RSM alone is not trustworthy for approximation of non-polynomial or non-linear variables.…”

Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

“…Response surface method (RSM) has been repeatedly used to optimization of new in vitro culture media for pear genotypes (Wada et al, 2013; Jamshidi et al, 2016). Previous investigations reported that ANN-GA models had a significantly higher accuracy of prediction than RSM and stepwise regression models (Ahmadi and Golian, 2011; Sedghi et al, 2012; Jamshidi et al, 2016). Moghri et al (2015) and Jamshidi et al (2016) indicated that RSM and regression models alone are not reliable for approximation of non-polynomial or non-linear variables.…”

Effect of processing conditions on the nutritive value of canola meal and presscake. Comparison of the yellow and brown‐seeded canola meal with the brown‐seeded canola presscake

“…CT) contribute to the dark color of canola meals. The tendency of tannin content to increase with intensity of seed coat color and it was reported that CT accumulation is most robust in young seed coats of black‐seeded line and only very little in the yellow‐seeded line …”