Now, sorting different commodities, with defects of specific size is made easy. This machine is designed unique with the powerful software that makes billions of right decisions to identify shape and size of defect, spot defect and watershed algorithm to arrive the area of every product pass through our high sensitivity, high resolution, high speed camera with 60,000 scans per second. What's more, this machine is built with embedded computer and can withstand any future inventions that will be made in the sorting algorithm. With its powerful 12288pixel dual side camera, 1 millisecond ejector response time and quad level sorting technology you can get your sorting done at great accuracy and speed. This series machines are the combination of best technology available today and our decade of experience in the color sorting industry to give you the ultimate benefit of getting consistent good purity in sorting with very minimum loss of good grain in the rejection which means more profit to you. Now you can demand for more quality output in our Camsort Digital series color sorters. Packaging Details: Color Sorting Machine.What''s more, this machine is built with embedded computer and can withstand any future inventions that will be made in the sorting algorithm. The absolute values of relative errors between the actual and predicted values were lower than 8.5%.Now you can demand for more quality output in our Camsort Digital series color sorters. The test results of optimal BELM model by two new cases revealed that the lowest R 2 and highest RMSE of BELM model were 0.9725 and 0.0563, respectively. It overcame the overfitting problems of ELM. In terms of prediction accuracy and execution time, BELM could achieve least similar or even better performance than ELM and BPNN. Then, to validate the robustness and effectiveness of BELM, the basic extreme learning machine (ELM) and traditional back-propagation neural network (BPNN) models have also been employed to predict the color quality. The effects of drying temperature (55, 60, 65, 70, and 75 ☌) and air velocity (3, 6, 9, and 12 m/s) on color change kinetics of mushroom slices during hot air impingement drying were firstly explored and the experimental results indicated that both drying temperature and air velocity significantly affected the color attributes. To alleviate this problem, a new model based on extreme learning machine integrated Bayesian methods (BELM) has been developed for the prediction of color changes of mushroom slices during drying process. However, it is difficult to quickly and accurately predict color change kinetics during drying as it is highly nonlinear, complex, dynamic, and multivariable. Establishing color change kinetics model is an effective way for better understanding the quality changes and optimization of drying process. Color is an important appearance attribute of fruits and vegetables during drying processing, as it influences consumer’s preference and acceptability.
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