Justification of the geometric dimensions of drum sorting machine

This article presents the results of research to determine the basic geometric dimensions of a drum sorting machine designed for potato sorting. A critical analysis of the current situation in this area has been carried out. A number of research works have been studied aimed at developing special methods to reduce manual labour and improve the quality of sizing and sorting potatoes. At the same time, it is important to sort by size with a low level of product damage, with high productivity. Known mechanical and robotic machines used for sorting potatoes: roller, drum, conveyor (belt) and combined. And also, with the help of machine and computer vision, laser backscattering of light, ultrasonic, visual and spectral analysis systems, optical, acoustic intelligent sorting systems. Among the mechanical ones, the drum sorting machine is the simplest in design. The disadvantage of this machine is that during operation the product to be sorted is only in the lower part of the drum, i.e. only part of the work surface is used. To eliminate the abovementioned disadvantage, a new design of the machine is recommended. Where the sorting surface is made of elastic mesh, the size of the holes increased in the direction of movement of the ends along the sorting surface. The ends are connected and pulled together on two drums. The holes of the elastic mesh material vary in size, the size of the holes increases from the beginning to the end of the sorting surface. The drum can be in a truncated cone or a cylinder shape. To ensure the efficient operation of the proposed machine, mathematical calculations are derived, including geometric and kinematic parameters.

Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds

Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895–2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with R2 over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (R2) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained R2 and SEP values of the seed model were comparable to the result of powder of 0.92–0.95 and 0.09–0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content.