Effect of hyperbranched poly(citric polyethylene glycol) with various polyethylene glycol chain lengths on starch plasticization and retrogradation

A series of hyperbranched poly(citric polyethylene glycol) (PCPEG) materials with varied polyethylene glycol (PEG) chain lengths as plasticizers were mixed with maize starch (MS) via cooking and film‐forming. The structure, pasting property, plasticization, aging property, moisture absorption and compatibility of plasticized starches were studied by means of Fourier transform infrared spectroscopy, X‐ray diffraction, rapid viscosity analysis, tension testing, moisture absorption measurements and scanning electron microscopy. Compared with PEG and citric acid, PCPEG was more effective in promoting starch chain movement and inhibiting the retrogradation of starch film. Also, PCPEG/MS had smaller moisture content. The longer the plasticizer chain, the better were the aging resistance and moisture resistance of starch. But with an increase of PEG chain length, mechanical properties of PCPEG/MS deteriorated and the compatibility between PCPEG and MS decreased. The hyperbranched derivative of PEG with longer chain exhibited improved plasticization and compatibility with starch. © 2019 Society of Chemical Industry     

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

This work intends to develop an online experimental system for screening of deoxynivalenol (DON) contamination in whole wheat meals by visible/near-infrared (Vis/NIR) spectroscopy and computer vision coupling technology. Spectral and image information of samples with various DON levels was collected at speed of 0.15 m s⁻¹ on a conveyor belt. The two-type data were then integrated and subjected to chemometric analysis. Discriminant analysis showed that samples could be classified by setting 1000 μg kg⁻¹ as the cut-off value. The best correct classified rate obtained in prediction was 93.55% based on fusion of spectral and image features, with reduced prediction uncertainty as compared to single feature. However, quantification of DON by quantitative analysis was not successful due to poor model performance. These results indicate that, although not accurate enough to provide conclusive result, this coupling technology could be adopted for rapid screening of DON contamination in cereals and feeds during processing.     

Wearing prediction of stellite alloys based on opposite degree algorithm

In order to predict the wearing of stellite alloys,the related methods of rare metals data processing were discussed. The method of opposite degree(OD) algorithm was put forward to predict the wearing of stellite alloys.OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation(BP) and radial basis function(RBF) neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.