Use of heat-moisture treated maize starch to modify the properties of wheat flour and the quality of noodles

Waxy, normal and high-amylose maize starches were subjected to heat-moisture treatment (HMT) and then added to wheat flour (WF) in different ratios (1%, 5% and 10%). The properties of blends and their cooked noodles were studied to investigate the effects of HMT starches. The incorporation of HMT starch in WF led to an increase in swelling power, peak viscosity and breakdown and to a decrease in setback, thus inhibiting retrogradation, hence enhancing resultant noodle softness. Compared to the same addition ratio of native starch to WF, HMT starch led to higher tensile strength and extensibility in resultant noodles. WF with added HMT starch had higher resistant starch than with native starch. This study showed that addition of HMT maize starch has potential to bring nutritional benefits. However, it is necessary to select the proper blending ratio and amylose content of starch to add, in consideration of its effect on noodle quality.     

Constructing developable surfaces by wrapping cones and cylinders

We model developable surfaces by wrapping a planar figure around cones and cylinders. Complicated developables can be constructed by successive mappings using cones and cylinders of different sizes and shapes. We also propose an intuitive control mechanism, which allows a user to select an arbitrary point on the planar figure and move it to a new position. Numerical techniques are then used to find a cone or cylinder that produces the required mapping. Several examples demonstrate the effectiveness of our technique.     

Solvent-free synthesis of C9 and C10 branched alkanes with furfural and 3-pentanone from lignocellulose

Jet fuel range branched alkanes were first synthesized under solvent-free conditions by the aldol condensation of furfural and 3-pentanone from lignocellulose followed by the one-step hydrodeoxygenation (HDO). Among the investigated solid base catalysts, CaO and KF/Al₂O₃ demonstrated the highest activity for the aldol condensation reaction of furfural and 3-pentanone. The aldol condensation product of furfural and 3-pentanone (liquid at room temperature) was directly hydrodeoxygenated to 4-methyl-nonane and 4-methyl-octane. These alkanes have low freezing points (174.1 K and 159.8 K) and can be blended into jet fuel without hydroisomerization. Among the investigated HDO catalysts, the Ni-Cu/SiO₂ exhibited the best performance.     

Electrochemical characteristics of heme proteins in hydroxyethylcellulose film

A stable film made from hydroxyethylcellulose (HEC) on pyrolytic graphite (PG) electrode was employed for incorporating hemoglobin (Hb), myoglobin (Mb) and horseradish peroxidase (HRP), and the electrochemical characteristics of the proteins were studied correspondingly. Experimental results revealed that HEC film could greatly accelerate electron transfer between the proteins and electrode, and the proteins showed a thin layer electrochemical behavior in the film. Moreover, all the proteins in the film exhibited good catalytic activity towards the reduction of hydrogen peroxide (H₂O₂) in the low H₂O₂ concentration range. In the high concentration range, H₂O₂ would exhibit toxicity effect on the proteins. The electrochemical properties and electrocatalytic abilities of the three heme proteins in HEC film have been compared, and the optimal conditions for H₂O₂ biosensor fabrication have been obtained.     

Effects of home preparation on pesticide residues in cabbage

Experiment was carried out to evaluate the pesticides (chlorpyrifos, p,p-DDT, cypermethrin, chlorothalonil) residue levels in cabbage in the process of home preparation by washing with different concentrations of acetic acid and sodium chlorine, and tap water, preserving in refrigerator, and stir-frying for different time. Results showed that washing by tap water and/or detergent solution for cooking are necessary to decrease the concentration of pesticide residues in cabbage. Washing with acetic acid solutions (at 10% concentration for 20 min) caused 79.8%, 65.8%, 74.0% and 75.0% loss of the above pesticides, respectively. Washing with NaCl solutions (at 10% concentration for 20 min) produced 67.2%, 65.0%, 73.3% and 74.1% loss, respectively, and washing by tap water (for 20 min) were 17.6%, 17.1%, 19.1% and 15.2% loss, respectively. The reductions due to the refrigeration (for 48 h) were 3.4%, 2.6%, 3.1% and 3.6%, respectively, and those due to the stir-frying (for 5 min) were 86.6%, 67.5%, 84.7% and 84.8%, respectively. The data indicated that washing by detergent solutions and stir-frying of cabbage are the most effective home preparations for the elimination of pesticide residues.     

Formation of Proto-Kranz in C3 Rice Induced by Spike-Stalk Injection Method

Introduction of C4 photosynthetic traits into C3 crops is an important strategy for improving photosynthetic capacity and productivity. Here, we report the research results of a variant line of sorghum–rice (SR) plant with big panicle and high spikelet density by introducing sorghum genome DNA into rice by spike-stalk injection. The whole-genome resequencing showed that a few sorghum genes could be integrated into the rice genome. Gene expression was confirmed for two C4 photosynthetic enzymes containing pyruvate, orthophosphate dikinase and phosphoenolpyruvate carboxykinase. Exogenous sorghum DNA integration induced a series of key traits associated with the C4 pathway called “proto-Kranz” anatomy, including leaf thickness, bundle sheath number and size, and chloroplast size in bundle sheath cells. Significantly, transgenic plants exhibited enhanced photosynthetic capacity resulting from both photosynthetic CO₂-concentrating effect and improved energy balance, which led to an increase in carbohydrate levels and productivity. Furthermore, such rice plant exhibited delayed leaf senescence. In summary, this study provides a proof for the feasibility of inducing the transition from C3 leaf anatomy to proto-Kranz by spike-stalk injection to achieve efficient photosynthesis and increase productivity.     

A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services

Selection of strategies that help reduce riverine inputs requires numerical models that accurately quantify hydrologic processes. While numerous models exist, information on how to evaluate and select the most robust models is limited. Toward this end, we developed a comprehensive approach that helps evaluate watershed models in their ability to simulate flow regimes critical to downstream ecosystem services. We demonstrated the method using the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program–FORTRAN (HSPF) model, and Distributed Large Basin Runoff Model (DLBRM) applied to the Maumee River Basin (USA). The approach helped in identifying that each model simulated flows within acceptable ranges. However, each was limited in its ability to simulate flows triggered by extreme weather events, owing to algorithms not being optimized for such events and mismatched physiographic watershed conditions. Ultimately, we found HSPF to best predict river flow, whereas SWAT offered the most flexibility for evaluating agricultural management practices.     

Improving classification-based diagnosis of batch processes through data selection and appropriate pretreatment

This work considers the application of classification algorithms for data-driven fault diagnosis of batch processes. A novel data selection methodology is proposed which enables online classification of detected disturbances without requiring the estimation of unknown (future) process behavior, as is the case in previously reported approaches.The proposed method is benchmarked in two case studies using the Pensim process model of Birol et al. (2002) implemented in RAYMOND. Both a simple k Nearest Neighbors (k-NN) and complex Least Squares Support Vector Machine (LS-SVM) are employed for classification to demonstrate the generic nature of the proposed approach. In addition, the influence of different data pretreatment methods on the classification performance is discussed, together with a motivation for selecting the correct pretreatment steps. Finally, the influence of the number of available training batches is studied.The results demonstrate that a good classification performance can be achieved with the proposed data selection method even with a low number of faulty training batches by exploiting knowledge on the nature of the to-be-diagnosed faults in the data pretreatment. This provides a proof of concept for classification-based batch diagnosis and demonstrates the importance of incorporating process insight in the construction of data-driven process monitoring and diagnosis tools.     

Corrosion kinetics and patina evolution of galvanized steel in a simulated coastal-industrial atmosphere

The corrosion kinetics and patina (corrosion products) layer evolution of galvanized steel submitted to wet/dry cyclic corrosion test in a simulated coastal-industrial atmosphere was investigated. The results show that zinc coating has a greater corrosion rate during the initial period and a lower corrosion rate during the subsequent period, and the patina composition and structure can greatly affect the corrosion kinetics evolution of zinc coating. Moreover, Zn₅(OH)₆(CO₃)₂ and Zn₄(OH)₆SO₄ are identified as the main stable composition and exhibit an increasing relative amount; while Zn₁₂(OH)₁₅Cl₃(SO₄)₃ cannot stably exist and diminish in the patina layer as the corrosion develops.     

Set-membership estimation from poor quality data sets: Modelling ammonia volatilisation in flooded rice systems

A set-membership (bounded-error) estimation approach can handle small and poor quality data sets as it does not require testing of statistical assumptions which is possible only with large informative data sets. Thus, set-membership estimation can be a good tool in the modelling of agri-environmental systems, which typically suffers from limited and poor quality observational data sets. The objectives of the paper are (i) to demonstrate how six parameters in an agri-environmental model, developed to estimate NH₃ volatilisation in flooded rice systems, were estimated based on two data sets using a set-membership approach, and (ii) to compare the set-membership approach with conventional non-linear least-squares methods. Results showed that the set-membership approach is efficient in retrieving feasible parameter-vectors compared with non-linear least-squares methods. The set of feasible parameter-vectors allows the formation of a dispersion matrix of which the eigenvalue decomposition reflects the parameter sensitivity in a region.