Phenolic acids in caryopses of two cultivars of wheat, rye and triticale that display different resistance to pre-harvest sprouting

 This study confirmed the significant role of phenolics in the dormancy of cereal caryopses. Investigations were conducted on two cultivars of wheat (Elena and Alba), rye (Amilo and Dańkowskie Złote), and triticale (Ugo and Bogo), with characteristic deeper or shallow dormancy, respectively. In germination studies, cultivars susceptible to sprouting (Alba, Dańkowskie Złote and Bogo) displayed higher germination percentages than those resistant to sprouting (Elena, Amilo and Ugo). The phenolic acid contents (i.e. free, liberated from soluble esters, and liberated from soluble glycosides) in caryopses were determined by an HPLC method. Caffeic, p-coumaric, ferulic and sinapic acids were the dominant phenolic acids detected. The majority of phenolic acids were found in the form of soluble esters. For all species examined, the levels of phenolic acids liberated from soluble esters and the total phenolic acid contents in caryopses showing shallow dormancy were higher than in those showing deeper dormancy. Slight differences in the UV spectra of extracts of phenolic compounds from caryopses showing shallow or deeper dormancy were noted. Received: 6 April 1999     

Modeling the tryptic hydrolysis of pea proteins using an artificial neural network

Experimentally determined values for the degree of hydrolysis (DH) were used with an artificial neural network (ANN) model to predict the tryptic hydrolysis of a commercially available pea protein isolate at temperatures of 40, 45, and 50 °C. Analyses were conducted using the STATISTICA Neural Networks software on a personal computer. Input data were randomized to two sets: learning and testing. Differences between the experimental and calculated DH% were slight and ranged from 0.06% to 0.24%. The performance of the educated ANN was then tested by inputting temperatures ranging from 35 to 50 °C. Very strong correlations were found between calculated DH% values obtained from the ANN and those experimentally determined at all temperatures; the determination coefficients (R²) varied from 0.9958 to 0.9997. The results so obtained will be useful to reduce the time required in the design of enzymatic reactions involving food proteins.     

Damage evolution and infrared thermography in woven composite laminates under fatigue loading

An analytical model based on cumulative damage has been used for predicting the damage evolution in composite materials. The model is verified with experimental data from a carbon/epoxy composite fatigued under tension–tension load. Fatigue tests of specimens have been monitored with an infra-red thermography system. By analysing the temperature of the external surface during the application of cyclic loading, it is possible to evaluate the damage evolution. The model agrees well with the experimental data, and it can be used to predict the evolution of damage in composites.     

Thermal fatigue effects on the temperature distribution inside IGBT modules for zone engine aeronautical applications

This work examines the thermal fatigue effects on the temperature distribution inside IGBT modules for aeronautical applications. Exactly, they are used in a very different application where temperature cycling due to the working environment is the most limiting fact. In this case, it is concluded that solder delamination does not present any restriction to module lifetime at short term (up to 60% of total delaminated area). In addition, it is proposed only determining the delaminated area behind devices, which is the main responsible of the thermal temperature increase.     

Pattern recognition applications for power system disturbance classification

This paper presents an automated online disturbance classification technique. This technique is based on wavelet multiresolution analysis and pattern recognition techniques. The wavelet-multiresolution transform is introduced as a powerful tool for feature extraction in order to classify different disturbances. Minimum Euclidean distance, k-nearest neighbor, and neural network classifiers are used to evaluate the efficiency of the extracted features.     

Multilevel assessment method for reliable design of composite structures

This work aims at demonstrating the interest of a new methodology for the design and optimization of composite materials and structures. Coupling reliability methods and homogenization techniques allow the consideration of probabilistic design variables at different scales. The main advantage of such an original micromechanics-based approach is to extend the scope of solutions for engineering composite materials to reach or to respect a given reliability level. This approach is illustrated on a civil engineering case including reinforced fiber composites. Modifications of microstructural components properties, manufacturing process, and geometry are investigated to provide new alternatives for design and guidelines for quality control.